Rong Du School of Economics and Management, Xidian University, Xi ’ an, Shannxi, China.

1. Overcoming Internal Knowledge Search through Firm-Institute Alliances: A Survey of Knowledge Flow in Xi’an, China

2. Rong DuSchool of Economics and Management, Xidian University, Xi’an, Shannxi, China. Ph.D. (Applied Mathematics), Xidian Univ. M.E. (Economics), Xi’an Jiaotong Univ. B.E. (Information and Control Engineering), Xi’an Jiaotong Univ.

3. Co-authors Shizhong Ai School of Economics and Management, Xidian University, Xi’an, Shannxi, China. Xiujun Cui, Xiaohu Rong, Jianning Lu Yanta Science and Technology Bureau, Xi'an, Shannxi 710071, China

4. 1. Introduction • Internal Knowledge Search People gain knowledge (new ideas, insights, experience, expertise, etc.) from internal sources (the people or units within an organization). • Why? The ability to acquire knowledge from external entities is limited by an organization’s own experience, expertise, and organizational knowledge.

5. 1. Introduction (continued) • External Knowledge Search People turn to external sources to effectively seek for new knowledge. • Hard! The ability to acquire knowledge from external entities is limited by an organization’s own experience, expertise, and organizational knowledge. It is technologically and geographically bounded.

6. 1. Introduction (continued) • Measures to Overcome Internal Knowledge Search Knowledge recombinations across technologies and knowledge sharing across organizations. Knowledge flows between technologically and geographically proximate entities. • Ideas in my Paper Form a firm-institute alliances mechanism to overcome the constraints of internalized knowledge search.

7. 1. Introduction (continued) • Purpose of our study Demonstrate the knowledge search in firms and institutes. Explore how many firms and institutes use firm-institute alliances mechanism to draw on the knowledge stocks of other organizations. Reveal the influence of geographic location and technological expertise on the efficacy of the mechanism.

8. 1. Introduction (continued) • Major Contribution of the Study Identify the impacts of firm-institute alliances on inter-organization knowledge flows and knowledge sharing. • Remainder of the Presentation Firm-Institute Alliances Mechanism and Knowledge Flow (some hypotheses) Methods (data, selection of sample, variables) Analysis and Results Discussion and Conclusion Plan for Further Research

9. 2. Firm-Institute Alliances Mechanism and Knowledge Flow • Findings on Knowledge Search The results of past searches for knowledge depend the way in new searches. Organizations rely heavily on their own experience. Organizations rely on their socially constructed practices, routines, and programs to drive the search for knowledge. It is difficult for organizations to recognize and absorb external knowledge from outsiders even when they seek to expand their knowledge stocks.

10. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Possible Solutions to Break Through the Restrictions Proper knowledge management mechanisms which facilitate knowledge flows within and among organizations. Suitable knowledge management contextual factors. Effective social relationships: alliances (most focus on vertical or horizontal ones)

11. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Firm-Institute Alliances A special alliance mechanism formed by firm(s) and institute(s) jointly. • Major Advantage of Firm-Institute Alliances Enable organizations break through the restrictions in knowledge search, and facilitate knowledge flows among organizations. • Why? Different Features!

12. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Features of Firms Emphasize on the provision of new products or services; are more concerned with applicable, profitable, and friendly-used knowledge; are application-oriented in their search for new knowledge. • Features of Institutes Focus on the development of theories in science and technology; are more concerned with novel, advanced, and revolutionary knowledge; are theory-oriented in their search for new knowledge.

13. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Two Different Results They both are bounded within their stereotypes in their search for knowledge if they do not join each other. They may reach beyond their organizational bounds or relational bounds and obtain external knowledge from each other if entering firm-institute alliances. • The Best Choice Join firm-institute alliances to benefit in knowledge search processes and then in innovations.

14. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Knowledge Flow in Firm-Institute Alliances Knowledge suitable for practical applications may travel along established ties from firms to institutes while knowledge distinguished for its theoretical innovation may travel from institutes to firms. • Benefits from Firm-Institute Alliances In the above way both parties may benefit from each other’s capability by acquiring the “real” external knowledge, which apparently differs from their own internal one.

15. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Hypothesis 1 The likelihood that a firm (institute) will employ the knowledge base of an institute (firm) increases with alliances between the firm and institute. • Implication This hypothesis suggests that the formation of firm-institute alliances is a useful mechanism for acquiring knowledge.

16. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Hypothesis 2 The likelihood that a firm (an institute) will employ the knowledge base of an institute (a firm) through alliancing increases when the institute is geographically proximate. • Hypothesis 3 The likelihood that a firm (an institute) will draw upon the knowledge stock of an institute (a firm) through alliancing increases with technological proximity.

17. 2. Firm-Institute Alliances Mechanism and Knowledge Flow (cont.) • Hypothesis 4 The likelihood that a firm (an institute) will employ the knowledge base of an institute (a firm) through alliancing increases when the institute is not geographically proximate if internet conditions hold. • Hypothesis 5 The likelihood that a firm (an institute) will draw upon the knowledge stock of an institute (a firm) through alliancing increases with technological distance if internet conditions hold.

18. 3. Methods 3.1. Data • Experts data • R&D projects data • Patents data

19. 3. Methods (cont.) • Experts data Contain information about work experience, education and training background, and even technical interests and hobbies. With the information one can identify experts moving to and from other organizations, part-time experts working for two or more organizations, and re-hired experts retiring from other organizations. Therefore, such information can partly characterize the interorganizational knowledge flow.

20. 3. Methods(cont.) • R&D projects data Encompass information about initiatives of projects, origins of the technologies involved in projects, the formation of project teams (nowadays often cross-functional or even cross-organizational teams), the final results of projects, etc. With such information one can understand the flow of ideas, technologies, and technological results. Hence, the information can reveal the interorganizational knowledge flow.

