research writing example
TRANSCRIPT
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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
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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
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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.