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8/2/2019 Research Writing Example

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Research Report Workshop 9 May 2009

Assoc. Prof. Dr. Nik Maheran

CHAPTER 1

INTRODUCTION

Environmental scanning is the internal communication of external information

about issues that may potentially influence an organizations decision making

process..Thus, this research investigates the impact of the extent of

environmental scanning to decision making quality particularly investment

decision for firms in Malaysia.

1.1 Background of the study

For more than forty years, there has been periodic reviews of the decision making

literature.

1.1.1 Environmental scanning

Environmental scanning is..

1.1.2 Decision making quality

1.1.3 Information Processing Capacity

However, in spite of many researches done on strategic decision making in

relation to environmental scanning, no management research has focused on

the impact of environmental scanning as an input to the strategic decision-

making process to investment decision quality. Issues of the extent of

scanning, scope of scanning, methods (formal vs. informal), etc. and their

impact on the quality of decisions made are amongst issues yet to be fully

addressed in the literature on environmental scanning

1.2 Problem Statement

Today's corporate world is undergoing unprecedented changes. The accelerating

pace of technology, integrating markets, and highly competitive market, place an

increasing demand to get strategic investment decision right. Malaysia like the restof the world is doubling its efforts in transforming the economy towards


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achieving higher value- added growth. Therefore, more efficient decision

mechanisms are required to support this transformation.

The wrong decisions made by the top management of Renong that is to cancel

of UEM put option and taking a huge short-term loan of RM3.1 billion (approx.

US $816 million), to acquire 32.6 percent stake in its holding company,

(Renong Bhd), as an effort to rescue Renong, was viewed by the analyst as a

bailout deal..

Previous research on environmental scanning and impact on investment decision-

making only looked at how investment decisions are made, (Eknem, 2005) and not

on how scanning should be done that will impact their investment decision-making.

This research therefore aims to fill such gap in the research of environmentalscanning and strategic decision making by looking at the impact of

environmental scanning behavior to investment decision-making quality

This research will analyze the investment decisions made by top managements in all

types of firms in Malaysia.

1.3 Purpose and Objective

The study on environmental scanning is well-recognized in strategic managementand decision making literature. However whether its impact to investment decision

making quality is not well researched especially in the developing country like

Malaysia. It also attempts to address the issues of how scanning should be

done in an organization to ensure quality decisions. Thus this study will attempt to

achieve the following:

1. To determine the extent of environmental scanning done by Malaysian

decision makers.

2. To determine the impact of environmental scanning on their investment

decision-making performance and quality.

3. To determine the contingent effect of information processing capacity on the

relationship between environmental scanning and investment decision-

making quality.


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1.4 Research Questions

Based on the above problem identification and the objectives, this study seeks

answers to the following research questions:

1. Do Malaysian firms practice environmental scanning?

2. How do Malaysian firms scan the environment?

3. What is the impact of environmental scanning behavior on their

investment decision-making quality?

4. Does information processing capacity (e.g. skills, personnel, decision-

making software, IT support) enhance the impact of investment decision

quality?

Definition of Terms

Table 2: Definition of Terms

Terms Definition

Environment As the relevant physical and social factors outside theboundary of an organization that are taken intoconsideration during the organizational decisionmaking (Duncan, 1972)

Scanning Is the means through which top managers perceiveexternal events and trends. (Hambrick, 1982, Culnan,1983)

Environmentalscanning

Is the acquisition and use of information aboutevents, trends, and relationships in an organizationsexternal environment, the knowledge of which wouldassist management in planning the organizationsfuture course and action. (Aguilar, 1967, Choo andAuster, 1993).

Informationprocessing capacity

How decision makers understand, predict, stimulate,interpret, store, retrieve, transmit, generate judgments, and solve problems based on theinformation gathered.An organization processesinformation to make sense of its environment, tocreate new knowledge, and to make decisions.Larkey and Sproull (1984).


