overcoming internal knowledge search through firm-institute alliances: a survey of knowledge flow in...

Overcoming Internal Knowledge Overcoming Internal Knowledge Search through Firm-Institute AlSearch through Firm-Institute Al

liances: liances: A Survey of Knowledge Flow A Survey of Knowledge Flow

in Xi’an, Chinain Xi’an, China

Ph.D. (Applied Mathematics), Xidian Univ.Ph.D. (Applied Mathematics), Xidian Univ.M.E. (Economics), Xi’an Jiaotong Univ.M.E. (Economics), Xi’an Jiaotong Univ.B.E. (Information and Control EngineerinB.E. (Information and Control Engineerin

g), Xi’an Jiaotong Univ.g), Xi’an Jiaotong Univ.

Rong DuRong DuSchool of Economics and Management, XiSchool of Economics and Management, Xidian University, Xi’an, Shannxi, China.dian University, Xi’an, Shannxi, China.

Co-authorsCo-authors

Shizhong Ai Shizhong Ai School of Economics and Management, XiSchool of Economics and Management, Xi

dian University, Xi’an, Shannxi, China.dian University, Xi’an, Shannxi, China.

Xiujun Cui, Xiaohu Rong, Jianning LuXiujun Cui, Xiaohu Rong, Jianning LuYanta Science and Technology Bureau, XYanta Science and Technology Bureau, X

i'an, Shannxi 710071, China i'an, Shannxi 710071, China

1. Introduction1. Introduction

• Internal Knowledge SearchInternal Knowledge SearchPeople gain knowledge (new ideas, insights, People gain knowledge (new ideas, insights,

experience, expertise, etc.) from internal experience, expertise, etc.) from internal sources (the people or units within an sources (the people or units within an organization).organization).

• Why?Why?The ability to acquire knowledge from The ability to acquire knowledge from

external entities is limited by an external entities is limited by an organization’s own experience, expertise, organization’s own experience, expertise, and organizational knowledge.and organizational knowledge.

1. Introduction 1. Introduction (continued)(continued)

• External Knowledge SearchExternal Knowledge SearchPeople turn to external sources to effectively People turn to external sources to effectively

seek for new knowledge. seek for new knowledge. • Hard! Hard! The ability to acquire knowledge from The ability to acquire knowledge from

external entities is limited by an external entities is limited by an organization’s own experience, expertise, organization’s own experience, expertise, and organizational knowledge.and organizational knowledge.

It is technologically and geographically It is technologically and geographically bounded. bounded.


• Measures to Overcome Internal Knowledge Measures to Overcome Internal Knowledge SearchSearch

Knowledge recombinations across technologies Knowledge recombinations across technologies and knowledge sharing across organizations.and knowledge sharing across organizations.

Knowledge flows between technologically and gKnowledge flows between technologically and geographically proximate entities. eographically proximate entities.

• Ideas in my Paper Ideas in my Paper Form a firm-institute alliances mechanism to ovForm a firm-institute alliances mechanism to ov

ercome the constraints of internalized knowleercome the constraints of internalized knowledge search.dge search.


• Purpose of our studyPurpose of our studyDemonstrate the knowledge search in firms Demonstrate the knowledge search in firms

and institutes.and institutes.Explore how many firms and institutes use Explore how many firms and institutes use

firm-institute alliances mechanism to draw firm-institute alliances mechanism to draw on the knowledge stocks of other on the knowledge stocks of other organizations. organizations.

Reveal the influence of geographic location Reveal the influence of geographic location and technological expertise on the efficacy and technological expertise on the efficacy of the mechanism. of the mechanism.

1. Introduction 1. Introduction (continued)(continued) • Major Contribution of the Study Major Contribution of the Study Identify the impacts of firm-institute alliances Identify the impacts of firm-institute alliances

on inter-organization knowledge flows and on inter-organization knowledge flows and knowledge sharing. knowledge sharing.

• Remainder of the PresentationRemainder of the PresentationFirm-Institute Alliances Mechanism and Firm-Institute Alliances Mechanism and

Knowledge Flow (some hypotheses)Knowledge Flow (some hypotheses)Methods (data, selection of sample, variables)Methods (data, selection of sample, variables)Analysis and ResultsAnalysis and ResultsDiscussion and ConclusionDiscussion and ConclusionPlan for Further Research Plan for Further Research

2. Firm-Institute Alliances 2. Firm-Institute Alliances Mechanism and Knowledge Mechanism and Knowledge

FlowFlow • Findings on Knowledge SearchFindings on Knowledge SearchThe results of past searches for knowledge The results of past searches for knowledge

depend the way in new searches. depend the way in new searches. Organizations rely heavily on their own Organizations rely heavily on their own experience.experience.

