eissn 2231-279x impact factor(gif): 0.376 issn 2249-0280

Editor-in-Chief

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Associate Editors

Dr. Saroj Dash Dr. Surendra Sisodia Mr. Abdul Rahman

Assistant Editors Ms. Swati Chauhan Ms. Ashu Bhojwani Managing Editor

Dr. Arif Anjum (India) Frequency :

Quarterly-Four Issues Per Year

Correspondence Address:

Indian Journal of Management Science, S.N.21, P.N.24, Mirza Ghalib Road, Malegaon Nasik, Maharashtra – 423203 (India) Contact: 0919764558895 Email: [email protected] Website: www.scholarshub.net

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EISSN 2231-279X Impact Factor(GIF): 0.376 ISSN 2249-0280

INDIAN JOURNAL OF

MANAGEMENT SCIENCE

Volume – III Issue – 3 July 2013

Volume I Issue 1 August 2011 EDITORIAL BOARD

Rohaizat bin Baharun Department of Management Faculty of Management Universiti Teknologi Malaysia Yasser Mahfooz, PhD Department of Marketing, College of Business Administration, King Saud University, Riyadh, Saudi Arabia Edhi Juwono Perbanas Economics School for Management Information Systems, Indonesia Dr.Mu.Subrahmanian Professor & Head, Department of Management Studies, Naya Engineering College, Chennai Prof. D. P. Singh Delhi college of engineering, Delhi Dr. Nafis Alam, School of Business, University of Nottingham, Malaysia Michael Sunday Agba, Department of Public Administration, Federal Polytechnic, Nigeria.

INDIAN JOURNAL OF MANAGEMENT SCIENCE (IJMS) EISSN 2231-279X – ISSN 2249-0280

www.scholarshub.net Vol.– III, Issue – 3, July 2013

INDEX

SN TITLE PAGE

NO.

1.

The Application of Effective Coaching Techniques in Designing a Coaching Plan for

Performance Improvement in Graduate Assistants Tracie V. Cooper & Donovan A. McFarlane (UUSSAA)

01-07

2.

A Hybrid Data Mining Approach to Construct the Target Customers Choice

Reference Model Shih-Chih Chen & Ruei-Jr Tzeng (TTaaiiwwaann)

08-15

3.

The Used of it Balanced Scorecard to Build the Performance Measurement Model of

Academic Information Systems (Case Study Academic Information System of Satya

Wacana) Paskah Ika Nugroho, Prihanto Ngesti Basuki & Evi Maria (IInnddoonneessiiaa)

16-22

4. Increasing the Accountability of the Institution through the Whistle Blowing System

Jony Oktavian Haryanto, Yefta Andi Kus Nugroho,

Rizal Edy Halim & Rizal Edwin Manansang (IInnddoonneessiiaa) 23-33

5. Agricultural TFP and R&D Spending in Iran

Solmaz Shamsadini, Saeed Yazdani & Reza Moghaddasi (IIrraann) 34-41

6. Ranking Indian Domestic Banks with Interval Data – The Dea Application

Dr. T. Subramanyam & Dr. R.V.Vardhan (IInnddiiaa) 42-47

7.

The Effects of Financial Reporting Quality on Stock Price Delay & Future Stock

Return Azam Pouryousof, Hilda Shamsadini & Mina Abousaiedi (IIrraann)

48-52

8. Gold Price Movements in India and Global Market

Shaik Saleem, Dr. M. Srinivasa Reddy & Shaik Karim (IInnddiiaa) 53-60

9.

The Kerala Building and other Construction Workers Welfare Fund Board – Social

Impact on Members Dr. Abdul Nasar VP & Dr. Muhammed Basheer Ummathur (IInnddiiaa)

61-70

10. A Study of Socio Economic Condition of Child Labour Engaged in Rag-Picking at

Silchar Shima Das, Dr. Amit Kumar Singh & Bidhu Kanti Das (IInnddiiaa)

71-78

11. Stock Market Anomalies: Empirical Evidence from Weekend Effect on Sectoral

Indices of Indian Stock Market Potharla Srikanth & P. Srilatha (IInnddiiaa)

79-85

12. Internet Banking: Does it Really Impacts Bank’s Operating Performance

Rajni Bhalla (IInnddiiaa)) 86-89



1

THE APPLICATION OF EFFECTIVE COACHING TECHNIQUES

IN DESIGNING A COACHING PLAN FOR PERFORMANCE

IMPROVEMENT IN GRADUATE ASSISTANTS

Tracie V. Cooper,

Faculty Support Coordinator

H. Wayne Huizenga School of Business

and Entrepreneurship

Nova Southeastern University,

Fort Lauderdale, Florida, USA

Donovan A. McFarlane,

Adjunct Professor of Marketing,

Nova Southeastern University

Adjunct Professor of Leadership Studies,

Bethune-Cookman University

Adjunct Professor of Business Administration,

Broward College

Visiting Professor of Management,

Keller Graduate School – DeVry University

Professor of Business Administration & Business

Research, Fredrick Taylor University

Faculty Blog Manager, Huizenga School of Business

Director, The Donovan Society, LLC, USA.

ABSTRACT

This paper examines effective coaching techniques that could potentially be incorporated into a coaching

plan to improve the performance of new-start graduate research assistants in an academic school and department at a university. From the perspective of a supervisory or managerial capacity, the authors play

the role of the prospective “Coach” responsible for faculty support, and therefore attempt to meet the

requirements of this office by working collaboratively through and with hired work-study graduate students

who serve as graduate research assistants in an academic department and school at a university. The opportunity for coaching unfolds in the scenario where four new start graduate students from the schools of

business and computer sciences are hired as research assistants in an academic department and must

effectively meet the needs of the faculty in being able to competently perform several tasks related to research. Most of the tasks are already within the ability-scope of these graduate students. However,

blending into their roles as newly hired employees and research assistants to the faculty support coordinator

and professors in this department and school requires developing familiarization with organizational

culture, process protocol, work study portfolio organization and competence in their new roles. This presents an opportunity for coaching using several techniques to address familiarization, competence, and

motivational and work-process issues. Thus, examining the literature on effective coaching and coaching

techniques, the authors in a coaching capacity will develop, design, and implement a Coaching Plan or program to address these competencies and work-needs-skills in this situation based on practical guidelines

or recommendations of previous research. This paper describes this opportunity for effective coaching and

presents relevant literature on coaching techniques and effectiveness, recommends a viable coaching plan and resolution to identify issues, and draws conclusion based on what constitutes success or effectiveness

in real-life situations. Additionally, broader implications for coaching strategies and techniques applied to

real problems, opportunities, or issues in organizational contexts and examined.

Keywords: Coaching, Coaching plan, Coaching Techniques, International Coach Federation (ICF),

Motivation, Performance.



2

Introduction:

Coaching is becoming more and more important as a process and performance improvement method and approach in

organizations across all fields. Coaching can be defined as “a process that enables learning and development to occur and thus

performance to improve” (Parsloe, 1999, p.8). Coaching effectiveness is what is important in today’s organizations as coaching

becomes both a corrective process and action to address performance, behavioral, and other issues across organizational

boundaries, and more and more managers attune to the coaching process and its application. According to Parsloe (1999), “To be a successful coach requires a knowledge and understanding of process as well as the variety of styles, skills and techniques

that are appropriate to the context in which the coaching takes place” (p. 8). Managers or supervisors must use effective

coaching techniques that cater to individual and group, as well as organizations needs.

The International Coach Federation [ICF] (2011) defines coaching as “partnering with clients in a thought-provoking and

creative process that inspires them to maximize their personal and professional potential” (p. 1). This definition takes a service-

provision or orientation to coaching, and coaching is in fact based on service-philosophy to individuals and organizations with

the end result being to improve performance and productivity. Coaching is indeed a creative process and it is the responsibility

of the coach to ensure that creative techniques or methods are used to address different coachee needs. Coaching is especially

important in helping new hires or new organizational members to improve their present skills levels as they are coached by

experienced organizational members and managers to perform important tasks effectively and efficiently to meet organizational

goals. While this is the case, most application of coaching seems to be in contexts involving organizational members or employees with significant time onboard, but lingering problems that affect attitude and work morale; hence, performance.

Literature Review:

The performance benefits of coaching are becoming more widely known and accepted and “coaching is [now] seen as having

clear and unique advantages, and is establishing itself alongside related activities, such as mentoring and counselling, as a key

development technique” (Phillips, 1996, p. 29). Coaching in organizational contexts fills several roles and confers several

benefits. According to the International Coach Federation [ICF] (2011) “Individuals who engage in a coaching partnership can

expect to experience fresh perspectives on personal challenges and opportunities, enhanced thinking and decision making skills,

enhanced interpersonal effectiveness, and increased confidence in carrying out their chosen work and life roles” (p. 1). The

benefits gained from coaching depend on how well the coach uses effective techniques that cater to individual skills

development or developing top talent that will serve the organization (Hunt & Weintraub, 2011). The coaching interaction is an important factor in considering coaching techniques as managers need to recognize that employees have a need to express

themselves as they influence organizational policy and decisions without authority.

According to Cohen and Bradford (2005) influence is important in human social interaction, and the coaching process involves

two-way influence, a process where the coach is influencing the coachee to make some form of change, progress, or

improvement; and a process where the coachee without vested managerial authority influences the views, decisions, and

strategies of the coach. Leadership coaching in organizations requires influence, and Wakefield (2006) argues that “Leadership

coaching is a vital tool for developing talent in organizations. Hunt and Weintraub (2011) certainly concur with this view.

Managers and supervisors who facilitate coaching must also recognize that both tasks and relationship are important in coaching

(Hunt & Weintraub, 2005). Thus, important concepts such as trust which functions to achieve influence and cooperation should be

integrated into the approach to coaching, especially where employees or coachees depend on their manager or coach to hone their

skills to maximize their performance and job security. According to Hunt and Weintraub (2005), “good relationships make it easier

to gain cooperation, it pays to be generous and engage in win-win exchanges” (p. 23). Managers and leaders who engage the coaching process to address performance-related individual and organizational opportunities and challenges must build effective

relationships with their employees in order to facilitate progress and get results.

Wakefield (2006) suggests engaging the four P’s that will help employees become more innovative problem solvers during the

coaching process. These four P’s are: (i) partnering for technological collaboration; (ii) possibilities for turning necessity into

opportunity; (iii) perspective by providing opportunity for individuals to broaden their problem-solving skills and experiences;

and (iv) practicing innovation throughout the coaching process and the organization using total quality management (TQM).

Coaching is a social process and the coach must bear in mind that people are the most important of organizational assets.

According to Case and Kleiner (1993), this fact must be recognized before managers can begin coaching their employees

effectively. Case and Kleiner (1993) assert that there are many methods or techniques to facilitate coaching. With this

understanding, they argue that coaching is not a method, but a combination of methods or practices applying different tactics

and strategies that are used to guide employees towards maximizing their potential in organizational work settings. Case and Kleiner (1993) list several techniques that they argue are coaching techniques: rewards, compensation, training, employee

development programs, goal setting, discipline, employee participation, and group participation problem solving.

Megginson and Clutterbuck (2005) describe coaching techniques from a goal-setting orientation. They believe that it is the

responsibility of the coach to help learners find a vision and the path towards achieving that vision. As such, coaching involves

techniques such as identifying, visioning, and motivation and must be effectively coordinated around timing. Megginson and



3

Clutterbuck (2005) believe that effective coaching involves the ability to influence employees who are able to identify

individuals who have been “helpful” in their career and have influenced them in ways which contribute to success or

performing successfully in their organizational roles. The process of visioning as a technique in coaching can be used in many

situations, and is especially powerful in goal-setting. According to Megginson and Clutterbuck (2005), the core of effective visioning is engaging all the learner’s senses and inner emotion. This inner emotion affects the individual coachee’s perceptions

and attitudes toward the coaching process. Visioning involves a process of visualization that asks questions such as: (a) where

do you want to be? (b) what do you see around you in terms of the environment and people? (c) how do you appear? (d) what

are you doing and why? and (e) how do you feel and why do you feel this way? Among other questions that attune the coachee

to the present situation, the need for change, and the goal or vision of what he or she wants to accomplish from the coaching

relationship or training are important.

Megginson and Clutterbuck (2005) believe that “Visioning is best used when the learner is relatively relaxed” (p. 12), and that

the technique requires the coach to engage the coachee to focus his or her whole consciousness into placing the self in a

possible future. This stands to reason, as coaching for performance improvement involves developing talent in the organization

to a certain optimum or to meet certain standards. Individuals and groups must be able to display certain levels of performance,

attitudes, work morale and skills to effectively increase productivity and organizational competitiveness. Therefore, the coach

must use this technique to foster a sense of potential and demonstrate to the coachee the ability to develop and apply the skills to reach that potential in a reasonable time frame. Organizational rewards and compensation can be used as techniques that

supplement this process, and Case and Kleiner (1993) argue that these not only serve in the roles of feedback, but as motivators

since “everyone in an organization gives of his or her abilities and efforts in exchange for rewards given by the organization”

(p. 8). Thus, rewarding and compensating; the manner in which these are done as performance-based indices, can significantly

contribute to overall coaching effectiveness and success.

In coaching individuals to improve their performance in the work setting, coaches must focus on building those defined set of

business or work-related skills that will affect individuals’ abilities to work independently, as well as part of teams and groups

(Butler, Forbes, & Johnson, 2008). As Case and Kleiner (1993) note, there are many methods or techniques of effective

coaching available to managers, but managers must be able to choose the best methods or techniques suited for particular

employees or subordinates. This requires remembering that people are individuals. Case and Kleiner (1993) argue that

coaching methods or techniques used must be refined or should be “changed in the event of continued poor morale and performance to ensure that resources are not merely being wasted” (p. 10).

Contemporary techniques in coaching are being developed across various organizations by managers and leaders to address

individual and organization specific performance and challenges. This includes the increasing use of the telephone to facilitate

coaching. According to Gaskell (2006) and Sparrow (2006), as confidence and expertise grow in coaching as a development

intervention, the telephone option is being increasingly used as a viable alternative to face-to-face meeting for coaching.

Gaskell (2006) argues that telephone coaching is catching on because it is convenient and less expensive. Managers are

increasingly conducting one-to-one coaching over the telephone and are getting significant results. This means that telephone

coaching is becoming more and more popular, and there are different companies and individuals using this technique. Sparrow

(2006) shows how telephone coaching forms the basis of account manager development programs at Elizabeth Arden, cosmetic

giant company. According to Sparrow (2006), telephone coaching has been successfully used by this company’s managers to

deal with professional and personal tensions in an effective manner. Telephone coaching holds good promise as a technique because of its cost-saving advantage, flexibility and convenience as

managers can be in different locations while providing instructions to employees as to performance on various issues.

According to Gaskell (2006) “Telephone coaching can work because there is something powerful about the voice entering the

mind of the coachee more directly” (p. 24). The coach on the other side of the line must however be a very good communicator

since the absence of face-to-face interaction sometimes creates communication problems in similar scenarios. The use of

telephone coaching also gives consideration to other coaching techniques making use of different technologies including the

computer, videos, and other forms of applied communication techniques.

Coaching is a highly dynamic process whose techniques depends on the coaching scenario and needs of the coachee and

organization, the expertise and creativity of the coach, and the duration of the coaching and level of knowledge required. Other

factors also come into play as well. Coaching can be applied at different levels within an organization, and coaching for

leadership succession is becoming an importantly recognized need in large corporations and businesses. Whatever the coaching

purpose or the techniques used, the coach must bear in mind that he or she is dealing with individuals and that individuals are unique and require different levels of training, communication, and assistance to improve their professional and personal skills.

Coaching involves influence and managers or supervisors responsible for coaching subordinates must develop the ability to

influence. This requires having technical expertise, excellent interpersonal and communication skills, and the understanding

that coaching does not mean control, but is a process of facilitating progress and opportunities for self-improvement. Coaching

requires setting clear goals, having objectives, developing an action plan, drafting a project schedule, giving employees

direction, giving reinforcement, keeping employees informed, resolving conflicts, delegating power, and promoting risk taking

where such has far more benefits than costs in the performance and personal improvement process (Case & Kleiner, 1993; see



4

Appendix 1: Steps in Effective Coaching Plan).

Methodology:

This article examines effective coaching techniques that could potentially be incorporated into a coaching plan to improve the

performance of new-start graduate research assistants in an academic school and department at a university. Four new-start graduate students from the schools of business and computer sciences were hired as research assistants in an academic

department and school of a university to effectively meet the needs of the faculty in being able to competently perform several

tasks related to research. From the perspective of a supervisory or managerial capacity, the authors play the role of the

prospective “Coach” responsible for faculty support, and therefore attempt to meet the requirements of this office by working

collaboratively through and with hired work-study graduate students who serve as graduate research assistants in an academic

department and school at a university.

The opportunity for coaching unfolds in the scenario where four new start graduate students from the schools of business and

computer sciences are hired as research assistants in an academic department and must effectively meet the needs of the faculty

in being able to competently perform several tasks related to research. Most of the tasks are already within the ability-scope of

these graduate students. However, blending into their roles as newly hired employees and research assistants to the faculty

support coordinator and professors in this department and school requires developing familiarization with organizational culture, process protocol, work study portfolio organization and competence in their new roles. This presents an opportunity for

coaching using several techniques to address familiarization, competence, and motivational and work-process issues. Thus,

examining the literature on effective coaching and coaching techniques, the authors in a coaching capacity will develop, design,

and implement a Coaching Plan or program to address these competencies and work-needs-skills in this situation based on

practical guidelines or recommendations of previous research. This article describes this opportunity for effective coaching and

presents relevant literature on coaching techniques and effectiveness, recommends a viable coaching plan and resolution to

identify issues, and draws conclusion based on what constitutes success or effectiveness in real-life situations. Additionally,

broader implications for coaching strategies and techniques applied to real problems, opportunities, or issues in organizational

contexts and examined.

The Coaching Opportunity:

Four new-start graduate students from the schools of business and computer sciences were hired as research assistants in an

academic department and school of a university to effectively meet the needs of the faculty in being able to competently

perform several tasks related to research. Most of the tasks are already within the ability-scope of these graduate students.

However, blending into their roles as newly hired employees and research assistants to the faculty support coordinator and

professors in this department and school requires developing familiarization with organizational culture, process protocol, work

study portfolio organization and competence in their new roles. The job performance of graduate assistants requires them to be

competent in performing the basic functions in described Table 1 below.

Table 1: Graduate Assistant General Job Description

Source: CareerPlanner.com, (2011).

Different skills set and competence levels will require assistance in meeting some of the assigned tasks given to graduate

assistants by professors and faculty support coordinator. Generally, faculty support coordinators are responsible for training or

coaching graduate assistants in meeting their job roles and in becoming familiar with different aspects of their jobs related to

organizational culture and the tools and equipment they will use to meet their job roles. The need for proficiency in these areas

(Table 1) and becoming part of the organizational culture provide opportunities for coaching and the development of coaching

relationships. The coaching opportunities from graduate assistant jobs allow coaches not only to develop their own coaching

skills, but to coach these graduate assistants who may become future faculty support coordinators or faculty support trainers

and managers. Thus, the benefits can be seen immediately in performance as well as, as an investment in organizational future. This coaching opportunity with graduate assistants provides for application of coaching skills and techniques on several levels.

The Coaching Plan:

The proposed Coaching Plan to address the opportunity of training these four graduate assistants to function at their maximum

and in an effective capacity will be based on “Solution-Focused Coaching”, which involves using a variety of techniques

Assists department chairperson, faculty members or other professional staff members in college or university, by performing any combination of following duties: Assists in library, develops teaching

materials, such as syllabi and visual aids, assists in laboratory or field research, prepares and gives

examinations, assists in student conferences, grades examinations and papers, and teaches lower-

level courses. May be designated by duties performed, or equipment operated.



5

described above to facilitate their skills and abilities in effectively performing their job functions and assigned tasks. The

dominant techniques that that will be used include telephone coaching, rewarding and compensation, and what could be called

instructional-face-to-face coaching. A combination of techniques will be used according to the specific needs of these

individuals and their levels of skills. It is reasonable to assume that some of these graduate assistants will have differing skills in terms of job-specific required competences and that their learning levels and communication skills might require unique

consideration in the application of coaching techniques. However, based on experience and the nature of their job functions,

instructional face-to-face coaching and telephone coaching are the core coaching techniques that will serve best to meet

coaching goals in both physical and virtual environments.

Instructional face-to-face coaching will probably be the most dominant techniques since the graduate assistants will mainly

need hands-on or technical skills to function in their current roles. For example, these graduate assistants must know how to

construct PowerPoint presentations, photocopy papers, scan and attached papers in emails, fax papers, use the Scantron, access

electronic databases for research and retrieval of articles and data, format papers for professional presentation and publication,

compile materials and folders for specific courses according to professors’ request, and perform other related academic

functions which may require the use of programs not limited to Excel, Access, and other functions in Microsoft Office, and

even use statistical software such as SPSS and PSPP.

Instructional face-to-face coaching will be a daily on-the-job process where the faculty support coordinator or other qualified and immediate supervisors in the department, including professors can coach graduate research assistants to improve their

current skills set and competences to meet their job requirements. This also provides opportunity to build lasting influence

relationships as these graduate assistants go on to further their education and even become faculty or future administrators.

Telephone coaching where the faculty support coordinator can provide instructions to graduate assistants in performing certain

job functions is an effective technique where face-to-face consultation is not an option. For example, at any specific time where

a faculty support manager or coordinator or supervisor over the graduate assistant is not present in the immediate office and a

graduate assistant needs direction in performing a task, for example scanning a document to email, a simple phone instructional

session could facilitate this. This also applies to more complex tasks which the graduate assistant might not be familiar with.

With experience and knowledge about all the required tasks and functions a graduate assistant may be asked to perform, an

experienced and knowledgeable faculty support manager or coordinator or graduate assistant supervisor can provide effective

telephone coaching that improves graduate assistants’ skills and performance almost immediately or over a very short period of time. Thus, as Sparrow (2006) demonstrates, telephone coaching is extremely useful in the coaching process.

Coaching Plan Resolution:

The above coaching techniques described in the literature review are designed to provide quick solutions with immediate

results, and in such an organizational setting and work situation, coaching is an applied-results oriented process where the

coachee immediately puts into practices those skills communicated or demonstrated by the coach, and this, mainly through an

instructional coaching approach. The overall coaching plan for responding to the coaching opportunity in this paper could be

described as a “Solution-Focused Coaching” because of the need for practical and applied performance skills by the coachees

to perform their jobs functions as graduate assistants.

Facilitating performance development and training through coaching requires understanding impacting variables of time,

responsibility, performance requirements on the part of coach and coachees, the level of skills training and assistance required, and the available and appropriate coaching techniques that will produce the best results from both human relations and

scientific viewpoints. Using the coaching plan described above, the coach should consider keeping the coaching brief and

solution-based (Wakefield, 2006). This does not only save organizational time as a valuable resource, but also will ensure that

both coach and coachee stay motivated and have a realistic time frame in which to bring the performance coaching session to

its close.

Effective and brief solution-focused coaching helps people to tap into their own resources to deal effectively with challenges by

making positive changes that can lead to success both personally and for their organizations (Wakefield, 2006). Furthermore, it

is based on finding solutions and this alone allows for the coach to focus specifically on resolving or addressing specific

problems and challenges rather than engaging in “umbrella coaching”. The aim of coaching in the case opportunity presented

in this paper requires applying specific techniques that address specific problem-solving issues and task necessitation. For

example, graduate assistants must conduct research and know how to identify and retrieve academic peer review articles from electronic databases. While most students at the graduate level would have some knowledge of this, fostering maximum skills

development in this task requires the coach to teach by demonstration; that is, showing and doing the required task as an

example. This will also provide opportunity for fostering further required competences such as compiling bibliographic lists

through the citation function, using exporting and importing functions, and other functions in electronic database for search and

retrieval during assigned research.

Given the functional responsibilities of graduate assistants as support to the faculty in research and other academic tasks, and

their current levels of skills, the types of coaching techniques that should be used should allow for practice and independence.

These students being in graduate schools would not require extensive training in performing academic functions. Thus,



6

instructional face-to-face coaching and occasional telephone coaching are the best and most applicable techniques. Additionally,

graduate assistants tend to develop many research and technical skills on their own through troubles-shooting and applying

problem solving techniques from their programs of study. Furthermore, through observation and mentorship, they will grow

into their roles naturally. Using telephone and instructional face-to-face coaching provides for communication and interaction and the appropriate levels of relationship that will foster the development of self and performance improvement. Telephone

coaching will also provide for a significant degree of independence, which is a major competence that faculty and

administrators in colleges and universities search for in students as potential graduate assistants.

Summary & Conclusion:

Coaching can represent a great opportunity for facilitating and fostering change through communication and interpersonal

interaction. Coaching as an effective work-motivation and performance enhancing process has been increasingly applied to

various organizations at different levels and in all kinds of industries. The benefits of coaching can be tremendous in terms of

its ability to boost worker morale, motivation, increase job performance and skills levels, and reduce employee turnover. When

coaching is effectively applied to address deficiencies in an organizational setting it not only serves as a diagnostic, curative,

and preventative approach to workplace problems and their consequences, but also adds value to human and capital resources. Coaching graduate assistants certainly requires having a good knowledge and understanding of the coaching process and

various techniques because of their levels of education, the special nature of their job requirements and responsibilities, and the

fact that they are working in academic environments where they are perhaps very familiar with coaching and already have

trainable skills sets required for their job roles. The different coaching techniques presented in this paper can be used at

different points to address specific coaching situations and individual needs. However, telephone coaching and face-to-face

instructional coaching techniques are ideal in meeting the coaching needs of graduate assistants and can facilitate the building

of relationships and performance improvement with convenience and effectiveness. The coach must remember that these

individuals have varying skills and needs and must develop a coaching plan with clear goals, objectives, and a reasonable time-

frame in which coachees acquire skills. Most importantly, they must provide clear directions and reinforcement and delegate

power to graduate assistants to foster independent problem solving and decision making skills.

Recommendations:

Before developing a coaching plan to address what is perceived to be performance related problems, the prospective coach

must first engage in several activities. These activities will serve both as diagnostic and assessment indicators that allow the

coach to gauge the levels of communication, interaction, develop appropriate coaching plan, and apply the most effective

techniques for success from an understanding of coachee needs, standards, and organizational goals. Thus, it is recommended

that the prospective coach develop an agenda which has the following components and plan of action:

1. An assessment of present skills sets and needs of the prospective coachee.

2. Clear understanding of what is important in a coaching relationship.

3. Develop trust that will build the relationship required for successful coaching.

4. Identify the coachee’s weaknesses and strengths, as well as the critical skills set needed to address existing performance gap.

5. Establish a clear and controlled objective for coaching and the coaching process. 6. Apply those techniques with the highest potential for instilling desired change and improvement.

7. Develop an effective plan for coaching that has assessment standards and procedures, as well as a clear time frame.

8. Make feedback and communication continuous; and most importantly,

9. Foster independence throughout the coaching process since the aim is to equip the individual for autonomous self-growth.

Coaching is an effective tool for performance improvement and the techniques available are diverse, and their successful

application will depend on the scenario, coachee readiness, the skills of the coach and a variety of internal and external

individual and organizational factors.

References:

[1] Butler, D., Forbes, B., & Johnson, L. (2008). An examination of a skills-based leadership coaching course in an MBA

program. Journal of Education for Business, Marc/April 2008, pp. 227-232; Taylor & Francis Inc. Retrieved from http://search.proquest.com/docview/202821891?accountid=14129

[2] CareerPlanner.com. (2011). Graduate Assistant: Job Description and Jobs. Retrieved from

http://www.careerplanner.com/DOT-Job-Descriptions/GRADUATE-ASSISTANT.cfm

[3] Case, T., & Kleiner, B.H. (1993). Effective coaching of organizational employees. International Journal of Productivity

and Performance Management, May/Jun 1993; 42, 3, pp. 7-10. Emerald Group Publishing, Limited. Retrieved from

http://search.proquest.com/docview/218430873?accountid=14129

[4] Cohen, A.R., & Bradford, D.L. (2005). Influence without authority, Second edition. Hoboken, NJ: John Wiley & Sons, Inc.

[5] Gaskell, C. (2006). Hello, how are you? It’s your coach calling. Training & Coaching Today, April 2006, p. 24. Reed


http://www.careerplanner.com/DOT-Job-Descriptions/GRADUATE-ASSISTANT.cfm




7

Business Information UK. Retrieved from http://search.proquest.com/docview/231093282?accountid=14129

[6] Hunt, J.M., & Weintraub, J.R. (2011). The coaching manager: Developing top talent business, 2nd edition. Thousand

Oaks, CA: SAGES Publications, Inc.

[7] International Coach Federation [ICF]. (2011). About Coaching. Lexington, KY: International Coach Federation. Retrieved from http://www.coachfederation.org/intcoachingweek/about-coaching/

[8] Parsloe, E. (1999). The Manager as Coach and Mentor. London, England: Chartered Institute of Personnel &

Development.

[9] Phillips, R. (1996). Coaching for higher performance. Journal of Workplace Learning, Vol. 8 Iss: 4, pp.29 – 32.

[10] Megginson, D., & Clutterbuck, D. (2005). Goal-seekers. Training & Coaching Today, September 2005, p. 12. Reed

Business Information UK. Retrieved from http://search.proquest.com/docview/231098307?accountid=14129

[11] Sparrow, S. (2006). Case Study. Training & Coaching Today, April 2006, p. 24. Reed Business Information UK.

Retrieved from http://search.proquest.com/docview/231093282?accountid=14129

[12] Wakefield, M. (2006). New views on leadership coaching. The Journal for Quality and Participation, Summer 2006, 29,

2 pp. 9-12. Association for Quality and Participation. Retrieved from


Appendix 1: Steps in Coaching Plan

*Set clear goals. It is essential that every employee knows what the project goal is. A good job cannot be done if the goal is not

clear. This requires good communication between the manager and his subordinates. The goal must be very specific and to do

this it must be measurable.

* Have objectives. Objectives must be created for every employee or group involved in a project. This breaks down the goals

into precise duties for each group or individual employee. Employees are more able to recognize their contributions towards

the goal when objectives are set. Objectives also serve as daily reminders of what is to be accomplished

* Develop an action plan. Action plans detail what is to be done and also monitor progress towards project completion. An

action plan should consist of checkpoints, activities, relationships and time. Checkpoints monitor progress towards completion.

Short-term checkpoints establish frequent feedback methods. More importantly, checkpoints help employees to monitor their own progress. Activities are the methods used from one checkpoint to the next. Highly detailed activities will save time in the

long run. Relationships imply the sequence of activities. Some activities may be done simultaneously. Sequencing requires

careful consideration. Finally, the time of project from start to finish must be estimated. This requires accurate estimates of

activity time.

* Draft a project schedule. The two most common methods of scheduling used are the Gantt Chart and the PERT Chart. Both

are disciplines of management science.

* Give employees direction. Managers cannot do large projects by themselves. Therefore they require a team of supporters and

collaborators. Developing a support group takes skill and an understanding of the perspective of others. Managers must be

open-minded and need to realize that people are alike and all have like needs. Employees must be treated as individuals in

order to be motivated.

* Give reinforcement. Allow people to volunteer for work. People who sign up do not need to be coerced to work. Give people opportunities to develop goals and objectives. This will build commitment to their work. Give encouragement to employees.

People like to be noticed and appreciated. so managers should not hesitate to give an “attaboy”.

* Keep them informed. Effective communication is required to keep employees informed. Some organizational structures can

be a barrier to good communication. This can create ambiguity, which will result in faulty information dispersal. People should

be regularly informed and this requires monitoring and feedback. Managers must also learn to be better listeners. Keeping

employees informed of progress will reduce anxiety and increase performance.

* Resolve conflicts. Disagreements between groups or individuals are unavoidable, since projects require the integration of

work from many people. Conflict is actually desirable, when it is used as a way of unleashing creativity and imagination.

Reasoning and logic must be used to resolve conflicts. Managers must gain acceptance by providing sound rationale for their

positions.

* Delegate power. Giving employees power encourages them to put in their best effort, ability and initiative. When managers share power, people at all levels feel that they contribute greatly towards reaching the previously set goals and objectives.

Managers must also be honest and competent as well as give direction and inspiration.