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Significance of the study

1.6.1 Theoretical Significance

The literature has generally agreed that environmental scanning has significant

positive impact on the performance of an organization, as attested to by several

studies such as Daft and Weick, (1984); Hambrick, (1981); Venkatraman (1989); and

Dess, (1987). Furthermore environmental scanning has been established as the first

step in the

The outcome of this research therefore, may add to the literature on strategic

management particularly strategic decision making which focuses specifically

on the impact of environmental scanning on companys investment decision-

making quality.

1.6.2 Practical Significance

One of the greatest challenges for managers of all organization today is

managing uncertainty. The future is not known with certainty; as a result

managers must do what they can to reduce uncertainty. It means reading the

signals, following the trends and scanning the external environment. Thus the

findings from this research will provide important guidelines as to the extent of

scanning and the areas of scanning that need to be focused when making

important investment decisions.

1.7 Conclusion

The primary focus of the study is to determine the impact of environmental scanning

behavior that is the extent of environmental scanning to the investment decision

making quality, looking at the information processing perspective. Since the current

study is prescriptive or normative in nature, the final implication of the study is to

identify how scanning should be done in order to achieve investment decision quality.


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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

Decisions are made almost every day by every human being. However it is a

complex process and must be well understood. In making a good decision, one that

is of quality, the decision makers must know a great deal about the industry and

social environment in which they work (Simon, 1987). ..

2.2 Decision Making

Many literature as cited by Haris (1998) viewed decision making as the process of

choosing among alternative courses of action for the purpose of solving problem or

attaining better situation regarding the opportunity that exist (e.g. Charlisle, 1979;

Stoner, 1982; Harrison, 1999). ..

Decision-making theory

In order to understand the investment decision making better, it is necessary to

understand the different theoretical perspectives to decision making.

Rational decision-making

In rational decision making, goals and alternatives are made explicit, the

consequences of pursuing different alternatives are calculated, and these

consequences are evaluated in terms of how close they are to the goals (Simon,

1987)..

2.5 Environmental Scanning

2.5.1 Definition

Environmental scanning is a managerial activity of learning about events and trends

in ..


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2.5.2 Environmental scanning process

Environmental scanning begins with gathering information about the external

environment. .

2.5.3 Environmental scanning behavior

Efforts by executives or decision makers to assess uncertainty and identify

opportunities in their environment are called scanning behavior

Amount of scanning

Scanning frequency (i.e. amount of scanning done by managers) is one the popular

dimension of scanning behavior used in earlier studies (Elenkov, 1997; May et al.,

2000; Sawyer, 1993). ..

2.5.4 Information Processing Capacity

2.5.5 Decision Making Quality

2.6 THEORETICAL FRAMEWORK

Many studies advocated that environmental scanning has positive impact on

organizational performance (e.g. daft and weick (2000); Ebrahimi (2000); etc).

Based on that relationship, it is proposed that the impact of environmental

scanning will lead to quality investment decisions.

The extent of environmental scanning in this study will be operationalized by looking

at the frequency of scanning, method of scanning, kind of information sought, and

sources of information used. However, a great extent of environmental scanning

does not necessarily translate into better decision. The impact of quantity and

quality of scanning activities will vary according to contextual situations. One

of the major context variables is the information processing capacity of the

organization. IPC is being measured via cognitive abilities of organizational

members (past experience with scanning in interpreting the environment); and the

ability of decision support system. Thus, the conceptual framework is depicted as in

figure 11 below


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Figure 9: Theoretical framework of the extent of environmental scanning and

investment decision making quality

2.7 HYPOTHESES

2.7.1 Scanning frequency

The literature indicated that scanning frequency is the number of times the manager

scans the environment to get an information. There are a number of evidence that

indicate organization which engages more in environmental scanning perform more

successfully than their counterparts who do not (e.g. Daft et al., 1988; ). Therefore,

the researcher expected that the more frequent the manager scans the

environment for information, the more timely information he/she will get to

capture the changing trend. These timely information will be useful for the

organization to identify problems and opportunities and therefore more able to

make the correct and quality decisions. Therefore it is hypothesize that:

H1: The greater the amount of environmental scanning done the better the quality of

the investment decisions made.

