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Knowledge transfer: an information theory perspective

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Knowledge Management Research & Practice

Abstract

Personalization and codification are two dominant knowledge transfer (KT) mechanisms found in organizations and organizational networks. This paper proposes a theoretical model of KT that explains organizations’ choice of KT mechanisms in terms of the tacitness of knowledge being shared and the corresponding information content. Shannon’s entropy, an information theoretical concept, has been used to define the constructs of tacitness and information content and explain their influence on the choice of the corresponding KT mechanisms. Contributions of the paper include (a) use of information content as a predictor of the choice of KT mechanisms, (b) development of an expression for tacitness, and an intuitive explanation of the tacit-explicit continuum, (c) characterization of product variety in terms of information content, and (d) development of a KT theoretical model that can be operationalized for predicting the choice of KT mechanisms in real-life situations.

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References

  • Adami, C. 2004. Information theory in molecular biology. Physics of Life Reviews 1 (1): 3–22. doi:10.1016/j.plrev.2004.01.002.

    Article  Google Scholar 

  • Ambrosini, V., and C. Bowman. 2001. Tacit knowledge: Some suggestions for operationalization. Journal of Management Studies 38 (6): 811–829.

    Article  Google Scholar 

  • Bar-Hillel, Y., and R. Carnap. 1953. Semantic information. The British Journal for the Philosophy of Science 4 (14): 147–157.

    Article  Google Scholar 

  • Birch, J. 2014. Propositional content in signalling systems. Philosophical Studies 171 (3): 493–512. doi:10.1007/s11098-014-0280-5.

    Article  Google Scholar 

  • Bloodgood, J.M., and M.A. Chilton. 2012. Performance implications of matching adaption and innovation cognitive style with explicit and tacit knowledge resources. Knowledge Management Research & Practice 10 (2): 106–117. doi:10.1057/kmrp.2012.3.

    Article  Google Scholar 

  • Boh, W.F. 2007. Mechanisms for sharing knowledge in project-based organizations. Information and Organization 17 (1): 27–58. doi:10.1016/j.infoandorg.2006.10.001.

    Article  Google Scholar 

  • Boh, W.F., T.T. Nguyen, and Y. Xu. 2013. Knowledge transfer across dissimilar cultures. Journal of Knowledge Management 17 (1): 29–46. doi:10.1108/13673271311300723.

    Article  Google Scholar 

  • Borgatti, S.P., and R. Cross. 2003. A relational view of information seeking and learning in social networks. Management Science 49 (4): 432–445.

    Article  Google Scholar 

  • Bou-Llusar, J.C., and M. Segarra-Ciprés. 2006. Strategic knowledge transfer and its implications for competitive advantage: An integrative conceptual framework. Journal of Knowledge Management 10 (4): 100–112. doi:10.1108/13673270610679390.

    Article  Google Scholar 

  • Buchler, J. 1955. Philosophical writings of Peirce (1868–1906). New York: Dover Publications Inc.

    Google Scholar 

  • Day, R.E. 2005. Clearing up “implicit knowledge”: Implications for knowledge management, information science, psychology, and social epistemology. Journal of the American Society for Information Science and Technology 56 (6): 630.

    Article  Google Scholar 

  • Dretske, F. 1981. Knowledge and flow of information. Cambridge: MIT Press.

    Google Scholar 

  • Dretske, F. 2008. Epistemology and information. Philosophy of Information. doi:10.1016/B978-0-444-51726-5.50007-8.

    Google Scholar 

  • Driscoll, M.P. 2004. Psychology of learning for instruction, Third ed. Upper Saddle River: Pearson Education Inc.

    Google Scholar 

  • Dyer, J.H., and K. Nobeoka. 2000. Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal 21 (3): 345–367. doi:10.1002/(SICI)1097-0266(200003)21:3<345:AID-SMJ96>3.0.CO;2-N.

    Article  Google Scholar 

  • Floridi, L. 2005. Is semantic information meaningful data. Philosophy and Phenomenological Research, LXX 2: 351–370. doi:10.1111/j.1933-1592.2005.tb00531.x.

    Article  Google Scholar 

  • Gorman, M.E. 2002. Types of knowledge and their roles in technology transfer. Technology 27 (3): 219–231.

    Google Scholar 

  • Gourlay, S. 2006. Conceptualizing knowledge creation: A critique of Nonaka’s theory. Journal of Management Studies 43 (7): 1415–1436. doi:10.1111/j.1467-6486.2006.00637.x.

    Article  Google Scholar 

  • Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(Special Issue: Knowledge and the Firm), 109–122. 10.1002/smj.4250171110.

  • Gupta, A.K., and V. Govindarajan. 2000. Knowledge flows within multinational corporations. Strategic Management Journal 21 (4): 473–496. doi:10.1002/(SICI)1097-0266(200004)21:4<473:AID-SMJ84>3.0.CO;2-I.

    Article  Google Scholar 

  • Hansen, M. T., N. Nohria, and T. Tierney. 1999. What’s your strategy for managing knowledge? Harvard Business Review, 77 (2): 106–116. Retrieved from http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Retrieve&list_uids=10387767&dopt=abstractplus.

  • Harms, W.F. 1998. The use of information theory in epistemology. Philosophy of Science 65 (3): 472–501.

    Article  Google Scholar 

  • Johnson, Bjorn, Edward Lorenz, and B.-A. Lundvall. 2002. Why all this fuss about codified and tacit knowledge? Industrial and Corporate Change 11 (2): 245.

    Article  Google Scholar 

  • Kakihara, M., and C. Sørensen. 2002. Exploring knowledge emergence: from chaos to organizational knowledge. Journal of Global Information Technology Management 5 (3): 48–66. doi:10.1080/1097198X.2002.10856331.

