ijsm-2012_vol_12-3

IJSM, Volume 12, Number 3, 2012 ISSN: 1555-2411

INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT®

SPONSORED BY:

Angelo State University San Angelo, Texas, USA

www.angelo.edu

Managing Editors:

Professor Alan S. Khade, Ph.D. Professor Detelin Elenkov, Ph.D. (MIT) California State University Stanislaus Angelo State University, Texas, USA

A Publication of the

International Academy of Business and Economics® IABE.EU

International Journal of Strategic Management, Volume 12, Number 3, 2012 ii

International Journal of Strategic Management® Volume 12, Number 3, 2012 ISSN: 1555-2411 Managing Editors: Dr. Alan S. Khade, California State University Stanislaus, USA

Dr. Detelin Elenkov, Angelo State University, Texas, USA Editorial Board: Dean/Dr. Cheick Wagué, Dean, South Stockholm University, Stockholm, Sweden Dean/Dr. Bhavesh M. Patel, Ahmedadab University, Ahmedabad, Gujrat, India Dr. Tahi J. Gnepa, California State University-Stanislaus, Turlock, CA, USA Dr. Zinovy Radovilsky, California State University East Bay, Hayward, California, USA Dean/Dr. Sid Howard Credle, Hampton University, Virginia, USA Professor/Dr. CB Claiborne, Texas Southern University, Houston, USA Dean/Dr. Khalid Alkhathlan, King Saud University- Al Kharj, Saudi Arabia Dean/Dr. Phapruke Ussahawanitchakit, Mahasarakham University, Thailand Dr. Vishnuprasad Nagadevara, Indian Institute of Management, Bangalore, India Dr. Ricarda B. Bouncken, University of Bayreuth, Bayreuth, Germany Dr. C. B. Claiborne, Texas Southern University, Houston, Texas, USA Dr. Ben-Jeng Wang, Tunghai University, Taichung, Taiwan, ROC Dr. Marius D. Gavriletea, Babes Bolyai University, Cluj-Napoca, Romania Dr. Benoy Joseph, Cleveland State University, Cleveland, Ohio, USA Dr. Ansgar Richter, European Business School, Oestrich-Winkel, Germany Dr. David Ward, European School of Economics, Milan, Italy Dr. Moshe Zviran, Tel Aviv University, Tel Aviv, Israel Dr. Michael Benham, European Business School, Germany Dr. Scott K. Metlen, University of Idaho, Moscow, Idaho, USA Dr. Fred N. Silverman, Pace University, White Plains, New York, USA Dr. Premilla D’Cruz, Indian Institute of Management, Ahmedabad, India Dr. Alain Nurbel, University of La Reunion, CERESUR, France Dr. Anand Desai, Ohio State University, Columbus, OH, USA Dr. Joy Bhadury, University of North Carolina, Greensboro, North Carolina, USA Dr. Aharon Tziner, Dean, Netnaya University College, Netnaya, Israel Dr. John S. Croucher, Macquarie University, Sydney, Australia Dr. Eric Girard, Siena College, Loudonville, New York, USA Dr. Ernesto Noronha, Indian Institute of Management, Ahmedabad, India Dr. Tom Badgett, Angelo State University, San Angelo, Texas, USA Dr. Dale H. Shao, Marshall University, Huntington, West Virginia, USA Dr. Lokman Mia, Griffith University, Brisbane, Queensland, Australia The IJSM is a peer-reviewed and publicly available journal listed in the Cabell’s Directories 2004-14 Editions. The IJSM is also listed in the Ulrich’s International Periodicals Directory since 2004. The IJSM is available online from the EBSCO Publishing and Cengage/Gale Publishing. The IJSM is sponsored by California State University, Channel Islands, USA. The IJSM is available in prestigious libraries such as US Library of Congress and British Library. The IJSM is a Registered Trademark of the IABE. The IJSM is a publication of the International Academy of Business and Economics. All rights reserved. ©2012 IABE. WWW.IABE.EU

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ISSN: 1555‐2411

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International Journal of Strategic Management, Volume 12, Number 3, 2012 iii

International Journal of Strategic Management Volume 12, Number 3, 2012, ISSN: 1555-2411

A Welcome Note from the Managing Editors: We proudly present to you the International Journal of Strategic Management (IJSM), Volume 12, Number 3, 2012 issue. In this issue, we present to you 9 research papers in business policy/strategy areas. IJSM is a publication of the Academy of International Business and Economics. IJSM is sponsored by the Angelo State University, San Angelo, Texas, USA. The IJSM is listed in the Cabell’s Directories of Refereed Publications 2004-14 Edition. The Journal is also listed in the Ulrich’s International Periodicals Directory since 2004. The IJSM is available online from the EBSCO Publishing and Cengage/Gale Publishing. The IJSM has the ISSN (ISSN: 1555-2411) and Call Number HD30.28 I553 issued by the Library of Congress, Washington. The academic foundations and real-world applications related to business and economics are rapidly changing. Challenges for everyone are increasing daily. IJSM presents a perspective on these developments with a special focus on the strategic responses of organizations facing such developments. The response to this volume has been very gratifying. We wish to thank all authors who submitted such an excellent selection of papers. All submitted work to the Journal goes through a rigorous double blind peer-review process of experts in the functional area. We wish to thank the scholars who contributed their time and expertise as reviewers for this issue. We are grateful to them and to our board members for donating their time for the cause of academics and research that makes this Journal possible. Our reviewers are a diverse group, from many academic areas and from many countries. We appreciate their dedication and especially for their work under very tight deadlines. This issue is dedicated to our contributors’ active participation in development of conceptual and applied work for the international strategic arena of business and economy. We are indebted to Angelo State University, San Angelo, Texas, USA for sponsoring IJSM and providing the invaluable editorial support necessary to the successful publication of the IJSM. Our website www.iabe.eu is completely redesigned for online paper submission, checking status of your paper, and more. We invite you to visit our website and create your member account. We welcome your comments and suggestions on this issue. We look forward to your paper submissions for future issues. Best regards, Alan S. Khade, Ph.D. Detelin Elenkov, Ph.D.(MIT) Managing Editors

International Journal of Strategic Management, Volume 12, Number 3, 2012 iv

International Journal of Strategic Management

Volume 12, Number 3, 2012; ISSN: 1555-2411 Page

MODERN COST MANAGEMENT STRATEGY IMPLEMENTATION AND FIRM PERFORMANCE: EVIDENCE FROM CHEMICAL MANUFACTURING BUSINESSES IN THAILAND

Kanoknate Prempree, Mahasarakham University, Thailand Phapruke Ussahawanitchakit, Mahasarakham University, Thailand

1

RISK TOLERANCE: A BEHAVIOURAL ANALYSIS Everton Anger Cavalheiro University of Cruz Alta Kelmara Mendes Vieira Federal University of Santa Maria Paulo Sérgio Ceretta Federal University of Santa Maria

14

INTELLECTUAL CAPITAL ORIENTATION AND SUSTAINABLE PERFORMANCE OF MEDICAL SERVICE BUSINESS: AN EMPIRICAL STUDY OF PRIVATE HOSPITALS IN THAILAND

Sumittra Jirawuttinunt, Mahasarakham University, Thailand Kannika Janepuengporn, Mahasarakham University, Thailand

25

HUMAN RESOURCE MANAGEMENT POLICIES AND PRACTICES (HRMPP): SCALE VALIDATION IN THE UNITED STATES

Gisela Demo, UCLA Anderson School of Management, Los Angeles, California, USA Késia Rozzett, University of Brasília, Brasília, Brazil

41

ACTIVITY-BASED COST MANAGEMENT STRATEGY AND CONTINUOUS PERFORMANCE IMPROVEMENT: EVIDENCE OF THAI ELECTRONIC FIRMS

Pitachaya Kaneko, Mahasarakham University, Thailand Phapruke Ussahawanitchakit, Mahasarakham University, Thailand

67

STRATEGIES IMPLEMENTATION AND EVALUATION OF THAILAND PUBLIC SECTOR TO IMPROVE PRIVATE SECTOR PERFORMANCE

Viput Ongsakul, National Institute of Development Administration (NIDA), Thailand

83

COMPETITIVE ADVANTAGE IN THAI SERVICE BUSINESSES: INVESTIGATING THE EFFECTSOF ORGANIZATIONAL DESIGN EFFECTIVENESS

Anirut Pongklee, Mahasarakham University, Thailand Sakcharoen Pawapootanont, Mahasarakham University, Thailand

88

ROLES OF RISK MANAGEMENT STRATEGY IN GOAL ACHIEVEMENT: EVIDENCE FROM THAI LISTED FIRMS

Sutika Rukprasoot, Mahasarakham University, Thailand Phapruke Ussahawanitchakit, Mahasarakham University, Thailand

98

SOYBEAN BRAZILIAN PRICE’S PREDICTABILITY VIA BOX-JENKINS METHOD Everton Anger Cavalheiro University of Cruz Alta Kelmara Mendes Vieira Federal University of Santa Maria Paulo Sérgio Ceretta Federal University of Santa Maria Juliano Nunes Alves University of Cruz Alta

114

TABLE OF CONTENTS

MODERN COST MANAGEMENT STRATEGY IMPLEMENTATION AND FIRM PERFORMANCE: EVIDENCE FROM CHEMICAL MANUFACTURING BUSINESSES IN THAILAND

Kanoknate Prempree, Mahasarakham Business School, Mahasarakham University, Thailand

Phapruke Ussahawanitchakit, Mahasarakham Business School, Mahasarakham University, Thailand

ABSTRACT Cost management strategy is important for business management effectiveness as it supports decision making, enhances competitive advantage and increases firm performance. This study aims at investigating the relationships among cost management strategy of three dimensions: cost containment, cost avoidance, and cost reduction, operational planning effectiveness, decision making quality, resource management capacity, and firm performance. A questionnaire was used to collect the data from 168 chemical manufacturing firms in Thailand. The results of this study show that cost avoidance focus and cost reduction capability have a significant positive relationship with firm performance. In sum, this study contributes to managerial by providing the knowledge that firm performance can be increased by cost management strategy implementation. Because the sample is only chosen from one industry as chemical manufacturing, the generalizability may need to be confirmed. Future research should cover a broader industrial manufacturing in order to increase the external reliability. Keywords: Cost Management Strategy, Operational Planning Effectiveness, Decision Making Quality, Resource Management Capacity, Firm Performance, Environmental Uncertainty 1. INTRODUCTION In the past two decades, business organization has been facing a change of business environments, such as increasing the competition of global firms respond to these conditions with better management approach (Kumar and Shafabi, 2011). The competition forces manufacturing firms to create the operational effectiveness and maintain their profitability; the most important managerial tools are cost management strategy (Zengin and Ada, 2010), and cost management strategy is considered as critical factors to increase revenue for the success companies (Kumar and Shafabi, 2011). Cost management strategy supports decision making and improves competitive advantage that results in a better resource allocation (Ellram and Stanley, 2008). In addition, cost management may be an integral feature of overall businesses’ management effectiveness and facilitate to determine accurately estimated cost before process starting and can help to forecast cost occurrence in the future. Cost management strategy effectiveness helps to finish the task with the spending of limited allocated resources and makes valuable to firms such as working capital invested reduction, lower cost per unit, and better quality of the process and product (Groth and Kinney, 1994). Cost management often refers to cost cutting and it’s commonly approached that firm managers use to respond to the decreasing sustainable profitability (Anderson, 2007). A number of prior research studied cost management with respect to supply chains, value chain management, target cost, activity-based costing, just in time (JIT), and inter-organizational cost management (e.g. Agndal and Nilsson, 2009; Anderson 2007; Anderson and Dekker, 2009; Backstrom and Lind, 2005; Nicolaou, 2002; Cooper and Slagmulder, 2004). Although, cost management had been investigated, few of prior research studied cost management of three dimensions. The present paper fills this niche, therefore the purpose of this paper is to study cost management strategy of three dimensions: cost containment, cost avoidance, and cost reduction. This paper studies the relationships among cost management strategy implementation, operational planning effectiveness, decision making quality, resource management capacity, and environmental uncertainty. The key research questions are: (1) how cost management strategy implementation relates to operational planning effectiveness, decision making quality, resource management capacity, and firm performance, (2) how the moderating effect of environmental uncertainty impacts on the relationships among cost management strategy implementation, operational planning effectiveness, decision making quality, and resource management capacity, (3) how operational planning

INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT, Volume 12, Number 3, 2012 1

H10a-c H11a-c H12a-c

H4, H5, H6

Modern cost management strategy implementation

Cost containment orientation Cost avoidance focus Cost reduction capability

Operational planning

effectiveness

Decision making

quality

Resource management

capacity

Firm

performance

Environment uncertainty

H7

H8

H9

H1a-c H2a-c H3a-c

effectiveness and resource management capacity relates to decision making quality, and (4) how decision making quality relates to firm performance. The remaining of this paper is organized as follows. The second section describes the literature reviews and hypotheses developments. Research methods described in the third and the fourth sections explain the results and discussions. The fifth involves contributions and the last section is a conclusion that includes limitations and directions of future research. 2. LITERATURE REVIEWS ON MODERN COST MANAGEMENT STRATEGY IMPLEMENTATON AND FIRM PERFORMANCE AND HYPOTHESES DEVELOPMENTS The relationships among modern cost management strategy implementation, operational planning effectiveness, decision making quality, resource management capacity, environment uncertainty and firm performance of chemical manufacturing businesses in Thailand are elaborately investigated. The conceptual model that links the aforementioned relationships is as shown in Figure 1.

FIGURE 1 A CONCEPTUAL MODEL OF THE RELATIONSHIPS BETWEEN MODERN COST MANAGEMENT

STRATEGY IMPLEMENTATION AND FIRM PERFORMANCE 2.1 Modern Cost Management Strategy Cost management is the process of controlling and planning cost structures toward cost behavior (Seuring, 2002). The purpose of cost management is to maximize the firms’ profit (Agrawal, Mehra and Siegel, 1998). Cost management starts with the understanding of what event generates cost and after that cost management can be doing well (Groth and Kinnery, 1994). Cost is changed by several events such as inflation, supply and demand effects, technological innovations, and production process change,

Control variables Firm size, Firm age


while information technology can help to acquire cost information and enhance cost management effectiveness (Groth and Kinnery, 1994). This study defines modern cost management strategy as three forms: (1) cost containment focuses on constraining future fixed cost or unit variable cost increases, (2) cost avoidance refers to the eliminated activities that generate costs of non-added values, and (3) cost reduction refers to an attempt to attain lower current fixed costs and variable costs associated with an essential activity (Groth and Kinnery, 1994). Cost containment techniques such as standard costing and budget system limit the highest cost that could be incurred. For effectiveness of these systems of cost containment requires monitoring which utilizes cost information reporting as product costing that provides the importance of up-to-date cost information (Cooper and Slagmulder, 2004). Also, cost avoidance techniques, for example, value chain management appropriately considers how cost is lower through the chain linked (Ellram and Stanley, 2008), and just in time (JIT) potentially manages lowest inventory cost, production efficiency and product quality (Nicolaou, 2002). Techniques of cost reduction include (1) value engineering that reduces cost in the product design stage such as reducing the number of product components, eliminating operating cost, improving labor and mechanical processes, replacing expensive parts by appropriate economically viable parts, pressured by suppliers to aggressively reduce costs (2) kaizen costing or continuous improvements that sets goals of cost-reduction and empowers workers to discover the approach to achieve costing goals, and (3) functional group management techniques that refer to the separation of the production process into autonomous groups and treating each group as a profit center that helps the group better understand the contribution of group to the overall profitability of organization (Cooper and Slagmulder, 2004). Limited resource and apparent continuous competition influence firms to better managing cost of production by implementing standard costing, budget system, monitoring cost information, and focusing on value added activities by eliminating non-value added activities through supplier coordination, and emphasizing on cost structure by analyzing cost and finding the way to reduce costs in the stage of pre-production. Firms with cost management strategy implementation are able to know when the amount of cost will incur in the future if they have current and future cost information. Thus, managers can achieve effective operational planning to make better decision making strategies and higher resource management capacities. As discussed above, the three forms of cost management strategy implementation can help managers enhance operational planning effectiveness, decision making quality, and resource management capacity. Thus, the hypotheses are proposed as follows: Hypothesis 1: Cost containment orientation will positively relate to (a) operational planning effectiveness, (b) decision making quality, and (c) resource management capacity. Hypothesis 2: Cost avoidance focus will positively relate to (a) operational planning effectiveness, (b) decision making quality, and (c) resource management capacity. Hypothesis 3: Cost reduction capability will positively relate to (a) operational planning effectiveness, (b) decision making quality, and (c) resource management capacity. 2.2 Firm performance Salter (1995) suggested that performance measurement of corporate and business unit has three dimensions: (1) effectiveness, (2) efficiency, and (3) adaptability. Some indicators of three dimensions are returns on investment, sales growth, and new product success, respectively. Furthermore, Salter (1995) argued that relative performance measures appropriate surrogates for objective measures in the single-industry sample. Morgan (2012) suggested that business performance consists of two aspects: market performance and financial performance. Market performance relates to customer behaviors. Higher sales volume, customer satisfaction increases, customer loyalty, and growth of market shares are indicators of market performance while the financial performance is measured in accounting terms. This study defines firm performance as a goal achievement and financial performance that are indicated by the net


income goal achievement, sales amount and market share increases, the better return on investment, and the growth and continuance of overall performance (Chai-Amonphaisal and Ussahwanitchakit, 2010; Tantiset and Ussahwanitchakit, 2010). Business operation focus on highest potential profit and a common approach is a cost control that is expected to produce the greatest overall financial performance (Healthcare Financial Management Association, 2012). Cost management strategy implementation success might generate value to the firm, for example, the greater control production activities results in better quality of procedure and lowers the unit cost of goods and cost variance. In addition, the consequence of the cost management success is firm value increasing and profit improvement that positively affects firms’ value greater than pricing (Groth and Kinney, 1994). Therefore, it can be expected that cost management implementation will increase firm performance. Thus, the hypotheses are proposed as follows: Hypothesis 4: Cost containment orientation will positively relate to firm performance. Hypothesis 5: Cost avoidance focus will positively relate to firm performance. Hypothesis 6: Cost reduction capability will positively relate to firm performance. 2.3 Operational Planning Effectiveness Operational planning is a basic process that all management procedures must implement for management success. The operational planning is explained in three points: (1) setting a goal of a functional department of the effort and behavior guidance of employee performance, (2) the standard for the operations and (3) the goals of functional unit aligns with operational planning to be able to control the group or individual performance (Chai-amonphaisal and Ussuhawanichakit, 2010). Operational planning effectiveness can occur through managers who do better planning to increase efficient performance. Thus, operational planning effectiveness is able to enhance better decision making and achieve the goal, vision and mission of firms (Chai-amonphaisal and Ussuhawanichakit, 2010). The definition of operation planning of this study is the existence of a framework of operation that is consistent with functional units and among the departments. The clearance process and method of the operational success evaluation system; the resource spending according to the particular planning; and the organization management goes along with the firm’s objective setting (Chai-Amonphaisal and Ussahwanitchakit, 2010; Hanpuwadal and Ussahwanitchakit, 2010). Firms with operation planning effectiveness may enhance decision making quality and after that firms can attain better performance. As discussed above, a hypothesis is proposed as follows: Hypothesis 7: Operational planning effectiveness will positively relate to decision making quality. 2.4 Resource Management Capacity Resource management is defined as the analyzing of resource requirements, the allocation of resources to each functional unit sufficient for efficiently operating to accomplish functional unit goals (Hanpuwadal and Ussahawanitchakit, 2010), and the correct and efficient spending of resources. When firms are able to better analyze, allocate, spend, and manage the resources, firms are able to make better decisions about the returns of the new project investments, and could remove or enter new products and product mixes. Therefore, the capacity of resource management could influence decision making quality and lead to better performance. As discussed above, a hypothesis is proposed as follows: Hypothesis 8: Resource management capacity will positively relate to decision making quality 2.5 Decision Making Quality Decision making is a primary activity of managers (Martinsons and Davison, 2007). Decision making is the selection process of a particular alternative for implementation and this process is supported by the evaluation of each alternative to assign quantitative values to consider available information about the alternative (Nutt, 1976). Decision making for this paper is defined as the possibility of an alternative setting and the effort to use better decision making consistent with the firm’s objectives (Chai-


Amonphaisal and Ussahawanitchakit, 2010) that leads to achieve better firm performance. Therefore, a hypothesis is proposed as follows: Hypothesis 9: Decision making quality will positively relate to firm performance. 2.6 Environment Uncertainty Environment uncertainty exists in a situation that decision-makers feel that they are not able to accurately define probability of the particular events or the change takes place, and the three common definitions of environment uncertainty are: (1) the state uncertainty refers to an inability to identify probability of the future events, (2) the effect of uncertainty refers to an insufficiency of cause-effect relationships information, and (3) the response uncertainty is an inability to forecast the outcomes accurately or consequences of the decision (Ashill and Jobber, 1999). Ashill and Jobber (1999) suggests that the level of environmental uncertainty depends on two characteristics of the environment: variability and complexity. Environment variability means the degree and frequency of environmental change, and environment complexity means the level of task environment relative with few or large number of factors. The level of complexity and variability of external environment includes customers, competitors, regulation, and labor unions (Ashill and Jobber, 1999; Habib, Hossain and Jiang, 2011). This research defines environment uncertainty as the external threat or instability of various aspects of the external business environment such as available material, economic, technological, and competitiveness, when firms perceive environment uncertainty, its effort to apply the new strategies and methods of operation, are meant improve the operational process, and adjust the way of predicting the consequent impacts of response environment uncertainty (Mathuramaytha and Ussahawanitchakit, 2010). Decision-makers who perceive the greater environment uncertainty are likely to rely on non-financial management accounting information to deal with external environment uncertainty more effectively (Hoque, 2004). Similarly, firms need better cost management strategy to avoid risk of environment uncertainty, for example, firms use just-in-time technique for cost avoidance and the risk of obsolete inventory are eliminated, reduce cost variance by standard costing using, and improve product design for fewer component parts and then reduce risk from the unpredictable of availability and prices of material fluctuation (Groth and Kinney, 1994). From external business environment uncertainty characteristics which firm is facing, firms would increase cost management strategy implementation for increasing operational planning effectiveness, decision making quality and resource management capacity. According to rationales discussed above, it can be expected that environment uncertainty might increase the relationships among cost management strategy implementation and operational planning effectiveness, decision making quality, and resource management capacity. Therefore, the hypotheses are proposed as follows: Hypothesis 10: Environment uncertainty positively moderates the relationships between cost containment orientation and (a) operational planning effectiveness, (b) decision making quality, and (c) resource management capacity. Hypothesis 11: Environment uncertainty positively moderates the relationships between cost avoidance focus and (a) operational planning effectiveness, (b) decision making quality, and (c) resource management capacity. Hypothesis 12: Environment uncertainty positively moderates the relationships between cost reduction capability and (a) operational planning effectiveness, (b) decision making quality, and (c) resource management capacity. 3. RESEARCH METHODS 3.1 Sample Selection and Data Collection Procedure In this study, the manufacturers of chemical products in Thailand were selected as the sample. Chemical business industry is attractive to research due to its industrial base of others. Chemical industries in


Thailand and is mixed and contained with products that require imported material and standard ingredients of material, thus chemical businesses require cost management strategy implementation. The sample was obtained from the database of the Department of Business Development in Ministry of Commerce of Thailand. A questionnaire-mailed survey was used to collect the data from 168 chemical product manufacturing firms. A cover letter, a return- addressed envelope and a copy of the questionnaire were sent directly to the accounting executives as a key informant whom has knowledge about cost management strategy and firm performance. With regard to 168 observations of this paper, the number is enough for the regression analysis that follows to Hair, Balck, Babin, and Anderson, (2010), who suggests that the desired level of sample size for multiple regression analysis is between 15 to 20 observations for each independent variable is sufficient. To test potential of non-response bias and to consider possible problems with non-response errors, a comparison of the first and the second wave data is recommended by Armstrong and Overton (1977). The results showed no significant differences between early and late respondents. As a result, non-response is not a problem with this study. 3.2 Questionnaire Development and Variable Measurement 3.2.1 Questionnaire Development A questionnaire was developed based on prior researches related to cost management strategy. It consists of six parts and all constructs were measured by multiple-item scales. Part one asks for personal information of the informant. Part two includes a question of the general information and history of business such as total assets, number of employees, and firm age. Part three through Part five, includes questions asked to measure each of constructs in a conceptual model. Items are designed by a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Finally, an open-ended question for informant’s suggestion and opinions are included in part six. 3.2.2 Variable Measurement 3.2.2.1 Dependent Variable Dependent variable is firm performance that refers to the success and achievement of firm’s goal. Four-item scale was adopted from Chai-Amonphaisal and Ussahwanitchakit (2010) and one-item was adapted from Tantiset and Ussahwanitchakit, (2010) to measure firm performance including profitability, goal achievement, and sale amount and market share increasing. 3.2.2.2 Independent Variables Independent variables comprise six variables. First, cost containment orientation refers to control the occurred current fixed and variable cost. Three-item scale was developed to assess the degree of a firm’s implementation of cost containment. Second, cost avoidance focus refers to the elimination or avoidance cost of unvalued-added activities. Three-item scale was developed which relates to the extent of firm implement cost avoidance. Third, cost reduction capability relates to the effort to reach in lowering current fixed and variable costs associated with essential activity. To measure the extent of firm adopted cost reduction, three-item scale was developed. Fourth, operational planning effectiveness relates to the operating procedure, the appropriated resource allocation, and the operating management alignment with firm goal. Two-item scale was adopted from Hanpuwadal and Ussahwanitchakit (2010) and two-item scale was adopted from Chai-Amonphaisal and Ussahwanitchakit (2010) for measuring the level of operational planning effectiveness. Fifth, decision making quality involves the firms’ ability to identify possibility alternative and making decision that consistent with firm objectives and the consequences of the decision. The extent of decision making quality was measured by five-item scale adopted from Tantiset and Ussahwanitchakit (2010). Finally, resource management capacity involves the capability of adequate budget setting for the operation unit or the project of the usefulness and efficiency of resources spending. Three-item scale was adopted from Hanpuwadal and Ussahwanitchakit (2010) for measuring the degree of resource management capacity.


3.2.2.3 Moderating Variable Moderating variable is environment uncertainty that refers to the insecurity, rapid and over time change of environment and unable to predict the future events or outcomes of decision and firm needs to seek the best way to improve the operational process to deal with environmental uncertainty. Two-item scale was adapted from Mathuramaytha and Ussahwanitchakit (2010) and one-item scale was developed to measure the degree of environmental uncertainty perception. 3.2.2.4 Control Variables Control variables are firm characteristics that include firm size and firm age. Size and age of firms may affect firm performance due to the large size and more operational experience may be able to accomplish the better performance (Tantiset and Ussahwanitchakit (2010). Firm size was measured by total asset and firm age was measured by number of years that firm has been operating. Dummy variable was used to proxy firm size and firm age. Total asset equals or higher than 50,000,000 Baht was represented by dummy value 1 and dummy value is 0 when total asset is lower than 50,000,000 Baht. To measure firm age, dummy value is 1 for more than 15 years and value is 0 for less than or equal 15 years. 3.3 Reliability and Validity Some constructs of this paper in the conceptual model were developed as new scales and adopted from prior researches. The face and content validity were verified by accounting academic experts. Confirmatory and exploratory factor analysis was utilized to examine the underlying relationship of a large number of items and to verify whether it can be reduced to a smaller set of factors. The factor analyses were done individually on each set of the items representing a particular scale; this approach is used for the limited observations reason. Factor loading values if greater than 0.50 are generally considered necessary for practical significant (Hair et al., 2010). All factor loadings are greater than cut-off 0.50. The measurement reliability was evaluated by Cronbach alpha coefficients. Generally agree on lower limit for Cronbach alpha is 0.70 (Hair et al., 2010). The scales of all measures meant appear to generate internal consistency between multiple measurements of a variable. Table 1 shows that the value of factor loadings indicated construct validity and Cronbach alpha coefficients indicated acceptable reliability for multiple-item scales.

TABLE 1 RESULTS OF MEASURE VALIDATION

Items Factor Loadings Cronbach Alpha

Cost Containment Orientation (CCO) 0.73-0.86 0.72 Cost Avoidance Focus (CAF) 0.76-0.83 0.70

Cost Reduction Capability (CRC) 0.82-0.85 0.78

Operational Planning Effectiveness (OPE) 0.69-0.86 0.79

Decision Making Quality (DMQ) 0.75-0.79 0.82

Resource Management Capacity (RMC) 0.80-0.89 0.79

Environment Uncertainty (ENU) 0.82-0.92 0.86

Firm Performance (FP) 0.63-0.91 0.89 3.4 Statistical Techniques To test the hypotheses of the relationships among cost management strategy, operational planning effectiveness, decision making quality, resource management capacity, environmental uncertainty, and firm performance of chemical manufacturing businesses in Thailand employ the ordinary least squares (OLS) regression analysis. With regard to independent-dependent relationships and metric-nonmetric data and the objective of this study that is to predict the change of dependent variable response to changes of independent variables, OLS is an appropriate method for examining hypotheses relationships (Hair et al., 2010). To understand the relationships in this study, the research models of these relationships are demonstrated as follows.


Equation 1: OPE = 001 + 1CCO + 2CAF + 3CRC + 4SIZE + 5AGE + Equation 2: OPE = 002 + 6CCO + 7CAF + 8CRC + 9ENU + 10(CCO*ENU) + 11(CAF*ENU) + 12(CRC*ENU) + 13SIZE + 14AGE + Equation 3: DMQ = 003 + 15CCO + 16CAF + 17CRC + 18SIZE + 19AGE + Equation 4: DMQ = 004 + 20CCO + 21CAF + 22CRC + 23ENU + 24(CCO*ENU) + 25(CAF*ENU) + 26(CRC*ENU) + 27SIZE + 28AGE + Equation 5: DMQ = 005 + 29OPE + 30RMC + 31SIZE + 32AGE + Equation 6: RMC = 006 + 33CCO + 34CAF + 35CRC + 36SIZE + 37AGE + Equation 7: RMC = 007 + 38CCO + 39CAF + 40CRC + 41ENU + 42(CCO*ENU) + 43(CAF*ENU) + 44(CRC*ENU) + 45SIZE + 46AGE + Equation 8: FP = 008 + 47DMQ + 48SIZE + 49AGE + Equation 9: FP = 009 + 50CCO + 51CAF + 52CRC + 53SIZE + 54AGE + 4. RESULTS AND DISCUSSION Table 2 shows the descriptive statistics and correlation matrix of all variables. To consider the possible problems of multicollinearity, variance inflation factors (VIFs) were used to verify the correlated of any single independent variable with a set of other independent variables. The VIFs range from 1.00 to 1.72, as recommended by Hair et al., (2010), it is well below the cut-off value of 10 indicating that the independent variables are not correlated with each other. Therefore, there are no significant multicollinearity problems encountered in this study.

TABLE 2 DESCRIPTIVE STATISTICS AND CORRELATION MATRIX

Variables CCO CAF CRC OPE DMQ RMC ENU FP

Mean 4.26 3.79 4.18 4.08 4.05 3.99 4.21 3.89 Standard Deviation 0.57 0.74 0.66 0.55 0.58 0.58 0.62 0.67 Cost Containment Orientation (CCO) Cost Avoidance Focus (CAF) 0.46*** Cost Reduction Capability (CRC) 0.53*** 0.35*** Operational Planning Efficiency (OPE) 0.61*** 0.33*** 0.44*** Decision Making Quality (DMQ) 0.55*** 0.46*** 0.45*** 0.69*** Resource Management Capacity (RMC) 0.47*** 0.46*** 0.45*** 0.64*** 0.72*** Environment Uncertainty (ENU) 0.27*** 0.22*** 0.33*** 0.17** 0.24*** 0.14 Firm Performance (FP) 0.28*** 0.33*** 036*** 0.42*** 0.47*** 0.41*** 0.39*** **p0.05, ***p<.01 The OLS regression analysis results of the hypotheses testing are shown in Table 3. Hypotheses 1-3 are to test the relationships among first dimension of cost management strategy as cost containment orientation (CCO) and operational planning effectiveness (OPE), decision making quality (DMQ), and resource management capacity (RMC), the results show that CCO significant positively related to OPE, DMQ, and RMC (1 = 0.52, p 0.01; 15 = 0.37, p 0.01; 33 = 0.22, p 0.01), thus Hypothesis 1 is supported. Hypothesis 2 tests the relationships among second dimension as cost avoidance focus (CAF) and OPE, DMQ, and RMC, the results show that CAF has the significant positive relationship with


DMQ and RMC (16 = 0.22, p 0.01; 34 = 0.29, p 0.01) but did not significantly relate to OPE, thus Hypothesis 2 is partially supported. Third dimension, cost reduction capability (CRC), has the significant positive relationships with OPE, DMQ, and RMC (3 = 0.14, p 0.10; 17 = 0.16, p 0.05; 35 = 0.22, p 0.01), hence Hypothesis 3 is supported.

The relationships between three dimensions of cost management and firm performance were tested, the results showed that CAF and CRC significant positively related to firm performance (51 = 0.23, p 0.01; 52 = 0.25, p 0.01) but CCO had no significant relationships, thus Hypotheses 5, 6 are supported but Hypothesis 4 is not. Hypothesis 9 tested the positive relationships between DMQ and firm performance and the result of Hypothesis 9 is supported (47 = 0.46, p 0.01).

TABLE 3 RESULTS OF REGRESSION ANALYSISa

Independent Dependent Variables Variables OPE OPE DMQ DMQ DMQ RMC RMC FP FP

CCO 0.52*** 0.51*** 0.37*** 0.34*** 0.22*** 0.19** 0.03 (0.08) (0.08) (0.08) (0.08) (0.08) (0.08) (0.09)

CAF 0.05 0.06 0.22*** 0.23*** 0.29*** 0.30*** 0.23*** (0.07) (0.07) (0.07) (0.07) (0.07) (0.07) (0.08)

CRC 0.14* 0.18** 0.16** 0.19*** 0.22*** 0.26*** 0.25*** (0.07) (0.08) (0.07) (0.08) (0.80) (0.08) (0.09)

ENU -0.06 0.01 -0.05 (.0.07) (0.07) (0.07)

CCO*ENU -0.14** -0.10 -0.03 (0.07) (0.07) (0.07)

CAF*ENU 0.13** 0.11 0.12** (0.06) (0.06) (0.06)

CRC*ENU 0.47 0.10 0.10 (0.06) (0.06) (0.06)

OPE 0.38*** (0.06)

RMC 0.47*** (0.06)

DMQ 0.46*** (0.07)

SIZE -0.17 -0.23 -0.33** -0.35** -0.25** -0.05 -0.11 0.17 0.07 (0.13) (0.14) (0.13) (0.13) (0.10) (0.14) (0.14) (0.15) (0.15)

AGE -0.12 -0.12 -0.05 -0.04 0.12 -0.23 -0.19 -0.19 -0.19 (0.13) (0.13) (0.13) (0.13) (0.10) (0.14) (0.13) (0.15) (0.15)

Adjusted R2 0.39 0.40 0.40 0.41 0.61 0.33 0.36 0.20 0.15 *p0.1, **p<.05, ***p<.01, a Beta coefficients with standard errors in parenthesis. Hypothesis 7 tests the relationships between OPE and DMQ, OPE significant positively related to DMQ (29 = 0.38, p 0.01). Hypothesis 8 examines the relationships between RMC and DMQ and the results indicate that RMC significant positively related to DMQ (30 = 0.47, p 0.01). Therefore, Hypotheses 7 and 8 are supported. Environment uncertainty (EUN) significant negatively moderates the relationships between CCO and OPE, and does not significantly moderate the relationships between CCO and DMQ and RMC, thus Hypothesis 10 is not supported. EUN significant positively moderates the relationship between CAF and OPE and RMC (11 = 0.13, p 0.05; 43 = 0.12, p 0.05), thus Hypothesis 11 is partially supported. Hypothesis 12 is not supported due to the fact that ENU does not significantly moderate the relationships between CRC and OPE, DMQ, and RMC. Firm size shows mixed results and


it significantly negatively relates to DMQ. Firm age does not significantly relate to OPE, DMQ, RMC, and firm performance. The results indicate that firms implemented with modern cost management strategy, cost containment and cost reduction have the operational planning effectiveness, decision making quality, and resource management capacity. Firms that focus on cost avoidance have decision making quality and resource management capacity. In addition, firms which emphasis on cost avoidance and cost reduction are able to achieve their goals and better performance. The results suggest that operational planning effectiveness and resource management capacity enhance the quality of decision making, and the consequences are firm performance improvement. These results are similar to those of Amoako-Gyampah and Acquaah (2008), which showed that operational efficiency impacted on quality production and leaded to better firm performance. Likewise, the study of James and Elmezughi (2010) reports that firms with cost leadership and activity-base costing having higher firm performance. Particularly, the results of this paper show that firms with the cost management strategy can enhance decision making quality and lead to superior firm performance. On the other hand, some of the results are not consistent as expected the operational planning effectiveness does not depend on cost avoidance focus, and cost containment does not relate to firm performance. The result suggests that firms with control cost techniques such as standard costing and budgeting do not have a direct effect on performance. However, it has an indirect effect on firm performance through decision making quality. There is mixed evidence of the moderating effect of environmental uncertainty on the relationships among three dimensions of cost management strategy implementation and operational planning effectiveness, decision making quality, and resource management capacity. The positive significant moderating effect of environment uncertainty on the relationships among cost avoidance focus and operational planning effectiveness and resource management capacity is consistent relevant to Lal and Hassel’s (1998) study. They found that when the environment is uncertainty, managers will have higher perceived with more sophistication management accounting system usefulness. Environment uncertainty significant negatively moderates the relationships between cost containment and operational planning effectiveness. The possible reason is when the rapid environment uncertainty happens and firms are unpredictable or firms are unable to increase the operational planning effectiveness to respond to uncertainty timely. Likewise, Hoque’s (2004) studied that the reported environment uncertainty had no significantly direct effect on organizational performance and had no significantly indirect effect through management’s use of non-financial performance measures. Firm size significant negatively relates to decision making quality, it indicates that large firms which are more formal cause will have lower communication effectiveness and decreasing decision making quality. Firm age did not significantly relate to all variables. This result suggests that operational planning effectiveness, decision making quality, resource management capacity and firm performance do not depend on the period of business running. 5. CONTRIBUTIONS 5.1 Theoretical Contribution The paper intends to clearly elaborate the relationships of cost management strategy, operational planning effectiveness, decision making quality, resource management capability, firm performance and the moderating effect of environmental uncertainty. Theoretical contribution of this study is providing knowledge for management accounting literature about cost management in terms of three dimensions: cost containment, cost avoidance, and cost reduction that impact on firm performance. 5.2 Managerial Contribution The results of this study provide important implications for firms’ executive as they indicate the positive relationships among cost management strategy, operational planning effectiveness, decision making quality, resource management capacity, and firm performance. To utilize the knowledge, firms can achieve goals and attain better performance when they implement cost management strategy. Therefore, these results help firms’ executives specify and consider the cost management strategy for implementation.