ENVIRONMENTALSCANING

Amount of scanningINVESTMENT

DECISION QUALIT

INFORMATIONPROCESSING CAPACITY

Organizational design Inform

ation support system


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2.7.5 Information processing Capacity

This study hypothesizes that IPC is a moderator to the impact of scanning activitieson the quality of the decision. DSS can increase efficiency and improve

service quality of the organization and thereby increase profits. Therefore,

researcher the believes that decision support system will help organizations to

process information more quickly to arrive at better quality decision. Based

on this premise, the researcher suggests the following hypothesis;

H2: The interaction of the extent of environmental scanning behavior and

decision/scanning skill and the experience of the decision maker will enhance the

quality of investment decision making.

2.8 Conclusion

The primary focus of this study is trying to address the issues of how scanning

should be done in an organization and what scanning behavior organizations should

adopt to ensure quality decisions


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CHAPTER 3

RESEARCH METHODOLOGY

3.1 Introduction

This study examines the relationship between environmental scanning and the

quality of investment decisions made by managers, and the role of information

processing capacity in the said relationship. This chapter describes themethodology used to address the questions, in terms of the research design,

the instruments, etc.

3.2 Research design

The purpose of this study is to explain the nature of relationships between the

extent of environmental scanning and the investment decision making quality.

Since the focus of this study is on extent of scanning and its impact, and that the

researcher is trying to address the questions of what scanning behavior

organizations should adopt to ensure quality decisions, it is therefore can be

established by engaging hypothesis testing method of research design.

Since the current research involves several variables, and the main focus is to know

how one variable is related to another - that is the researcher would like to see the

nature, direction and significance of the relationships between the variables used in

the study, this study is correlational in nature. It does not attempt to establish

cause-effect relationships. Furthermore, the extent of the researchers interference

with the study is minimal as it is conducted in natural environment of the organization

through interviews and self-administered questionnaires at the workplace.

Since the study is correlational and not causal in nature, cross-sectional data will

be sufficient. This choice is also made in recognition of the time undertake this study.

The data relates to an investment decision, which therefore forms our unit ofanalysis. The extent of scanning involved in making the decision, and its subsequent


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quality will be measured. This unit of analysis is also chosen to enhance greater

internal validity. Choosing the manager or the organization making the decisions

will only confuse the issues to be addressed, as managers and organizations make

many decisions, some would involve a great amount of scanning, and some none at

all, and the associated quality is high for some and low in others.

3.3 Variables and Measurement

The conceptual framework of the study consist of the extent of environmental

scanning as independent variables, investment decision quality as the dependent

variable, and information processing capacity as moderating variables.

3.4 Questionnaire Design

Based upon a review of the literature, several items have been designed by the

researcher specifically for this study (e.g. information on information processing

capacity, investment decision making quality). However, majority of the items on

information of extent of environmental scanning will be adopted from past literature

(Choo, 1993; Hambrick, 1979; Walters, 1996) and the nature of decision was

adopted from Mahmood (2001). The questionnaire items were arranged in five

primary sections: (1) information related to general background of the decision

makers, the companies, and the specific decision made within the last two years; (2)

information related to extent of environmental scanning; (3) information related to

information processing capacities; (4) information related to nature of decision; (5)

information related to quality of investment decision making. Items in this study will

use nominal measures for Section A and interval measure, rated on a 5-points Likert-

like scale for Section B, C, D and partly ordinal and interval scale for Section E

(which is according to Sekaran, (2000) will provide satisfactory results and reduce

bias).

3.5 Population and Sample

The main interest of the study is to identify the impact of environmental scanning to

investment decision making quality. Therefore, thepopulation of the study is the

investment decision per se. The data will be obtained from sources such as

Registrar of Companies (ROC). The sample size for the study is determined by arule of thumb proposed by Sekaran (2000). As according to Sekaran (2000), sample


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of one hundred to five hundred is enough to generalize the population. Therefore,

one hundred samples will be chosen to participate in this research.

Multistage cluster sampling will be used in this study for data collection method.

Since the primary focus of the study is investment decision making.

3.8 Data Analysis Method

The data collected were analyzed using the SPSS for Windows. This analysis is to

obtain (1) Goodness of measures which consists of the response rate; sample profile

in terms of demographic information and other profiles; the non-response bias; and

validity and reliability test; and (2) Inferential analysis, including the descriptive

analysis; test of differences and the results of the hypothesis testing.

