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: majharul.talukder@canberra.edu.au b ABM Abdullah, UniSA College &School of Management, University of South Australia, Adelaide, SA 5000, Australia. E-mail: abm.abdullah@unisa.edu.au 0 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? 1 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. 2 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 3 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 4 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 5 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. 6 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. 7 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 8 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. 9 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. 10 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 12 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. References Abdel-Halim, A.A. 1983. Effects of task and personality characteristics on subordinate responses to participative decision making. Academy of Management Journal, 26: 477-484. Ariely, D. & Zauberman, G. 2000. On the making of an experience: The effects of breaking and combining experiences on their overall evaluation. 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