Proceedings of 9th Asian Business Research Conference

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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
The Role of Experience, Decision Environment and
Decision Type in Successful Decision Making
Majharul Talukder a and ABM Abdullah b
The paper outlines a study undertaken to identify the types of impact that
prior experience, the decision environment and the decision type could have
upon the performances and outcomes of the decisions made. The objective
was to portray the relationships between decision performances, prior
experience, decision environments and decision types in order to offer advice
in the building of decision support systems. Survey data was collected in a
large Bangladeshi pharmaceutical company which supported the hypotheses
that managers’ prior experience is positively related to the decision
performance. Moreover, decision performance is affected by uncertainty in
the business environment where the decisions are made and the level of unstructuredness of the business decisions. These findings from a developing
country correspond to research in developed countries. The implications of
this study are that organizations need to actively consider who is taking the
decisions within the organization and how likely they are to be able to make
effective decisions based upon their career history and levels of experience.
Keywords: Decision making, Experience, Decision environment, Decision type,
Decision performance.
1. Introduction
It is generally accepted that people learn from their experiences and try to apply such
knowledge in making successful future decisions (Hamel & Prahalad, 1994). In the
business world, experience is critical, such that it is almost impossible for anyone to
become a top decision maker in any organization without having had a range of
decision making experiences throughout their career (Holcomb, Holmes & Connelly,
2009). Experience is an important way of acquiring decision making skills; individuals
develop the habit and action of thinking according to their predisposition to the
organizational environment (Hamel & Prahalad, 1994).
Ashmos, Duchon and McDaniel (1998) proposed that predisposition is the result of
two conditions: firstly, existing organizational rules and routines which they refer to as
rule orientation and, secondly, organizational past performance. Hamel and Prahalad
(1994) asserted that the experiences individuals acquire are likely to affect the
thinking process they retain over their life. Previous studies have found that routines
and patterns originating from day-by-day experiences persist for a long time and
these internal thinking patterns become a powerful force in determining subsequent
organizational action (Holcomb et al., 2009). Milliken and Lant (1991) suggest that
past performance affects the way decision makers respond to strategic issues; in
particular past performance is believed to activate certain psychological processes
that affect decision makers choices and actions.
______________________________________________
a
Majharul Talukder, Discipline of Management Studies, Faculty of Business, Government & Law,
University of Canberra, ACT 2601, Australia. E-mail: [email protected]
b
ABM Abdullah, UniSA College &School of Management, University of South Australia, Adelaide, SA
5000, Australia. E-mail: [email protected]
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Scholars agree that past failures or past successes are significant drivers of
managerial decision behavior (Cowan, 1991). Milliken and Lant (1991), for example,
argue that recent experiences with success and failure activate psychological and
internal processes that alter the way managers interpret their environments and
respond to strategic issues. Past failures are often believed to rigidify actions
because decision makers develop a particular mindset over the period (Staw et al.,
1981). It is also posited that past successes may lead to overconfidence, combined
with a failure to vigorously process information. There is compelling theory for each
of these lines of argument regarding how past performance may affect strategic issue
participation. While the explanations and predictions vary, all suggest that past
performance is a factor in explaining both the decision participation and the future
decision outcome.
In addition to prior experience, the decision environment also plays a crucial role in
executive decision making (Nadkarni & Barr, 2008). The decision environment can
be characterized as slow or fast changing business environments linked to different
levels of uncertainties (Nadkarni & Barr, 2008). In today’s fiercely competitive global
business arena, the business environment can change dramatically very quickly.
Such drastic changes lead to high levels of uncertainty that decision makers have to
deal with in order to make any effective business decision. Similarly, the decision
type, which can be categorized as structured, semi-structured or unstructured also
plays an important role in determining the outcome of any decision made (Osborne,
Stubbart & Ramaprasad, 2001). While it is relatively easy to make a structured
decision which leads to a positive outcome in the end, it is extremely difficult to make
an unstructured decision that leads to positive business results, owing to the
complexity and ambiguity involved in the decision.
The purpose of this study was to identify the type of impact that prior experience, the
decision environment and the decision type could have upon the performances and
outcomes of the decisions made. The objective was to build mathematical models
which portray the relationships between decision performances, prior experience,
decision environments and decision types. Such a model can be used to build
decision support systems which assist managers in making better decisions in
different situations by analyzing the decision-situation.