* Promote risk taking. Organizations should stress the rewards of success rather than the consequences of failure. Time should be allowed

for experimentation and creativity. Innovation requires support and should be enhanced by communication and open exchange of ideas.

Source: Thomas J. Case & Brian H. Kleiner. (1993). “Effective coaching of organizational employees” in International

Journal of Productivity and Performance Management, pp. 7-8.

****


http://www.coachfederation.org/intcoachingweek/about-coaching/






8

A HYBRID DATA MINING APPROACH TO CONSTRUCT THE

TARGET CUSTOMERS CHOICE REFERENCE MODEL

Shih-Chih Chen,

Assistant Professor

Department of Accounting Information

Southern Taiwan University of Science and

Technology, Taiwan

Ruei-Jr Tzeng,

Department of Information Management

Tatung University, Taiwan

ABSTRACT

Marketing, the prevailing commercial activity of enterprises, is an important strategy to increase

customer loyalty and potential customer for more profit. To maximize profit with limited resources, it would be more profitable for enterprises to choose the right target customers. Therefore, it is necessary

to build up an efficient, objective and accurate target customer choice model. Using data mining

techniques to find the target customers is a traditional way. However, most studies in the past mainly focused on finding the high accuracy classifier, but different classifiers perform differently in varied

situations. So this study is to propose a target customer choice model by integrating support vector

machine, neural network and K-Means algorithm into a two-phase analysis methodology. The research results indicate that the integrated methodology is effective in simultaneously enhancing classification

accuracy and reducing Type I and Type II errors.

Keywords: Data Mining, Support Vector Machine, Neural Network, K-Means.



9

Introduction:

With the business environmental change and increasingly fierce competition, the enterprise must face how to

improve the interests of business and make enterprise more competitive. The previous mass marketing is already

out of date, now enterprise must to search niche market and create the merchandise that fit it. Peppers (1999) mentioned that one-to-one economic system will become mainstream in the future, this economic model emphasize

the customized production and one-to-one marketing. Therefore, for the future changes, quickly and accurately to

find the target customers, maximize the interests of marketing with limited resources is important.

In the past, find target customer always using the different classifiers to improve classification accuracy, but don’t consider the classification error. For instance, when a customer wanted to buy products, but the classifier

misjudgment him, this produces Type I error. When a customer didn’t want to buy products, but the classifier

misjudgment him, this generates Type II error. This study proposes a two-stage target customers choice model to upgrade classification accuracy and reducing statistical Type I and Type II error. So this study proposed a two-stage

data mining methodology. First, we separately compute the accuracy with support vector machine and neural

network. Second, by using K-Means algorithm to re-classification target customers, we can upgrade the classification accuracy and reduce Type I and Type II error results.

Literature Review:

Data Mining:

The principle of data mining is to find useful information or knowledge from the data, it’s also known as data archeology, data model analysis. Technology Review (2001) awarded data mining is one of the ten emerging

technologies that affect human life in the 21st century, this shows the importance of data mining. Fayyad et al.

(1996) defined data mining is a process that using automatic or semi-automatic methods to analyze large amounts of data. The research (Scott, 2006) that should take advantage of information technology systems, make all users

can depend on their needs to find really useful information rather than search for useless message.

In the analysis of data mining functions, Berry & Linoff (1997) proposed six analysis functions, this is a brief

description of the various analysis functions: (1) Classification: Without first giving the characteristics of each category and clearly defined, and then through

the prepared training data to build a model, Let yet classified data to be classified in each category.

(2) Algorithm: Let the high homogeneous data be clustered in the same group, the principle is that the same group has high homogeneity and between the different group has highly heterogeneous.

(3) Prediction: Speculate value may be incurred in the future or the future trend.

(4) Estimation: To deal with the continuity value, according the existing continuity value to estimate the unknown continuity data. (5) Affinity Grouping: To explore an event or data will appear in a same time, this is used to generate association rules.

(6) Description and Visualization: At different angles or different levels to describe complex data, help to make decisions.

Support Vector Machine:

Support vector machine is a machine learning technique that based on statistical learning theory and follow the structural risk minimization principle, now widely used in classification problems. Vapnik (1995) proposed SVM,

this is the principle of support vector machine, letting the independent variables and the dependent variable from

the original nonlinear corresponding relationship elevated to the high dimensional vector space, and looking for a hyperplane to separate the data into two class in this vector space, making distance between the two class farthest in

feature space to achieve the best classification results.

Since support vector machine has performed very well in classification problems, it is widely used in document

classification (Joachims, 1998), image recognition (Pontil & Verri, 1998) and biological technology (Yu et al., 2003). The advantages of support vector machine is good summarized ability and training speed, and the SVM's

architecture is based on solving a binary programming problem, it can make up for local extreme problem in neural

network, therefore, the study will use support vector machine with neural network to analyze.

Neural Network:

Neural network theory originated in the 1950s, by the 1980s, Hopfield proposed neural network, by this time, expert

system encountered a bottleneck, neural network has gradually taken seriously. neural network simulated biological



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nervous system to build a simplified neural system mode, using parallel computing that similar to human brain and

self-learning ability, and making system can be accumulated experience through repeated training to achieve the

learning effect. Until today, neural network still has new architecture and theories been proposed, because operational speed of computer is more quickly, making neural network more powerful and more widely used.

Research on neural network developed rapidly in recent years, application fields include industrial management,

biology, medicine, business and credit Scoring (Stern, 1996; Vellido & Vaughan, 1999; Zhang & Hu, 1998), neural network is very suitable for classify and predict because it can self-organizing, self-learning and generalization.

K-Means Algorithm:

K-Means algorithm was first proposed by James MacQueen in 1967. The k-means approach to algorithm performs

an iterative alternating fitting process to from the number of specified clusters. It is one of the simplest unsupervised learning algorithms. With the advantage of good efficiency and simple concept, K-Means algorithm is

widely used in various types of data mining and statistical analysis software.

K-Means algorithm is often applied in a variety of researches such as document algorithm (He et al., 2003), data watermarking (Zhang et al., 2001), and graphic retrieval (Kanungo et al., 2000). In multivariate perspective, if the

attribute of the real world be abstracted into a vector, it will be able to be calculated by K-Means algorithm. A

variety of studies use K-Means algorithm as the analytical tool because of its abstract application.

Research Methodology:

The purpose of this research is to enhance the accuracy when choosing target customers, and meanwhile reduce

misspecification rate (including type I and type II errors) when classifying. To achieve the goal, a two-phase target

customer choice model is proposed. First of all, we classify the customer data as control group and tested group, and then step into the first phase. Input the data of control group into neural network and support vector machines

class models. Run the models and calculate the class accuracy. Compare the results of neural network and support

vector machines, if the results are identical, it will be the finale result whether consists with the original data or not,

else we will step into second phase to analyze data by using K-Means algorithm. The second phase purposes to cluster the unclassified data by using K-Means algorithm. We divide the customer

data into two clusters, including good customer cluster and bad customer cluster. Then calculate the distance

between the unclassified data and the cluster centers of two clusters by using K-Means algorithm. In this research, we define the distance between the unclassified data and the cluster center of the good customer cluster as VG

(value of distance from cluster (good)’s center to data), and the distance between the unclassified data and the

cluster center of the bad customer cluster as VB (value of distance from cluster (bad)’s center to data). When VG<VB, the data has higher similarity with the good customer cluster, and be clustered to the target customer

cluster; otherwise be clustered to un-target customer cluster. Finally, we pour the consistent result from the first

phase output and the re-judgment result form the second phase back to the customer data. We calculate the accuracy

by support vector machine and neural network again, and observe the effectiveness. The process architecture shows in Figure 3.1.

Fig. 3.1: Target customer choice model

In this study, we use IBM SPSS Modeler, which is a popular data mining tool in recent years, in the windows environment. SPSS Modeler was originally named SPSS Clementine (PASW Modeler), and was since acquired by



11

IBM in 2009. Today, we call the new version modeler as IBM SPSS Modeler in which was renamed by IBM in

2010. We choose SPSS Modeler as the data mining tool, because it products directly help improve business

processes in many real-life cases. For example, Cablecom GmbH, is the largest cable network operator in Switzerland. By using SPSS Predictive Analytics, Cablecom has continuously seen customer churn rates decrease

from 19 percent to 2 percent. In another case, through the use of SPSS Modeler, Dutch insurance

firm FBTO Verzekeringen, has also increased conversion rates by 40 percent and decreased its direct mailing costs by 35 percent. Base on the effect of the real-life cases, in this study, we attempted to use SPSS Modeler as a data

analyzing and model building platform.

The first phase analysis:


The support vector machine operation process is divided into two parts, operates as following:

Construct the classification system:

The data from this study is nonlinear partitioned dataset, can’t find a hyperplane in the original space, required through kernel function to covert the data from the original space to the high dimensional feature space, and

classifying it in this space. We can simplify the complex computational problem become through kernel function.

There are four commonly used kernel functions:

Linear: ( , ) ,T

i j i jK x x x x

Polynomial: ( , ) ( )T d

i j i jK x x x x r ,

>0

Radial Basis Function: ( , ) exp( )d

i j i jK x x x x ,

>0

Sigmoid: ( , ) tanh( )T

i j i jK x x x x r

Kernel function is the key to construct a good performance support vector machine, but the different problems need different kernel function. In this research, we adopt polynomial kernel to construct the classification system because

it is good to obtain higher benefit in nonlinear and high dimensional data, and the parameter that we adjust only C

value and Gamma value, it's not easy to have too much deviation. (Hsu et al., 2003) Using different C value and Gamma value will generate different accuracy rate, we through SPSS Modeler to find

the best parameter, then we can get better classification performance.

Calculate the correct rate:

Using the support vector machine with set parameter to classify data and calculate the correct rate.

Neural Network:

The neural network has different modes. e.g., back propagation network, Hopfield network and radial basis

function network, and back propagation network is the method that is the most commonly used in commercial research (Vellido et al., 1999). Therefore, we using the multilayer perception in back propagation network to

analyze data.

Back propagation network is a multilayer feedforward network and it has input layer, hidden layer and output layer.

Input layer neurons major role in transmission, and hidden layer and output layer are neurons that really work. Input layer neurons expressed as the number of input variables, in this study, the number of input layer neurons

represent variables of customer data, the output layer represent determine customer that is target customer or not,

when the output shows “yes”, represents this data attributable to target customer, if the output shows “no”, represents this data can’t attributable to target customer, and hidden layer represents the interaction between

processing unit in input layer.

The second phase analysis:

In order to reduce Type I and Type II errors in the classification system, we perform the class predictions by using

support vector machine and neural network in first phase. The method will step into the second phase if the class

http://acronyms.thefreedictionary.com/FBTO



12

predictions from two classifiers are not the same. In the second phase, we devoted to cluster the customer data by

using K-Means algorithm. We compare the unclassified data with target customer cluster and non-target customer

cluster. The unclassified data will be clustered into the cluster according to their similarity. To begin with, we define the cluster centers of each cluster by using K-means algorithm and vector the unclassified data. Then

compare the distance between the unclassified data and the cluster centers of each cluster by using a mathematical

calculation known as the Euclidean distance (Buttrey & Karo, 2002; Davidson, 2002). After the VG and VB of the unclassified data are calculated by K-means algorithm, the unclassified data is able to

be clustered. When VG<VB, the data has higher similarity with the good customer cluster, and therefore be

clustered to the target customer cluster; otherwise be clustered to un-target customer cluster.

This study using K-means algorithm in the second phase because of it won’t be affected by the quality of training data. K-means algorithm clusters data not based on the pre-defined categories but based on the similarity of the

data, and the methods in the first phase may produce errors due to the quality of training set. Therefore, in the

second phase, we analyze the inconsistent data from the first phase by using K-means algorithm to get the better results.

The Analysis of Case:

This study uses the data of a Portuguese banking institution that from the UCI machine learning database. The bank

marketing data set contains 4521 instances and 17 attributes. There use 16 attributes to describe the customer data and the condition of the bank marketing (phone cells), including 7 numeric attributes, 6 categorical attributes and 3

binary attributes. The target attribute represents whether the customers subscribe the long-term bank deposits or

not, including 521 “yes” and 4000 “no”. We define the customer in which has subscribed as the target customer, and process analysis.

To begin with, we divide the data set into training set and test set. The result of proportion show about 80% and

20% for training set and test set. Training set contains 3604 samples, including 418 “yes” and 3186 “no”. Test set contains 917 samples, including 103 “yes” and 814 “no”.

The first phase analysis:


In this study, we use the SVM modules of SPSS Modeler to classify, and select polynomial kernel to construct the classification system. After repeated tests and cross-validation, we find that when the value C=2 and Gamma=0.3

will achieve the best classification results. Using support vector machine with set parameter to classify the test set.

Fig. 4.1 shows, the average accuracy of test set is 85.5%, the classification accuracy of 817 samples “no” is 91.2%, the classification accuracy of 103 samples “yes” is 40.8%%, Type I error is 59.2% and Type II error is 8.8%.

Table 4.1: Support vector machine classification result

Original class Classified class

NO YES

NO 742(91.2%) 72(8.8%)

YES 61(59.2%) 42(40.8%)

Neural Network:

In this research, we use the multilayer perception of SPSS Modeler to analyze. In this case, the number of input

layer neurons expressed as 16 attributes of customer data, and the number of output layer neurons expressed as target attribute. Setting the hidden layer of multilayer perception to two levels, after repeated tests and cross-

validation, we setting the first level of hidden layer to 3, and setting the second level of hidden layer to 4, using the

neural network with set parameter to classify test set and calculate the accuracy of classification. Fig. 4.2 shows, the average accuracy of test set is 90.4%%, the classification accuracy of 817 samples “no” is 96.6%%, the

classification accuracy of 103 samples “yes” is 41.7%%, Type I error is 58.3% and Type II error is 3.4%.



13

Table 4.2: Neural network classification result


NO YES

NO 786(96.6%) 28(3.4%)

YES 60(58.3%) 43(41.7%)

The second phase analysis:

In this phase, we compare the classification results from support vector machine and neural network. If the two

classifications are consistent, the classification will be the finale result whether it consists with the original data or

not. Otherwise, the procedure will step into second phase, to analyze data by using K-Means algorithm. First, to divide the customer data into good customer cluster and bad customer cluster. We calculate the VG (value of

distance from cluster (good)’s center to data) and VB (value of distance from cluster (bad)’s center to data) of the

unclassified data by using K-means algorithm. When VG<VB, the data has higher similarity with the good customer cluster, and will be clustered to the target customer cluster; otherwise will be clustered to un-target

customer cluster.

NO.98 customer in Figure 4.1, for example, is the data that classed by support vector machine and neural network.

We compared the classification results produced from two classifiers, and found that they are not the same. Next, we calculate the data by using K-means algorithm of SPSS Modeler, and figure out VG=1.852, VB=2.042. If

VG<VB, the no.98 customer is similar to the target customer cluster. Therefore, it comes to a conclusion that no.98

customer is clustered to the target customer cluster.

Figure 4.1: K-Means algorithm flowchart

Through support vector machine and neural network classification, there are 814 samples that judgment is same, output them for result. And 103 samples that judgment is not same, the original data as "yes" are 35 samples, as

"no" are 68 samples. Importing this data to second phase analysis, through K-Means algorithm, there are 53

samples be clustered to non-target customer cluster, 50 samples be clustered to target customer cluster.

For example, in Table 4.3, the five data are not clustered to the target customer cluster in original. After analyze data by using K-means algorithm, we get the four data that can be clustered to the target customer cluster because

of their VG<VB. The rest of the unclassified data may be deduced by analogy.

Table 4.3: Examples of reassigned results

NO. VG VB Original Reassigned

98 1.852 2.042 no yes

180 1.558 1.603 no yes

201 1.623 1.703 no yes

224 1.433 1.477 no yes

346 1.843 1.793 no no

http://tw.dictionary.yahoo.com/dictionary?p=figure



14

Finally, to verify the effect, we analyze customer data of the two-phase model output by using support vector

machine and neural network. As shown in Table 4.4 and Table 4.5, after analysis, we get the classification

accuracies as 98.91% and 94.55%. Both of them are higher than the initial classification accuracies from support vector machine and neural network. Also, Type I error and Type II error are reduced. Consequently, the simulations

show that two-phase target customer choice model in this study not only increasing the accuracy of classification

but also reducing the Type I and Type II error.

Table 4.4: Using SVM to verify the result of two-phase model


NO YES

NO 786(99.6%) 3(0.4%)

YES 7(5.5%) 121(94.5%)

Table 4.5: Using NN to verify the result of two-phase model


NO YES

NO 772(97.9%) 17(2.2%)

YES 30(23.4%) 98(76.6%)

Conclusion:

Increasing global competition is changing the environment facing most enterprises today. For any enterprise, it is

an important issue that how to reduce costs, promote the interests of marketing, or find out the potential customers.

In recent years, various data mining methods have been widely used in marketing and customer relationship management fields. If a enterprise is able to collect a lot of customer data and analysis useful information, it will

become a leader of the field.

In this research, we presents a two-phase target customer choice model. First, we perform the class predictions by using support vector machine and neural network. Comparing the class predictions, if the judgment is not the same,

it will proceed to the next phase. The second phase attempts to analysis the customer data by K-Means algorithm.

We cluster the customers by comparing VG and VB. The simulations show that our methods not only increasing

the accuracy of classification but also reducing the Type I and Type II error. The proposed approach appears an excellent performance, and shows that this study has contribution on practice and academic value at the same time.

To believe firmly, the advantages of our two-phase target customer choice model are helpful to reduce marketing

costs, find out the potential customers and increase enterprise profits for the enterprises.

References:

[1] Berry, M.J.A., & Linoff, G. (1996). Mastering Data Mining, the Art and Science of Customer Relationship

Management. NY: John Wiley and Sons.

[2] Buttrey, S.E. & Karo, C. (2002). “Using K-nearest- neighbor classification in the leaves of a tree,” Computational Statistics and Data Analysis, 40(1), 27-37.

[3] C. W. Hsu, C. C. Chang and C. J. Lin (2003). “A Practical Guide to Support Vector Classification,”

Technical Report, Department of Computer Science and Information Engineering, University of National Taiwan, Taipei, 1-12.

[4] Davidson, I. (2002). “Understanding K-means non-hierarchical clustering,” SUNY Albany Technical Report, 2-25.

[5] Fayyad, U. & Piatetsky-Shapiro, G. &Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Databases,” Advances in Knowledge Discovery and Data Mining, Calif.: AAAI Press, 37–54.

[6] H. Zha, C. Ding, M. Gu, X. He, and H.D. Simon. (2001). “Spectral rlaxation for k-means clustering,” Neural

Information Processing Systems vol.14 (NIPS), 1057–1064.

[7] He, J., A. Tan, C. L. Tan, and S. Y. Sung, (2003). “On quantitative evaluation of clustering systems,” Clustering and Information Retrieval Anonymous, 105-134.

[8] IBM SPSS Modeler 15 Applications Guide (2011).



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[9] J. B. MacQueen (1967). “Some Methods for classification and Analysis of Multivariate Observations,”

Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of

California Press, 1, 281-297. [10] Joachims, T. (1998). “Text categorization with support vector machines,” In Proceedings of European

conference on machine learning (ECML). Chemintz, DE, 137–142.

[11] Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., and Wu (2000).“An efficient K-means clustering algorithm: Analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine

Intelligence, 24(7), 881–892.

[12] Peppers, D. and Rogers, M. (1999), The One to One Future, Doubleday, N.Y.

[13] Pontil, M. & Verri, A. (1998). “Support vector machines for 3D object recognition.,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6), 637–646.

[14] S. Moro, R. Laureano and P. Cortez. (2011). “Using Data Mining for Bank Direct Marketing: An Application

of the CRISP-DM Methodology,” In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, 117-121.

[15] Scott, N. (2006). The basis for bibliomining: frameworks for bringing together usage-based data mining and

bibliometrics through data warehousing in digital library services. Information Processing and Management, 42, 785-804.

[16] Stern, H. S. (1996). “Neural Networks in Applied Statistics,” Technometrics, 38, 205-216.

[17] UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/

[18] Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer-Verlag, New York. [19] Vellido, A., Lisboa, P. J. G., & Vaughan, J. (1999). “Neural networks in business: a survey of applications

(1992–1998),” Expert Systems with Applications, 17, 51-70.

[20] Yu, G. X., Ostrouchov, G., Geist, A., & Samatova, N.F. (2003). “An SVM-based algorithm for identification of photosynthesis-specific genome features,” In 2nd IEEE computer society bioinformatics conference, CA,

USA, 235–243.

[21] Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). “Forecasting with artificial neural networks: the state of the

art,” International Journal of Forecasting, 14, 35-62.

****

http://archive.ics.uci.edu/ml/



16

THE USED OF IT BALANCED SCORECARD TO BUILD THE

PERFORMANCE MEASUREMENT MODEL OF ACADEMIC

INFORMATION SYSTEMS (CASE STUDY ACADEMIC

INFORMATION SYSTEM OF SATYA WACANA)

Paskah Ika Nugroho,

Faculty of Economics and Business

Satya Wacana Christian University, Indonesia.

Prihanto Ngesti Basuki,

Faculty of Information Technology

Satya Wacana Christian University

Indonesia.

Evi Maria,

Faculty of Information Technology

Satya Wacana Christian University

Indonesia.

ABSTRACT

The aim of this research is to make a model of performance measurement of academic information

system to facilitate the auditors in conducting a periodically performance measurement of Satya

Wacana Academic Information System using IT Balanced Scorecard. SI performance measurement model was developed through systematic measures in the form of the action process, reflection,

evaluation, and innovation by applying the method of survey research, development, experiments ,

and evaluation. Performance measurement modeling of Academic Information Systems (SIASAT) in SWCU has been done by making a framework model which was developed by considering the

following parameters: (a) the duties and functions of the university, (b) the aspects of university

management, (c) the duties and functions of the IT organization in university, (d) the need of

information system for academic activities, and (e) the methodology of IT basic framework used, which is the IT Balanced Scorecard (IT-BSC).

Keywords: IT Balanced Scorecard, Academic Information System, Performance Measurement.



17

Introduction:

The use of Information Technology (IT) in Higher Education institutions especially for the use of information systems and the

Internet can not be separated due to the demands of the stakeholders (Indrajit, 2006). IT Management in Higher education

institution is a Critical Success Factor (CSF) for leaders and partners of Higher education institutions (Henderi, 2010).

However, the complexity of IT implementation makes the leaders of the various levels in the Higher education institutions and

stakeholders have difficulty in managing the IT. The complexity of IT implementation in higher education institutions in

Indonesia happens because the higher education institution does not have a specific framework model when establishing the

information system (Mutyarini and Sembiring, 2006). As a result, the benefits of using IT is not comparable to the investments

value which has already been incurred.

Satya Wacana Christian University is one of the universities, which has already used IT as an infrastructure and facility to provide services for students, lecturers and all the staff, and also assists the running of the activities around the work units. In

carrying out its main activity, that is to provide educational services, SWCU has supported by IT of Satya Wacana Academic

Information Systems (SIASAT). IT management has been applied in SWCU, but it has not been applied using a well-structured

method and approach. On the other hand, IT implementation must be controlled because the control provides reasonable

assurance to management that the implementation process has been done in accordance with the plans and goals of the

organization (Maria, 2011).

Each IT process requires a controlled IT measurement to indicate the performance of IT in achieving the control objectives and

facilitate the management to make improvements to the performance of IT. IT performance measurement can be performed by

using IT Balanced Scorecard IT where the IT performance is measured from 4 perspectives: corporate contribution, user

orientation, operational excellence, and future orientation (Van Grembergen, 2000). IT Balanced Scorecard is an effective

method of managing IT organizations as well as evaluating the success and development of the system/application, the

development of computer and network investment, quality of products and IT services, as well as improving the quality of human resources, even though, most universities in Indonesia have not been using this method (Prabowo, 2007).

In addition, monitoring and evaluating towards SIASAT performance has not been done periodically, but only if there are

complaints from the working units about the SIASAT service (Maria and Haryani, 2011). This condition is not consistent with

the results of Maniah and Surendro’s research (2005) which stated that SI performance measurement must be done

periodically to ensure the sustainability of IT operations used by the organization or company as well as to assess the

sustainability between the planning and implementation of the system. Since the importance of SI performance measurement

should be done periodically, this research will try to make a model of performance measurement of academic information

system to facilitate the auditors in conducting a periodically performance measurement of SIASAT using IT Balanced

Scorecard.

Literature Review:

IT Balanced Scorecard:

The balanced scorecard can be applied to the IT function and its processes as Gold (1994) and Willcocks (1995) have conceptually

described and has been further developed by Van Grembergen and Van Bruggen (1997) and Van Grembergen and Timmerman (1998).

IT-BSC has four perspectives: (1) Corporate Contribution, contains a measure which indicates how the management (the manager)

evaluates/views the IT organization; (2) User orientation contains a measure which indicates how users evaluates/sees the results of

the IT organization, (3) Operational Excellence contains a measure of the effectiveness and efficiency of the IT process, and (4)

Future orientation contains a measure which describes how IT position within the next challenge.

Performance Measurement:

Mulyadi (2001) defines performance measurement as a process of assessment on the company operational activities in a

particular period, whether it has been done based on the defined goals or not. The main purpose of the performance measurement is that the leader of the company has an objective basis in giving the compensation in accordance with the

achievement which has been done by each department as a whole. It is expected that all of these will give motivation and

stimulation in each section to work more effectively and efficiently.

Previous Researches:

IT model development and performance measurement can adopt IT standards such as ITIL, ISO/IEC 17799, COSO dan

COBIT (O’Donnell, E, 2004). Van Grembergen’s research (2000) discussed about how the IT balanced scorecard (IT-BSC) can

be linked to the business balanced scorecard (BU-BSC) and in this way support the IT/business governance and alignment

processes. The considered aspects in the IT application in IT-BSC method are corporate contribution, customer (user)

orientation, operational excellence and future orientation. IT-BSC method used to measure the performance of the

implementation system of Enterprise Resource Planning systems (ERP) at the University. The method was continuously developed to make a strategic plan which is in accordance with the mission of educational institutions to continue in surviving



18

in the business competition (Sa'adi and Suhardi, 2006).

IT Balanced Scorecard has not been widely used to measure the performance of information systems in universities in

Indonesia, but actually this method is very effective for managing the IT organization and evaluating its success (Prabowo,

2007). This is because the Universities in Indonesia did not have a specific model of the framework when they build their

academic information system (SI), so Mutyarini and Sembiring (2006) created an academic architecture Information system

model by adapting the architecture of Monash University which used TOGAF in order to achieve the mission of Tri Dharma

higher education.

The previous research on IT in SWCU as the research object, including the study of Maria and Haryani (2011) who found that the

supervision and the assessment towards the IT performance in SWCU has not been carried out periodically, just only if there are complaints from the users (the working units) about the IT service. This research produced a model of information audit system

which is developed using the COBIT framework especially for delivery and support (DS) domain. Maria‘s research (2011) also

found that so far the IT management in SWCU has been done, but it has not been done using the structured method and approach.

This research also choose to use COBIT framework in doing the comparison among the academic information systems because

COBIT can notice the link between the business goals without neglecting the IT process as the focus. Maria’s research, et al

(2012) found that IT in SWCU has been well managed where IT processes to support business goals has been standardized,

documented and communicated well. There should be a continuous monitoring and evaluation of the IT in SWCU, so the quality

of IT services in SWCU can be improved day by day in accordance with what is expected.


This type of research is a combination of descriptive studies that describes the phenomenon that actually occurs in an event or population and exploratory research that found a model of the SI Academic performance measurement done by doing an

approach on the "Research and Development", that was a research program which was followed up by doing some

development programs. SI performance measurement model was developed through systematic measures in the form of the

action process, reflection, evaluation, and innovation by applying the method of survey research, development, experiments ,

and evaluation.

The location of this study, Satya Wacana Christian University Salatiga Indonesia, was chosen on purpose. Primary data of this

study was the results of guided interviews and observation. While secondary data such as documents, reports and policies are

taken based on the SIASAT

The steps of this study are as follow:

a. Preliminary studies

In the initial study, there were prelimanary research on previous studies, literature and standards that support the research topic, guided questionnaire drafting, and SIASAT understanding.

b. Data collection

At this stage, the data was obtained by interview, observation, and questionnaires given to the relevant units and users of

SIASAT. The secondary data is also collected from related units of SIASAT.

c. Development of performance measurement model of IS

At this stage, development of performance measurement of IS was managed by interviews, observation and related

documents to state parameters and Critical Success Factor (CSF), which will be used as constraints to determine criteria of

performance measurement of SIASAT based on IT BSC perspective. Then we mapped the steps to measure the

performance of acedemic information systems.

d. Conclusions

In the final stage of this research, a conclusion from all research processes was stated.

Result and Discussion:

Brief description of the Satya Wacana Academic Information Systems:

The internet-based of SWCU Academic Information System (SI), known as SIASAT , is an application used to record data

from each student's academic administration from the entry (admission) to the exit (graduation). This application can be

accessed easily via the SWCU homepage, http://www.uksw.edu address, then go to the SIASAT menu in the ACADEMIC

group, or directly go to http://siasat.uksw.edu. These applications provide an online and a real time of academic information.

All students who are listed as SWCU students, have the right to access the application via the homepage institution. It is

important for SWCU students to know and master this application and its operations in order to see the financial obligations

that they have to pay, course registration, and see the results of their study for each semester.

SIASAT Performance Measurement Model by Using IT Balanced Scorecard:

Pyle (2003) stated that the development of performance measurement model will be based on one of modelings, i.e. the model

which is developed by its constituent components, such as business processes and their correct data components. Performance

measurement modeling of Academic Information Systems (SIASAT) in SWCU has been done by making a framework model



19

which was developed by considering the following parameters: (a) the duties and functions of the university, (b) the aspects of

university management, (c) the duties and functions of the IT organization in university, (d) the need of information system for

academic activities, and (e) the methodology of IT basic framework used, which is the IT Balanced Scorecard (IT-BSC). These

parameters are expected to be the factors that determines the performance of academic information systems which were

observed, and how these parameters can be controlled and regulated, in order to obtain a desired performance system. The

relationship between the 5 parameters in creating the measurement model of academic information system performance by

using IT-BSC is presented in Figure 1.

Figure 1. The relationship between the parameters in creating the performance measurement model

The main parameters of duties and functions of university in this research is the implementation Tridharma High Education,

such as lectures, working in laboratory, practical work, the implementation of the final project, research and training, and the

implementation of community service. Those are the things that encourage the chief of SWCU to formulate its business goals

by using four perspectives of Balance Scorecard (BSC). The business goals of SWCU are presented in Table 1. To achieve

those business goals, IT infrastructure is provided in the form of the use of computers, information systems implementation,

and the use of internet technology. The information system was built in accordance with the internal business processes of

SWCU starting from prospective new students since they enroll, be accepted, join the lectures, until graduate.

Table 1. Business goals SWCU

BSC Perspective SWCU Business Goals

Financial

1 Provide a good return on investment of IT-enabled business investments

2 Manage IT-related business risk

3 Improve corporate governance and transparency.

Customer

1 Improve customer orientation and service

2 Offer competitive products and services

3 Establish service continuity and availability

4 Create agility in responding to changing business requirements

5 Achieve cost optimisation of service delivery

Internal

1 Improve and maintain business process functionality

2 Lower process costs

3 Provide compliance with external laws, regulations and contracts

4 Provide compliance with internal policies

5 Improve corporate governance and transparency

6 Manage business change

7 Improve and maintain operational and staff productivity

Learning &Growth Manage product and business innovation

SWCU leaders also formulate the main aspects that need to be considered in the management of a university. University management

should pay attention to the availability of resources, the process aspects and the content aspects. These parameters need to be

formulated, since universities in Indonesia do not have a standard framework for building and managing academic information

system (Mutyarini and Sembiring, 2006). Those aspects will be managed by the organization's culture, values and work ethic and are



20

manifested in the form of organization structure and management systems in universities as presented in Table 2.

The parameters of IT management organization in SWCU are handled by the Bureau of Information Systems Technology

(BTSI). IT developments are constantly increasing, so that SWCU must make arrangement in the IT management organization.