3.8.1 Goodness of Data

To verify the goodness of data, the present study observed the response rate;

sample profile in terms of demographic information and other profiles; the

non-response bias; and validity and reliability test. For reliability and validity

test, the present study used (1) Cronbachs alpha and (2) Construct validity. .

Factor analysis was conducted to assess the validity of the theorized

dimensions tapped by the items in the measure. The present study used

exploratory factor analysis to verify whether the 16 items of the independent variable,

7 items of the control variable and the 4 items of the moderating variable in the

survey instrument correctly capture the environmental scanning behavior, information

processing capacity and nature of decision, respectively.

The researcher identifies and interprets factors based on the criteria whereby,

each item should load 0.50 or greater on one factor and 0.50 or lower on the other

factors. The criteria were applied by deleting items that showed loading of less than


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0.50 on all factors and items whose loading were greater than 0.50 on two or more

factors.

Once the data is ready for analysis (i.e., out-of-range/missing response, etc.,

are cleaned up, and the goodness of the measures is established), the researcher is

ready to test the significance and hypotheses already developed for the study.

Inferential Analysis

Descriptive analysis

The major objective of the survey is to have an overview of how investment decision

is impacted by the amount of scanning done, the method of scanning used and the

source of information utilized by the decision maker (i.e., the conceptualization of the

extent of environmental scanning). Based on the data collected from the

questionnaire survey, descriptive statistics will be computed.

Test of Differences

Test of differences was also used in the present study to explore if the

scanning behavior - amount of scanning, method and sources used differ

across profiles of the respondents and decisions. The intention is to test those

differences and to understand further the scanning behavior of Malaysian decision

makers as well as to provide the reader an opportunity to become familiar with the

characteristics of the variables and demographic profiles. Statistical techniques used

to evaluate these differences are t-testand One-way ANOVA.

Correlation Analysis

Correlation analysis was also conducted to provide an initial picture of the

interrelationships among the fourteen variables of interest. This correlation


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results will be an early indicator whether the independent and dependent variable will

show significant relationship in the multiple regression. Therefore, bivariate

correlations were used to assess and understand to the extent each variable is

related to each dimension of the independent variables and also to see the direction

and significance of the bivariate relationships of the variables used in the study.

Hypotheses Testing

Since the current research involved several variables, and the main focus is to know

how one variable is related to another, that is, the researcher would like to see the

nature, direction and significance of the relationships of the variables used in the

study, regression analysis will provide this information that is, it will indicate the

direction, strength and significance of the relationships of all the interval or ratio

variables in this study. To test the hypotheses under study we used Paired-T-Test

and multiple linear regressions. Paired T-Test was used when there are two

related samples ofobservations to see if the means on two related variables differ

from one another. Multiple regressions were employed to assess simultaneous

relationships and also for assessing the individual effects of the several independent

variables.

Therefore:

o Hypotheses 1, 2, and 3 were tested using regression analysis to enable the

researchers to determine whether a significant positive relationship exists

between the two variables.

Multiple regression was also employed to assess simultaneous relationships and

also for testing the individual effects of the several independent variables. The beta

coefficient will be examined as it tells how strongly the independent variable

associated with the dependent variable is. It is equal to the correlation coefficient


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between the two variables. The Durbin Watson statistics was also examined to

determine whether the assumption of independent errors is tenable. The

closer to two that the value is, the better will be the assumption . R is the

correlation between the observed and predicted values of the dependent variable.

The sign of R (positive or negative) indicates the direction of the relationship. The

absolute value of R indicates the strength, with larger absolute values

indicating stronger relationships.

R squared is the proportion of variation in the dependent variable

explained by the regression model. The values of R squared range from 0 to 1,

small values indicate that the model does not fit the data well. The sample R

squared tends to optimistically estimate how well the model fits the population.

Besides R squared, we also look at theANOVA table to check how well the

model fits the data. If the significance value of the F statistics is small (smaller

than say 0.05) the independent variables do a good job in explaining the

variation in the dependent variables. If the significance value of F is larger than

say 0.05 then the independent variables do not explain the variation in the dependent

variables and the null hypothesis that all the population values for the regression

coefficients are 0 is accepted.

