مطالعه مقایسه ای بر روی روند رفتارهای کارآفرینی شرکت در استراتژی های مختلف : استفاده از نظریه شناخت اجتماعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|9471||2006||14 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 31, Issue 2, August 2006, Pages 207–220
The influences on entrepreneurial-behavioral trends of environmental uncertainties, decision styles and inter-organizational relations and the differences in the choice of entrepreneurial behaviors among enterprises in different strategic posture from the social cognitive perspective were investigated through empirical study on 86 samples selected from the IC industry with stratified random sampling. Hypotheses were verified with the neural fuzzy set network model. The findings indicated: the more uncertain the environment is, the more rushing startup enterprises tend to be, and the more progressive the conservative enterprises are; and the former concerns are more about market relations than the latter. Moreover, entrepreneurs making decisions intuitively tend to rush entrepreneurial behaviors but those making decisions rationally to progressive entrepreneurial behaviors. By verifying data with the non-linear neural fuzzy set network model and investigating the subject matter with the a social cognitive approach through an empirical study on knowledge- and capital-intensive bigger enterprises, this study has revolutionized past studies focusing on labor-intensive SMEs.
In the knowledge economy with continuous unexpected change, the challenges faced by the budding entrepreneur and success factors of entrepreneurship have been the studies by academics and practitioners alike. Gartner (1985) believes that to start a new business, the individual, environment, organization and the entrepreneurial journey itself are the four main contributing factors. In his paper, Timmons (1999) wrote about a model for entrepreneurship management. Timmons believed that opportunity is a key factor in the building of a business and that the source of this opportunity generally arises from the external environment. The success of the entrepreneurial effort is due also in no small part to the decision style of the entrepreneur. Decision style can be categorized into rational and emotional decisions, which are greatly different from each other. In addition, the social cognitive theory also emphasizes the interactive relationship between the individual, environment and behavior (Bandura & Cervone, 1986). Recently, there have been many studies on the impact of decision style or management approach on the performance of a startup business (Allinson et al., 2000; Krueger, 2000; Lee & Tsang, 2001); or the impact of the environment and decision style on the performance of a startup business (Khatri & Ng, 2000). We can therefore deduce that the common factor of the performance of a startup business is decision style. For businesses with different entrepreneurial strategies, there are still many conflicting views and conclusions on the way these respective startup businesses adapt to their environments, structure of the relationships between organizations and the entrepreneurial behavior (Covin, 1991; Karagozoglu & Brown, 1988; Miller, 1983). Before, most studies on relationships between organizations focused on the interdependence of the internal organization or the relationship of the vertical supply chain (Johnson, 1999; Webster, 1992); and less emphasis was on the impact of the relationship between organizations on entrepreneurial behavior or the entrepreneurial spirit. Timmons (1999) believed that on top of leveraging the opportunity, startup teamwork and resources, the entrepreneur must always make the right changes and choices in different phases of the business startup. Searching for external opportunities for new business helps the success of a startup business. However, other ways of interaction and relationships among organizations are also the crucial factors of the success and growth of a startup business. In summary, this study applies the social cognition theory and shows that in addition to the different style of adaptation to environment uncertainty, the decision style of the manager and type and structure of the relationship between organizations are also key factors that impact entrepreneurial behavior. One of the objectives of this study is to understand the impact of the above three factors on entrepreneurial behavior. In recent years, studies have generally had the following characteristics: (1) most of these studies focused on traditional small- to medium-sized companies. There were relatively few studies conducted on medium to large high tech industries with higher capital intensity. Comparisons between companies with different strategic postures were also rare; (2) most of the studies focused on management theory or strategy to discuss the process of entrepreneurial management. For example, Spinelli and Timmons (2003) pointed out that the factors that impact value creation during the startup process are internal resources, external opportunities and startup team, complemented by communication, leadership and creativity. Social cognition theory and theory of planned behavior (TPB) were rarely applied; (3) the methodology is mostly case analysis or multivariate analysis based on the high volume of responses to our surveys. Less focus was placed on verifying the data with the non-linear fuzzy neural method. In our research, we analyzed the data with the non-linear fuzzy neural method because of the relatively lower error rate, accurate measurement of inter-variant correlation and fewer constraints on application. Based on the above, our study objectives are: (I) Use of the social cognitive theory to study the impact of environment uncertainty, decision style and relationship between organizations on the entrepreneurial behavior. (II) Whether different strategic postures applied by companies have varying impact on entrepreneurial behavior intention. (III) Use of the non-linear fuzzy neural method to verify the assumptions and analyze the data.