BTSI plays an important role for the success of the implementation process. This is because BTSI not only manage the

technical aspects of IT but also play a role in determining the organizational culture in SWCU in using IT. BTSI consists of 2

parts: (1) IT section which is in charge of audio-visual section, parts of the communication network and the Internet, and parts

of computer, (2) information system section which is in charge of software parts, parts of information systems management,

parts of flexible learning & web, and parts of documentation and training. The functions which are managed by BTSI are: (1)

the function of technology development and the application of information systems, (2) the function of maintenance of information systems applications, database, digital documentation of information system, the content of learning resources,

networks and computers, (3) the functions of settings and monitoring of IT implementations in the form of change management

and user relationship, release system and audit.

Table 2. The key aspects of Universitites Management

Organizational Culture, Values and Work Ethics

Resources Process Content

Lecturers and Non-

lecturers resources

Funds

Facilities and

Infrastructure

IT Infrastructure

The key process is to run Tri Dharma University,

which consists of:

1. Education and Teaching

2. Researches

3. Community Service

The curriculum and its management,

which consist of instructional materials,

the results of the study, the results of

community service, scientific forums. Supporting processes, which include the

processes of: academic administration, students

and alumni, financial administration, cooperation

and external relations, and promotion

Information Systems Knowledge Management both tacit

knowledge and explicit knowledge.

Organizational Structure and Management Systems in Universities

On the other hand, the parameters of the IT framework methodology used in this study use IT Balanced Scorecard (IT BSC).

Based on those IT organization functions in SWCU, it is necessary to establish IT Balanced Scorecard for each of those

functions which is essentially a derivative of the IT Balanced Scorecard for universities. The process of identifying Critical

Success Factor (CSF) is needed to determine the limits of performance measurement criteria in each IT process. The CSF of

Academic Information Systems is presented in Table 3.

Table 3. The Critical Success Factors of Academic Information System based on IT-BSC Perspective

IT-BSC Perspective CSF Academic Information System

Business Contribution Control costs, increase revenue and improve service coverage

User Orientation Customer value proposition that includes the rates, quality, service provided, service

and partnerships

Operational Excellence Improvement of internal processes by implementing the operations management,

customer management and innovation.

Future Orientation Enhanced capabilities and skills through the strengthening of human capital,

strengthening of information capital, and strengthening of organization capital

For the parameters of the IS (Information System) requirements related to academic activities of universities which consist of

information about admissions, student registration, course registration, grades, and graduation. Admission information consists of

information regarding enrollment of prospective new students which covers registration process, the data inputting, photo-taking, selection, selection, announcement of selection result, printing of Rector Decree about new admission and information regarding re-

signing up includes taking an acceptance letter, registration payment, informing a bank payment receipt, filling out a registration form,

and obtaining student’s number. The importance of IS related to student registration such as providing information about types of

registration, information of procedures, requirements, and student registration deadline. Student registration is an activity of

registration or recording of active-status as the University's student and must be done by students each semester.

Course registration is a subject registration process as a participant of a course in the current semester. Subject registration

process includes academic supervision, financial (dispensation), internet, course registration schedule of study

program/department. IS ought to provide information related to grade of subject for each student and each semester which will

be presented either in a study result card or an academic transcript. As for graduation activities, IS should provide information

includes registration, graduation ceremony, and diploma delivery.

After determination of the CSF and know the needs of IS for the academic activities, it should begin mapping the steps what needs to be done in order to measure the performance of academic information system. The methods of academic information



21

system performance measurement based on IT BSC Perspective are presented in Table 4.

Table 4. The Methods of Academic Information System Performance measurement based on IT BSC Perspective.

IT-BSC Perspective Academic Information System Measurement

Business Contribution

1. Performing control towards IS, for example by comparing with the actual budget, analysing

the use of budgets, calculating the cost of IS per number of staff.

2. Calculating the financial benefits derived from selling products and services.

3. Doing business assessment of the projected new IS when the university will create and

develop IS, for example by evaluating business based on economical information and

performing financial evaluation based on Return on Investment (ROI), Net Present Value

(NPV), Internal Rate of Return (IRR), Payback Time (PB).

4. Doing business assessment of IS functions such as calculating the percentage of capacity

related to IT strategy projects, analyzing the relationship between development/new

infrastructure and the investment/investment displacement.

User Orientation

1. Assessment towards BTSI associated with applications that have been addressed, the

percentage of applications that have been completed, etc..

2. Cooperation with the users of information systems when it will carry out the functions of IT

organization, for example by calculating the number of users involved in the manufacturing

process and the development of IS application. 3. Analyzing the IS’s user satisfaction by measuring the level of user friendliness on the

application, calculating the index of user satisfaction, counting the number of applications

and system availability.

Operational

Excellence

1. Conducting an analysis towards the efficiency of software development, for example in terms of the average increase of unexpected budget, maintenance activities, the average

number of delayed response of the application.

2. Conducting an analysis towards the efficiency of such operations by computing network

availability, response time per category per person, the percentage of work which is done

ontime, the ratio of operating costs of the system used.

3. Conducting an analysis towards the acquisition and application of personal computers if

there is any upgrade.

4. Conducting an analysis towards problem sloving if the system is on trouble, for example by

calculating the average charge time, troubleshooting time, the percentage of problems

answered in a timely manner.

5. Conducting an analysis towards the training of the IT users.

Future Orientation

1. Conducting an analysis towards the training and expertise of IT staff both in terms of the

budget which is owned by institutions and trained individuals based on the age, expertise,

etc.

2. Conducting an analysis towards the age of the applications and opportunities for investment in new technologies.

SWCU Academic Information System Measurement Model by using the IT-BSC is illustrated in Figure 2:

Universities’ Business Goals by

using BSC Perspective

Aspects of IT management in

University

Critical Success Factor

The methods of Academic IS

performance measurement

The Methodology of IT Framework

(IT BSC Perspective)

Resources, Process, and Content

IT Management organization

The need of IS for academic

activities in Universities

Business Contribution

User Orientation

Operational Excellence

Future Orientation

Figure 2. Academic SWCU SI measurement model using IT BSC.



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Conclusion:

Based on those IT organization functions in SWCU, it is necessary to establish IT Balanced Scorecard for each of those

functions which is essentially a derivative of the IT Balanced Scorecard for universities. The process of identifying Critical

Success Factor (CSF) is needed to determine the limits of performance measurement criteria in each IT process. After

determination of the CSF and know the needs of IS for the academic activities, it should begin mapping the steps what needs to

be done in order to measure the performance of academic information system.

References:

[1] Dorian Pyle. (2003). Business Modeling and Data Mining. Morgan Kaufmann Publishers, ISBN:155860653X.

[2] Gold, C. (1994). US measures — a balancing act. Boston: Ernst & Young Center for Business Innovation.

[3] Henderi. (2010). Good IT Governance: Framework and Prototype for Higher Eduation. Creative Communication and

Inovative Technology Journal vol 3, no.2 ISSN: 1978-8282.

[4] Indrajit, Eko. (2006). Mengukur Tingkat Kematangan Pemanfaatan Teknologi Informasi untuk Institusi Pendidikan

(Suatu Pendekatan Kesiapan Pemegang Kepentingan/Stakeholder). Conference proceeding of ICT for Indonesia,

Bandung: 3-4 May 2006, 116-120.

[5] ISACA. (2004). COBIT Student Book. IT Governance Institute.

[6] Maniah and Surendro. (2005). Usulan Model Sistem Informasi (Studi Kasus: Sistem Informasi Perawatan Pesawat

Terbang). National Seminar of Information Technology Application, Yogyakarta: 18th June 2005.

[7] Maria. (2011). Perbandingan Sistem Informasi Akademik Universitas Kristen Satya Wacana Menggunakan COBIT Framework. Journal of Eonomic Foccus, Vol X, Issue-2, 140-149.

[8] Maria, dan Haryani. (2011). Audit Model Development of Academic Information System: Case Studi on Academic

Information System of Satya Wacana. Journal of Art, Science & Commerce, Researchers World, Vol II, Issue-2, 12-24.

[9] Maria, et al. (2012). The Measurement of Information Technology Performance In Indonesian Higher Education Institutions in

The Context of Achieving Institutional Business Goals Using COBIT Framework Version 4.1: Case Studi Satya Wacana

Christian University Salatiga. Journal of Art, Science&Commerce, Researchers World, Vol III, Issue-3(3), 9-19.

[10] Mulyadi. (2001). Balanced Scorecard. Jakarta: Salemba Press: p 416-420.

[11] Mutyarini and Sembiring. (2006). Arsitektur Sistem Informasi Untuk Institusi Perguruan Tinggi Di Indonesia.

Conference proceeding of ICT for Indonesia, Bandung: 3-4 May 2006, 102-107.

[12] O’donnell, E. (2004). Discussion of Director Responsibility for IT Governance: A Perspective on Strategy. International

Journal of Accounting Information Systems 5: p 101-04.

[13] Prabowo, Harjanto. (2007). Implementasi IT Balance Scorecard di Perguruan Tinggi. National Seminar of Information Technology Application, Yogyakarta: 16st June 2007.

[14] Sa’adi and Suhardi. (2006). Pengukuran Kinerja Penerapan Sistem Enterprise Resource Planning (ERP) di Universitas

dengan Metode IT-Balaced Scorecard (IT-BSC). Conference proceeding of ICT for Indonesia, Bandung: 3-4 May 2006.

[15] Van Grembergen, W. and Van Bruggen, R. (1997). Measuring and improving corporate information technology through

the balanced scorecard technique. Proceedings of the Fourth European Conference on the Evaluation of Information

Technology, Delft: October 1997, 163-171.

[16] Van Grembergen, W. and Timmerman, D. (1998). Monitoring the IT process through the balanced scorecard. Proceedings

of the 9th Information Resources Management (IRMA) International Conference, Boston: May 1998, 105-116.

[17] Van Grembergen, W. (2000). The Balanced Scorecard Technique and IT Governance. Accessed in

http://www.isaca.org/Certification/CGEIT-Certified-in-the-Governance-of-Enterprise-IT/Prepare-for-the-Exam/Study-

Materials/Documents/The-Balanced-Scorecard-and-IT-Governance.pdf

[18] Willcocks, L. (1995). Information Management. The Evaluation of Information Systems Investments. London: Chapman & Hall.

****

http://www.isaca.org/Certification/CGEIT-Certified-in-the-Governance-of-Enterprise-IT/Prepare-for-the-Exam/Study-Materials/Documents/The-Balanced-Scorecard-and-IT-Governance.pdf

http://www.isaca.org/Certification/CGEIT-Certified-in-the-Governance-of-Enterprise-IT/Prepare-for-the-Exam/Study-Materials/Documents/The-Balanced-Scorecard-and-IT-Governance.pdf



23

INCREASING THE ACCOUNTABILITY OF THE INSTITUTION

THROUGH THE WHISTLE BLOWING SYSTEM

Jony Oktavian Haryanto,

Satya Wacana Christian University,

Indonesia.

Yefta Andi Kus Nugroho,

Satya Wacana Christian University,

Indonesia.

Rizal Edy Halim,

University of Indonesia, Indonesia.

Rizal Edwin Manansang,

Coordinating Ministry for Economic

Affairs Republic of Indonesia, Indonesia.

ABSTRACT

Along with the development of the organization, the organization's control can no longer rely on a structural approach that is run through a top-down approach but must be pursued through non-

structural, bottom-up approach. Whistleblowing system presents to answer this challenge considering

that this system puts the control nodes of an organization on all its members. This study is specifically trying to find a whistleblowing system model that can become a guide in the implementation for

companies in Indonesia. This research is done by using surveys and interviews starts at a state-owned

enterprise, two government agencies and two multinational companies in Indonesia which have

whistleblowing system. Research results indicate that the empirical model of whistleblowing system is more suitable for the conditions of Indonesia.

Keywords: organizational control, structural approach, non-structural approach, whistleblowing system.



24

Introduction:

Organization’s control has a strategic role in achieving organizational goals. According to Ouchi (1979), it is

described as the organization's control mechanism on what an organization can be managed to achieve its

objectives and targets. Meanwhile, according to Anthony and Govindarajan (2001) also Lowe and Machin (1988), the control of the organization is a process of examination both formal and informal to help managers ensure that

all resources are used efficiently and effectively to achieve the goals of the organization (company). In the end, the

control is intended to keep the employees from doing something the organization does not want them to do, or not

to fail to do something on what they should do. In line with the growth of an organization, the control system must keep up to it to suit the needs. Organizational

control systems not only ensure their objectives are achieved, but at the same time pressing the cheating behavior of

its members which can cause huge losses, possibly will lead to the failure to achieving company’s goals. In essence, an effort to avoid cheating behavior should also be done as part of the organization's control.

Various types of control with vertical structural approach have been applied, but fraud committed by the

corporation's board of directors or employees which caused huge losses still happened. Some of these scandals are Enron, Tyco, Arthur Anderson, Lehman's Brothers, etc. Different interests often lead to fraud (deviation). Just an

example, Enron is an ambitious company that was claimed destroyed by the lack of confidence in the company,yet

it did not leave any traces but the angry employees and shareholders. Sadly falling from a prestigious place to place

so contemptible in a fairly short time (Greenspan, 2008). Cheating behavior due to the performance of the parties in an organization can occur because of strong personal

interests. To overcome this, the individuals in the organization are well-rewarded with incentives based on

performances. However, despite of being given a great reward, corruption scandals still happen such as those shown above. Motivation diverse of all members of the organizations or companies not necessarily correspond to

the interests of the company, as well as opportunistic behavior and limitations of principal agent to convince the

agent in order to perform all the activities for the benefit of shareholders or principals, those make organization’s control even more important.

But in reality, the function of the existing control is not always successful. Fraud or corporate scandals that aims to

enrich themselves or a group, still occur, causing loss or bankruptcy for the company. Structured vertical control

mechanism between superiors and subordinates and the establishment of Internal Control Unit (ICU), also the code of conduct which has been available in some companies, as well as some programs strengthening the corporate

culture are not yet capable on performing the function of an optimal control. The existence of organizational control

mechanisms require a form or sharpening of theory and practice. One of the control mechanisms that organizations need is a whistleblowing system.

Whistleblowing system is not a new system. From the observation of researchers, there are only few companies in

Indonesia who implement this system. This fact suggests that there may be things that are not compatible between

this system and companies in Indonesia. On the other hand, there is the possibility of indifference from companies in Indonesia about the importance of applying this whistleblowing system. Whereas control system will work well

if there is support from the performers as well as its appropriateness to the local environment. Exploration of

environmental conditions in the implementation of whistleblowing system will help dissemination of application and system development in Indonesia.

Literature Review:

Definition, Meaning, and Nature of Organizational Control:

An understanding of the organization's control system is very diverse, starting from the approach that only focuses

on aspects of accounting, to the concept of a broad organizational control , including any actions taken by managers

to achieve organizational goals. In the editorial, the understanding of the organization's control was first placed on

the term 'control'. Control is the process used to ensure that all members of the organization doing their best in achieving the goals of the organization (Schendel and Hofer, 1979). Therefore, controls as a system is the basis of

the structure of the organization. This occurs because of the complexity of an organization that is affected by

several factors, such as internal and external conditions that require changes in organizational control systems. Organization's control system was first introduced by Anthony (1965) with the notion of the process by which

managers ensure that resources are obtained and used effectively and efficiently in the accomplishment of the

organisation's objectives'. While Langfield-Smith (1997) considered to limit further research that considers the



25

control of the organization that includes the extent of control by using the report based planning and monitoring

activities.

Control practices arise from the consciousness of managers and integrated informal mechanisms of a spontaneous reaction from employees all the time. One thing that is integrated is a complex matter and the potential for escape

from a network established over time to address various managerial needs. When combined, all the elements will

affect the attitudes, motivations, perceptions and behavior of employees (Marginson, 2002; Simons, 1995) Specific mechanisms to achieve control described by Cirka (1997) by dividing it into: simple controls, control on

technology, bureaucracy and administration control, and concertive and culture control. Control on concertive and

culture associated with shared values, norms and conformity to social systems and beliefs. Efforts to control the

behavior represents a complex and elusive activities in order to try to apply the self control to every human being. Social standards and group interaction of a formal control system explain there is a control on behaviour within the

organization (Davilia, 2000).

Lowe and Machine (1988) stated that, if an organization sets its goals in written and unwritten terms, explicit and implicit, plus the possibility for contradiction and conflict , then how could they plan and build a coherent and

effective control system? Therefore, ultimate controls intended to keep the employees from doing something the

organization does not want them to do, or not to fail to do something the employee should do. In fact, the variety of human nature and organizational behavior make this control as a daunting task.

The Evolution of Organizational Control Systems:

In addition to the internal demands as an organization grows larger and becomes more complex, the control system

must also reflects the needs of the ever-changing external environment. Contingency theory explains, when the external environment becomes more complex and dynamic, the uncertainty increases and the appropriate

organizational structures and control strategies must also be changed to fit the situation. Contingency theory within

the larger organization serves to examine the relationship between organizational characteristics, such as organizational structure or control system of an organization which depends on the specific conditions of the

organization (Donaldson, 2001)

According to Van de Ven and Drazin (1985), when the conditions of task uncertainty increases, it needs to be

coordinated with programming and hierarchical manner, which is substituted with horizontal communication channels. Lawrence and Lorsch (1967) proposed that a dynamic environment tends to lead to adaptation with less

formalized control system. Govindarajan (1988) concluded that for each task has various uncertainties, the behavior

needed to achieve effective performance is also very diverse. So because the differences affect differences in behavior control system, superior performance can be achieved by performing a control system adapted to the

uncertainty of the task. According to Galbraith (1975) and Davilla (2000) the effectiveness of formal control

systems are only suitable for the limited uncertainty situation or circumstances. While the use of control systems

with social and informal mechanisms are more appropriate. According to Harrison and McKinnon (1999) and Van der Stede (2001) on the evolution, there is no mutual

decision that underlines the dimension of the control system. When one of the parties convey some dimensions that

can be used as the underlines for some characteristics of control systems, others deliver some of the literature that is still there and it is against it. There are three dimensions that have been identified and associated with control

strategies and operational phases of a company. The first dimension is the dimension of formal and informal. This

dimension indicates how far an organization believes in an explicit mechanism, written and documented (eg, regulations, procedures, and policies) in guiding resource and employee behavior (Cirka, 1997; Ferner 2000; Floyd

and Lane, 2000; Galbraith, 1975, Harrison and McKinnon, 1999; Thomas, 1998; Whitley, 1999). The second

dimension is the flexibility - inflexibility of a manager. This dimension indicates how far will the authority be given

to junior managers in determining a decision while interpreting the rules and procedures in doing his job (Covin dan Slevin, 1991; Geary dan Dobbins, 2001; Govindarajan, 1988; Harrison dan McKinnon, 1999; Marginson, 2002;

Whitley, 1999). Finally, the dimensions of stringency or budget flexibility, which refers to how far the budget will

restrict an activity of resource allocation and the conduction of performance evaluation (Geary and Dobbins, 2001; Govindarajan, 1988; McKnight et al., 2001; Simons, 1995; Shih and Yong, 2001)

Although some of the above dimensions are frequently presented, but there are several examples of other

dimensions, such as about how far the control system is centralized or decentralized, a clear and unequivocal regulation (hint) from the center that must be obeyed, priority is given to self-control, the relative emphasis on

compliance and compliance (conformance) as well as the level of detail and complexity.



26

Whistleblowing System:

The definition of whistleblowing is a disclosure done by organization members (either still active or retired), to

those who are entitled to do corrective actions, about the illegal, immoral behaviour or other illegal practices

committed by members of the organization (Dandekar, 1990; Goldberg, 1987, James, 1984. Micelli and Near, 1992; Near and Micelli, 1985). According to Bowie (1982) the disclosure is only based on consciousness, not a hidden

agenda or greed.

Whistleblowing is considered as a voluntary thing in disclosing the fraud as part of a pro-social behavior (Dozier

and Miceli 1985; Miceli and Near 1985; Trevino and Weaver 2001). Furthermore, Trevino and Weaver (2001) refer that whistleblowing as an organization citizenship behavior (organizational citizenship behavior (OCB) - a subset of

pro-social behavior (Organ, 1990). Basis of organization citizenship is voluntary , being useful to society, and extra

behavior in an organization (Organ, 1990). Justice in an organization is as antecedent of organizational citizenship behavior (Moorman 1991; Bies and Tripp, 1993; Eskew 1993; Greenberg 1993; Moorman et al. 1993; Podsakoff

and MacKenzie 1993; Robinson and Morrison 1995).

Chung et al. (2004) described a manipulation between a state of the organization with regulatory approach or principle approaches. In the approach to the rules, an organization emphasizes the need for adherence to various

types of organization regulations, while the principle-based approach emphasizes the importance of individual

values and independent views (opinions). They found that generally the individuals within an organizations that

perform rule-based approach tend to dislike whistleblowing system when compared with individuals who are in the organization with the principles-based approach.

Management Accounting and Internal Control System (Internal Auditor) has no role and function to report

wrongdoings in the organization. If they do so, it will put them at risk of losing their jobs and or career as a revenge from the reported or offended parties (Porter 2003). Organization’s pro-social behavior is a more inclusive

construction than the OCB (Organ, 1990). Pro-social behavior could be needed (eg, because of his role) or

voluntary (extra role) and is defined as an action within the organization who tries to help a person to whom it should be directed (Brief and Motowidlo, 1986).

OCB can only be defined as an extra role and is defined as behavior that depends on a person's freedom and

wisdom. It is not directed or explicitly recognized by the formal system of incentives and hence aggregately will

promote the effectiveness and functioning of an organization. This behavior is not required to be done as part of the job description, but only as a personal choice (Organ, 1988; Organ, 1990). So the disclosure made by the internal

auditor is not considered as an OCB. Instead, disclosures made by the accountant is an OCB behavior because such

action is not a part of his duties and obligations. Studies on the willingness of a person to conduct cooperation in the organization when it is not required, first

proposed by Barnard (1938). He said there are five major categories: 1) cooperation with others, 2) to protect the

organization, 3) voluntary for constructive ideas, 4) self training, and 5) maintain the character or good behavior

towards the organization (Katz, 1964) . The five categories are narrowed and called OCB (Bateman and Organ, 1983). A common listing of OCB used by Researchers is altruism, conscientiousness, civic virtue, courtesy, and

sportsmanship (Smith et al., 1983; Graham 1986a; Organ 1988; Moorman 1991; Niehoff and Moorman 1993;

Podsakoff and Organ, 2000; Cohen-Charash and Spector 2001). Altruism (to prioritize others), as the opponent to egoism is also a pillar for preparation of whistleblowing system.

The OCB of altruism is defined as helping others specifically in face to face situations. Prudence

(conscientiousness) represented by obeying all norms as a good employee and do something extra of what should be done (Organ, 1988; Schnake et al., 1993; Lepine and Van Dyne, 2002). Civic virtue is described as participating

in the management of an organization's governance, although it will cost or put them at risk (Graham, 1986b;

Podsakoff and Organ, 2000). So, whistleblowing is an example of civic virtue OCB not only for internal auditors

but also for employees. Courtesy as a form of communication with others before taking action can be elaborated by not complaining for things that are trivial or insignificant (Organ, 1988; Lepine et al., 2002). Examples of OCB

such as making constructive statements about the department, training for new employees, making suggestions for

improvement of the organization, and respecting the spirit of the rules (Bateman and Organ, 1983). Whistleblowing leads to a dilemma for managers and is often perceived as a threat. But in the era of appreciation

and utilization of employee involvement, the authors believe that it is time for the manager to see that

whistleblowing can be a valuable resource. If one is considered as a committed employee who can provide useful information as part of problem solving mechanism, then the manager can take action in ways that will help out the

company. Whistleblowing can be characterized as an OCB, a responsive action for justice in an organization, and



27

motivation to do so is based on social exchange (Micelli and Near, 1992). Although some states in the U.S. provide

an incentive to embellish the whistleblowing program, most of the whistleblowers who reported fraud, based their

reports on the expectation that violations or unethical behavior must be stopped (Miceli and Near, 1992).

The Connection Between Organizational Control System with Whistleblowing:

When controlling an organization, a manager regularly and personally participate in the decision making and

problem solving with their subordinates. This system is called the interactive control system that can be done in

person as face to face (Simons, 1995a). On the other hand, the organization's control system as a tool can also be based on action control (based on behavioral constraints such as sorting duties and authorities, preaction reviews

such as monitoring the expenditures, action accountability in terms of clarity of communication, and redundancy).

These can be done with personal control (selection and placement, training, and job design and provision of necessary resources), cultural control (codes of conduct, group-based rewards, intraorganizational transfers,

physical and social arrangements, and the tone at the top) and result control (performance measurement and linking

performance to compensation) (Merchant and Van der Stede, 2007; Simons, 1994). Control of organization is a tool to carry out the internal monitoring mechanism. The linkage between the

whistleblowing and organization control system should consider the effectiveness of formal control systems which

is applied only on a limited situation or uncertainty. While the use of control systems with social and informal

mechanisms are more appropriate (Galbraith, 1975). Furthermore, contingency theory explains that when the external environment becomes more and more complex and dynamic, the uncertainty increases, thus the

appropriate organizational structures and control strategies must also be changed to adjust. Next, Van de Ven and

Drazin (1985) found that when task uncertainty increases, programming and coordination by hierarchy are substituted with horizontal communication channels. Lawrence and Lorsch (1967) proposed that a dynamic

environment tends to lead to adaptation with less formalized control system. Finally, whistleblowing programs as a

subset of organizational citizenship behavior theory and pro-social behavior which shall report fraud charges ,(it is) not put as obligation in a job description, but considered peripheral. Subjects who come to report do not have to be

superiors to subordinates, but any employee can do so if any indications of fraud committed by members of the

organization occur ( this can be colleagues or superiors). Cheating behavior should be agreed as a deviation from

the norms and values of the organization. Indonesia has unique conditions that must be observed. Compared to some previous studies, Indonesia has a

uniqueness as a developing country which rules of law and regulations have not been so well-implemented,

including weak system of witness protection. So this study aims to map the factors that affect whistleblowing program in strengthening the organization's control system in Indonesia.

Research Methods:

Research Type and Design:

This research is descriptive research with the aim to obtain an overview of the effects of the antecedents of individual commitment, organizational work purposes, and the whistleblowing to organizational performance.

This study is a qualitative research, with in-depth interviews with 5 (five) managers from several companies that

have implemented a whistleblowing system to explore the variables related to the effectiveness implementation of whistleblowing systems, advantages and disadvantages and implementation practices in the real business world,

especially in the context of Indonesia. We can not mention the name of these 5 companies due to privacy and

request from the managers who are interviewed. The method used for this research is a qualitative method used in exploring the construction that will be examined

as well as to try to explain more about these relationships. Qualitative research methods are also used to explore the

relationships between variables in a preliminary study. In-depth interviews carried out to check the relationship

between the construct and to test the extent to which an understanding of the concepts used in this study.

Results and Discussion:

From the interviews stated above, it was found that virtually every organization has a system of reporting fraud.

Although not exactly the same with the concept of whistleblowing that is developed in Western countries, but the bottom line is that organizations have serious concerns to identify fraud committed by its members. For example, in

one of the largest state-owned enterprises in Indonesia (later on we call PT X), they have adopted a policy of "Clean



28

Company”. Through this program, any employee can report fraud without fear of identity revealed. Organizations

outsource to a third party in handling complaints and incoming information. Outsourcing is done in order to ensure

the identity of the complainant and to ensure that all statements in accordance with the order of priority followed up by the company. The system of third-party filter any incoming information and then forward it to the relevant

parties. In the period of time when the report was not followed up, the system will continue to warn that there

would be a real act of leadership on such information. The advantage of this system is its independence and ensure the confidentiality of the complainant. But on the other hand, these systems have drawbacks in terms of costs ,to the

involvement of external parties in the company's internal problems which are often highly sensitive and confidential.

In an interview with one of the leaders of foreign private bank (later on we call Bank Z), found that organizational

commitment and leadership are the keys to success or to fail the whistle blowing system. When there is a strong commitment from the organization and leadership to encourage members to report every fraud, the record shows an

increasing reports which is very good sign. For example in the year 2011 as many as 70% of cases were

successfully dismantled, those were originating from this report. In the coming year, the organization is thinking to give awards to each entry and report that can be proved. Award in the form of financial support as much as three

million rupiah (U.S. $ 300) is an example of the organization's commitment to encourage reporting. Viewed from

the theoretical standpoint this award actually is a deviation from its own system of whistle blowing (Miceli and Near, 1992). Trevino and Weaver (2001) stated that the initial concept of whistleblowing reports aimed at

improving the performance of organizations and not for individual awards. Organization's commitment and strong

leadership will create awareness of all members of the organization about the importance of reporting any fraud.

One of the cement companies which is the Multi National Corporation (MNC) has a reporting system since 2010. This system is a fairly new as a response to the company's desire to have a system of fraud reporting. Any reports

flow in, are directed to the chief executive officer (CEO) for them to set priorities and conduct further investigation.

The advantage of this system is that all reports are handled directly by the CEO without many parties involved to make the investigation remain confidential. In addition to it, CEO will quickly respond to any reports which tend to

cause larger damage to the company. While the weakness of the system is a busy CEO will possibly run slow to

look into various reports coming in.

Given the research is done in large organizations, it was found that most of the fraud committed by members of the organization will eventually be caught. This is often as a result of inequities in the benefit distribution obtained

through fraud.

The whistleblowing system development are focused on socialization and fostering awareness.

Among the many types of violations, a good example comes from one of the biggest private

bank in Indonesia which focuses on fraud and violation of code of conduct. It is based on the analysis that those may hurt the company in the future. In this bank, whistleblowing is handled

by the whistleblowing system called fraud and complain. The principle of whistleblowing

system that runs through the whistleblower hotline is that no matter how trivial the

information may seem, such as the anonymous letter, should not be ignored.

Whistlebowing system at one of the biggest state-owned company which has been initiated

since 2006 and officially launched In August 2008. The system oversees six violations, namely: regulatory violations, theft, fraud, corruption, bribery, and deceptions. Implementation

was undertaken by a task force team to follow up all reports received. This company uses an

outsourcing from Delloitte, to act as reports beneficiary, processing and reporting incoming information to the management. The principle that must be obeyed in using 3rd party is

transparency, independence, and confidentiality. It has also been through a process of

consultation with forensic experts, and technology applications.

It actually shows that no matter how big the organization is and or how neat a fraud committed, as long as it has a

system that allows members of the organization to report, the fraud would have no chance to escape. Thus the

company does need to have mechanisms that can be realized in the form of a special phone line, email, complaint letters, etc. But amid efforts to organize a good whistleblowing system, the organization also has disadvantages as

revealed in the following interview:



29

Constraints in the implementation of whistleblowing systems in goverment are (1) the work is

administrative, not many willing to do it, and (2) Reduction of the authority of the Inspectorate

investigation field. The impact is, designed nomenclature changes to the establishment investigation agents, which makes whistleblowing centered only in one party. This can trigger

public unrest and internal Finance department unrest worrying about the objectivity.

After getting feedback from the interviews, there shows a more comprehensive picture about the implementation of

whistleblowing which has been done in these organizations. What no less important is the result of interviews,

pointing a theoretical basis that is generally used to examine the phenomenon of whistleblowing, which is theories

of power (Blau and Scott, 1962) or the theory of justice (Moorman, 1991). However, in the Indonesian context, it seems both of these theories are not strong enough to describe the phenomenon. Power approach, clearly not

suitable for the conditions in Indonesia because Indonesia is a democratic country. Awareness of this condition has

been initiated in the past decade and is reflected in the social life of the community. Next is justice approach which suggests the company to organize all of the existing system to a well-defined, transparent system ,in order to make

the employees treated fairly. Perceptions of justice has not been realized in Indonesia noticing the circumstances

haven’t been reflecting an ideal conditions related to justice. For example, witness protection in Indonesia is still very weak. This makes the organization avoids sanctioning, instead,merely raises awareness and vigilance. Here are

excerpts of this interview:

Compared to most existing whistleblowing systems, whistleblowing systems in PT X has a unique, objective system that does not focus on the search for who is at fault but (focus on ) the

growing awareness among the employees of the company, as well as the lack of an incentive

system. It is due to the weakness of witness protection programme in Indonesia.

The implementation of whistleblowing systems in Indonesia should be approached with the Social Learning Theory.

This theory was originally proposed by Bandura (1977), which stated that the learning process occurs when there is

an interaction between the environment, behavior, and experience (Pfeffer, 1982). Whistleblowing system formed from experience, the everchanging environement, and the effort to show the behavior.