3.9 Conclusion

To conclude, this study chooses to investigate the impact of environmental scanning

on investment decision making from an Information Processing perspective.

It is expected that the findings of this study will show that the amount of

scanning done coupled with the correct methods and proper sources used will lead to


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quality decision making. However, good analyzing capability of the decision maker

will enhance the quality decision made.


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CHAPTER 4

DATA ANALYSIS AND FINDINGS

This chapter reports the results of the data analysis and evaluates them

according to the stated objectives and research problems. It has two primary

purposes The second section describes the inferential analyses, which are results

taken from statistical analysis including the descriptive analysis; test of differences and

the results of the hypothesis testing. Finally a brief summary of the findings will be

presented.

4.1 Goodness of Data

4.1.1 Response Rate

after the questionnaire was distributed, emails were sent to those who had not

yet responded. After another month, the researcher followed up with phone calls.

Finally a total of 126 questionnaires were returned. However, 9 were unusable as they

contain in-complete responses. The final sample size used for data analysis was 118.

Table 4.1 displays the details.

Table 4.1:

Rate of return in terms of number and percentage

No. of questionnaire %

Questionnaire

distributed on 24th June

30th July 2006

345 100%

Total questionnaire

received126 36.5%

Received (in the first

two weeks)

3830.1%

Received (in one month

after the first reminder12 9.5%

Received (after 1 month after 2nd reminder and

68 54.8%


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phone calls)

Usable questionnaire 118 93.7%

Unusable

questionnaires9 7.1%

4.1.2 Sample Profile

Respondent Profile

4.1.4 Validity and Reliability

Validity Test


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Table 4.6

Measure of sampling adequacy, partial correlation (anti-image matrices) and descriptive statistics for environmental scanning

Q1 Q2 Q3 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q13 Q14

Q1 .810(a)Q2 -.088 .700(a)

Q3 .086 -.635 .679(a)

Q5 -.363 -.043 .174 .772(a)

Q6 -.089 -.268 .222 .001 .741(a)

Q7 -.187 -.172 -.005 -.205 -.046 .757(a)

Q8 .112 -.148 .102 .188 .042 -.444 .725(a)

Q9 -.084 .163 -.069 -.088 -.340 -.213 -.118 .798(a)

Q10 .050 .230 -.223 -.233 -.320 .079 -.391 .013 .719(a)

Q11 -.004 .112 -.154 -.080 .136 -.151 -.247 .053 -.117 .865(a)

Q13 -.001 -.042 -.169 -.199 -.233 .325 -.164 -.131 .188 -.092 .712(a)

Q14 -.078 -.122 -.069 -.006 .056 -.116 .155 .122 -.190 -.182 -.539 .786(a)

Mean

Std dev

3.2735

1.27728

117

3.2564

1.38922

117

3.3846

1.25839

117

3.1624

1.32585

117

3.5641

1.20620

117

3.5128

1.29731

117

3.4017

1.24610

117

3.6068

1.21028

117

3.4274

1.1694

1

117

3.1880

1.33217

117

3.5385

1.33609

117

3.4701

1.20754

117Kaisers MSA

Bartletts Test of SphericitySig.

.749

585.004.000

122


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123


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Table 4.7

Factor analysis and reliability test result on extent of scanning

Reliability Test

Table 4.12:

Reliability coefficients for the major variables

VariablesNumber of

items

Item

dropped

Reliability

Cronbach

Alpha

Method of scanning (Formal/Informal)

TechnologyEconomic

Competition

44

4

-.723.753

.624

Source of scanning (Personal/

Impersonal)

Technology

Economic

Competition

4

4

4

-

.670

.776

.598

Source of scanning (External/Internal)