6. CONCLUSION Business competition forces firms to create the effective operation and retain profitability. Cost management strategy is an important managerial tool for response to these conditions (Zengin and Ada, 2010). This study examines the relationships among cost management strategy, operational planning effectiveness, decision making quality, resource management capacity, and firm performance and the moderating effect of environmental uncertainty of chemical manufacturing businesses in Thailand. 168 chemical product manufacturing firms are the sample of this study. The results suggest that firms which implement cost management strategy of three forms: cost containment, cost avoidance, and cost reduction enhance operational planning effectiveness, decision making quality, resource management capacity, and firm performance while environmental uncertainty gives rise to a mixed moderating effect. The sample of this study is 168 chemical manufacturer businesses in Thailand which many limit the generalizability, thus future research should cover the broader industries in order to increase the reliability. Furthermore, the moderating effect of environmental uncertainty is not significant and with mixed signs suggesting that future research should include other moderating variables to study. REFERENCES: Agndal, Henrik and Nilsson, Ulf. 2009. Interorganizational Cost Management in the Exchange Process. Management Accounting Research, 20: 85-101. Agrawal, Surendra P., Mehra, Satish and Siegel, Philip H. 1998. Cost Management System: An Operational Overview. Managerial Finance, 24(1): 60-78. Amoako-Gyampah, Kwasi and Acquaah, Moses. 2008. Manufacturing Strategy, Competitive Strategy and Firm Performance: And Empirical Study in a Developing Economy Environment. International Journal of Production Economics, 111: 575-592. Anderson, Shannon W. 2007. Managing Costs and Cost Structure throughout the Value Chain: Research on Strategic Cost Management. Handbook of Management Accounting Research, Elsevier Ltd. 481-506. ________ . and Dekker, Henri D. 2009. Strategic Cost Management in Supply Chains, Part 1: Structural Cost Management. Accounting Horizons, 23(2): 201-220. Armstrong, Scott J. and Overton, Terry S. 1977. Estimating Nonresponse Bias in Mai Surveys. Journal of Marketing Research, 14(August): 396-402. Ashill, Nicholas J. and Jobber, David. 1999. The Impact of Environmental Uncertainty Perceptions, Decision-Maker Characteristics and Work Environment Characteristics on the Perceived Usefulness of Marketing Information Systems (MkIS): A Conceptual Framework. Journal of Marketing Management, 15: 519-540. Backstrom, Henrilk and Lind, Johnny. 2005. Modern Management Strategies and Business Networks. Problems and Perspectives in Management, 1: 37-46. Chai-Amonphaisal, Korravee and Ussahawanitchakit, Phapruke. 2010. Strategic Management Accounting and Corporate Performance of Thai-listed Companies: A Mediating Effect of Management Process. International Journal of Strategic Management, 10(1): 1-23. Coad, Alan F. and Cullen, John. 2006. Inter-Organisational Cost Management: Towards an Evolutionary Perspective. Management Accounting Research, 17: 342-369.


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Seuring, Stefan. 2002. Cost Management in Supply Chains – Different Research Approaches. Heidelberg. Slater, Stanley, F. 1995. Issues in Conducting Marketing Strategy Research. Journal of Strategic Marketing, 3: 257-270. Tontiset, Nattawut and Ussahawanitchakit, Phapruke. Building Successful Cost Accounting Implementation of Electronics Manufacturing Businesses in Thailand: How Do Its Antecedents and Consequences Play a Significant Role? Journal of Academy of Business and Economics, 10(3): 1-25. Zengin, Yasemin and Ada, Erhan. 2010. Cost Management through Product Design: Target Costing Approach. International Journal of Production Research, 48(19): 5593-5561. AUTHOR PROFILES: Kanoknate Prempree earned her M.S. from Thammasat University, Thailand in 1999. Currently, she is a Ph.D. (Candidate) in Accounting at Mahasarakham Business School, Mahasarakham University, Thailand. Dr. Phapruke Ussahawanitchakit earned his Ph.D. at Washington State University, USA in 2002. Currently, he is an associate professor of accounting and Dean of Mahasarakham Business School, Mahasarakham University, Thailand.


RISK TOLERANCE: A BEHAVIOURAL ANALYSIS

Everton Anger Cavalheiro University of Cruz Alta Kelmara Mendes Vieira Federal University of Santa Maria Paulo Sérgio Ceretta Federal University of Santa Maria

ABSTRACT The traditional perspective of financial theory suggests an implicit rationality on decision making. Historically, researches have revolved around demographic, social and economic heuristics, thus neglecting the emotional, cognitive and behavioral suppositions, related to financial decision making. In this sense, this study aims to evaluate which are the determining factors for risk tolerance. So, we carried out a survey on 815 individuals residing in Santa Maria, Julio de Castilhos and Cruz Alta, Brazil. Afterwards, we performed a CFA and, eventually, a regression analysis. Generally and consistently, the suppositions for rationality were refuted, though consistent to the Prospect Theory, validating the numerous studies that demonstrate the violation of the rationality suppositions. The heuristics which are traditionally used in order to determine the level of risk tolerance have not shown to be significant in this research. The cognitive, emotional and behavioral dimensions of decision making have shown to be significant. Keywords: risk; risk tolerance; Behavioral Finance 1. INTRODUCTION The premise that currently supports most of the modern economic and financial theory is based on the rationality held by the economical agents. This conceptual aspect suggests that all economical agents are completely rational and that they use all the available information in the best way possible. As a consequence, individuals will choose their optimal option that will in turn maximize their satisfaction (Mosca, 2009). We can find in this context of rationality for financial decision the Expected Utility Theory (EUT) that was shaped by Von Neumann and Morgenstern (1944). EUT is an axiomatic theory that is based in the premise that the rational human being makes decisions by comparing the promised utility for each alternative (multiplying the expected utility for each option by the respective probability and choosing the highest value). One of the main axioms in EUT is the one on rationality, which subsidizes the one on utility and suggests that individuals will make their choices based in expected utility, so as to maximize their wealth. However, Allais (1953), as well as Edwards (1961), Quiggin (1982), Segal (1989), Quiggin and Wakker (1994), demonstrated that human beings often violate the rationality axiom, as suggested by EUT. Among financial decisions, behavior facing risk is one of the central themes. Risk tolerance is a determining factor when it comes to choosing how to allocate assets and, as a consequence, it directly influences the creation of products and the definition of investment and funding strategies. In this context, several studies seek to identify factors that influence risk tolerance, but many questions are yet to be answered, especially regarding its determinants. Several heuristics are used in order to determine the level of risk tolerance in individuals, which suppose a strong correlation between the demographical and social/economical characteristics. However, few studies demonstrate the influence of the cognitive, behavioral end emotional dimensions on financial decision making. Considering the importance of risk tolerance, the setback of financial theories that approach rationality, the discrepancy of results when compared to determining factors and the scarcity of studies that demonstrate the influence on cognitive, emotional and behavioral dimensions in risk tolerance, this research sought to answer the following question: which are the determining factors in risk tolerance for financial decisions?


2. REVIEW 2.1 The traditional perspective on risk and the Expected Utility Theory Risk, according to the traditional conception, is objective and of a quantitative nature. It is based in past information (occurrence of an event followed by a statistical evaluation) so as to make a decision in order to increase the safety of results. In this sense, risk definition, according to EUT, supposes that the investor evaluates the investment risk according to the change that it carries as far as wealth is concerned. Ricciardi (2004) states that according to EUT, risk is analyzed by relating the expected return in terms of utility. Another relevant point to be highlighted is that EUT works with the concept that the investor is perfectly rational when making decisions, always preferring the alternative that presents a greater increase of his expected wealth. Moore (1968) described it as objective risk: the word “risk” commonly denotes only future events where the probabilities for the alternative results are known. Probability is a measure for the relative frequency for an event and is strictly applicable to events that are repeated in nature. Thus, it shows distribution, and such observations can be analyzed and statistical inferences can be carried out. When there are a great number of observations available, the highest frequency observed, bias-free, gets closer to the objective risk, via the probability for the event to happen. The Expected Utility Theory (EUT) is the main theory to process – a in a statistical manner – the problems regarding economical decision. It was initially launched by Von Neumann and Morgenstern (1944), although there is evidence, in the case of Baron (2008), e.g., that the first scientific work on EUT was developed by Daniel Bernoulli, in 1738, as an attempt to solve the Saint Petersburg Paradox. Utility can be defined as “the level of satisfaction that somebody has when consuming a good or performing an activity”. The terms “utility” or “preference” are frequently used in order to define the decision maker’s attitude facing the choice. They basically refer to the relationship between alternatives, in which the decision maker prefers one instead of the other always choosing the one that offers more “expected utility”, as quoted by Pindyck and Rubinfeld (2005). According to EUT, a rational individual always needs to have imperative preferences, i.e., one must never abstain from acting rationally. In this concept, a rationally acting individual must agree and act consistently to the presented axioms. Meanwhile, some evidence for inconsistencies was found in some of these axioms. 2.2 The cognitive and behavioral perspective on risk The basic assumption of modern finance states that man is a rational being and a maximizer for expected utility. However, literature on markets’ irrationality is fertile. The idea that markets could behave in an irrational manner was against the principles of expected utility. However, according to Kahnemann and Riepe (1998), financial decisions are made in times of high complexity and great uncertainty. Often, the moment’s emotional stress at the moment of financial decision is huge. This ambiance makes the investor trust intuition which often plays a crucial role in financial decisions. This is the context where the prejudices that push them away from rationality come up. In this sense, discussion on human rationality and, as a consequence, the validity of EUT, has opened a new path for a new area in Finance that is currently being developed and called Behavioral Finance. This area is commonly defined as the application of Psychology to Finance, in an attempt to explain the financial decision of individuals. For Behavioral Finance, decisions made according to a problem follow, in some cases, an identifiable pattern that can and should be contemplated by an economical and financial model. The field of Behavioral Finance is precisely the identification of how emotions and cognitive mistakes may influence the decision making process and of how such behavioral patterns can determine changes in the market.


2.2.1. Excessive confidence bias Excessive confidence, or overrating personal skills, is maybe the behavioral bias that has a greater number of studies confirming its existence. For some researchers it gets to be the element with the strongest influence on the decision making process. It is vastly observed in individuals who imagine they own a decision making skill that is superior to the average population. Biais, Hilton, Mazurier and Pouget (2002) created an experimental market to study the influence of excessive confidence on the performance of investment portfolios. In this study, researchers demonstrate that, the more an individual suffers from excessive confidence, the worse the performance for his investment portfolio is, when compared to other investors. Pompiam (2006) quotes that, in its most basic form, excessive confidence maybe summarized as unjustified faith in an intuitive reasoning, in judgments or cognitive skills. The concept of excessive confidence bias is based in the set of cognitive and psychological experiences that directly influence the decision making process, overestimating both the anticipating skills and the precision of the information that underlies them. Fallaciously, they tend to compare the amount of information to its quality, making an individual believe that the more information he has, the more prepared he will be, without even analyzing its validity. Another perverted consequence of excessive confidence is the reluctance in assuming a mistake. This feeling of aversion to regret shapes another bias that is commonly studied in Behavioral Finance: cognitive dissonance. 2.2.2. Cognitive Dissonance When a new piece of information starts conflict with pre-existent perceptions, individuals often feel a mental discomfort, which is a phenomenon known as cognitive dissonance. In Psychology, cognitions represent attitudes, emotions, beliefs and values, and cognitive dissonance corresponds to an unbalanced condition that takes place when contradictory cognitions collide. According to Pompain (2006), the concept of cognitive dissonance inscapes the answer of individuals when trying to harmonize cognitions and, thus, to relief their mental discomfort. Pompain (2006) quotes that the difficulty to accept the mistake in a decision is perceived as a contestation of such decision and this becomes an emotional threat. Most people avoid dissonant situations or even ignore potentially relevant information so as to avoid psychological conflicts. Scholars have identified different aspects of the cognitive dissonance and that participate in the decision making process: selective perception and selective decision making. Individuals who suffer from selective perception only register information that confirms the path chosen, thus producing an incomplete vision of reality and, as a consequence, imprecise. Since they are unable to objectively analyze the available evidence, they become more and more likely to make calculation and prejudiced mistakes in their future decisions. On the other hand, selective decision making takes place when the commitment to the decision is high, thus forcing the individual to rationalize his actions in such a way that they do not enter a conflict with his decision, even when there is an exorbitant economical cost to it. Many studies show that individuals will subjectively and continuously reinforce decisions or commitments made or taken in the past. In order to weather the dissonance that comes from recognizing mistakes in the past, investors often associate their failures to external events opposite to assuming a bad decision. Naturally, people who lose the opportunity of learning from their past will be prone to new calculation mistakes, thus renewing the anxiety cycle, discomfort, dissonance and denial. Another bias that is associated to cognitive dissonance is the self-attribution bias. 2.2.3. Self-attribution bias Self-attribution bias refers to the tendency individuals have to attribute their success to innate features, such as talent for anticipating or their own intelligence, although their failures are often attributed to external influences, such as bad luck. Pompian (2006) quotes that the self attribution bias is a cognitive phenomenon that makes individuals attribute their negative results to situational factors and their gains to innate factors of their own nature. This bias can be divided into analysis forms: self-enhancing bias, which represents how prone individuals are to claim an irrational degree of credit for their success; self-protecting bias, represents the corollary to the irrational denial of responsibility for failure. The author concludes that the self-enhancing bias may be explained by a cognitive approach, because individuals are naturally more biased to credit their success rather than their failures, since


they intend to have success, instead of failing. Self-protecting bias can be explained from an emotional point of view. Psychologists argue the human being’s need to keep their self-esteem by instigating psychological protection, so as to decrease the psychological pain of assuming guilt for wrong decision. The irrational attribution of success and failure can harm an investor in two primary ways. First, people who are not able to understand their own mistakes are, as a consequence, unable to learn from their own mistakes. Second, investors grant a disproportional credit to the positive results of their investments, making them excessively confident about their future decisions. 2.2.4. Excessive Optimism Bias Investors may be excessively optimistic about markets, economy and the potential value increase of assets they have invested in. According to Pompian (2006), many investors believe a bad investment will not happen to them, but only to others. These neglects may harm the profitability of their investment portfolios, because individuals may not recognize the potential consequences of their investment decisions. Daniel Kahneman and Daniel Lovallo describe the excessive optimism bias in a more technical way. Researchers marked a tendency of investors to adopt an internal vision, with a clear personal involvement, instead of an external vision, without personal involvements. The external vision, not passionate, evaluates the current situation regarding results obtained in the past, relating and analyzing them in the most unbiased way possible. The process of external vision replaced by internal vision is the one that distinguishes excessive optimism, thus harming the rational decision and implying in predictions that are too “pink”, influenced by feelings that are related to present situations in a biased manner. Pompian (2006), quotes that most investors are inclined towards an internal vision, influenced by their feelings. This approach, according to the author, is traditional and rooted, and it comes in an intuitive way. Since the path to think about an investment is complex, due to the need to analyze the available data and to pay special attention to unique or uncommon details, the perception of the need to gather stats about a case rarely comes up in an investor’s mind. 2.2.5. The fear of missing a gain opportunity Mosca (2009) comments that the fear of missing a gain opportunity in a specific investment that others are participating in is a stronger motivator for the acquisition of a specific asset, when compared to the fear a financial loss, as long as most of his peers have made the same mistake. Such fear of being left out is the main fuel that drives the herd movements and, consequently, the forming of bubbles. Research led by DeMarzo, Kremer and Keniel, Stanford and Duke Universities, confirm that most fear, not the loss itself, but the risk of seeing their investments having a worse performance when compared to other investors. These researchers demonstrate that individuals care first about the wealth – compared to other people or members of their community. So, for these authors, fear #1, regarding managing their property, is to be poor while other get richer. Generally, people and companies follow the behavioral pattern of their peers because, by acting in such a way, they are fighting the risk that other might be investing in the next big winner, while they are out (MOSCA, 2009). There is, hence, a strong influence or pressure exerted by the observed or assumed behavior of our peers, where the final decision to allocate assets ends being determined by the perception of the evolution of wealth when compared to the other members of the group. 2.3 The emotional and social perspective on risk Nofsinger (2005) quotes that finances have followed modern economy quite a lot, which seems to be seen as a branch of exact sciences. To that respect, neoclassical finance theory tends to ignore the influence of social factors in the finance decision context, and a great part of the Traditional Finance is modeled in a Robinson Crusoe-like economy, i.e., isolated from the social system to which it belongs to. For the author, economy is not a physical system, but yet a complex system of human interactions. Humor affects the way investors analyze judgments (Nofsinger, 2002). People in a good mood make more optimistic judgments than people in a bad mood. Being in a bad mood makes investors more critical; it helps them exercise a more detailed analysis. As an alternative, people in a good mood will tend to use less critical ways to process information. That aspect particularly affects relatively abstract decisions, about which people do not have complete or exact information. Naturally, this situation perfectly describes the investment context. According to the author, bad mood causes a more critical analysis of judgments and good mood tends to cause


decisions taken without much analysis. So, investment decision making is directly influenced by the individual’s mood. Nofsinger (2005) comments that conversation is important for stock market. Brokers interact with clients and other brokers. Analysts communicate with executives. Individual investors talk to their families, neighbors, colleagues and friends about investments. Shiller (1995) perform their research in institutions and on individual investors about their communication patterns. Authors conclude that the directing of interpersonal communications is very important in investments decisions. Hong, Kubik, and Stein (2005) analyze portfolio managers so as to test the premise that fund managers that work in the same city are more prone to exchanging investment ideas by word of mouth. Authors demonstrate that managers in the same city are more prone to exchange the same type of stocks and conclude that investments are consistent to the information that is being distributed by these interactions. 3. METHOD This research was carried out with the inhabitants of Santa Maria, Julio de Castilhos and Cruz Alta (Brazil). A total of 815 questionnaires were applied. The main technique to define the determining factors for risk tolerance was the Exploratory Factorial Analysis. In order to answer the problem of this research “we used the multivariate technique, called multiple regression analysis. Risk tolerance is a concept that has implications for individual investors, as well as managers in finance, or investment managers, for example. Droms and Strauss (2003) quotes that, for individual investors, risk tolerance will determine the adequate composition of assets in an optimized portfolio, as far as risk and return are concerned regarding each individual’s needs. The tool for collecting data was adapted from Droms and Strauss (2003) so as to determine the level of the individuals’ risk tolerance. The tool for collecting data was adapted from Droms and Strauss (2003)so as to determine the level of the individuals’ risk tolerance. In order to make this measure more quantitative, the participant was given the possibility of assigning a score (0-10), depending on how much he/she agreed with each one of the six questions. When assigning a zero score, the participant showed not to agree to the statement and when assigning ten, he/she utterly agreed. With the new scale, the sum of the values pointed out by the participants for each of the six questions could range from zero (totally intolerant to risk) to sixty points (totally tolerant to risk). 4. RESULTS In order to find the determining factors for risk tolerance, we initially performed a factorial analysis. Adequacy and specificity tests performed on the sample were considered satisfactory, because the results from the Kaiser-Meyer-Olkin (KMO) equals 0,828 and the Bartlett’s test showed a qui-square equal to 6.447,219 and significance equal to 0,000. Table 1 shows variance explained by factors with eigenvalues superior to 1. Table 1: Extracted Factors and respective eigenvalues and explained variance

Factor Eigenvalue Explained variance

Percentual Accumulated 1 5,913 25,709 25,709 2 2,221 9,655 35,364 3 1,972 8,574 43,938 4 1,371 5,96 49,898 5 1,339 5,82 55,718 6 1,177 5,116 60,834 7 1,047 4,553 65,387

Table 1 shows that the seven selected factors (with eigenvalues bigger than 1) explain, altogether, 65.39% of the data total variance, excluding other 16 factors that showed eigenvalues smaller than or


equal to 1. On Table 2 we show the factorial cargo on each of these seven factors, as well as the variables for each factor. Table 2: Factorial cargo obtained for each factor and respective variable

Variable Factors

1 2 3 4 5 6 7

Enjoys a lot of luxury in life 0,77

Enjoys owning things that impress people 0,74

Better life if had many things that does not have now 0,73

Would be much happier if could buy more things 0,65

Upset if unable to buy all desired things 0,60

Money means pleasure 0,50

Afraid of losing an opportunity everyone takes 0,78

Relieved because own mistake is the same as everyone else’s

0,75

Afraid of having worse results than others 0,71

Make same decisions as most people 0,61

Tranquility / peace 0,82

Enthusiasm 0,79

Happiness 0,75

Able to identify the best moment to invest 0,79

Gains are a direct result of his/her competence 0,78

Instincts contribute for choosing investments 0,71

Prefers spread payments even if total is more expensive 0,77

Buys on spread payments instead of waiting to have money 0,71

Finds it normal to get into debt so as to buy things 0,66

Comments if there is loss 0,86

Comments if there is profit 0,83

Cognitive disonance 0,80

Losses are caused by invisible factors 0,79

All factors presented satisfactory factorial cargo (bigger or smaller than 0.50) and hence we kept them for this study, such as suggested by Hair et al. (1998) – cargo greater than 0.30 is significant. After estimating the factorial cargo, we named the factors. The first factor was called “materialism” for the interest in material goods and emotional association, whether by acquisition, or by the impossibility of acquiring such goods. Fournier and Richins (1991) quote that society nowadays lives


an era of compulsive materialism. Authors have studied materialism in several different countries and concluded that the popular meaning of materialism involves notions of possessing or achieving the best, and wishing for wealth as an objective itself. For these authors, this notion is associated to objectives, such as the search for happiness, demonstration of social status, self-affirmation and feeling of superiority. The second factor was called the “left out effect”, because a common way to simplify the decision making process is simply to follow the pack; to do what everyone else is doing. We have the innate necessity to act according to the other members of the group in which we are in. Mosca (2009) quotes that acting in such a way brings comfort and security, even because making a mistake along with others is less awkward. Pompain (2006) quotes that when we act differently from our social group, our subconscious enters a conflict with pre-existent perceptions and individuals often feel a mental discomfort – a phenomenon known as cognitive dissonance. Cognitions, in Psychology, represent attitudes, emotions, beliefs and values, and cognitive dissonance is an unbalanced condition that takes place when contradictory cognitions cross. Psychologists conclude that individuals perform pseudo-rationalizations so as to synchronize their cognitions and keep their psychological stability. Thus, individuals modify their behaviors or cognitions in order to reach a new cognitive harmony. However, such changes are not always made in a rational way. Such pseudo-rationalizations can make individuals ignore potentially relevant information so as to avoid psychological conflicts, thus elevating their risk tolerance level. The third factor was called “emotion”, because both the psychologists and the economists that analyzed the role of emotion in decision making realized that feelings and emotions that are unattached to the subject can affect decisions (Loewenstein, Weber, Hsee, & Welch, 2001). The term “unattached”, in this context, means that emotions are not related to the decision to be made. Nofsinger (2001) quotes that emotions interact with the evaluation’s cognitive process and end up leading to a decision. Sometimes, emotional reactions diverge from reasoning and logic so as to determine the decision making process. In fact, the more complex and uncertain the situation is, the more emotions influence the decision (Forgas, 1995). Cavalheiro et al. (2011) quotes that financial decisions are complex and include uncertainty and can be influenced by feelings, emotions or mood. That is called misattribution bias, i.e., people generally let themselves being unduly influenced by feelings when making a financial decision. The fourth factor is called self-attribution bias via self-enhancement. Self-attribution bias is a cognitive phenomenon that makes an individual associate their negative results to situational factors and their gains to innate factors of their nature (Pompian, 2006). This bias can be divided into analysis forms: a) self-enhancing bias, that represents how prone individuals are to claim an irrational degree of credit to their success and b) self-protecting bias, representing the corollary effect to the irrational denial of responsibility for failure. The fifth factor is called “indebtedness”. The sixth factor is called “talking about investments”. People learn from interacting with each other. The human-being observes other people’s behavior because he wants to interpret what they are thinking, but what he really likes is to take the most of the conversation’s social interaction. People talk about subjects that they are enthusiastic about, topics that they are interested in and even about what upsets them. Conversation is an important way to get information and detect emotional reactions, and this helps to make an opinion. The last factor was called self-attribution bias by self-protection. Self-protection bias is taken as the attribution of personal failure to external influences, such as bad luck (Pompiam, 2006). Self-protection bias can be explained from an emotional point of view, for the human need to keep self-esteem. This effect is connected to the difficulty humans have in recognizing their mistakes, because this recognition takes the individual to a level of unwanted psychological pain, directly influencing financial decisions. In order to evaluate the liability of factors generated from the factorial analysis, we used Cronbach’s Alpha. According to Hair et al. (1998), Cronbach’s alpha should be bigger than 0.6 (because it is considered to be an exploratory factorial analysis). On Table 3, we show the variables that make up each factor and their respective results for Cronbach’s alpha.


Table 3: Variables and Cronbach’s alpha for each factorFactor Variables Cronbach’s alpha Materialisme 88, 86, 80, 84, 90 e 74 0,8282 Being left out effect 60, 59, 61 e 58 0,7911 Emotion 44, 45 e 43 0,7429 Self-enhancement 32, 34 e 35 0,7188 Indebtedement 87, 83 e 81 0,6487 Talk about investments 36 e 38 0,7584 Self-protection 31 e 33 0,4813

On Table 3 self-protection stands out with a Cronbach’s alpha smaller to the one established by Hair et al. (1998) and, since it is no longer possible to exclude any variable because there are only two, we calculated the variables’ average for each factor. In order to check the influence of variables and factors on risk tolerance, we performed a multiple regression analysis. Risk tolerance was considered as an exogenous variable. Results of the chosen model, via stepwise, are shown on Table 4. Table 4: Regressors, weights and coefficient significance of the OLS regression model in order to explain the exogenous variable – risk toleranceRegressors Coef. std. deviation t test t test sig. FIV Emotion factor 0,698272 0,14154 4,9330 0,0000 1,2060Being left out effect factor 0,397497 0,14311 2,7770 0,0056 1,5880Cash-on.stock effect 0,295800 0,09810 3,0150 0,0026 1,2710Cognitive disonance 0,399484 0,10626 3,7600 0,0002 1,2000Self-protection 0,446919 0,10104 4,4230 0,0000 1,2710Excessive confidence bias 0,509397 0,11599 4,3920 0,0000 1,4820Risk as an opportunity 0,438070 0,10048 4,3600 0,0000 1,1650Self-attribution factor 0,676986 0,13476 5,0240 0,0000 1,3940Save before you spend 0,331377 0,10160 3,2620 0,0012 1,2640Already incurred in cost 0,346609 0,09310 3,7230 0,0002 1,2540Spending on expensive things

0,270591 0,10385 2,6060 0,0093 1,3700

Excessive confidence 0,210590 0,09934 2,1200 0,0343 1,2230Excessive optimisme 0,338406 0,13329 2,5390 0,0113 1,4080

The Stepwise model selected 13 regressors, 3 factors of which were used (emotion, self-attribution and being left out effect) and 10 variables. The determination coefficient (adjusted R2) was 0.93. We can observe on Table 4 that all values for the t test were significant, as well as the ones for the f test (811,634 and sig. 000). The Akaike Information Criteria was equal to 5.713,168 and the Schwarz Criteria was equal to 5.774,309. On the other hand, the White test for heterocedasticity rejected the null hypothesis Qui-square = 381,476245 with sig. 0,000), indicating the existence of heterocedascity, of a specification error, or both, although the FIV index suggests the inexistence of multicollinearity. In order to correct the heterocedascity effect, we performed a new estimate for the parameters, now with variances and standard deviation with a corrected heterocedascity according to White Table 5: Regressors, weights and coefficients significance of the minimum square model with corrected heterocedascity in order to explain the exogenous variable – risk tolerance Regressors Coef. std. deviation T test T test sig. FIV Emotion factor 0,949704 0,107794 8,8100 0,0000 1,1230 Being left out effect 0,459063 0,140252 3,2730 0,0011 1,5590 Cash on stock effect 0,253982 0,096049 2,6440 0,0083 1,2280 Cognitive disonance 0,386345 0,118740 3,2540 0,0012 1,1940 Self-protection 0,703214 0,097752 7,1940 0,0000 1,1870 Excessive confidence 0,492706 0,125111 3,9380 0,0001 1,4670 Risk as opportunity 0,459499 0,097596 4,7080 0,0000 1,1420 Self-attribution bias 0,751971 0,115600 6,5050 0,0000 1,3410 Save before you spend 0,358285 0,088313 4,0570 0,0001 1,1600 Already incurred in cost 0,360764 0,091277 3,9520 0,0001 1,2400 Spending on expensive things

0,276512 0,107104 2,5820 0,0100 1,3110


On Table 5 we can observe that all t test values were significant (the variables for “excessive optimism” and “excessive confidence bias” were excluded from the model because they were not significant at the t test). The sample determination coefficient (adjusted R2) was 0.3492. Although the sample determination coefficient had been inferior to the previous mode, the Akaike Information Criteria and the Schwartz Criteria were 3.380,183 and 3.431,918, respectively. All FIV indicators were close to one, indicating the absence of multicollinearity in this model. The Qui-square test (0.651 and sig 0.72220), for residual normality (Doornik-Hansen test), accepted the null hypothesis for equal distribution of data with normal distribution. 5. FINAL CONSIDERATIONS The basis that supports most of the financial theories is founded upon the utter rationality of economical agents. This approach suggests that all economical agents are totally rational and use all available information in the best possible way. The heuristics used so as to determine the risk tolerance level of individuals and that suppose a strong correlation between demographic, social and economical features have not shown to be significant in this research. The cognitive and emotional dimension of the decision making process has shown to be significant. Emotion and cognitive bias such as: self-attribution, excessive trust, cognitive dissonance, being left out effect, cash on stock effect and already incurred in costs have shown to be significant in this research, thus showing cognitive and emotional features during the decision making process, that are traditionally neglected in risk tolerance studies. Considering that the regression estimated model attends to the basic presuppositions, it is possible to state that, for the selected sample, emotion have a direct and positive association to an individual’s risk tolerance. This association – that can be understood as the misattribution bias – validates Nofsinger (2001), who demonstrates that this bias generally makes people permeable to being influenced by feelings when making a financial decision. Via this result, it is possible to conclude that people in a good mood make more optimistic judgments than people in a bad mood, and tend to use less critical ways to process information, thus elevating their tolerance level. Humans show a natural tendency to follow the decisions made by the group. This behavioral effect can be observed by the “being left out effect”. The factor, in the selected sample, showed a positive association to risk tolerance, and it was possible to conclude that the bigger the effect, the bigger the risk tolerance is. This result contributes to what DeMarzo, Kremer and Keniel at Stanford and Duke Universities, suggest – they confirmed that most individuals do not fear loss itself, they are afraid of watching their applications having a worse performance than other investors. People and companies tend to follow their peers’ behavior, because when acting that way they are fighting the fear that other people may be investing in the next big investor, whereas others would be out. The “being left out effect” is potentially harming because it makes people assume more risks in their financial decisions and, hence, they would tend to neglect their ability to assume risks, which can lead to damage to their patrimony, by unduly exposing it to risk. Empirical international literature demonstrates that, after having profit or loss, people feel inclined to assuming greater risks. People who gamble call it “cash in stock” and, after making some money, amateurs do not consider it their money. Regression showed that, for the selected sample, the increase of this effect is associated to an increase of risk tolerance which could generate an increase in markets negotiations, since investors could believe that they would be risking something that does not belong to them. The Cognitive Dissonance variable has shown to be directly and positively associated to risk tolerance. This result can be understood as human nature – to dissociate the acknowledgement of guilt for one’s mistakes in decisions made by individuals. Assuming guilt for one’s own negative results is to assume that the wrong decisions were made and that generates a mental discomfort that in turn leads to psychological pain. In order to balance or even avoid such discomfort, it is easier to associate negative results in decisions to external aspects. It was possible to observe in this research that, for the selected sample, the lack of acknowledgement was directly and positively associated to a


greater risk tolerance level. This result tends to be harming, since when avoiding acknowledgement for one’s mistakes, one cannot learn from those mistakes, which can lead to the same mistakes and recurrent negative results in their investments portfolios. The misattribution bias shows two sides: self-enhancement and self-protection. Self-protection has a similar origin to the previous variable, since one avoids the association between the error’s guilt and the decision-maker. The basic difference is that the mistake is associated to unpredictable circumstances, which would decrease the psychological pain coming from making the wrong decision. This variable showed to be positively associated to risk; so, we can conclude that, for the selected sample, a greater effect is associated to a greater risk tolerance and - just like in the previous effect - assuming new risks without even learning from previous mistakes could lead to persistent negative results. The self-attribution bias showed a positive relation to risk tolerance, thus indicating that the bigger the effect of this bias, the bigger the risk tolerance is. This bias might be the most harming one, because it makes individuals believe they have a superior capacity than they really have. This belief leads to a greater level of self-confidence, less attention to details and, as demonstrated in our research, a greater risk tolerance, which is particularly concerning, because it could lead to wrong decision when allocating assets. The excessive confidence bias showed a significant and positive relationship to risk tolerance in this research. This result validates Nofsinger (2001) demonstrating that individuals who show excessive confidence underestimate the risk they are taking. Underestimating risks can lead to choices that carry an unwanted risk level, thus not considering the capacity one has to take them, as well as a possible psychological pain of seeing that the obtained results are inferior to what was expected. This bias should, preferably have minimal influence when managing wealth, because of the loss coming from potentially biased decisions. Materialism, due to the need for consuming expensive objects, has shown to be positively and significantly associated to risk tolerance. Empirical literature demonstrates that the popular meaning of materialism involves notions of owning or achieving the best. Damage associated to this factor takes place when one loses track of the objective for which one is taking a risk. Taking a risk exclusively for the wish of a new standard of wealth, without parameters or final objective may make individuals assume more and more risks without realizing the potential damage associated to their decisions. On the other hand, aversion to debts, for the need to save before you spend, has shown to be positively related to risk tolerance; this fact, along with other quoted variables, may also be harmful by restricting opportunities for investments. According to traditional economical theories, people should consider present and future costs and benefits when making a decision, not considering past costs. However, we have the natural tendency to avoid this dissociation, especially when there is a need to acknowledge mistakes in the past. This bias has shown to be associated to risk tolerance, that can generate unwanted results while investing time and assets that have consistently shown to be harmful. The decision to keep assets with loss has shown to be a natural protection against the pain associated to acknowledging wrong decisions, but it is inconsistent with the assets’ wealth. When looking for the answer to the research problem, it was possible to observe that the financial decision is influenced by biases that positively influence risk tolerance. Perhaps the most significant message to take from this research can be interpreted by the need of self-knowledge, in order to minimize such effects when making a financial decision, o as to avoid potentially harmful risks, for not answering the capacity to take risks. REFERENCES: Allais, M. (1953). Le comportement de l'homme rationnel devant le risque: Critique des postulats et axiomes de l'école Américaine. Econometrica: Journal of the Econometric Society, 503-546. Baron, J. (2008). Thinking and deciding: Cambridge Univ Pr. Biais, B., Hilton, D., Mazurier, K., & Pouget, S. (2002). Psychological Traits and Trading Strategies. SSRN eLibrary.