Based on some analysis and consideration of the above, then draft is made about a whistleblowing system

implementation model as shown in the following figure.

Whistleblowing Model Implementation ( picture1)

Leadership

Organization’s

commitment

Organization’s

understanding

Compliance

Organization’s

awareness

Whistle BlowingPerformance of

the Organization

Conclusion:

It is important for organizations to continue to develop whistleblowing system as one of the mechanisms on reporting fraud committed by its members. System implemented in state-owned PT X and PT Y and Bank Z as

multinational companies, suggests that organizations should initiate and develop their existing reporting systems to

whistleblowing systems. Implementation of whistleblowing systems adopt all subordinate statements and followed



30

up by a special section or directly to the CEO. Thus the company can differentiate between slander and trustable

reports worth following-up. In addition to the involvement of high-level management is to reduce the potential

conflicts of interest and ensure the direct action of the leaders. The study also showed that in order to implement whistleblowing systems, it requires the organization's

commitment to clean up the company and reduce the potential for fraud that may be committed by its members.

Without a strong commitment from the organization to do the cleaning and facilitate all reports ,the implementation will surely experience problems.

On the other side, leadership is also a positive influence on the successful implementation of whistleblowing.

Without a shift in mindset about the importance for companies to adopt whistleblowing system, it will become

difficult to apply and face many obstacles. This often occurs because the application left without strong leadership and a true understanding of the system. If this occurs then the whistleblowing would just be a "lip service" , not a

strong-willed implementation.

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****



34

AGRICULTURAL TFP AND R&D SPENDING IN IRAN

Solmaz Shamsadini,

Ph.D. Student,

Department of Agricultural Economics,

Science and Research Branch, Islamic Azad University, Tehran, Iran.

Saeed Yazdani,

Professor,


Science and Research Branch,

Islamic Azad University, Tehran, Iran.

Reza Moghaddasi,

Assistant Professor,


Science and Research Branch,

Islamic Azad University, Tehran, Iran

ABSTRACT

Investing in research and development spending (R&D) affects total factor productivity (TFP).

Recently new theories of economic growth have emphasized the relationship between R&D and TFP

and also identified a number of channels through which a country’s R&D affects TFP of its trade

partner. This study seeks to estimate the effect of agricultural R&D and education spending and some other factors on agricultural TFP in Iran during 1971 to 2011. Agricultural TFP is calculated using

Kendrick Index and the model is estimated by OLS method using E-Views 7.0.

all explaining variables in the model, effect on agricultural productivity in different lags positively with 5% confidence. The optimum lag is determined using Akaike information, Schwarz and Hannan-

Quinn criterion. The results show elasticity of R&D spending in agriculture, education expenditure in

agriculture, government investing in agriculture and rainfall is 0.13 by 5 lags, 0.10 by 2 lags, 0.14 by 1

lag and 0.17 at the same time in agriculture TFP function. R&D spending in other sectors (except agriculture) and import of capital inputs in agriculture are contained in the model as research spill-over.

The elasticity of these two factors is estimated 0.09 by 5 lags and 0.04 by 2 lags. Rainfall with highest

elasticity (0.17) is the most effective factor in agriculture TFP model.

Keywords: Agricultural Research and Development, Total Factor Product, spill-over.



35

Introduction:

Productivity growth is an important consideration in agriculture. One way to stimulate the productivity growth rate is to

increase the rate of spending in agricultural R&D.

Recently a large body of research has considered the importance of research and development (R&D) in influencing output

growth and total factor productivity. Most of these literatures provide theoretical and empirical models that cumulative R&D

spending is the main engine of technological progress and productivity growth (see Aghion and Howitt (1998), Grossman and

Helpman (1991) and Romer (1990).

R&D investments are still central to agricultural productivity growth. Alston et al. (1999) in the introduction of their recent

book on the theme underline that “Throughout the twentieth century improvements in agricultural productivity have been

closely linked to investments in agricultural R&D and to policies that affect agricultural R&D”.

Pardy, P. G., et al. (2012) showed Countries with larger (smaller) agricultural economies are likely to invest more (less) in

agricultural R&D simply because of a congruence effect (Pardey, Kang and Elliott 1989) and concluded that the intensity at

which the Asia & Pacific region invests in agricultural R&D has grown much more modestly from 0.43 percent of agGDP

(agriculture share of GDP) in 1960 to 0.52 in 2009. While this region has sustained growth in agricultural R&D spending at a

comparatively rapid pace, averaging 5.1 percent per year since 1960, agricultural output has grown at reasonably rapid rate as

well (3.71 percent per year). Thus the growth in spending on agricultural R&D has more than kept pace with the growth in the

value of output, such that the region’s research intensity has inched up over time and increasingly so after the mid-1990s.

Given the importance of agricultural R&D to the growth of the sector, many works have been devoted to reporting measures of

the returns to domestic agricultural R&D (see recently Esposti (2000) and for a survey Alston et al. (2000). But in a world

where the international trade of agricultural products and the dissemination of knowledge are widespread, domestic agricultural

productivity depends not only on domestic R&D but also on foreign R&D efforts. This point has been fully recognised, among others, by Hayami and Ruttan (1985) where they emphasise that a country can acquire substantial gains in agricultural

productivity by borrowing advanced technology which exists in other countries.

An empirical evidence has been provided by Coe and Helpman’s (1995) seminal contribution where they find that accumulated

spending on R&D by a country and by its trade partners helps to explain the growth of total factor productivity.

Coe, D. T. (2008) considered that the importance of international R&D spillovers has long been recognized, although estimates

of their empirical significance at the macroeconomic level were often elusive. The search for R&D spillovers across countries

received a boost in the 1990s with the development of new growth models by Romer (1990), Grossman and Helpman (1991),

and Aghion and Howitt (1992), and by the application of the ideas from these models together with new empirical techniques

to expanded data sets by Coe and Helpman (1995) and Coe, Helpman, and Hoffmaister (1997).

Gutierrez, L. and Gutierrez, M. M. (2005) analyses, within the new growth theory framework and using panel co-integration

techniques, the effect of agricultural international technological spillovers on total factor productivity growth for a sample of 47 countries during the period 1970-1992. They concluded that the United States R&D capital stock has the strongest effect on

total factor productivity of its trade partners. A 1 per cent increase in the R&D capital stock in this country increases total factor

productivity by an average of 0.087 per cent for the full sample of 47 countries. The effect is stronger for the subset of

countries located in temperate zones, where the elasticity rises to 0.123, whereas tropical countries are less influenced by R&D

in the United States. European countries are well integrated. A 1 per cent increase in the R&D capital stock in France increases

total factor productivity in Italy by 0.09 per cent, in the Netherlands by 0.14 per cent, in UK by 0.08 per cent. Japan and the

USA are less influenced, with elasticities respectively of 0.003 and 0.005 per cent. Similar effects are easily verifiable for an

increase in R&D capital stock in Italy, in the Netherlands and in UK.

Khaksar, H. and Karbasi, A. (2005) have computed agricultural TFP of Iran during 1978-2002 using turn-quist Index and considered

the impact of agricultural R&D spending on it using Almon Distributing Lag. They concluded that if agriculture R&D spending

increases 1 percent, agriculture TFP will increase 0.28 percent by 5 lags in long-run and the impact will remain to 3 years.

Bagherzadeh, A. and Komeijani, A. (2010) considered the impact of agriculture R&D spending on agricultural TFP of Iran during 1979-2009 using Almon Distributing Lag and concluded that the long-run elasticity of this factor is 0.17 percent and rate of return of

investing in agricultural R&D spending is 0.36 percent that is much lower comparing the world mean rate (0.51) [7].

Mehrabi, H. and Javdan, E. (2011) have investigated the relationship between agricultural R&D expenditure and agricultural

TFP for Iran during 1974-2007 using Auto Regression Distributing lag model. They computed agricultural TFP using

Kendrick’s Index for selected data and concluded that R&D spending has positive significant effect on TFP in both long-run

and short run in agriculture sector. That is 1 percent increase in agricultural R&D spending will increase agricultural TFP 0.1

percent. They suggest R&D spending is one of the main factors to improve agriculture growth.

Agricultural R&D spending in Iran:

Agricultural research and the agricultural extension organization in Iran were inaugurated in 1930. This organization began to

investigate weather conditions, reallocation of cultivated crops, introducing new production methods and new efficiency factors and promoting new agricultural technologies. The Government determined financial expenditure annually. As Table 1



36

shows, expenditure for agricultural research increased from 26% to 50% during the period. Spending on agricultural education

was mostly at college level and increased over the period. Total agricultural research expenditure had negligible growth (1 per

cent per year) from 1980 to 1987 because of the circumstances induced by war.

Table1: Averages of Total Research Expenditure, Agricultural Research Expenditure and

agricultural education Expenditure in Iran in 1971- 2010 (million Rials)

Year Research expenditures Agricultural research expenditures Agricultural education expenditures

1971-1980 8797.26 2366.82 7385.64

1981-1990 34097.64 13525.26 12944.39

1991-2000 505272.5 255254.7 110335.8

2001-2011 2748634.7 1385762.74 792654.3

Iran Annual budget

Methodology:

This section presents a theoretical model that links TFP to the spending on R&D in agricultural sector as Gutierrez et.al (2005)

are considered. Assume that agricultural output is produced in a competitive environment and has a Cobb-Douglas production form that contains two important factors; Labor and Capital; and also non durable intermediate inputs.

, α, β>0 , α+β<1 (1)

Where Y is agricultural output, A is a constant, K is capital and L is the amount of labor used to product the final agricultural

output. Output is a function of the Xj non durable intermediate inputs, numbered from 1 to N, used in the production process.

From equation1, we not first that the production function shows diminishing marginal productivity for each input K,L and Xj

and constant returns to scale in all inputs together. Second, the marginal productivity of intermediate input j is dependent of the

quantity employed of intermediate input j. thus the innovation of new types of intermediate inputs do not tend to make any

existing types obsolete. The technological progress can be seen as improvements in the number N of intermediate inputs and

we assume that this advance requires purposive effort in the form of R&D.

Defining the price of intermediate input as pj and setting output price py=1, from profit function maximization we can derive

the demand for input j.

(2)

In these models, the inventor of new intermediate goods is usually seen as a monopolist who retains a monopoly right over the

production and sale of the good that uses his/her design. Assuming a marginal unit cost to produce the intermediate goods, a

monopolist will set the price maximizing the following expression.

Max (Pj-1)Xj (3)

Substitiuting (2) in (3), the solution for monopoly price is

Pj = P = [1/(1-α-β)]>1 (4)

We can now introduce (4) in (2) and utilizing the result in (1) we end with the following production function

(5)

Where a=α/(α+β), b=β/(α+β) and by definition (α+β)=1, i.e. the production function shows constant returns to scale on the two

inputs K and L. the variable F, usually defined as total factor productivity, can be written as

(6)

Given α and β as well as A values, it is clear from the above expression that in this model total factor productivity depends on the

available assortment of intermediate inputs N: the more intermediates are used in production, the higher is total factor productivity.

If the flow of these intermediate goods is proportional to real spending on research and development Re, we have that

(7)

Where δ is a parameter that links, in each period, the growth rate of the number of intermediate inputs to the R&D spending.

We therefore have a relationship between current total productivity and cumulative R&D investment. This is central to the

innovation based endogenous model and our empirical specification.

Until now innovation has been associated with an expansion in the range of intermediate products used in the production process. We

can think of this activity as basic innovation which means new kinds of goods or method of production. Aghion and Howitt (1992)

and Grossman and Helpman (1991, Ch. 4) also introduce innovation as improvements in the quality of intermediate inputs.

If we assume that in each period the improvements in the quality of products are proportional to real spending in R&D, then a

link between total factor productivity and cumulative R&D expenditure can be found once more.



37

Agricultural Total Factor Productivity:

Kendrick Index:

Kendrick's index of total factor productivity for the case of value added as output, and two inputs can be written as:

(8)

Where TFP, VA, L, K and E stand for total factor productivity, value added, labor, capital stock and energy use in agriculture

sector respectively. α, β and δ denote the elasticity of labor, capital stock and energy use with respect to value added

respectively in the base year.

Naturally we have

constancy of factor elasticities over time. The assumption of constant returns has recently received empirical support from Mundlak et al. (1997).

Parametric approach consists in econometric estimation of production functions to infer contributions of different factors and

of an autonomous increase in production over time, independent of inputs. This later increase which is a shift over time in the

production function can be more properly identified as technological progress. It is one of the factors underlying productivity

growth. Cobb-Douglas Specification is applied for agriculture production function:

VA=ALαK

βE

δ (9)

Where, VA, L, K and E refer to value added, labor, capital stock and energy use in agriculture sector. α, β and δ give factor

shares respectively for labor, capital stock and energy use in agriculture. A describes initial conditions. Log-linear form this

function can be written as:

lnVA = lnA + αlnL + βlnK +δlnE (10)

where lnVA, lnL, lnK and lnE present logarithm of value added, labor, capital stock and energy use in agriculture.

Finally, agriculture TFP function is estimated using OLS method. 6 explaining factors are contained in the model to be estimated how much they can affect agriculture TFP in selected period of time. The model is written as:

ln(TFP)t= f {ln(Re)t,ln(Ed)t, ln(OR)t, ln(Imca)t, ln(Ra)t, ln(Aginv)t } (11)

Equation1 represents the total factor productivity function in the agricultural sector that has been computed by the Kendrick’s

index for the selected time period and contains three factors; capital stock, labor and energy use. In this equation, lnTFP, lnRE,

lnEd, lnRa, and lnAginv present respectively logarithm of agriculture total factor productivity, agricultural research and

development spending, agricultural education expenditure, rainfall and government investing in agriculture sector respectively.

Two other factors are also contained in the model to show research spill-over effects on agriculture sector; lnOR and lnImca

that represent logarithm of research and development expenditure in other sectors (except agriculture) and import of

agricultural inputs respectively. The following other studies have also investigated the effects of these variables on agricultural

TFP Ali. S(2004), Huffman. W. E and Evenson. R. E (2001), Kiani. A. K, Iqbak. M and Javad. T (2008), . Rosegrant, M. W.

and Evenson, R. E. (1995).

Data:

All the variables used in this study are collected as time series data for 1971 to 2011. Agricultural TFP is calculated using the

Kendrick’s Index that contains agricultural value added and three important factors; agricultural capital stock, labor and energy use.

Data for agricultural value added is collected from the Statistics Center of Iran. Data for agricultural capital stock and labor is

obtained from Central Bank of Iran for selected time period. Data for energy use in agriculture is obtained from Energy balance

sheet of Iran. Data for research and development expenditure in agriculture and other sectors, and also spending on agricultural

education are collected from annual budget books of Iran. Government investment in agriculture and import of capital inputs in

agriculture sector data is collected from Statistics Center of Iran. Rainfall data is collected from aerology website.

Results:

First step of using data for variables in the model is to test the stationary because we have used time series data for all variables. Augment

Dicky-Fuler test (ADF), Philips-Peron test (P-P) and KPSS test are applied for the variables and the results are shown in table3.

Table3. Testing stationary using ADF, P-P and KPSS tests.

Logarithm of Variable Abbreviated name ADF test P-P test KPSS test Integration degree

Agricultural capital stock lnK -6.09 -6.13 0.08 I(1)

Agricultural labor lnL -3.58 -6.07 0.13 I(1)

Energy use in agriculture lnE -4.68 -4.81 0.18 I(1)

Agriculture value added lnVA -8.05 -12.94 0.3 I(1)

Agricultural total factor productivity lnTFP -2.37 -6.08 0.09 I(1)

Research and development spending

in Agriculture lnRe -5.26 -6.27 0.09 I(1)

Education spending in agriculture lnEd -7.65 -7.59 0.1 I(1)



38

Research and development spending

in other sectors lnORe -7.89 -7.89 0.19 I(1)

Import of capital goods in

Agriculture lnImca -4.24 -4.05 0.06 I(0)

Government investiment in

agriculture sector lnAginv -7.52 -7.57 0.06 I(1)

Raining lnRa -6.39 -6.48 0.07 I(0)

Source: Calculated by the author.

As results in table 3 shows, logarithm of Import of capital goods in Agriculture and rainfall are stationary at level and logarithm

of Agricultural capital stock, Agricultural labor, energy use in agriculture, Agricultural total factor productivity, Research and

development spending in Agriculture, Education spending in agriculture and Research and development spending in other

sectors are stationary by first difference.

As Engle-Granger and Sargan and Bhargava (1983) indicate, we can be use variables that they are not in the same level of stationary, if the residuals are stationary and the variables have long run relationship. So we have to analysis Engle-Granger test

and co-integration regression Durbin-Watson tests on the residuals of the models that will be regressed in last section

(Noferesti, 1995).

Agriculture Total Factor Productivity:

For computing agricultural TFP, production function must be estimated as presented in previous section. A Cobb-Doglaus

function including agriculture capital stock, labor and energy use in agriculture is estimated considering constant return to scale

in this part. The results are shown in table 4. The coefficients present the production elasticity of each factor.

Table4: Agriculture Cobb-Daglaus production function estimation

lnE lnK lnL Constant Parameters

0.15 0.17 0.67 -3.67 Coefficient

0.07 0.04 0.08 1.14 Std-Error

2.10 4.00 7.92 -3.19 t-Statistic

R2: 0.98 h-Durbin-Watson:1.96

Source: Calculated by the author

As results in table 4 shows, all coefficients are positive and significant in 5% confidence. Agricultural labor is the most

effective in estimated production function. As the production elasticity of labor, capital stock and energy use in agriculture is

0.67, 0.17 and 0.15 percent respectively. Sum of these elasticities equals 1 and they can be used as factor share of value added for computing Kendrick total factor productivity index.

Agricultural Total Factor Productivity is calculated for 1971 to 2011 using Kendrick’s Index. The results are shown in table 5.

Table5. Agriculture Total Factor Productivity in Iran (Kendrick’s Index).

TFP Year TFP Year TFP Year TFP Year TFP Year TFP Year

3.66 2006 3.48 1999 2.76 1992 2.07 1985 2.24 1978 1.88 1971

3.80 2007 3.56 2000 3.10 1993 2.09 1986 2.19 1979 1.95 1972

3.58 2008 3.44 2001 3.23 1994 1.97 1987 2.28 1980 2.05 1973

3.68 2009 3.71 2002 3.57 1995 2.12 1988 2.26 1981 2.14 1974

3.76 2010 3.76 2003 3.69 1996 2.03 1989 2.26 1982 2.28 1975

3.86 2011 3.52 2004 3.63 1997 2.38 1990 2.21 1983 2.36 1976

- 2012 3.62 2005 3.84 1998 2.48 1991 2.21 1984 2.36 1977


In the last part, equation 11 is estimated to determine the effective factors that effect on agriculture TFP. OLS method is applied

to estimating the model using E-Views 7.0. The results are shown in table 6.



39

Table6: Estimated coefficients of rural poverty index of Iran

Regsessor Coefficient Standard Error t-statistic

Constant 1.97 0.22 8.95

lnRe(-5) 0.13 0.03 4.09

lnEd(-2) 0.10 0.04 2.60

lnORe(-5) 0.09 0.04 2.14

lnImca(-2) 0.04 0.02 2.49

lnRa 0.17 0.06 2.77

lnAgInv(-1) 0.14 0.04 3.77

R-squared :0.95 Durbin-Watson :1.71


As table 6 shows, all explaining variables in the model, effect on agricultural productivity in different lags positively with 5%

confidence. The optimum lag is determined using Akaike information, Schwarz and Hannan-Quinn criterion. All the variables

used in the model are in logarithm form, so the coefficients are presented as the elasticity of each factor on dependant variable.

According to table 6, rainfall is the most effective factor in agricultural TFP, that is, 1 percent increase in rainfall (millimeter

per year) will increase agriculture TFP 0.17 percent. Bagherzadeh, A. and Komeijani, A. (2010) obtained a 0.18 percent elasticity of rainfall in agriculturae TFP model in Iran. It is obvious enhancement in raining prepares better condition for

cropping. In a country like Iran that is facing droughts some years a major problem is irrigating agricultural lands and rainfall

plays an important role in production process. Storing water in dams is suggested to such countries to provide a favorable

condition for agriculture.

1 percent increase in agricultural R&D, will enhance agricultural TFP 0.13 percent by 5 lags. As Alston, J. M. and Pardey, G. P.

(2007) are considered, best lag period for R&D spending is 2 to 7. Khaksar Astaneh, H. and Karbasi, A. (2005) and Thirtle, C. ,

Lin, L. and Piesse, J. (2003) obtained the best lag of R&D efficiency is 5 lags. Bagherzadeh, A. and Komeijani, A. (2010)

concluded agricultural R&D spending affects TFP by 6 lags in Iran. Research and development spending does not effect on

agricultural growth and TFP immediately, but R&D outputs must be learnt, accepted and applied by farmers.

A large amount of new technologies used in agriculture, are borrowed from developed countries that are trade partners. While

we have contained these foreign technologies in the model as spill-over; import of capital inputs in agriculture. Spending on Import of such capital goods is borrowing and using knowledge and more efficiency factors in production process. That is, 1

percent increasing in import of capital inputs in agriculture sector will improve agricultural TFP 0.04 percent by 2 lags in Iran.

Importing modern agricultural machines has a large share of this factor and usually is accepted by farmers after 1 year to be

used for next cropping year.

Another spill-over factor that is contained in the model is R&D spending in other sectors (except agriculture). Because of the

relationship between agriculture sector with other economic sectors; Industry, Services and Oil sector, any improvement in

these sector may affect agricultural input productivity. As result show, 1 percent increase in R&D spending in other economic

sectors will increase agricultural TFP 0.09 percent by 5 lags. R&D spending in agriculture is more effective than other sectors

on agricultural input productivity.

Education spending in agriculture is one of the most important factors that cause improvement in agriculture and input

productivity. New technologies are often not accepting by rural farmers immediately. Teaching, training and extending the

usages of modern findings and research outputs plays the impotent role in applying the new technology in rural agriculture. As results show, 1 percent increase in education expenditure in agriculture will increase agricultural TFP 0.10 percent after 2 years.

Research outputs are not usable without training and extending to the farmers and 2 lags show the acceleration applying new

technologies by training farmers.

Last factor that is contained in the model is government investment in agriculture and is presented positive effectively.

Agricultural TFP will increase 0.14 percent, if government increases investing in agriculture 1 percent after 1 year. Mehrabi,B.

H. and Javdan, E. (2011) shows a 0.17 percent elasticity for this factor in TFP model in long-run in agriculture sector in Iran.

Totally, we have tested the stationary of residual of the estimated model. The results are shown in table 7.

Table7: Engel-Granger and CRDW test.

Dependent Variable Engle-Granger test CRDW

LTFP -4.23** 2.84*

The null hypothesis has a unit root at 1% (**) and 5% (*).


According to table7, residual time series of the previous estimated model is stationary in level and as Engle-Granger and Sargan and Bhargava (1983) indicate, the results are reliable .



40

Conclusions:

This paper addresses how much do agriculture R&D and R&D spill-over affect total factor productivity in the agricultural

sector In Iran. Although this is not a new question, only recently has the new economic growth literature provided theoretical as

well as empirical models to analyse this field of research.

This paper answers to this problem by computing total factor productivity in the agricultural sector during the period 1971-

2011 using Kendrick’s Index and uses this variable to analyse its relationship with domestic and foreign public R&D spending

in agriculture. Results show agriculture total factor productivity is positively and significantly influenced not only by its

domestic R&D capital stock but also by the foreign R&D capital stock of its trade partners. 6 factors are contained in the agriculture TFP model; agriculture R&D spending, agriculture education expenditure,

government investing in agriculture and rainfall; and two factors as spill-over; R&D spending in other sectors and import of

capital inputs in agriculture.

Augment Dicky-Fuler, Philips-Peron and KPSS test is applied for all variables used in the model to test their stationary.

Logarithm of import of capital inputs in agriculture and rainfall time series data are stationary in level and all other variables

are stationary by first difference.

We estimated agriculture TFP model using OLS model by E-Views 7.0 and the results are shown in table 6. All explain

variables show positive significantly effect on TFP by different lags. 1 percent increase in R&D spending in agriculture,

education expenditure in agriculture, R&D spending in other sectors, import of capital inputs in agriculture, government

investing in agriculture and rainfall will increase agriculture TFP respectively 0.13 percent by 5 lags, 0.10 percent by 2 lags,

0.09 percent by 5 lags, 0.04 percent by 2 lags, 0.14 percent by 1 lags and 0.17 percent at the same time.

R&D spending in agriculture is more effective than R&D spending in other sectors. Rainfall is the most effective and import of capital inputs in agriculture is the least effective factor in agriculture TFP model.

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[20] Mundlak, Y., Larson, D. and Butzer, R. (1997). The Determinants of Agricultural Production Function : a Cross-

Countries Analysis. World Bank Working Paper, 1827. Washington, DC: World Bank.

[21] Noferesti, M., 1995. Unit Root and Co-integration in Price of Rural Area. Econometrics.

[22] Pardey, P.G., M.S. Kang, and H. Elliott. "The Structure of Public Support for National Agricultural Research Systems:

A Political Economy Perspective." Agricultural Economics 3(4)(December 1989): 261-278.

[23] Philip G. Pardey, Julian M. Alston, and Connie Chan Kang, 2012, Agricultural Production, Productivity and R&D over

the Past Half Century: An Emerging New World Order, International Association of Agricultural Economists (IAAE)

Triennial Conference, Foz do Iguaçu, Brazil, 18-24 August, 2012.

[24] Romer, P. (1990). Endogenous Technical Change. Journal of Political Economy, 98: 71-102. [25] Mark W. Rosegrant and Robert E. Evenson, 1995, Total Factor Productivity and Sources of Long- Term Growth in

Indian Agriculture, International Food Policy Research Institute 1200 Seventeenth Street, N.W.Washington, D.C.

20036-3006 U.S.A.

[26] Sargan, J.D. and A. Bhargava, 1983. Testing Residual from Least Square Regression for Being Generated by the

Gaussian Random Walk. Econometrica Journal, 51: 153-174.

[27] Thirtle1, C., Lin, L. and Piesse, J., 2003, The Impact of Research Led Agricultural Productivity Growth on Poverty

Rrduction in Africa, Asia and Latin America, 25th International Conference of Agricultural Economists (IAAE), ISBN

Number: 0-958-46098-1

****



42

RANKING INDIAN DOMESTIC BANKS WITH

INTERVAL DATA – THE DEA APPLICATION

Dr. T. Subramanyam,

Guest Faculty, Dept. of Statistics,

Pondicherry University, Pondicherry, India.

Dr. R.V.Vardhan,

Assistant Professor, Dept. of Statistics,

Pondicherry University, Pondicherry, India.

ABSTRACT

Data Envelopment Analysis (DEA) is a non-parametric approach used to measure the relative efficiency of organizational units where multiple inputs and outputs make comparison difficult. The

present study aims at evaluating the relative efficiency of decision making units (DMUs) with interval

data. In this case the relative efficiency will lie within an interval. In this paper we constructed the relative efficiency bounds. The DMUs were classified into different categories. A ranking method was

proposed to rank the DMUs in each category to identify the best performing banks in each category.

This new methodological techniques were applied for the data relating to the Indian Domestic Banks.

Keywords: Banks, Data Envelopment Analysis, Interval Data, Ranking, Efficiency.



43

Introduction:

Data Envelopment Analysis is the optimization method of mathematical programming, based on linear

programming technique for measuring the performance of organizational units where the presence of multiple

inputs and outputs makes comparison difficult. DEA was first introduced by Charnes et.al, in the year 1978 to measure the relative efficiency of DMUs. Theoretical development of DEA has been quite remarkable, because of

its use in different public and private sector issues.

In DEA, CCR (1978) and BCC (1984) are the basic models to measure the efficiency of DMUs in constant (CRS)

and variable returns to scale (VRS) environments respectively. DEA alone classifies the DMUs into two dichotomous groups: efficient and inefficient. Efficient group receives the score 1 and inefficient group score lies

between 0 and 1. These basic models have some weakness in ranking the DMUs. Since all the efficient DMUs

having the equal score 1, one cannot decide which DMU having the better rank in their respective environment. In order to differentiate the efficient units Anderson & Peterson (A&P) developed super efficiency ranking method. In

spite of its popularity there were several criticisms about the A&P ranking method. Cooper & Tone developed

another ranking method based on the slack variables of the dual problem. All these methods were utilized to evaluate the efficiency and rank the DMUs using accurate data. If the data is an

inaccurate, these models disallowed to calculate the efficiency and to rank the DMUs. Inaccurate data may be

probabilistic, interval, ordinal or fuzzy. In this case the efficiency of a particular DMU will lie within an interval.

In recent years, in different applications of DEA, inputs and outputs have been observed whose values are indefinite. Such indefinite data are called „inaccurate data‟. A Few number of researchers devoted their findings to develop the

theoretical methodology with interval data to identify the bounds of the relative efficiency of the DMUs (Despoits

et.al, 2002; Jahanshahloo et.al, 2004). The present paper focused on evaluating the efficiency and ranking the DMUs using interval data. To rank the

DMUs the basic method applied is A&P super efficiency ranking method. The present paper is divided into six

sections. After introduction, section-I includes a brief review of literature about the basic CCR-DEA model. The DEA models with interval data are discussed in section-II. Section-III is devoted to discuss the DEA ranking

methods using interval data. In section IV we discussed about the Indian Domestic banks and input, output

selection. An empirical application with Indian Domestic banks is discussed in section-V. Section-VI presents the

concluding remarks of the present study.

Basic Data Envelopment Analysis Model:

Charnes, Cooper and Rhodes (1978) introduced a linear programming technique to measure the efficiency of

Decision Making Units (DMUs) in a competitive environment where similar inputs are employed to produce similar outputs.

Suppose, we have „n‟ decision making units (DMUs) with „m‟ inputs and „s‟ outputs. Let nj ,....,2,1,DMUj is

to be evaluated under investigation with the input and output vectors mjjjj xxxX ,..., 21 and

sjjjj yyyY ,..., 21 where 0Y and 0 jjX .

The basic CCR model to evaluate the input technical efficiency of kDMU is

n1,2,....,j m;1,2,...,i1,2,...n;jε;v,u1;xv; 0xvyu :yuMaxθ ji

m

1i

iki

s

1r

m

1i

ijirjr

s

1r

rkrk

------------- (1)

jv and iu are the input and output weights computed by solving the equation (1). The kDMU is said to be an

efficient with the optimum weights **,vu if and only if ,1* otherwise kDMU is said to be an inefficient.

Interval Data Envelopment Analysis Models:

For any, it is possible to construct a class interval by identifying the lower and upper bound of the given input and

output variables. The lower and upper bound of the ith input of the jDMU be

L

ijx and U

ijx . Let L

rjy and U

rjy be the



44

lower and upper bound of the rth output of the

jDMU respectively. For every lower and upper bound the following

conditions are to be satisfied. U

ij

L

ij xx and U

rj

L

rj yy .

i.e., U

ij

L

ijij xxx , and U

rj

L

rjrj yy y ,

The CCR Model for evaluating the efficiency of kDMU with the given interval data is as follows:

n1,2,....,j m;1,2,...,i1,2,...n;j

; εv,u1, x, xv, 0 x, xvy ,y u:y ,y uMaxθ

ji

m

1i

U

ik

L

iki

s

1r

m

1i

U

ij

L

iji

U

rj

L

rjr

s

1r

U

rj

L

rjr

k

---------- (2)

The above problem doesn‟t allow the researcher to evaluate the efficiency. Whenever a researcher deals with an

interval data the efficiency itself lie within an interval. For each and every DMU it is possible to identify two relatively efficient bounds. i.e., lower and upper bound. The following are the two LPP models to evaluate the two

bounds.

Upper Bound of the Relative Efficiency:

The upper bound of the relative efficiency of kDMU is evaluated by solving the following linear programming problem:

n1,2,....,j m;1,2,...,i k;j 1,2,...n,j

εv,u1;xv0;xvyu, 0xvyu:yuMaxθ

ri

m

1i

L

iki

s

1r

m

1i

L

iki

U

rkr

s

1r

m

1i

U

iji

L

rjr

s

1r

U

rkrU

k

------------- (3) In this problem, the particular DMU is evaluated in its best condition and the other DMUs are evaluated in their

worst condition, such that U

k k .