Research Questions
This study will be guided by the following research questions:
1. What is the relationship between managers’ job experiences and
managerial decision performance?
2. What is the relationship between the type of business environment and
managerial decision performance?
3. What is the relationship between the type of decision made and managerial
decision performance?
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
2. Literature Review
2.1 Job Experience and Decision Performance
Throughout the years, experience has played a crucial role in making successful
decisions. Cognitive researchers predict that task experience will influence executive
activities (Cowan, 1991), whilst in the domain of problem formulation, Kilmann (1989)
investigated the relationship between managerial experience and executives’ thinking
about the process of formulating organizational problems for decision making. He
measured the thinking involved in this process by evoking executives’ descriptions of
their activities and used the total number of years in a managerial position as the
measure for experience. His analysis showed that management experience had an
influence on the way problems were defined and solved. Significant results were
found in relating the success rate of the decisions taken to total years of experience.
To address the effect of management experience on executives’ description of the
problem-formulation process in more detail, Cowan’s (1991) study focused on
executive experience with specific types of organizational problems. The reason is
similar to the logic employed by Dearborn & Simon (1958) which related descriptions
of problem types to executives’ functional backgrounds. Both Cowan (1991) and
Dearborn & Simon (1958) suggest that what an executive has learned previously is
retained and subsequently brought to bear upon later activities, helping to inform
interpretations that act as behavioral drivers.
It is becoming increasingly clear that prior experience plays a critical role in
interpretive activities leading to behavior and performance (Cowan, 1991; Talukder,
Harris & Mapunda, 2008). Problem solving experience is an important factor in
understanding executive thinking and decision making. Brown (1982) examined
whether the process by which individuals learn from experience could be expected to
yield outcomes consistent with the normative predictions. The results of a laboratory
experiment suggested that a subject’s behavior is consistent with their utilizing
reference points during the learning process. As a result, their equilibrium decision
rules will merely promise results above their subjective criteria, thereby suggesting
that the alternative optimality of an agent’s decision rule will depend upon their
criteria of success and experience.
Many different aspects can characterize patterns of experience over time. Among the
most important is the trend of an experience (Ariely & Zauberman, 2000), its rate of
change and the maximum and final intensities associated with the experience. In
their study, Ariely and Zauberman (2000) tested these different aspects in a way that
allowed their relative importance to be compared. The conclusions were that the
trend of an experience was the most important predictor of overall evaluation of the
alternatives. In addition, the rate of change of the initial part of the experience, the
maximum and final intensities and the duration of experience, were also found to play
an important role in the overall alternative evaluations. In sum such characteristics of
experience over time have been shown to have a positive impact on overall
evaluations, such that an increase in any of them increases overall alternative
evaluations.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Decision makers differ in their capacity to perform decision making tasks (Taylor,
1975). One attribute of a decision-maker which has been found to be significant in
determining their information processing ability is their age and acquired experience;
these contribute significantly to both the manner in which decisions are reached
(Kirchner, 1958) and the decision quality (Birren, 1964; Weir, 1964). The older
decision makers are far more susceptible to the dysfunctional effects of information
overload owing to their higher exposure to the large volume of information they have
had to process throughout their career (Taylor, 1975; Ligon, Abdullah & Talukder,
2007).
With experience, managers acquire expertise in making decisions (Perkins & Rao,
1990). Previous studies comparing experts and novices from different organizations
suggest that experts have more highly developed cognitive structures (Nadkarni &
Barr, 2008). They are more efficient in organizing information in their memory and
repertoiring a set of rules for using that information, which allows them effective
problem structuring and successful problem solving (Chi, Feltovich & Glasn, 1981).
Harmon and King (1985) found that experts use facts and heuristics to solve
problems. In the real world, experienced managers are likely to search for more
information, at the same time, restricting themselves to relevant and important
information (Chiesi, Spillich & Voss, 1979).
In a situation where managers are provided with information, as opposed to having to
acquire it, they differ significantly in their valuation of provided information (Perkins &
Rao, 1990). More experienced managers place more importance on relevant cues
and less importance on irrelevant cues (Taylor, 1975). Consequently, experience
plays a significant role in determining the amount of information sought and the way
that it is evaluated (Perkins & Rao, 1990); such differences in information evaluation
leads to variations in decisions and in addition, experienced managers understand
the uncertainties and their consequences better than inexperienced managers
(Beach, 1975; Nisbett et al., 1983). Therefore, we propose the following hypotheses
to test the relationships between managers’ decision performance and their job
experience.