Organizations rely on their socially constructed Organizations rely on their socially constructed practices, routines, and programs to drive the practices, routines, and programs to drive the search for knowledge. search for knowledge.

It is difficult for organizations to recognize and It is difficult for organizations to recognize and absorb external knowledge from outsiders absorb external knowledge from outsiders even when they seek to expand their even when they seek to expand their knowledge stocks.knowledge stocks.

2. Firm-Institute Alliances 2. Firm-Institute Alliances Mechanism and Knowledge Flow Mechanism and Knowledge Flow

(cont.)(cont.) • Possible Solutions to Break Through Possible Solutions to Break Through

the Restrictionsthe RestrictionsProper knowledge management Proper knowledge management

mechanisms which facilitate knowledge mechanisms which facilitate knowledge flows within and among organizations.flows within and among organizations.

Suitable knowledge management contextual Suitable knowledge management contextual factors.factors.

Effective social relationships: alliances (most Effective social relationships: alliances (most focus on vertical or horizontal ones)focus on vertical or horizontal ones)


(cont.)(cont.) • Firm-Institute Alliances Firm-Institute Alliances A special alliance mechanism formed by firm(s) A special alliance mechanism formed by firm(s)

and institute(s) jointly. and institute(s) jointly. • Major Advantage of Firm-Institute Alliances Major Advantage of Firm-Institute Alliances Enable organizations break through the restrictiEnable organizations break through the restricti

ons in knowledge search, and facilitate knowlons in knowledge search, and facilitate knowledge flows among organizations. edge flows among organizations.

• Why? Different Features!Why? Different Features!


(cont.)(cont.) • Features of FirmsFeatures of FirmsEmphasize on the provision of new products Emphasize on the provision of new products

or services; are more concerned with or services; are more concerned with applicable, profitable, and friendly-used applicable, profitable, and friendly-used knowledge; are application-oriented in their knowledge; are application-oriented in their search for new knowledge.search for new knowledge.

• Features of InstitutesFeatures of InstitutesFocus on the development of theories in Focus on the development of theories in

science and technology; are more science and technology; are more concerned with novel, advanced, and concerned with novel, advanced, and revolutionary knowledge; are theory-revolutionary knowledge; are theory-oriented in their search for new knowledge. oriented in their search for new knowledge.


(cont.)(cont.) • Two Different Results Two Different Results They both are bounded within their They both are bounded within their

stereotypes in their search for knowledge stereotypes in their search for knowledge if they do not join each other. if they do not join each other.

They may reach beyond their organizational They may reach beyond their organizational bounds or relational bounds and obtain bounds or relational bounds and obtain external knowledge from each other if external knowledge from each other if entering firm-institute alliances. entering firm-institute alliances.

• The Best ChoiceThe Best ChoiceJoin firm-institute alliances to benefit in Join firm-institute alliances to benefit in

knowledge search processes and then in knowledge search processes and then in innovations. innovations.


(cont.)(cont.) • Knowledge Flow in Firm-Institute Knowledge Flow in Firm-Institute

Alliances Alliances Knowledge suitable for practical applications Knowledge suitable for practical applications

may travel along established ties from may travel along established ties from firms to institutes while knowledge firms to institutes while knowledge distinguished for its theoretical innovation distinguished for its theoretical innovation may travel from institutes to firms. may travel from institutes to firms.

• Benefits from Firm-Institute Alliances Benefits from Firm-Institute Alliances In the above way both parties may benefit In the above way both parties may benefit

from each other’s capability by acquiring from each other’s capability by acquiring the “real” external knowledge, which the “real” external knowledge, which apparently differs from their own internal apparently differs from their own internal one. one.


(cont.)(cont.) • HypothesisHypothesis 1 1The likelihood that a firm (institute) will The likelihood that a firm (institute) will

employ the knowledge base of an institute employ the knowledge base of an institute (firm) increases with alliances between the (firm) increases with alliances between the firm and institute.firm and institute.