Lower Bound of the Relative Efficiency:

To obtain the lower bound of the relative efficiency of kDMU , we solve the following linear programming problem:

n1,2,....,j m;1,2,...,i ,k;j 1,2,...n,j

εv,u; 1xv , 0xvyu , 0xvyu:yuMaxθ

ri

m

1i

U

iki

s

1r

m

1i

U

iki

L

rkr

s

1r

m

1i

L

iji

U

rjr

s

1r

L

rkrL

k

---------- (4)

In the above problem the DMU is evaluated in its worst condition and the other DMUs in their best

condition. The obtained efficiency will always satisfy the condition

L

k k . Therefore, we observe that

U

k

L

k ,k .

Classification of DMUs:

We classify the DMUs into three categories. In category-I, all the DMUs are efficient both in their best and worst

conditions 1 U

j

L

j , which is denoted by E++. In category-II, the DMUs are efficient in their best condition

and inefficient in their worst condition 1 ,1 U

j

L

j which is represented as E+ and category-III contains all

inefficient DMUs which are inefficient in their best and worst conditions 1 ,1 U

j

L

j and is denoted by E-.



45

Ranking DMUS:

Ranking of DMUs with interval data seems to be very difficult. In other words, if two or more DMUs fall under

same category, how one can decide which bank is functioning in better environment with the better rank than the

other? To overcome this difficulty we suggest a two stage DEA method removing the boundedness conditions from the general LPP methods (3) and (4).

The following are the two stages to evaluate the possible efficiency scores.

Stage-I: DMU under evaluation is in its worst condition and the other DMUs in their best condition.

knj1,2,....,j m;1,2,...,i

1,2,...n,j ε;v,u 1;xv , 0xvyu:yuMaxθ

ri

m

1i

U

iki

s

1r

m

1i

L

iji

U

rjr

s

1r

L

rkr1

k

Stage-II: DMU under evaluation is in its best condition and the other DMUs in their worst condition.

n1,2,....,j m;1,2,...,ik;j 1,2,...n,j

ε;v,u; 1xv , 0xvyu:yuMaxθ

ri

m

1i

L

iki

s

1r

m

1i

U

iji

L

rjr

s

1r

U

rkr2

k

In the above two problems, we relaxed the boundedness condition of the objective function from the constraints to

get the possible maximum score of the objective function. The average score is calculated by using the relation

n,1,2,k ,2

θθθ

2

k

1

kR

k

From the above criteria we suggested that, if any DMU having the greater efficiency will be awarded with a better

rank and so on.

Indian Domestic Banks:

In India commercial banks were operating under three different ownerships, namely, government, private and

foreign. In public sector we have 27 commercial banks, in private 23 commercial banks and 28 commercial banks

were functioning under foreign ownership. According to the report of ICRA limited, a rating agency, the public sector banks hold over 75 percent of total assets of banking industry. It indicates the importance of the Indian

domestic banks. To know which domestic bank is functioning under efficient environment, we must evaluate the

efficiency. To gauge the efficiency of a commercial bank, first we model a commercial bank appropriately to meet the needs and objectives of the analyst.

To model a commercial bank we have two basic approaches. i.e., intermediate and production approach. In

intermediate approach banks viewed as intermediate funds between depositors and borrowers. In production approach a commercial bank resources produce services to the customers.

In the present study we pursued production approach to model a commercial bank. The inputs that it employs are

Number of Employees, Fixed Assets, outputs that produces are Deposits, Advances, and Investments.

Inputs Outputs

1. No. of Employees

2. Fixed Assets

1. Deposits

2. Advances

3. Investments

Empirical Applications:

The present study devoted to investigate the efficiency of Indian Domestic Banks. In India 27 public sector banks

were operating under the government ownership. The data collected from the RBI Bulletins for the academic years

2008 and 2009. The performance of each bank is evaluated with the interval data. The basic model to evaluate the lower and upper bound is CCR-CRS model. The results are shown in the Table (1).

We evaluated the relative efficiency bounds by assuming 2008 and 2009 data as lower and upper bound respectively.



46

The DMUs were classified into three categories on the basis of the efficiency score of the lower and upper bounds.

From the table (1), we observe that the only one bank, i.e., IDBI, Ltd., is fully efficient which falls under the

category E++. Under category E+ we have 52 percent (14 out of 27) banks and 44 percent banks (12 out of 27) fall under the category E -.

Category E++ E+ E- Total Banks

No. of Banks 01 14 12 27

The two stage DEA model employed to gauge the possible efficiency scores of the DMUs in each category. We

calculated the average efficiency score for each DMU and basing on this average score corresponding ranks are also given in Table (1).

Conclusions:

This study attempts to investigate the efficiency and ranking the Indian Domestic banks with interval data. The main aim of this paper is to construct the relative efficiency bounds of efficiency score and also to rank the DMUs

which fall under the same category. This ranking method helps us to know which bank is functioning in the

efficient environment comparing to the other banks in the same category. The study states that the only one bank

IDBI, Ltd. is the fully efficient bank among all the Indian Domestic banks which is assigned with Rank „one‟. The remaining banks in number 14 and 12 fall under the category E+ and E- respectively.

Overall, the present study facilitates the ranking method whenever the interval data appears in the literature. This

will help as the base for ranking the decision making units with interval data.

References:

[1] Andersen A and Petersen, N.C., 1993. A procedure for ranking efficient units in data envelopment

analysis.Mgmt.Sci.39, 1261-1264. [2] Banker, R.D., Charnes, A., Cooper, W.W., (1984), Some Models for estimating technical and scale

inefficiencies in data envelopment analysis: Management Science 30, pp 1078-1092.

[3] Berg, S.A., Forsund, F.R., Hjalmarsson. L., and Suominen, M. 1993. Banking efficiency in the Nordic

countries, Journal of Banking and Finance 17: 371 – 388. [4] Charnes, A., Cooper, W.W., and Rhodes, E. 1978. “Measuring the efficiency of decision making units”,

European Journal of Operational Research 2, 429-444.

[5] Despotis, D.K., Smirlis, Y.G., 2002. “Data Envelopment Analysis with Imprecise data”, European Journal of Operational Research 140, 24-36.

[6] Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., and Moradi, M., 2004. “Sensitivity Analysis and stability analysis

in DEA with interval data”, App. Math. Comput, 156, 463-477.

[7] Mlima, A.P., Hjalmarsson, L., 2002. Measurement of Inputs and Outputs in the Banking industry. Tanzanet Journal 3(1): 12-22.

[8] Sealey, Jr. C.W., and Lindley, J.T., 1977. Inputs, outputs and a theory of production and cost at depository

financial institutions. Journal of Finance 4: 1251-1266.

Table (1)

Bank Name L

k U

k Category 1

k 2

k R

k Ranks

State Bank of India 0.5606 1.0000 E+ 0.5606 1.2117 0.8862 9

State Bank Bikaner & Jaipur 0.7178 1.0000 E+ 0.7178 1.0683 0.8931 8

State Bank of Hyderabad 0.6977 1.0000 E+ 0.6977 1.2826 0.9902 6

State Bank of Indore 0.8525 1.0000 E+ 0.8525 1.3094 1.0810 4

State Bank of Mysore 0.3657 1.0000 E+ 0.3657 1.2645 0.8151 11

State Bank of Patiala 0.7996 1.0000 E+ 0.7996 1.3148 1.0572 5

State Bank of Travancore 0.7809 1.0000 E+ 0.7809 1.1354 0.9582 7

Allahabad Bank 0.4847 0.9219 E- 0.4847 0.9219 0.7033 20



47

Andhra Bank 0.5574 1.0000 E+ 0.5574 1.1613 0.8594 10

Bank of Baroda 0.5384 1.0000 E+ 0.5384 1.0699 0.8042 13

Bank of India 0.5005 0.9735 E- 0.5005 0.9735 0.7370 16

Bank of Maharashtra 0.4257 1.0000 E+ 0.4257 1.1422 0.7840 15

Canara Bank 0.4498 0.8606 E- 0.4498 0.8606 0.6552 22

Central Bank of India 0.3921 0.7546 E- 0.3921 0.7546 0.5734 27

Corporation Bank 0.7562 1.0000 E+ 0.7562 1.6149 1.1856 2

Dena Bank 0.4838 0.9378 E- 0.4838 0.9378 0.7108 19

IDBI Ltd. 1.0000 1.0000 E++ 1.7243 3.7483 2.7363 1

Indian Bank 0.3910 0.8105 E- 0.3910 0.8105 0.6008 26

Indian Overseas Bank 0.4407 0.9421 E- 0.4407 0.9421 0.6914 21

Oriental Bank of Commerce 0.6699 1.0000 E+ 0.6699 1.5008 1.0854 3

Punjab & Sind Bank 0.3760 0.8649 E- 0.3760 0.8649 0.6205 25

Punjab National Bank 0.4338 0.8238 E- 0.4338 0.8238 0.6288 24

Syndicate Bank 0.5585 1.0000 E+ 0.5585 1.0404 0.7995 14

UCO Bank 0.4985 0.9377 E- 0.4985 0.9377 0.7181 18

Union Bank of India 0.4519 0.9989 E- 0.4519 0.9989 0.7254 17

United Bank of India 0.5062 0.7833 E- 0.5062 0.7833 0.6448 23

Vijaya Bank 0.6000 1.0000 E+ 0.6000 1.0227 0.8114 12

****



48

THE EFFECTS OF FINANCIAL REPORTING QUALITY

ON STOCK PRICE DELAY & FUTURE STOCK RETURN

Azam Pouryousof,

Department of Management,

Accounting, Payame Noor University, I.R., Iran.

Hilda Shamsadini,

Department of Accounting, Bam Branch,

Islamic Azad University, Bam, Iran.

Mina Abousaiedi,

Department of Accounting, Kerman Branch,

Islamic Azad University, Kerman, Iran.

ABSTRACT

The purpose of this research is to survey the effects of financial reporting quality on stock price delay

and future stock return. In capital markets with poor or medium efficiency, cross-sectional disclosure

of stock price and as a result the stock price will mainly delay. In this research we also study this question: does the quality of accounting information have influence on the reflection delay of

accounting information in stock price? On the other hand stock price delay is risky for investors, so

investors return premium to compensate this adverse selection. Therefore, the second question arises: does stock price delay relate to the association of financial reporting quality & future stock returns?

The statistical populations in this research are all firms accepted in Tehran stock exchange, using

elimination method in sampling; the firm was elected as a sample, received information such as: financial reporting quality, future stock return and stock price delay which were analyzed through

model …….. and the findings indicated that there is no significant association between financial

reporting quality and stock price delay. But there is such significant association between financial

reporting quality and future stock return.

Keywords: stock price delay, financial reporting quality and future stock return.



49

Introduction:

In capital markets with poor or medium efficiency, cross-sectional disclosure of information and as a result stock price to

newly-arrived information will delay. On the other hand stock price delay is risky for investors; therefore, we survey this

question in this research: does the quality of accounting information as a kind of information imperfection have influence on

accounting information reflection delay in stock price? And can we relate stock price delay to the association between financial

reporting quality and future stock returns?

Theoretical principals:

In efficient capital market (complete disclosure of information and rational investors) stock price is balanced on the basis of

newly-arrived information. Therefore, the main volume of financial research surveys the information imperfection such as information asymmetry and incomplete information (Barry and Brown 1984; Merton, 1987; Easley et al., 2002; Hou and

Moskwitz, 2005; Lambert et al., 2007).

In incomplete information, cross-sectional disclosure of stock price and as a result stock price adjustment will delay.

(Verrecchia, 1980; Callen, 2000). In this research we also survey this question: does the quality of accounting information (as a

kind of information imperfection) have influence on accounting information reflection delay in stock price?

Stock price delay is risky for investors because it may be in contrary to the general information which appears in price.

Therefore, investors return premium to compensate this adverse selection. The second question: is stock price delay related to

the association of financial reporting quality and future stock return?

Since stock price delay is related to both accounting and non-accounting information, and return premium for delay is

associated with financial and non-financial indexes of the firm, this research will impel us to analyze return premium (due to

delay) and accounting & non-accounting sources. Therefore, we must seek documents to show the association between expenses, capital and financial reporting quality.

In this research, the quality of financial reporting is defined as the effect of financial reporting in the prediction of stockholders’

salary in future cash flow. And it is expected that the poor quality of financial reporting is economically costly and will result in

a decrease in the adjustment of stock price and an increase in the firm capital expenses. When the pre-existing information set

is poor or imprecise, investors’ cash flow forecasts are poor or imprecise, and there is also likely heterogeneity in investor

opinion about the amounts, timing and uncertainty of future cash flows.

In this research, we distinguish between stockholders’ available information and newly-arrived information. Stockholders use

the existing information to forecast cash flow, then they can estimate stock price and by disclosure of newly-arrived

information, they will be able to update cash flow forecast in order to determine stock price. Here we supposed that accounting

information is part of information which is used to forecast cash flow by investors.

As a result, poor quality of financial reporting is related to poor quality of existing information and it decreases the quality of

cash flow forecast. After publishing the related new information, the investors revise their forecast of cash flow so; stock price estimation is accompanied by uncertainty, because investors are interested in stock price revaluation based on increasing

awareness or imitation of other investors. These revaluations are continued until prices cover the main values. (Verrecchia,

1980; Callen, 2000). Therefore, in this research we determine stock price delay with difference in the quality of the existing

accounting information.

This research is based on Verrecchia studies; he has determined the speed of stock price adjustment based on the quality of newly-

arrived information. He supposed that the quality of existing information of investors is fixed. But in this research, based on the

experiments in other similar studies, stock price speed is determined with difference in the quality of existing accounting information

and the quality of newly-arrived information is supposed to be the same as the quality of existing information.

Stock price delay is measured on the basis of the firm return rate correlation with general return of the market and the quality of

financial reporting is determined using the general information of financial statements. To evaluate the quality of financial

reporting, some models are presented based on accrual quality, special items quality, recent continuous losses and unexpected profits. (Li, 2008). In this research we use accrual quality to evaluate financial reporting quality, because it is more robust scale

in associated with variability control of cash flow and operational uncertainty index.

Hypothesis:

The effects of financial reporting on future cash flow forecasts, shows financial reporting quality; when the pre-existing

information set is poor or imprecise, investors’ cash flow forecasts are poor or imprecise, and there is also likely heterogeneity

in investor opinion about the amounts, timing and uncertainty of future cash flows. As a result, the poor quality of financial

reporting is related to the poor quality of existing information; then it decreases the quality of cash flow forecasts.

After publishing the related new information, the investors revise the cash flow forecasts so; stock price estimation is

associated with uncertainty, because investors are interested in stock price revaluation based on increasing awareness or

imitating other investors. These revaluations are continued until prices reflect the main values. Therefore, in this research, delay in stock price adjustment is determined with difference in the quality of existing accounting information and we expect

that the stock price adjustment (stock price revaluation by investors) to have higher delay in the condition that the quality of



50

financial reporting is poor. Therefore, the first hypothesis of this research is as follows:

1- There is a significant association between financial reporting quality and stock price delay.

Since, it is expected that stock price adjustments has higher delay in the condition that the quality of financial reporting is poor,

the investors ask for higher return in this condition. Because stock price delay is risky for investors, because it may be in

contrary to that general information which appears in prices; therefore, investors return premium to compensate this adverse

selection. Therefore, the second hypothesis of this research is as follows:

2- Investors predict higher future stock return, when the quality of financial reporting is poor.

It is worth noting that the research data in this article is cross-sectional and analysis of collected data based on correlation

method. Moreover, this research is a kind of relation-finding research in the field of capital market. For the research hypothesis to be tested at first we analyze the research information through Kolmogorov- Smirnov Test, if the

information distribution is normal, Pearson Test is used and if it is not normal Spearman Rank Correlation Test is used.

The statistical populations in this research are: all firms accepted in Tehran stock exchange, using systematic elimination

method in sampling; some firms were elected as samples which:

- Their fiscal year is leading to 19/03/2012

- Their relative data such as 3-month reports are available

- Two weeks after publishing the 3-month reports, their stock will be exchanged

Previous studies:

In a research, Verrecchia, R (1980), surveyed the association of price adjustment speed with the quality of accounting

information. He supposed that the quality of existing information is fixed and indicated that the speed of price adjustment will increase due to increasing the quality of newly-arrived information.

Callen et al (2000) examined stock price delay and future stock return, relation of financial reporting quality & the delay of

stock price adjustment in a research under the name of Accounting Quality. The result suggested poor accounting quality

causes the stock price adjustment to have higher delay and investors evaluate higher future stock return in poor accounting

quality condition.

Research Variables:

A) Independent Variable:

Financial reporting quality is defined as financial reporting effects on forecasting stockholder’s equity in future cash flow. In

this research we use Accrual Quality to evaluate financial reporting quality, according to (Francis et al 2005; Dechow and Dicher 2002; McNichols 2002) studies as following model:

CAcct = γ1,t + γ2,t CFOt-1 + γ3,t CFOt + γ4t CFOt+1 + γ5,t Δrev + γ6,t PPEt + et

CAcct = Current Accrual (or Changes in Capital Flow)

CFO = Cash Flow at the Beginning

Δrev = Changes in Incomes at the end of period in respect of the beginning

PPE = Properties & Equipments

All the variables in the above-mentioned model, to eliminate the inflation effect, are balanced through collecting assets; it

means that the variables are divided to assets collection.

B) Dependent Variable:

1- Stock Price Delay: Investors use all the existing data to forecast the company cash flow and as a result the company value. Following to disclosure of new data concerning the company, investors will update their estimation of cash flow and reach a

new price for the stocks. Based on traditional paradigms of efficient capital markets, price adjustment occurs quickly and

completely, but the results of the observational research indicate that the effect of finding new data on price stock is appeared

with a delay.

In Hou and Moskowitz, 2005 model, stock price delay average is calculated through the sold stock return and market return in

4 times (after publishing 3-month financial reporting) as follows:

Ri, t = ai + βi Rm,t + Σn=1 to 4 δi,n Rm,t-n + εi,t

Ri,t = Stock Return (i) in Period (t)

Rm, t = Market Return in Period (t)

If information reflects with delay in stock price, some of δi,n shall not be zero and market return will be added to stock pr ice

after publishing 3-month reports (uncontrolled price adjustment).

Above equation is calculated another time with this restriction that all δi,n are zero, in other words we supposed that newly-arrived information have influence on stock price speedily (controlled price adjustment). Then price delay of D is calculated as

follows:

D = 1- (R2 restricted / R2 unrestricted)



51

D is similar to Fisher test about the importance of accrual correlation in Hou and Moskowitz model. If the variation percent of

discussed return in this model is higher, D will be higher too. Hence, new information reflection delay in stock price (Stock

price delay) will increase D.

Hypothesis Test:

First Hypothesis: There is a significant association between financial reporting quality and the firms’ stock price delay.

To examine this hypothesis, we calculated the correlation coefficient between financial reporting quality and stock price delay

of the firms for the period of seven years separately. The results are as follows:

Firms Stock Price Delay

2002 2003 2004 2005 2006 2007 2008

Financial

Reporting

Quality

Correlation Coefficient -0.011 0.017 0.008 -0.024 -0.12 -0.029 0.005

Statistic t -0.093 0.145 0.0683 -0.205 -1.032 0.247 0.0427

Significance Level 0.926 0.887 0.947 0.841 0.305 0.806 0.969

Table 1- correlation coefficient between financial reporting quality and firms’ stock price delay during the years 2001 to 2007

Correlation coefficient is negative during the years: 2002, 2004, 2005 & 2006. Therefore, during the said years there is a

reverse relation between financial reporting quality and firms’ stock price delay, in other word during these years the delay

amount of price adjustment has decreased due to increasing the quality of financial reporting. Certainly since in all years

discussed here, the Significance Level is higher than 0/05, there is no significant association between the above-mentioned

variables. In other words the first hypothesis is not confirmed.

Second Hypothesis:

Investors forecast higher future stock returns for firms when the quality of financial reporting is poor:

To examine this hypothesis, we calculate the correlation coefficient between financial reporting quality and firms’ future stock

returns. The results are as follows:

Firms Future Stock Returns

2002 2003 2004 2005 2006 2007 2008)

Fin

an

cia

l

Rep

orti

ng

Qu

ali

ty

Correlation

Coefficient -0.044 -0.361 0.065 -0.014 0.027 -0.117 0.084

Statistic t -0.376 -3.307 0.55 -0.119 0.231 -1.0065 0.72

Significance Level 0.705 0.001 0.58 0.905 0.817 0.319 0.472

Table 2- correlation coefficient between financial reporting quality and firms’ stock price delay during the years 2001 to 2007

Correlation coefficient between the said variables is negative during the years: 2008, 2002, 2004 & 2006. This shows that the

second hypothesis is accepted. Correlation coefficient in 2001, 2002, 2004 & 2006 is positive; hence, there is a direct but

incomplete association between financial reporting quality and future stock returns.

Regression Analysis:

Using simple linear regression analysis, we want to study the association between the mentioned variables and determining appropriate paradigm from relation between financial reporting quality with stock price delay and firms’ future stock returns

for future research and presenting forecast model. In this research two simple linear regression models are presented; in the

first model, financial reporting quality has been considered as an independent variable and the firms’ stock price delay as a

dependent variable.

(Financial Reporting Quality) × 0.02 – 7.731E – 10 = Stock Price Delay

Table 3: Regression Coefficient Estimation for stock price delay against firms’ financial reporting quality

Regression

Estimation

Standard Deviation

Coefficient Statistics T

P-Value

Significance Level

Constant Coefficient 0.02 0.015 1.348 0.178

Financial Reporting Quality -7.731E-10 0.00 -0.024 0.981



52

The above table indicates that as financial reporting quality increases one unit (7.731, ×10-10) the amounts of stock price delay decreases.

The second regression model is as follows: in this model, financial reporting quality has been considered as an independent

variable and future stock returns as dependent variables.

(Financial Reporting Quality) 27.651-2.424×10-6= Future Stock Returns

Table 4- Regression Coefficient Estimation for future stock returns against firms’ financial reporting quality

Regression

Estimation

Standard Deviation

Coefficient

Statistics T P-Value

Significance Level

Constant Coefficient 27.651 3.059 9.039 0.00

Financial Reporting Quality -2.424E-6 0.00 -0.365 0.715

Considering the fact that significance level pertaining to financial reporting quality in the above model is more than 0.05, with

95 percent assurance, we can say that the above model is not efficient and appropriate. After studying the hypothesis, we came to the conclusion that there is no significant linear association in the level of %95

between financial reporting quality and stock price delay. But, considering that the correlation coefficient between financial

reporting quality and future stock returns is negative, investors forecast higher future stock returns when the quality of financial

reporting is poor. Its model is as follows:

Future stock returns = 27.651 – 2.424 × 10-6

(financial reporting quality)

Conclusion:

In efficient and half-efficient capital market, when the related new information is published, investors revise their forecast of

cash flow. Hence, stock price estimation is accompanied by uncertainty, because investors are interested in revaluation of stock

price base on increasing awareness or imitation of other investors. These revaluations are continued until prices cover the main

values. Therefore, it is expected that, delay in stock price adjustment shall be determined by difference in the existing accounting information quality and we may observe more delay in stock price adjustment (stock price revaluation by investors)

when the quality of financial reporting is poor.

Our expectation of existing reverse relation between financial reporting quality and stock price delay was confirmed, but

association between above-motioned variables is not statistically significant as we cannot relate stock price delay to financial

reporting quality. Lack of association between financial reporting quality and stock price delay may be due to the restriction

which exits in Delay measurement model in stock price adjustment or market inefficiency. Seemingly, we need more extensive

research to reach a final conclusion: because measuring the delay variable in stock price adjustment has no record in Iran; on

the other hand, as we saw the detailed results of the first hypothesis in chapter four, is has been confirmed that there is no

association between financial reporting quality and stock price delay in 2008, 2004, 2005 & 2006 and there has been positive

association between financial reporting quality and stock price delay in 2002, 2003 & 2007.

Since, it is expected that we observe more delay in stock price adjustment while the quality of financial reporting is poor; investors ask more returns in this condition. Because stock price delay is risky for investors and it may be contrary to such

general information which appears in prices. Therefore, it is expected that investors return premium to compensate this adverse

selection. As we expected, reverse association between stock returns and financial reporting quality was confirmed. Of course,

detailed statistical results indicates that reverse association between stock returns and financial reporting quality was confirmed

only for years: 2008, 2002, 2004 & 2006; and investors forecast higher future stock returns as the financial reporting quality

increases in years 2003, 2005 & 2007. Certainly, considering the records of this hypothesis test in Iran capital market, we can

conclude that as financial reporting quality deceases, investors forecast higher future stock returns. Acceptance the above

conclusion will lead us to accept that stock exchange is efficient.

References:

[1] Callen at al.(2010), Accounting Quality, Stock Price Delay and Future Stock Returns, Journal of Accounting and Economics, 5:63-92.

[2] Callen at al, (2000), large time and small noise asymptotic results for mean reverting diffusion Processes with

applications, Economic theory, 16:401-419.

[3] Dechow, Patricia and Ilia Dichev. (2002), the quality of accruals and earning, The Accounting Review, 77:35-39.

[4] Hou, Kewei and Tobias Moskowitz, (2005), Market frictions, Price delay and the cross- Section of expected returns,

Review of Financial studies, 18(3): 981-1020.

[5] Li, Feng, (2008). Annual report readability: current earning and earning persistence. Journal of Accounting and

Economics, 45:221-247.

[6] Vettecchia, Robert. (1980), the rapidity of price adjustments to information, Journal of Accounting and Economics, 2:63-92.

****



53

GOLD PRICE MOVEMENTS IN INDIA

AND GLOBAL MARKET

Shaik Saleem,

Research Scholar, Department of Management Studies,

Sri Venkateswara University, Tirupati, Andhra Pradesh, India.

Dr. M. Srinivasa Reddy,

Professor,

Department of Management Studies, Sri Venkateswara University,

Tirupati, Andhra Pradesh, India.

Shaik Karim,

Research Scholar,

GITAM School of International Business,

GITAM University, Visakhapatnam,

Andhra Pradesh, India.

ABSTRACT

The price of gold varies from country to country as there are some very influential factors to affect its

rate nationally and internationally. In the international markets when in gold is traded online, its price depends upon the dominated currency that is US Dollar in most of the online trading markets. In

online commodity exchanges, the Live Gold Rates are updated time to time whereas in the physical

markets the prices changes and vary from country to country. This paper attempts to study the gold prices movement in INR and Key Currencies, impact of exchange rate, inflation rate and gold reserves

on gold prices movement in India. It was found that there exists positive and significant correlation

between the gold prices movement in INR and Key Currencies and there exists seasonal variation in

gold prices movement between INR and key currencies. The study also shows significant impact of exchange rate, inflation rate and gold reserves on gold prices movement in India.

Keywords: Currencies, Exchange rate, Gold reserves, India, Inflation rate, Seasonal variation.



54

Introduction:

Thousands of years ago people found shiny rock in a creek and thereby human race got introduced to the Gold for first time.

Gold, the metal, particularly the yellow metal witnessed a drastic change in its characteristics. In ancient days it was luxurious

for the mankind; today the same gold is the need for mankind. People they prefer the need to have the investment in the gold

for various reasons. It is evident from the history the importance of gold as the best medium of exchange between countries,

but today gold has lost its importance as there was an end of determining foreign exchange rate in terms of gold in Bretton

Woods Agreement.

The price of gold varies from country to country as there are some very influential factors to affect its rate nationally and

internationally. In the international markets when in gold is traded online, its price depends upon the dominated currency that is

US Dollar in most of the online trading markets. In online commodity exchanges, the Live Gold Rates are updated time to time whereas in the physical markets the prices changes and vary from country to country. The variations in Gold Rates are similar

to price of crude oil. The crude oil rate changes in the international markets and conversely affects the national markets of the

different countries. The crude oil price is given in US Dollars and then the countries calculated their local price of petroleum

products on various factors. Countries have different policies for the export and import of the goods that is why they design the

policies accordingly which results in unlike gold rates among their neighboring and other countries. The variation in prices is

due to the cost of physical delivery, storing and ordering cost, local taxation and conversion of price from US Dollar to

local currency. Following are some of the factors that affect the prices in different countries.

Inflation affects the gold rate:

Gold is an inflation hedge that is used by the countries to secure their economy by hedging gold against their inflation rate.

Mostly the developed countries hedge gold to balance their economy that may be disturbed by the increase in inflation. The gold rate goes up with the increase in inflation rate and the countries that hedge gold against inflation will not face recession. It

is one of the financial instruments that help the economy in stabilizing its position in the international community. Some of the

developing countries have increased inflation rates that may be affected by the decrease in the foreign currency rate. Every

country has its own policy-makers who advent economic policies according to the needs of the country to bring out the

maximum result in developing their economy that's why the price of gold varies from country to country.

Import tax and duties affect gold rates:

Countries impose tax to force the investors and importers contribute in the national economy. Some of the taxes are imposed

directly while some of them are indirectly levied. Gold is a premium commodity that brings more revenue to the tax authorities

and stability in the economy. India's revenue from import of gold almost doubled in 2010-11 as compared to the previous year,

revenue turnover in respect of customs duty collected from the import of gold was Rs 2,553.52 crore in 2010-11 against Rs 1,567.64 crore in 2009-10. The gold rates are therefore subject to increase with the addition of import tax and duties. Every

country has its own Income Tax ordinance and rules to charge tax over the imports of global homogenous commodities. Gold is

one of those durable commodities that are taxed differently indifferent countries. That's why the Live Gold Rates tend to vary

from country to country.

Central Banks affect the gold rates:

The central bank of a country plays a leading role in setting the price of gold as it often hedge the gold against its central

reserves. The banks and gold mining companies can manipulate the gold prices as they have a large amount of raw and refined

gold in their reserves. Banks can affect the rate in case they undergo the sale or purchase of gold in bulk or the mine-owners

increase the production or reduce the output of gold. Gold is traded internationally but it is treated in a dissimilar way when it

is comes to the national boundaries. The central banks have the amount of gold and they may buy more gold when they find a decrease in their gold reserves against their holdings.

Hence, in this context this study is undertaken to observe any relationship in gold price movements in India and the Global

Market and to observe the impact of various factors on gold prices in India.

Objectives of the Study:

1. To study the trend in gold price movements in Indian rupee and key currencies of the world.

2. To identify the association between gold price movement in Indian rupee and key currencies of the world.

3. To study the impact of foreign exchange rate between INR/USD, Inflation rate and Gold Reserves on gold prices in India.

Literature Review:

There are many studies Koutsoyiannis (1983), Sjaastad (1986), Cengiz Toraman (2011) and Sujit (2011) investigating the price

of gold in the literature. These studies dealt with different variable and determined the relationship between gold prices and US



55

dollar, inflation rate, stock return and oil prices in general. Most of studies deal with gold price movements in US and other

developed countries, Indian based studies are less in number, and these studies are mostly in relation to stock market. Hence,

there was a need to carry this study.


For the purpose of study to make comparison between the gold price movement in Indian market and Global market, historical

gold prices in INR, USD, GBP, JPY, CAD, EUR and CHF on monthly and yearly basis were taken for 32 years from 1981 to

2012 from World Gold Council. And historical exchange rates of INR/USD, Inflation rates in India and Gold Reserves in Metric tons in India from 1981 to 2012 were taken. Data were analyzed in this study by using seasonal index by simple average

method, correlation analysis, and regression analysis and for interpreting the results of hypothesis testing student’s t-test and

ANOVA have been used.

Hypotheses:

As the study is about knowing the gold price movements in various markets, which may show variations in the trend of all

markets. Hence, following hypotheses were developed:

1. To test the significance of the value of Karl Pearson co-efficient between the gold price movement in INR and key

currencies, the following hypothesis has been developed. H0: There is no association between gold price movements in INR and key currencies.

H1: There is an association between gold price movements in INR and key currencies.

2. To test the significance of seasonal variability of Gold prices in Indian and Key currencies market, following hypothesis

has been developed:

H0: Seasonal variability of gold prices in all the markets does not differ significantly.

H1: Seasonal variability of gold prices in all the markets does differ significantly.


Seasonal Variation in the Gold Prices in Indian and Key Currencies market:

The present study is a time series study covering a period from 1981 to 2012. This period was chosen because it covers both

pre and post liberalization period, which may show a good variability as before 1991 the gold prices were not determined by

the market forces but rather fixed by the Government from time to time and also cover the period of crisis in financial markets.