Technology

Economic

Competition

4

44

-

.707

.790

.689

Items Types of informationFactor Loading

1 2 3Q1 Demographic trends affecting demand .065 -.120 .798

Q2 Advances in technology .754 .110 -.013

Q3 New concepts in technology .829 .165 -.279

Q5 Changes in societal values affecting demand .065 -.005 .774

Q6 Product comparable to competitors .073 .146 .626

Q7 Information about cash and investment techniques -.073 .711 .244

Q8 Internal budgeting and control systems -.015 .923 -.087

Q9 Improve sales level and pattern -.194 .342 .582

Q10 Companys performance (e.g. expenses, cost) information .031 .666 .127

Q11 New organizational design .338 .591 -.121

Q13 Technology information for product/service enhancement .784 -.180 .290

Q14 Technology information for product/service efficiency .804 -.053 .165

Reliability Cronbach Alpha .827 .786 .732

Eigenvalues 4.505 1.797 1.300

Percentage of Common variance 37.539 14.977 10.831

Cumulative % 37.539 52.516 63.347


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4.2 Inferential Analysis

4.2.1 Descriptive analysis

Overall Descriptive Analysis

Table 4.13

Descriptive statistics of Independent VariablesDecision N=118

Variables Mean

Std.

DeviationX1: Extent of scanning

Technology

Economic/financial

Competition

3.369

3.371

3.392

.985

.988

.936

X2: Method of scanning

Technology

Economic/financial

Competition

3.413

3.469

3.405

1.046

.848

.752

X3: Source of scanning

(Personal/Impersonal)

Technology

Economic/financial

Competition

2.913

2.989

3.068

.743

.863

.767

X4: Source of scanning (External/Internal)

Technology

Economic/financial

Competition

2.866

3.047

2.867

.779

.917

.747

Notes: X1 scale range: 1(not at all) to 5 (great amount)X2 scale range:1 (informal) to 5 (formal)

X3 scale range: 1(impersonal) to 5 (personal)

X4 scale range: 1(external) to 5 (internal)

4.2.2 Test of Differences

.. One-way ANOVA is used to evaluate those differences. Tables 4.16 to

4.20 present the results of one-way ANOVA comparing the extent of scanning done,

method used and sources of information used on different environmental sectors across

the demographic profiles and profiles of the decisions. Refer also the SPSS output

appended in Appendix C.


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Table 4.16

ANOVA results - Extent of scanning by demographic profile

DEMOGRAPHIC

EXTENT OF SCANNING

Technology Economic Competition

F-value p-value F-valuep-

valueF-value p-value

Respondent profile:

Position

Education level

Field of expertise

Years in the industry

Years in the current firm

Years in current position

2.369

.480

3.062

.342

.640

1.037

.098

.620

.051

.795

.591

.358

.455

.956

5.072

.466

1.010

.845

.636

.387

.008

.706

.391

.432

.222

.893

.448

.495

1.834

.039

.801

.412

.640

.687

.145

.962

Company profile:

Type of industry

Years in operation

Size of company

- No. of employees- Average revenue

1.628

.893

4.702

1.702

.205

.447

.011

.187

.043

1.701

.738

.154

.836

.171

.480

.857

.061

.643

.536

.110

.805

.589

.586

.896

Decision profile:

Type of decision

Size of investment

.238

2.410

.870

.071

2.580

1.856

.057

.141

4.676

1.572

.004

.200

4.2.3 Test of Relationship

Correlation Analysis

.

.


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Table 4.22

Correlation Between Variables

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

Mean Std. Dev Technol

ogy

(X1)

Econom

ic

(X2)

Compet

ition

(X3)

Method

Econom

ic

(X4)

Method

Technol

ogy

(X5)

Method

Compet

ition

(X6)

Souce1

Technol

ogy

(X7)

Source1

Econom

ic

(X8)

Source1

Compet

ition

(X9)

Souce2

Technol

ogy

(X10)

Source2

Econom

ic

(X11)

Source2

Compet

ition

(X12)

IPC

(X13)

Decision

Quality

(X14)