Cavalheiro, E. A., Vieira, K. M., Ceretta, P. S., Trindade, L. L., & Tavares, C. E. M. (2011). Misattribution Bias: The Role of Emotion in Risk Tolerance. The IUP Journal of Behavioral Finance, Vol. VIII, No. 3, pp. 25-44, September. Droms, W. G., & Strauss, S. N. (2003). Assessing risk tolerance for asset allocation. Journal of Financial Planning, 16(3), 72-77. Edwards, W. (1961). Behavioral decision theory. Annual review of psychology, 12(1), 473-498. Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological bulletin, 117(1), 39. Fournier, S., & Richins, M. L. (1991). Some theoretical and popular notions concerning materialism. Journal of Social Behavior & Personality. Gujarati, D. N. (1995). Basic econometrics. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5): Prentice hall New Jersey. Hong, H., Kubik, J. D., & Stein, J. C. (2005). Thy Neighbor's Portfolio: Word‐of‐Mouth Effects in the Holdings and Trades of Money Managers. The journal of Finance, 60(6), 2801-2824. Kahneman, D., & Riepe, M. W. (1998). Aspects of investor psychology. The Journal of Portfolio Management, 24(4), 52-65. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological bulletin, 127(2), 267. Moore, B. J. (1968). An introduction to the theory of finance: Assetholder behavior under uncertainty: Free Press. Mosca, A. (2009). Finanças comportamentais: gerencie suas emoções e alcance sucesso nos investimentos. Coleção Expo Money. Rio de Janeiro: Elsevier. Nofsinger, J. R. (2002). Do optimists make the best investors? Corporate Finance Review, 6(4), 11-17. Nofsinger, J. R. (2005). Social mood and financial economics. The Journal of Behavioral Finance, 6(3), 144-160. Pindyck, R., & Rubinfeld, D. (2005). Microeconomics (6th edn): Upper Saddle River, NJ: Pearson Prentice Hall. Pompian, M. M. (2006). Behavioral finance and wealth management. How to build optimalportfoliosforprivate clients. Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behavior & Organization, 3(4), 323-343. Quiggin, J., & Wakker, P. P. (1994). The axiomatic basis of anticipated utility: A clarification. Journal of Economic Theory, 64(2), 486-499. Segal, U. (1989). Axiomatic representation of expected utility with rank-dependent probabilities. Annals of Operations Research, 19, 359-373. Von Neumann, J., & Morgenstern., O. (1944). Theory of Games and Economic Behavior: Princeton University Press, Princeton, NJ.


INTELLECTUAL CAPITAL ORIENTATION AND SUSTAINABLE PERFORMANCE OF MEDICAL SERVICE BUSINESS:

AN EMPIRICAL STUDY OF PRIVATE HOSPITALS IN THAILAND

Sumittra Jirawuttinunt, Mahasarakham Business School, Mahasarakham University, Thailand Kannika Janepuengporn, Mahasarakham Business School, Mahasarakham University, Thailand

ABSTRACT The purpose in this study is to examine the relationships between intellectual capital orientation (human capital, structural capital and relational capital) and sustainable business performance via knowledge management effectiveness and organizational innovation. In addition, it also explores the moderating effects of transformational leadership in the relationships of the model. Here, 102 private hospitals in Thailand are chosen as the sample of the study. According to medical service business in Thailand, the results of OLS regression analysis indicate that three dimensions of intellectual capital orientation have significant influence on sustainable business performance through knowledge management effectiveness and organizational innovation. For the moderating effects, transformational leadership increases only some relationships. Potential discussion with the research results is effectively implemented in the research. Theoretical and managerial contributions are explicitly provided. Conclusion and directions of the future research are highlighted. Keywords: Intellectual Capital, Human Capital, Structural Capital, Relational Capital, Transformational Leadership, Knowledge Management Effectiveness, Organizational Innovation and Sustainable Business Performance 1. INTRODUCTION In the modern economic perspectives, knowledge can be viewed as the most powerful weapon for business competition (Nonaka and Toyama, 2003; Youndt et al., 2004). Accordingly, Guthrie (2001) and Juma and Payne (2004) argued that winning companies do not gain benefits with only tangible assets, but they mostly emphasis on access to intangible assets such as intellectual capital (IC). More than 50% of firm’s value creation in today’s economy is expected to come from the success of the firm’s intellectual capital rather than the use of material goods (Chareonsuk and Chansa-ngavej, 2008). Many researchers claim that three important factors of intellectual capital are human capital, structural capital, and relational or customer capital that can be seen as a source of competitive advantage in the organization because it cannot be easily imitated and substituted by competitors (Bontis et al., 2000; Roos et al., 2001; Sharabati et al., 2010). Hence, intellectual capital orientation becomes a part of strategic assets of the firm to gain sustainable competitive advantage. From organizational point of view, a number of academics have recognized that IC directly affects organizational innovation and firm performance. (Edvinsson,1997; Hsu and Sabherwal, 2011; Marr et al., 2003) However, in a rapidly changing business environment, knowledge resources does not warranty sustained competitive advantages of firm because changes can be disruptive and unpredictable (Hsu and Sabherwal, 2011). Consequently, the interpretation of direct effects between IC and innovation, and between IC and firm performance, should be considered. Even though intellectual capital perspective has been widely applied to various researches, only few researches clearly explain how and why IC has been transformed into innovation and business performance either directly or indirectly. Thus, this paper tries to bridge both direct and indirect relationships between IC, organizational innovation and business performance based on KM and IC literature by using KM effectiveness as the mediation. In medical service business, IC is essential for business and survival (Veltri et al., 2011). This business is knowledge-intensive industry, highly innovativeness and well balance in the use of people-centered and process-centered (Peng et al., 2007). It is believed that development of IC model specific to the health care sector is particularly significant because of the high degree of management complexity of health care business, in which professional skills, know-how, technological innovation and relational capabilities are a


symbol of an important driving force in achieving high performance (Veltri et al., 2011). Although, the value of IC in medical service business is able to highlight the key features of a successful firm, the attention of IC research has so far not been investigated within the context of medical service sector. Hence, the private hospitals in Thailand were chosen to be investigated for this study as they are a part of medical service business that can generate tremendous revenue for the country by using IC as product of excellence (Thailand Productivity Institute, 2012). Particularly, Thai government now has systematically promoted health care service business as a medical hub of Asia in the year 2015 (The National Economic and Social Development Board, 2012). This causes private hospital in Thailand to increase medical service innovation by focusing on IC and KM effectiveness in order to gain sustainable business performance. Here, intellectual capital orientation is defined as the sum of knowledge that firm intent to utilize for gaining competitive advantage and its components are human capital (knowledge, skill, competencies and individual abilities), structural capital (system, structure, strategy, and culture) and relational or customer capital (interactions among individuals and their network of relationships) (Edvinsson, 1997; Petty, and Guthrie, 2000; Roos et al., 2001; Sharabati et al., 2010). This research suggests that firms with IC orientation turn to the major challenge for formulating corporate value such as KM effectiveness, organizational innovation and ultimately, leading to sustainable business performance. Absolutely, the main aim of this research is to empirically examine the role of intellectual capital on KM effectiveness, organizational innovation and sustainable business performance of a specific medical service business, the private hospitals in Thailand. Moreover, the moderating variable as transformational leadership is also highlighted. The main research questions is how intellectual capital orientation affect KM effectiveness, organizational innovation and sustainable business performance, and how transformational leadership moderates KM effectiveness, organizational innovation and sustainable business performance relationships. This paper contributes to existing literature by providing the evidence of the impact of IC on KM effectiveness, organizational innovation and sustainable business performance in medical service business sector. This research is outlined as follows: The first part presents the literature reviews on IC orientation that leads to KM effectiveness, organizational innovation and sustainable business performance. The second details research methods, including data collection, measurement, statistics, and results are discussed and shown. Consequently, contributions, limitations, future directions, and conclusion are presented. 2. RELEVANT LITERATURE REVIEWS AND RESEARCH HYPOTHESES DEVELOPMENT The knowledge-based view of firm (KBV) as the theoretical lens explains how IC orientation (human capital, structural capital and relational capital) affects sustainable business performance. KBV focuses on the importance of knowledge resources as the most important assets in leveraging and managing firm competitiveness. Moreover, knowledge-based capabilities are considered the most strategically important ones to create and sustain competitive advantage (Nonaka, 1991; Barney, 2001). IC is viewed as the stock of knowledge assets that are owned by organization and drive organizational value creation capabilities (Marr 2004). In this research, IC consists of human capital, structural capital and relational capital. The strength of IC which influences the management of knowledge has potential to converting individual tacit knowledge into organizational knowledge, and transforming knowledge into valuable one to company (Shih et al., 2010). Thus, firms with high IC orientation as a key success factor to improve productivity tend to enhance KM effectiveness (Hsu and Sabherwal, 2011) organizational innovation (Subramaniam and Youndt, 2005) and lastly, achieve sustainable business performance (Chen et al., 2005; Cohen and Kaimenakis, 2007). Moreover, transformational leadership plays a critical role to increase the relationships of IC and business performance (Zagoršek et al., 2009). Accordingly, a conceptual model of this research is shown in Figure 1.


FIGURE 1 CONCEPTUAL MODEL OF THE RELATIONSHIPS BETWEEN

INTELLECTUAL CAPITAL ORIENTATION AND SUSTAINABLE BUSINESS PERFORMANCE

2.1 Intellectual Capital Orientation (IC) The intellectual capital perspective has since been adopted by a number of academics; many definitions are being proposed for complex concept. A review of previous research defined IC as a collection of intangible assets (resources, capabilities, and competence) that drive organizational performance and value creation (Roos and Roos, 1997; Bontis, 1998). According to a new interpretation, IC is defined as the sum of all knowledge assets utilized for competitive advantage and to create wealth for the enterprises (Bontis, 2004; Subramaniam and Youndt, 2005). Nahapiet and Ghoshal (1998) and Phusavat and Kanchana (2007) suggest that any knowledge capabilities, creativities, organizational structure, and relations that can generate knowledge storage and transform to value belong to the classification of IC. A broader IC definition points out that it is the difference between a company’s market value and book value (Schiuma and Lerro, 2008). Although there is not yet a consensus in literature on dimensions of IC, many academics point out that IC is composed of three dimensions: human capital, structural capital and relational or customer capital (i.e. Bontis, 1998; Ross et al., 2001; Sharabati et al., 2010) whereas Subramanian and Youndt (2005) propose three elements of IC as human capital, organizational capital and social capital. Likewise, Chen et al., (2004) allocated IC into four categories: human capital, customer capital, structural capital and innovation capital. In addition, Tseng and Goo (2005) categorized IC framework in terms of human capital, organizational capital, innovation capital and relationship capital. According to Schiuma et al., (2008), IC is arranged into five elements: human capital, structural capital, organizational capital, social capital and stakeholder capital. From the categorization mentioned above, there are different definitions and classifications of intellectual capital due to different research backgrounds. In this research, intellectual capital orientation is defined as the sum of knowledge that firms are intent to utilize for gaining competitive advantage and its components are human capital (knowledge, skill, competencies and individual abilities), structural capital (system, structure, strategy, and culture) and relational capital (interactions among individuals and their network of relationships), following IC aspects in healthcare sector of Peng et al., (2007). Human Capital (HC). The term “human capital” refers to the degree of competencies, knowledge, skills, experience and abilities of individual employee that creates economic value (Youndt et al., 2004; Wiig, 1997). Human capital plays an important role as invisible and strategic assets and facilitates the business strategy to create competitive advantage (Hatch and Dyer, 2004). In addition, human capital including education, training, know-how and capabilities directly associates with greater productivity (Marimuthu et al., 2009). The study of Peng et al., (2007) confirms that human capital is the most important and reflects the mission of healthcare service. Characteristics of human capital such as creativity, skill and expertise tend to generate new idea and knowledge that supports KM success (Jennex and Olfman, 2005). It is obvious that the way to achieve the success of KM could reach a certain degree of impact on employees’ human capital (Birasnav and Rangnekar, 2010). Furthermore, Hsu and Sabherwal (2011) suggest that human capital is positively associated with knowledge capabilities. Hence, increasing employee skills and


abilities are expected to create future returns through knowledge management effectiveness and business performance. A review literature also suggests that human capital with the value of creativity of employee improves innovativeness (Santos-Rodrigues et al., 2010). A recent study of Alshekaili and Boerhannoeddin (2011) asserts that knowledge management mediates the relationship between human capital and innovation performance. Furthermore, the study of Zerenler et al., (2008) asserts that employee capital is positively related to innovation performance. Drawing from KBV, Hatch and Dyer (2004) find that human capital had a significant impact on sustained firm performance. Thus, the hypothesis is proposed as follows: Hypothesis 1: Human capital is positively associated with (a) knowledge management effectiveness, (b) organizational innovation and, (c) sustainable business performance. Structural Capital (SC). Structural capital is defined as everything owned by the organization that supports employees in their work and remains with an organization even when people leave (Longo and Mura, 2011; Roos et al., 1997). Structural capital consists of four core elements: system, structure, strategy and culture which improve productivity through knowledge-sharing, retention and well-organized procedure (Huang and Hsueh, 2007). In healthcare service, service quality, information technology, reporting system and hospital’s image are essential (Peng et al., 2007). Also, Birasnav and Rangnekar (2010) propose that structural capital such as problem solving approach, communication-oriented culture and innovative-supportive culture improves KM activities. In addition, Hsu and Sabherwal (2011) proposed that organizational capital such as technology, structure, and strategy is positively affected knowledge capability. The study of Tai-Ning et al., (2011) indicated that structural capital is the system and procedures that improve business efficiency through innovative capabilities. Following Jassawalla and Sashittal (2002), firms with innovative-supportive culture provide organizational innovation According to the empirical study of Zerenler and Hasiloglu (2008), structural capital is positively associated with innovation performance in automotive supplier industry. Previous studies conducted by Bontis et al., (1998) and Sharabati et al., (2010) assert that structural capital is positively related to business performance. Hence, these ideas lead to posit the following hypothesis: Hypothesis 2: Structural capital is positively associated with (a) knowledge management effectiveness, (b) organizational innovation and, (c) sustainable business performance. Relational Capital (RC). Relational capital is defined as the values which is created through the relationships between an organization and its stakeholders such as customers, competitors, partners, suppliers, shareholders, and society (Sharabati et al., 2010). Relationships between employees and external stakeholders stimulate the creation, acquisition, and exploitation of knowledge, while intra-firm relationships are a source of knowledge development and exchange (Longo and Mura, 2011). Since cultivating a good cooperation with stakeholders, knowledge possesses by customers, competitors and partners may be beneficial to corporate innovation and influence the development of products or process (Amara and Landry, 2005) From KBV, knowledge acquires from the partner can be utilized to create the competitive capabilities and performance. The study of Liu et al., (2010) indicates that relational capital influences the acquisition of knowledge among alliance outcomes. In addition, Kale et al., (2000) use relational capital as a part of latent variables in a study of factors influencing the formation of inter-firm alliances and find a positive correlation between relational capital and knowledge transfer. Furthermore, prior study of Hsu and Fang (2009) shows that relational capital actually improves new product development performance through organizational learning capability. Likewise, Hsu and Sabherwal (2011) conclude that relational knowledge possessed by teams can be utilized for innovation. According to Carmeli and Azeroual (2009), relational capital builds knowledge combination capabilities and leads to radical and incremental innovations. Further, a number of studies show that relational capital positively affects business performance (i.e. Amiri et al., 2010; Huang and Hsueh, 2007; Sharabati et al., 2010). Hence, the following hypothesis is formulated: Hypothesis 3: Relational capital is positively associated with (a) knowledge management effectiveness, (b) organizational innovation and, (c) sustainable business performance.


2.2 Knowledge Management Effectiveness (KM) Knowledge management effectiveness is defined as the effective phrase for a group of processes and practices used by organizations to increase their value (Marr et al., 2003). KM involves creating a learning culture to continuously create, share, and use knowledge for the purposes of developing new opportunities (Nonaka and Toyama, 2003). According to Zheng (2005), KM effectiveness comprises three components: knowledge acquisition effectiveness, knowledge sharing effectiveness, and knowledge application effectiveness. On the other hand, Lindsey (2002) proposes a conceptual KM effectiveness model in terms of two main constructs: knowledge infrastructure capability and knowledge process capability. In addition, Hsu and Sabherwal (2011) explain KM effectiveness as of two functions: knowledge enhancement capability and knowledge utilization capability that increase corporate value. Drawing from KBV, knowledge capabilities is a core resource of the firm that can gain sustained competitive advantage because of its value, uniqueness and complexity (Eisenhardt and Martin, 2000; Grant, 1996). In addition, a high degree of tacit knowledge resources is generally difficult to understand and absorb for outside spectators (Lam, 2000). In a similar vein, various studies focus on the role of KM in the innovation process by suggesting that KM is positively related to firm innovativeness (Chen et al., 2010; Huang and Hsiao, 2010). Likewise, Jashapara (2005) states that KM is the important factor in assessment and measurement firm success. Many studies confirm that KM effectiveness improves business performance (i.e. Mohrman et al., 2003; Zack et al., 2009; Daud and Yusoff, 2011). Hence, the following hypothesis is formulated: Hypothesis 4: Knowledge management effectiveness is positively associated with (a) organizational innovation and, (b) sustainable business performance. 2.3 Organizational Innovation (OI) Organizational innovation refers to the creation or adoption of an idea or behavior new to the organization (Damanpour and Evan 1984; Damanpour 1996). Innovative organization is characterized as intelligent and creative, capable of learning effectiveness and creating new knowledge (Nonaka 1994). The resource-based view of the firm argues that organizational innovation as one of organizational capabilities provides the stimulus necessary to achieve a competitive advantage in the marketplace (Barney 1991). Innovation has a considerable impact on corporate performance by producing an improving market position that conveys competitive advantage and superior performance. Mol and Birkinshaw's (2009) propose organizational innovation as a source of sustainable competitive advantage. A large number of studies focus on the relationships between innovation and higher business performance (i.e.Calantone et al., 2002; Guan and Ma, 2003). Organizational innovation has the potential to significantly increase firm performance by helping to gain access to knowledge assets and building of value adding capabilities that is specific characteristics, hard to imitate and, therefore, sustainable (Hamel, 2006). As described above, the hypothesis is proposed as follows: Hypothesis 5: Organizational innovation is positively associated with sustainable business performance. 2.4 Transformational Leadership (TL) Transformational Leadership refers to the leader moving the subordinate beyond immediate self-interests (Bass et al., 2003). Bass and Avolio (1994) have characterized transformational leadership as encompassing four unique but interrelated behavioral components: inspirational motivation (articulating an appealing and/or evocative vision), intellectual stimulation (promoting creativity and innovation), idealized influence (charismatic role modeling), and individualized consideration (coaching and mentoring). Prior studies have found that leaders who display transformational behaviors are able to realign their followers’ values and norms, promote both personal and organizational changes, and help followers to exceed their initial performance expectations (e.g., Jung 2001; Shin and Zhou, 2003). In addition, employee learning orientation and transformational leadership were positively related to employee creativity in insurance industry (Gong et al., 2009). Likewise, Jung et al., (2008) and Khan et al., (2009) assert that transformational leadership has a positive impact on organizational innovation. Moreover, the study of Abdulai et al., (2012) shows that transformational Leadership moderates the relationships between the elements of intellectual capital and firm competitive capability. Hence, the following hypotheses are formulated:


Hypothesis 6: The relationships between human capital and (a) knowledge management effectiveness, (b) organizational innovation, and (c) sustainable business performance will be positively moderated by transformational leadership. Hypothesis 7: The relationships between structural capital and (a) knowledge management effectiveness, (b) organizational innovation, and (c) sustainable business performance will be positively moderated by transformational leadership. Hypothesis 8: The relationships between relational capital and (a) knowledge management effectiveness, (b) organizational innovation, and (c) sustainable business performance will be positively moderated by transformational leadership. 3. RESEARCH METHODS 3.1 Sample and Data Collection Procedure The population and sample of this research are 380 private hospitals in Thailand chosen from data file of the Department of Business Development. Ministry of Commerce, Thailand. (http://knowledgebase.dbd.go.th/DBD/Main/login.aspx, 5 March, 2012). Private hospitals are interesting to be focused on because the knowledge of employees working and organizational knowledge of private hospital are considered important elements effectively running medical service business in competitive environment (Peng et al., 2007). The key participants in this study are executive director, general director or general manager of each firm. A mail survey was used for data collection. The questionnaires were sent to 380 private hospitals. With regard to the questionnaire mailing, 4 surveys were undeliverable because some firms were no longer in business, address errors or had moved to unknown locations. Removing the undeliverable from the original 380 mailed, the valid mailing was 276 surveys, from which 104 responses were received. Due to 2 incomplete questionnaires, they were deducted from further analysis. Of the surveys completed and received, only 102 are usable. The effective response rate is approximately 27.13%. According to Aaker et al., (2001), the response rate for a mail survey, without an appropriate follow-up procedure if greater than 20%, is considered acceptable. Finally, the non-response was tested for independent two samples. A comparison of early responses and late responses data is recommended by Armstrong and Overton (1977). T-tests comparing the first 51 survey responses received with the last 51 survey responses across firm’s four characteristics (i.e. number of employees, number of years in business, amount of capital invested, and sale revenue per year) did not find any significant differences between the two groups. Thus, it appears that non-response bias does not pose a significant problem for this research. 3.2 Questionnaire Development and Variable Measurement 3.2.1 Questionnaire Development To examine the relationships mentioned earlier, the questionnaire of this study was developed to assess the dimensions of intellectual capital orientation, and moderators. There are five parts in a questionnaire. Part one asks for personal information. Part two asks about private hospital information. Part three and four, all questions deal with the measurement of intellectual capital orientation and the outcomes. Lastly, an open-ended question for suggestions and opinion is included. 3.2.2 Variable measurement In the conceptual model, all of variables were measured on five point Likert scale, ranging from ‘1 = strong disagree’ to ‘5 = strong agree’, except control variable. The variable measurements of dependent, independent, moderator, and control variables are described as below: Sustainable business performance is the dependent variable of this research. It is measured by long term in sales growth, profitability, market share, outstanding service over competitor and customer acceptance.


This construct is adapted from Jirawuttinaunt and Ussahawanitchakit (2011) including four-item scale. Human Capital is measured by qualities of employees such as competencies, experience and creativity abilities. This construct is adapted from Bontis (1998) and Sharabati et al., (2010) including four-item scale. Structural Capital is measured by a link of firm’s potential to apply administrative system, organization structure, organizational culture, motivating strategies, and information access for serving increasing productivity. This construct is adapted from Bontis (1998) and Sharabati et al., (2010) including four-item scale. Relational Capital is measured by the firm being able to cooperate and cultivate a good friendship with stakeholders. This construct is adapted from Bontis (1998) and Sharabati et al., (2010) including four-item scale. Knowledge Management Effectiveness is measured by the level of knowledge acquisition, knowledge sharing and knowledge application. This construct is adapted from Zheng (2005) including four-item scale. Organizational Innovation is measured by the firm being able to create new product or service, new process, and applying new technology to improving service and R&D in developing new product or service and new process. This construct is adapted from Peng et al., (2007) including four-item scale. Transformational Leadership is measured by the extent to which leadership actions and behaviors result in the corporate mission and vision, and stimulate idea generation and innovation This construct is adapted from Abdulai et al., (2012) including three-item scale. The control variables are also likely to affect the relationships. In this research, there are two of them including firm age and firm capital; because different age may present different organizational attributes and resource deployment (Chen and Huang, 2009). This study defines firm age as the number of years the firm has been established. Also, firm capital may impact the capacity of a firm to implement business strategies in order to achieve superior performance (Ussahawanitchakit, 2007). It is measured by amount of capital invested. 3.3 Validity and Reliability With respect to the confirmatory factor analysis, this analysis has a high potential to inflate the component loadings. Factor analysis was utilized for construct validity. This analysis has a high potential to expand the component loadings. Hence, a cut-off at 0.40 was adopted (Nunnally and Berstein, 1994). All factor loadings in this research are greater than the 0.40 cut-off and are statistically significant. The reliability of the measurements in this research was evaluated by Cronbach alpha coefficients. In the scale reliability, Cronbach alpha coefficients are greater than 0.70 (Nunnally and Berstein, 1994). The scale of all measurement appears to produce internally consistent results; thus, these measures are deemed appropriate for future analysis as they express an accepted validity and reliability. Table 1 shows the results for both factor loadings score between 0.7-0.9 indicating that there is construct validity, and Cronbach alpha for all variables are shown between 0.8-0.9.

TABLE 1

RESULTS OF MEASURE VALIDATION

Items Factor

Loadings Cronbach Alpha

Number of Items

Sustainable Business Performance (PER) .815-.889 .906 4 Human Capital (HC) Structural Capital (SC) Relational Capital (RC) Knowledge Management Effectiveness (KM) Organizational Innovation (OI) Transformational Leadership (TL)

.912-.956

.769-.901

.761-.829

.779-.947

.752-.891

.883-.908

.941

.874

.800

.868

.848

.891

4 4 4 4 4 3

3.4 Statistic Test The Ordinary Least Square (OLS) is utilized to assess all hypotheses in this study. Because both dependent and independent variables in this study were neither nominal data nor categorical data, OLS is an appropriate method for examining the hypothesized (Hair et al., 2006). After all is said and done, the model of the relationships mentioned above is shown below.


Equation 1: KM = 01+ 1FA + 2FC + 3HC + 4SC + 5RC + Equation 2: KM = 02+ 6FA + 7FC + 8HC + 9SC + 10RC + 11TL + 12(HC*TL) + 13(SC*TL) + 14(RC*TL) + Equation 3: OI = 03+ 15FA + 16FC + 17HC + 18SC + 19RC + Equation 4: OI = 04+ 20FA + 21FC + 22HC + 23SC + 24RC + 25TL + 26(HC*TL) + 27(SC*TL) + 28(RC*TL) + Equation 5: OI = 05+ 29FA + 30FC + 31KM + Equation 6: PER = 06+ 32FA + 33FC + 34HC + 35SC + 36RC + Equation 7: PER = 07+ 37FA + 38FC + 39HC + 40SC + 41RC+ 42TL+ 43(HC*TL) + 44(SC*TL) + 45(RC*TL) + Equation 8: PER = 08+ 46FA + 47FC + 48KM + 49OI + 4. RESULTS AND DISCUSSION The descriptive statistics and correlation matrix for all variables are shown in Table 2. With respect to possible problems relating to multicolinearity, all the correlation coefficients of independent variables are smaller than 0.8. The problem of multicolinearity of independent variables in this model is therefore not significant (Hair et al., 2006). Variance Inflation Factors (VIF’s) was used to check multicolinearity problem among independent variables. The VIF’s ranged from 1.182 – 7.668 are below the cut-off value of 10 recommended by Hair et al., (2006) meaning that the independent variables are not correlated with each other. Therefore, there are no substantial multicolinearity problems encountered in this study. In addition, Table 2 shows the correlation matrix for all variables used in the regression analysis.

TABLE 2

DESCRIPTIVE STATISTICS AND CORRELATION METRIX FOR ALL CONSTRUCTS

Variables HC SC RC TL KM OI PER

MEAN 4.039 4.181 4.024 4.167 3.992 3.566 3.774

S.D .725 .587 .571 .649 .582 .754 .657

HC

SC .629**

RC .693** .763**

TL .570** .713* .684**

KM .585** .683** .678** .546**

OI .586** .485** .578** .522** .686**

PER .481** .624** .515** .453** .654** .614**

**. p <0.01, * p < 0.05

4.1 Influence of Intellectual orientation and consequences Table 3 presents the OLS regression analysis of intellectual capital orientation (human capital, structural capital and relational capital) on KM


effectiveness, organizational innovation and sustainable business performance. The results reveal that human capital has a significant positive impact on KM effectiveness (b3 = 0.214, p<.05), organizational innovation (b17 = 0.396, p<.01) and sustainable business performance (b34 = 0.198, p<.10). Therefore, Hypotheses 1a, 1b and 1c are supported. All in all, Hypothesis 1 is fully supported consistent with prior literatures. The later, structural capital shows a significant influence on KM effectiveness (b4 = 0.278, p<.05), and sustainable business performance (b35 = 0.422, p<.01). On the contrary, structural capital has no significant relationships with organizational innovation (b18 = -0.016, p>.10). Therefore, Hypotheses 2a and 2c are supported but Hypothesis 2b is not. These results imply that structural capital has a direct effect on business performance and indirect effect via KM effectiveness consistent with Daud and Yusoff (2011). However, the finding shows that structural capital has no significant impact on organizational innovation similar to Carmona-Lavado et al., (2010) who reveals that organizational capital has no direct effect on product innovation. This result may due to the fact that most structures of private hospital in Thailand are based on family-owned organizations which Kuo and Wu (2007) suggest that family ownership structure has a negative effect on organizational innovation. Besides, the non-significant effect of structural capital may due to firms spending too much on information system or R&D expenditures, in turn, leading to reduced new product development performance (Hsu and Fang, 2009). Next, relational capital has a positive effect on KM effectiveness (b5 = 0.260, p<.05) and organizational innovation (b19 = 0.266, p<.05) but with no direct impact on sustainable business performance (b36 = 0.008, p>.10). Following the previous literature of Zhao et al., (2011), relational capital does not demonstrate any significant impact on the probability in short term performance. This result provides that relational capital has an indirect effect on business performance through KM effectiveness and organizational innovation. Thus, Hypotheses 3a and 3b are supported but hypothesis 3c is not.

Subsequently, the results in Table 3 show that KM effectiveness has a significant positive influences on organizational innovation (b31= 0.693, p<.01), and sustainable business performance (b48= 0.356, p<.01). Similar to Chang and Lee (2008), this result implies that it is vital for organizations to create KM effectiveness and organizational innovation to gain the most economic rents. Therefore, Hypotheses 4a and 4b are strongly supported. In addition, following the results of numerous literature (i.e. Guan and ma, 2003; Hamel, 2006), the finding reveals that organizational innovation is positively associated with business performance (b49= 0.363, p<.01). Thus, Hypothesis 5 is supported. For the moderating effect of transformational leadership on three IC elements (HC, SC, RC), the results show that transformational leadership moderates the relationships between human capital and KM effectiveness (b12= 0.561, p<.05), and the relationship between relational capital and organizational innovation (b13= 0.239, p<.10) but has no effect on other relationships. Hence, Hypotheses 6a and 8b are supported whereas 6b, 6c, 7a, 7b, 7c, 8a, and 8c are not. This finding can explain that transformation leadership has influence on human capital to increase KM effectiveness as well as on relational capital to create organizational innovation which confirm by the study of Birasnav et al., (2011) and Abdulai et al., (2012) who indicates that transformational leadership moderate the element of intellectual capital and firm internal competitive capability relationships. Surprisingly, on the contrary to the proposal, transformational readership is significantly and negatively affected structural capital and KM effectiveness relationship (b12= -0.535, p<.05) which support the study of Singh (2008) who explains that supportive styles of leadership, one of transformational leadership elements, is significantly and negatively associated with the art of knowledge management practices for competitive advantage in software firms in India. However, the absence of other relationships of transformational leadership might be affected by almost previous studies that treat transformation leadership as an independent variable. Therefore, when this research treats transformational leadership as moderator, it may have some effects on others variables, and in turn, make it not significant.


TABLE 3

RESULTS OF OLS REGRESSION ANALYSIS a

Independent Variables

Dependent Variables 1 2 3 4 5 6 7 8 KM KM OI OI PER PER OI PER

Human Capital (HC) .214** (.098)

.260*** (.091)

.396*** (.113)

.379*** (.112)

.198* (.112)

.190* (.112)

Structural Capital (SC) .278** (.112)

.181 (.113)

-.016 (.128)

-.089 (.141)

.422*** (.127)

.519*** (.141)

Relational Capital (RC) .260)** (.116)

.194* (.113)

.266** (.133)

.184 (.140)

.008 (.132)

-.007 (.140)

Knowledge Management Effectiveness (KM)

.693*** (.078)

.356*** (.102)

Organizational Innovation (OI) .363*** (.098)

Transformation Leadership (TL) .043 (.097)

.208 (.120)

-.002 (.120)

HC x TL .561*** (.114)

.182 (.142)

-.096 (.142)

SC x TL -.535*** (.133)

-.261 (.165)

.095 (.165)

RC x TL .088 (.115)

.239* (.142)

.126 (.142)

FA .374** (.158)

.238 (.147)

.153 (.182)

.172 (.182)

.445** (.180)

.551*** (.182)

-.187 (.166)

.416** (.162)

FC -.204 (.143)

-.027 (.135)

-.369 (.164)

-.310* (.167)

-.014 (.163)

-.092 (.167)

-.228 (.153)

.159 (.151)

Adjusted R2 .544 .630 .402 .432 .411 .432 .476 .502 aBeta coefficients with standard errors in parenthesis, *** p < 0.01, **. p <0.05, * p < 0.10

5. CONTRIBUTIONS AND DIRECTIONS FOR FUTURE RESEARCH 5.1 Theoretical Contribution and Directions for Future Research This research is intended to provide a clearer understanding of the relationships among IC orientation, KM effectiveness, organizational innovation, and sustainable business performance. IC orientation consists of three dimensions, namely, human capital, structural capital and relational capital. It provides unique theoretical contribution expanding on previous knowledge and literature of IC, KM effectiveness, organizational innovation, and sustainable business performance. With respect to the results, element of intellectual capital (HC, SC, and RC) has a direct effect on sustainable business performance and indirect effect via KM effectiveness and organizational innovation. However, transformational leadership is almost not the moderator of the relationships. Then, future research needed to conceptualize the measurement to find out why transformational leadership is almost does not fully moderate the aforementioned relationships. 5.2 Managerial Contribution This research provides some relevant managerial implications. The results can help medical service business executives identify and justify key components that may be more critical in a rigorously competitive market. For medical service businesses, they should understand, manage, and give priority to capital to provide IC, KM effectiveness, organizational innovation to increase sustainable business performance. The findings imply that human capital and structural capital appear to impact directly on the organizational performance of medical service business of Thailand whereas relational capital appears to impact indirectly via KM effectiveness and organizational innovation. This makes the organization gain more advantage over competitors. Also, medical service executives should understand more about IC and its critical role in exposing value creation. In other words, medical service executives should pay more attention to what IC resources are considered important for competitive advantage of the firm, and whether the relative performance indicators are being used to measure how those IC resources create firm value.