From the exhibit -1 it is clear that the gold price movements shown a downward trend in the years 1981 - 1982, and thereafter

showing increasing trend up to 1996. From 1997 again the gold prices in India started falling down and geared up from 2000

and continuing the same trend till. Further, the gold price movements in US Gold Market is not showing a constant trend over a

period of time from 1980 – 2004. From 2004 it is observed a good increasing trend in gold price in US at higher pace.

Exhibit – 1: Gold Price Movements in INR and Key Currencies from 1981 to 2012

Year INR USD EUR JPY GBP CAD CHF

1981 3969.5 459.7 361.0 100991.2 226.8 551.2 902.8

1982 3560.1 375.8 345.7 93804.8 215.8 463.2 765.8

1983 4279.2 424.2 440.3 100874.6 279.6 523.1 890.3

1984 4066.2 360.4 425.7 85459.1 269.7 466.2 844.2

1985 3888.6 317.3 394.2 75457.9 246.4 433.0 776.7

1986 4615.5 367.5 351.0 61646.3 250.9 510.5 657.3

1987 5751.8 446.5 365.9 64389.5 272.4 591.6 664.5

1988 6041.6 437.0 351.9 55981.8 245.5 538.1 638.2

1989 6154.4 381.4 325.6 52580.7 233.0 451.6 623.1

1990 6695.9 383.5 282.8 55491.7 215.9 447.5 533.2

1991 8205.2 362.2 278.0 48692.8 205.3 414.9 518.8

1992 9632.6 343.7 253.5 43546.7 195.7 415.3 483.2

1993 11189.9 359.8 301.1 39894.6 239.6 464.4 531.7

1994 12047.1 384.0 319.5 39243.0 250.8 524.3 524.9

1995 12450.7 384.2 292.0 36109.8 243.5 527.3 453.9

1996 13713.1 387.7 300.8 42140.0 248.7 528.7 478.9

1997 12006.5 331.1 292.3 40022.4 202.3 458.0 480.2



56

1998 12128.9 294.2 264.3 38473.8 177.5 436.2 426.4

1999 12001.6 278.8 261.5 31666.5 172.2 414.2 418.5

2000 12530.1 279.0 302.6 30073.4 184.2 414.3 471.0

2001 12786.8 271.0 302.8 32914.8 188.2 419.7 457.2

2002 15056.0 310.0 328.2 38760.5 206.4 486.6 481.6

2003 16915.2 363.5 321.2 42060.3 222.3 508.3 488.5

2004 18517.4 409.2 329.1 44230.2 223.3 531.9 508.0

2005 19624.6 444.9 358.6 49117.4 245.1 538.4 555.2

2006 27372.2 604.3 480.8 70233.8 327.9 685.2 756.4

2007 28733.2 696.7 507.4 81849.4 347.7 745.0 833.9

2008 37768.7 871.7 593.3 90251.5 472.3 925.5 941.3

2009 47025.2 973.0 697.8 90862.3 621.9 1105.7 1053.4

2010 55973.2 1224.7 925.1 107171.6 792.5 1261.1 1274.1

2011 73394.9 1568.6 1128.4 124770.9 979.1 1553.5 1388.6

2012 89061.5 1668.1 1297.2 133141.5 1052.2 1666.7 1563.5

Source: World Gold Council

It is evident from the exhibit - 1 that the gold price movements in European Gold Market follows the same line of trend of US gold market, fluctuations in the gold prices from 1980 – 2004 and thereafter a high speed increasing trend in gold prices. But it

was found decreasing trend in the gold prices in the Japan gold market from 1981 – 1995, bit increase in the gold prices in the

year 1996 and again down trend up to 2001. Thereafter increasing trend is observed in the gold prices. London gold market

had witnessed fluctuations from 1980 to 2004 and from 2005 onwards an increasing trend is noticed in the gold prices in

London. The gold markets in Canada and Switzerland also show fluctuations in gold prices from 1981 to 2000, later years

increasing trend in gold prices is observed.

From the exhibit-1 it is clear that somehow seasonal variations are there in the Indian gold market and the other markets further

the seasonality is not varying at high rate in all the markets on an average. The demand for gold is somewhat high in and

changing time to time in India; this may be due to India is one of the major countries of consumer of gold. Further the demand

for gold in India increases from the August and continue up to December as these are the festivals months, people consider

purchasing of gold as good act during these months. As the seasonal indices observed with minimum of 93.68% and maximum

of 108% is varying more than the other countries seasonal indices having a range between 99% as minimum to 103% as maximum on an average.

It was found high positive correlation between the gold price movements in INR and USD, EUR, GBP, CAD, CHF and JPY of

0.961, 0.944, 0.954, 0.967, 0.804 and 0.634 respectively. Showing increase in gold price in said currencies will lead to increase

in the gold price in INR at higher proportion in same direction or vice-versa. Further on testing the significance of correlation,

the relationship between INR and USD, EUR, GBP, CAD, CHF AND JPY is found to be significant in exhibit - 2 at 5% level

of significance. Hence, there is a correlation between gold price movements in INR and USD, EUR, GBP, CAD and CHF.

With the help of ANOVA it was found that the gold price movements in all the currencies do not differ significantly at 5% level

of significance shown in Exhibit – 3. If to observe the prices in months then there exists significant variability.

Exhibit – 2: Correlation analysis between Gold Price in INR and Key Currencies

INR USD EUR JPY GBP CAD CHF t value p-value

INR 1

USD 0.961 1

19 0

EUR 0.944 0.983 1

15.59 0

JPY 0.634 0.792 0.821 1

4.49 0

GBP 0.954 0.991 0.99 0.78 1

17.51 0

CAD 0.967 0.995 0.982 0.771 0.992 1

20.78 0

CHF 0.804 0.915 0.943 0.956 0.916 0.904 1 7.39 0

Exhibit – 3: Average Seasonal Indices of Gold Prices in INR, USD, EUR, JPY, GBP, CAD and CHF

Month INR USD EUR JPY GBP CAD CHF

Jan 93.7 97.7 97.3 99.6 97.3 98.9 99.1

Feb 95.3 98.5 98.7 100.3 98.7 99.7 100.5



57

Mar 95.6 97.8 97.5 99.2 98.4 98.6 98.9

Apr 95.5 98.3 97.9 100.1 97.8 98.5 99.4

May 97.7 98.8 99.1 99.6 98.6 98.9 100.2

Jun 98.4 98.3 99.3 99.3 98.5 98.4 99.6

Jul 99.1 98.5 99.2 99.1 98.2 98.1 98.8

Aug 100.9 99.8 100.5 99.8 99.5 99.3 99.3

Sep 104.2 102.1 102.5 101.5 102.3 101.3 101.4

Oct 104.7 102.6 102.1 100.6 102.8 101.9 100.8

Nov 107.3 103.6 103.0 100.4 103.7 103.0 101.2

Dec 107.7 103.7 103.0 100.5 104.2 103.5 100.9

ANOVA

Source of

Variation SS df MS F P-value F critical

Months 344.6166283 11 31.32878439 13.6777363 0.00 1.936958

Currencies 0.000623457 6 0.00010391 0.00 1 2.23948

Error 151.1726593 66 2.290494837

Total 495.789911 83

Fig. 1.2 : Seasonal Indices of Gold Prices in INR, USD, EUR, JPY, GBP, CAD and CHF

It was found in the study from exhibit - 4 that the correlation between the gold price movement in India and exchange rate

between INR/USD works out to 0.64, low positive correlation of 0.08 found between inflation rate and gold price movement

and high positive correlation of 0.86 found between gold reserve in metric tons in India and gold prices in India. Further it was

found significant relationship between exchange rate and gold prices in India. If exchange rates goes up there is possibility that the gold prices in India will move up relatively high. This could be because of in International Market the value of gold is

determine in US Dollar and US is one of the major gold producers of world. And countries they purchase gold from IMF as

reserve which is also denominated in US Dollars. Moreover there is a significant relationship between gold reserves and gold

prices in India and gold prices in India is having insignificant relationship with the inflation. Indicates changes in the gold

reserves will cause good change in gold prices and change in inflation rate may cause less change in gold prices.

Exhibit – 4: Correlation between Gold Price Movements in India and Exchange Rate between

INR/USD, Inflation rate and Gold Reserves

INR INR/USD

Inflation

Rate

Gold

Reserves t value p-value

INR 1

INR/USD 0.641 1

4.57 0

Inflation Rate 0.08 -0.313 1

0.43 0.66

Gold Reserves 0.86 0.69 0.078 1 9.22 0

Impact of Exchange rate INR/USD on Gold Prices in India:

The impact of Exchange rate on gold prices movement in India is change in Re. 1 in exchange rate will cause change of Rs.



58

882.42 in the gold prices in India through regression analysis shown in exhibit – 5. And the effect of exchange rate on gold

price movement in India found significant by using both t-test and ANOVA at 5 % level of significance.

This may be because currency plays a very important role in determining commodity prices and rupee depreciation has been a

major factor that has affected prices of commodities in the Indian markets. Taking the example of gold itself, the yellow metal

has witnessed sharp gains in the Indian markets as a weaker rupee supported gains. And as many countries they import gold

from international market, which is mostly represented in terms of US Dollar.

Exhibit – 5: ANOVA

df SS MS F Significance F

Regression 1 5409004657 5.41E+09 20.91757 7.74447E-05

Residual 30 7757599406 2.59E+08

Total 31 13166604064

Coefficients

Coefficients Standard Error t -Stat P-value

Intercept -9383.353924 6820.790917 -1.3757 0.1791

INR/USD 882.4184812 192.9385298 4.573573 7.74E-05

Impact of Inflation rate on Gold Price Movement:

The impact of inflation rate on gold prices movement in India is change in 1% in inflation rate will cause change of Rs. 506.14

in the gold prices in India through regression analysis shown in exhibit – 6. And the effect of inflation rate and gold price

movement in India found insignificant by using both t-test and ANOVA at 5 % level of significance.

This may be due to rupee depreciation stresses upon imports becoming expensive. As in the international market gold prices

are denominated in US dollars, the rise in the exchange rate could affect the commodity prices which are imported from the

other countries. Later athese imports become expensive this can cause rises in the domestic prices of the commodities.


ANOVA


Regression 1 77832580.48 77832580 0.17839546 0.675767669

Residual 30 13088771483 4.36E+08

Total 31 13166604064

Coefficients

Coefficients Standard Error t Stat P-value

Intercept 14899.84164 10327.81593 1.44269 0.159468126

Inflation Rate 506.1440585 1198.346036 0.422369 0.675767669

Impact of Gold Reserves in metric tons in India on Gold Price Movement in India:

The impact of Exchange rate on gold prices movement in India is change in 1 unit in gold reserve will cause change of Rs.

236.96 in the gold prices in India through regression analysis shown in exhibit – 7. And the impact of gold reserves on gold

price movement in India found significant by using both t-test and ANOVA at 5 % level of significance.

It was in the year 2009 when RBI purchased 200 metric tons worth $6.7 billion of gold from International Monetary Fund

(IMF) as part of the foreign exchanges reserves management operations, which was highest share in the total gold reserves sold

by the IMF. And made the gold prices to go up in the international market and national market.


ANOVA


Regression 1 9735181805 9735181805 85.11207077 2.88845E-10

Residual 30 3431422259 114380742

Total 31 13166604064



59

Coefficients


Intercept -66043.75651 9407.29945 -7.02047988 8.39171E-08

Gold Reserve 236.9617285 25.685181 9.225620346 2.88845E-10

Impact of Exchange rate INR/USD, Inflation rate and Gold Reserves on Gold Prices in India:

The joint impact of exchange rate between INR/USD, Inflation rate and Gold Reserves in metric tons on gold price movement

in India is studied through multiple regression and results presented in exhibit – 8. Further the regression statistics found

significant at 5 per cent level of significance for 3 and 28 degrees of freedom. The effect of inflation rate and exchange rate on

gold price movement in India found insignificant because it is rupee depreciation which is reflected in inflation and inflation rate later affects the exchange rate. And the impact of gold reserves in metric tons on gold price movement in India found

significant.

Exhibit – 8: Impact of Exchange rate INR/USD, Inflation rate and Gold Reserves on Gold Prices in India

Regression Statistics

Multiple R 0.864016481

R Square 0.746524479

Adjusted R Square 0.719366388

Standard Error 10917.56747

Observations 32

ANOVA - Table


Regression 3 9829192240 3.28E+09 27.48810528 1.71138E-08

Residual 28 3337411824 1.19E+08

Total 31 13166604064

Coefficients


Intercept -65602.30028 10592.78596 -6.19311 1.09018E-06

INR/USD 184.9683459 210.1672976 0.880101 0.386296514

Inflation Rate 401.8976552 729.9445275 0.550587 0.586283509

Gold Reserve 210.1479148 40.04108164 5.248308 1.407E-05

Conclusion:

From the present study it is clear that there exists no significant difference in gold price movements in INR and Key Currencies

but, if month wise to consider then there exists significant difference. Further the relationship between the gold price

movement in India and Key currencies market were found significant, which may affect the gold price movement in India due

to change in gold price of that particular currency. Further the change in INR/USD does effect significantly the gold price

movements in India in a higher manner i.e., change in exchange rate INR/USD will bring comparatively much change in the

gold price in India.

The impact of inflation rate found lesser than exchange rate and also insignificant as it is having low degree of association, further gold reserves is having lesser impact than exchange rates and inflation rates, still it has significant impact due to high

degree of association with gold price movements. When the joint impact of exchange rate, inflation rate and gold reserves

studied together on gold price movements in India, also shows significant and considerable impact. Moreover, if INR get start

floating in the International Market then there is possibility that change in INR/USD will affect more the gold prices in India.

References:

[1] Aggarwal, R., & Soenen, L. A. (1988). The nature and efficiency of the gold market. The Journal of Portfolio

Management, 14, 18-21.

[2] Baur, D. G., & Thomas K. McDermott (2010). Is Gold a Safe Haven? International Evidence, Journal of Banking &

Finance, 34, 1886–1898.

[3] Cengiz Toraman et.al. (2011). Determination of Factors Affecting the Price of Gold: A Study of [4] MGARCH Model. Business and Economic Journal, 2(4), 37-50.

[5] Deutsche, W. (2011). Central banks and major investors join gold rush. Retrieved from http://www.dw-

world.de/dw/article/0,,15292029,00.html.



60

[6] Koutsoyiannis, A. (1983). A Short-Run Pricing Model for a Speculative Asset, Tested with Data

[7] from the Gold Bullion Market, Applied Economics, 15, 563–581.

[8] Lakshmi K (2007). Should India add more Gold to its Foreign Exchange Reserves, Retrieved from

http://ssrn.com/abstract=977127.

[9] Mahdavi, Saied & Zhou, S., (1997). Gold and Commodity Prices as Leading Indicators of Inflation: Tests of Long-run

Relationship and Predictive Performance, Journal of Economics and Business, 49, 475-489.

[10] Mani Ganesh & Srivyal Vuyyuri (2004). Gold Pricing in India: An Econometric Analysis, Retrieved from

http://ssrn.com/id=715841.

[11] Salent, S., & Henderson, D. (1978). Market Anticipation of government policies and the price of gold, Journal of Political Economy, 86, 227-249.

[12] Sjaastad, L., & Scacciavillani, F., (1996). The price of gold and the exchange rate, Journal of International Money and

Finance. 15, 879-897.

[13] Sujit, B. & Rajesh Kumar, B (2011). A Study on Dyanamic Relationship Among Gold Price, Oil Price, Exchange Rate

and Stock Market Returns, International Journal of Applied Business and Economic Research, 9(2), 145-165.

[14] Tandon, K., & Urich, T. (1987), International Market Response to Announcements of U.S. Macroeconomic Data,

Journal of International Money and Finance, 6(1), 7l-84

[15] World Gold Council (2009, 2010 and 2011), Quarterly Gold Demand Trends, Retrieved from (http:www.gold.org).

****



61

THE KERALA BUILDING AND OTHER CONSTRUCTION

WORKERS WELFARE FUND BOARD –

SOCIAL IMPACT ON MEMBERS

Dr. Abdul Nasar VP,

Associate Professor, Department of Commerce,

KAHM Unity Women’s College,

Manjeri, Kerala, India

Dr. Muhammed Basheer Ummathur,

Associate Professor & Head, Department of

Chemistry, KAHM Unity Women’s College,

Manjeri, Kerala, India

ABSTRACT

This paper looks into the social dimensions of Kerala Building and Other Construction Workers

Welfare Fund Board (KBOCWWFB) from the members’ perspective. The study is presented on a member-non-member basis. To pinpoint the regional differences a district wise analysis is also

attempted. The analysis showed that the Board has made positive impact on training and job

satisfaction of the members and education of their children. The study also revealed that the trade unions in the construction sector play a dominant role in the enrolment and disbursement of benefits to

the members.

Keywords: Construction industry, Members and non-members, Educational assistance, Trade Unions.



62

Introduction:

The Kerala model of development accords a prominent position to provide security to the working population in the

informal sector1. At present, there are 24 Welfare Fund Boards run by Tripartite Boards consisting of

representatives of workers, employers and the Government. In most Boards, the Government has retained the powers to give directions on policy matters. While successive State Governments continued to earmark substantial

resources and efforts to strengthen the Welfare Fund system, the present crisis afflicting many of the Boards needs

to be seen as an opportunity to reform the system. Even though the efforts made by Kerala in the field of social

service sector is laudable and appreciable; several questions arise now, such as approach, coverage, real content of the scheme, financial aspects, future operational efficiency and its impact on the workers. By following a

development policy entirely different from that of the other States in the country, the maintenance and improvement

of the quality of social services in Kerala have become extremely difficult2.

Realising the need for Social Security Schemes for the unorganised sector workers, Kerala Government has

initiated several progressive measures to provide Social Security to workers in the unorganised sector such as

agricultural workers, toddy workers, cashew workers, construction workers, etc. Among these, Kerala Building and Other Construction Workers Welfare Fund Board (KBOCWWFB or the Board) is unique in nature and worth

emulating for other unorganised sector workers3. Implemented in 1990, the Board has so far covered 14 lakhs

employees out of 16 lakhs working in the construction sector. Even though the coverage is satisfactory to a certain

extent, there is conflicting views regarding the impact of the scheme on the employees and the way in which the schemes are implemented. The success of a Welfare Fund Board has to be evaluated not merely on the basis of

number of members enrolled to it but also on the basis of the impact it has made on the socio-economic conditions

of its beneficiaries.

Review of Literature:

Vijaya Sankar, P. S. (1986) in a study on Head Load workers4 states that the basic objective of all Welfare Funds is

to provide a measure of social security and insurance for workers who are vulnerable to risks and uncertainties and

do not have any other institutional protection arising from their employment status. Vijaya Kumar, S. (1986) in his case study

5 found that trade unionism emerged as an insurance against job security and wage bargaining, but

subsequently it accentuated the process of segmentation in the labour market. In the process, workers belonging to

the powerful union established their working right in dominant sector while the weak were pursued to the less dominant segment. Anand, S. (1986) pinpointed the difficulties in providing welfare facility to the migrant

construction workers in Kerala due to the mobility of construction workplaces6. Jayasree, S. (1994) examined the

socio-economic and health status of women construction workers in the unorganised sector7 and found the impact

of welfare measures implemented by the Government and the extent of union participation among them. Women in

this sector suffer more due to their powerlessness, immobility and lack of bargaining power. Duvvury, Nata & Sabu

M George (1997) made an evaluation of the Welfare Funds in Kerala8. But the study makes only an overall

evaluation of all welfare schemes and not any specific one. A study on unemployment by Dolly Sunny (2000) found that in Kerala high priority was given for expansion of social and general services while production and

employment-oriented projects were either neglected or ignored9. Ignatius Pereira (2003) discussed reports

10 about

the seriousness of the role of labour mafia with the backing of powerful trade unions. He observed that trade unions are compelling to give employment to the workers given in the list supplied by them in some parts of Kerala. John,

C.P. (2004) through a socio-psychological analysis of the pensioners of KBOCWWFB showed that the breakdown

of the joint family and the emergence of nuclear family system create socio-psychological tensions in the lives of the elderly population

11. Personal and family liabilities compel a good proposition of the elderly construction

workers to engage in some kind of economic activities. Programmes will have to be developed to promote family

values and invigilate the young generation on the necessity and desirability of inter-generational bonding and

continuity. He offers some comments and suggestions to improve the welfare of the construction workers and the activities of KBOCWWFB.

Review of literature on construction industry shows that only few studies have been undertaken in India. These

studies highlight the general socio economic background of the construction workers and the nature and functioning of construction labour markets. In Kerala, despite the burgeoning construction and related activities, surprisingly

very few studies have been made to analyse the different dimensions of construction industry as a major form of

economic activity.



63

Methodology:

A well-drafted interview schedule was used to collect data from the respondents. The first part of the interview

schedule evaluates the socio-cultural, educational and family background of the construction workers and the

second part is entirely devoted to questions, which indirectly measure the impact of the Board on its members. The data for the study were collected from the construction workers; both members and non-members. The performance

and functioning of the Board was primarily analysed by collecting data from the offices of KBOCWWFB, offices

of other Welfare Fund Boards in Kerala, Labour Department; Government of Kerala, the publications and records

of various trade unions, Department of Economics and Statistics, Kerala Planning Board and other related agencies. The districts selected for the study were Thiruvananthapuram (Trivandrum) as the capital of the State, Ernakulam

as the district in which construction activities take place on a mass scale, Malappuram as the district where the

people spent a major portion of their earnings from gulf countries on construction activities and Wayanad as the district having least construction activities and lowest number of membership in the Welfare Fund Board. Stratified

random sampling technique was used for the purpose of the sampling. The sample size is selected under

proportional allocation method. Equal number of members and non members (300 each) were selected from Thiruvananthapuram, Ernakulam and Malappuram districts. As a district having the least construction activity, only

100 members each were selected from Wayanad. The period of this study covers the whole life of the Board since

its inception in 1990. However, the fieldwork for the study was conducted during 2005-2007.


Role of the Board on the Recruitment Pattern:

Mode of getting the job is an important factor influencing the socio-economic conditions of workers in any sector.

In the past, most of the jobs were ancestral and reserved for certain castes or families. But this situation has changed now. Due to the regular availability of jobs, reduction in the job opportunities in other sectors and

comparatively higher wage rates, there is an influx of new workers into this sector.

It is quite natural that the Board has its influence on the mode of recruitment of the people in to the industry. More

than one-third of the members and one half of the non-members got their present job by their own effort (Table 1). Labour contractors play a significant intermediary role in getting the job. Whenever, any contractor, employer or

owner wants employees, these labour contractors are ready to supply them. But for this, they charge commission

either in the form of reduction in the wages paid to the workers or ‘tips’ from the owners or contractors.

Table 1: Mode of Recruitment to the Industry (Percentage)

Mode of Recruitment Member Non-member Total

Own efforts 34.2 56.3 45.25

Labour contractor 13.8 6.9 10.35

Labour society 3 1.6 2.3

Other workers 14.4 15.8 15.1

Union 6.8 0 3.4

Welfare Fund Board 10.3 0 5.15

Employment exchange 4 0.7 2.35

Local influence 4.5 3.4 3.95

Political influence 1.3 0.6 0.95

From father/ancestors 5.6 12.9 9.25

Others 2.1 1.8 1.95

Total 100 100 100

The percentage of members who got job through Welfare Fund Board is only 10.30. This shows that the role of the

Board in recruiting people to the industry is too meager and insignificant. In fact, the Board should design a

scientific system of recruitment in order to obtain higher levels of workmanship. This can go a long way in improving the goodwill and public image of the Board.

The enrolment of traditional caste to the Board is also meager mainly because of the fact that trade union leaders

are least interested to enroll them due to their lack of political association to the union. At the same time there is



64

complaint from the trade union leaders that a practice is emerging among individuals belonging to certain caste in

many parts of the State to enroll the persons belonging to a particular caste to the Board even though they are not

doing the construction job. Only 6.80 per cent of members got work through the trade unions and none of the respondents belonging to non-members agree trade union leaders have some role in procuring job to them. From

the workers’ point, it is advantageous as it ensures more employment and better wages. But, on the other hand, it

also enhances conflict with the public because they have to pay a higher wages. The study reveals that the role of the trade unions and the Welfare Fund Board is negligible in procuring job to the workers in this sector. The trade

union authorities are not giving due attention in this regard and concentrate mainly on the enhancement of the

number of employees enrolled to the Board through their union for the purpose of enhancing their political base.

Impact of Training on the Workers:

The technological revolution taking place in every field has its impact in this sector also. The method and technology of

different stages of construction work are changing frequently. The employees can cope up with these changes only through

training. Apart from creating confidence among workers, training improves their work efficiency. Towards this end the Board has launched an Advanced Building Technology Training Institute at Thiruvananthapuram. However, the institute has

not been successful in realising its objectives. The members and their children were not ready to undergo training even at

free cost offered by the Board. In fact, the Board has offered certain amount as stipend to the trainees. The members were of

the opinion that since ample employment opportunities exist in the sector, even to non trained workers, the time spent for training will be a waste which could otherwise be utilised for earning wages. Thus, the majority of the employees or their

children are not willing to spare few weeks for getting training. They get jobs without training and hence are not ready to

forego the wages of the training period. The stipend given by the Board is not attractive to the workers. Those who were trained by the Institute responded that the training has great impact on their workmanship. About 34 per

cent of the Institute trained members got better offer in multinational companies immediately after the training. Thus, the

Board has immense impact on its members in sharpening their skills which ultimately leads to better job prospects. Since non-members have no affiliation with the Board, they have no chance for free training offered by the Board.

Any similar training programmes offered by outside agencies are highly expensive and unaffordable to them.

Satisfaction of Members with the Construction Work:

The satisfaction in continuing a job depends on many factors such as regularity of employment, wage rate, working conditions,

future prospective, social security, etc. Large numbers of workers are attracted to the organised sector solely because of the

security provided by the sector such as regularity of work12

, leave with pay, provision for the future, etc. The Welfare Fund Boards are mainly constituted to provide social security to the workers in the unorganised sector. As revealed by the survey

(Table 2) the welfare fund for the workers in construction sector has succeeded in providing some satisfaction to members.

Table 2: Level of Satisfaction of the Board Members with the Existing Work

Scale Level of Satisfaction Members (percentage)

1 Well satisfied 41

2 Fairly satisfied 27

3 Unsatisfied 16

4 Fairly unsatisfied 6

5 Neutral 10

Total 100.00

The reasons to stick on the construction work are given in Table 3.

Table 3: Reasons for Satisfaction in the Construction Work

Reasons for Satisfaction Percentage of Members

Regularity of employment 23.25

Higher wage rate 14.55

Welfare benefits of the Board 26.40

Good working conditions 04.55

Future prospective 10.60

Other reasons 20.65

Total 100.00



65

Social security measures offered by the Board seem to be one of the main reasons for satisfaction with the existing

work. It must be noted that there is hectic construction activities going on in Kerala due to the influx of petro-dollar

to the State. Thus, there is persistent demand for construction workers.

Couples working together:

If the workers are getting sufficient income, they usually do not like to see their spouse working especially in fields

like construction where manual labour is required. However, in some construction sites couples are working. The

survey revealed that the wives of 25.70 per cent of respondents are working either in construction field or other fields. Table 4 shows that family responsibility acts as a major hindrance to majority of the wives in undertaking any job.

The general belief is that the wife has to go for work only when the income of the family head is not adequate. In

some traditional communities, there is no practice of women participating in outdoor activities.

Table 4: Reasons for Wife Not Working (Percentage)

Reasons Members Non-members Total

Wife employed 23.30 28.10 25.70

Adequate income 7.90 2.70 5.30

Family responsibility 32.00 31.20 31.60

Unwillingness to do work 6.40 10.40 8.40

Non availability of suitable job 16.30 9.70 13.00

No practice of going for work 8.20 7.80 8.00

Other reasons 5.90 10.10 8.00

Total 100.00 100.00 100.00

Pearson Chi-square: 148.683, df = 17, p = . 000000

Further, as the calculated p value is less than 0.05, there exists significant difference among members and non-

members in the reasons of their wife not going for work.

Child Labour in Construction Industry:

Even though child labour is prohibited in India, due to the availability of job suitable to the children and

comparatively higher wages of the industry, children are working in the construction sector. There is no provision either in the Central or State Acts to enroll these child workers to the Welfare Fund Board. However, it may be

noted that children of 15 years and above are allowed to enroll as per the Tamil Nadu Act.

When the working conditions are good, wage rate is attractively high and there is regularity of employment, people

like to continue their ancestral job. About ¾ of the members and non-members do not like to see their children working in the construction sector as in Table 5.

Table 5: Child Labour and Workers’ Willingness

Workers’ willingness Member Non-member Total

Like to see children working in the

construction field 24.90 25.10 25.00

Do not like to see children working in the construction sector

74.60 72.80 73.70

Not responded 0.50 2.10 1.30

Total 100.00 100.00 100.00

Pearson Chi-square = 16.2465, df = 3, p = . 001011 Thus, even after 15 years of establishment of the Welfare Fund Board, it has failed to create a sense of security even

among the members. As the Chi-square = 16.2465, df = 3, p =. 001011; since calculated value p < 0.05, there is no

significant difference among members and non-members in seeing their children working in the construction sector.



66

Impact on Education:

Among all other assets, education is considered as the most precious and invaluable wealth in the world. Kerala is a

State which has been declared as cent per cent literate. Considering the importance of education, the Board gives

various assistances for the education of the members’ children13

. In terms of number, educational assistance is one of the largest benefits given by the Board to its members. Table 6 shows the amount of scholarships given by the

Board for various courses to the members’ children.

Table 6: Rate of Scholarships given by the Board for various courses

Sl. No. Name of Courses Rate of scholarship

(Rs per year )

1 School Final 250

2 Plus2/VHSE/T.H.C/T.T.C/Certificate Courses, Nursery Teachers Training 600

3 I.TI/I.T.C/J.T.S 720

4 Poly technique courses /J.D.C 900

5 Computer Courses/ P.G. Courses, Nursing Diploma (General),

B.Ed/M.Ed/H.D.C/ P.T/C.A/Journalism 1200

6 P.G.D.C.A, Paramedical courses, Professional courses/M.B.A/M.C.A/

Health Inspector Course/L.L.B 2400

7 Degree Courses/D.T.P/M.B.T 840

Source: Kettida Nirmana Thozhilali Masika – Various Issues.

The number and amount of cash awards and scholarships are increasing over the years. This shows an increased pressure on the part of the members to get more scholarships. The Board so far distributed Rs 356.55 lakhs by cash

award and scholarship among 65566 beneficiaries in the State (Table 7). This constitutes only about 2 per cent of

the total benefit disbursed by the Board. Although this benefit is an insignificant proportion of total disbursements, there is a steady increase in the number of beneficiaries and the amount awarded, except during 2006-2007.

Table 7: Scholarships and Cash Awards Disbursed by the Board

Year No. of

beneficiaries

Amount

sanctioned

Total benefit

paid

Expenditure as a percentage

of total welfare benefits

1991-1992 58 36200 597250 6.06

1992-1993 465 261050 4350275 6.00

1993-1994 630 61100 3788380 1.60

1994-1995 654 617900 4903227 12.58

1995-1996 641 300400 18004877 1.67

1996-1997 933 201400 30968542 0.65

1997-1998 956 967850 46960412 2.10

1998-1999 1492 1155890 66563730 1.76

1999-2000 1826 1258970 104960635 1.06

2000-2001 2350 1520100 113664703 1.18

2001-2002 2668 1282800 143101693 0.90

2002-2003 4792 3267250 200316943 1.63

2003-2004 15772 5003650 205842218 2.43

2004-2005 12562 7572140 265411363 2.85

2005-2006 14309 8358210 314941164 2.65

2006-2007 5458 3789910 269148664 1.41

Total 65566 35654820 1793524076 1.99

Source: Annual Reports of KBOCWWFB; various years.



67

Even though in terms of number, educational assistance is the second largest (next to pension), the amount is the

least. On an average only 1 to 2 per cent of the total benefits are paid as educational assistance. It was highest

during the year 1994-1995. However, in the next year it was declined. During the years 1996-1997 and 2001-2002, it is even less than one percentage. Thus, the educational assistance is very meagre especially under the present

situation where people give more emphasise for the education of their children. Further, the cost of education is

increasing at a higher rate. Hence, it requires an increase in the various assistances provided by the Board to the education of the members’ children.