H1: There is a significant difference in managerial decision performance
based upon their job experience.
2.2 Business Environment and Decision Performance
The business environment consists of a combination of various forces which are
beyond the control of management, thereby offering opportunities and threats to the
decision makers (Ward et al., 1995). A review of the history of management research
on the environment encompasses three perspectives (Bourgeois, 1980). The first
focuses on groups external to the organization that impinge on its activities. The
second focuses on the attributes of external forces such as dynamism and
complexity (Dess & Beard, 1984), whilst the third perspective is concerned with
managerial perceptions about environmental attributes as explained by Swamidass &
Newell’s (1987) construct of perceived environmental uncertainty. Uncertainty of
related environmental dynamism is related to unpredictable change in the business
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
environmental conditions faced by firms (Dass & Beard, 1984). Thus, the notion of a
dynamic environment is similar to the high velocity environment where rapid and
discontinuous change takes place (Bourgeois & Eisenhardt, 1988).
The organizational environment is a significant source of contingencies faced by an
organization (Tosi & Slocum, 1984). Environmental variables or properties have
major implications for all aspects of management including strategy formulation and
process control (Goll & Rasheed, 1997). Multiple theoretical arguments have been
advanced which suggest that the environmental context is a major determinant of the
appropriateness of the rational decision process. Fredrickson & Iaquinto (1989)
suggested the adoption of rational comprehensive processes in stable environments
and their avoidance in uncertain dynamic environments. In a dynamic environment, a
comprehensive process is destined to fail as complete information is not available,
relationships are not certain and the future is highly unpredictable (Fredrickson &
Iaquinto, 1989). Miller & Friesen (1983) argue that a dynamic environment must be
studied more carefully and diligently to afford executives with an adequate degree of
mastery. Eisenhardt (1989) also found that successful decision making in high
velocity environments requires more information, the consideration of more
alternatives and more expert opinion.
Highly dynamic, uncertain environments intensify the challenges to decision makers,
often complicating their decision making efforts. Greater analytical effort must,
therefore, be devoted to understand and master opportunities and threats (Goll &
Rasheed, 1997); during highly uncertain conditions, more attention must be paid to
the selective pursuit of economical and competitive policies (Miller & Friesen, 1989).
Additional risk taking, pro-activeness and strong emphasis on novelty, can lead to
negative outcomes when economic conditions are more taxing (Miller & Friesen,
1989). Therefore, we propose the following hypothesis to relate the business
environmental conditions to the decision performance of the managers.
H2: There is a significant difference in managerial decision performance
based on their decision making environment.
2.3 Decision Type and Decision Performance
In many organizations, managers attempt to initiate structure in the environment and
to clarify the situation for decision making. The less structure in the work
environment, the more diligence the managers have to exercise to make decisions
which may lead to good performance (Sagie et al., 1995). It is always easier for a
manager to make repetitive or structured decisions, as the information requirements
are lower. However, making novel decisions demands a higher degree of cognitive
involvement as the information gathering and analysis requirements are much higher
in unstructured decisions (Moldoveanu, 2009; Eisenhardt, 1989). A situational
variable reflecting the ambiguity involved in an organizational decision is related to
the nature of the decision: Gist, Locke & Taylor (1989) have found significant
relationships between the types of decisions (strategic or tactical) that managers
have to make and the level of ambiguity associated with these decisions. The
strategic decisions are long-term decisions which deal with future uncertainties and
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
are very ambiguous in nature; they could be described as “if” decisions indicating
what actions are needed if something happens or not (Sagie et al., 1995).
According to Sagie et al. (1990), ambiguity is not constant along the decision making
process, it changes based on the decision type. Strategic decisions are more
complex and characterized by the highest level of ambiguity (Sagie et al., 1990), (i.e.
when determining which course of action should be taken in the future); after this
decision is made clarity and certainty are likely to increase to some extent for the
lower level decisions. Consequently, ambiguity and the level of complexity decreases
for the tactical level decisions (Sagie et al., 1995). It has been found that the initiation
of a decision structure by top management is more beneficial during the uncertain
strategic decision making rather than the tactical decisions process (O’Driscoll &
Beehr, 1994). Conversely, the more the situation has been explained during the
tactical decisions, the less management direction consultation is needed (AbdelHalim, 1983). Therefore, we propose the following hypothesis to test the relationship
between decision type and decision performance.