5.3 Limitations and Future Research Directions This research has some limitations that should be mentioned. Firstly, the data obtained only from private hospital businesses in Thailand. Future research is needed to collect data from different groups of sample and/or a comparative population such as public hospital in order to verify the generalizability of the study and increase the level of reliability. Secondly, this research is conducted on a small sample size. If with larger sample size, it is expected to make the results more distinct. Lastly and surprisingly, transformational leadership is almost not the moderator of intellectual capital orientation that needs future research to apparently reconfirm. 6. CONCLUSION Intellectual capital is increasingly recognized as an important strategic asset for sustainable corporate competitive advantages. Our results underline the importance of IC in enhancing business performance in medical service business in Thailand. The purpose in this study is to examine the relationship between intellectual capital and sustainable business performance via KM effectiveness and organizational innovation as mediators. The model is tested using data collected from mail survey of 102 private hospitals in Thailand. On the whole, the results of the OLS analysis largely confirm our conceptual framework and hypotheses. All in all, the empirical results indicate that intellectual capital (HC, SC, RC) has a positive impact on KM effectiveness, organizational innovation and sustainable business performance. This implies that knowledge and competency of employee, internal organizational systems, organizational information and knowledge appear to directly impact the organizational performance of medical service business of Thailand. On the other hand, relational capital has an indirect effect on business performance via KM effectiveness and organizational innovation. Furthermore, we find that human capital is the most important IC to explain the effects of KM effectiveness, organizational innovation on business performance. Based on these results, it follows that the optimal procedure for medical service companies is to focus on all the three components of IC in order to increase firm performance. The results of the study support the notion that firms which actively nurture and increase their IC are likely to obtain superior performance. Our findings have important implications for developing countries to describe IC which being increasingly recognized as the major driver of corporate growth. Additionally, further study may consider finding practical reasons why some constructs were found with no relationships supporting hypotheses by reviewing extensive literature, or collecting data from a larger sample. In summary, this research contributes significantly toward understanding how medical service business in Thailand generate intellectual capital orientation to increase KM effectiveness, organizational innovation and ultimate, achieve sustainable business performance. REFERENCES: Aaker, David A.,Kumar, V. and Day, George S. Marketing Research. 7th ed. New York : John Wiley and Sons. 2001. Abdulai, Mohammed-Sani; Kwon, Y and Moon, J., “Intellectual Capital and Firm Performance: An Empirical Study of Software Firms in West Africa’, The African Journal of Information Systems, Volume 4, Number 1, Pages 1-30, 2012. Alshekaili, Salim A. R. and Boerhannoeddin, A. “Human capital approach towards enhancing innovation performance in Omani industrial firm: The role of knowledge management” Progress in Business Innovation & Technology Management, Volume 1, Pages 23-33. 2011. Amara, N and Landry, R., “Source of information as determinants of novelty of innovation in manufacturing firms: evidence from the 1999 statistics Canada innovation survey”, Technovation, Volume 25, Number 3, Pages 245-259. 2005. Amiri, A. N., Jandghi, G., Alvani, S. M., Hosnavi, R., and Ramezan, M. “Increasing the Intellectual Capital in Organization: Examining the Role of Organizational Learning”, European Journal of Social Sciences, Volume 14, Number 1, Pages 98-108. 2010. Armstrong, J. Scott and Overton, Terry S. “Estimating non-response Bias in Mail Surveys”, Journal of Marketing Research, Volume 14, Number 3, Pages 396-402. 1977.


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HUMAN RESOURCE MANAGEMENT POLICIES AND PRACTICES (HRMPP): SCALE VALIDATION IN THE UNITED STATES

Gisela Demo, UCLA Anderson School of Management, Los Angeles, California, USA

Késia Rozzett, University of Brasília, Brasília, Brazil ABSTRACT Given the strategic relevance of Human Resource Management (HRM) in organizations and the lack of comprehensive instruments to assess policies and practices of HRM, the main objective of this study is to develop and validate a reliable and valid scale to measure employees perceptions regarding policies and practices of Human Resource Management (HRM) implemented by organizations. Three studies with different national samples were conducted for the development and validation of the Human Resource Management Policies Scale (HRMPPS) in the United States (US) using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Scale reliability was assessed by Cronbach’s alpha and Jöreskog’s rho. A six-factor model was generated showing high-reliability and good fit. Construct validity was provided through convergent, discriminant and nomological validity, being the latter assessed through the correlation between HRM practices and well-being at work. Finally, the scale generalizability was tested in a different sample by conducting a replicative analysis on the measurement model and structural model obtained. This research is a starting point to provide a comprehensive, psychometrically and operationally valid measure of employees’ perceptions regarding the most widely studied HRM policies and practices. As practical implications, the six-factor HRMPPS model could be used as a diagnostic tool to identify HRM areas where specific improvements are needed, as well as an instrument of evaluation for managers who wish to improve employees’ well-being. Limitations and directions for future researches are discussed. Keywords: human resource management policies and practices; exploratory factor analysis; confirmatory factor analysis; scale validation; well-being at work Acknowledgement We would like to thank the Brazilian National Counsel of Technological and Scientific Development (CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico) for the grant that funded this research.

1. INTRODUCTION

Given the strategic relevance of Human Resource Management (HRM) in organizations, the lack of comprehensive instruments to measure employees’ perceptions about policies and practices of HRM and the importance to validate a scale in different countries to improve its generalizability, the main objective of this study is to validate in the US the Human Resource Management Policies and Practices Scale (HRMPPS), developed and validated first in Brazil by Demo, Neiva, Nunes and Rozzett (2012).

In order to test the nomological validity of the HRMPPS, that means, its ability to behave as expected with respect to some other constructs to which it is related, this study also aims to confirm results from other studies showing that HRM policies and practices have a great influence on employees’ well-being at work (Rubino, Demo and Traldi, 2011; Baptiste, 2008; Nishii, Lepak and Schneider, 2008; Turner, Huemann and Keegan, 2008; Gelade and Iviry, 2003).

According to Huselid (1995), work on the measurement of HRM policies and practices is extremely limited and this is still true. The only scale found in the literature, called High-Performance Work Practices was developed and validated by Huselid (1995), with 13 items and a .67 Cronbach’s alpha. It was based on a prior work from Delaney, Lewin and Ichniowski (1989), which resulted in a 10-item HRM practices questionnaire. These measures and also some indexes of HRM practices identified by advocates of the “high commitment” approach (Guest, 1998; Pfeffer, 2005; Marchington and Wilkinson, 2005) have been


used in researches. However comprehensive instruments with higher reliability for measuring HRM policies and practices are demanded.

Furthermore, if the HRMPPS shows theoretical consistency and also good psychometric indexes when validated in a different country (US), it will be a comprehensive, psychometrically and operationally valid measure to be used in relational studies from both Human Resource, and Management and Organizations fields. Additionally, it could be used as a diagnostic tool to identity HRM areas where specific improvements are needed, as well as an instrument of evaluation for managers who wish to improve employees’ well-being.

First of all, a literature review about human resource management, its strategic role in organizations, and human resource management policies, including their constitutive definitions, is presented. After, the study conducted by Demo et al (2012) concerning the procedures for the development and validation of the HRMPP scale in Brazil are detailed, since it is the basis for the present study. The method used is then described, detailing the review of the items to make the scale suitable for application in the US, as well as the procedures applied in the 3 studies conducted to validate the scale in America. In sum, study 1 aimed to select items based on Exploratory Factor Analysis (EFA); study 2 intended to examine the factor structure by running a Confirmatory Factor Analysis (CFA), as well as to provide scale reliability and construct validity through convergent, discriminant and nomological validity; and study 3 aimed to test the scale generalizability by checking if the factor structure obtained in the CFA would remain stable in a different sample. Finally, the results are presented and discussed and final remarks are made, pointing the research limitations and its practical implications as well as highlighting directions for future research. 2. LITERATURE REVIEW This section first presents the theoretical background of HRM and HRM policies and after details the study conducted by Demo et al (2012) to develop and validate a HRMPP scale, used as basis for this research.

2.1 Theoretical Background Many authors understand HRM’s current role in the organizations as being strategic. One of the distinctive features of HRM is that better performance is achieved through the people in the organization (ALDamoe, Yazam and Ahmid, 2012). Ulrich, Halbrook, Meder, Stuchlik and Thorpe (1991) stated that the competitive panorama is constantly being changed and has been demanding new models of competitiveness which in turn require organizational capacities that will enable the companies to better serve their customers and distinguish them from their competitors. These organizational capacities come from the redefinition and redistribution of HRM practices, functions and professionals.

By summarizing what authors such as Guest (1987), Storey (1995), Legge (2006) and Bohlander and Snell (2009) say, it is possible to observe that people have been taking over a strategic and relevant role in organizations, and therefore cohesive and coherent theories - aligned to both planning and organizational strategy - must properly sustain HRM. In this meaning, HRM policies and practices may vary among organizations and should be aligned with business strategy (Chênevert and Tremblay, 2009). Boxall and Purcell (2000) add that the effects of individual HRM practices depend on both the nature of the effects of other HRM practices and the business strategy. Also, Lim (2012) argues that external business environment has strong influence on the HRM activities.

From the perspective of Strategic HRM, policies and practices can be mutually reinforced and create a strong impact on organizational goals (Morris and Snell, 2010). Moreover, HRM policies are guided by the logic of skills developed in accordance to the requirements of business processes (Serpell and Ferrada, 2007). Thus, they provide tools to capture and communicate the strategic vision and objectives of the organization in clear terms that can be more easily understood and requested (Vakola, Soderquist and Pratascos 2007). On such context, the development of scales that allow an estimation of the perception of HRM policies aims to identify to what extent they are applicable to various organizations and aligned to the organization's strategy. In addition, a scale can translate how HRM policies are associated with business strategy, because only so they will be effective (Legge, 2006).


HRM must also not have the traditional role of support anymore, but instead it must constitute essential competence in reaching the organizational and individual objectives and results, once human resource are valuable and constitute a source of competitive advantage. Uysal (2012) indeed found strong, positive and significant correlations among the main HRM policies cited on the literature such as staffing, training, performance evaluation and compensation. These results are important for understanding the inter-relationships between HRM practices, in order to enhance the effect of HR systems on employees’ organizational outcomes.

Therefore, organizations have turned to the perspective of creating competitive advantage. Consequently themes related to the areas of organizational strategy and theory converge, originating comprehensive implications for HRM and putting its primordial function under discussion. According to the Resourced Based View by Barney (1991), the creation of competitive advantage depends on prerequisites that may be closely related to the HRM area, since resource must be valuable and rare to the organization, may never be imitated or replaced, and the organization must be able to explore them. Also, Beauvallet and Houy (2010) support that the key mechanism and decisive variable that would justify the competitive advantages of companies alleged as being “lean enterprises”, or the ones practicing “a lean management”, are directly related to HRM.

Organizational policy can be defined as principles established for leading a company, a general course of action in which some practices are developed collectively, in a constructive way, aiming to reach certain objectives (Singar and Ramsden, 1972). HRM policies define the attitude, expectations and values of the organization concerning the way of treatment of the individuals, and serve as point of reference for the development of organizational practices and for decisions made by people, besides causing an equal treatment among individuals (Armstrong, 2009).

In this study the term “HRM policy” means an organizational articulated proposal, with theoretical and practical constructions within human relations aiming to reach the desired results. Thereby, HRM policies, operationalized by HRM practices, define theoretical and practical referential built to make possible the reaching of the objectives and purposes of the organization, operating as thinking and acting guides for the HRM area.

Some research results have pointed out positives relations between HRM policies and variables like commitment, productivity, profitability and quality, among others (Guest, 1987; Schneider and Bowen, 1985; Ulrich et al., 1991). In their meta-analysis, Combs, Liu, Hall and Ketchen (2006) found that relations between HRM practices and organizational outcomes are stronger in industries than in service companies. Studies have also been conducting in cultures other than the American and European ones. Majumder (2012) verified strong relationship between HRM practices and employee’s satisfaction in Bangladesh private banks. Kim and Lee (2012) found evidence that HRM policies and practices improve strategic capabilities and firm performance in management consult firms at South Korea. The study of Demo (2010) showed positive and strong relationship between HRM policies and organizational justice in both private and public Brazilian organizations.

Similarly, other researchers have shown that HRM policies and practices affect performance of organizations favorably (Boselie, Dietz and Boon, 2005; Subramony, 2009; Menezes, Wood and Geladi, 2010). Guest and Conway (2011) confirmed the association between more HRM practices, higher HR effectiveness and a range of performance outcomes. Besides, ALDamoe et al. (2011) concluded that employee retention is likely to mediate in the relationship between HRM practices and organizational performance. Employee perceptions of HRM policies and practices also influence discretionary work effort and co-worker assistance (Frenkel, Restubog and Bednall, in-press). On the other hand, the effectiveness and acceptance of HRM policies are related to values and organizational culture (Stone, Stone-Romero and Lukaszewski, 2007).

There is a consensus indeed that HRM practices generate higher organizational performance when integrated to business strategy (Guest and Hoque 1994; Ezzamel, Lilley and Willmott, 1996). And this is also true for small firms. The study conducted by Katou (2012) showed that HRM policies have a positive effect on organizational performance through employee attitudes (satisfaction, commitment, motivation) and employee behaviors (absences, turnover, disputes). In summary, HRM policies and practices assume special connotation in development, appreciation and retention of talents. They also promote


employee’s commitment and, as a result, goodwill on their part to act in a flexible and adaptive manner towards excellence in the organizations (Legge, 2006). An entrepreneurial strategy aiming production and supply of added-value products and services must concern the development and the implementation of HRM policies resulting in well-qualified employees (Legge, 2006).HRM policies and practices considered in the current study were based on the literature review used for HRMPPS development and validation in Brazil (Demo et al., 2012). According to Kerlinger and Lee (2008), constitutive definition is the one that typically appears as a term definition in dictionaries and theories. Authors should elaborate such concepts based on a literature review of other concepts and studies that work as a theoretical support. And the items of the scale are HRM practices. Policies are indeed implemented by practices (Legge, 2006).

Chart 1 summarizes the selected policies as well as its constitutive definitions elaborated from the literature review. The main authors who were reviewed in the development of the theoretical background for each HRM policy are pointed out.

CHART 1.CONSTITUTIVE DEFINITIONS OF HRM POLICIES AND ITS THEORETICAL

BACKGROUND HRM Policy Constitutive Definition and Authors Reviewed

Recruitment and Selection (RS)

Organizational articulated proposal, with theoretical and practical constructions, to look for employees, encourage them to apply, and select them, aiming to harmonize people’s values, interests, expectations and competences with the characteristics and demands of the position and the organization. Authors reviewed: Armstrong, 2009; Bohlander and Snell, 2009; Dessler, 2002; Lievens and Chapman, 2010; Mathis and Jackson, 2003.

Involvement (I)

Organizational articulated proposal, with theoretical and practical constructions, to create an affective bond with its employees, contributing to their well-being at work, in terms of acknowledgement, relationship, participation and communication. Authors reviewed: Bohlander and Snell, 2009; Dessler, 2002; Dietz, Wilkinson and Redman, 2010; Mathis and Jackson, 2003; Muckinsky, 2004; Sisson, 1994; Ulrich et al., 1991; Siqueira, 2008.

Training, Development and Education (TD&E)

Organizational articulated proposal, with theoretical and practical constructions, to provide for the employee systematic competence acquisition and to stimulate continuous learning and knowledge production. Authors reviewed: Bohlander and Snell, 2009; Dessler, 2002; Dutra, 2001; Goldstein, 1996; Sisson, 1994; Winterton, 2007.

Work Conditions (WC)

Organizational articulated proposal, with theoretical and practical constructions, to provide the employees good work conditions in terms of benefits, health, safety and technology. Authors reviewed: Bohlander and Snell, 2009; Dessler, 2002; Loudoun and Johnstone, 2010; Mathis and Jackson, 2003; Osborn, Hunt and Schermerhorn, 1998; Sisson, 1994; Ulrich, 2001.

Competency-Based Performance Appraisal (CBPA)

Organizational articulated proposal, with theoretical and practical constructions, to evaluate the performance and competences of the employees, subsidizing the decisions about promotions, career planning and development. Authors reviewed: Bohlander and Snell, 2009; Dessler, 2002; Devanna, Fombrun and Tichy, 1984; Dutra, 2001; Latham, Sulsky and Macdonald, 2007; Mathis and Jackson, 2003.

Compensation and Rewards (CR)

Organizational articulated proposal, with theoretical and practical constructions, to reward the employees’ performance and competences in terms of remuneration and incentives. Authors reviewed: Bohlander and Snell, 2009; Dessler, 2002; Devanna, Fombrun and Tichy, 1984; Dutra, 2001; Gerhart, 2010; Hipólito, 2001; Sisson, 1994


2.2 Development and validation of the HRMPPS in Brazil The study of the development and validation of the HRMPPS in Brazil (Demo et al., 2012) was the basis for the validation in the US. Item generation of the HRMPPS validated in Brazil was based on a broad literature review as well as on interviews with employees of various companies. Regarding the interviews, the analysis of categorical thematic content recommended by Bardin (2011) was used for the identification of categories and its indicators. The categories that emerged from content analysis were consistent with the six main policies cited in the most recently literature review about HRM policies namely recruitment and selection, involvement, training, development and education, working conditions, competency-based performance appraisal and rewards (Gerhart, 2010; Dietz, Wilkinson and Redman, 2010; Lievens and Chapman, 2010; Armstrong, 2009; Bohlander and Snell, 2009; Dessler, 2002; Winterton, 2007; Latham, Sulsky and Macdonald, 2007; Mathis and Jackson, 2003; Goldstein, 1996; Sisson, 1994; Ulrich et al., 1991; Devanna, Fombrun and Tichy, 1984). Then, 88 items were developed.

As to theoretical analysis of the items, we followed the steps proposed by Kerlinger and Lee (2008). At first, they were submitted to semantic analysis so that their understandability to the population members could be verified and doubts could be solved. At the end of the semantic analysis, 20 items were considered unclear, doubting and repeated by the analysts. These items were crossed off and HRMPPS remained with 68 items. Finally, twelve experts in HRM field (professors, HRM researchers and HRM managers) were exposed to the definition of each factor/HRM policy plus a related explanation and asked to allocate each of the 68 statements to an appropriate factor or to a “not applicable” category. After the judges’ analysis, 18 items did not reach an application concordance to the factors for 80% of the judges or did not fit into only one factor and were deleted from the instrument. After all, HRMPPS counted 50 items in its application version, with a 5-point Likert scale, varying from “I totally disagree” to “I totally agree”.

Thereafter, HRMPPS was validated through EFA and CFA. The results presented a multifactorial instrument with 40 items distributed in 6 factors consistent with the literature review and explained about 58% of the construct’s total variance. It also has high reliability by displaying Cronbach’s alphas higher than .80 in the 6 obtained factors, according to the threshold recommended by authors such as Nunnally and Bernstein (1994), besides Jöreskog’s rho higher than .76, as proposed by Chin (1998). Specifically: recruitment and selection policy (α=.84; ϱ=.81), involvement policy (α=.93; ϱ=.92), training, development and education policy (α=.88; ϱ=.88), work conditions policy (α=.84; ϱ=.76), competency-based performance appraisal policy (α=.86; ϱ=.92) and rewards policy (α=.81; ϱ=.77). At last, HRMPPS has validity by displaying high-quality factor loadings, with 70% of the items classified as good, very good and excellent, according to Comrey and Lee’s (1992) criterion.

After, the six-correlated-factor structure obtained in the exploratory analysis was tested in other sample, remaining stable. Finally, the CFA conducted using the maximum likelihood method in other different sample confirmed the validity of HRMPPS by showing conceptual adequacy to the structure obtained in the exploratory analysis and satisfactory fit. The final model showed 112 parameters, with χ2(708) = 2178.4, p<0.001; p<0.001 or NC=3.07; CFI = .90; RMSEA=.057 (confidence interval from .55 to .60). 3. METHODS First of all, the review of the scale for application in the US, regarding its translation for English and the content validity of the items, are presented. Then, the three studies conducted for the development and validation of the Human Resource Management Policies and Practices Scale (HRMPPS) in the United States (US) are detailed. Three different national samples were collected online using MTurk in order to ensure the presence of a broad variety of industries located in the United States. This diversification indicates sampling variability and representativeness. Data from study 1 (n=409) were used to select items based on EFA. Then, CFA was used on data obtained in study 2 (n=400) to examine factor structure, as well as to provide construct validity through convergent and discriminant validity and a structural model including the construct Well-Being at Work (WBW) was used to test for nomological validity. Scale reliability was assessed by Cronbach’s alpha and Jöreskog’s rho. Finally, data from study 3 (n= 285) were used to test the scale generalizability by conducting a replicative analysis on both the measurement model and structural model used to test


nomological validity in study 2, and by checking if the structure obtained through the CFA remained stable in a different sample.

3.1 HRMPPS Review for Application in the US In order to make the HRMPPS suitable for application in the US, the 40 items were translated to English by a specialist in translation and retranslated to Portuguese by the authors of the scale. Then, an English Professor from a university in California checked out the translation to English. Following the item generation steps proposed by Kerlinger and Lee (2008), two faculty members and one PhD student from the Management and Organization area of a Business School of a university in California served as judges to evaluate the content/validity of the items. As a result, the 40 statements remained for the application in the US, with a 5-point Likert scale, varying from “I totally disagree” to “I totally agree”. The 40 items can be found in Appendix 1. 3.2 Study 1: Exploratory Factor Analysis The sample for this study was collected online using MTurk. Data were collected from 449 employees of various organizations. Of the employees, 67% were male, 46% were Asian, Asian-American or Pacific Islander, 80% were under the age of 36, 53% had a Bachelor degree, and 55% had been at the company for fewer than 5 years. The respondents came from a multitude of industries, such as computer hardware/software (19.1%), education (12.1%), healthcare/medical (7.7%), internet ASP (7.4%), engineering/architecture (7.2%), finance/banking/insurance (6.7%), accounting (4.2%), retail (3.5%), telecommunications (2.7 %), consulting (2.5%), and others (26.9%).

The data were examined (searched for incorrect values, missing data and outliers) and the assumptions for multivariate analysis were checked, following the procedures recommended by Tabachnick and Fidell (2007) and Hair, Black, Babin, Anderson and Tatham (2009). The final sample counted then with 409 subjects. Hair et al. (2009) say that for an adequate sample size, it is necessary to have between 5 and 10 individuals for each item of the instrument. However, the authors state that any factor analysis with less than 200 individuals can hardly be considered suitable. To Tabachnick and Fidell (2007), a factor analysis is compromised with less than 300 individuals. Similarly, Comrey and Lee (1992) class 300 as a good sample size. The sample size with 409 subjects attended, therefore, all the criteria cited.

To perform the EFA, the correlation matrix, the matrix determinant and the results of the Kaiser-Meyer-Olkin (KMO) sampling adequacy test were analyzed regarding factorability. For factor extraction, Principal Components Analysis (PCA) was used. Once the matrix was considered factorable, the eigenvalues, percentage of explained variance of each factor, scree plot graphic and parallel analysis were then examined in order to determine the quantity of factors to be extracted. After defining the quantity of factors, a Principal Axis Factoring (PAF) analysis was run using Promax rotation - since correlation among factors was expected, which is common in behavioral phenomena. Cronbach’s alpha was used to check reliability or internal consistency of each factor.

3.3 Study 2: Confirmatory Factor Analysis and Construct Validity The sample for this study was also collected online using MTurk. Data were collected from 450 employees of several companies. Of the employees, 58% were male, 45% were Asian, Asian-American or Pacific Islander, 85% were under the age of 40, 55% had a Bachelor degree, and 48% had been at the company for fewer than 5 years. The respondents came from a multitude of industries, such as education (13.4%), computer hardware/software (13.2%), engineering/architecture (9.4%), healthcare/medical (5.8%), finance/banking/insurance (5.8%), admin/support/waste management/remediation services (4.8%), telecommunications (4.8%), internet ASP (4.3%), accounting (4.3%), food service (3.5%), retail (3.5%), manufacturing (3.3%), consulting (2.8%), and others (21.1%).

The data were examined and the assumptions for multivariate analysis were checked, following the procedures recommended by Myers (1990), Menard (2002), Tabachnick and Fidell (2007) and Hair et al. (2009). The final sample counted then with 400 subjects. Byrne (2009) and Kline (2011) state that for a CFA, an adequate sample size would be 10 subjects for variable. On the other hand, the authors state


that a minimum of 200 individuals is always required. The sample size with 400 subjects attended, therefore, the criteria.

In this study, two measurement models were tested and compared: the one-factor model and the six-factor model. To determine which structure adjusts better to HRMPPS, its fit was evaluated by using AMOS through the following indexes: NC (normatized chi-square or chi-square value divided by the model’s degrees of freedom = CMIN/DF), CFI (Comparative Fit Index) and RMSEA (Root Mean Square Error of Approximation), as recommended by Kline (2011). The internal consistency was measured through composite reliability, also known as Dillon-Goldstein’s rho or Jöreskog’s rho, as proposed by Chin (1998). Dillon-Goldstein’s rho is a better reliability measure than Cronbach’s alpha in Structural Equation Modeling, since it is based on the loadings rather than the correlations observed between the observed variables.

Finally, construct validity, “the degree to which a measure assesses the construct it is purported to assess” (Peter, 1981, p. 134), was examined in this study through convergent, discriminant, and nomological validity. 3.4 Study 3: Scale Generalizability The sample for this study was collected online using MTurk as well. Data were collected from 305 employees of multiple organizations. Of the employees, 70% were male, 60% were Asian, Asian-American or Pacific Islander, 90% were under the age of 40, 54% had a Bachelor degree, and 56% had been at the company for fewer than 5 years. The respondents came from a multitude of industries, such as computer hardware/software (16.3%), education (14.5%), engineering/architecture (9.9%), healthcare/medical (6.7%), finance/banking/insurance (5.7%), internet ASP (5.3%), accounting (5.3%), and others (36.3%).

The data from this study were used to test the scale generalizability by conducting a replicative analysis on both the measurement model and structural model used in study 2, assessing the correlation between HRM policies and well-being at work. For this purpose, we used as measure for the perceptions of HRM policies the six-factor HRMPPS validated in the EFA (study 1) and confirmed trough the CFA (study 2). Also, the measure used to assess employees well-being at work was the three-factor Well-Being at Work (WBW) scale developed and validated by Paschoal and Tamayo (2008). This scale was translated to English by one of its authors, retranslated to Portuguese by the authors of this paper and it was finally checked out by an English Professor from a university in California.

The WBW scale has good psychometric parameters and addresses both the affective dimension of well-being at work as well as the eudaimonica dimension of achievement and expression. This instrument comprises 30 items divided into three factors: (1) positive affect, with nine items and the reliability index Cronbach's alpha (α) equal to .95, (2) negative affect, with 12 items and α=.94, (3) fulfillment, composed of nine items and α = .92. Items related to positive and negative affect are supposed to be answered according to a five-point scale, ranging from 1 (not at all) to 5 (extremely) and the items related to fulfillment in accordance with a scale of five points of agreement, ranging from 1 (strongly disagree) to 5 (agree completely).

The data were examined and the assumptions for multivariate analysis were checked, following the procedures recommended by Myers (1990), Menard (2002), Tabachnick and Fidell (2007) and Hair et al. (2009). The final sample counted then with 285 subjects. In order to run a structural model, it is important to select a sample that has a minimal statistical power of .80 (Cohen, 1992). Through the program GPower 3.1, we obtained the minimum sample size of 146 with a .95 statistical power. Additionally, according to Kline (2011), for simple models, which is the case of this study, 200 subjects is the minimum recommended. Thus, the sample size with 285 subjects attended both criteria. 4. RESULTS This section presents the results of exploratory factor analysis, confirmatory factor analysis, construct validity and scale generalizability.


4.1 Exploratory Factor Analysis The analyses’ results confirmed the matrix high factorability to perform the exploratory factor analysis. KMO was 0.959, classified by Kaiser (1974) as marvelous. The determinant of the matrix was extremely close to zero indicating that the number of factors is lower than the number of items. Through Principal Components Analysis, it was possible to decide how many factors would be extracted. All the criteria adopted (eigenvalues higher than 1.0, explained variance percentage of each factor above 3%, scree plot graphic visual analysis and parallel analysis) pointed to the existence of 6 factors.

Thus HRMPPS, after 11 iterations, resulted in a multifactorial instrument with 32 items, distributed in 6 factors (subscales), representing HRM policies. The policies are compatible with the theoretical review done, explaining 58% of the construct’s total variance and meeting Hair et al.’s (2009) criterion that says a scale needs to have enough factors in order to explain about 60% of the construct variance.

The validity or quality of the items that composed each factor was also analyzed, based on Pasquali’s (2008) statement that a valid item is the one that well represents the factor, that is, an item with a good factor loading. The minimum acceptable load was .40 (Tabachnick and Fidell, 2007). Comrey and Lee (1992) classified items with loadings higher or equal .71 as excellent; higher or equal .63 as very good; higher or equal .55 as good; higher or equal .45 as reasonable; and higher or equal .32 as poor. Thus, as to the items’ quality, 70% of them were classified as excellent, very good and good.

Concerning the reliability, internal consistency or precision of the factors, Pasquali (2008) states that values above .70 indicate that the scale is reliable, while values above.80 indicate good reliability (Field, 2009). Nunnally and Bernstein (1994, pp. 264-265) say that “in the early stages of predictive or construct validation research,” it may be satisfactory to “have only modest reliability, e.g., .70”. For other scenarios, Nunnally and Bernstein (1994) go on to state that .80 or even .90 may be required. Peterson’s (1994) meta-analytical study on Cronbach’s alpha showed that reliable alphas have a .77 mean and .79 median.

All 6 factors showed high reliability, with alpha coefficients higher than .80, following the threshold recommended by authors such as Nunnally and Bernstein (1994), and Peterson (1994). Tables 1, 2, 3, 4, 5 and 6 summarize the main information of each factor.

TABLE 1. DESCRIPTION OF THE ITEMS IN FACTOR 1 (RECRUITMENT AND SELECTION)

Variable Description Quality Loading

HR4 The organization I work for uses various selection instruments (e.g., interviews, tests, etc.).

Very Good .70

HR3 Selection tests of the organization where I work are conducted by trained and impartial people.

Very Good .64

HR2 The organization I work for has competitive selection processes that attract competent people.

Good

.58

HR1 The organization I work for widely disseminates information about both external and internal recruitment processes.

Good .58

HR5 The organization I work for discloses information to applicants regarding the steps and criteria of the selection process.

Reasonable .49

HR6 The organization I work for communicates performance results to candidates at the end of the selection process.

Poor .40

Note: This factor had a total of six items and reliability of .81 (Cronbach’s α in EFA).


TABLE 2. DESCRIPTION OF THE ITEMS IN FACTOR 2 (INVOLVEMENT) Variable Description Quality Loading

HR8 The organization I work for is concerned with my well-being. Excellent .82 HR9 The organization I work for treats me with respect and

attention. Excellent .73

HR10 The organization I work for seeks to meet my needs and professional expectations.

Excellent .72

HR16 In the organization where I work, there is an environment of trust and cooperation among colleagues.

Excellent .71

HR15 In the organization where I work, there is an environment of understanding and confidence between managers and

employees.

Very Good .70

HR13 The organization I work for recognizes the work I do and the results I achieve (e.g., in oral compliments, in articles in

corporate bulletins, etc.).

Good .60

HR11 The organization I work for encourages my participation in decision-making and problem-solving.

Reasonable .49

HR14 In the organization where I work, employees and their

managers enjoy constant exchange of information in order to perform their duties properly.

Reasonable .45

HR18 In the organization where I work, there is a consistency

between discourse and management practice. Poor .43

Note: This factor had a total of ten items and reliability of .91 (Cronbach’s α in EFA). TABLE 3. DESCRIPTION OF THE ITEMS IN FACTOR 3 (TRAINING, DEVELOPMENT & EDUCATION)

Note: This factor had a total of three items and reliability of .82 (Cronbach’s α in EFA).

TABLE 4. DESCRIPTION OF THE ITEMS IN FACTOR 4 (WORK CONDITIONS)


HR26 The organization I work for provides basic benefits (e.g., health care, transportation assistance, food aid, etc.).

Very Good .65

HR29 The organization I work for is concerned with the safety of their employees by having access control of people who enter the

company building/facilities.

Good .60

HR28 The organization I work for has programs or processes that help employees cope with incidents and prevent workplace accidents.

Good .56

HR25 The organization I work for is concerned with my health and quality of life.

Reasonable .48

HR30 The facilities and physical condition (lighting, ventilation, noise and temperature) of the organization I work for are ergonomic,

comfortable, and appropriate.

Poor .40

Note: This factor had a total of five items and reliability of .81 (Cronbach’s α in EFA).


HR21 I can use knowledge and behaviors learned in training at work. Very Good .69

HR22 The organization I work for stimulates learning and application of knowledge.

Reasonable .52

HR19 The organization I work for helps me develop the skills I need for the successful accomplishment of my duties (e.g., training,

conferences, etc.).

Reasonable .48


TABLE 5. DESCRIPTION OF THE ITEMS IN FACTOR 5 (COMPETENCY-BASED PERFORMANCE

APPRAISAL)


HR35 The organization I work for disseminates competency-based performance appraisal criteria and results to its employees.

Excellent .84

HR34 The organization I work for discusses competency-based performance appraisal criteria and results with its employees.

Excellent .74

HR33 In the organization where I work, competency-based performance appraisal provides the basis for an employee development plan.

Good .61

HR32 In the organization where I work, competency-based performance appraisal is the basis for decisions about promotions and salary

increases.

Reasonable .53

HR31 The organization I work for periodically conducts competency-based performance appraisals.

Reasonable .49

Note: This factor had a total of five items and reliability of .86 (Cronbach’s α in EFA).

TABLE 6. DESCRIPTION OF THE ITEMS IN FACTOR 6 (COMPENSATION AND REWARDS)

Item Description Quality Loading

HR38 In the organization where I work, I get incentives such as promotions, commissioned functions, awards, bonuses, etc.

Very good .66

HR40 In the organization where I work, my salary is influenced by my results.

Good .56

HR37 The organization I work for offers me a salary that is compatible with my skills, training, and education.

Reasonable .48

HR39 The organization I work for considers the expectations and suggestions of its employees when designing a system of

employees rewards.

Reasonable .45

Note: This factor had a total of four items and reliability of .84.

By comparing the HRMPPS validated in Brazil and the HRMPPS validated in US, regarding reliability, number of items and validity, it’s possible to see similar parameters, as shown on Table 7, driving us to the conclusion that the six-factor structure validated in Brazil remained stable, with fewer items, on the validation in a different country, being suitable for application in US organizations.

TABLE 7. RELIABILITY OF THE SCALES

Factor HRMPPS Brazil HRMPPS US

Recruitment and Selection α=.84 α=.81 Involvement α=.93 α=.91

Training Development and education α=.88 α=.82 Work Conditions α=.84 α=.81

Competency-Based Performance Appraisal α=.86 α=.86

Compensation and Rewards α=.81 α=.84 Number of items 40 32

Quality of items 70% classified as excellent, very good and good

60% classified as excellent, very good and good

Total variance explained 58% 58%


4.2 Confirmatory Factor Analysis and Construct Validity As to dimensionality assessment, Byrne (2009) states that in a confirmatory factor analysis, a one-factor model should be tested before a multiple-factor model. So, in this study, two measurement models were tested and compared: Model 1, one-factor model (see Figure 1) and Model 2, six-factor model structure obtained in the EFA (see Figure 2). Two CFAs were run and the maximum likelihood method was used to estimate both models.

According to Kline (2011), values which indicate satisfactory adjust for a model are: for NC (CMIN/DF), values 2.0 or 3.0 or, at most, up to 5.0; for CFI, values higher than .90 and for RMSEA, values lower than .05 or up to .08. Model 1 showed 97 parameters, with χ2(464) = 1554.14, p<0.001 or NC=3.35; CFI =.81; RMSEA=.08 (confidence interval from .07 to .08). Therefore, the one-factor model has provided only moderate levels of fit (Figure 1).


FIGURE 1. MODEL 1 FOR HRM POLICIES AND PRACTICES LATENT VARIABLE: HRM POLICIES AND PRACTICES

χ2(464) = 1554.14.4 p<0.001

NC =3.35 CFI =.81 RMSEA=.08


On the other hand, the hypothesized six-factor model (Model 2) was tested and confirmed, providing a good fit in all indexes (Figure 2). The model showed 117 parameters, with χ2(449) = 979.54, p<0.001 or NC = 2.18; CFI = .91; RMSEA = .05 (confidence interval from .05 to .06). The factor loadings of the items in this confirmatory validation were between .47 and .84, showing a good quality of items, according to Comrey and Lee (1992).

FIGURE 2. MODEL 2 FOR HRM POLICIES AND PRACTICES

LATENT VARIABLES: RECRUITMENT AND SELECTION (RS); INVOLVEMENT (I), TRAINING, DEVELOPMENT and EDUCATION (TD&E); WORK CONDITIONS (WC); COMPETENCY-BASED

PERFORMANCE APPRAISAL (CBPA); COMPENSATION AND REWARDS (CR)

Taken together, model 2 was found to outperform model 1 on all measures. Summary statistics for these two models are shown in Table 8.

χ2(449) = 979.54 p<0.001 NC =2.18 CFI=.91 RMSEA=.05


TABLE 8. COMPARISON OF THE INDEXES OBTAINED FOR THE HRM POLICIES AND PRACTICES CONSTRUCT

Indexes Model 1: one general

factor Model 2: six correlated

factors CMIN or χ2 (df) 1554.14(464), p<0.001 979.54(449), p<0.001

NC (χ2/df) 3.35 2.18

RMSEA .08 .05

CFI .81 .91 The result of this analysis suggested that HRM policies in United States organizations are a multi-dimensional construct that consists of six dimensions. It is important to emphasize that, in the confirmatory analysis, the same multifactorial structure of 32 items distributed in 6 factors were kept, in agreement with the reviewed literature and with the exploratory validation, such that the interpretation of the factors is the same displayed in the tables 1 to 6. At last, the originated results confirmed the HRMPPS’ validation by showing the conceptual suitability of the structure obtained in the exploratory analysis and satisfactory fit.