• Implication Implication This hypothesis suggests that the formation This hypothesis suggests that the formation

of firm-institute alliances is a useful of firm-institute alliances is a useful mechanism for acquiring knowledge.mechanism for acquiring knowledge.


(cont.)(cont.) • HypothesisHypothesis 2 2The likelihood that a firm (an institute) will emplThe likelihood that a firm (an institute) will empl

oy the knowledge base of an institute (a firm) toy the knowledge base of an institute (a firm) through alliancing increases when the institute hrough alliancing increases when the institute is geographically proximate.is geographically proximate.

• HypothesisHypothesis 3 3 The likelihood that a firm (an institute) will draw The likelihood that a firm (an institute) will draw

upon the knowledge stock of an institute (a firupon the knowledge stock of an institute (a firm) through alliancing increases with technolom) through alliancing increases with technological proximity.gical proximity.


(cont.)(cont.) • HypothesisHypothesis 4 4The likelihood that a firm (an institute) will emplThe likelihood that a firm (an institute) will empl

oy the knowledge base of an institute (a firm) toy the knowledge base of an institute (a firm) through alliancing increases when the institute hrough alliancing increases when the institute is not geographically proximate if internet conis not geographically proximate if internet conditions hold.ditions hold.

• HypothesisHypothesis 5 5 The likelihood that a firm (an institute) will draw The likelihood that a firm (an institute) will draw

upon the knowledge stock of an institute (a firupon the knowledge stock of an institute (a firm) through alliancing increases with technolom) through alliancing increases with technological distance if internet conditions hold.gical distance if internet conditions hold.

3. Methods3. Methods

3.1. Data3.1. Data

• Experts dataExperts data

• R&D projects dataR&D projects data

• Patents data Patents data

3. Methods (cont.)3. Methods (cont.)

•Experts dataExperts dataContain information about work experience, eduContain information about work experience, edu

cation and training background, and even teccation and training background, and even technical interests and hobbies. hnical interests and hobbies.

With the information one can identify experts mWith the information one can identify experts moving to and from other organizations, part-tioving to and from other organizations, part-time experts working for two or more organizatime experts working for two or more organizations, and re-hired experts retiring from other orons, and re-hired experts retiring from other organizations. ganizations.

Therefore, such information can partly characterTherefore, such information can partly characterize the interorganizational knowledge flow. ize the interorganizational knowledge flow.

3. Methods3. Methods (cont.)(cont.)

•R&D projects dataR&D projects dataEncompass information about initiatives of projeEncompass information about initiatives of proje

cts, origins of the technologies involved in projcts, origins of the technologies involved in projects, the formation of project teams (nowadayects, the formation of project teams (nowadays often cross-functional or even cross-organizas often cross-functional or even cross-organizational teams), the final results of projects, etc. tional teams), the final results of projects, etc.

With such information one can understand the flWith such information one can understand the flow of ideas, technologies, and technological reow of ideas, technologies, and technological results. sults.

Hence, the information can reveal the interorganHence, the information can reveal the interorganizational knowledge flow. izational knowledge flow.


•Patents dataPatents dataWe collected patent data from firms and We collected patent data from firms and

institutes under our survey, who have institutes under our survey, who have applied, sold, or bought patents. applied, sold, or bought patents.

Through such patent data, one can track Through such patent data, one can track knowledge flows across organizations, knowledge flows across organizations, technological areas, and geographic technological areas, and geographic regions. regions.


3.2. Selection of Sample3.2. Selection of Sample • All state-owned firms and research institutes (iAll state-owned firms and research institutes (i

ncluding institutes in universities), and parts oncluding institutes in universities), and parts of private firms and institutes registered in Distrf private firms and institutes registered in District Yanta, Xi’an, China. ict Yanta, Xi’an, China.

• District Yanta is famous for its great scientific aDistrict Yanta is famous for its great scientific and technological resources. There are numerond technological resources. There are numerous research institutes and high-tech firms. us research institutes and high-tech firms.


• SurveySurveyFinancially supported by Yanta Science and TecFinancially supported by Yanta Science and Tec

hnology Bureau, Xi'an, Shannxi. hnology Bureau, Xi'an, Shannxi. The surveyed organizations include 182 firms anThe surveyed organizations include 182 firms an

d 67 institutes. d 67 institutes. These firms engage in a wide variety of lines, ranThese firms engage in a wide variety of lines, ran

ging from manufacturing industry to services iging from manufacturing industry to services industry. ndustry.