نتیجه گیری انگلیسی
For Assumption 1—studying the correlation between environment uncertainty and entrepreneurial behavior intention. Each variable comprises two membership functions—Low and High. Using the zero-order sugeno fuzzy model, the rules database will go through 120 iterations of learning with an average testing error of 0.3014 to test generate results as shown in Fig. 3. In the chart, ● represents the test data while ♦ represents the data generated by the fuzzy neural network model. From the chart we can see that there is a close correlation between ‘environment uncertainty’ and ‘entrepreneurial behavior intention’ and Assumption 1 is accepted. Taking the test further with Hypotheses 1.1 and 1.2 to ascertain the impact of environment uncertainty on the entrepreneurial enterprise and the conservative enterprise, Fig. 4(a) shows that, in a situation where there is high environment uncertainty, the entrepreneurial enterprise tends to progress towards rapid behavior, commensurate with the assertion that the senior management of the entrepreneurial enterprise actively pursues speedy, flexible business goals; and affirming that more pressure from the external environment will generate structural changes or instigate aggressive plans to meet the associated challenges. From Fig. 4(b) we can see that conservative enterprise continues to hang onto the conservative approach even during times of high environment uncertainty, behavior with the assertion that the conservative enterprise favors a stable conservative approach in business operation and highlights the different strategies adopted by companies with different strategies. Therefore, Hypotheses 1.1 and 1.2 can be accepted. To test Assumption 2—understanding the structure and type of relationship between organizations and correlation and entrepreneurial behavior intention, we also used membership functions and the rules database, going through 165 iterations of learning with an average testing error rate of 0.2857. The results are displayed in Fig. 5 where we can see that there is a strong correlation between ‘the structure and type of relationship between organizations’ and ‘entrepreneurial behavior intention’. Therefore, Assumption 2 is accepted. In testing Assumption 2.1 and Assumption 2.2, Fig. 6(a) shows that the ‘entrepreneurial type’ is correlated to ‘the structure and type of the market relationship’, indicating that the entrepreneurial type tends to adopt the structure and type of the market relationship. This is in line with the appetite for new markets and risk taking characteristic of the entrepreneurial enterprise so Assumption 2.1 is accepted. Fig. 6(b), however, shows that there is no correlation between the ‘conservative type’ and the ‘the structure and type of the social relationship’ so Assumption 2.2 is not accepted. The reason is probably due to the fact that the inter-organizational relationships in Taiwan's high technology industry are not as established as those in the traditional small to medium-sized enterprises. This is especially so when the respective departments or individuals in the organizations are relatively autonomous, highlighting that a transactional market relationship is the key. To test Assumption 3—examining the relationship between decision style and entrepreneurial behavior intention using membership and the rules database running 120 iterations of learning with an average testing error rate of 0.3451. The results of the test are shown in Fig. 7 where we can see that there is a strong correlation between ‘decision style’ and ‘entrepreneurial behavior intention’ so we accept Assumption 3. In addition, testing Assumption 3.1 and Assumption 3.2, both Fig. 8(a) and (b) show that an intuitive decision style drives radical entrepreneurial behavior, while a rational decision style drives progressive entrepreneurial behavior so we accept Assumption 3.1 and Assumption 3.2. The subject of our study is the high technology IC industry where the product lifespan is short, competition is strong and the rate of product innovation is very high. As such, as for entrepreneurial behavior, our study has discovered that decision style, environment uncertainty and structure and type of relationship between organizations are key drivers. Although subsequent related studies have suggested that social network relationships also play a key role (Simsek et al., 2003; Tsai and Ghoshal, 1998) in impacting the entrepreneurial path to a new business, our results have shown otherwise. One of the reasons is the subject of our research that should be considered a large-scale enterprise as opposed to the small to medium-sized enterprises being studied in past research. Also, industries related to the IC industry already have mature products and well-established markets. Of course we will not minimize the significance of social relationships but in the context of the specific industry, this factor is of secondary importance. Our contribution to the research is the application of the fuzzy neural network model to verify correlation hypotheses and through surveys collect original data, a prerequisite of using this research analysis model. Our emphasis on the significance of organizational behavior and use of cross sectional data enables future researchers to slice and dice the data, maybe even deriving different conclusions. Also, organizational variables can always be added e.g. further examination of entrepreneurial behavior of different organizational type. If we could be more certain and clear about our assumptions through the application of different research and testing methods, e.g. multivariate statistical analysis or fuzzy neural network model to highlight the gaps between the outcomes, then we would have made a considerable contribution to the research in this field. Our research findings and study of the theory have highlighted the following implications: (1) generally, the external environment, decision style of internal senior management and impacts on the structure of interactive relationship among enterprises have direct impact on future entrepreneurial behavior intention of enterprise. Different decision cognitive style and appetites for risk among the internal management will affect entrepreneurial direction and spirit. (2) There are distinct differences between the way enterprises with different strategic postures respond to environment uncertainty and their decision styles, clearly indicating that strategy is a contributing factor in individual or overall behavior in an enterprise. (3) in the IC industry where there are relatively larger-scaled operations and rapidly changing technologies, a stable and regulated market environment brought about by a mature and systemized market relationship are key success factors. (4) From our research findings, we can claim that a mature and regulated enterprise will implement robust strategies that enable the smooth and reliable operation of the entrepreneurial strategy. (5) Although our study uses a perspective from the social cognition theory, which expounds the correlation between the individual, environment and behavior, we have not analyzed the relationship between the environment and the individual decision style. The reason is because we have focused on the strategic behavior, i.e. treating environment uncertainty and decision style equally as important independent variables that impact entrepreneurial behavior intention. (6) We have adopted the sampling method of 86 samples. The response to this survey has not been as favorable as expected because for one reason, we had a lot of questions in the survey. Also, in recent years, the market has been inundated with surveys of all sorts and entrepreneurs, being focused on ensuring the smooth operation of their business, have not been as quick to respond. Although the fuzzy neural network model has a relatively low error rate, it has a prerequisite of 100 samples and above, something our research has not met. That being said, we have learnt a lot from this process. For future studies, we intend to use a different Algorithm method, increase the rate of response to our surveys to ensure that we collect sufficient data for better analysis results.