استراتژی های تکامل دانش و عملکرد سازمانی: تحلیل تناسب استراتژیک

The rapid growth of electronic commerce on the Internet provides a platform for organizational knowledge to be changed faster than ever. The process by which knowledge assets of an organization change over time to cope with the pressure of environmental variation is called knowledge evolution. In this paper, we adopt the strategic fit theory to examine whether different knowledge evolution strategies would affect organizational performance in different circumstances. We adopt the concept from natural evolution to define two knowledge evolution strategies: knowledge mutation that relies on internal knowledge sources and knowledge crossover that takes advantage of external sources such as online communities and professional consultants. A survey was conducted to explore the effects of different strategies on organizational performance, as measured by the balanced scorecard (BSC). The results show that knowledge mutation and crossover have impacts on different aspects of organizational performance. In addition, many industrial factors, such as environment variation, knowledge density, and organizational factors, including IT capability and sharing culture, are found to have moderating effects. The findings of this research will help organizations choose the right strategy for knowledge enhancement and light up new directions for further research.

With the rapid development of e-commerce and the trend of globalization, the environment surrounding organizations is dramatically changing. Under these circumstances, knowledge assets have become an importance source of competitive advantages to most organizations. Peter Drucker (1999) stated that knowledge would replace tangible assets, such as equipment, capital, material, or labor as the key production factor; knowledge workers are replacing traditional labor to become an important enabler of organizational value. As such, how to manage knowledge assets effectively has become a critical issue to organizations in the Internet age. Knowledge management also plays a key role for e-businesses to cumulate their valuable intangible assets for higher competitive advantages. More and more organizations are taking advantage of external knowledge sources such as online communities (e.g., blogs and social networking websites) to enhance their competitiveness. Knowledge could become an intangible product to be traded in electronic commerce. However, not much research has investigated whether different knowledge acquisition strategies may affect organizational performance and under which circumstances a particular strategy has a better effect. Many models have been proposed to manage valuable organizational knowledge. Early research on knowledge management (KM) proposes the perspective that focuses on the process of knowledge creation and sharing in organizations (Nonaka, 1994, Davenport and Prusak, 1998 and Alavi and Leidner, 1999). A well-known model is the knowledge creation cycle proposed by Nonaka (1994), which suggests that knowledge creation activities include socialization, externalization, combination, and internalization. An organization should properly manage the process of knowledge creation, storage, retrieval, transfer, and applications. Alavi and Leidner (1999) provided a nice review of the process view of knowledge management. Another research line adopts the resource-based view that treats knowledge as organizational resources to investigate its effect on organizational capabilities and firm performance (Hamel and Prahalad, 1990, Grant, 1991, Bharadwaj, 2000, Billinfer and Smith, 2001, Gold et al., 2001, Lee and Choi, 2003, Liu and Wang, 2009 and Schroeder et al., 2009). These studies have found significant impacts of KM activities on organizational creativity and firm performance. As KM is a continuous and dynamic process, understanding the patterns of knowledge development, their driving forces and organizational context is also an important issue. A better understanding of the interaction between knowledge development and organizational context allows us to know more about how different evolutionary strategies affect organizational performance. Zollo and Winter (2002) proposed a knowledge evolution cycle to explain how knowledge assets adapt to environmental pressure. They added an additional stage to Darwin’s evolution process to include variation, selection, replication, and retention. Organizational knowledge evolves through these four stages recursively. This model defines the stages of knowledge evolution but fails to identify potential evolutionary strategies, nor provides empirical evidence to show the relationship between knowledge evolution, organizational context and firm performance. In this research, we extend the knowledge evolution model by conceptually defining and empirically testing two knowledge evolution strategies that organizations use to enhance its knowledge and whether there exists a fit between evolution and organizational factors. The remainder of the paper is organized as follows: Section 2 reviews major literature concerning knowledge evolution and the strategic fit theory. Research model and hypotheses are developed in Section 3. Section 4 shows the results of our survey research. Finally, implications and conclusions are described in Section 5.