H3: There is a significant difference in managerial decision performance
based upon decision type.
3. Methodology
A quantitative research design was selected to develop an overview of the
relationships between the variables (Punch, 2005; Creswell, 2008). To test the
hypothesized relationships, data was collected using a closed question survey
questionnaire. The survey questionnaire consisted of questions related to the
participants’ demographics, their current, as well as previous job experience, and the
success rate of their decisions. In the second part of the questionnaire, twenty-five
questions were asked to measure participants’ perceptions about the role of level of
experience, role of decision type, and role of decision environment on their decision
performance. The respondents were asked to evaluate the extent they agreed or
disagreed with the statements that describe their individual performance on a seven
point Likert scale from 1= Strongly Disagree to 7= Strongly Agree.
Prior to conducting the survey, a pilot test was conducted to determine if the
questionnaire was clear and ready for use. Five experts, two academic professionals
and three senior managers from different companies, were asked to complete the
questionnaire and comment on its clarity and user friendliness. Based on their
feedback, minor changes in the wording of some questions were made to ensure that
the questionnaire was easy to understand.
The survey was undertaken in the Orion Pharmaceutical (BD) Ltd., which is one of
the biggest pharmaceutical companies currently operating in Bangladesh with
approximately 7% market share; it is also one of the most successful pharmaceutical
companies in Bangladesh. The location was chosen as much previous research has
been undertaken in developed countries and it was considered to be a useful
contribution to see whether the findings would support previous research or,
alternatively, show differences that might be attributed to being undertaken in a
developing country. The company has experienced phenomenal growth in the past 6
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
years due to the marketing strategy untaken by the company's management. This
organization was deemed to be one of the best to study the impact of managerial
decisions on company success within a developing country owing to the significant
government scrutiny the company faced during the period of 2007-2008 which led to
more openness in the organizational processes. The company employs around 1000
management level employees in various departments. The sample of 139 executive
level employees we have taken represents all major divisions of the company
including marketing, human resources, finance and distribution and provides a 14%
sample of the organization.
The final stage of quantitative study was to analyze the collected data using a series
of quantitative methods which included exploratory factor analysis, analysis of
variance and multiple regression analysis. These were all chosen in order to explain
the way that variables impacted upon or interrelated with each other.
4. Data Analysis and Discussion
In this section of the paper we will firstly, describe the sample and then analyze the
questionnaire data. The implications will then be explored.
Participants
Most of the participating executives were male. Frequency distribution shows that out
of 139 participants, 108 (77.7%) were male and 31 (22.3%) were female (please
refer to table 1). The mean value for participants’ gender is 1.22. Standard deviation,
skewness and kurtosis values for the participants’ gender are 0.418, 1.345, -0.193.
An absolute skewness value of more than 1 indicates that the distribution of
participants is skewed to the right (1.345), but an absolute kurtosis value of less than
1 indicates that the distribution is normal (please refer to table 6). Normal distribution
of the participants is important for the validity of statistical analysis (Hair et al., 2009).
Table 1: Distribution of participants based on gender
Frequency
Percent
Valid Percent
Cumulative Percent
Male
108
77.7
77.7
77.7
Female
31
22.3
22.3
100.0
Total
139
100.0
100.0
Most of the participants (77%) are between the ages of 25-39 years (between 25-29
years 27.3%, 30-34 years 31.7%, and 35-39 years 18%). About 14% (20 out of 139)
participants are more than 45 years old. Only 5% (7 out of 139) participants are less
than 25 years old (please refer to table 2). The average age for the entire sample is
33.1. Standard deviation, skewness and kurtosis values are 1.42, 0.642 and -0.439
respectively. Absolute values for both skewness and kurtosis less than 1 indicate that
the participants are normally distributed based on their age groups.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Table 2: Distribution of participants based on age
Frequency
Percent
Valid Percent
Cumulative Percent
20-24 Years
7
5.0
5.0
5.0
25-29 Years
38
27.3
27.3
32.3
30-34 Years
44
31.7
31.7
64.0
35-39 Years
25
18.0
18.0
82.0
40-44 Years
5
3.6
3.6
85.6
>=45 Years
20
14.4
14.4
100.0
Total
139
100.0
100.0
Among the participants 35% hold bachelor degrees and 64% hold masters degrees.