And the surveyed institutes specialize in a wide vAnd the surveyed institutes specialize in a wide variety of technological territories, ranging froariety of technological territories, ranging from high technologies to traditional technologiem high technologies to traditional technologies. s.

3. Methods3. Methods (cont.)(cont.) • QuestionnaireQuestionnaireIndicators (variables) measuring three data Indicators (variables) measuring three data

categories.categories.4 additional questions measuring the contexts:4 additional questions measuring the contexts: *formation of firm-institute alliances. *formation of firm-institute alliances. *technological similarity.*technological similarity. *geographic proximity between alliance *geographic proximity between alliance

members. members. *condition under which cross-technology and *condition under which cross-technology and

cross-region knowledge flows were cross-region knowledge flows were motivated. motivated.


3.3. Variables3.3. Variables • KFE: knowledge flow with experts.KFE: knowledge flow with experts.

• KFR: knowledge flow with R&D projects.KFR: knowledge flow with R&D projects.

• KFP: knowledge flow with patents.KFP: knowledge flow with patents.

• To measure knowledge flow between firms To measure knowledge flow between firms and institutes, we design a nominal and institutes, we design a nominal dependent variable KF, which is a dependent variable KF, which is a weighted additive function of KFE, KFR, weighted additive function of KFE, KFR, KFP.KFP.


We predict KFE, KFR, and KFP as We predict KFE, KFR, and KFP as functions of experts data, R&D functions of experts data, R&D projects data, and patent data. projects data, and patent data.

Therefore, we define the following Therefore, we define the following three variables: three variables:

• NENE

• NRNR

• NP NP

3. Methods3. Methods (cont.)(cont.) • NE: the number of the experts who moved NE: the number of the experts who moved

from, or are part-time hired by, or once from, or are part-time hired by, or once retired from other organizations in the past 3 retired from other organizations in the past 3 years, but now are full-time or part-time years, but now are full-time or part-time employed by the surveyed organization. employed by the surveyed organization.

• NR: the number of the cross-organization NR: the number of the cross-organization projects that were carried out collaboratively projects that were carried out collaboratively by other organizations and the surveyed by other organizations and the surveyed organization in the past 3 years. organization in the past 3 years.

• NP: the number of the patents that were NP: the number of the patents that were transferred from or to other organizations in transferred from or to other organizations in the past 3 years. the past 3 years.


To describe the contexts which influence NE, To describe the contexts which influence NE, NR, and NP, and then the knowledge flow NR, and NP, and then the knowledge flow between firms and institutes, we use between firms and institutes, we use following four variables:following four variables:

• AM: firm-institute alliances mechanismAM: firm-institute alliances mechanism

• TS: technological similarityTS: technological similarity

• GP: geographic proximityGP: geographic proximity

• CN: the condition associated with cross-CN: the condition associated with cross-technology and cross-region knowledge technology and cross-region knowledge flow. flow.

4.4. Analysis and ResultsAnalysis and Results

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

4. Analysis and Results 4. Analysis and Results (cont)(cont) ResultsResults• Table 2 Descriptive StatisticsTable 2 Descriptive Statistics• Table 3 Relationship between knowledge Table 3 Relationship between knowledge

flow and firm-institute alliance flow and firm-institute alliance • Table 4 Relationship between knowledge Table 4 Relationship between knowledge

flow and technological similarity flow and technological similarity • Table 5 Relationship between knowledge Table 5 Relationship between knowledge

flow and geographic proximityflow and geographic proximity• Table 6 Relationship between knowledge Table 6 Relationship between knowledge

flow and internet condition flow and internet condition

5. Conclusion and 5. Conclusion and DiscussionDiscussion

ConclusionConclusion • Many people recognize and search for Many people recognize and search for

internal knowledge within their own internal knowledge within their own organization while they rarely recognize organization while they rarely recognize and search for external knowledge. and search for external knowledge.

• Establishing firm-institute alliances Establishing firm-institute alliances mechanism can be helpful to overcome mechanism can be helpful to overcome the internal knowledge search. the internal knowledge search.

• In addition, external knowledge search is In addition, external knowledge search is affected by both technological and affected by both technological and geographic contexts. geographic contexts.