Educational Qualification of the Children of the Respondents (Above 5 Years of Age):

Educational assistance is one of the main attracting benefits of the Board members. The various schemes of

assistance are mainly framed to promote the educational status of the members’ children. As the Board gives assistance for the education from high school level onwards, 69.30 per cent of members have children studying

from high school level to professional courses while among non-members it is only 38.80 per cent (Table 8).

Further, during the survey, the members reported that the scholarship granted by the Board were of immense use in meeting the educational expenses of their wards.

Table 8: The Educational Qualification of the Members’ Children

Children Illiterate Lower

Primary

Upper

Primary HS HSS Degree

Professional

degrees Total

Member 1.20 15.70 13.80 41.60 17.20 8.60 1.90 100

Non-Member 2.70 29.40 29.10 27.10 8.30 2.10 1.30 100

Total 1.95 22.55 21.45 34.35 12.75 5.35 1.60 100

Aspiration of the Members Regarding the Education of their Children:

Education of children is considered as the main concern of people irrespective of level of income and socio-

economic status as most of the members and non-members have great aspirations regarding the education of their children. Even though most of them are satisfied with the existing conditions of work, many of them do not like to

see their children as workers in the construction sector. There is a feeling among the employees in this sector that

they were compelled to select this work due to lack of education and this should not happen to their children. However 15 per cent of members’ and 18.50 per cent of nonmembers’ children could not go for education as they

assist their parents in the construction work. The percentage of members having no children at the age of education

is 6.88 and that of non-members is 8.66. In this context the educational scholarships and cash awards introduced by

the Board become more relevant. Table 9 depicts a picture of the educational aspirations of members and non-members about their children.

Table 9: Aspirations Regarding Higher Education of Children (Percentage)

Aspiration for higher education Member Non-Member Total

Not interested 3.10 1.70 2.40

SSLC 5.60 12.80 9.20

Plus 2 14.70 17.50 16.10

Degree 37.20 29.70 22.10

Post Graduation 13.70 11.30 12.50

Professional education 21.20 14.80 18.00

Technical work 2.30 5.80 4.05

Not responded 2.20 6.40 4.30

Total 100.00 100.00 100.00

Pearson Chi-square: 189.404, df = 9, p = 0.00000.

As the calculated value of Pearson Chi-square = 189.404, df = 9, p = 0.00000; since P < 0.05, the association between

members and non-members is highly significant with regard to educational aspiration regarding the higher education



68

of their children. Majority of the members responded that the educational assistance of the Board has influenced their

educational aspirations and they are confident in meeting the educational expenses of their children.

Satisfaction of the Board Members about Educational Assistance:

Majority of the members agree that the Board assistance has promoted the education of their children (Table 10).

Table 10: Satisfaction of the Board Members about Educational Assistance

Satisfaction Percentage of members

Satisfied 62.00

Unsatisfied 28.00

Neutral 10.00

Total 100.00

Trade Union Activity:

Construction workers are enrolled to the Board only on the production of a certificate from the contractor, labour

officer or registered trade union leader to the effect that the worker has worked for a minimum of 90 days

construction work during the previous year. But the contractors and labour officials are generally reluctant to issue such certificates due to the fear of future unfavorable consequences. But the trade union leaders are generally ready

to issue such certificates to any person, even without a ‘construction back ground’. They consider this as a medium

to propagate their political ideology and thus to increase their union membership. The survey (Table 11) reveals that

all the Board members are also members of the trade union. Among non-members only 16.80 per cent are members of a trade union and the majority is not affiliated to any political union.

Table 11: Trade Union Activity

Trade union membership Member Non-member Total

Member of trade union 100.00 16.80 58.40

Not a member 0.00 83.20 41.60

Total 100.00 100.00 100.00

Status of Membership:

During the survey an attempt was also made to analyse the level of union activities of the Board members. About one-third of the Board members are office bearers of various trade unions and the remaining two- third are only

primary members in various unions. Among non-members, only 8.33 per cent are office bearers while 91.67 per

cent have only primary membership in trade unions (Table 12).

Table 12: Status of Membership

Status of membership Member Non-member Total

Member 67.60 91.67 71.06

Office bearer 32.40 8.33 28.94

Total 100.00 100.00 100.00

This shows that there is high political involvement among Board members compared to non-members. It is quite

natural since the Board itself is a politically motivated one and trade union leaders control it, the members have to

be part and parcel of these trade unions. It was also found that compared to non-members, members occupy key positions in the trade union leadership.

Working Area of Trade Union:

The study also looked into the intensity of the trade union activities among the Board members. It is a common fact that

there are trade union leaders among the Board members and non-members working from local to national levels (Table 13).



69

Table 13: Working Area of the Workers in the Trade Union

Area Members Non-members Total

Local 32.40 43.20 37.80

Taluk 20.28 18.36 19.32

District 28.80 27.17 27.98

State 14.35 10.12 12.24

National 4.17 1.15 2.66

Total 100.00 100.00 100.00

The Role of Trade Union Leaders in the Enrolment and Disbursement of Benefits The trade unions in the construction sector play a dominant role in the enrolment as well as disbursement of

benefits. The enrolment is mainly done through the trade union leaders and in most cases the members approach the

trade union leaders for getting benefits from the Board. However, there is difference of opinion among the members about the role of trade unions. Table 14 gives a picture of the extent of satisfaction among the members about the

role of trade union leaders.

Table 14: Satisfaction of the Members about the Role of Trade Union

Role of trade union Ernakulam Malappuram Wayanad Thiruvananthapuram Total

Satisfied 90.00 88.33 75.00 54.33 77.30

Not Satisfied 10.00 11.67 25.00 45.67 22.70

Total 100.00 100.00 100.00 100.00 100.00

Almost all members in the Board are enrolled through trade unions. In the disbursement of benefits also the trade union leaders assist the members in filling the application form, submission of application for benefits in the

District Executive Office of the Board and also in processing the application in the office.

Reasons for Dissatisfaction of the Trade Union Leadership:

The various reasons for dissatisfaction among the members about the role of trade unions are analysed in Table 15.

It was observed that 32.16 per cent of the members are dissatisfied due to the delay in submitting documents for enrolment and disbursement of benefits even after collecting the documents from members. The trade union leaders

reported that they usually wait for getting more applications from members so that the transaction cost could be

reduced. About one-fourth of the sample members find over politicalisation of membership as their cause of dissatisfaction. Once membership in the Board is taken through trade unions, it becomes a political trap. The trade

union leaders may compel the members to participate in the various programmes organised by the political parties.

In Wayanad district 80 per cent of members see over politicalisation of membership while in Thiruvananthapuram

district it is only 13.87 per cent.

Table 15: Reasons for Dissatisfaction about the Trade Unions

Reasons for dissatisfaction Ernakulam Malappuram Wayanad Thiruvananthapuram Total

Over politicalisation of membership 26.67 25.71 80 13.87 24.67

Cheating of members 20 14.29 20 20.44 19.38

Delay to submit documents 36.66 22.86 0 39.42 32.16

Over charging of members 0 14.28 0 21.9 15.42

Other reasons 16.67 22.86 0 4.37 8.37

Total 100 100 100 100 100

Pearson Chi-square: 120.233, df = 18, p = . 000000

There is a practice among trade union leadership to collect some additional amount to the union fund in addition to

their usual monthly subscription. This, according to them, is to meet the administrative cost of the union. There are many complaints against the unions that the members are overcharged.



70

Conclusion:

This paper explains the district wise analysis of various social impacts by the Kerala Building and Other

Construction Workers Welfare Fund Board (KBOCWWFB) among its members. The data are also compared with

non-members to understand the effectiveness of the Board. The districts selected for the study are Thiruvananthapuram, Ernakulam, Malappuram and Wayanad. The study reveals that the role of the trade unions

and the Welfare Fund Board is negligible in procuring job to the workers in this sector. The Board has launched an

Advanced Building Technology Training Institute at Thiruvananthapuram. Those who were trained by the Institute

responded that the training has great impact on their workmanship and most of them got better offer in multinational companies immediately after the training. In terms of number, educational assistance is one of the

largest benefits given by the Board to its members. Majority of the members responded that the educational

assistance of the Board has influenced their educational aspirations and they are confident in meeting the educational expenses of their children. The trade unions in the construction sector play a dominant role in the

enrolment as well as disbursement of benefits. The enrolment is mainly done through the trade union leaders and in

most cases the members approach the trade union leaders for getting benefits from the Board. It is also found that compared to non-members, members occupy key positions in the trade union leadership. As revealed by the survey,

the Board has succeeded in providing satisfaction to 68 per cent of the members.

References:

[1] A. Sivananthiran & C.S. Venkata Ratnam. (2005). Informal Economy: The Growing Challenge for Labour Administration. Indian Industrial Relations Association (IIRA), New Delhi, International Labour Organization.

[2] K. K. George. (1993). Limits to Kerala Model of Development: An Analysis of Fiscal Crisis and its

Implications. Centre for Development Studies, Thiruvananthapuram. Monograph Series, p. 133. [3] Abdul Nasar, V. P., Aboobacker Sidheeque, K. T. & Muhammed Basheer, U. (2013). Kerala Building and

Other Construction Workers Welfare Fund Board – A Macro Picture. International Journal of Research

in Commerce and Management, 3(3), 25-38.

[4] Vijaya Sankar, P. S. (1986). The Urban Casual Labour Market in Kerala – A Study of the Head-Load Workers of Trichur (M. Phil Thesis). Centre for Development Studies, Thiruvananthapuram.

[5] Vijaya Kumar, S. (1986). Working Conditions and Wage Rates of Head load Workers -A Case Study (M. Phil

Thesis). University of Kerala, Thiruvananthapuram. [6] Anand, S. (1986). Migrant Construction Workers: A Case Study of Tamil Nadu Workers in Kerala (M. Phil

Thesis). Centre for Development Studies, Thiruvananthapuram.

[7] Jayasree. S. (1994). Women in the Unorganised Sector – A Case Study of Women Unorganised Workers in Kerala (Ph. D Theses). University of Kerala, Thiruvananthapuram.

[8] Duvvury, Nata & Sabu. M. George. (1997). Social Security in the Informal Sector - A Study of Labour

Welfare Funds in Kerala. Centre for Development of Imaging Technology, Thiruvananthapuram.

[9] Dolly Sunny. (2000). Unemployment and Employment of Educated Youth in Kerala. The Indian Journal of Labour Economics, International Series No: ISSN 0971-7927, Volume 43, Number 4, December.

[10] Ignatius Pereira. (2003). Law to curb `labour mafia' soon. The Hindu daily, Kollam, May 25.

[11] John C. P. (2004). Social Security and Labour Welfare with Special Reference to Construction Workers in Kerala. Discussion paper 65, Kerala Research Programme on Local Level Development, Centre for

Development Studies, Thiruvananthapuram.

[12] Labour Welfare and Social Security. Chapter 3.5, Tenth Five Year Plan, 2002-07, National Development Council. [13] Human Development Report .(2005). State Planning Board, Government of Kerala, Prepared by Centre for

Development Studies, Thiruvananthapuram, Kerala.

****



71

A STUDY OF SOCIO ECONOMIC CONDITION OF CHILD

LABOUR ENGAGED IN RAG-PICKING AT SILCHAR

Shima Das,

Research Scholar,

Department of Management Mizoram University,

Aizawl, India

Dr. Amit Kumar Singh,

Assistant Professor, Department of Management

Mizoram University, Aizawl, India

Bidhu Kanti Das,

Assistant Professor, Department of Management

Mizoram University, Aizawl, India

ABSTRACT

A more serious and vulnerable group of the urban poor that is growing rapidly in the towns and cities is that of working children, with a home or without a home. Many of them may be just runaways, as a

result of broken home, allure by the city life, migration of their families, and have no other alternative

than to work. In this paper we made an attempt to find out the socio economic condition of child labour engaged rag picking in Silchar. Also, we try to find out the forcing factor for the children to

choosing the work and solution to solve this problem.

Keywords: Rack-pikers, child labour, Child work.



72

Introduction:

The term ‘child labour’ means different things to different societies. A universally accepted definition of child

labour is not available. There are differences between child labour and child work. ‘Child work’ refers to occasional

light work done by children which in most of the societies is considered to be an integral part of the child’s socialization process. While helping parents at home and in family farms, children learn to take responsibility and

pride in their own activities, acquire certain skills and prepare themselves for the task of adulthood. ‘Child labour’

implies children prematurely leading adult lives, working long hours for low wages under conditions damaging to

their health and to their physical and mental development, sometimes separated from their families, frequently deprived of meaningful educational and training opportunities that could open up for them a better future.

It is true that if one wants to see a nation, he should see its children. No doubt work is worship but it never meant

the child labour. The problem of child labour is a burning problem of the world, and largest share of child labour of the world is in India. From the time immemorial, it had been a concern of the social reformers, the legislators, the

jurists, the philosophers, the politicians and economists, etc. Children’s are blooming flowers of the nation, nobody

should be allowed to pluck these flowers, rather they need their protection from the worst conditions prevailing in any society. The smile on their lips and innocence in their eyes required to grow further. As poverty is the root

cause of the child labour and India where more than thirty percent of the people leaves below the poverty line, two

meals in a day is the biggest worry of the people, where to have sufficient meals two times in a day is the goal of

life. It is not only the feeding of his own self, but feeding of his children too. These leads to the migration of the poor people in urban areas and putting their children at work where no other option left behind. Apart from it, the

long illness, death of earning member of the family, breaking down of family, left and run away children leads to

the problem of child labour in urban places. A more serious and vulnerable group of the urban poor that is growing rapidly in the towns and cities is that of

working children, with a home or without a home. Many of them may be just runaways, as a result of broken home,

allure by the city life, migration of their families, and have no other alternative than to work. Again they may not have sufficient skill and knowledge to work in an establishment. Also law prohibits the employer to employ them.

So, they take picking recyclable rags from dustbins, dumping grounds and other unhygienic places and selling it for

their livelihood.

Objectives:

The present study have the following objectives

1. To highlight the socio-economic background of children engaged in rag picking in Silchar.

2. To identify the health condition of children engaged in rag picking in Silchar. 3. To suggest measures to improve the conditions of the children in rag-picking work.


The present study is descriptive in nature and following are the outline of the methodology.

Sources of Data: in this study we utilized both primary and secondary data. The primary data was collected through direct interview, observation, schedules and case studies. The primary data sources were visiting different places of

Silchar town where child rag-pickers are accessible and working. The secondary data sources comprises of reports

published by government and NGOs, books, news papers, magazines and journals. The whole Silchar town was taken as universe of the study. The purposive sampling method was employed to select the sample for the study. It

is estimated that there are 300 child rag pickers in Silchar, therefore; 150 child Rag pickers (both boys and girls)

was selected for the present study.

Literature Review:

For the study of socio economic conditions of child labour engaged in rag picking at silchar following literature

were consulted to get an fair idea about the national scenario about the child labour working in different sectors and

its similarities with silchar. Assam. A survey of children at work (Mendelvich, 1979) tries to highlight the problem of child labour in India and its

causes. In fact, the problem of child labour in India may be seen as the result of traditional attitudes, urbanisation,

industrialisation, migration, and lack of schools or the reluctance of parents to send their children to schools, etc. In



73

the ultimate analysis, main case is extreme poverty and agriculture being the main occupation of the majority of

population requiring more hands.

A report on the committee on child labour (ministry of Labour, Government of India, December, 1979) indicated that in our country, the tradition of educational learning outside home was confined to the upper caste, the

privileged classes. Children of the producing classes learnt the necessary skills and work in the family. Step by step

these children get steeped in the ethos of labour. Thus poverty and child labour always make each other and tend to reinforce themselves in families and communities. For a number of tasks, employers prefer children to adults.

Children can be put on non-status, even demeaning jobs, without much difficulty. Children are more amenable to

discipline and control. Child labour is also cheaper to buy and is a greater source of profit. In fact child workers are

not organised on lines of trade unions which can be militantly fight for their cause. Child labour is also justified on the ground that it trains the child's fingers in the required skill.

Taking the case of Haryana State, a study conducted on the working children in Hisar (Sharma, 1982) revealed that

a majority of the child workers joined the labour force due to acute poverty of their family, death and chronic illness of the earning members there was no source to supplement their family income. Children came from

different states. About 4/5 of the children came from the families whose average monthly income was less than Rs.

300 and the size of the family was 8 on an average. The social circumstances which also motivated the child workers to seek jobs were company of friends, rude behaviour of father and lack of affection in the family.

Another study on the working condition of children employed in unorganised sector (CSIR, 1984) which was based

on the sample of 900 male and female child worker below the age of 16 years indicated that the majority of children

employed in match units in Sivakasi were girls (67 percent). Only 8 percent were children below 10 years of age and a majority of the child workers (71 percent) were in the age group of 13-16 years.

A study on working children in urban Delhi (ICCW, New Delhi, 1997) has tried to examine the extent, causes and

consequences of child labour practices in Delhi. The study found that most of the children were employed in workshops and the children employed in tea stalls, dhabas and as domestic servants come from Uttar Pradesh and

Bihar. The average monthly income of their families was Rs. 321.50 and the average size of the household with

working children was 5. The number of working children per household generally increased with family size. The

daily hours of work in most of the establishments were 6-10; and against the maximum of six hours (for young person’s between the ages of 12 and 18) laid down in the Delhi Shops and Establishment Act. Nearly 50 percent of

the children in registered tea stalls and dhabas worked for more than 12 hours a day. The environment and working

conditions are unsatisfactory and most of the establishments are situated in the walled city and are located in lanes and by-lanes. The lighting and ventilation in these working areas are just sufficient to carry on the work but

sanitation and hygiene cannot be simply thought of in such conditions. Children engaged in manufacturing and

servicing earned less than Rs. 60, domestic workers earned 26-50 and those in shops and dhabas earned 26-50. The child workers in auto repair and cycle repair shops are being given Rs. 30 as wages. These children have to be

satisfied with low status

Gangrade and Ghatia’s (1983) reports on women and child workers in unorganised sector indicate that India has the

largest number of working children. The brick kiln industry in Stwarigaon near Delhi attracts poor rural families who work from October to June when there is no agriculture work. Families are paid on a piece work basis ranging

from Rs. 18-21 per 1000 bricks. So they use their children to increase their production. The children risk injury

from the work as well as silicosis of the lungs after three or four years of exposure to brick dust. A study on the child labour - a socio-economic perspective by Singh (1990) revealed that economic conditions of

41.5 percent of the worker's families forced them to undertake carpet weaving, 14 percent of the child workers

parents felt motivated to put their children in labour market who were getting in it bad company and in case of 13 percent child workers, they themselves wanted to earn and live like their colleagues in the community. A majority

of children 62.1 percent were illiterate and in rest of them education varied from first standard to eight standard. Of

the total child labour force, 72.5 percent of the child workers came from backward caste families, 19.1 percent from

scheduled caste and 5.5 percent from upper caste families. The employer’s preference to have child labour indicates that 33.5 percent preferred to employ children because they work hard. For 18.5 percent of the employers, child

labour is cheaper then adult workers and for 15 percent of the employers motivation has been that they can be put to

any job. It is also indicates that 39.9 percent child workers earned between Rs. 151 and Rs. 200 per month, 35.4 percent between Rs. 101-150, 44 percent between Rs. 51-100, 4.1 percent earned Rs. 50 or less whereas 18

respondents did not earn anything. It is also found that majority of the child workers accepted that they worked for

11 or more than 11 hours per day.



74

Maurya (2001) in his study ‘Child Labour in India’ highlights about the legal provisions against exploitation of

child labour. This paper talks about the government of India’s ratification of six ILO Conventions concerning

working children and enacted appropriate laws for protecting them from economic exploitation and from performing any work that is, in a way, likely to be hazardous or harmful to their health or physical, mental,

spiritual, moral and social development. There are a number of enactments in the country which protect and

safeguard the interests of child labour. The employment of children below 14 years of age has been prohibited under (i) the Children (Pledging Labour) Act, 1933 (ii) The Factories Act, 1948 (iii) The Mines Act, 1952 (iv) The

Motor Transport Workers Act, 1961 (v) The Bidi and Cigar Workers ( Condition of Employment) Act,1966 (vi)

The plantation Labour Act, 1951 and (vii) The Child Labour (Prohibition and Regulation) Act, 1986. Apart from all

these legal provisions he found there is still a need to expand network of enforcement machinery required for enforcing various existing laws on child labour in the country. He said in his paper, this exercise, if done, will

certainly go a long way in saving the precious future of millions of working children in India.

Association for Development (2004) conducted a study on the problems of street and working children living railway stations in Delhi. The main objectives were to identify the needs and problems in the day-to-day life of

these children as well as abuse by various authorities and other sections of the society. The study was conducted

among children staying at New Delhi, Old Delhi and Hazrat Hizamuddin railway stations. A random sample of 100 respondents was taken for the study in the age group of 4-17 years. The findings of the study shown that 39 % of

the children were from U. P. followed by 26% from Bihar, 7% were from Delhi. Some of the children did not know

the name of their village. Most of them were from families belonging to the lower income group. 47% mentioned

abused by parents as the reason for leaving their home. Out of 100 respondents 52% did not desire to go back to their families. 36% replied in the affirmative to go to any institution like a home, and the remaining 64% said they

wish to remain on the street. It was also seem that most of the respondents often travelled to places outside Delhi

due to lack of home or a permanent place to stay. The major problems of these children faced in their daily life were harassment by police and lack of basic need of shelter most of these children were addicted to drugs also.

There is not any available data on the status of child labour in Silchar, hopefully this may be the first study to get

the socio economic condition of child labour engaged in rag picking.

Findings of the study:

Statistics deal with large mass of inter-related data. To make the study more useful and collection of most reliable

data, efforts have been taken to collect and arrange it in a systematic way. Collection of statistical data necessitates

a pre-consideration of the type of sampling to be undertaken. If a detailed and exhaustive enquiry is to be made, the census type of enquiry is unavoidable; but if the case is otherwise, other techniques of sampling may be effectively

used. As the scope of investigation in my study is large, it is difficult to apply census method for collection of

primary data. Therefore, the sample method of enumeration and collection have been used. For collection of

primary data, field survey was conducted by canvassing personal interview and response were filled up in an interview schedule. 150 child rag-pickers were interviewed, and there response were recorded and scrutinized. On

the basis of the data so collected, tabulation, analysis, and interpretation have been made as follows:

Table: 1 Analysis of data

Parameters Total no of

respondent Divisions

Chi-square

value

Age 150

4Years - 6 Years 11

31.867 7 Years - 9 Years 37

10 Years -12 Years 59

13 Years - 15 Years 43

Religion 150

Hindu 87

73.560 Muslim 60

Christian 3

Literacy Level 150 Illiterate 105

24 Literate 45

Attending schools 150 Yes 2

142.107 No 148



75

Reasons for not attending

schools 148

Can’t afford 88 Analysis were made on

percentage

Parents did not send 6

Others 54

Native Place of the

Respondents 150

Silchar 84 Analysis were made on

percentage Outside silchar 66

Types of family 150 Nuclear 141 Analysis were

made on

percentage Alone 9

Years of Working as Rag Pickers

150

0 -1 year 10 2

Analysis were

made on

percentage

1 – 3 year 20 10

3 – 5 years 46 5

5- 7 years 46 0

7 - More 11 0

Age of the child when

started rag picking 150

Age Male Female

Analysis were made on

percentage

3 - 5 2 0

6 – 8 31 7

9 - 11 95 10

12 – 14 5 0

Reasons for preferring the Job

150

Reason Male female

Analysis were

made on

percentage

Getting Money 33 4

Getting food 91 13

Getting freedom 2 0

Don’t know 7 0

Forcing factor to join

Rag picking 150

Voluntarily 2 Analysis were

made on

percentage

Parents 41

Relatives 33

Self 74

Nature of Work 150

Regular 105 Analysis were made on

percentage

Part time 43

Occasional 2

Daily working hours of

Child rag Pickers 150

Working hours Full time Part time or

occasional Analysis were made on

percentage

3 - 5 13 9

5 - 8 50 34

8 - 12 42 2

Daily collection of Rags 150

Analysis were made on

percentage

1 – 3 Kg 24 6

4 – 6 Kg 44 29

7 – 9 Kg 28 9

10 – 12 Kg 9 1

Types of House 150

Own 27 Analysis were made on

percentage

Rent 89

Others (specify) 25

Sanitation facility 150

Yes 33 Analysis were

made on percentage

No 117

Daily Income 150

Rs.01 – Rs.30 72

72.773 Rs.31 – Rs.60 52

Rs.61 – Rs.90 21



76

Rs.91-Rs.120 5

Contribution to

family/parents 150

Yes 108 Analysis were

made on

percentage No 42

Personal Habits 150

Chewing of pan/saada 129

Analysis were

made on

percentage

smoking 99

Drinking alcohol 45

Using drugs 03

Consume tea/coffee 150

Sickness/injury of child

rag pickers 150

Any other 83 8.640

No 57

Physical Appearance 150

Dirty & unclean 54 Analysis were

made on

percentage

Looking suffering from some disease/ Malnourished

90

Good health 6

Relation with

Community People 150

Friendly 10 Analysis were

made on

percentage Rejected 120

Others 20

Perception of social

status (affected by this

profession)

150

Yes 2 Analysis were

made on

percentage

No 13

Cann’t say anything 135

Job Satisfaction 150 Yes 35

42.667 No 115

Response Regarding the

State of Abuse 150

Abused 135 96

Not Abused 15

Source: Field study conducted in Silchar in 2010

The child rag pickers are started rag picking at the tender age of four. A vast majority of them are found in the

age group of 7 to 12 years, It was found that a vast majority of them are male, and a small portion of them are

female. Female child participation in this work is very less because of high level of risk involvement in work

place.

Majority of child rag pickers are Hindus and a sizeable percent of them are Muslim also. A negligible percentage

of Christan children were also found working as rag picker.

A vast majority of the rag pickers are illiterate; rest of them can write their name. Very few of them completed

primary level. Except few all of them are not attending school.

Majority of them are not attending school, because they can’t afford. Others are not attending school because of

various reasons like, parents were not sending, they have to earn their livelihood and contribute in their families.

Majority of the child rag pickers were from Silchar. Others were migrated from different parts of Barak valley

either with family or without family.

A vast majority of the child rag pickers were belonging to nuclear families with a large number of siblings. It was

found that few of them have grandparents also.

Majority of the child rag pickers were staying in rented houses and other places. Only a small percent of them are

living in their own home.

A small percent of street children had been found. They were generally staying at railway stations, bus stops and

other places; they are not staying at any fixed place. Generally other people are also staying with them.

Majority of them are collecting rag for last 3 years to five years. A small percent of them were working for one

years or less than that. And majority of them had started rag picking at the age of 2 to 11 years. Few of them have

started even at the age of four. They are working mainly for two reasons i.e., getting food and getting money.

Majority of them had chosen this work by their own. Other prominent portion was put in this work by their parents. These children’s are generally collecting plastic, papers including news papers, tins and irons, bottles



77

canes, and food items also. It is found that majority of them are generally working five to eight hours; another

sizable percent were working even more than eight hours.

A vast majority of them are regular full time rag pickers. A sizable percent of them are working as part time rag

pickers. Part time rag pickers are generally involved with others job also, like begging, working as a coolie or part time helper at tea stalls or shops at footpaths.

A vast majority of them are collecting 3 to 5 Kg rags per day. And a sizeable percent of them are collecting 5 Kg

or less then it, and a small percent of them are collecting more than 5 kg.

Vast majority of the child rag-pickers are selling their rags to adult rag-pickers for variety of reasons like, rag

dealers are far away from the place of collection, and it needs extra cost to them. A sizable percent are selling directly to rag-dealers only.

A vast majority of the child rag picker and their family are living either on rented house or other places. A small

percent is found to living in their own houses. Again majority of them are living in a one room.

It is found a vast majority of them didn’t have any sanitary facility, they used to go river bank, open fields or

public toilets in bus stops, railway station, or any such places where the facility is provided for general people.

Except few all of them didn’t have electric facility in their home.

It is found that majority of them are earning Rs.30 or less then Rs. 30 per day. Majority of them are spending

their money for fooding, lodging only. A vast majority of them are contributing in their family.

A small percent of them were saving money for their future purposes.

A vast majority of the child rag pickers responded that their income is sufficient for their livelihood.

It is found that a sizable percent of the child rag pickers are engaged with other income also. They are generally

working as beggar, coolie part time helper at tea stall or other establishments. Others are working as full

time/regular rag-picker.

Majority of them have personal habits like, consuming tea, chewing pan or saada, and smoke.

A vast majority of them had some sickness or injuries in last six months.

Majority of them are suffering from skin disease or Cut and injury. Other is suffering from Respiratory problems

and frequent fever.

It is found that a vast majority they had consulted for their disease either doctors in government hospitals or

medicine shops. Remaining who had not consulted for their illness, they fell it was not necessary, or other

worker/parents has given some medicine or advised for curing that illness. Regarding affordability of the medication expenses, them had said, they can afford the medication expenses.

It was found only 3.33 percent of child rag-pickers are physically disabled. Remaining of them is physically fit.

Maximum child rag-pickers are dirty and unclean, they also seems to be suffering from some disease or highly

malnourished.

It is found that all of the child rag pickers are interacting with the people of community/ society. Vast majority of

them feel that community peoples behavior are rejecting in nature. Only a small percent of them had responded that they find community people are friendly. And a very small percent of the child rag-pickers were feels that

due to their profession, their social life is affected. And all most all of them were not able to say anything

regarding their social life.

A vast majority of the child rag-pickers were not satisfied with their job.

It was observed that except few all of the child rag pickers were the victim of different kinds of abuse, majority

of them are generally abused by adult rag pickers, buyers, shop-owners, and adult people. Kind of abuses faced by them were generally economic and physical.

Conclusion:

The study which was conducted on socio economic condition of child labour engaged in rag picking at Silchar, Assam found that majority of the respondents were belonging to the street children or the children from the family which are

below poverty line. As well as these children were suffering from various diseases like skin diseases, prolonged caugh &

colds. Majority of the children have bad habits like chewing tobacco, pan and smoke. They are dirty unclean and not

satisfied with their job. To reduce this problem government and non government organization intervention is required. Specially, government should provide boarding schools where these children can be accommodated.



78

References:

[1] Ahmed, I. (1999). Getting Rid of Child Labour. Economic and Political Weekly , 34 (27), 1815-1822.

[2] Association for Development. (2004). A Study on the Problems of Street and Working Children at railway

station in Delhi. New Delhi: Association for Development. [3] Baruah, A. (2003). Child Abuse. New Delhi: Reference Publications.

[4] Bhadra, M. (1999). Girl Child in Indian Society. New Delhi: Rawat Publications.

[5] Brick, C. P. (2002). Street Children, Human Rights and Public Health: A Critique and Future Directions.

Annual Review of Anthropology , 31, 147-171. [6] Chaitanya, K. (1991). Child Labour among Digaru of Arunachal Pradesh. Economic and Political Weekly ,

26 (36), 2084-2085.

[7] CSIR (1984). (2005). Working Condition of Children Employed in Unorganised Sector. In M. R. Biju, Human Right in a Developinf Society (pp. 294-295). New Dehi: Mittal Publications.

[8] Dak, T. M. (2000). Child Labour in India. New Delhi: Serial Publication.

[9] Gangrade, K. D., & Gahtia, J. A. (1983). Women & Child Workers in unorganized sectors. New Delhi: Concept Publishing Co.

[10] Government of India. (1969). Report of the National Commission on Labour. New Delhi: Ministry of Labour,

Governemnt of India.

[11] Gunn, S. E., & Ostos, Z. (1992). Dilemmas in tackling child labour: The case of scavenger children in the Philippines. International Labour Review , 131, 629-646.

[12] I.C.C.W. (1997). Working Children in Urban Delhi. New Delhi: Indian Council for Child Welfare.

[13] Joshi, S. C. (2006). Child labour: Issues, Challanges and Law. New Dehi: Akansha Publishing House. [14] Kak, S. (2004). Magnitude and profile of child labour in 1990s - Evidence from the NSS data. Social Scientist , 32, 43-73.

[15] Khandelwal, A., Raj, N., & Ghosh, R. (1998). Child Labour in the Sports Goods Industry: Jalandhar - a case

study. Noida: V.V. Giri National Labour Institute. [16] Kumar, K. (1991). Banning Child Labour. Economic and Political Weekly , 26 (52), 2983.