Only 0.7% participants (1 out of 139) did not possess a bachelor degree (please refer
to table 3). This educational profile reflects the overall executive qualifications in
Bangladesh because of the high unemployment rate and over supply of qualified
personnel (SPB, 2007). Mean value of participants educational qualification is 2.63.
Standard deviation, skewness and kurtosis values are 0.498, -0.736 and -1.032
respectively (please refer to table 6). Absolute skewness and kurtosis values close to
one indicate that the distribution of the participants based on their education
qualification is normal.
Table 3: Distribution of participants based on education
Frequency
Percent
Valid Percent
Cumulative Percent
HSC
1
.7
.7
.7
Bachelor Degree 49
35.3
35.3
36.0
Masters Degree
89
64.0
64.0
100.0
Total
139 100.0
100.0
Approximately seventy percent (69.8%) of the participating executives had been
working with their current company for 1-15 years. Only 14.74% had been working
with their current employer for more than 15 years. There is no participating
executive who has been working with the current employer for 21-25 years (please
refer to table 4). The average length of participants’ employment with their current
company was 3.02 which indicates that the average length of current employment is
between 6-15 years. Standard deviation, skewness and kurtosis values for
participants’ current experience are 1.592, 0.914 and 0.553 (please refer to table 6).
Absolute values of less than 1 for both skewness and kurtosis indicate that the
participants are normally distributed based on length of their current employment.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Table 4: Distribution of participants based on current experience
Frequency
Percent
Valid Percent
Cumulative Percent
<1 Year
22
15.8
15.8
15.8
1-5 Years
37
26.6
26.6
42.4
6-10 Years
35
25.2
25.2
67.6
11-15 Years
25
18.0
18.0
85.6
16-20 Years
10
7.2
7.2
92.8
>25 years
10
7.2
7.2
100.0
Total
139
100.0
100.0
Most of the participating executives did not have previous job experience with any
other company. Fifty five percent of the participants mentioned that they have less
than 1 year previous job experience. Thirty seven percent participants stated that
they have 1-5 years of job experience (please refer to table 5). Only 7.2%
participants have stated that their previous job experience was between 6-15 years.
Average value for participants’ previous job experience is only 1.55. Standard
deviation, skewness and kurtosis values are 0.733, 1.484 and 2.414 (please refer to
table 6). Absolute values of more than 1 for both skewness and kurtosis imply that
the distribution of participants based on their previous experience is skewed to the
right and flatter than normal distribution.
Table 5: Distribution of participants based on previous experience
Frequency
Percent
Valid Percent
Cumulative Percent
<1 Year
77
55.4
55.4
55.4
1-5 Years
52
37.4
37.4
92.8
6-10 Years
5
3.6
3.6
96.4
11-15 Years
5
3.6
3.6
100.0
Total
139
100.0
100.0
Table 6: Descriptive statistics of the participants
Descriptive Statistics
Std.
N
Min
Max
Mean
Dev
Skewness
Kurtosis
Std.
Std.
Statistic Statistic Statistic Statistic Statistic Statistic Error Statistic Error
Gender
139
1.00
2.00 1.2230 .41778 1.345 .206 -.193 .408
Age
139
1.00
6.00 3.3094 1.41856 .642
.206 -.439 .408
Education 139
1.00
3.00 2.6331 .49846 -.736 .206 -1.032 .408
Cur. Exp
139
1.00
7.00 3.0288 1.59229 .914
.206
.553 .408
Pre. Exp
139
1.00
4.00 1.5540 .73399 1.484 .206 2.414 .408
Valid N
139
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Factor Analysis
Factor analysis of the collected data with varimax rotation has yielded three discrete
factors with eigenvalues more than 1 (7.168 for factor 1, 3.868 for factor 2 and 2.109
for factor 3). Total variance explained by these three factors is 54.88% (please refer
to table 7).
The first factor extracted through factor analysis is “Experience”. This factor
comprises 7 out of 8 items given to the participants for rating. One item is excluded
due to low factor loading. All the items included in this factor have factor loadings
more than .500 (range is .501 to .739) and none of these items has a cross-loading
of more than .300 for other factors (please refer to table 8 for factor loadings and
descriptions of the factor items). This factor shows high internal consistency as its
Cronbach’s alpha is .826. According to Peterson (1994), the acceptable alpha value
for a valid factor is .70 for social science research.