21. 3. Methods(cont.) • Patents data We collected patent data from firms and institutes under our survey, who have applied, sold, or bought patents. Through such patent data, one can track knowledge flows across organizations, technological areas, and geographic regions.

22. 3. Methods(cont.) 3.2. Selection of Sample • All state-owned firms and research institutes (including institutes in universities), and parts of private firms and institutes registered in District Yanta, Xi’an, China. • District Yanta is famous for its great scientific and technological resources. There are numerous research institutes and high-tech firms.

23. 3. Methods(cont.) • Survey Financially supported by Yanta Science and Technology Bureau, Xi'an, Shannxi. The surveyed organizations include 182 firms and 67 institutes. These firms engage in a wide variety of lines, ranging from manufacturing industry to services industry. And the surveyed institutes specialize in a wide variety of technological territories, ranging from high technologies to traditional technologies.

24. 3. Methods(cont.) • Questionnaire Indicators (variables) measuring three data categories. 4 additional questions measuring the contexts: *formation of firm-institute alliances. *technological similarity. *geographic proximity between alliance members. *condition under which cross-technology and cross-region knowledge flows were motivated.

25. 3. Methods(cont.) 3.3. Variables • KFE: knowledge flow with experts. • KFR: knowledge flow with R&D projects. • KFP: knowledge flow with patents. • To measure knowledge flow between firms and institutes, we design a nominal dependent variable KF, which is a weighted additive function of KFE, KFR, KFP.

26. 3. Methods(cont.) We predict KFE, KFR, and KFP as functions of experts data, R&D projects data, and patent data. Therefore, we define the following three variables: • NE • NR • NP

27. 3. Methods(cont.) • NE: the number of the experts who moved from, or are part-time hired by, or once retired from other organizations in the past 3 years, but now are full-time or part-time employed by the surveyed organization. • NR: the number of the cross-organization projects that were carried out collaboratively by other organizations and the surveyed organization in the past 3 years. • NP: the number of the patents that were transferred from or to other organizations in the past 3 years.

28. 3. Methods(cont.) To describe the contexts which influence NE, NR, and NP, and then the knowledge flow between firms and institutes, we use following four variables: • AM: firm-institute alliances mechanism • TS: technological similarity • GP: geographic proximity • CN: the condition associated with cross-technology and cross-region knowledge flow.

29. 4.Analysis and Results Topics for Analysis • Knowledge Flow with Experts. • Knowledge Flow with R&D Projects. • Knowledge Flow with Patents. • Firm-Institute Alliances. • Technological Similarity. • Geographic Proximity. • Internet Conditions.

30. 4. Analysis and Results (cont) Results • Table 2 Descriptive Statistics • Table 3 Relationship between knowledge flow and firm-institute alliance • Table 4 Relationship between knowledge flow and technological similarity • Table 5 Relationship between knowledge flow and geographic proximity • Table 6 Relationship between knowledge flow and internet condition

31. 5. Conclusion and Discussion Conclusion • Many people recognize and search for internal knowledge within their own organization while they rarely recognize and search for external knowledge. • Establishing firm-institute alliances mechanism can be helpful to overcome the internal knowledge search. • In addition, external knowledge search is affected by both technological and geographic contexts.

32. 5. Conclusion and Discussion (cont.) Conclusion (cont.) • Firm-institute alliances with technological similarity and geographic proximity facilitate interorganization knowledge flows by increasing mobile experts, collaborative projects, and transferred patents. • Whlie for the firm-institute alliances without technological similarity and geographic proximity, great internet conditions are necessary to facilitate interorganization knowledge flow.

33. 5. Conclusion and Discussion (cont.) Implication Managers have some discretion in considering 1) what firm-institute alliance may be established to reach out for external knowledge. 2) what conditions may be deployed to overcome internal knowledge search to fill in the gaps of their existing context.

34. 5. Conclusion and Discussion (cont.) Discussion In contrast to some previous studies, we have several innovations. • We simultaneously considered multiple contexts and demonstrated several findings. • We reach beyond the focus on the flows of knowledge that are codified in patents. We used several data categories, which can reflect the interorganizational flow of not only explicit knowledge but also tacit knowledge. • We measured alliance mechanism, technological similarity, geographic proximity, and internet conditions by respondents’ answers, simplifying the process of data collection and data analysis.

35. 5. Conclusion and Discussion (cont.) Discussion Our study has two limitations, which must be acknowledged and be start of further research. • Our reliance on the “Yes or No” answers to the additional questions in our questionnaire makes our measures glancing for alliance mechanism, technological similarity, geographic proximity, and internet conditions. While it may be reasonable if one adopts better methods to describe alliance mechanism and other contexts more exactly. • The survey we conducted is not a random one, with our sample restricted in one region. It may be more reasonable if one undertakes a similar survey in a more wide area.

36. Acknowledgement We appreciate the suggestions of Prof. Qiying Hu, who is with the School of International Business and Management, Shanghai University. Thanks to all other participants. Financial support for this project was provided by Yanta Science & Technology Bureau. This research is also partially supported by the National Natural Science Foundation of China under No.70471068.

37. Plan for Further Research (1) Integration and sharing of inter-disciplinary knowledge and its representation by mathematical models. (2) Creation and transfer of knowledge by comprehensive model analysis. (3) Adapting the knowledge sharing model to the needs of decision-making processes. Search for a chance to conduct internationally collaborative research.

38. Welcome to Xi’an, ChinaWelcome to Xidian University for collaborative research.

39. Thanks

40. Any Questions?