By comparing the fit provided in the six-factor final model (Model 2) obtained in the CFA in the American sample with the fit obtained in the six-factor final model in the Brazilian sample, we observe better fit and indexes in the American model with 32 items. In addition, chi-square difference was significant, indicating that the American model is better than the Brazilian one. Table 9 shows this comparison.

TABLE 9. COMPARISON OF THE RESULTS OBTAINED IN BRAZILIAN AND AMERICAN MODELS

FOR HRM POLICIES AND PRACTICES

Six-factor model in Brazil (40 items)

Six-factor model in US (32 items)

CMIN or χ2 (p) 2178.4 (p<0.001) 979.5 (p<0.001)

Df 708 449

NC (χ2/df) 3.07 2.18

RMSEA .06 .05

CFI .90 .91

∆χ2(259) = 1198.9, p<0.001

4.1.1 Reliability Assessment To assess the reliabilities of the six subscales of HRM Policies and Practices, Jöreskog’s rho was computed for each factor. Chin (1998) recommends that acceptable scores for the Jöreskog’s rho should be higher than 0.7. The results were satisfactory, ranging from .73 through .90 for all the six factors. Specifically: recruitment and selection (ϱ=.77), involvement (ϱ=.87), training, development and education (ϱ=.73), work conditions (ϱ=.80), competency-based performance appraisal (ϱ=.90) and rewards (ϱ=.83). 4.2.2 Construct validity Construct validity is the degree to which a set of measured items actually reflects the theoretical latent construct that those items are supposed to measure (Hair et al, 2009). In this study, the construct validity


of the HRMPPS was examined by assessing convergent, discriminant, and nomological validity.

Convergent validity refers to the degree of agreement in two or more measures of the same construct. According to Hair et al. (2009), there are several indicators of convergent validity, for example, examining factor loadings and the reliability of the factors. As we have seen, the reliability of all six factors were above ϱ=.70, indicating appropriate convergence (Hair et al., 2009). In addition, all items of the HRM Policies and Practices measure loaded significantly positive on their specified factor (see Figure 2). 31 of the 32 items had loadings over .5 (Hair et al., 2009) on the factors to which they were assigned which is indeed a test of convergent validity of the scale. We may thus state that the scales for these six dimensions of HRMPP possessed convergent validity.

Discriminant validity indicates the degree to which measures of conceptually distinct construct differ. It was assessed in this study as follows: AFC was performed for a selected pair of constructs/factors, allowing correlation between the two constructs. The chi-square value of this model has been noted. Then, the AFC was performed again for the same pair of constructs, setting the correlation between the two constructs equal to 1. The chi-square value of the second model has been noted.

Thereafter, we calculated the difference between the values of the chi-squares and also the difference of degrees of freedom for both models. Then, we analyzed the difference of the chi-square values and the difference of degrees of freedom in a chi-square table: statistically significant values indicate the existence of discriminant validity. The test was conducted on each pair of constructs, resulting in 15 tests. The results on Table 10 showed that all chi-square differences are significant. There is evidence, then, that the constructs are different and have discriminant validity.

TABLE 10. DISCRIMINANT VALIDITY

Constructs Recruitment

and Selection Involvement TD&E

Work Conditions

CBPA

Involvement ∆χ2(1)=113.7

p<0.001

Training, Development and Education

∆χ2(1)=138.1

p<0.001 ∆χ2

(1)=82.2 p<0.001

Work Conditions

∆χ2(1)=122.1

p<0.001 ∆χ2

(1)=63.4 p<0.001

∆χ2(1)=98.2

p<0.001

Competency-Based Performance Appraisal

∆χ2(1)=138.7

p<0.001 ∆χ2

(1)=95.1 p<0.001

∆χ2(1)=120.4

p<0.001 ∆χ2

(1)=84.1 p<0.001

Compensation and Rewards

∆χ2(1)=115.1

p<0.001 ∆χ2

(1)=56.5 p<0.001

∆χ2(1)=91.4

p<0.001 ∆χ2

(1)=50.5 p<0.001

∆χ2(1)=71.3

p<0.001

Nomological validity shows the ability of a scale to behave as expected with respect to some other constructs to which it is related. It can be tested by examining if correlations between constructs make sense in a theory of measurement (Hair et al., 2009). There are well-grounded theoretical reasons to expect a strong and positive association between HRM policies and practices and well-being at work (Rubino et al., 2011; Baptiste, 2008; Nishii et al., 2008; Turner et al., 2008; Gelade and Iviry, 2003). Thus, in the current context, nomological validity would be demonstrated if the scores of the measures of HRM policies were positively and significantly correlated with well-being at work.

An assessment of the nomological validity of the HRMPPS was conducted through the structural equation


modeling analyses depicted on Figure 3. The findings supported the hypothesis that there is a positive correlation (r=0.814, p<0.001) between HRM policies and practices (HRMPP) and well-being at work (WBW). To remember, the 3 observed variables for well-being at work latent variable: positive affect, negative affect and fulfillment. Consequently, there is evidence of nomological validity for the proposed HRMPPS.

FIGURE 3. NOMOLOGICAL VALIDITY

In sum, we found evidence of convergent validity, discriminant validity, and nomological validity, and thus our findings lend support to the construct validity of the six-factor model of HRM policies and practices. 4.3 Scale Generalizability Even though our proposed factorial structure has a good fit with the data (Figure 2) and we have used a broad sample from various organizations located in US, we recognize that the results could be specific to this particular sample. Although it can be said that the sample represents a cross-section of a large number of firms, the generalizability of the HRMPPS could be still questionable. To provide evidence on generalizability of HRMPPS, a replicative study on a wide and different sample is essential, as shown in Figure 4.

As far as the measurement model is concerned, the data in this study exhibit a satisfactory level of fit: 31 parameters, with χ2(26)=87.36, p<0.001 or NC=3.36; CFI =.96; RMSEA =.09 (confidence interval from .07 to .11). Moreover, all 9 items were significant and loaded as predicted on their respective factors. These results provide further evidence to suggest that the proposed scale validated in this study is a reliable operational measure for HRM policies in a variety of American industries. After, analyzing our structural model (figure 4), we verified that our data support the assertion that there is a positive correlation between HRM policies and well-being at work (r = 0.839, p<0.001).

To sum up, the results are encouraging in terms of scale generalizability. The 32-item 6-factor HRM policies scale proposed in this study was found to have a high degree of reliability and validity and so it can be applied to a wide array of industries in the United States.

χ2(26) = 109.25 CFI=.94 p<0.001 RMSEA=.09 NC =4.20


FIGURE 4. SCALE GENERALIZABILITY

5. DISCUSSION

This section discusses the theoretical consistency of the scale validated in this study, academic and managerial implications of the results obtained and also points out limitations and directions for further researches. 5.1 Theoretical Consistency of the HRMPPS This paper reports a series of studies on the development and validation of a measure of HRM policies and practices in US organizations. The HRMPPS was found to demonstrate a high degree of reliability and construct validity. Nevertheless the numbers resulted from the previous analysis performed being very satisfactory, it is also necessary to analyze HRMPPS’s theoretical consistency or validity of expression from the revised literature, verifying if the scale’s items are coherent with the theoretical concepts used to support it. Kerlinger and Lee (2008) have said that it is not appropriate to hold a factor that has only a mathematic meaning, for the factor must be relevant in the scientific theoretical context. Furthermore, validity of expression must be established before any theory test when using CFA, because without an understanding of the content or meaning of each item, it is impossible to express and correctly specify a theory of measurement (Hair et al, 2009).

Concerning the recruitment and selection policy and its practices, Dessler (2002), and Mathis and Jackson (2003) have suggested as important points the vast disclosure of external and internal recruitment processes, as well as of information concerning the selective process’ stages, criteria, performance and results. The importance of using several selection instruments, defended by authors like Bohlander and Snell (2009), Dessler (2002), and Mathis and Jackson (2003), is also an aspect of HRMPPS and is the one with highest loading. And, finally, there are items, with a strong factor loading indicating its representativeness in the construct, illustrating the ideas of Lievens and Chapman (2010). They say that professionals responsible for recruitment and selection process have to be capable and impartial once they perform a fundamental and determinant role in the process. According to these authors, companies with a good organizational image become more attractive and have the possibility of selecting best-prepared professionals. They are also present in the scale.

χ2(26) = 87.36 CFI=.96 p<0.001 RMSEA=.09 NC =3.36


Dietz, Wilkinson and Redman (2010) discussed the involvement policy and its practices by legitimating the employees’ participation in decision-making and greater integration among them. Bohlander and Snell (2009) highlighted the importance of the existence of an environment of understanding, cooperation and confidence as a way of creating involvement, and the coherence between managerial speech and practice, points which are present in HRMPPS. Muckinsky (2004) and Siqueira (2008) emphasized the respectful and attentive treatment given to the employees, as well as caring for their well-being as fundamental aspects to involve them. It is important to note that the illustrative items of this point are the ones with the highest factor loadings of the involvement policy, showing thus the convergent validity of the items with the concept they represent. The other involvement’s items also have theoretical support. Bohlander and Snell (2009), Dessler (2002), Mathis and Jackson (2003) and Sisson (1994) alerted to the importance of continuous acknowledgement and feedback, exchanging information, as important practices of involvement, and Ulrich et al. (1991) emphasized the necessity of establishing partner relationships with the employees from the identification of their needs, values and worries.

The scale’s items referring to the TD&E practices also have theoretical support. Goldstein (1996) and Winterton (2007) explained the differences between the concepts of training, development and education, emphasizing the importance of evaluating training impact at work. Such item presented the highest factor loading in the HRMPPS’ validation. Sisson (1994) and Dessler (2002), in turn, discussed the need that the organizations have to invest especially in actions of development and education given their strategic character (long term). Thus, modern methods of training, managerial development and career management assume special connotation. In this context, Dutra (2001) affirmed that TD&E policy plays an important role in the development of necessary competences to perform functions, illustrating distance education and the corporate universities’ model as innovative in this process. Finally, Bohlander and Snell (2009) indicated that the stimulus for learning and sharing knowledge must be in the heart of a TD&E policy.

Regarding the items referring to the work conditions policy and its practices, Loudoun and Johnstone (2010) dealt with occupational health, quality of life at work and work safety, referring to a suitable environment and auspicious conditions to maintain the physical, psychic and mental welfare of the individuals, synthesizing items with high factor loading in the HRMPPS’ validation. Sisson (1994), Osborn, Hunt and Schermerhorn (1998), Dessler (2002) and Mathis and Jackson (2003) confirmed the relevance of offering basic benefits to the employees and this was the most representative item of the work conditions policy in the HRMPPS’ validation. Mathis and Jackson (2003) also pointed the importance of an ergonomic approaching on the project of functions, environment and positions. At last, Ulrich (2001) stressed that managers constantly neglect the access to suitable and necessary technology, in its broadest sense (hardwares, softwares, office supplies), for a good function’s performance, although it is fundamental, deserving special attention. In the same way, the existence of workplace amenities and conveniences, as banks, snack bars, among others, is a point recommended by the author, aiming to make work conditions more attractive to the employees.

Items representing practices of the competency-based performance appraisal policy also have found support in the reviewed literature. According to Latham, Sulsky and Macdonald (2007), performance’s management feedback to the employees is a crucial point, referring to the goals and results reached. From that, it’s important to remember that the criteria’s definition for the performance evaluation can be elaborated together with the employees as well as it should be disseminate throughout the organization, stimulating their involvement and participation in the process. These were the items with the highest factor loading in this policy. Aligned to these ideas, Dessler (2002), Mathis and Jackson (2003) and Bohlander and Snell (2009) defended the performance evaluation as the principal subsidy for elaborating an employees’ development plan and for decision-making regarding promotion and salary raise. Dutra (2001) emphasized the need of evaluating, besides performance, the employees’ competences since they might be indicatives of the potential for future contribution to the organization. Finally, Devanna, Fombrun and Tichy (1984) certified the need of periodical evaluations and, in this sense, Bohlander and Snell (2009) recommended a maximum period of 1 year between an evaluation and the next, having 6 months as the ideal period.

Finally, respecting the items representing practices validated in the compensation and rewards policy, Gerhart (2010) argued that it must be object of careful choices of the managers, being able to act as one


of the most impacting strategic policies of an organization. According to the author, the main questions for decision making are “how to pay” (offered rewards), considered the most strategic one in the decision-making process, and “how much to pay”. Referring to “how much to pay”, the remuneration must be compatible with the employee’s education and skills as Sisson (1994) and Bohlander and Snell (2009) defend. Also, Devanna, Fombrun and Tichy (1984) understood that a compensation policy must comprehend, besides remuneration, rewards and incentives like awards, bonus and promotions. The incentives were translated in the item with the highest factor loading in the rewards policy validation. On the same line, Hipólito (2001), Dutra (2001) and Dessler (2002) highlighted variable remuneration methods, such as prizes, gratification, profit participation and action options, a competency-based promotion and the broadbanding (fewer, but broader pay ranges indicating an easier way to professional promotion) as remuneration tendencies in the new millennium.

Thereafter, we might affirm that HRMPPS’s 32 items indeed have theoretical support, greatly corresponding to the literature reviewed throughout this paper.

The items of the Human Resource Management Policies and Practices Scale developed and validated in Brazil by Demo et al. (2012) were the basis for the validation conducted in the US. The comparison between the two scales, regarding the exploratory and the confirmatory factor analyses, drove us to the conclusion that the six-factor structure validated in Brazil remained stable in the American sample with respect to its validity and reliability, but the model validated in the US presented a leaner structure, with fewer items: 32 instead of 40 in its Brazilian version. Smaller instruments may have a higher response rate because it is likely that respondents’ questionnaire fatigue could be a contributory factor to the response rate (Saunders, Lewis and Thornhill, 2000). Besides, the 32-item-measurement model used to run the CFA, trough the structure equation modeling, also showed better fit in all indexes than the 40-item-model, indicating that the American version of HRMPPS could be more suitable for test in other countries and cultures.

In sum, the three studies performed in this paper produce a six-factor measure for HRM policies and practices with reliability, construct validity and theoretical consistency to be used in US companies. 5.2 Academic and managerial implications The present study makes both academic and practical contributions, and suggests several applications for the research.

First, we explore the strategic nature of HRM, provide a clear conceptualization of the construct, and then develop a conceptual model with the six most cited in the literature policies, namely, recruitment and selection; involvement; training, development and education; work conditions; competency-based performance appraisal; compensation and rewards. Though some of the ideas expressed in this conceptual model are familiar to HRM specialists, its value is in integrating these various notions to provide a more comprehensive and holistic picture of HRM policies and practices. Second, we provide empirical evidence on the testable scales that are both reliable and valid. This gives a new theoretical insight into how HRM policies and practices can be managed to provide superior organizational outcomes. Third, the model was empirically tested and found to have substantial association with well-being at work, indicated by positive affect, negative affect, and fulfillment.

As to the managerial implications, our findings confirm the long-held belief that HRM policies and practices are a critical success factor for well-being at work. There is a consensus in the literature that HRM policies and practices positively impact well-being at work. Nishii et al. (2008) assert that HRM practices should be designed to enhance well-being at work. Similarly, Turner et al. (2008) point out that HRM has traditionally two roles, a management support, providing the organization with competent people to perform the work processes, and also a support staff, looking after their well-being. Also, Gelade and Ivery (2003) found significant correlations between HRM practices, work climate and organizational performance, emphasizing the contribution of HRM policies and practices for employees’ well-being. In the same vein, the study performed by Baptiste (2008) showed that HRM practices significantly impact both well-being at work and organizational performance. Additionally, the results obtained by Rubino et al. (2011) highlight the strong influence of the involvement and compensation policies to promote greater well-being at work.


In this meaning, our scale might be an important evaluation instrument for managers to improve employee’s well-being at work. Beyond, there is theoretical and empirical evidence that HRM policies and practices indeed affect performance of organizations favorably (e.g., Guest and Hoque 1994; Ezzamel, Lilley and Willmott, 1996; Boselie, Dietz and Boon, 2005; Subramony, 2009; Menezes, Wood and Geladi, 2010; Guest and Conaway, 2011; Katou, 2012). Consequently, since the Human Resource Management Policies and Practices Scale comprehends the most widely studied HRM policies and practices, it might support managers decision-making and problem-solving regarding identification of HRM areas where specific improvements are needed. 5.3 Limitations and directions for future research Our proposal represents a first attempt to build and test a conceptual framework of HRM policies and practices. Then, a first limitation is that the present findings are therefore indicative rather than conclusive. In spite of the scale’s validation in Brazil, it would be useful to further assess the generalizability of the HRMPPS to other business environments such as European and Asian countries. Moreover, with more replicative and creative research, a more comprehensive conceptual framework related to HRM policies and practices can be developed in the future.

Second, although the results of this study do provide support that HRM practices have a positive influence on well-being, it is important to note that well-being is a multi-dimensional construct that may be characterized in a number of ways. Thereby, it would be useful to explore the complexities of the relationship between HRM practices and alternative dimensions and measures of well-being in future studies. For instance, it would be used the Satisfaction with Life Scale validated by Diener, Emmons, Larsen and Griffin (1985) to measure one’s global, cognitive assessment of one’s life as a whole, and the Positive Affect and Negative Affect Schedules validated by Watson, Clark and Tellegen (1988) which measure the experience of positive and negative emotions at work.

Another limitation is that because of the cross-sectional nature of the data, questions regarding causality remain unanswered. Thereby, the relationships between HRM practices and well-being at work may not be interpreted as proof of a causal relationship, but rather as lending support for a prior causal scheme. The development of a time-series database and testing of the HRM practices association with well-being at work in a longitudinal framework would provide more insights into probable causation.

In this meaning, there could be a need of alteration or even deletion of original items. Additionally, items representing HRM practices very disclosed and mentioned as important in literature could be included in further validations, such as: additional benefits and convenience at work place (e.g., partnerships with gyms, recreation centers and other establishments, rest area, banks, post-offices, beauty saloons), flexible benefits plan, labor gymnastics program and other leisure and health benefits the organization can offer, in the work conditions policy; internal recruitment prioritization over external, in the recruitment and selection policy; social gatherings, events and sports to promote employees interaction and the existence of internal communication channels, in the involvement policy; and periodically survey for training needs as well as investments on education (e.g., full or partial sponsorship of undergraduate degrees, postgraduate programs, language courses) in the TD&E policy. Specifically, the TD&E policy was composed by three items only, the minimum required for a scale (Hair et al, 2009; Field, 2009). Despite it has shown good reliability, future validations may considerate adding items to this factor.

Finally, continued refinement of the HRMPPS is recommended based on further research on new HRM trends and perspectives and changes in business environments, so that a valid measure of HRM policies and practices can be ensured on an ongoing basis.

We may conclude, in spite of the limitations pointed, that the main objective of this study was reached and a multifactor instrument to measure employees perception regarding HRM policies and practices was produced showing theoretical consistency, reliability, construct validity and the possibility to be applied to a wide array of industries in the United States.

Considering the increasing research attention paid to the new strategic role of HRM policies in organizations, this study provides a comprehensive operational measure of HRM policies. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of HRM policies and practices can be built.


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Appendix 1. HRMPPS application version items

1 The organization I work for widely disseminates information about both external and internal recruitment processes.

2 The organization I work for has competitive selection processes that attract competent people. 3 Selection tests of the organization where I work are conducted by trained and impartial people. 4 The organization I work for uses various selection instruments (e.g., interviews, tests, etc.). 5 The organization I work for discloses information to applicants regarding the steps and criteria of the

selection process. 6 The organization I work for communicates performance results to candidates at the end of the

selection process. 7 The organization I work for follows up on the adaptation of employees to their functions. 8 The organization I work for is concerned with my well-being. 9 The organization I work for treats me with respect and attention. 10 The organization I work for seeks to meet my needs and professional expectations. 11 The organization I work for encourages my participation in decision- making and problem- solving. 12 The organization I work for encourages interaction among its employees (e.g., social gatherings, social

events, sports events, etc.). 13 The organization I work for recognizes the work I do and the results I achieve (e.g., in oral

compliments, in articles in corporate bulletins, etc.). 14 In the organization where I work, employees and their managers enjoy constant exchange of

information in order to perform their duties properly. 15 In the organization where I work, there is an environment of understanding and confidence between

managers and employees. 16 In the organization where I work, there is an environment of trust and cooperation among colleagues. 17 The organization I work for favors autonomy in doing tasks and making decisions. 18 In the organization where I work, there is a consistency between discourse and management practice. 19 The organization I work for helps me develop the skills I need for the successful accomplishment of my

duties (e.g., training, conferences, etc.). 20 The organization I work for invests in my development and education promoting my personal and

professional growth in a broad manner (e.g., full or partial sponsorship of undergraduate degrees, postgraduate programs, language courses, etc.).

21 I can use knowledge and behaviors learned in training at work. 22 The organization I work for stimulates learning and application of knowledge. 23 In the organization where I work, training needs are identified periodically. 24 In the organization where I work, training is evaluated by participants. 25 The organization I work for is concerned with my health and quality of life. 26 The organization I work for provides basic benefits (e.g., health care, transportation assistance, food

aid, etc.). 27 The organization I work for provides additional benefits (e.g., membership in gyms, country clubs, and

other establishments, etc.). 28 The organization I work for has programs or processes that help employees cope with incidents and

prevent workplace accidents. 29 The organization I work for is concerned with the safety of their employees by having access control of

people who enter the company building/facilities. 30 The facilities and physical condition (lighting, ventilation, noise and temperature) of the organization I

work for are ergonomic, comfortable, and appropriate. 31 The organization I work for periodically conducts competency-based performance appraisals. 32 In the organization where I work, competency-based performance appraisal is the basis for decisions

about promotions and salary increases. 33 In the organization where I work, competency-based performance appraisal provides the basis for an

employee development plan. 34 The organization I work for discusses competency-based performance appraisal criteria and results

with its employees.


35 The organization I work for disseminates competency-based performance appraisal criteria and results to its employees.

36 The organization I work for remunerates me according to the remuneration offered at either the public or private marketplace levels.

37 The organization I work for offers me a salary that is compatible with my skills, training, and education. 38 In the organization where I work, I get incentives such as promotions, commissioned functions,

awards, bonuses, etc. 39 The organization I work for considers the expectations and suggestions of its employees when

designing a system of employee rewards. 40 In the organization where I work, my salary is influenced by my results.


ACTIVITY-BASED COST MANAGEMENT STRATEGY AND CONTINUOUS PERFORMANCE IMPROVEMENT: EVIDENCE OF THAI ELECTRONIC FIRMS

Pitachaya Kaneko, Mahasarakham Business School, Mahasarakham University, Thailand


ABSTRACT The study attempts to integrate the key components of activity-based cost management strategy in the new model. The objectives of this study are to examine the effects of activity-based cost management strategy on continuous performance improvement through two mediators: resource integration quality and intellectual capital advantage. This study also examines the effects of organizational communication and organizational coordination as moderators on the relationships among activity-based cost management strategy, resource integration quality, intellectual capital advantage, and continuous performance improvement of electronic firms in the Federation of Thai Industries (F.T.I). Questionnaire was utilized for data collection and 225 financial managers of electronic firms are the sample of this study. With the results of the study, activity-based cost management strategy is strongly positive which has a significant continuous performance improvement through resource integration quality and intellectual capital advantage which become mediators. However, the results indicate a partially significant positive effect of the moderators, organizational communication and organizational coordination on the relationship among activity-based cost management strategy, resource integration quality, intellectual capital advantage and continuous performance improvement. Additionally, the results also suggest that organizational coordination is suitable as an antecedent than a moderator, because it has a direct effect both on resource integration quality and intellectual capital advantage, but it has no indirect effect on continuous performance improvement. To explicitly verify the linking of the aforementioned antecedents to continuous performance improvement, future study needs to resort to antecedent and mediating variables and include them in the conceptual model in order to increase the contributions and benefits of the study. Keywords: Activity-Based Cost Management Strategy, Cost Management Orientation, Product Planning Focus, Resource Integration Quality, Intellectual Capital Advantage, Continuous Performance Improvement, Organizational Communication, Organizational Coordination 1. INTRODUCTION The fierce global competition both of local and international markets forces manufacturers to compete in quality, cost, and time to the market aspects of their products. These changes have an impact on the environment around companies change continuously and are demanding lower prices, extend higher quality of products/services, suitable product planning and aggressive pricing. The availability and relevance of accounting information underlies many business decisions. The popularity of activity-based cost management which has presented since 1980s has subsequent evolution and revolution debate both of management accounting literature and practices. However, it still concentrates on assessing the integrity of the ABC process (Cokins, 1996; Gupta, 2008; Gupta and Galloway, 2003; Kennedy and Affleck-Graves, 2001; Krumwiede, 1998), factors impacting the success of implementation, the goal of ABC and ABCM is to increase profits by estimating the various manufacturing costs more accurately to become a strategic objective and create share holder value through increasing long-term profits, continuous performance improvement. The term activity-based cost and activity-based cost management are sometimes used interchangeably (Cokins, 1996) and have increasingly influenced managers who utilize this information for decision making and seek new strategies, techniques and resources with concerning more complex cost management systems in order to create continuous performance improvement. ABC is a cost accounting methodology that aims to allocate overhead costs effectively and traces cost by using resource and activity cost drivers that reveal activities and objects consumption patterns on the basis of a cause-effect relationship. Cost drivers in ABC model are utilized to establish a transitional mapping between resources, activities and cost objects (Kostakis, Boskou, and Palisidis, 2011). While activity-based cost


management refers to fundamental management philosophy that focuses on the planning, controlling, executing, and measuring activities as a strategy for the key to competitive advantage (Rasmussen et al., 1999; Roberts et al.,1996). Additionally, increasing the value for activities process can increase the competitive advantage to ensure the survival of firm in the world of high uncertainty and competitiveness (Beheshti, 2004; Drury and Taules, 2005; Rattanaphaptham and Ussahawanitchakit, 2010). Prior research focuses on the interest and motivation about the effects of ABC adoption; implementation; and techniques on firm performance or firm success (Kennedy and Affleck-Graves, 2001) which includes ABC effectiveness as firm’s resource and capability to create competitive advantage leading to enhance financial performance. There are several potential questions about ABC and ABCM implementation as “If ABC has demonstrated benefits, why firms are not actually employing it” and “Is ABCM implementation actually suitable for every firm?” However, implementation of ABC or ABCM itself may be not succeed and could not increase value added of stakeholders. It needs to be correlated with the others variables which are true value drivers on firm performance (Shields, Dang and Kato, 2000). Anderson (1995); Foster and Swenson (1997); Krumwiede (1998); Malmi (1997); argue that successful implementation depends on organizational and technical factors including specific characteristics in their business strategy and organizational structure that leads certain firms to adopt and implement ABCM. Both ABC and ABCM may have an indirect rather than a direct effect on performance through an intervening variable that mediates the relationships. Managers of firm will be concerned about the level of suitable implementation ABCM and the influence factors as a strategy to set their targets. Additionally, only a few researches examine the relationships between implementation of activity-based cost management and firm performance (Gunasekaran and Sarhadi, 1998). This study, thus, focuses on the contribution from implementation of activity-based cost management strategy in electronic firms as well as in manufacturing and services firms (Lammert and Ehrsam, 1987; Cooper and Kaplan, 1991; West and West, 1997) to achieve the important goals such as product development and pricing; target cost reduction; key performance evaluation; and industry cost comparison, two dimensions of activity-based cost management strategy (cost management orientation and product planning focus) are utilized in this study. The remainder of this study is organized in seven sections as follows. The second section reviews theoretical framework, literature reviews and hypotheses development is presented in the third section. The fourth explains about research methods, sample selection and data collection procedure and variable measurement of each construct. The fifth shows the results and discussion. The sixth describes the contributions of this study and the suggestions for future research. The conclusion is presented in the last section. 2. THEORETICAL FRAMEWORK This study applies the following concepts of the resource-based view, contingency theory, and planning theory to explain the situation and understand about the relationship among these variables that have shown in Figure 1. The resource-based view (Barney, 1991) is defined firm resources as all assets, capabilities, organizational processes, firm attributes, information, knowledge, innovations and intellectual capital controlled by a firm. Firms will have increased competitive advantages when they could create value and succeed their strategy based on firm resources. This theory explains how firm resources and their capabilities have a strongly positive impact on firm performance (Rattanaphaphtham and Ussahawanitchakit, 2010). Thus, this research proposes that the effectiveness of activity-based cost management is a firm resource to create more competitive advantages leading to continuous performance improvement. According to contingency theory, organizational structure is a function of context which simultaneously determined by a variety of endogenous and exogenous contextual factors such as business strategy, organizational commitment, environmental uncertainty, organizational communication and coordination, and competitors (Anderson and Young, 1997; Liu and Pan, 2007). Hence, this study treats two contextual factors: organizational communication and coordination as moderating effects which encourage firms to obtain continuous performance improvement.


Planning theory is a theoretical framework with the synthesis of concepts from multiple fields and based on concept “How organizational leaders might best consider practicing and set their targets in the future.” This theory does not only concern the changes too rapid for prediction to be accurate, but also gain creditability as effective tools to prepare for an uncertain future and to improve performance in a dynamic environment (Chermack et al., 2001; Thaweechan and Ussahawanitchakit, 2011). In this study, we apply activity-based cost management strategy, managerial accounting strategy as a functional management strategy, for a proxy of management strategy. We also apply this theory to study the relationships between product planning focus on continuous performance improvement through mediators resource integrated quality and intellectual capital advantage following RBV theory and organizational communication and coordination based on contingency theory as moderators among these variables on continuous performance improvement. 3. LITERATURE REVIEWS AND HYPOTHESES DEVELOPMENT According to the theoretical framework, the probable relationship concerns among several constructs are visible. This research proposes a conceptual model for empirical research in the topic “Activity-Based Cost Management Strategy and the Influence Factors on Continuous Performance Improvement” as shown in Figure 1.

FIGURE 1

MODEL OF THE RELATIONSHIPS BETWEEN ACTIVITY-BASED COST MANAGEMENT STRATEGY AND CONTINUOUS PERFORMANCE IMPROVEMENT

3.1 Activity-Based Cost Management Strategy Activity-based cost management (ABCM) has been accepted as more accurate in the costing and pricing of products or services with the focus on decision making of planning, controlling, executing and measuring all activities as a strategy for the key to competitive advantage (Rasmussen et al., 1999; Roberts et al.,1996). Activity-based cost management strategy (ABCMS) is defined as strategy with advanced management accounting system ABCM to create more sophisticated decision-making approach utilizing sophisticated management accounting systems with more complex, more challenging and more competitive business environment (Kallunki and Silvola, 2008). To increase competitive advantage and decrease non-value added activities and cost, managers should utilize activity-based cost management as a strategy for formal controls and planning (Zaman, 2009). Thus, we employ cost management orientation and product planning focus which included concept of production and marketing function as dimensions of activity-based cost management strategy in this study. 3.1.1 Cost Management Orientation Cost management orientation (CMO) is based on firm strategic cost management systems that emphasize on better business process, implementation and profit by focusing on 1) the achievement of allocation of direct and indirect costs, provision of cost information creditability, and cost reporting usefulness to support management decision making, 2) the achievement focuses on increasing efficiency


and managerial practices that concern about market oriented cost management such as lower the customer cost, higher customer satisfaction, cost of maintenance and cost to improve the implementation in order to achieve organization goals (Ratanaphaptham and Ussahawanitchakit, 2010; Zaman, 2009). Additionally, environment changes are required in the functional structures of the organizations and the interaction between cost management oriented and other structures of the organization including the belief to be the most valued members of the organization. 3.1.2 Product Planning Focus Product planning focus (PPF) is a strategic planning concept: 1) to assign production planning in all aspects from workforce activities to product delivery, concerning with the efficient use of resources, distinguished characteristics that the production planning is focused on the actual production, whereas operations planning looks at the operation as a whole (O’Conner, 2004; O’Farrell, 2011), 2) to utilize capable forecasting and planning output of product/ service, 3) to lead to increase customer responsiveness, quality, and satisfaction by new product development and products to the market quickly than major competitors (Chenhall, 2004; Choe, 2004; Ratanaphaptham and Ussahawanitchakit, 2010). Camal, Acar and Tanriverdi (2006) argued that product planning could increase sustained competitive advantage to encourage firms to achieve high performance. 3.2 Resource Integration Quality Resource integration quality (RIQ) refers to the process by which managers manage corporate resources, internal innovation, knowledge and capabilities to gain and use effectively and efficiently in the achievement of the organizational targets (Pavlov and Bourne, 2007) and to achieve competitive advantage (Pungboonpanich and Ussahawanitchakit, 2010). Prior researchers (e.g. Labodova, 2004; Myhr and Spekman, 2002) reveal that efficiently planning, forecasting, and controlling have a strongly positive impact on resource integration quality. In this study, we apply two dimensions of activity-based cost management strategy as a strategy focuses on efficiently planning, forecasting and controlling cost and production, and employ resource integration quality as a mediating variable of activity-based cost management strategy on continuous performance improvement. 3.3 Intellectual Capital Advantage Intellectual capital advantage (ICA) refers to the value of best practice of a real business asset, or organization’s employee knowledge, business training and any useful information that helps company gain competitive advantage. Intellectual capital advantage is focused on value added of asset, knowledge, and all information systems and resources of company to gain new customers, create new products, or improve business over competitors such as human capital, information capital, alliances, brand awareness and instructional capital (Agbejule and Saarikoski, 2006; Gupta et al., 2008). Prior researches on cost accounting found that implementation of activity-based cost management can enhance cost and profit information and also assist managers in understanding and evaluating how resources are suitably used to create firm’s value-chains in delivering strategic outcomes and firm performance such as customer satisfaction, return on investment and profitability (Cadez and Guilding, 2008; Krumweide, 1998). We apply this variable as a mediating variable of activity-based cost management strategy on continuous performance improvement. Thus, the hypotheses are proposed as follows: Hypothesis 1a: The greater the cost management orientation is, the more likely that firms will achieve more resource integration quality. Hypothesis 1b: The greater the cost management orientation is, the more likely that firms will achieve more intellectual capital advantage. Hypothesis 2a: The greater the product planning focus is, the more likely that firms will achieve more resource integration quality. Hypothesis 2b: The greater the product planning focus is, the more likely that firms will achieve more intellectual capital advantage.