The second factor yielded from the factor analysis was “Decision Making
Environment”. This factor comprises only 5 out of nine items given to the participants
for rating. All included items have factor loadings more than .500 (range .576 to .699)
and none of them have a cross-loading of more than .300 (please refer to table 8 for
factor loadings and descriptions of the factor items). Cronbach’s alpha for this factor
is more than the threshold level .70 (.705).
The third extracted factor was “Decision Type”. This factor consists of 5 out of 8
items given to the participants for rating. Three items were excluded due to low factor
loadings and high cross loadings. All included items have factor loadings more than
.600 (range .626 to .825) and no item has a cross-loading more than .350 (please
refer to table 8 for factor loadings and descriptions of the factor items). Similarly to
the two previous factors, this factor also shows high internal consistency (Cronbach’s
alpha .868).
Table 7: Details of the total variance
Total Variance Explained
Extraction Sums of
Rotation Sums of Squared
Initial Eigenvalues
Squared Loadings
Loadings
% of Cumulative
% of Cumulative
% of Cumulative
Component Total Variance
%
Total Variance
%
Total Variance
%
1
7.168 28.672
28.672 7.168 28.672
28.672 5.188 20.751
20.751
2
3.868 15.470
44.143 3.868 15.470
44.143 4.994 19.977
40.728
3
2.109 8.435
52.578 2.109 8.435
52.578 2.962 11.849
52.578
Extraction Method: Principal Component Analysis.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Table 8: Construct measures and their loadings
Construct
Experience
(α= .826)
Decision Making
Environment
(α= .705)
Decision Type
(α= .868)
Indicators
Previous experience helped me to make successful business decisions
Experience helps managers to make better decisions
Previous experience provides helpful insights for future decision-making
Experience playing a crucial role in all sorts of business decisions
Previous experience plays an important role for future decision-making
Prior experience definitely affects business decision making
The success of the business decisions are affected by a manager’s experience
Loading
0.501
0.715
0.699
0.660
0.673
0.725
0.739
Business environment plays an important role in business decisions
Business decisions are affected by the operating environment
Business environments affect business decisions significantly
It is necessary to consider the operating environment before making decision
Making successful decisions in the business environment is very challenging
0.699
0.672
0.665
0.618
0.576
It is relatively easy to make structured business decisions
Decision type affects decision performance
Making unstructured business decisions is very challenging
Unstructured business decisions are more complex and difficult to make
Routine decisions have low a information requirement
0.815
0.626
0.786
0.807
0.661
α= Cronbach alpha
Hypothesis Testing
H1 is supported at 5% level of significance, which means managerial decision
performance varies significantly based on managerial experience (please refer to
table 10 for the summary of the ANOVA results) implying that job experience have
significant influence on the decisions made by the managers.
Between Groups
Within Groups
Total
Table 9: ANOVA result for experience
Success Rate
Sum of
Squares
df
Mean Square
F
23.613
16
1.476
7.280
24.732
122
0.203
48.345
138
Sig.
0.000
H2 is supported at 5% level of significance (please refer to table 11 for the summary
of the ANOVA results), which indicates that the managerial decision environment
plays a significant role in the success or failure of the decision (s) made by the
managers.
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Table 10: ANOVA result for decision environment
Success Rate
Sum of
Squares
df
Mean Square
F
Between Groups
10.642
16
0.665
2.152
Within Groups
37.703
122
0.309
Total
48.345
138
Sig.
0.010
Like H1 and H2, H3 is supported at 5% level of significance (please refer to table 12
for the summary of the ANOVA results). Support for this hypothesis implies that
managerial decision performance is significantly influenced by the type of decisions
(eg. Structured, semi-structured, unstructured) made by the managers.
Between Groups
Within Groups
Total
Table 11: ANOVA result for decision type
Success Rate
Sum of
Squares
df
Mean Square
F
25.727
12
2.144
11.943
22.618
126
0.180
48.345
138
Sig.
0.000
Regression Analysis
Regression analysis was conducted to build a model that can predict the managerial
decision performance based on their job experience, decision environment and types
of decisions they make. For the model, decision performance is considered as the
dependent variable and age, gender, level of education, experience, decision
environment, and decision type have been considered as independent variables. For
this analysis, aggregate scores of job experience, decision environment and decision
type factors are used.