5.1. Summary of findings In this paper, we have proposed two knowledge evolution strategies and empirically evaluated how they affect organizational performance as assessed by the balanced scorecard. The results show that different knowledge evolution strategies have affected different dimensions of organizational performance. Knowledge mutation that relies on internal creation of new knowledge has significant impacts on the improvement of internal process, while knowledge crossover that takes advantage of external knowledge sources can benefit financial and customer dimensions. It implies that when the goal of knowledge management is for improving business processes, internal innovation may be better than seeking advice from outside sources, but when the goal is to improve customer satisfaction and retention, bringing in outside expertise will be better than relying on internal knowledge. In addition to the overall relations, we have also investigated the contingency effect derived from the strategic fit theory that stresses the importance of matching organizational strategy with its environmental and organizational features ( Datta, 1991, Miller, 1992, Doty et al., 1993 and Chan et al., 1997). We identified four contingency variables (two related to the industry and two related to the organization itself) and investigated how these variables may moderate the impact of knowledge evolution on firm performance. Table 6 summarizes the result of our hypothesis testing.5.2. Managerial implications The study has contributed toward our understanding of the relationship between knowledge evolution strategy and firm performance, which has valuable managerial and theoretical implications. Our research findings support the argument that different strategies affect different aspects of organizational performance in different environmental settings. This has plenty of managerial implications. For example, organizations in the high environmental variation sector (such as mobile phones or computer products) can enhance its performance by adopting the crossover strategy that brings in more new knowledge from the outside bodies to keep pace with the rapidly changing customer needs. Organizations in the low environmental variation sector can focus more on knowledge enhancement over time through internal knowledge sharing, which may not have significant effect on the financial performance but can enhance their learning and growth. Adopting the crossover strategy can help improve the financial performance. For practitioners, the findings inform them of the effect of different knowledge evolution strategies, which will be very helpful in implementing knowledge management. Managers may use the reported relationships to choose a proper strategy for improving a specific performance dimension. General procedures for such as purpose include (1) determining the target dimension for improvement, (2) determining the characteristics of the industry and organization, and (3) using the identified relationships to choose a suitable strategy. For example, if a firm intends to improve its learning and growth, and the firm is in a low variation, low knowledge density, high IT capability and high knowledge sharing setting, then using internal knowledge mutation will be more effective than bringing outside experts for knowledge crossover. On the other hand, if the target is to improve customer satisfaction, and the firm is in a high environment variation, high knowledge density industry, then bringing outside expertise for knowledge crossover will be better than internal mutation through brainstorming or other means. The findings also can be applied to the management of e-commerce business. As the technology and applications of e-commerce are changing rapidly, the environmental uncertainty is higher than most other industries and the knowledge density is high. Knowledge management for e-commerce needs to use the crossover strategy that takes advantage of external knowledge sources.5.2. Managerial implications The study has contributed toward our understanding of the relationship between knowledge evolution strategy and firm performance, which has valuable managerial and theoretical implications. Our research findings support the argument that different strategies affect different aspects of organizational performance in different environmental settings. This has plenty of managerial implications. For example, organizations in the high environmental variation sector (such as mobile phones or computer products) can enhance its performance by adopting the crossover strategy that brings in more new knowledge from the outside bodies to keep pace with the rapidly changing customer needs. Organizations in the low environmental variation sector can focus more on knowledge enhancement over time through internal knowledge sharing, which may not have significant effect on the financial performance but can enhance their learning and growth. Adopting the crossover strategy can help improve the financial performance. For practitioners, the findings inform them of the effect of different knowledge evolution strategies, which will be very helpful in implementing knowledge management. Managers may use the reported relationships to choose a proper strategy for improving a specific performance dimension. General procedures for such as purpose include (1) determining the target dimension for improvement, (2) determining the characteristics