3.4 Continuous Performance Improvement Continuous performance improvement (CPI) in this study is defined as firm’s effort to seek out continually and apply new ways to enhance sustained performance in long-term. Continuous performance improvement actually forces everyone in business either employees or managers to continue improving themselves, in turn, resulted in economic benefits increase in four categories: improving stakeholder confidence; management and firm performance; organization learning; and innovation assets (Holton et al., 2010; Prempanichnukul and Ussahawanitchakit, 2010). Following the theoretical framework and prior literature reviews, the hypotheses are proposed as follows: Hypothesis 3: The greater the resource integration quality is, the more likely that firms will achieve more continuous performance improvement. Hypothesis 4: The greater the intellectual capital advantage is, the more likely that firms will achieve more continuous performance improvement. 3.5 Organizational Communication Organizational communication (OCM) in this study is defined as the process by which information exchanged and understood by two or more people, usually with an intention to motivate or influence behavior. It focuses on three dimensions: diverse communication implementation; openness and flexible communication channel; and media utilization efficiency. The importance of organizational communication relationships continues to be of an interesting topic (Jahanson et al., 2008) because it helps managers achieve the goal, especially in increasing or reducing critical problems, giving information or commitment. According to contingency theory, this research determines organization coordination as the moderator of relationships among activity-based cost management strategy, resource integration quality, and intellectual capital advantage include relationships among resource integration quality, intellectual capital advantage, and continuous performance improvement. Hence, the hypotheses are proposed as follows: Hypothesis 5a: The greater the cost management orientation is, the more likely that firms with higher organizational communication will achieve more resource integration quality. Hypothesis 5b: The greater the cost management orientation is, the more likely that firms with higher organizational communication will achieve more intellectual capital advantage. Hypothesis 6a: The greater the product planning focus is, the more likely that firms with higher organizational communication will achieve more resource integration quality. Hypothesis 6b: The greater the product planning focus is, the more likely that firms with higher organizational communication will achieve more intellectual capital advantage. Hypothesis 7: The greater the resource integration quality is, the more likely that firms with higher organizational communication will achieve more continuous performance improvement. Hypothesis 8: The greater the intellectual capital advantage is, the more likely that firms with higher organizational communication will achieve more continuous performance improvement. 3.6 Organizational Coordination Organizational coordination (OCO) in this study focuses on goal sharing behavior of firms and it is defined as the extent to which activities, people, routines, and assignments work together to accomplish overall objects or activities and link together to different parts of an organization to accomplish a collective set of task that leads toward common goals (Avadikyan et al., 2001; Lai, Wong, and Cheng, 2008). According to the contingency theory, this research determines organization coordination as the moderator of the relationships among activity-based cost management strategy, resource integration quality, and intellectual capital advantage include relationship among resource integration quality, intellectual capital advantage, and continuous performance improvement as same as organizational communication. Hence, the hypotheses are proposed as follows:


Hypothesis 9a: The greater the cost management orientation is, the more likely that firms with higher organizational coordination will achieve more resource integration quality. Hypothesis 9b: The greater the cost management orientation is, the more likely that firms with higher organizational coordination will achieve more intellectual capital advantage. Hypothesis 10a: The greater the product planning focus is, the more likely that firms with higher organizational coordination will achieve more resource integration quality. Hypothesis 10b: The greater the product planning focus is, the more likely that firms with higher organizational coordination will achieve more intellectual capital advantage. Hypothesis 11: The greater the resource integration quality is, the more likely that firms with higher organizational coordination will achieve more continuous performance improvement. Hypothesis 12: The greater the intellectual capital advantage is, the more likely that firms with higher organizational coordination will achieve more continuous performance improvement. 4. RESEARCH METHODS 4.1 Sample Selection and Data Collection Procedure In this study, the sample selection included 850 members of electronic firms in the Federation of Thai Industries (F.T.I). A mail survey procedure via the questionnaire was used for data collection to examine the relationships. The key informants were financial managers of these firms. With regard to the questionnaire mailing, 51 surveys were undeliverable because of firms had moved to unknown locations and some dismissed due to the flooding in 17 provinces of Thailand. Deducting the undeliverables from the original 850 mailed, the valid mailing was 799 surveys, from which 238 responses were received. Of the surveys completed and returned, 225 were usable. The effective response rate was approximately 28.16%. According to Aaker, Kumar and Day (2001), the response rate for a mail survey of this paper with an appropriate follow-up procedure if greater than 20%, is considered acceptable. The non-response bias was calculated by comparing the results of early and late respondents as recommended by Armstrong and Overton (1977). Additionally, respondents were compared with non-respondents in terms of sample characteristics, such as authorized capital of the company, firm tenure, and number of employees. Non-response bias was investigated by t-test, and results were not significantly different, indicating that non-response bias did not appear to be a problem in this paper. 4.2 Variables To measure each construct in the conceptual model, all variables in table 1 are anchored by five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree) excluding control variables and show numbers of items in order to tap each variable. Additionally, all constructs are developed for measuring from definition of each constructs and examine the relationship from theoretical framework and prior literature reviews. Thus, the variables measurement of dependent variable, independent variables, and control variables of this study are described as follows: 4.2.1 Dependent Variable The dependent variable in this paper is continuous performance improvement referred to in the prior literature which consists of five items to assess four dimensions, namely the increasing outcomes from financial and non-financial performance; creativity intellectual; market share; sales and after service; and overall performance (Choe, 2004; Holton et al., 2010). 4.2.2 Independent Variables This study considers six independent variables. Firstly, the core constructs – two dimensions of activity-based cost management strategy are cost management oriented and product planning focus. Following literature reviews, cost management orientation is measured by ten items dealing with three categories: cost driver fitness; cost calculation accuracy and cost information (Chenhall, 2004; Pungboonpanich and


Ussahawanichakit, 2010; Zaman, 2009). While, product planning focus is measured by eight items dealing with three dimensions: pricing decision; production process decision; and product development (Chenhall, 2004; Zaman, 2009). Secondly, resource integration quality is measured by four items concerning the efficiency of financial and non-financial resource utilization, material requirement planning, information network system and alliances. Thirdly, product planning focus is measured by four items concerning with value added of asset, knowledge, and all information systems and resources of company to gain new customers, create new products, or improve business over competitors such as human capital, information capital, alliances, brand awareness and instructional capital (Abhijeet and Gupta, 2010). Finally, the next two moderator variables are organizational communication and organizational coordination. Both of them are measured by three items. 4.2.3 Control variables In this study, three control variables are considered. The first is firm size measured by operational capital and represent as a dummy variable which 1 is firms with more operational capital than 25 million baht, while 0 is otherwise. The second is firm tenure measured by years of firms operation and representing as a dummy variable which 1 is firms with operation experiences more than 10 years, while 0 is otherwise. The third is firm revenue measured by operation income per year and also representing as a dummy variable which 1 is firms with operation income more than 25 million baht, while 0 is otherwise. 4.3 Methods In this paper, factor analysis was implemented to assess the underlying relationships of a large number of items, and conducted separately on each set of the items representing a particular scale due to limited observations. This analysis are adopted by Nunnally and Bernstein (1994), all factor loadings were greater than cut-off value was at 0.40 and were statistically significant. Reliability of the measurements was evaluated by Cronbach Alpha Coefficients. In the scale reliability, Cronbach Alpha Coefficients are 0.76 - 0.88 as being greater than 0.70 (Nunnally and Bernstein, 1994). The scales of all measures appear to produce internally consistent results. Hence, these measures are conceived appropriate for further analysis because they revealed an accepted validity and reliability in this study. Table 1 presents the results for both factor loadings and Cronbach Alpha for multiple-item scales used in this paper. The ordinary least squares (OLS) regression analysis is used to test and examine the hypothesized relationships among activity-based cost management strategies, which consist of cost management orientation and product planning focus, resource integration quality, intellectual capital advantage, and two moderators: organizational communication and organizational coordination, on continuous performance improvement of electronic firms in The Federation of Thai Industries. The OLS regression analysis is an appropriate method for examining the hypothesized relationships (e.g. Aulakh, Kotabe and Teegen, 2000). With the interest of understanding the relationships in this study, the research models of the aforementioned relationships are depicted as follows.


Items Factor Loadings Cronbach Alpha

Cost Management Orientation (CMO) 0.66-0.84 0.85 Product Planning Focus (PPF) 0.75-0.85 0.85

Resource Integration Quality (RIQ) 0.80-0.89 0.86

Intellectual Capital Advantage (ICA) 0.84-0.91 0.88

Organizational Communication (OCM) 0.86-0.91 0.79

Organizational Coordination (OCO) 0.90-0.94 0.84

Continuous Performance Improvement (CPI) 0.86-0.89 0.76

Equation 1: RIQ = 01 + 1CMO + 2PPF + 3CAP + 4FTE + 5REV +


Equation 2: RIQ = 02 + 6CMO + 7PPF + 8OCM + 9CMO * OCM + 10PPF * OCM

+ 11CAP + 12FTE + 13REV + Equation 3: RIQ = 03 + 14CMO + 15PPF + 16OCO + 17CMO * OCO + 18PPF * OCO + 19CAP + 20FTE + 21REV + Equation 4: ICA = 04 + 22CMO + 23PPF + 24CAP + 25FTE + 26REV +

Equation 5: ICA = 05 + 27CMO + 28PPF + 29OCM + 30CMO * OCM + 31PPF * OCM + 32CAP+ 33FTE + 34REV + Equation 6: ICA = 06 + 35CMO + 36PPF + 37OCO + 38CMO * OCO + 39PPF * OCO + 40CAP + 41FTE + 42REV + Equation 7: CPI = 07 + 43RIQ + 44ICA + 45CAP + 46FTE + 47REV + Equation 8: CPI = 08 + 48RIQ + 49ICA + 50OCM + 51RIQ * OCM + 52ICA * OCM + 53CAP + 54FTE +55REV + Equation 9: CPI = 09 + 56RIQ + 57ICA + 58OCO + 59RIQ * OCO + 60ICA * OCO + 61CAP + 62FTE + 63REV +

5. RESULTS AND DISCUSSION Table 2 presents the descriptive statistics and correlation matrix for all variables. The potential problems relating to multicollinearity, variance inflation factors (VIFs) were used to provide information about their correlation on the extent to which non-orthogonality among independent variables inflates standard errors. The VIFs are ranged from 1.17 to 3.80, well below the cut-off value of 10 recommended by Neter, Wasserman and Kutner (1985), meaning that the independent variables are found that there are some correlations with each other over 0.80 score (Hair, 2010) which may have an effect on this problem. However, there is no significant problems from the multicollinearity encountered after the VIF’s testing in this study.

TABLE 2 DESCRIPTIVE STATISTICS AND CORRELATION MATRIX

Variables CPI CMO PPF RIQ ICA OCM OCO CAP FTE REV Mean 3.55 3.95 3.95 3.63 3.77 3.57 3.68 2.52 3.22 2.93 S.D. 0.66 0.60 0.66 0.64 0.69 0.70 0.72 1.09 0.93 1.02 CPI CMO 0.59*** PPF 0.61*** 0.84*** RIQ 0.66*** 0.74*** 0.77*** ICA 0.69*** 0.73*** 0.74*** 0.82*** OCM 0.59*** 0.63*** 0.64*** 0.69*** 0.71*** OCO 0.60*** 0.69*** 0.69*** 0.70*** 0.76*** 0.81*** CAP 0.10 0.19*** 0.20*** 0.13 0.11 0.05 0.07 FTE 0.11 0.19*** 0.21*** 0.16** 0.10 0.07 0.06 0.35*** REV 0.19*** 0.23*** 0.22*** 0.18*** 0.13 0.15** 0.18*** 0.66*** 0.32***

***p<.01, **P<.05


Table 3 shows the results of OLS regression analysis of the relationships among cost management orientation, product planning focus, organizational communication, and organizational coordination on resource integrated quality and intellectual capital advantage.



Dependent Variables RIQ ICA

Model1 Model 2 Model 3 Model 4 Model 5 Model 6 CMO 0.32*** 0.23*** 0.21*** 0.42*** 0.36*** 0.29***

(0.13) (0.12) (0.13) (0.13) (0.12) (0.12) PPF 0.50*** 0.39*** 0.38*** 0.40*** 0.29*** 0.25***

(0.08) (0.08) (0.08) (0.08) (0.08) (0.07) OCM 0.30*** 0.28***

(0.05) (0.05) OCO 0.31*** 0.39***

(0.06) (0.06) CMO * OCM 0.09* 0.11**

(0.05) (0.05) PPF * OCM -0.02 0.08*

(0.05) (0.05) CMO * OCO 0.01 0.10*

(0.05) (0.05) PPF * OCO -0.01 0.03

(0.06) (0.05) CAP -0.06 -0.02 -0.02 -0.02 0.01 0.02

(0.11) (0.11) (0.11) (0.12) (0.11) (0.10) FTE -0.01 0.01 0.02 -0.04 -0.01 0.00

(0.11) (0.10) (0.10) (0.11) (0.10) (0.10) REV 0.04 0.02 0.00 -0.03 -0.05 -0.08

(0.11) (0.11) (0.11) (0.12) (0.11) (0.11) Adjusted R2 0.61 0.66 0.65 0.59 0.66 0.68

*p<.10, **p<.05, ***p<.01, a Beta coefficients with standard errors in parenthesis. The first two hypotheses focus on the relationships among cost management orientation (CMO), product planning focus (PPF) on resource integration quality (RIQ) and intellectual capital advantage (ICA). The results of Hypotheses 1-2 have presented in Table 3. It indicates that cost management orientation in Models 1-3 (H1a: 1= 0.32, p<.01; 6= 0.23, p<.01; 14= 0.21, p<.01) and Models 4-6 (H1b: 22=0.42, p<.0127= 0.36, p<.0135= 0.29, p<.01), Product Planning Focus in Models 1-3 (H2a: 2= 0.50, p<.01; 7= 0.39, p<.01; 15= 0.38, p<.01) and Models 4-6 (H2b: 23=0.40, p<.01; 28= 0.29, p<.01; 36= 0.25, p<.01) has a strong positively significant effect on RIQ and ICA. CMO has more strong impact than PPF in ICA, but less impact in RIQ. It means that the greater the higher two elements (CMO and PPF) of activity-based cost management strategy are, the more likely that firms will achieve more resource integrated quality and intellectual capital advantage. Thus, Hypotheses 1a,b and 2a,b are supported. Hypotheses 5-6 in Table 3, we test the interaction between independent variables (CMO and PPF) and a moderator, organizational communication (OCM) on RIQ and ICA. The result of H5a shows the interaction between CMO and OCM on RIQ (Model 2, H5a: 9= 0.09, p<.10) and the result of H5b indicates the interaction between CMO and OCM on ICA (Model 5, H5b: 30= 0.11, p<.05). It indicates that the greater the cost management orientation is, the more likely that firms with higher organizational communication will achieve more resource integration quality and intellectual capital advantage. Thus, Hypothesis 5 is supported. However, the result of the interaction between PPF and OCM on RIQ (Model 2, H6a: 10= -0.02, p>.10) is not supported, while the interaction between PPF and OCM on ICA (Model 5, H6b: 31= 0.08, p<.10) is supported. It means that the greater the intellectual capital advantage


is, the more likely that firms with higher organizational communication will achieve more only intellectual capital advantage. Thus, Hypothesis 6 is partially supported. In Models 3 and 6, we substitute organizational communication to organizational coordination and test the interaction between independent variables (CMO and PPF) and organizational coordination, which are also presented in Table 3. The result of H9a shows the interaction between CMO and OCO on RIQ (Model 3, H9a: 17= 0.01, p>.10), while H9b shows the interaction between CMO and OCO on ICA (Model 6, H9b: 38= 0.10, p<.10). The result of H10a indicates the interaction between PPF and OCO on RIQ (Model 3, H10a: 18= -0.01, p>.10), while H10b indicates that the interaction between PPE and OCO on ICA (Model 6, H10b: 39= 0.03, p<.10). These findings reveal that there is a significant effect of the interaction between CMO and OCO on ICA in H9b. Thus, Hypothesis 9 is a partially supported. However, Hypothesis 10 is not supported. Additionally, we find the direct effect of organizational coordination on resource integration quality (Model 3, 16= 0.31, p<.01) and the direct effect on intellectual capital advantage (Model 6, 37= 0.39, p<.01). Additionally, we also find the direct effect of organizational communication on RIQ (Model2, 8= 0.30, p<.01) and the direct effect on ICA (Model 5, 29= 0.28, p<.01).


Independent Dependent Variable (CPI) Variables Model 7 Model 8 Model 9

RIQ 0.31*** 0.23*** 0.28*** (0.08) (0.09) (0.09)

ICA 0.42*** 0.34*** 0.32*** (0.08) (0.09) (0.09)

OCM 0.19*** (0.07)

OCO 0.15* (0.08)

RIQ * OCM -0.08 (0.08)

ICA * OCM 0.08 (0.08)

RIQ * OCO -0.01 (0.07)

ICA * OCO 0.08 (0.07)

CAP -0.07 -0.06 -0.06 (0.13) (0.13) (0.13)

FTE -0.03 -0.02 -0.02 (0.12) (0.12) (0.12)

REV 0.13 0.11 0.10 (0.13) (0.13) (0.13)

Adjusted R2 0.49 0.50 0.50 *p<.10, **p<.05, ***p<.01, a Beta coefficients with standard errors in parenthesis. Table 4 shows the results of OLS regression analysis of the relationships among resource integrated quality (RIQ), intellectual capital advantage (ICA), organizational communication (OCM), and organizational coordination (OCO) on continuous performance improvement (CPI). Firstly, in Hypotheses 3 and 4, we test the relationships of RIQ and ICA on CPI respectively. The result indicates that RIQ in Models 7-9 (H3: 43= 0.31, p<.01; 48= 0.23, p<.01; 56= 0.28, p<.01) and ICA in Models 7-9 (H4: 44= 0.42, p<.01; 49= 0.34, p<.01; 57= 0.32, p<.01) have strong positively significant effect on CPI. It concludes that the greater the resource integration quality and intellectual capital advantage is, the more


likely that firms will achieve more continuous performance improvement. Thus, Hypotheses 3 and 4 are strongly supported. It refers that activity-based cost management strategy has an impact on continuous performance improvement through resource integration quality and intellectual capital advantage which play a role as mediating variables. Secondly, we test the interaction effect of RIQ and OCM and interaction effect of ICA and OCM, in Hypotheses 7 and 8. The results indicate that the interaction effect of RIQ and OCM (H7: 51= -0.08, p>.10) and the interaction effect of ICA and OCM (H8: 52= 0.08, p>.10) have no impact on CPI. Thus, Hypotheses 7 and 8 are not supported.

Finally, we test the interaction effect of RIQ and OCO and the interaction effect of ICA and OCO on CPI in Hypotheses 11 and 12. The results indicate that the interaction effect of RIQ and OCO (H11: 59=-0.01, p>.10), and the interaction effect of ICA and OCO (H12: 60= 0.08, p>.10) have no impact on continuous performance improvement. Thus, Hypotheses 11 and 12 are not supported. Both OCM and OCO have no impact on the relationships among RIQ and ICA on CPI. However, we find that both OCM (in Model 8: 50= 0.19, p<.01) and OCO (in Model 9: 58= 0.15, p<.10) have a direct effect on CPI. Additionally, we also utilize control variables, namely firm size (CAP), firm tenure (FTE), and operation income (REV) in Models 1-9 for controlling the influence effect. However, the results indicate that there is no significant effect of control variables in this study.

6. CONTRIBUTIONS AND DIRECTIONS FOR FUTURE RESEARCH 6.1 Theoretical Contribution and Directions for Future Research This study is intended to provide a clearer understanding of the activity-based cost management usefulness as business strategy to achieve performance improvement. This research also enriches the previous literature on usefulness of activity-based cost management as a strategy for goal achievement. This study measures activity-based cost management strategy based on prior literature as an indirect effect on continuous performance improvement through mediating variables in uncertainty environment. Prior research (e.g. Foster and Senson, 1997; Krumweide, 1998) reveals that activity-based cost management as a useful strategy which is appropriate for manufacturing and service firms to enhance their performance improvement. However, the success of implementation on activity-based cost management strategy will impact on suitable of firms characteristic and structures (Foster and Swenson, 1997; Krumwiede, 1998, 2001; Malmi, 1997). 6.2 Managerial Contribution This study also provides more contribution to firm owners and managers to settle decision making their planning and control. Managers can apply the suitable level of relationships among activity-based cost management strategy, cost management orientation and product planning focus, resource integration quality, intellectual capital advantage, organizational communication, and organizational coordination to the actual business situations and utilizing their resources and capabilities in order to achieve continuous performance improvement. 7. CONCLUSION The objectives of this study are to examine 1) the effects of activity-based cost management strategy on continuous performance improvement through two mediators: resource integration quality and intellectual capital advantage, and 2) to examine the effects of organizational communication and organizational coordination as moderators on the relationships among activity-based cost management strategy, resource integration quality, intellectual capital advantage, and continuous performance improvement of electronic firms in the Federation of Thai Industries (F.T.I). Drawing on the work of Chenhall (2004); Krumwiede, 1998; and Shield (1995), it explains about the relationships between activity-based cost management and behavioral implementation factors, including business strategies and their usefulness for implementation practices (cost management orientation and product planning focus). The results from the sample of 225 financial managers of electronic firms are strong positively supported with the


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AUTHOR PROFILES: Pitachaya Kaneko earned her M.B.A. from Kasetsart University, Thailand in 1998. Currently, she is a Ph.D. (Candidate) in Accounting at Mahasarakham Business School, Mahasarakham University, Thailand. Dr. Phapruke Ussahawanitchakit earned his Ph.D. at Washington State University, USA in 2002. Currently, he is an associate professor of accounting and Dean of Mahasarakham Business School, Mahasarakham University, Thailand.


STRATEGIES IMPLEMENTATION AND EVALUATION OF THAILAND PUBLIC SECTOR TO IMPROVE PRIVATE SECTOR PERFORMANCE

Viput Ongsakul, National Institute of Development Administration (NIDA), Bangkok, Thailand

ABSTRACT This survey research revealed the status of strategies implementation of public sector in Thailand aiming to improve private sector performance. The research data is based on 905 samples which consist of two parts. The first part is data from citizen evaluation, 755 samples, and the second part is from civil servant evaluation, 150 samples. The model that we construct to evaluate strategies implementation of public sector is based on 4 dimensions which are efficiency, competency, participation and governance. The study reveals that efficiency and governance are most advance dimensions in strategies implementation while competency and participation are less advance dimensions. Keywords: Strategy, Strategy Management, Strategy Implementation, Strategy Evaluation, Public Management, Private Sector Performance, Public Reform, New Public Management 1. INTRODUCTION World Bank has done a study on the ease of doing business in each country and released report each year from 2003 to present. The report has shown the rank of countries in 10 indicators: Starting a Business, Dealing with Construction Permits, Getting Electricity, Registering Properties, Getting Credit, Protecting Investors, Paying Taxes, Trading Across Borders, Enforcing Contracts, and Resolving insolvency. Thailand was rank in top 20 for many years (World Bank, 2003-2011). Thai government set target to move Thailand into Top 10 rank and has initiated strategies to reform government agencies to serve better, faster, and cheaper. The reform strategies have been carried out by the Office of Public Development Commissions (OPDC). The strategies aimed to improve efficiency of service process, competency of civil servants, participation of citizens, and governance in administration. OPDC has set four strategy implementations and created many projects and initiatives for each strategy. The first strategy implementation was to improve overall efficiency of service process. The strategy involved initiating two large-scale service improvements, the Service Links and Government Counter Services. The Service Links acts as government gateway for handling requests requiring government approvals or services such as home construction permit, work permit, firm permit, tax submission service, social security service etc. Service Links are located in city center and sky train terminal providing a single location to make inquiries or submit requests to various public agencies. The Government Counter Services further increase convenience by locating service counters of public agencies that provide basic service such as identification cards, household certificates, name change certificates, birth and death certificates, passport, police reports for stolen wallet and losses of documents etc. The second strategy implementation involved improve competency of civil servants. The competency base management and Knowledge Management (KM) programs were started. The aims were to improve the public servant ability and to provide more pay to qualified servants. Later, the organizational restructure came in place, where the new structure is leaner by outsourcing some public works to efficient providers. The new format of government agency such as Public Organization, Office of Commissioners has been used for new type of government works. The young blood and talented people were recruited and trained for 22 months in the PSED (Public Service Executive Development) program. After their graduation, they were sent to work in strategic departments and provinces and qualified under the Fast Track career path. The third strategy implementation was to promote citizen participation in public policies and public works. The Public and Private Partnership or PPP was formed. Its main objective was to champion collaborations between government, businesses, NGOs in policy making and policy implementation. The 19 clusters initiative within provincial network collaboration was created, aiming to promote business


activities within and across provincial levels. The integrated provincial administration and the integrated ministry administration were formed by area based and function based accordingly. They were managed by joint key performance indicators (joint KPIs) as well as their own KPIs. The People’s Audit for Thailand (PATH) program was kicked off. The key emphasis was based on focusing on people’s need and participation in public service in order to increase the understanding, satisfaction, and trust. The forth strategy implementation was the adopting governance administration. The Royal Decree on Good Governance was proclaimed, where as it has prescribed the criteria and procedures for good governance. The government agencies are bound to follow the criteria and procedures for good governance. The provincial good governance committees were created, where their roles were to monitor provincial agency and make recommendations on improving public services to the governors. After years of implementation, OPDC would like to know the results and progress. This research was set by OPDC and founded by NIDA’s Business School to evaluate and monitor progress as well as the result. The research was based on survey of 905 samples consisted of two parts. The first part is citizen evaluation, 755 samples and the second part is civil servant evaluation, 150 samples. 2. THE MODEL The strategies implementation evaluation model is constructed base on four dimensions which are efficiency, competency, participation, and governance (See Figure 1). In each dimension, the evaluation has set to see the results of each initiative and activity by using Satisfaction Index. The Index is based on Likert scale, which runs from one to five. One is least agree and five is most agree. There are five question in efficiency dimensions, six questions in competency dimension, four questions in participation dimension, and three questions in governance dimension. The method of the survey is based on quota random sampling on two set of groups: The first group is civil servants (the service provider) and the second group is citizen (the service receiver). The data collection has been set for 200 service provider samplings and 800 service receiver samplings. The descriptive statistical analysis of each dimension is used to see the average and compare the result with other dimension

FIGURE 1: STRATEGIES IMPLEMENTATION EVALUATION MODEL

Efficiency

Governance

Competency Participation


3. RESULTS The response from the first group is 150 out of 200 (75% response rate) and the second group is 755 out of 800 (94.37% response rate). The overall satisfaction index of the first group is 3.33 and the second group is 3.07. The satisfaction index of each dimension for the first group and second group are shown in Table 1 and Table 2 respectively.

TABLE 1: SATISFACTION INDEX FOR THE FIRST GROUP (CIVILSERVANTS) SATISFACTION INDEX ON STRATEGIES IMPROVING IN AVERAGE

1. Efficiency 3.51 2. Competency 3.09 3. Participation 3.18 4. Governance 3.38

Overall 3.33

TABLE 2: SATISFACTION INDEX FOR THE SECOND GROUP (CITIZENS) SATISFACTION INDEX ON STRATEGUES IMPROVING IN AVERAGE

1. Efficiency 3.14 2. Competency N/A 3. Participation 2.92 4. Governance 2.86

Overall 3.07 The t-test two samples with 95% confident interval shows that there is no significant difference in Satisfaction Index among two groups. It means that these two groups of responds answered are homogenous response and can be combined. The next tests are based on comparing between each pair of parameters to see the differences between Satisfaction Index using t-test with 95% confident interval and the results are shown in TABLE 3.

TABLE 3: HYPOTHESIS TESTING BETWEEN PAIR OF PARAMETERS

Pair of Parameters

Confident Level p-value Results

Efficiency and Competency

95% 0.972 Cannot reject the hypothesis of no differences between two parameters

Efficiency and Participation

95% 0.001 Reject the hypothesis of no differences between two parameters

Efficiency and Governance


Competency and Participation


Competency and Governance


Participation and Governance

95% 0.795 Cannot reject the hypothesis of no differences between two parameters

From the t-test results, we can see that there are no statistical differences between efficiency and competency, participation and governance. But there are statistical differences between efficiency and participation, efficiency and governance, competency and participation, competency and governance. Therefore, the efficiency and competency can be classified as one group and participation and governance are in one group. It means that people (citizens and civil servants) see the same results from strategies implementations in efficiency and competency, participation and governance, but see the different result between group of efficiency and competency and group of participation and governance.


4. DISCUSSION The response from the survey confirmed that there are more satisfaction in strategies results in efficiency and competency than participation and governance. Thai Government needs to continuously improve efficiency of public service to maintain satisfaction level because citizens now expect a similar quality of service from both the public and private sectors (Jackson, 2001). With the shift in expectations, public agencies are generally faced with constant demand for improvement, especially in the areas of efficiency and quality (Wilson et al., 2001; and Finn and Thomas, 2008). To increase the level of service quality, empowerment of staff, and participation of citizens, all of which are essential for sustaining future improvements in service delivery (Foley, 2008). Therefore competency development and participation are the keys to improve government service, but Thai Government doing a good job in competency development and need improvement in participation. Finally, given the trends in urbanization and life-styles or living culture of city dwellers (who simply have no time to skip work to contact public agencies during the weekdays), any initiative for service improvement must address these issues (Vos and Westerhoudt, 2008). Thai Government needs to recognize the difference between lifestyle of urban citizen and rural citizen and response in providing public service differently. In order to have the difference service standard for urban and rural citizen, the different in service standard should still be “good difference”. Therefore the need to improve governance within the public agency is important. REFERENCES: Finn, A. and Thomas, D., “Prioritizing Areas for the Development and Delivery of Government e-Content and e-Service: An Appraisal of the Alberta Supernet”, Electronic Government, an International Journal, Volume 5, Number 2, Pages 103- 119, 2008 Foley, J., “Service Delivery Reform within the Canadian Public Sector”, Employee Relations, Volume 30, Number 3, Pages 283- 303, 2008 Jackson, P., “Public Sector Added Value: Can Bureaucracy Deliver?”, Public Administration, Volume 79, Number 1, Pages 5- 29, 2001 Vos, M. and Westerhoudt, E., “Trends in Government Communication in the Netherlands”, Journal of Communication Management, Volume 12, Number 1, Pages 18- 29, 2008 Wilson, C., Leckman, J., Cappuccino, K., and Pullen, W., “Towards Customer Delight: Added Value in Public Sector Corporate Real Estate”, Journal of Corporate Real Estate, Volume 3, Number 3, Pages 215- 221, 2001 World Bank, Doing Business 2004: Understanding Regulations, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2003. World Bank, Doing Business 2005: Removing Obstacles to Growth, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2004. World Bank, Doing Business 2006: Creating Jobs, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2005. World Bank, Doing Business 2007: How to Reform, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2006 World Bank, Doing Business 2008, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2007.


World Bank, Doing Business 2009, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2008. World Bank, Doing Business 2010: Reforming through Difficult Times, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2009. World Bank, Doing Business 2011: Doing Business in a More Transparent World, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2010. World Bank, Doing Business 2012: Doing Business in a More Transparent World, A copublication of the World Bank and the International Financial Corporation, Washington DC, 2011. AUTHOR PROFILE: Dr. Viput Ongsakul is an assistant professor at the Graduate School of Business Administration, National Institute of Development Administration (NIDA) in Bangkok, Thailand. He is currently Director of Ph.D.in Business Program and Area Coordinator for the Operations Management Department. He obtained a Bachelor of Engineering from Chulalongkorn University, Bangkok, Thailand. He also has a Ph.D. and an M.SC. in Industrial Engineering/Operations Research from Texas Tech University, U.S.A.


COMPETITIVE ADVANTAGE IN THAI SERVICE BUSINESSES: INVESTIGATING THE EFFECTS OF ORGANIZATIONAL DESIGN EFFECTIVENESS

Anirut Pongklee, Mahasarakham University, Thailand Sakcharoen Pawapootanont, Mahasarakham University, Thailand

ABSTRACT This paper to examines the role of organizational design effectiveness that affects service marketing image, specialize human capital, and firm performance. In addition, the researchers explore the key antecedents of organizational design effectiveness that include three factors of competence development in business relationship, customer induced uncertainty and technological opportunity. The model is tested using data collected from mail survey of 149 companies from hotel businesses in Thailand. The results indicate partial support for the hypotheses derived from the conceptual model. Thus, contribution and suggestions are also provided for future research. Keywords: Organizational Design Effectiveness, Service Marketing Image, Specialize Human Capital, Firm Performance, Competence Development in Business Relationship, Customer Induced Uncertainty, and Technological Opportunity 1. INTRODUCTION This research takes a strategic perspective and proposes a model based on the resource-based view (RBV) of the firm linking organizational design effectiveness to their consequences. RBV is employed for better understanding how and why firms gain and sustain competitive advantage. This perspective emphasizes performance differences based on firm heterogeneity. Firms vary in their unique bundles of resource and in the capabilities derived from such resources. Resources that are valuable, unique and difficult to imitate can provide the competitive advantage (Barny, 1991). The RBV suggests that an industry may be heterogeneous in terms of the resources they control and these resources are imperfectly mobile across firms (Barney, 2001). This intrinsically suggests that resources are valuable in and of themselves, impulse the selection of strategy and that competitive advantage is derived through a combination of unique organizational resources to enhance business competition in the competitive intensity markets (Hamel and Prahalad, 1994). Truly, RBV is applied for two reasons: firstly, organizational design effectiveness is a valuable resource of firm because of it is the firm culture to create and commit to integrate marketing activity to every function of firm that generates good coordination and enhances customer value. Lastly, organizational design effectiveness becomes a unique resource of firm that it cannot be easily imitated by rivals because these activities are more complex and take time of accumulating this capability. Thus, RBV is more appropriate to derive the conceptual model of this research. Research field of strategic management focus on configuration paradigm that provides and modified organisational structure will enhance firm competitiveness. Prior research on organizational design is different in the aims of the study and lacks serious investigation in the construct. Gebauer et al., (2010) explore the patterns of service strategy changes in manufacturing firms and indicate how each pattern is interrelated with modifications in organizational design elements. Homburg et al., (2000) described a range of dimensions such as structure, coordination, culture, and power that allow comparisons of organizations. Chowdhurya, and Miles, (2006) examined the unique design characteristics of service organizations that focus on the factors predicted organizational design choices. Moreover, prior investigations remain less information and lack empirical study of organizational design effectiveness in terms of organizational resource that can provide firm performance. The organizational design effectiveness literature signals that lack of the few academics exist with a systematic empirical research for the effects of organizational design effectiveness on service marketing image, specialize human capital, and firm performance. This research directly addresses this important knowledge gap by advancing and testing the notion of organizational design effectiveness. This research has theoretical contribution that encompasses the resource-based view of the firm and adds up the literature of organizational design effectiveness in terms of practitioner’s intent. Therefore, the primary objective of this research is to investigate the relationships among organizational design effectiveness, service marketing image, specialize human capital, and firm performance. The final


is to empirically investigate the role of competence development in business relationship, customer induced uncertainty and technological opportunity on organizational design effectiveness. The remaining part of this study is structured as follows. First, the relevant literature on all construct is reviewed. Second, the research method of the study is detailed. Third, the results of the empirical study are discussed. Finally, the study ends with theoretical and managerial contributions, suggestions for future research and conclusion. 2. RELATION MODEL AND HYPOTHESES The conceptual framework is drawn from a review of existing organizational design effectiveness. This conceptual framework attempts to investigate the relationships among organizational design effectiveness, service marketing image, specialize human capital, and firm performance. Then, this study attempts to investigate the antecedence organizational design effectiveness that includes competence development in business relationship, customer induced uncertainty and technological opportunity. This model is based on the resource-based view, as shown in Figure 1 below. 2.1 Organizational Design Effectiveness Organizational design effectiveness refers the capacity to produce a strong a formal, guided process for integrating the people, information, and technology of organization that used matches the form of organization as closely as possible to the purpose the organization seek to achieve. The organizational design concept is based on managerial decisions about the service orientation of human resources, organizational structure, and service process development (Gebauer, et al. 2009). Thus, this is organizational culture that are provides major decisions about align their organizational design with service strategy changes. Organizational design effectiveness s focuses on the concept of strategic change in service orientation towards the realigning and establishing business structure, process, and human capital that are integrated and committed to the continuous creation of unique value for customers. Organizational design effectiveness is culture and a key variable in the enhancement of organizational performance (Gebauer, et al. 2009) that becoming a source of sustained competitive advantage (Barney, 1991). To survive in turbulence of external environment organizations must be able to cope with increasing complexity and high-speed change (Brown and Eisenhard, 1995). In these contexts, companies with the capacity to innovate the organizational design will be able to respond to challenges faster and to exploit new products and market opportunities (Miles and Snow, 1997). Murphy and Wang, (2006) suggest that when firm are integrated organizational functions, the service marketing in many firm functions is orientated to the long term growth. The goal is to deliver long-term value to customers, and the measure of success is long-term customer satisfaction. Then, the development of services firm corresponds with the alterations in resources, structure, measurement and performance, and processes that are provide and enhance human capital in the organization (Neu and Brown, 2005). Capabilities of integrated organizational function allow firms to develop flexible strategies by effectively coordinating and consuming their process, technology and human resources in order to enhance firm performance (Rodoula et al., 2010) Therefore, the hypotheses are posited as follows: Hypothesis 1a: Organizational design effectiveness has a positive effect on service marketing image. Hypothesis 1b: Organizational design effectiveness has a positive effect on specialize human capital. Hypothesis 1c: Organizational design effectiveness has a positive effect on firm performance. 2.2 Service Marketing Image Service marketing image refers to positive attributes of the service marketing including expertise, trustworthiness, attractiveness and power that are convince customer interest. Image is generally conceived of as the outcome of a transaction whereby signals discharged by a service marketing department are acquired by a receptor and organized into a mental perception of the sending unit (Stern, B. et al., 2001). Corporate image is more involved in the service industry, which is conditioned by service features: intangibility, inseparability, heterogeneity, and perishability (Davies , 1996). Lehtinen and Lehtinen (1991) argue that the corporate images are positive relationship with service quality. According to Gronroos (1990) Firm image makes an important dimension of service quality. Image is considered to be an outcome of a service functional and technical quality. Marketing image is the most significant factor in the customer’s quality perception (Lehtinen and Lehtinen 1991). Barich and


Kotler (1991) identify groups of elements stipulating marketing image: selling power, channels of distribution, communication, service, promotion, price, product or service. This marketing value can increase firm’s profitability through higher customer life time value (Rust et al., 2004) and affecting sales growth and market share through customer attitudes toward brands and services and customer satisfaction to cultivate lifetime customer loyalty. Therefore, the hypothesis is posited as follows: Hypothesis 2: Service marketing image has a positive effect on firm performance.