[17] Madan, G. R. (1993). Indian Social Problem: Social Disorganization and Reconstruction. New Delhi: Allied

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[18] Mahajan, P. (2006). Status of Child Labour. New Delhi: Adhyan Publishers. [19] Mahanti, N. (2005). Educational Rehabilitation of Rag-Picker Children in Seemapuri - A Dream Come True.

New Delhi: Inter India Publications.

[20] Margaret, G., & Smit, R. F. (2004). The Children of Neglect When No One Care. New York: Routledge. [21] Nangia, P. (1987). Child Labour: Cause - Effect Syndrome. New Delhi: Janak Publishers.

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Child Development.

[23] Pati, R. M. (Ed.). (1991). Rag Picking Children: Rehabilation of Child Labour in India. New Delhi. [24] Payal, G. (2008). Social Dimensions of Child Labour in India. New Delhi: Om Publications.

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Journal of Population Economics , 10 (4), 377-386. [26] Rao, K. H., & Rao, M. M. (1998). Employers' View of Child Labour. Indian Journal of Industrial Relations , 34 (1), 15-38.

[27] Sahoo, U. C. (1995). Child Labour in Agrarian Society. Jaipur: Rawat Publications.

[28] Sen, R. K., & Dasgupta, A. (2003). Problems of Child Labour in India. New Delhi: Deep & Deep Publications. [29] Sethi, G. R. (2004). Street Children: A Windo to the Reality. Indian Pediatrics , 41, 219-220.

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[35] UNDP. (2000). Human Development Report. New York: Oxford University Press.

****



79

STOCK MARKET ANOMALIES:

EMPIRICAL EVIDENCE FROM WEEKEND EFFECT

ON SECTORAL INDICES OF INDIAN STOCK MARKET

Potharla Srikanth, M.Com., M.Phil., UGC-NET., ACMA., PGDT., PGDIBO., PGDFM., NCFM., (Ph.D.)

Assistant Professor

Dept. of Commerce, Post Graduate College,

Constituent College of Osmania University,

Secunderabad. A.P., India

P. Srilatha, M.Com, PGDBA.,(Ph.D).

Ph.D. Scholar,

Department of Management,

JNTU, Hyderabad, India

ABSTRACT

The objective of the present study is to analyze the existence of a week-end effect in the selected CNX

indices. The present study considers the week-end effect in the selected sectoral CNX indices such as

Banking, the FMCG (Fast Moving Consumer Goods), the IT (Information Technology) and Pharma (Pharmaceuticals) during the period of 10 years from 1

st April, 2001 to 31

st March, 2011. The analysis

reveals that out of five week days, the highest returns were generated in Banking and Pharma sectors

on Wednesday; Thursday in the IT and Friday in the FMCG sectors. Under simple OLS (Ordinary Least Squares) regression, only the IT sector is experiencing week-end effect whereas under the

GARCH method, all the sectors except the IT are experiencing weekend effect.

Keywords: Stock market anomalies, weekend effect, CNX sectoral indices.



80

Introduction:

Many studies documented evidence to support the view that there is randomness in stock prices of the Indian stock market. The

volatility in the stock prices is due to many factors viz., speculation, inflation, rising oil prices, interest rates, announcement of

corporate results/announcements, Government regulations, corporate restructuring, goods prices, money supply, exchange rates,

other political, social, economic and global events. Thus, the stock market in India is not fully efficient yet. Besides, there exist

anomalies such as calendar effect, week-day effect, week-end effect and market sentiments, creating, thereby, opportunities for

arbitrage. Hence, the study of capital market volatility assumes great importance to the Indian investors, regulators, brokers,

policy makers, dealers and researchers especially in the developing countries like India. The present study attempts to scrutinize the existence of a week-end effect in the selected CNX indices. The present study

considers the week-end effect in the selected sectoral CNX indices such as Banking, FMCG (Fast Moving Consumer Goods),

IT (Information Technology) and Pharma (Pharmaceuticals).

Literature Review:

Vipul Kumar Singh and Prof.Naseem Ahmad (2011) investigated volatility forecasting performance of the GARCH(1,1)

class models on different time series with and without parameter restrictions comprising closing prices of 1900 daily

observations of Nifty index for 23 sectors during 1st June 2001 to 31st December 2008. The sum of the GARCH coefficient is

close to one in almost all cases indicating the persistence of conditional variance. It is found that the TGARCH and PGARCH

specification to be preferred as it more reliably describes the Nifty index volatility processes1. Abhijeet Chandra (2011)

examined various seasonal patterns in returns in the stock markets across the world. These patterns often referred to as anomalies, can be seasonal. Results reveal that the turn-of-the-month effect and the time-of-the-month effect have significantly

existed in BSE SENSEX returns. Returns in the first few days of the month are found to be positively significant compared to

the remaining days of the month. Different time segments of a month, however, witness significantly varying returns. The

evidence of this study strongly supports the existence of calendar effects in the returns of the BSE SENSEX2. Manpreet Kaur

(2011) observed seasonal anomalies existing in stock returns in India. The daily closing prices of two indices- BSE 500 and

S&P CNX 500 have been used to examine the presence of month-of-the-year and day-of-the-week effects in the Indian stock

market during January 2002 to December 2009. The findings show presence of month-of-the-year effect but absence of day-of-

the-week effect in Indian stock market. This indicates that the Indian stock market is not fully efficient yet. The existence of

month-of-the-year effect may provide opportunities to formulate profitable trading strategies so as to earn the increased return

that does not commensurate with the risk3. Pratap Chandra Patri (2008) examined the stylized facts of stock returns, model

and estimate the time varying volatility, persistence of Indian stock market and the asymmetric impact of shock on volatility.

There is evidence of non-normality, time varying conditional volatility, and volatility clustering and leverage effect in Indian stock market. There is evidence of predictable time varying volatility. Periods of high/low volatility tend to cluster and

volatility showed high persistence. Negative shock increases the future volatility more than the positive shocks of the same

magnitude. The GJR-GARCH (1, 1) is the best volatility model according to the log likelihood value and to the diagnostic test

of the model's residuals. The GJR-GARCH model reduced the kurtosis level the most and had the lower Jarque-Bera statistic

value4. P K Mishra (2010) investigated the nature and characteristics of stock return volatility in the capital market of India in

the aftermath of global market slowdown by using the GARCH class models. The results provide the evidence of time varying

stock return volatility over the sample period spanning from January 1991 to August 2009. It is further found that the effect of

bad news is relatively greater in causing market volatility in India5.

Objective of the study:

The objective of this paper is to examine week-end effect in the returns of S&P CNX sectoral indices. The study also focuses on identifying the non-randomness of the selected sectoral indices returns during the trading days in a week.

Hypothesis:

In order to attain the above stated objective, the following hypothesis has been formulated:

Null hypothesis (H0): There is no week-end effect on S&P CNX sectoral indices.

Alternative hypothesis (H1): There is a week-end effect on S&P CNX sectoral indices.

Data and Methodology of the study:

The study covers a period of 10 years from 1st April, 2001 to 31st March, 2011. An attempt has been made to analyze the week-end

effect on the selected sectors. Sectors selected for the study are Banking, FMCG, IT and Pharma. CNX-Bank Index is used as proxy for Banking sector; CNX-FMCG for the FMCG Sector; CNX-IT for the IT sector; and CNX-Pharma for Pharmaceutical sector.

The study considers daily prices of the selected sectoral indices of the NSE. The indices daily prices are converted into natural

logarithmic returns and the same is used as inputs for statistical analysis. It is the general practice to use log returns for making

research with time-series data relating to financial markets as the log returns will take into account the compounding effect of



81

returns. Descriptive statistics are used to provide simple summaries about the sample data. The measures used to describe the

data set are measures of central tendency and measures of variability or dispersion such as mean, standard deviation, Skewness

and Kurtosis. Simple Ordinary Least Squares (OLS) Regression equation has been estimated by taking log returns of daily

prices of selected indices as dependent variable and all the week-day dummy variables (except Friday) as predictors. A constant

(C) is included in the following equation as exogenous variable to represent week-end effect.

Following French (1980), daily dummy variables are created to test for the day-of-the-week effect by estimating the following equation:

Rit = α1iD1 + α2iD2 + α3iD3 + α4iD4 + C + εt

Where D1…D4 are the days of the week; α1i-α4i = coefficients to be estimated and εt = Random error term for day t.

In the above equation, D1 is a dummy variable which takes the value 1 if day t is a Monday and 0 for all other days of the week (days fall on Monday = 1; days falls on other days = 0); D2 is dummy variable which takes the value 1 if day t falls on Tuesday and 0 for all other days of

the week (days fall on Tuesday = 1; days fall on other days = 0); The remaining dummy variables are defined in the same manner.

The standard error measures the statistical reliability of the coefficient estimates. The value of t-statistic evaluates the contribution of

each independent variable to regression model. R-squared measures the success of the regression in predicting the values of the

dependent variable within the sample while Adjusted R-squared attempts to correct R-squared to more closely reflect the goodness of

fit of the mode in the population. Durbin –Watson (DW) Statistic for autocorrelation of the AR (1) type measures the auto-correlation

of the residuals. Autocorrelation refers to the correlation of a time series with its own past and future values.

After estimating regression equation under simple OLS method, again the regression equation has been estimated by using

Autoregressive conditional Heteroskedasticity (ARCH) method which categorizes predictors into two equations i.e., mean

equation and variance equation. Week-day dummy variables and constant are classified under mean equation; and under

variance equation, unconditional volatility is represented by constant (C); the effect of news on the log returns of daily prices of selected indices is denoted by one period lagged squared residuals [RESID (-1) ^2] and the effect of old news or conditional

volatility is represented by GARCH (-1). After arriving at the results under the ARCH regression, comparison has been made

between the results under simple OLS regression and the results under the ARCH regression.


Based on the methodology discussed above, the analysis revealed the following results:

Descriptive Statistics for log return of sector indices: Descriptive Statistics like mean, standard deviation, skewness and Kurtosis have been computed to describe the characteristics

of the sample data.

Table 1: Descriptive Statistics for log return of Sectoral Indices

Week day

CNX

Sectoral

indices

N Mean Std.

Deviation Skewness Kurtosis

N Statistic Statistic Statistic Std. Error Statistic

Monday

BANK 499 0.0009570 0.0250786 -0.138 0.109 8.100

FMCG 499 0.0004657 0.0163316 -1.032 0.109 8.883

IT 499 0.0004318 0.0260096 0.062 0.109 6.829

PHARMA 499 0.0004093 0.0151469 -0.251 0.109 9.179

Tuesday

BANK 499 0.0005560 0.0203976 0.292 0.109 3.624

FMCG 499 0.0005716 0.0141258 -0.099 0.109 4.530

IT 499 0.0010571 0.0214997 0.442 0.109 3.556

PHARMA 499 0.0005548 0.0134316 -0.539 0.109 5.609

Wednesday

BANK 498 0.0016914 0.0198363 0.201 0.109 1.962

FMCG 498 0.0002795 0.0134729 0.225 0.109 2.437

IT 498 0.0005042 0.0238025 -0.463 0.109 9.075

PHARMA 498 0.0013590 0.0127047 -0.073 0.109 1.368

Thursday

BANK 499 0.0007111 0.0194559 -0.285 0.109 1.632

FMCG 499 0.0003068 0.0133516 -0.176 0.109 2.629

IT 499 0.0010922 0.0239565 -1.563 0.109 16.142

PHARMA 499 0.0001882 0.0126437 -0.445 0.109 2.367

Friday

BANK 489 0.0010265 0.0219534 -0.979 0.11 6.246

FMCG 489 0.0007876 0.0144468 -0.149 0.11 2.550

IT 489 -0.0050136 0.1089919 -20.71 0.11 447.826

PHARMA 489 0.0007818 0.0136050 -0.561 0.110 5.153

Source: Authors‟ calculations



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Table 1 presents the basic statistics of returns series from the four sectoral indices. The mean return is positive on all days for

all the sectors except on Friday for the IT. The highest return is reflected on Wednesday in Banking and Pharma; on Friday in

the FMCG; and on Thursday in the IT. The lowest return is reflected on Tuesday in Banking; on Wednesday in the FMCG; on

Thursday in Pharma; and negative returns on Friday in the IT sector. The standard deviation of daily log returns is highest on

Monday and lowest on Thursday for Banking, FMCG and Pharma sectors. On the contrary, it is interesting to note that the

standard deviation is highest on Friday and lowest on Tuesday for the IT sector. This is due to obvious reason that Monday,

being the first day-of-the-week, the stock market is highly volatile and closes with a low variance eventually. Thus, based on

the means of daily log returns for sectoral indices, the best return sectors are in the order of Bank, Pharma and IT followed by

FMCG sectors. However, based on the standard deviations, the risky sectors follow the order of IT, Bank, FMCG and Pharma. Further, the week-end effect (Friday effect) is quite apparent in the IT sector caused by negative mean returns and higher

standard deviation.

The kurtosis of all sectors investigated shows consistently positive value, suggesting that the series are leptokurtic that means

all series have a thicker tail and higher peak than a normal distribution. The Skewness of the distribution of log returns of

selected sectoral indices prices is found to be negative on almost all the days indicating that the left tail is longer; the mass of

the distribution is concentrated on the right of the figure and it has relatively few low values. This signifies the high probability

of relatively more number of large returns in the distribution of the series.

Estimation of Regression equation under Simple OLS method:

Table 2: Simple OLS Regression equation for estimating log returns of CNX sectoral indices daily prices

Dummy Variable Sectoral indices Coefficient Std. Error t-Statistic Prob.

Monday

BANK -0.0000695 0.001364 -0.050930 0.9594

FMCG -0.0003220 0.000915 -0.351650 0.7251

IT 0.0054450 0.003365 1.618357 0.1057

PHARMA -0.0004670 0.000855 -0.546334 0.5849

Tuesday

BANK -0.0004700 0.001364 -0.344827 0.7303

FMCG -0.0002160 0.000915 -0.236018 0.8134

IT 0.0060710 0.003365 1.804205 0.0713

PHARMA -0.0003210 0.000855 -0.376035 0.7069

Wednesday

BANK 0.0006650 0.001365 0.487106 0.6262

FMCG -0.0005080 0.000916 -0.554822 0.5791

IT 0.0055180 0.003366 1.639057 0.1013

PHARMA 0.0004830 0.000855 0.564683 0.5723

Thursday

BANK -0.0003150 0.001364 -0.231105 0.8173

FMCG -0.0004810 0.000915 -0.525297 0.5994

IT 0.0061060 0.003365 1.814644 0.0697

PHARMA -0.0006880 0.000855 -0.805064 0.4209

Constant

BANK 0.0010260 0.000970 1.058618 0.2899

FMCG 0.0007880 0.000651 1.210667 0.2261

IT -0.0050140 0.002391 -2.096646 0.0361

PHARMA 0.0008760 0.000604 1.451448 0.1468


On Monday, Tuesday and Thursday, regression coefficients of all the sectors except CNX IT are negative indicating negative

impact on the daily log returns of the sectors indices; and on Wednesday only regression coefficients of FMCG is negative and

that of other sectors is positive. On all the weekdays, highest standard error is recorded only in the case of log returns of daily

prices of CNX-IT indices which indicates that the volatility in the distribution of log returns of CNX-IT is very high compared

to other selected sectors. The results of the study disclose that none of the week days have statistically significant impact on the

log returns of daily prices of selected sectoral indices. However, Tuesday, Wednesday and Thursday are documenting a

significant impact on log returns of daily prices of CNX IT index (P<0.10). In the regression equation, weekend effect has

been included as constant (C). „p‟ value of constant (C) is significant only in the case of CNX IT sector, indicating the week-

end effect only in the CNX-IT sector.



83

Table 3: R-squared, Adjusted R-squared and Durbin-Watson Statistic under simple OLS Regression

CNX Sectoral indices R-squared Adjusted

R-squared Durbin-Watson statistic

BANK 0.000332 -0.001281 1.746765

FMCG 0.000167 -0.001447 1.930864

IT 0.001919 0.000309 2.001626

PHARMA 0.000909 -0.000695 1.826558


From Table 3, it is observed that the R-squared value is almost equal to zero indicating that the proportion of variance in

dependent variable explained by the regression model is very poor. Adjusted R-squared is negative in all the sectors except IT,

indicating that the predictors are statistically not useful in fitting the regression model. Durbin-Watson test results reveals that

the log returns of daily prices of CNX-IT sector index are not experiencing any autocorrelation in its series, because the test

statistic value is very close to 2. Further, there is a presence of positive autocorrelation in the case of other selected sectoral indices.

Estimation of Regression equation under GARCH (1, 1) model:

After finding the presence of heteroskedasticity in the series of log returns of selected sectoral indices, ARCH method is used

in estimating the regression equation.

Table 4: Regression under GARCH (1,1) equation for estimating log returns of CNX sectoral indices daily prices

Mean Equations

Dummy Variable CNX Sectoral indices Coefficient Std. Error z-Statistic Prob.

Monday

BANK 0.000195 0.000978 0.199255 0.8421

FMCG -0.000300 0.000674 -0.444748 0.6565

IT 0.000804 0.001206 0.666755 0.5049

PHARMA 0.000145 0.000570 0.253658 0.7998

Tuesday

BANK -0.000619 0.001040 -0.595284 0.5517

FMCG -0.000247 0.000744 -0.331956 0.7399

IT -0.000749 0.001212 -0.618016 0.5366

PHARMA -0.000502 0.000709 -0.708532 0.4786

Wednesday

BANK -0.000133 0.001025 -0.130201 0.8964

FMCG -0.000636 0.000744 -0.854722 0.3927

IT 0.001335 0.001417 0.941789 0.3463

PHARMA 0.000488 0.000647 0.753027 0.4514

Thursday

BANK -0.000336 0.001021 -0.328544 0.7425

FMCG -0.000386 0.000723 -0.533999 0.5933

IT 0.000799 0.001102 0.724967 0.4685

PHARMA -0.000202 0.000656 -0.307379 0.7586

Constant

BANK 0.001579 0.000706 2.235130 0.0254

FMCG 0.001106 0.000509 2.175069 0.0296

IT 0.000277 0.000918 0.301815 0.7628

PHARMA 0.000910 0.000441 2.060715 0.0393

Variance Equation

CNX Sectoral Indices

BANK FMCG IT PHARMA

Constant

Co-efficient 0.00000872 0.0000133 -0.00000771 0.0000171

Standard Error 0.00000145 0.00000164 0.00000075 0.00000248

z-statistic 6.0274920 8.1064460 -10.276020 6.8898930

Prob. 0.000001 0.000001 0.000001 0.000001

RESID(-1)^2

(ARCH)

Co-efficient 0.107985 0.159811 0.372222 0.175544

Standard Error 0.008618 0.010789 0.020454 0.014592

z-statistic 12.52957 14.81176 18.19829 12.03017



84

Prob. 0.000001 0.000001 0.000001 0.000001

GARCH (-1)

Co-efficient 0.876385 0.779128 0.851701 0.733536

Standard Error 0.009206 0.012901 0.007094 0.024717

z-statistic 95.19243 60.39307 120.0651 29.67704

Prob. 0.000001 0.000001 0.000001 0.000001

Source: Authors‟ calculations.

As shown in table 4, the regression coefficients for the selected sectors are positive except FMCG on Monday; on Tuesday, the

regression coefficients are reflecting negative impact on log returns of daily prices of all the selected indices; on Wednesday,

Banking and the FMCG sectors are experiencing negative impact while the IT and Pharma are experiencing positive impact; on

Thursday, all the sectors except the IT are experiencing negative impact. Constant in the regression equation is positive thus

indicating a positive weekend effect on log returns of daily prices of all the selected sectors.

Just as in the case of OLS regression, highest standard error is recorded for IT sector confirming comparatively highest

volatility in the log returns of daily prices of IT sector index. Z-test results are showing that none of the week days are

exhibiting statistically significant impact on the log returns of daily prices of selected sector indices. However, all the selected sectors, except the IT, are experiencing weekend effect.

Under variance equation, regression coefficient of ARCH shows the effect of news on the market and GARCH Coefficient

shows the effect of old news on the market. The coefficient of constant is a measure of unconditional volatility. The coefficients

of both the ARCH and GARCH variables in the Variance Equation are highly statistically significant (P<0.01) and the sum of

ARCH and GARCH is close to one. It indicates that shocks to the conditional variance are highly persistent.

Table 5: R-squared, Adjusted R-squared and Durbin-Watson Statistic under ARCH Regression

CNX Sectoral

indices R-squared

Adjusted

R-squared Durbin-Watson statistic

BANK -0.000312 -0.003140 1.746286

FMCG -0.000325 -0.003153 1.929995

IT -0.000338 -0.003166 2.000636

PHARMA -0.000060 -0.002874 1.826566


From Table 5, it is observed that R-squared value is negative indicating that the proportion of variance in dependent variable

explained by the regression model is very poor. Adjusted R-squared is also negative in all the sectors indicating that the predictors are statistically not useful in fitting the regression model.

Comparison of regression equation results under simple OLS regression and GARCH (1, 1) regression model:

Under simple OLS regression method, Monday and Tuesday are revealing negative impact on all sectors except the IT, whereas

under GARCH (1, 1) method, Monday is exhibiting positive impact on all the sectors except the FMCG. Under OLS regression

method, Tuesday is exhibiting a negative impact on all the sectors except IT, whereas under GARCH (1, 1) method, IT has

negative impact on all the sectors. Under simple OLS regression method, Wednesday is showing a positive impact on all the

sectors except the FMCG, whereas under GARCH(1,1) Banking and the FMCG are experiencing negative impact while the IT

and Pharma are experiencing positive impact. Under both the methods of regression, Thursday is exhibiting negative impact

on all the sectors except the IT. Also, under simple OLS regression, the results of t-test reveal that none of the above week days

have statistically significant impact except on the IT sector(P<0.10) whereas under GARCH(1,1) none of the above week days have statistically significant impact on any of the selected sectors. Under simple OLS regression, none of the sectors except the

IT are showing a weekend effect, whereas under GARCH (1, 1) in contrast, all the sectors except IT are experiencing week-end

effect. The main reason for such opposite results obtaining under two different methods can mainly be attributed to the

presence of heteroskedasticity in log returns of daily prices of the selected sectoral indices. The results of the study prove that

simple OLS regression results will be spurious when heteroskedasticity is present in the time series data.

Conclusion:

The main purpose of the present study is to capture the stock market anomalies present in the form of week-end effect on the

stock prices. The analysis reveals that Banking and Pharma sectors have provided highest return on Wednesday; the IT sector

has provided highest return on Thursday; and FMCG has provided highest return on Friday.

Under simple OLS regression method, none of the selected sectoral indices are experiencing week-day effect except the IT, whereas regression results under GARCH method reveal that all the selected sectoral indices except the IT sector are experiencing weekend

effect. The regression results under GARCH clearly indicate the presence of conditional volatility in the selected sectors. This



85

explains the rationale behind occurrence of altogether different results under simple OLS regression method.

The study discloses the fact that the stock prices of the IT sector are relatively very highly volatile which is reflected in highest

value of standard error. It is mainly because this sector is highly determined by the Foreign Institutional Investors on a large

scale besides its excessive dependence on exports, which are, in turn, influenced by the international market conditions. The

reasons for the presence of week-end effect in Indian stock market may be attributed to certain factors like short- selling,

investors‟ optimism between Monday and Friday, release of some good or bad news by corporate bodies on Friday.

On identifying the anomalous behavior of stock market in the form of week-end effect on the selected sectors in Indian stock

markets, it can be concluded that still the Indian stock market is not informational efficient. Thus, short term investors like

portfolio managers, mutual funds, institutional investors and other individual investors should keep in mind such type of market anomalies while managing their portfolios in the Indian stock market.

References:

[1] Vipul Kumar Singh and Prof.Naseem Ahmad (2011),” Modeling S&P CNX Nifty Index Volatility With GARCH Class

Volatility Models: Empirical Evidence From India”, Indian Journal of Finance, Vol.5, No.2, pp.34.

[2] Abhijeet Chandra (2011), “Stock Market Anomalies: A Test of Calendar Effect in the Bombay Stock Exchange (BSE)”,

Indian Journal of Finance, Vol.5, No.5, May 2011, pp.23.

[3] Manpreet Kaur (2011), “Seasonal Anomalies in Stock Returns: Evidence From India”, Indian Journal of Finance, Vol.5,

No.5, May 2011, pp.43.

[4] Pratap Chandra Patri (2008) “Econometric modeling of time-varying conditional heteroskedasticity and asymmetry in

volatility using GARCH and non-normal distribution: the case of National Stock Exchange of India”, Indian Journal of Economics and Business, Vol.7, No.1, pp.129-143.

[5] P K Mishra (2010), “A GARCH model approach to capital, market volatility: the case of India”, Indian Journal of

Economics and Business, Vol.9, No.3, pp.631-641.

****



86

INTERNET BANKING: DOES IT REALLY IMPACTS

BANK’S OPERATING PERFORMANCE

Rajni Bhalla,

Assistant Prof. in Commrece

Panjab University Constituent College,

Nihal Singh Wala, Moga, India.

ABSTRACT

The development of the electronic banking industry can be discovered to the early 1970s. Information

technology has introduced new ways of providing banking services to the customers, such as ATMs and Internet banking. The concept and scope of e-banking is at nascent stage. But still Internet

banking is one of the major developments in the financial service sector in recent years. It is a tool to

attract as well as to retain the customers in the global banking sector. Internet banking enables its various users to use different alternatives available for different purposes like to retrieve account

information online, to make different transactions using internet banking technology or to get

information regarding any type of financial product or service. At first sight the Internet is the best medium for carrying out banking activities as it cut down the cost and accelerate the speed of

information transmission. There is an extent of dissimilarities in the services which are offered by the

banks with the coming out of internet banking services. So, it has been tried with the help of this paper

to study the nature, expansion and degree of internet banking services and also how these services put impact on the operational performance of banks. The present study is an attempt to scrutinize how

internet banking impacts operational performance of Indian Banks. This paper also includes critical

analysis of various peer reviewed, scholarly literature on the subject of the impact of internet banking on operating performance of banks.

Keywords: Internet Banking, Operational performance, Indian Banks, Information Technology.



87

Introduction:

The development of the electronic banking industry can be discovered to the early 1970s. Technology has

introduced new ways of providing banking services to the customers, such as ATMs and Internet banking (Singh,

Chhatwal, & et al., 2002). This concept of internet banking is still in the halfway stage. But still Internet banking is one of the major developments in the financial service sector in recent years. It is a tool to attract as well as to retain

the customers in the global banking sector (Sharma, 2011). Internet banking enables its various users to use

different alternatives available for different purposes like to retrieve account information online, to make different

transactions using internet banking technology or to get information regarding any type of financial product or service. IT Act, 2000 (Information Technology Act) enacted by India to provide legal recognition to electronic

transactions and other related means of electronic commerce (Srivastva, 2007). ICICI bank is the initiator of

providing internet banking services in India. Now various public and private sector banks are delivering internet services to their customers. These banks currently offer “Fully Transactional Websites” to its customers. The

facilities which are enjoyed by the through internet banking facility includes: account summary, online shopping,

online payment of bills, mobile recharge, inter account transfer, seeking products and their rates information, apply for loans online, payment of taxes online, cheque book request, credit card payments/ statements, facilities to

contact account managers, etc. (Geetha and Malarvizhi, 2012).

At first sight the Internet is the best medium for carrying out banking activities as it cut down the cost and

accelerate the speed of information transmission. There is an extent of dissimilarities in the services which are offered by the banks with the coming out of internet banking services. So, it becomes necessary to study the nature,

expansion and degree of internet banking services and also how these services put impact on the operational

performance of banks.

Role of E–Banking in the Indian Banking Sector:

„Any time, Any Where Banking‟ i.e. Internet Banking is a replacement of banks traditional offerings to the

customers. Initially the internet banking services were launched in the metropolitan cities and banks situated in the

urban areas of India but as time passes these services were also introduced in the semi urban areas and rural areas (Keivani, Jouzbarkand, and et al., 2012). The use of internet banking is one of the factors having influence in the

growth of Indian banking industry. Today no one can imagine his or her life without internet banking because our

daily needs are now directly depend upon the e-banking. Whether we are going for the shopping or whether we want to pay our monthly bills, e-banking is now providing a great help to us to do all this no time. The use of

internet banking has placed the banking personnel out of scene due to which the customers find it difficult to

undergo with the transaction offered by the banks to the customers. (E-Banking and its role in today‟s society). The internet banking also provides new opportunities to the banks to explore the new ways of providing value added

services to the customers to expand their customer base as well as business (TNO Report on E-Commerce in

Banking Sector, 2001).

Internet Banking and Operational Performance of the Banks:

As internet banking is now a vital element of the banking sector then it can be rightly said that it is an inseparable

part of the banks. Internet banking has a great impact on the operational performance of the banks. Internet banking

has quite high initial set-up costs following highly savings in future. Internet banking has changed the methods and techniques of marketing, advertising, pricing, financing etc. Revenues of the banks have increased after the

adoption of internet banking as banks have provided the information regarding their e-products to the customers on

the websites in detail. Research proves that the processing time of the transactions has been considerable reduced

with the introduction of internet banking and also workload of the employees has been decreased due to the division of work and less processing time (Kaushal, 2011). In today‟s world the bank having modern and high

technology are treated as brand banks. Customers also presume that it becomes necessary for the banks to follow

new and modern technology to become a brand. The competitive ability of the banks is also augmenting due to the increasing competition in the banking sector which also has a positive influence on the operating efficiency of the

banking sector. Where internet banking offers relief to the customers at the same time it provides cost cutting to the

banks by eradicating physical documentation. Cost of communication through WWW i.e. World Wide Web is also least as compared to other means of communication. Internet banking saves time of bank as well as those of

customers (Kaushik, 2012).



88

The use of internet in the banking sector has direct relationship with the profitability. Ceteris paribus, the profit

margin of banks increased with the investment in electronic banking and also reduction in costs and increase in

non-interest income also increases the ROA and ROE (Gupta and Islamia, 2008). Compared to the traditional methodology, online banking is an economical forthright way to conduct banking business, exchange of

personalized information and buying and selling of goods and services from any place at any time (Jalal, Marzooq,

and et al., 2011). This only is sufficient reason for banks to congregate to Internet and to provide maximum of their services through Internet and as soon as possible. In order to maintain the cost efficiency, banks have to constantly

upgrade the changing and well- tested technologies. The banking sector also has to consider the additional security

measures in the internet banking because internet is a public domain and demands sufficient and additional security

measures (Report on Internet Banking by RBI, 2008).

Operational Performance of the Banks: Internet and Non-Internet Banks:

The internet banking has simply added another delivery channel to the already available existing channels. Due to

this the number of banks providing financial services through internet is increasing at a rapid rate in India. Now customers without leaving their homes or place of business can use their banking services easily. But the banks

which are still not using the internet as a medium of banking i.e. non-internet banks are lacking in operational

efficiency and performance as compared to the internet banks. As the internet banks are providing trading services

to their customers with the help of fully transactional websites which results in the more revenue generations to these banks as their customer base has been increased and also non- interest income has been improved. But the

non-internet banks are only dependent upon the customers who can physically visit their banks and such banks also

find difficult to expand their customer base because now customers are looking for the more comfortable services which the non-internet banks are looking hard to provide with great efficiency as internet banks are providing. The

asset quality of the internet banks is also higher than the non-internet banks (Malhotra, Singh, 2009). The Internet

delivery channel of banks serves as complementary mean of transacting with customers rather than a substitute for physical branches. Despite the large investment in the Internet as a channel of distribution, the branch network

remains an important channel for retail banking product and it adds more in the operational efficiency of the

internet banks (Hernando, Nieto, 2006).

Conclusion:

The present paper is an attempt to study the impact of internet banking on the operational performance of the banks

in India. The analysis indicates that the internet banks are more efficient and showing better performance in terms

of profitability, asset quality, reduction in overhead expenses etc. as compared to non-internet banks. From the research I come to know that internet banking is really a way forward to the Indian banking industry. Where the

internet banking is providing comfortable services to customers on one hand, also on the other side, it helps in

cutting down cost. The main aim of the banking sector to shift towards electronic means is to increase their

clientage, to serve the customers with best of the services, to facilitate them and to boost customers‟ loyalty.

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