The overall model is significant at 5% level of significance (F-value 12.044, P<
0.000). R-square value 0.392 indicates that 39.2% variations in the dependent
variable is explained by the model (please refer to table 13). D-W value less than 4
implies absence of autocorrelation in the error terms. At the same time, VIF values
less than 10 indicate that there is no multicollinerity (please refer to table 14).
Table 12: Regression model summary
Model Summary
Model R
1
.626
Adjusted
R Square Square
.392
.359
R Std. Error
Estimate
.47386
11
of
the
Durbin-Watson
1.934
Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
Table 13: ANOVA result for regression analysis
Sum of Squares
df
Mean Square F
Regression 18.930
7
2.704
12.044
Residual
29.415
131
.225
Total
48.345
138
Sig.
.000
In the model, participating managers gender, age, previous job experience and type
of decision environment and decision type came up as significant predictors of
decision performance (please refer to table 15). Participants’ current experience and
decision type have not come up as significant predictors of decision performance at
5% level of significance. However, it is noteworthy to mention that for both decision
type and decision environment, the coefficients are negative indicating the negative
correlations between decision type, decision environments and decision
performance. These indications lead to the conclusion that higher uncertainty in the
environment and highly unstructured decisions affect decision performance
negatively which supports previous research findings.
Table 14: Coefficients for regression analysis
Coefficients
a
Unstandardized
Coefficients
Std.
B
Error
4.845 .335
-.202 .100
-.192 .077
.009
.083
.256
.088
Model
1 (Constant)
Gender
Age
Education
Job
Experience
Decision
.043
Type
Decision Env. -.329
Standardized
Coefficients
Beta
Collinearity
Statistics
Sig.
.000
.045
.014
.911
.004
Tolerance VIF
-.143
-.461
.008
.317
t
14.452
-2.021
-2.496
.112
2.901
.932
.136
.944
.388
1.073
7.341
1.060
2.578
.010
.336
4.173
.000
.688
1.454
.048
-.586
-6.845 .000
.634
1.576
Regression Model
Decision Performance = 4.845 – 0.143*(Gender) – 0.461*(Age) + 0.317*(Job
Experience) + 0.336*(Decision Type) – 0.586*(Decision Environment)
5. Conclusion
All three proposed hypotheses have been supported by the collected data indicating
that managers’ job experience, business environment, as well as the types of
decisions made, plays an important role in the success or failure of decisions made
by the managers. The regression model shows that managers’ job experience and
decision type are positively related to the decision performance. However, their age,
gender and decision environment are negatively related. The negative relationship
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Proceedings of 9th Asian Business Research Conference
20-21 December, 2013, BIAM Foundation, Dhaka, Bangladesh ISBN: 978-1-922069-39-9
between gender and decision performance implies that male managers are better
decision makers than the female managers.
Contrary to the previous findings, the findings of this study indicate that young
managers are better decision makers than their older and more experienced peers.
This is probably due to the number of decisions made by the young managers are
fewer compared with the older managers who have made lot of business decisions in
their career. Consequently, their success rate percentage may be less than the
young colleagues. Moreover, it is also possible that due to their lower positions
young managers end up making most of the structured decisions, whereas older
managers hold higher positions and are responsible for making unstructured
strategic decisions.
As expected, it has been found that both decision types and decision environments
are significantly related to the decision performance. This finding supports previous
research and signifies that decision performance is affected by the uncertainty of the
business environment where the decisions are made and the level of unstructuredness of the business decisions. What is of interest is that the findings are in
line with research in developed countries indicating that the factors framing decision
making are the same in developing countries. This allows research findings
elsewhere to be applied to developing countries in a meaningful way.
The implications of this study are that organizations need to actively consider who is
taking the decisions within the organization and how likely are they to be able to
make effective decisions based upon their career history and levels of experience. If
there are complex, unstructured decisions to be taken, organizations may need to
implement processes that will allow their managers to reflect upon what antecedents
they are bringing to bear upon the situation and whether they need to actively seek
alternative information or explanations prior to making the decision.
7. Limitations and Future Direction
This study has been conducted in a single company and the company culture could
have affected the outcome of the survey study significantly. At the same time, only
20% of the survey participants are women executives, which could also affect our
findings. In the future this study should be conducted in multiple organizational
settings and try to balance the number of male and female participants.
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