FIGURE 1 COMPETITIVE ADVANTAGE IN THAI SERVICE BUSINESS: INVESTIGATING

THE EFFECTS OF ORGANIZATIONAL DESIGN EFFECTIVENESS

2.3 Specialize Human Capital Specialize human capital refers the unique capabilities, experiences, knowledge and skills of the workers that match core competency of organization. Employee abilities and experiences are largest capital of the modern firms (AL-Ma'ani and Jaradat, 2010). Accordingly, service organizations began to take the issue of building the specialize human capital, the importance it earns, by focusing on organizational process, which build and support creativity, and through employee development, to secure its vitality and effectiveness that are encouraging creativity and service innovation. The literature does suggest a positive effect of human resource management on firm performance. Human capital allows the company to develop capabilities that enhance innovation and that innovation is what positively affects performance (Hurley and Hult, 1998). Specialize human capital is similar to firm’s intangible assets that provide organizational competency such as new service process and more effective communication and retention program with customer. Thus, this capability can serve as a source of firm performance and provide competitive advantage (Hunt and Morgan 1995). Therefore, the hypothesis is posited as follows: Hypothesis 3: Specialize human capital has a positive effect on firm performance. 2.4 Competence Development in Business Relationship Competence development in business relationship refers to the extent to which a firm develops it competencies through adapting with customer and competitors action in the organizational environment. Nowadays, service firm confront change in the buying behavior of customer that is the substantial increase in the buying power of the customers. In addition, Besides, Firm face to excellently competitors


that shown new and powerful strategy and action to business competition (Kotler and Keller, 2009). Thus, business in modern economic can’t ignore the customer and competitor action that are affecting firm operation, performance and competitive advantage. The ability to respond to change of customer and competitor has been found to positively impact organization strategy and firm performance. Competence development in business relationship means that a firm can instantaneously prepare for operational change and volatile customer demands (Stank and Lackey, 1997) that provide and improve the new process, technology and human capital development. Therefore, the hypothesis is posited as follows: Hypothesis 4: Competence development in business relationship has a positive effect on organizational design effectiveness. 2.5 Customer Induced Uncertainty Customer induced uncertainty refers to the vagueness that arises from customer diversity, opportunism, and interactions to an organization (Chowdhurya and Miles, 2006). Nowadays, many firms confront more complex external environment. In the emerging era of continuous innovative production and service, all of business firm will increasingly need to involve customers in organizational activities (Miles et al., 1997). As the interaction diversity of the customer profile of such an organization increases that are increasing the mutability of demand (Tansik, 1990). Thus, customer diversity potentially presents a high level of mutability into the organizational process (Skaggs and Youndt, 2004). A highly diverse and complex customer base requires organizational agents and staff to continually adjust their approach to completing transactions. Organizations with highly diverse customers demand will therefore have trouble in forecasting activities to prosperously complete business activities and transactions (Bowen and Jones, 1986). Customer-induced uncertainty provides a more inclusive means of understanding and improves organizational design (Chowdhurya and Miles, 2006). Correspondingly, increased customer induced uncertainty will force businesses to adopt strategic management paradigm or firm’s capability that enables streamlined creation of new structure, process, and capability of organization staff for customers. It enhances information to gain access, provides new process of business services, and improves delivery superior services. Therefore, the hypothesis is posited as follows: Hypothesis 5: Customer induced uncertainty has a positive effect on organizational design effectiveness. 2.6 Technological opportunity Technological opportunity refers to the extent to which the opportunities related to technology for business operation. Technological opportunities provide success of radical innovations, in introducing new technological and resource opportunities (Fai, 2007) that are prepare resource or input for organizational design process. New technology improves firm architecture that provides more efficiently organizational facilities. Then, these opportunities promote human competency development that effect firm competitive advantage. Mitchell and Singh, (1996) has shown that discoverers of technological opportunities can access resources for exploitation most effectively through collaboration that provide process of knowledge management between partner. The importance of synergies of knowledge is apparent in evident to strategic integration (Geringer, 1988). Besides, technological opportunity influences the productivity of research and development (Klevorick et al. 1995). Correspondingly, increased technological opportunities will provide and force businesses to adopt business’s structure and process that enables streamlined creation of new organizational design. It improves delivery superior services. Therefore, the hypothesis is posited as follows: Hypothesis 6: Technological opportunity has a positive effect on organizational design effectiveness. 3. RESEARCH METHODS 3.1 Sample The suggested research model was tested with data obtained from a random sample of 630 companies from hotel businesses in Thailand. This study focuses on CEOs and top executive as target respondents. A questionnaire was sent to each respondent along with a cover letter and a postage-paid return envelope. The final sample consisted of 149 usable responses resulting in a response rate of 23.65% which is considered acceptable.


No non-response bias was found since there were no differences between the mean responses of the first and the last quartiles (Armstrong and Overton (1977). In this study, respondents were compared with non-respondents in terms of sample characteristics, such as total assets and authorized capital of the company. To test for non-response bias, the data for the early respondents as the first-wave questionnaire and late respondents were compared (Churchill, 2006). The rationale was that late respondents are more akin to non-respondents than those early ones. Non-response bias was investigated by t-test, and results found no significant differences, indicating that non response bias did not appear to be a problem in this investigation. 3.2 Variable Measurements To measure of independent variables, mediating variables, dependent variables, moderating variable and control variables are detailed as follows. The measurements used in this research were on a 5- point Likert-type scale (where: 1 = strongly disagree; 2 = disagree; 3= undecided; 4 = agree; 5 = strongly agree).The variables in this research were measured with multiple-item scale and based on previous research and literature review. The independent variable, as organizational design effectiveness, was measured by six items, which relate to the capacity to produce a strong a formal, guided process for integrating the people, information, and technology of organization. As for, the mediator variables; Service marketing image was measured by five items, which were positive attributes of the service marketing including expertise, trustworthiness, attractiveness and power that are convince customer interest. Specialize human capital was measured by four items, which related the unique capabilities, experiences, knowledge and skills of the workers that match core competency of organization; competence development in business relationship was measured by five items, which indicates the extent to which a firm develops it competencies through adapting with customer and competitors action in the organizational environment. Customer induced uncertainty was measured by four items which indicates the vagueness that arises from customer diversity, opportunism, and interactions to an organization. Technological opportunity was measured by five items which indicates the opportunities related to technology for business operation. Technological opportunities provide success of radical innovations, in introducing new technological and resource opportunities. For the dependent variable as firm performance was measured by six items adapted from Troilo et al., (2009). Also, the control variables were also likely to affect the relationships as firm age because different age may present different organizational attributes and resource deployment (Chen and Huang, 2009) which may behave differently in firm performance. Therefore, this study consists of this variable as control variables to measure possible effects. This study defines firm age as the number of years the firm has been established. 3.3 Reliability and Validity


Variables Factor Loadings Cronbach's AlphaOrganizational Design Effectiveness (ODF) 0.852-0.883 0.889 Service Marketing Image (SMI) 0.737-0.898 0.707 Specialize Human Capital (SHC) 0.542-0.961 0.809Firm performance (FPM) 0.725-0.947 0.824 Competence Development in Business Relationship (CDR) 0.803-0.864 0.857 Customer Induced Uncertainty (CIU) 0.920-0.925 0.919 Technological Opportunity (TNO) 0.634-0.876 0.807 Reliability of each construct was evaluated using the coefficient or Cronbach’s Alpha. The reliability of each construct was higher than the cut-off value of 0.70 as recommended by Nunnally and Bernstien, (1994). In the scale reliability, Cronbach’s alpha coefficients are 0.707-0.919 as being greater than 0.70. The scale of all measures is internally consistent with the results. Factor analysis is employed to test the validity of data in the questionnaire. Items are used to measure each construct extracted to be one only principal component. Factor loading for each construct presents a loading value higher than 0.4. All factor loadings are 0.542-0.961 as being greater than the 0.4 cut-off and are statistically significant. That is,


factor loading of each construct should not be less than 0.4 (Nunnally and Bernstien, 1994). The scales of all measure are internally consistent with the results. Hence, these measures are deemed appropriate for further analysis because they revealed an accepted validity and reliability in this study. Table 1 on previous page shows the results of both factor loadings and Cronbach’s Alpha for multiple-item scales used. 3.4 Statistical Technique The multiple regression analysis is used to test the hypotheses relationships among organizational design effectiveness, service marketing image, specialize human capital, and firm performance. Then, this study attempts to investigate the antecedence organizational design effectiveness that includes competence development in business relationship, customer induced uncertainty and technological opportunity. In this research, the models of the aforementioned relationships are depicted as follows. Equation 1: SMI = β01 + β1ODF + β2FA + ε Equation 2: SHC = β 02 + β3ODF + β4FA + ε Equation 3: FPM = β 03 + β5ODF + β6SMI + β7SHC + β8FA + ε Equation 4: ODF = β 04 + β9CDR + β10CIU + β11TNO + β12FA + ε 4. RESULTS AND DISSCUSSION Table 2 shows the correlation matrix for all variables. Variance inflation factors (VIF) were used to check potential problems relating to multicollinearity, which non-orthogonally among independent variables inflates standard errors. The VIFs range from 1.07 to 2.79 well below the cut-of value of 10 recommended by Neter et al., (1985).Thus, they did not correlate with each other between independent variables. Hence, there are no substantial multicollinearity problems encountered in this study. TABLE 2

RESULTS OF CORRELATION MATRIX

Variables ODF SMI SHC FPM CDR

CIU ODF 1.00 SMI -0.457 1.00SHC 0.632* -0.405* 1.00FPM 0.747* -0.308* 0.773* 1.00CDR -0.244* -0.082 -0.098 -0.035 1.00

CIU -0.221* 0.057 -0.394* -0.363* 0.012 1.00

TNO 0.626* -0.193* 0.865* 0.859* -0.239* -0.285*** p < .05, *** p < .01, Correlation is significant at the 0.01 level (2-tailed)

Table 3, in Model 1, shows the results of the relationships between organizational design effectiveness and service marketing image in H1a. The results indicate that between organizational design effectiveness has a negative effect on service marketing image (b1 = -.279, p < .01). Therefore, Hypothesis H3 is not supported. The finding is inconsistent with Rodoula et al., (2010) suggesting that capabilities of integrated organizational function allow firms to develop flexible strategies. The marketing image is positive perception of customer that is more long time to build this competitive advantage. Thus, service firm are concentrate the organizational change effectiveness that use effort to realigning process, technology and staff capability. Then, marketing image of service firm can be dropped. Therefore, Hypotheses H1a is not supported. Beside, Table 3, in the Model 2, shows the results of the relationships between organizational design effectiveness and specialize human capital in H1b. The results indicate that organizational design effectiveness has a positive effect on specialize human capital (b3 = .780, p < .01). Neu and Brown (2005) argue that the development of services firm corresponds with the alterations in resources, structure, measurement and performance, and processes that are provide and enhance human capital in the organization. Therefore, Hypothesis H1b is supported. Moreover, Table 3, in Model 3, shows the results of the relationships between organizational design effectiveness and firm performance in H1c. The results indicate that organizational design effectiveness has a positive effect on firm performance (b5 = .320, p < 0.01). The finding is consistent with Rodoula et al., (2010) suggesting that firms with higher organizational design effectiveness tend to integrate the technology, processes, and activities that promote the continuous creation of superior value for customers that enhance firm performance. Therefore, hypothesis H1c is supported. Table 3, in model 3, shows the results of the


relationships among service marketing image, specialize human capital and firm performance are in H2, and H3. The results indicate that service marketing image has a positive effect on firm performance (b6 = .152, p < .01). This result implies that’s firms that are more marketing image value can increase firm’s profitability through higher customer life time value (Rust et al., 2004) and affecting sales growth and market share through customer attitudes toward brands and services and customer satisfaction to cultivate lifetime customer loyalty. Therefore, Hypothesis H2 is supported. Then, the results indicate that specialize human capital has a positive effect on firm performance (b7 = .493, p < .01). This result implies that firms are to develop existing and potential human capital with regard to the unique services. Hurley and Hult, (1998) argue that human capital allows the company to develop capabilities that enhance innovation and that innovation is what positively affects performance. Therefore, Hypothesis H4 is supported.

TABLE 3 RESULTS OF REGRESSION ANALYSIS


Dependent VariablesSM SHC FPM OD

Model Mode Mode Mode

Organizational Design Effectiveness (ODF) -.279 .780* .320* (.076 (.067 (.056

Service Marketing Image (SMI) .152* (.04Specialize Human Capital (SHC) .493* (.053Competence Development in Business Relationship (CD .210* (.06Customer Induced Uncertainty (CIU) .043 (.050Technological Opportunity (TNO) .743* (.053Firm Age (FA) -.77 -.64 .234* .245* (.153 (.134 (.099 (.093

Adjusted R2 .318 .475 .722 .673* p < .10, ** p < .05, *** p < .01, Bata coefficients with standard error in parenthesis

Table 3, in model 4, shows the results of the relationships among competence development in business relationship, customer induced uncertainty and technological opportunity and organizational design effectiveness in H5, H6, and H7. The results indicate that competence development in business relationship and technological opportunity have a positive effect on organizational design effectiveness (b9 = .210, p < .01; b11 = .743, p < 0.01). The relationships may be explained as when firms confront the situation of more new customer capability and information technology growth. The ability to respond to change of customer and competitor has been found to positively impact organization strategy and firm performance. Competence development in business relationship means that a firm can instantaneously prepare for operational change and volatile customer demands (Stank and Lackey, 1997) that provide and improve the new process, technology and human capital development. Correspondingly, increased customer induced uncertainty will force businesses to adopt strategic management paradigm or firm’s capability that enables streamlined creation of new structure, process, and capability of organization staff for customers. It enhances information to gain access, provides new process of business services, and improves delivery superior services. Therefore, Hypothesis H5 and H6 are supported. Then, the results indicate that customer induced uncertainty have a positive effect on organizational design effectiveness (b10 = .043, p > .05). Therefore, Hypotheses H5 are not supported. 5. CONTRIBUTIONS AND DIRECTIONS FOR FUTURE RESEARCH 5.1 Theoretical contribution and directions for future research This study contributes to marketing literature in several aspects. First, this investigation empirically verifies the theoretical tenets of RBV theory that resources and capabilities produce different performance results depending on the complex process in which a firm integrates organizational design effectiveness.


Second, this research fulfils the gap of a systematic empirical research on the effects of organizational design effectiveness. The research results provide information for strategic management research field that initiates to test the concept of organizational design effectiveness. Previous research on the role of configuration concept in the capability of organizational design effectiveness has not taken into explicit consideration the complementary contribution of the business firms. However, this investigation has several limitations that should be mentioned. The first, this study used questionnaire to collect data by mail survey. Thus, the empirical validity may be biased. Future research should attempt to overcome the limitations of this research. One key point is based on small sample size because the sample this research are Thai listed firms. In a sense, the result can be considered as a starting point for investigations in other industry for future research. Therefore, future research needed to collect data from larger sample in order to increase reliability and valid generalization. On the other hand, in order to examine the effect of organizational design effectiveness on company performance, future research should also use longitudinal studies. As organizational learning and innovation processes require some time to affect performance. 5.2 Managerial contribution Besides theoretical implications, the study has several managerial implications. The general implication is that managers should be aware that emphasizing on organizational design effectiveness requires changes in the company's process, managerial technology as well as employee's viewpoints and behaviours. Therefore, an organization hoping to enhance corporate performance through organizational design effectiveness should improve its human development processes. Some recommendations in this line are the following. First, firms should promote the acquisition of new capability, for example by making managerial polity attend consolidating the development of new technology within the firm. Second, they should enhance the new unique process of service work that is interpreted within the firm. Thirdly, firms should try to continuously develop human resource. 6. CONCLUSION The purpose of this study is to investigate the influences of organizational design effectiveness, service marketing image, specialize human capital, and firm performance. In addition, this study attempts to investigate the antecedence organizational design effectiveness that includes competence development in business relationship, customer induced uncertainty and technological opportunity on organizational design effectiveness in Thai service businesses. The results reveal that organizational design effectiveness is significantly and positively related to specialize human capital, and firm performance. Moreover, marketing value driven, and customer insight are significantly and positively related to firm performance. Furthermore technological opportunity is significantly and positively related to organizational design effectiveness. REFERENCES: Aaker, D. A., Kumar, V. and Day, G. S., Marketing Research, John Willey and Sons, New York, 2001. AL-Ma'ani, A. I. and Jaradat, N., “Impact of Human Capital on the Organization Performance”, Interdisciplinary Journal of Contemporary Research in Business, Volume 2, Issue 4, Page 63-73, 2010. Armstrong, J. S., and Overton, T. S., “Estimating Non-response Bias in Mail Surveys”, Journal of Marketing Research, Volume 14, Number 3, Pages 396-402, 1977. Barich H., Kotler, P., “A Framework for Marketing Image Management.” Sloan Management Review, Volume 94, Page 94-104, 1991. Barney, J., "Firm resources and sustained competitive advantage", Journal of Management, Volume 17 Number 1, Pages 99-120, 1991. Barney, J., "Is the Resource Based View a Useful Perspective for Strategic Management Research? Yes", Academy of Management Journal, Volume 26 Number 1, Pages 41-56. 2001. Bowen, D. E., and Jones, G. R., A transaction cost analysis of service organization–customer exchange. Academy of Management Review, Volume 11, Pages 428–41, 1986.


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Rodoula, H. T., Irini, D. R., and Kehagias, D. J., “Tracing Customer Orientation and Marketing Capabilities Through Retailers' Websites: A Strategic Approach to Internet Marketing”, Journal of Targeting, Measurement and Analysis for Marketing, Volume 18, Issue 2, Pages 79-95, 2010. Rust, R. T., Ambler, T., Carpenter, G. S., Kumar, V., and Srivastava, R. K., “Measuring Marketing Productivity: Current Knowledge and Future Directions”, Journal of Marketing, Volume 68, Pages 76–89, 2004. Stank, T.P., Emmelhainz, M.A., and Daugherty, P.J., "The Impact of Information on Supplier Performance," Journal of Marketing Theory & Practice , Volume 4, Issue4, Pages 94, 1996. Tansik, D. A., “Balance in service systems design”, Journal of Business Research, Volume 20, Issue 1, Pages 55 – 61, 1990. Troilo, G., De Luca, M. L., and Guenzi, P., “Dispersion of Influence between Marketing and Sales: Its Effects on Superior Customer Value and Market Performance”, Industrial Marketing Management, Volume 38, Pages 872–882, 2009. Stern, B., Zinkham, G. M., and Jaju, A., “Marketing images: Construct definition, measurement issues, and theory development,” Marketing Theory, Volume 1, Issue 2, Page 201–224 AUTHOR PROFILES: Dr. Anirut Pongklee earned his Ph.D. from Mahasarakham University, Thailand in 2010. Currently, he is a lecturer of marketing at the Faculty of Accountancy and Management, Mahasarakham University, Thailand.

Sakcharoen Pawapootanont earned his Master of Business Administration from the University of Ottawa, Canada in 1988. Currently, he is a lecturer of management at Faculty of Accountancy and Management, Mahasarakham University, Thailand.


ROLES OF RISK MANAGEMENT STRATEGY IN GOAL ACHIEVEMENT: EVIDENCE FROM THAI LISTED FIRMS

Sutika Rukprasoot, Mahasarakham Business School, Mahasarakham University, Thailand


ABSTRACT This study aims at investigating the impacts of risk management strategy on goal achievement through mediating influences of internal control efficiency, operational value increase and organizational resource exploitation and moderating effects of environmental complexity. Risk management strategy consists of (1) identifying events and circumstances relevant to an organization's achievement of its goals and objectives (IE); (2) assessing these events and circumstances in terms of likelihood and magnitude of impact (AE); (3) determining a strategy for responding to the identified threat or opportunity (DS); and (4) monitoring the subsequent evolution and impact of the events (ME). 128 information Thai Listed firms were chosen as the sample of the study. The results present that AE and DS have a significant positive influence on internal control efficiency, operational value increase and organizational resource exploitation. Inversely, ME has a negative influence on operational value increase, organizational resource exploitation, especially it has a significant negative influence on internal control efficiency. However, IE has no influence on internal control efficiency operational value increase, organizational resource exploitation. Likewise, operational value increase and organizational resource exploitation have a potential positive influence on goal achievement while internal control efficiency has no relationship with goal achievement. Thus, further study may consider finding practical reasons why it is so by reviewing extensive literature. Potential discussion with the research results is effectively implemented in the study. Theoretical and managerial contributions are explicitly provided. Conclusion and suggestions and directions for the future research are highlighted. Keywords: Risk Management Strategy, Internal Control Efficiency, Operational Value Increase, Organizational Resource Exploitation, Environmental Complexity, Goal Achievement 1. INTRODUCTION Nowadays, environmental uncertainty has been increasing rapidly making firms adapt their competitive advantage. The possibility of the outcome needs to identify uncertainties, estimate their impact, analyze their interactions, and control them by risk management strategy so as to deal with it for goal achievement. Risk management has become a main part of the firm’s activity and it main aim is to help all other management activities reach its’ aim directly and efficiently (Tchankom, 2002). Similarly, risk management is a strategy to increase the value management function (Gupta, 2011). Recently, risk management strategy has been likely to become a main driver that influences improved business outcomes, such as efficiency, effectiveness, performance, and goal achievement. However, fewer organizations had real all-encompassing risk management strategy (Tchankom, 2002). Adequate basic system and service is not available in companies for implementing enterprise wide risk management (Gupta, 2011). Effective risk management can improve organizational performance. Risk management strategy helps firms achieve their goals. It is a framework that entails the following: (1) identifying events and circumstances relevant to an organization's achievement of its goals and objectives, (2) assessing these events and circumstances in terms of likelihood and magnitude of impact, (3) determining a strategy for responding to the identified threat or opportunity, and (4) monitoring the subsequent evolution and impact of the events (Arnold et al., 2011). To expand and increase the benefits and advantages of the risk management strategy on the goal achievement relationships, internal control efficiency, operational value increase, and organizational resource exploitation are hypothesized to become mediators and environmental complexity as a moderator of the relationships. The importance of risk management strategy has managed the firms by integrating strategic planning, operations management, performance management and internal control.


To identify and proactively address risks and opportunities, organizations create value for shareholders (Arnold et al., 2011). Meanwhile, environmental complexity or external competition pressure can increase the importance of exploration activities (Garcia et al., 2003). Accordingly, the relationships among risk management strategy, internal control efficiency, operational value increase, organizational resource exploitation, goal achievement relationships, and environmental complexity are examined. To clearly verify the aforementioned relationships, information Thai – Listed firms are chosen as the sample of the study. Risk management strategy is of four stages:(1) identifying events and circumstances relevant (IE) to an organization's achievement of its goals and objectives (2) assessing the events and circumstances (AE) in terms of likelihood and magnitude of impact (3) determining a strategy for responding to the identified threat or opportunity (DS), and (4) monitoring the subsequent evolution and impact of the events (ME). Therefore, risk management strategy is hypothesized to become a key determinant of driving and explaining goal achievement via mediating effects of internal control efficiency, operational value increase, and organizational resource exploitation. In this study, environmental complexity is a moderator of the relationships. Hence, the objective of this study is to investigate the influences a risk management strategy on goal achievement through internal control efficiency, operational value increase and organizational resource exploitation as mediators and environmental complexity as a moderator relationship of Thai–listed firms. Correspondingly, the key research questions are: (1) how risk management strategy has a significant effect on internal control efficiency, operational value increase, organizational resource exploitation, (2) how internal control efficiency, operational value increase, organizational resource exploitation has an important influence on goal achievement, (3) whether the moderator has an effect of environmental complexity is the relationship between four stage of risk management strategy and its consequence, internal control efficiency, operational value increase, organizational resource exploitation (4) whether moderating an effect on environmental complexity has the relationship among internal control efficiency, operational value increase, organizational resource exploitation has and goal achievement , (5) whether the aforementioned relationships are positive, and (6) whether internal control efficiency, operational value increase, organizational resource exploitation are mediators of these relationships. This study is outlined as follows. The first section reviews existing significant literature in the areas of management strategy, internal control efficiency, operational value increase, organizational resource exploitation, goal achievement relationships, and environmental complexity, links between the concepts of the aforementioned variables, and develops the key research hypotheses of those relationships. The second explicitly details research methods, including data collection, measurements, and statistics. The third gives the results of the analysis and the corresponding discussion. The final summarizes the findings of the study, points out both theoretical and managerial contributions, and presents suggestions for further research and the limitations of the study. 2. RISK MANAGEMENT STRATEGY AND GOAL ACHIEVEMENT Accordingly, risk management strategy is the main determinant of driving goal achievement through mediating functions of internal control efficiency, operational value increase, and organizational resource exploitation. The relationships between risk management strategy and goal achievement are systematically investigated. Thus, the conceptual, linkage, and research model presents the associations between risk management strategy and goal achievement as shown in Figure 1 on next page.


FIGURE 1 CONCEPTUAL MODEL OF THE ROLE OF RISK MANAGEMENT STRATEGY

ON GOAL ACHIEVEMENT H8a-d (+) H9a-c (+) For the most important thing, identifying and proactively dealing with risks and opportunities, organizations create value for shareholders. The benefits of risk management are enhancing resource allocation (Arnold et al., 2011). For this reason, identification is the first stage in risk management process that presents and leads role for effective risk management, which has to start with the question as 1) How can organizational resource be threatened?, 2) What adverse effect can prevent the organizational from achieving its goals? , and 3) What favorable possibility can be revealed? Thus, IE is greater internal control efficiency, operational value increase and organizational resource exploitation. Therefore, the aforementioned relationships are hypothesized as shown below. Hypothesis 1a: The greater the identifying of the events and circumstances is, the more likely that firms will achieve internal control efficiency. Hypothesis 1b: The greater the identifying of the events and circumstances is, the more likely that firms will achieve operational value increase. Hypothesis 1c: The greater the identifying of the events and circumstances is, the more likely that firms will achieve organizational resource exploitation. 2.2 Assessing the Events and Circumstances Assessing events and circumstances (AE) refers to an ability of risks analysis in terms of likelihood and magnitude of impact (COSO, 2004). The aim of the risks assessing is to determine the entity’s exposure to the identified risks, taking into account their probability and incidence, risk classification in terms of tolerance to risk on the basis of the existing criteria (Turlea and Stefănescu,2009). Hence, AE is important needed to consider its impact of the entity. Accordingly, firms classified as the risks into dimensions are likelihood of occurrence and magnitude of impact on achievement of objectives. After focusing on the high risks and defining control strategies for containing them, then, if performing all of this appropriately, after that what is the residual risk is and

Risk Management Strategy

1. Identifying the Events and Circumstances

2. Assessing the Events and Circumstances

3. Determining a Strategy Responding to the Threat or Opportunity

4. Monitoring the Subsequent Evolution and Impact of the Events

Goal Achievement

Operational Value Increase

Organizational Resource Exploitation

Internal Control Efficiency

Environmental Complexity

H1a-c (+) H2a-c (+) H3a-c (+) H4a-c (+)

H5 (+) H6 (+) H7 (+)

Control variables Firm Age Firm Size


where it is acceptable, during of this risk assessment, which is continuing process, firms have found that they are able to improve the quality of their processes and the management of their operations. Thus, well assessing the events and circumstances are greater internal control efficiency, operational value increase and organizational resource exploitation. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 2a: The greater the assessing of the events and circumstances is, the more likely that firms will achieve internal control efficiency. Hypothesis 2b: The greater the assessing of the events and circumstances is, the more likely that firms will achieve operational value increase. Hypothesis 2c: The greater the assessing of the events and circumstances is, the more likely that firms will achieve organizational resource exploitation. 2.3 Determining a Strategy for Responding to the Threat or Opportunity Determining a strategy for responding to the identified threat or opportunity (DS) refers to the reaction approach selecting to resolve the risk of firm (COSO, 2004). Consequently, they are identified and analyzed as the options and chosen as the strategy based on the decisional criteria focused on results or opportunities; it is implemented the chosen strategy (Turlea and Stefănescu, 2009). Also, firms enhance the relationships between risk management and organizational performance. Higher levels of risk management activity are actually associated with increased strategic flexibility and improved performance (Arnold et al, 2011). Thus, determination on how to respond to assessed relevant risks management has effective processes to respond to improved performance. Highlights of assimilate strategic planning, operations management, performance management, and internal control are important to risk management strategy which the enterprise identifies and proactively addresses risks and opportunities, and organizations create value for shareholders. Additionally, the benefits of risk management enhance resource allocation and assuring well management (Arnold et al., 2011). Thus, DS is the greater internal control efficiency, operational value increase and organizational resource exploitation. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 3a: the greater the determining of a strategy for responding to the threat or opportunity is, the more likely that firms will achieve internal control efficiency. Hypothesis 3b: the greater the determining of a strategy for responding to the threat or opportunity is, the more likely that firms will achieve operational value increase. Hypothesis 3c: the greater the determining of a strategy for responding to the threat or opportunity is, the more likely that firms will achieve organizational resource exploitation.

2.4 Monitoring the Subsequent Evolution and Impacts of the Events Monitoring the subsequent evolution and impact of the events (ME) refers to observe, adjust and improve activity for organization accomplishment (COSO, 2004). Risk management procedures provide necessary information to top management needed to monitor for changing the impacts for an organization's well-being (Arnold et al., 2011). The nature of follow-up monitoring suggests that these aspects of Internal Audit Function (IAF) quality help prevent material weaknesses from occurring. These activities increase the internal control effectiveness (Lin et al., 2011). Hence, monitoring of the effectiveness of risk management strategy has a major role for the existence of the relationship feedback for improvement and enhances internal control efficiency. According to the problems found and treatment of accidents in follow-up audit, internal auditors can assess whether the new control measure is useful. The results of the analysis and suggestions will provide for the management in order to improve control measures. In the entity-level control, “Monitoring” is the most important objective, the internal control through continuous and point-in-time assessment processes (Huang et al., 2011). As a result, internal audit should be included in its annual plan the


complete review of the framework for managing operational risk and the review of the policies, process and procedures for identification, assessing, monitoring and control/mitigate operational risk (Ana, 2007). Therefore, monitoring is a feedback-learning-improvement by internal audit activities to remediation of control problems. Thus, well monitoring the subsequent evolution and impact of the events are greater internal control efficiency, operational value increase and organizational resource exploitation. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 4a: The greater the monitoring of the subsequent evolution and impact of the events is, the more likely that firms will achieve internal control efficiency. Hypothesis 4b: The greater the monitoring of the subsequent evolution and impact of the events is, the more likely that firms will achieve operational value increase. Hypothesis 4c: The greater the monitoring of the subsequent evolution and impact of the events is, the more likely that firms will achieve organizational resource exploitation. 2.5 Internal Control Efficiency Internal control efficiency refers to the process established to provide reasonable assurance regarding the achievement of successful, reliable and competent operations (Petrovits et al., 2011). At the beginning of the twenty-first century, following a number of large corporate scandals and failures, corporate governance became extremely an important topic. A key part of corporate governance is a strong internal control culture, and this includes the internal audit function because it is a valuable source of internal and external risk information. Given the importance of the internal control necessities as a means to improve the governance of firms, the reporting effects of strong versus weak internal controls by examining how the quality of internal controls is related to conservatism in financial reporting (Goh and Li, 2011). In the same way, risk management must ensure that control processes identify both risks and opportunities that may affect the achievement of objectives. The identified risks and opportunities are complimented with appropriate responses. An efficient operational risk management framework will improve and reinforce the internal controls of the organization (Ferna´ndez-Laviada, 2007). Accordingly, internal control has an effect on goal achievement. The strength of internal control system enhances an organization's ability to identity events may affect the achievement (Arnold et al 2011). Thus, internal control efficiency leads to which goal achievement. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 5: The greater the internal control efficiency is, the more likely that firms will achieve goal achievement. 2.6 Operational Value Increase Operational value increase refers to the performance efficient as expected from guidelines for add quality development (Molina-Castillo et al., 2011). The first stage in risk management is risk identification. Once risk identification is complete, risk analysis is used to identify the likelihood of which the risks that have been identified will happen. While there are several formal methods that can be used for risk analysis, the most performance success maintains open lines of communication throughout their organizations helping to understand the issues related risk and how to avoid them. Thereby insuring greater probability their performance will come to a successful and satisfying conclusion (Cervone, 2006). Thus, several processes of operational value increase enhance the greater success. Especially, operational value increase leads to goal achievement. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 6: The greater the operational value increase is, the more likely that firms will achieve goal achievement. 2.7 Organizational Resource Exploitation Organizational resource exploitation refers to obtaining advantage from resources invested to improve and expand innovation knowledge, skills and processes (Arnold et al., 2011). Accordingly, the benefits of


risk management enhance resource allocation. Thus, firms minimize the risks and failures in the process enhancing preference for the existing organizational competences that increase its reliance on process improvements. For decision making for exploitive activities to maintain performance expectation, firms should research and develop strategy over environmental factor keeping in mind on exploitation allocation decision and may be more profitable to focus on exploitation activities. Also, in highly competitive or unsuccessful environments, a prudent myopic strategy is to focus on exploitation activity (Garcia et al., 2003). Thus, well organizational resource exploitation leads to better increased goal achievement. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 7: The greater the organizational resource exploitation is, the more likely that firms will achieve goal achievement. 2.8 Environmental Complexity Environmental complexity refers to the heterogeneity or variety activities from the market turbulence or competitive intensity is dispersing organizational activities (Molina-Castillo et al., 2011). Similarly, to explain for operations, environmental complexity would be best understood as the variety and diversity of additional factors that operations have to consider because of the presence in environments with an extended scope, such a global environment (Kinra and Kotzab, 2008). Also, external competition pressure can increase the importance of exploration activities as competitors pursue the same market opportunities. Correspondingly, information acquired from environmental scanning is subsequently utilized in the strategic management process strategic and decision-makers collect, interpret, and utilize information from the external environment in formulating their firms’ future strategies (Ebrahimi, 2000). The increasing complexity of enterprise environment, risk management techniques will develop continuously, and simultaneous technique will move forward, and need more practitioners to be involved not only to enrich, but also to improve practicality of findings. (Wang and Li, 2011). Therefore, more environmental complexity leads to better risk management strategy increase internal control efficiency, operational value increase and organizational resource exploitation. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 8a: Environmental complexity relationships positively moderate the relationships between identifying the events and circumstances and (a) internal control efficiency (b) operational value increase, and (c) organizational resource exploitation. Hypothesis 8b: Environmental complexity relationships positively moderate the relationships between assessing the events and circumstances and (a) internal control efficiency (b) operational value increase, and (c) organizational resource exploitation. Hypothesis 8c: Environmental complexity relationships positively moderate the relationships between determining a strategy for responding to the threat or opportunity and (a) internal control efficiency (b) operational value increase, and (c) organizational resource exploitation. Hypothesis 8d: environmental complexity relationships positively moderate the relationships between monitoring the subsequent evolution and impact of the events and (a) internal control efficiency (b) operational value increase, and (c) organizational resource exploitation. As a result, external competition pressure can increase the importance on exploration activities as competitors pursue the same market opportunities. Competitive forces often do not allow the stressed firm to focus primarily on exploitation activities. They much continually launch technologically superior products in order to maintain market share (Garcia et al., 2003). However, increased perceived environmental complexity did not decrease but increased executives scanning efforts of environmental sectors did. Thus, environmental complexity brings firms more activities


to seize opportunity. With the increasing complexity of enterprise environment, risk management techniques will develop continuously. As well as in other emerging economies, complexities are not only the obstacles and difficulties but also they can be the source of greater opportunities (Wang and Li, 2011). Thus, more environmental complexity leads to better increased goal achievement. Therefore, the abovementioned relationships are hypothesized as shown below. Hypothesis 9a: Environmental complexity relationships positively moderate the relationships between internal control efficiency and goal achievement. Hypothesis 9b: Environmental complexity relationships positively moderate the relationships between operational value increase and goal achievement. Hypothesis 9c: Environmental complexity relationships positively moderate the relationships between organizational resource exploitation and goal achievement. 3. RESEARCH METHODS 3.1 Sample Selection and Data Collection Procedure For data collection of this study, the sample firms will be randomly selected from Thai-Listed firms in all industries (excluding possible delisting companies). There are 473 Thai-listed firms (www.set.or.th on December 12, 2011). The key informants are Internal Audit Executive (IAE). With regard to the questionnaire mailing, 2 surveys were undeliverable because some firms were with unclear locations and had moved to unknown locations. Deducting the undeliverable from the original 473 mailed, the valid mailing was 471 surveys, from which 128 responses were received as the surveys completed and returned; the effective response rate was approximately 27.18%. According to Aaker, Kumar and Day (2001), the response rate for a mail survey, without an appropriate follow-up procedure, if greater than 20% is considered acceptable. To test potential and non-response bias and to detect possible problems with non-response errors, non-response bias is conducted by the comparison of early and late responses by demographic data. The assessment and investigation of non-response-bias was centered on comparison of first wave and second wave data as recommended by Armstrong and Overton (1977). The result showed no significant differences. 3.2 Variables All variables obtained from the survey. Goal achievement is the dependent of the study and it is defined as an ability of firm to generate value for financial and non-financial performances (James, 2004). Five-item scale was developed to evaluate the level of goal achievement both financial and non-financial performance, namely, business goal achievement corporate innovative, corporate market share, customer satisfaction, and achieve both financial and non-financial performances and goal excellence more than competitors. Risk management strategy refers to the process of the organization and the whole staffs are involved and intended to provide reasonable insurance in terms of the achievement of objective (COSO 2004). This process is intended to identify the potential events which could have effects on the entity and to manage the risks within the limits of its aversion to risk. It is taken into account when drawing up the entity strategy and its activities risk management strategy have 4 stages concluding identifying the events and circumstances, assessing the events and circumstances, determining a strategy for responding to the threat or opportunity, and monitoring the subsequent evolution and the impact of the events are independent variables of the study. Each of which is defined as below. First, identifying events and circumstances refers to an ability to discover something or situation important that affecting an entity’s objectives (COSO, 2004). Events both internal and external affecting achievement of an entity’s objectives must be identified, and distinguished between risks and opportunities. Opportunities are guided back to management’s strategy or objective-setting processes.