X1 3.3686 .98473 1.000

X2 3.3708 .98803 .569** 1.000

X3 3.3919 .93626 .353** .497** 1.000

X4 3.4693 .84768 .331** .482** .298** 1.000

X5 3.4131 1.04563

.971** .433** .335** .338** 1.000

X6 3.4053 .75159 .332** .354** .323** .541** .332** 1.000

X7 2.9129 .74293 -.028 -.081 -.027 .032 .007 .115 1.000

X8 2.9891 .86289 -.141 -.013 -.110 -.005 -.174 -.045 .469** 1.000

X9 3.0682 .76744 -.150 -.166 -.226* -.132 -.133 -.156 .495** .502** 1.000

X10 2.8664 .77885 -.209* -.158 -.265** .152 -.179 .135 .569** .315** .346** 1.000

X11 3.0468 .91704 -.116 .054 -.052 .180 -.116 .158 .366** .666** .330** .449** 1.000

X12 2.8674 .74649 -.198* -.119 -.200* .058 -.184* .122 .397** .386** .561** .436** .506** 1.000

X13 3.8729 .60953 .502** .372** .340** .318** .487** .386** -.092 -.245** -.294** -.089 -.095 -.214* 1.000

X14 3.7323 .70879 .344** .387** .296** .209* .294** .309** -.098 -.158 -.160 -.081 -.142 -.136 .513** 1.00

126


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Research Report Writing workshop 9 May 2009


Interaction variable

IPC*Tech 3.493*IPC*Econ -.977

IPC*Comp .486

IPC*Tech(M) -2.452*IPC*Econ(M) 1.470**

IPC*Comp(M) -

2.480***

IPC*Tech(S1) .071

IPC*Econ(S1) -.606

IPC*Comp(S1) -.153

IPC*Tech(S2) -.054

IPC*Econ(S2) .397

IPC*Comp(S2) .839

***significant at the 0.01 level, ** significant at the 0.05 level, * significant at the 0.1 level

Table 4.28 below provides a summary of hierarchical regression results for direct

relationship and the moderating effect of the relationships between environmental scanning

behavior and investment decision quality

Table 4.2

The specific hypotheses and summary of hypotheses testing resultHypotheses testing Technology (a) Economic (b) Competition (c)

DirectModerate

dDirect

Moderate

dDirect Moderated

H1: The greater the

amount of environmental

scanning done the better

the quality of the

investment decisions made

X X * X

H2: Investment decision

that focuses more on

formal methods ofenvironmental scanning

will have better quality

than those that focus more

on informal methods)

n/a

X *

Supported * weakly supported X not supported

RESEARCH FORMAT-EXAMPLE


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Supported * weakly supported X not supported

4.3 Conclusion

In summary, the current studys usable questionnaire of 118 collected via convenient

sampling was used for the analysis of the study. Various levels of decision makers who

make capital investment decisions in the last two years from various types of companies

were analyzed. From the test of goodness of data, it was found that the data was

reasonably good and provide useful basis for the analysis and inference. Result obtained

from factor analysis and scale reliability testing also gave satisfactory output.



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CHAPTER 5

DISCUSSION AND CONCLUSION

The purpose of this chapter is to discuss and draw conclusions about the research findings

and to offer interpretations of the results that may have implications for the future scanning

research and for executives concerned with the scanning task. The general outline of the

chapter is as follows: (1) Recapitulation of major findings; (2) Discussion; (3) Implications

for research; (4) Implications for executives; (5) Methodological limitations; (7)

Recommendations for future research; and (8) Conclusion.

5.1 Recapitulation of Major Findings

This research was initiated to understand the nature of environmental scanning undertaken

in lieu of investment decision making among Malaysian decision makers.

5.2 Discussion

5.2.1 Descriptive results

5.3 Implication

5.3.1 Theoretical Implication

5.3.2 Practitioners Implication



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5.4 Limitation

There are a number of limitations inherent in this study. The identified limitations are: (1)

the sample data selected, and (2) the definition of the environment.

5.4.1 Sample selected

5.4.2 Definition Of Environmental Sectors

5.5 Recommendation for Future Research

5.6 Conclusion

APPENDIX

References

Abels, E. (2002). Hot Topics: Environmental Scanning. Bulletin of the American society

for Information and Science and Technology. 28(3), 1-4.

Abelson, R.P. and Levi, A. (1985). Decision making and decision theory. In Lindzey, G.

and Aronson, E (Eds), the handbook of Social Psychology, 3rd Ed. New York:

Random House, 231 309.

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