5. Conclusion and Discussion5. Conclusion and Discussion (cont.)(cont.)

ConclusionConclusion (cont.) (cont.)• Firm-institute alliances with technological simFirm-institute alliances with technological sim

ilarity and geographic proximity facilitate interilarity and geographic proximity facilitate interorganization knowledge flows by increasing morganization knowledge flows by increasing mobile experts, collaborative projects, and transobile experts, collaborative projects, and transferred patents. ferred patents.

• Whlie for the firm-institute alliances without teWhlie for the firm-institute alliances without technological similarity and geographic proximitchnological similarity and geographic proximity, great internet conditions are necessary to fay, great internet conditions are necessary to facilitate interorganization knowledge flow. cilitate interorganization knowledge flow.


ImplicationImplicationManagers have some discretion in Managers have some discretion in

considering considering 1) what firm-institute alliance may be 1) what firm-institute alliance may be

established to reach out for external established to reach out for external knowledge.knowledge.

2) what conditions may be deployed to 2) what conditions may be deployed to overcome internal knowledge search overcome internal knowledge search to fill in the gaps of their existing to fill in the gaps of their existing context. context.


DiscussionDiscussionIn contrast to some previous studies, we have severIn contrast to some previous studies, we have sever

al innovations. al innovations. • We simultaneously considered multiple contexts We simultaneously considered multiple contexts

and demonstrated several findings. and demonstrated several findings. • We reach beyond the focus on the flows of knowlWe reach beyond the focus on the flows of knowl

edge that are codified in patents. We used severaedge that are codified in patents. We used several data categories, which can reflect the interorgal data categories, which can reflect the interorganizational flow of not only explicit knowledge but nizational flow of not only explicit knowledge but also tacit knowledge. also tacit knowledge.

• We measured alliance mechanism, technological We measured alliance mechanism, technological similarity, geographic proximity, and internet cosimilarity, geographic proximity, and internet conditions by respondents’ answers, simplifying tnditions by respondents’ answers, simplifying the process of data collection and data analysis. he process of data collection and data analysis.


DiscussionDiscussionOur study has two limitations, which must be Our study has two limitations, which must be

acknowledged and be start of further acknowledged and be start of further research. research.

• Our reliance on the “Yes or No” answers to Our reliance on the “Yes or No” answers to the additional questions in our questionnaire the additional questions in our questionnaire makes our measures glancing for alliance makes our measures glancing for alliance mechanism, technological similarity, mechanism, technological similarity, geographic proximity, and internet conditions. geographic proximity, and internet conditions. While it may be reasonable if one adopts While it may be reasonable if one adopts better methods to describe alliance better methods to describe alliance mechanism and other contexts more exactly. mechanism and other contexts more exactly.

• The survey we conducted is not a random The survey we conducted is not a random one, with our sample restricted in one region. one, with our sample restricted in one region. It may be more reasonable if one undertakes It may be more reasonable if one undertakes a similar survey in a more wide area. a similar survey in a more wide area.

AcknowledgementAcknowledgement

We appreciate the suggestions of Prof. Qiying HWe appreciate the suggestions of Prof. Qiying Hu, who is with the School of International Busiu, who is with the School of International Business and Management, Shanghai University. ness and Management, Shanghai University.

Thanks to all other participants. Thanks to all other participants. Financial support for this project was provided Financial support for this project was provided

by Yanta Science & Technology Bureau. by Yanta Science & Technology Bureau. This research is also partially supported by the This research is also partially supported by the

National Natural Science Foundation of China National Natural Science Foundation of China under No.70471068. under No.70471068.

Plan for Further ResearchPlan for Further Research

(1) Integration and sharing of inter-(1) Integration and sharing of inter-disciplinary knowledge and its disciplinary knowledge and its representation by mathematical models. representation by mathematical models.

(2) Creation and transfer of knowledge by (2) Creation and transfer of knowledge by comprehensive model analysis. comprehensive model analysis.

(3) Adapting the knowledge sharing model (3) Adapting the knowledge sharing model to the needs of decision-making to the needs of decision-making processes. processes.

Search for a chance to conduct Search for a chance to conduct internationally collaborative research.internationally collaborative research.

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

ThanksThanks

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overcoming internal knowledge search through firm-institute alliances: a survey of knowledge flow in...

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