Four-item scale was implemented to evaluate firms which provide their activities for identifying events and circumstances. Second, assessing the events and circumstances refers to an ability of risks analysis in terms of likelihood and magnitude of impact (COSO, 2004). This assesses both inherent and residual risk for the determining to manage. Four-item scale was exploited to evaluate the degree to which firms assess these events and circumstances. Third, determining a strategy for responding to the threat or opportunity refers to the reaction approach that selecting to resolve or risk management of firm (COSO, 2004), such as, avoiding, accepting, reducing, or sharing risk by developing a set of actions. Four- item scale was investigated to measure the degree of determination in response to assess relevant risks of firms. Lastly, monitoring the subsequent evolution and impact of the events refers to the observation and improvement of activity for organization accomplishment (COSO, 2004). Four-item scale was used to investigate the degree of firms monitoring. Internal Control Efficiency refers to the process established to provide reasonable assurance regarding the achievement of successful, reliable and competent operations (Petrovits et al., 2011). Policies and procedures are established and implemented to help ensure the risk responses that are effectively carried out by management (COSO, 2004). Five-item scale was investigating which to evaluate internal control efficiency of firms. Operational value increase refers to the performance efficiency expected from guidelines development for added quality (Molina-Castillo et al., 2011). Five-item scale was employed to investigate the operational value increase of firms. Organizational resource exploitation refers to the obtained advantage from resources investing for improving and expanding innovation knowledge, skills and processes (Arnold et al., 2011). Also, the benefits of ERM are enhancing resource allocation. Five-item scale was employed to investigate the organizational resource exploitation of firms. Environmental complexity refers to the heterogeneity or variety activities from the market turbulence or competitive intensity that disperses organizational activities (Molina-Castillo et al., 2011). Complexity relates to an ability to predict the effects of environmental trends on the firm. Five-item scale was used to investigate the degree environmental complexity of firms. The control variables were likely to affect the relationships, including firm age and firm size. Firm age (FA) becomes a control variable because uncertainty environment and complexity increase managerial opportunism and reduce risk value (Leech and Leahy, 1991).The dummy variable which 0 means firm has the period of time in proceeding business lower than 15 years, and 1 means firm has the period of time in proceeding business equal or more than 15 years. In this study, firm size (FS) is treated as a control variable which is defined as total assets of the firm invested. It is a dummy variable which 0 is firm with total assets lower than 10,000,000,000 Baht, and 1 is firm has total assets equal or more than 10,000,000,000 Baht. 3.3 Methods Factor analysis was firstly utilized to examine the confirmatory factor analysis. This analysis has a high potential to inflate the component loadings. Thus, a higher rule-of-thumb, a cut-off value of 0.40, was adopted (Nunnally and Bernstein, 1994). All factor loadings are greater than the 0.40 cut-off and are statistically significant. The reliability of the measurements was secondly evaluated by Cronbach alpha coefficients. In the scale reliability, Cronbach alpha coefficients are greater than 0.70 (Nunnally and Bernstein, 1994). The scales of all measures appear to produce internally consistent results; thus, these measures are deemed appropriate for further analysis because they express an accepted validity and reliability in this study. Table 1 below presents the results for both factor loadings and Cronbach alpha for multiple-item scales used in this study.



Items Factor

Loadings Cronbach

Alpha

Goal achievement (GA) .80-.89 .90

Identifying the Events and Circumstances (IE) .82-.88 .88

Assessing the Events and Circumstances (AE) .85-.89 .90

Determining a Strategy For Responding to the Threat or Opportunity (DS) .80-.88 .88

Monitoring the Subsequent Evolution and Impact of the Events (ME) .89-.92 .92

Internal Control Efficiency (IC) .72-.88 .89

Operational value increase (OV) .67-.87 .86

Organizational resource exploitation (OR) .82-.91 .92

Environmental complexities(EC) .80-.90 ..88

The ordinary least squares (OLS) regression analysis is used to test and examine the hypothesized effects of risk management strategy on goal achievement via internal control efficiency, operational value increase and organizational resource exploitation as mediators, which environmental complexities as a moderating variable. Because all dependent variable, independent variables, and control variables in this study were neither nominal data nor categorical data, OLS is an appropriate method for examining the hypothesized relationships (Aulakh, Kotabe and Teegen, 2000) with the need to understand the relationships in this study, the research models of the aforementioned relationships are shown as follows.

Equation 1: IC = 01 + 1IE + 2AE + 3DS + 4 ME +5 FA+6 FS + Equation 2: IC = 02 + 7IE + 8AE + 9DS + 10 ME + 11EC + 12(IE * EC)

+ 13 (AE * EC) + 14 (DS * EC) +15 (ME * EC) +16 FA+17 FS +

Equation 3: OV = 03 + 18IE + 19AE + 20DS + 21 ME+22FA+23 FS + Equation 4: OV = 04 + 24IE + 25AE + 26DS + 27ME + 28 EC + 29 (IE * EC)

+ 30 (AE * EC) + 31 (DS * EC) +32 (ME * EC) +133 FA+34 FS +

Equation 5: OR = 05 + 35IE + 36AE + 37DS + 38ME +39FA+40FS + Equation 6: OR = 06 + 41IE + 42AE + 43DS + 44ME + 45 EC + 46 (IE * EC) + 47 (AE * EC)

+ 48 (DS * EC) +49 (ME * EC) +50 FA+51 FS +

Equation 7: GA = 07 + 52 IC + 53 OV + 54 OR +55 FA+56FS + Equation 8: GA = 08 + 57IC + 58OV + 59OR +60 EC+61 (IC* EC + 62(OV *EC)

+ 63 (OR *EC) +64 FA+65 FS + Where:

GA = Goal Achievement IE = Identifying the Events and Circumstances AE = Assessing the Events and Circumstances DS = Determining a Strategy for Responding to the Threat or Opportunity ME = Monitoring the Subsequent Evolution and Impact of the Events IC = Internal Control Efficiency OV = Operational value increase OR = Organizational resource exploitation EC = Environmental complexities FA = Firm Age


FS = Firm Size = error term

4. RESULTS AND DISCUSSION In Table 2, the descriptive statistics and correlation matrix for all variables are presented. With respect to potential problems relating to multicollinearity, Variance Inflation Factors (VIFs) were used to provide information on the extent to which non-orthogonality among independent variables inflates standard errors. The VIFs range from 1.08 to 4.9, well below the cut-off value of 10 as recommended by Neter, Wasserman and Kutner (1985), meaning the independent variables are not correlated with each other. Therefore, there are no substantial multicollinearity problems encountered in this study. Table 3 presents the results of OLS regression of the relationships among risk management strategy, internal control efficiency, operational value increase, organizational resource exploitation, goal achievement, and moderating effect of environmental complexity on risk management strategy and goal achievement relationships. Risk management strategy includes Identifying the events and circumstances (IE), assessing the events and circumstances (AE), determining a strategy for responding to the threat or opportunity (DS), and monitoring the subsequent evolution and impact of the events (ME). Interestingly, AE has a significant positive influence on internal control efficiency (b2 = 0.35, b8 =.37, p < 0.01), operational value increase (b19 = .46, p < 0.01 and b25 =.32, p < 0.05), and organizational resource exploitation, (b36 =.45, b42 =.47, p < 0.01). In the existing literature, AE is an activity gaining the importance due to current business environment with a global focus and competition.

TABLE 2

DESCRIPTIVE STATISTICS AND CORRELATION MATRIX

Variables GA IE AE DS ME IC OV OR EC FA FS

Mean 3.86 4.10 4.01 4.08 4.07 4.15 3.90 3.93 3.65 .8425 .3657

s.d. .62 .57 .60 .62 .63 .55 .57 .58 .70 .3701 ..484

GA

IE .547**

AE .718** .784*

*

DS .604** .750*

* .800**

ME .562** .748*

* .789** .837**

IC .610** .623*

* .694** .720** .587**

OV .819** .621*

* .729** .686** .606** .758**

OR .723** .601*

* .690** .644** .578** .714** .801**

EC .168 .104 .146 .145 .151 .125 .204* .252**

FA .008 .169 .078 .103 .050 .105 .001 .002 .118

FS .199* .179* .272** .185* .161 .179* .198* .158 .135 .066

**. P< 0.01, * P< 0.05.


This technique results in an increased effectiveness of performance (Ahmed, Kayis and Amornsawadwatana, 2007).Thus, manager awareness to assessing the events and circumstances of impact are analyzed, considering likelihood and impact. It is the alertness of managers to evaluate and calculate the magnitude of impact of threat continuously. Accordingly, assessing the events and circumstances is positively related to goal achievement via mediating effect of internal control efficiency, operational value increase, and organizational resource exploitation. Hence, it becomes a key determinant of driving and explaining internal control efficiency, operational value increase, and organizational resource exploitation. Therefore, Hypotheses 2a, 2b, and 2c are supported. In addition, DS has a significant positive influence on internal control efficiency (b3 = 0.54, b9 = 0.55 p < 0.01), operational value increase, (b20 = 0.31, p < 0.05), (b26 = 0.33, p < 0.01), organizational resource exploitation (b37 = 0.30, b43 = 0.29 p < 0.05). The existing literature highlights the importance of risk management to manage the enterprise by integrating strategic planning, operations management, performance management, and internal control. Additionally, the benefits of risk management are enhancing resource allocation (Arnold et al., 2011). In order for risk management to generate some strategic opportunities, it must be perceived and implemented in a strategic way rather than as a routine business function (Gupta, 2011). Thus, the perception of corporate managers in determining a strategy for responding to the threat or opportunity must take responsibility or deal with greater internal control efficiency, operational value increase and organizational resource exploitation. Accordingly, a strategy for responding to the threat or opportunity has a significant positive influence on internal control efficiency, operational value increase and organizational resource exploitation. Hence, it becomes a key determinant of driving and explaining internal control efficiency, operational value increase, organizational resource exploitation Therefore, Hypotheses 3a, 3b, and 3c are supported. In contrary, the results show that ME has a significant negative influence on internal control efficiency (b4 and b10 = -0.22, p < 0.10). Besides, the result also shows that the ME has no relationship with operational value increase and organizational resource exploitation. In the existing literature, cost savings force the implementation to closely follow the existing and approved traditional internal audit program. The facts are necessary for trade-offs between effectiveness, efficiency and timeliness of audit procedures and determining how to make continuous monitoring of business process control implementations valuable (Alles et al., 2006). Besides, mixed evidence on the relation between internal control and earnings quality could be due to the existence of additional monitoring mechanisms (Feng et al., 2009). With the increased complexity, uncertainty and risks in business operations, workflow monitoring is gaining growing attention in business process controlling and supervision. Also, using customized monitoring plan and proactive monitoring process, the workflow monitoring activities can be executed flexibly and efficiently. The application of intelligent agents for such flexible, adaptive and collaborative workflow monitoring is investigated through an intelligent monitoring system in securities trading (Wang et al., 2005). Hence, the approach, monitoring the subsequent evolution and impact of the events needs a severe change in companies (Gupta, 2011). Therefore, Hypotheses 4a, 4b, and 4c are not supported. Surprisingly, IE has no relationship with internal control efficiency, operational value increase, and organizational resource exploitation. In the existing literature, the tools for identifying risks do not vary much between the persons responsible for identifying risks. Companies do not use the modern tools of risk analysis, which may be due to convention or lack of awareness or skills (Gupta, 2011). Two of the most common reasons for not implementing a risk management program are cost and benefit (McGrew and Bilotta, 2000).Thus, limited resources to identify is the reason for the lack of internal control efficiency. Therefore, Hypotheses 1a-1c, and 4a-4c are not supported. For the mediating effects of the risk management strategy on goal achievement relationships, operational value increase has a positive association with goal achievement (b53 and b58 = 0.64, p < 0.01). In the existing literature, the importance of risk management is integrating strategic planning, operations management, performance management, and internal control, that by identifying and proactively addressing risks and opportunities, organizations create value for shareholders. (Arnold et al., 2011). Therefore, Hypothesis 6 is supported.


Organizational resource exploitation has a potential positive association with goal achievement (b54 and b59 = 0.30 and .28, p < 0.01). In the existing literature, minimizing the risks and failures in the innovation process commensurately enhances its preference for the existing organizational competences that increase its reliance on process improvements to provide quality (Molina-Castillo et al., 2011). Then, corporate proactiveness has a positive direct influence on firm performance. Therefore, Hypothesis 7 is supported. While both operational value increase and organizational resource exploitation have a positive effect on goal achievement; internal control efficiency has no influence on goal achievement. In the existing literature, the cost/benefit balance of auditor testing of internal controls is highly controversial (Bedard and Graham, 2011). IT internal control quality negatively moderates the association between accounting earnings and market valuation (Stoel and Muhanna, 2011) Thus, internal control efficiency is not relative to goal achievement. Then, internal control efficiency has no influence on goal achievement. Therefore, Hypothesis 5 is not supported. Hypothesis 8a to hypothesis 8d concentrated on hypothesized moderating effect of environmental complexity on the relationships between four stages of risk management strategy and its consequence, internal control efficiency, operational value increase, organizational resource exploitation. The findings shown in Table 3 indicated that the moderating variable has no positive effect of environmental complexity on the relationships between risk management strategy and its consequence, internal control efficiency, operational value increase, organizational resource exploitation. Correspondingly, Information acquired from environmental scanning is subsequently utilized in the strategic management process strategic and decision-makers collect, interpret, and utilize information from the external environment in formulating their firms’ future strategies (Ebrahimi, 2000). Therefore, Hypotheses 8a, 8b, 8c and 8d are not supported. Hypothesis 9a to hypothesis 9c concentrated on hypothesized moderating effect of environmental complexity on the relationships between internal control efficiency, operational value increase, organizational resource exploitation and consequence, goal achievement. The results showed that environmental complexity relationships negatively moderates the relationships between internal control and environmental complexity (b61 = -.23, p < 0.05). The reason is that complexity is a function of the degree of the actor's ignorance about the reality's working principles. When facing natural complexity both problems and solutions have to be able to create rather than cover up the data (Vasconcelos and Ramirez, 2011).Hence, higher level environmental complexity uncertainty may impose internal control efficiency demands, which conversely engender negative impact on goal achievement. Besides, the interactions among operational value increase, organizational resource exploitation, and environmental complexity have no positive effect on goal achievement. Therefore, Hypotheses 9a, 9b and 9a are not supported.

5. CONTRIBUTIONS AND DIRECTIONS FOR FUTURE RESEARCH 5.1 Theoretical Contribution This study is a emphasis on a clearer understanding of the risk management strategy which is the main determinant of driving goal achievement through mediating functions of operational value increase and organizational resource exploitation. According to the results in this study, further study is needed to identify the events and circumstances, monitoring the subsequent evolution and impact of the events and internal control efficiency so as to find some explanations about why identifying the events and circumstances, monitoring the subsequent evolution and the impact of the events and internal control efficiency do not affect the relationships. Also, future research should use a mixed method procedure including in-depth field interviews qualitative fieldwork to help identify the important risk factors suitable in Thailand context which is important for such an emerging market.


TABLE 3 RESULTS OF OLS REGRESSION ANALYSISa

Independent

Variables Dependent Variables

IC1 IC2 OV1 OV2 OR1 OR2 GA1 GA2 IE .10 .08 .12 .16 .13 .11 (.10) (.11) (.10) (.10) (.11) (.11)

AE .35*** .37***

.46*** .32**

.45***

.47***

(.12) (.13) (.12) (.13) (.13) (.14)

DS .54*** .55*** .31**

.33***

.30** .29**

(.12) (.13) (.12) (.12) (.13) (.13) ME -.22* -.22* -.08 -.02 -.12 -.13

(.12) (.13) (.12) (.12) (.13) (.13)

EC .04 .11* .19**

*

(.07) (.06) (.07) IE*EC .02 -.16 .09

(.10) (.09) (.10) AE*EC -.12 .28 -.15

(.13) (.12) (.13) DS*EC .24 .01 .21

(.15) (.14) (.15) ME*EC -.19 -.13 -.20

(.14) (.13) (.15) IC -.92 -.75 (.08) (.08)

OV .64**

* .64***

(.10) (.10)

OR .30**

* .28***

(.09) (.09) EC -.04 -.03

(.05) (.06) IC*EC -.23**

(.11) OV*EC .16

(.10) OR*EC .05

(.09) FA .05 .10 -.24 -.28 -.21 -.20 .04 .01

(.17) (.18) (.17) (.17) (.18) (.18) (.15) (.14) FS -.01 .02 .03 .03 -.04 -.05 .11 .04

(.13) (.13) (.13) (.13) (.14) (.14) (.11) (.11) Adjusted R2 .55 .54 .55 .57 .49 .51 .66 .67

*p<.10, ** p<.05, ***p<.01, a Beta coefficients with standard errors in parenthesis. 5.2 Managerial Contribution This study helps top management and stakeholder of Thai-listed firms understand the role of risk management strategy and the key components that may be a more important determinant of its organizational performance, which companies will attempt to improve the risk management strategy of their company in their efforts for goal achievement. One of the main implications for managers is that risk


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AUTHOR PROFILES: Sutika Rukprasoot earned M.B.A. from Kasetsart University, Thailand. Currently, she is a Ph.D. student in Accounting at Mahasarakham Business School, Mahasarakham University, Thailand. Dr. Phapruke Ussahawanitchakit earned his Ph.D. at Washington State University, USA in 2002. Currently, he is an associate professor of accounting and Dean of Mahasarakham Business School, Mahasarakham University, Thailand.


SOYBEAN BRAZILIAN PRICE’S PREDICTABILITY VIA BOX-JENKINS METHOD

Everton Anger Cavalheiro University of Cruz Alta Kelmara Mendes Vieira Federal University of Santa Maria Paulo Sérgio Ceretta Federal University of Santa Maria

Juliano Nunes Alves University of Cruz Alta

ABSTRACT This paper analyses the efficiency of the Box-Jenkins methodology in the forecast of monthly returns of the soybean price paid in Brazil. At first, in order to determine the exogenous variable, the logarithm return of this commodity was calculated. Subsequently, the best period for the series simulation was verified. Afterwards, the simulations were carried out and the model was validated. The results suggest relative predictability for the soybean price, denoting some sort of inefficiency in this market due, especially, to the period following the American crisis, in which the soybean price was shown to be more predictable in t + 1 modeling. Keywords: Time series, Box-Jenkins, Soybean 1. INTRODUCTION Among worldwide and Brazilian greatest bean producers, soybean was the one that showed a greater percentual growth in last few years. According to the data coming from the USA Department of Agriculture – USDA –, global soybean production grew from 44 million tons in 1970, to over 220 million tons in 2008. Besides, in August 2009, there was a 15% increase in global production (32 million tons). 2010/2011 projections – considering the favourable weather conditions for the American harvest – point out that the American production for 2010/2011 is anticipated for 260.9 million tons, which represents a 406% growth. This number depicts a substantial growth, whereas other cultures have grown much less, such as wheat (75%) that moved up from 300 to 792 million tons, and rice (40%) that moved up from 310 to 432 million tons, in the same period (Trennepohl & Paiva, 2011). In 2010, Brazil accounted for 26.2% of all soybean global production, corresponding to 67.5 million tons, grown in a 59.80 million acre area, equivalent to all the UK territory (CONAB, 2010). Still in 2010, soybean accounted for around 9% of all exports, 5.6% of agricultural GDP and 1.25% of the total Brazilian GDP. Regarding soybean price, one can say, according to Jun & Chao (2010), that there are many determining factors for its variation, including the weather, the family consumption level, the consumption structure, offer and demand, national and international stock in the futures market and the soybean circulation system. This circulation system has non-linear features typical of a dynamic system and of the evolution law, sustained by the application of the chaotic sequence so as to study fluctuation and price forecast law. Recent research, such as Righi & Ceretta (2011), was performed using time series analysis. In this sense, Righi and Ceretta (2011) showed that daily quotations for some Brazilian commodities (soybean, cotton, coffee and sweet corn) do not follow the anticipation for market efficiency, thus generating opportunities arbitrage. Considering the statement by authors, we defined the following research problem: “Is Box Jenkins methodology efficient enough to prove non-randomness in the forecast of monthly returns of the soybean price paid in Brazil?” Box-Jenkins models, also known as Autoregressive Integrated Moving Average (ARIMA), were suggested by Goerge Box and Gwilym Jenkins in the early 70’s (Box, Jenkins, & Reinsel, 1970). Box-Jenkins, methodology is known for being an interactive and complex procedure that produces an integrated moving average and autoregressive model, adjusted for seasonal and trend factors, besides estimating adequate pondering parameters, testing the model and repeating the cycle, if need be.


In economics, there are stationary and non-stationary series. Generally, stocks return and GDP growth are not, among other examples, stationary. Thus, a non-stationary series is the one that does not float around the same average and may have a deterministic or stochastic nature. A non-stationary data series with a stochastic trend moves around floating averages and has the configuration as shown on Equation (1).

ttt yy 1 (1)

The stationarity concept is the main idea that one must keep in mind so as to estimate a time series, especially for ARIMA models. The stochastic process, or the time series, ...}2,1,0{},,{ tyt is

weakly stationary if i) 2

ty ; ii) ty for all t , and iii) tjtt yy .

The first condition only states that the second not centred moment must be finite, though it is unequal for all periods. The second condition states that the average is the same for all periods, even if distribution for the random variable changes through the course of time. The third condition states that variance is always the same for all periods where auto-covariance does not depend on this factor, yet only on time distance between observations.

2. METHODOLOGY In order to answer this research problem, we performed a time descriptive analysis where we used the logarithmic return of the prices paid to producers in Brazil as the exogenous variable, between February 1990 and December 2011. In order to calculate return, we used secondary data based in (IPEADATA, 2011). Tsay (2005) mentions that most studies on finances time series use returns, instead of the assets themselves. In this sense, the author comments that there are two main reasons to use returns in finance related studies: firstly, for the average investor, assets return is an adequate measure for comparing investments opportunities and, secondly, return series are easier to deal with than a price series, since the first ones show more attractive statistical features. Among such features, non-bias is common in non-stationary data series. Tsay (2005) says that using logarithmic returns in financing studies is indicated by the hypothesis that assets return is independently and identically distributed (i.i.d.) with an average and variance 2 .

After calculating the logarithmic returns, we performed the random walk test, so as to prove whether or not the data series would show non-random features, which would denote an opportunity to perform modelling for the time series forecast. Then, we verified the series stationarity using the augmented Dickey-Fuller methodology. In order to estimate and identify the parameters for the ARIMA model, we used the ordinary least squares method, as suggested by Makridakis et al. (2008). The criterion for choosing this model was the existence of white noise behaviour within the residues of each model. When diagnosing the residues, we used the Ljung-Box Q test and the autocorrelation Function. In order to analyse the predictability of soybean price, we used the sample determination coefficient R2. This criterion seeks to measure the proportion or percentage of variation for y anticipated by models, as shown in Equation (2).

.ˆ

1 2

1

2

12

N

i

N

i

yy

yR

(2)

Two other indicators were used: MSE and MAE, which are shown in Equations (3) and (4).

.ˆ1 2

1 N

ii yyN

MSE

(3)

.ˆ1 22

1 ii

Nyy

NMAE

(4)

Additionally, we analysed the Theil inequality coefficients, also known as U. The denominator for U is MSE, but the scale for the denominator is such that U exists in the interval from 0 to 1; where U=0


would be a perfect forecast adjustment to the observed value and where U=1 would show the worst performance for the model’s anticipation. The Theil inequality coefficient was shown in Equation (5).

.

ˆ11

ˆ1

2

1

2

1

2

1

N

i

N

i

N

ii

yN

yN

yyNU

(5)

Besides the Theil inequlity coefficient, we anlysed the proportions for UM and US (bias proportion and variance proportion), which allow to break down the error to its characteristical sources. According to Pindyck and Rubinfield (1991), the bias proportion (UM) analyses a possible systematic error, since it measures how much the average values for the simulated and effective series can deviate from each other. Whatever the value for the inequality coefficient (U), it is expected that UM is close to 0. An elevated value for UM (above 0.1 or 0.2) would be worrying, since it would indicate the presence of systematic bias, and so the models would need to be checked again. In Equations (6) and (7), bias and variance proportions are shown, respectively.

.

/12

2

A

t

S

t

AS

M

yyT

yyU

(6)

./1

2

2

A

t

S

t

ASS

yyTU

(7)

In (6 and 7) S

y ,A

y , S and A we observed the average and the observed and estimated

standard deviation errors, respectively. The variance proportion US, according to Pindyck and Rubinfield (1991), indicates the capacity to replicate the variability rate for the variable we are interested in. If US was high, that would mean that the effective series floated a great deal, or vice-versa. That would also be worrying and could lead to revising the models. In order to evaluate the forecasts’ success, as guided by Ivakhnenko, Ivakhnenko and Müller (1993), we used Equation (8).

.minˆ

2

1

2

12

N

i

N

iii

yy

yy (8)

The results obtained in (8) and that are lower or equal to 0.5 would be considered adequate; the ones

between 0,5 < 2 < 0,8 would be considered satisfactory; and the ones greater than 1 would be

considered as false information and the modelling would be considered inefficient.

3. RESULTS Based on the monthly negotiation prices for soybean, we calculated the logarithmic returns. Then we tested the hypothesis that monthly returns would show features typical of random walks, which would invalidate the study. Picture 1 shows the test results.


Picture 1 – random walk test for a random data series and for monthly logarithmic returns for soybean [ February 1990 – December 2011 ].

On the left, Picture 1 shows that there were three data series generated. For a better comparison, all series were computed using the same sequence of random figures, according to a normal distribution. Initially, we calculated a pure random walk series (see graphic). The second data series was computed as a trend (dotted line named “rwdrift(ls)”). The last series with a deterministic trend was adjusted by normally distributed errors (det.trend+noise(ls)). On the right, procedure was identical. The only exception was the use of a logarithmic return series on the soybean average price paid to producers in Brazil (February 1990 – December 2011). On the series simulated with random figures (graphic on the left), one can tell that the trended series and the deterministically trended series, which is error adjusted, show similar behaviours. On the other hand, the random walk does not show an evident relation to the other series. On the right, where we used the logarithmic returns for soybean price, we can see that the two first series (trended and deterministically trended adjusted to error) show similar behaviours and the random walk series appears to follow the first tow series’ behaviour, denoting that the series presents non-random behaviours and that could be detected in stochastic deterministic models. We can also note that there is a behaviour change in the random walk series as of the 50th observation. Thus, we performed the simulation excluding the first 50 observations (February 1990 – March 1994). Picture 2 shows the new test, where the first 50 logarithmic returns were excluded.

Picture 2 – random walk test for the monthly logarithmic returns of soybean [ April 1994 – December

2011 ] We can see in Picture 2 that the random walk series show to be even better adjusted to the other two simulated data series, denoting that the logarithmic returns series shows non random behaviours and that could be detected in deterministic stochastic models. Excluding the first 50 observations (February 1990 – March 1994) can be explained by a economics perspective: as of a near future period (October 1994) the Real plan was initiated in Brazil and that significantly modified the inflation memory within the Brazilian society, enhancing the Brazilian productive processes, especially the agro-business sector and soybean. Those interested in replicating this test for this or other series can get better information in Pfaff (2008). According to Gujarati (2003), stationarity is needed for performing Box-Jenkins models. For this work, we used the augmented Dickey-Fuller test (ADF)., suggested by Dickey & Fuller (1979) so as to test the presence of a unitary root in the series. The Dickey-Fuller test is based on the following model in Equation (9):

tttt yy 1

(9)

where:

1

1j

i

(10)


where y denotes the dependent variable and ∆ denotes the subtratction operator (∆yt = yt - yt-1). The parameters to be estimated are α, β and η. The statistics ττ and τμ and τ presented by Dickey & Fuller (1981) correspond to the t test used to estimate the variable coefficient yt-1 in Equation [9]. These statistics are specified for a model that includes a constant, a trend and a lag (ττ), a model that includes a lag and a constant (τμ), as well as a constant-less and trend-less model (τ). The hypothesis tested in these models correspond to a null hypothesis stating that it is not stationary (H0 : yt is not I(0) or η = 0); against the alternative hypothesis that it is not an integrated series, i.e., it is a stationary series (H1: yt is I(0)). Table 1. Results of the unitary root from the Augmented Dickey-Fuller test for the null hypothesis stating that the monthly logarithmic returns of the soybean price paid to producers in Brazil are not stationary Total Lags Ττ Τμ Τ

1 -9.041*** -9.065*** -9.005*** 2 -7.119*** -7.129*** -7.060*** 3 -6.702*** -6.702 ** -6.612*** 4 -6.875*** -6.856*** -6.711*** 5 -6.419*** -6.407*** -6.257***

*** indicates the null hypothesis is rejected for a significance level of 1%. Initially, we applied the unitary root test to the logarithmic return of the soybean price paid to producers. We can see on Table 1 that the null hypothesis that the indexes are not stationary must not be rejected for all simulations. Gujarati (2003) says that, in order to use the Box-Jenkins method, we must have in hands a stationary time series or a series that can become stationary with one or more lags. According to Pokorny (1987), the Box-Jenkins method’s objective is to spot and estimate a statistical model that can be interpreted as having been generated by the sample data. If such estimated model can be used to make predictions, we should assume that such model’s features are constant through time, especially in future periods. Thus, the reason to demand stationary data is that any model that is to be inferred from these data can be itself interpreted as stationary or stable, thus offering a valid basis for forecast. The models’ analysis was carried out using the autocorrelation function (ACF) coefficient, accompanied by the residues Ljung-Box test. Autocorrelation is verified when at least one of the autocorrelation coefficients is different from zero and when the p-value, as well as the Ljung-Box Q-statistic are small enough to reject the null hypothesis that states that the errors in the models have no correlation. It was considered a 95% interval for the autocorrelation coefficients different from zero and for the Ljung-Box test. Picture 3 shows the ACF test results for the residues and the p-value for the Ljung-Box statistic.

Picture 3 – results for the ARIMA model (1,1,1) residues test

Picture 3 shows the ARIMA model diagnosis graphics (1,1,1). We can see that the ACF statistic for residues shows that no correlation can be not null and that the p-values for the residues independence test, measured by the Ljung-Box statistic, they are always above 70%. It is important to


note that the Ljung-Box statistic was initially suggested by Ljung & Box (1978), where the tested null hypothesis states that the data (model residues) are independently distributed; and the alternative hypothesis states that the data are not independently distributed. Having p-values always above 70%, it is impossible not to reject the null hypothesis, thus being possible to conclude that the model is properly adjusted, an the residues seem to be white noise. After estimating and validating the model, we then move on to the forecast process. We carried out 70 forecasts, only for t+1, i.e., only for one step (month) ahead, between April 2006 and December 2011. It is important to note that in this period there was the so-called American crisis, basically consisting of a credit crisis in the banking sector. Symptoms were however perceived in other sectors, especially the agricultural productive sector. In this sense, Krugman et al. (1999) says that there is no universally accepted formal definition for the concept of crisis, but we know them when we see them. According to the author, the basic element is a type of circular logic, where investors run away from an investment because they fear that it can go down, and where there are many (although not necessarily all of them) pressures for going down that come precisely from such capital flee. The author quotes that such crisis has been a recurring feature in international economy, since gold and silver coins were replaced by coin-paper. The global systemic global crisis coming from the USA strongly affected the Brazilian economy. De Freitas (2009) says that happened as far as both external trade and financial flux, including commercial credit lines and market application of Brazilian equity. In Brazil, the most immediate effect was the downfall in stock markets, caused by the major selling of stocks to foreign speculators that literally stepped on each other to repatriate their equity in order to cover their loss in their own countries. As a consequence, there was an expressive dollar high. This rise of the American currency directly influenced the Brazilian agro-business sector. In order to limit periods, we used a theoretical limit based in the article by De Freitas (2009). According to this author, the period the most crisis turbulence was from September 2008 to May 2009 – nine months. This period was called “during the crisis”. The moment made up of the previous twenty-six months (June 2008 – August 2008) before that period was called “before the crisis”. Last, the “post-crisis” period was from June 2009 and December 2011 (32 months). The forecasts results are shown in Table 2.

Tabela 2: Forecasts’ results before, during and after the 2008 American crisis, for the logarithmic return of the soybean monthly price paid to producers in BrazilCategory R2 Correlation Signals MSE MAE U UM US Ivakhn

enko All sample 0.1491 0.3861 0.6563 0.0027 0.0387 0.0287 0.1067 0.0000 1.0007

Before the crisis 0.1382 0.3718 0.7143 0.0023 0.0384 0.0251 0.1024 0.0000 1.0163

During the crisis 0.1220 -0.3492 0.1111 0.0087 0.0793 0.0777 0.0002 0.0003 1.7961

Post-crisis 0.3845 0.6201 0.7778 0.0013 0.0302 0.0173 0.0011 0.0001 0.6191

In Table 2, we can see that, according to the Ivakhnenko criterion, shown in Equation [8], the logarithmic return forecast for soybean price, in all sample and for the period before the crisis, was ineffective. Positively, we note that the Theil U, the variance proportion (UM) and the error bias proportion (US) have shown to be adequate, thus indicating the absence of a systematic error in the forecast, which would denote that significant information – contained in the original series – had not been modelled. In the period of time contemporary to the 2008 American crisis, results have completely degenerated and the Ivakhnenko criterion, Ivakhnenko and Müller (1993), showed the performed forecasts to be unsatisfactory and the results to be misinformation. Yet in the period after the crisis (June 2009 – December 2011), forecasts have shown to be satisfactory (Ivakhnenko criterion equals 0.6191). In 77.78% of the cases, the forecasts’ signs were right, with a special emphasis for square-R that was 0.3845. Other indicators, such as MSE and MAE significantly improved, when compared to other analysed periods.


These results denote a new behaviour for soybean prices paid to producers in Brazil after the 2007/2008 crisis. The series has shown to be more predictable with the Box-Jenkins model, for the post-crisis period. This research corroborates what was suggested by Righi and Certta (2011), who have demonstrated that there is mild inefficiency for the Brazilian soybean prices series, thus opening the possibility for arbitrage procedures and abnormal returns for this type of investments, as well as opportunities for the farmer to plan how to sell this commodity in more favourable moments. 4. FINAL CONSIDERATIONS In this research we aimed to assess the predictability of the monthly return of soybean price paid to producers in Brazil. Initially, the logarithmic return of this series was calculated. Then, we tested the hypothesis that stated that returns would follow a random walk which would prevent predictability. In this test, we noted that soybean prices’ returns show different features depending on the period – before implementing the Real plan (October 1994) and after this moment. Thus, we used 194 months in order to simulate the modelling parameters and another 64 months to carry out forecasts (April 2006 – December 2011). The forecasts results have shown to be unsatisfactory for all sample; however, the results obtained in the simulations performed after the American crisis (May 2009 – December 2011) showed that the analysed commodity prices have become more predictable for the ARIMA model (1,1,1). The Box-Jenkins model has shown to be able to demonstrate the returns non-randomness, denoting inefficiency for this market, arbitrage opportunities and abnormal return to investors, as well as the opportunity for producers in that region to plan their sales to more favourable periods.

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