of the industry and organization, and (3) using the identified relationships to choose a suitable strategy. For example, if a firm intends to improve its learning and growth, and the firm is in a low variation, low knowledge density, high IT capability and high knowledge sharing setting, then using internal knowledge mutation will be more effective than bringing outside experts for knowledge crossover. On the other hand, if the target is to improve customer satisfaction, and the firm is in a high environment variation, high knowledge density industry, then bringing outside expertise for knowledge crossover will be better than internal mutation through brainstorming or other means. The findings also can be applied to the management of e-commerce business. As the technology and applications of e-commerce are changing rapidly, the environmental uncertainty is higher than most other industries and the knowledge density is high. Knowledge management for e-commerce needs to use the crossover strategy that takes advantage of external knowledge sources. 5.3. Research implications The findings have indicated several key concepts that will be useful for future research. First, no single knowledge evolution strategy is capable of improving all aspects of organizational performance. Knowledge development driven by internal forces tends to benefit internal performance, whereas that driven by external forces tends to benefit external performance. We interpret this observation from the coordination theory perspective. The interdependence of the involving parties determine the coordination needs and the knowledge evolution strategy that matches the coordination needs will prevail. This is an innovative view to knowledge management that may trigger future research in the area. Second, we have reported strong evidence to support the strategic fit theory in knowledge management. The findings from our moderating analysis show that both industrial factors (environmental variation and knowledge density) and organizational factors (IT capability and sharing culture) may change the impact of knowledge evolution on firm performance. Therefore, the strategy for knowledge evolution should change, if the environment changes or the nature of the organization changes. This implies that the strategic fit theory and the evolutionary view of organizational knowledge are useful new angles in knowledge management. A possible extension from this foundation is to further examine the mechanism by which knowledge evolution affects different aspects of organizational performance. Theories of organizational learning may be a useful new direction for enriching the current findings. Another possible theoretical extension is to examine whether the rapid increase of electronic commerce may change the effect of knowledge evolution strategy. This is particularly useful in the wave of online community and social networking, which are often considered to be useful external knowledge sources. So far, we do not know how (or whether) external knowledge from online communities affects organizational knowledge evolution. 5.4. Research limitations Although our study has shown some interesting findings for organizational knowledge management, it is not without limitations. First, the analysis is based on a survey conducted on Taiwanese companies. The sampling was not totally random and the generalizability of the findings to other regions or cultures may be limited. Knowledge management is more art than science in some aspects; organizational cultural plays an important role, as we have found in our study. Therefore, it would be interesting to conduct a survey in another culturally different region. A comparative analysis of the findings from different countries may also be interesting. The second issue is the relatively small sample size of the study. Although the total sample size of 97 is acceptable for PLS analysis, the sample size of sub-groups in conducting the moderator analysis becomes small, which might cause a lower statistical power. Hence, the result of the moderation analysis should be used more carefully. A third limitation is that we did not investigate different types of knowledge in our study. Evolution of marketing knowledge may be different from that of product development knowledge in the same organization. This was not consider in our study but could be investigated in the future. The balanced scorecard was arbitrarily chosen as our performance measures, due to its multi-dimensional coverage. There might be other approaches that can better demonstrate the effect of KM. Developing a better method to measure the impact of KM on organizational performance is an issue for future research. Another potential issue is the crowding effect. In some cases, hiring external consultants may reduce the motivation for developing new knowledge internally and eventually damage the long-term performance. Alternatively, it may be possible that “internal” drivers (e.g., drop in productivity) induce changes to existing knowledge stocks but new knowledge for supporting the changes is only available from “external” sources (e.g. consultants). This raises another potential research issue of choosing a particular strategy in different contexts. Some scholars may want to see a qualitative study when knowledge evolution is analyzed. Exploring the insight from the longitudinal perspective can definitely increase the value of this research line.