    Article  Google Scholar 

  • Koenig, M. 2001. Don’t fall for that false dichotomy! Codification vs personalization. KM World Magazine

  • Kogut, B., and U. Zander. 1992. Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science 3 (3): 383–397.

    Article  Google Scholar 

  • Leonard, Dorothy, and Sylvia Sensiper. 1998. The role of tacit knowledge in group innovation. California Management Review 40 (3): 112–132.

    Article  Google Scholar 

  • Lin, S.-W., and L.Y.-S. Lo. 2015. Mechanisms to motivate knowledge sharing: integrating the reward systems and social network perspectives. Journal of Knowledge Management 19 (2): 212–235. doi:10.1108/JKM-05-2014-0209.

    Article  Google Scholar 

  • Liu, Hongmei, Kah-Hin Chai, and James F. Nebus. 2013. Balancing codification and personalization for knowledge reuse: A Markov decision process approach. Journal of Knowledge Management 17 (5): 755–772.

    Article  Google Scholar 

  • Martín-Pérez, V., N. Martín-Cruz, and I. Estrada-Vaquero. 2012. The influence of organizational design on knowledge transfer. Journal of Knowledge Management 16 (3): 418–434. doi:10.1108/13673271211238742.

    Article  Google Scholar 

  • Martinkenaite, I. 2011. Antecedents and consequences of inter-organizational knowledge transfer: Emerging themes and openings for further research. Baltic Journal of Management 6 (1): 53–70. doi:10.1108/17465261111100888.

    Article  Google Scholar 

  • McLean, L. (2004). A review and critique of Nonaka and Takeuchi’s Theory of organizational knowledge creation. In Fifth International Conference on HRD Research and Practice across Europe by AHRD and UFHRD.

  • Modrak, V., P. Krus, and S. Bednar. 2015. Approaches to product variety management assuming configuration conflict problem. FME Transactions 43 (4): 271–278. doi:10.5937/fmet1504271M.

    Article  Google Scholar 

  • Nahapiet, J., and S. Ghoshal. 1998. Social capital, intellectual capital, and the organizational advantage. The Academy of Management Review 23 (2): 242–266.

    Google Scholar 

  • Nakano, D., J. Muniz, and E.D. Batista. 2013. Engaging environments: Tacit knowledge sharing on the shop floor. Journal of Knowledge Management 17 (2): 290–306. doi:10.1108/13673271311315222.

    Article  Google Scholar 

  • Nilsen, P. 2015. Making sense of implementation theories, models and frameworks. Nilsen Implementation Science 10 (53): 1–13. doi:10.1186/s13012-015-0242-0.

    Google Scholar 

  • Nonaka, I. 1991. Knowledge creating company, 96–104. Brighton: Harvard Business Review.

    Google Scholar 

  • Nonaka, I. 1994. A dynamic theory of organizational knowledge creation. Organization Science 5 (1): 14–37.

    Article  Google Scholar 

  • Nonaka, I., and G. von Krogh. 2009a. Perspective-Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory. Organization Science 20 (3): 635–652. doi:10.1287/orsc.1080.0412.

    Article  Google Scholar 

  • Nonaka, I., and H. Takeuchi. 1995. The knowledge creating company: How Japanese companies create the dynamics of innovation. Oxford: Oxford University Press.

    Google Scholar 

  • Nonaka, I., and R. Toyama. 2003. The knowledge-creating theory revisited: Knowledge creation as a synthesizing process. Knowledge Management Research & Practice 1 (1): 2–10. doi:10.1057/palgrave.kmrp.8500001.

    Article  Google Scholar 

  • Nonaka, I., and G. von Krogh. 2009b. Tacit knowledge and knowledge conversion: Controversy and advancement in organizational knowledge creation theory. Organization Science 20 (3): 635–652. doi:10.1287/orsc.1080.0412.

    Article  Google Scholar 

  • Polanyi, M. 1966. The tacit dimension. New York: Doubleday & Company Inc.

    Google Scholar 

  • Prencipe, A., and F. Tell. 2001. Inter-project learning: Processes & outcomes of knowledge codification in project based firms. Research Policy 30 (2001): 1373–1394.

    Article  Google Scholar 

  • Ranucci, R.A., and D. Souder. 2015. Facilitating tacit knowledge transfer: Routine compatibility, trustworthiness, and integration in M & As. Journal of Knowledge Management 19 (2): 257–276. doi:10.1108/JKM-06-2014-0260.

    Article  Google Scholar 

  • Rathi, D., L.M. Given, and E. Forcier. 2014. Interorganisational partnerships and knowledge sharing: the perspective of non-profit organisations (NPOs). Journal of Knowledge Management 18 (5): 867–885. doi:10.1108/jkm-06-2014-0256.

    Article  Google Scholar 

  • Rovik, K.A. 2016. Knowledge transfer as translation: Review and elements of an instrumental theory. International Journal of Management Reviews 18 (3): 290–310. doi:10.1111/ijmr.12097.

    Article  Google Scholar 

  • Rusly, F., P. Yih-Tong Sun, and J.L. Corner. 2014. The impact of change readiness on the knowledge sharing process for professional service firms. Journal of Knowledge Management 18 (4): 687–709. doi:10.1108/JKM-01-2014-0007.

    Article  Google Scholar 

  • Jasimuddin, Sajjad M., C. Connell, and J.H. Klein. 2013. A decision tree conceptualization of choice of knowledge transfer mechanism: the views of software development specialists in a multinational company. Journal of Knowledge Management 18 (1): 194–215. doi:10.1108/JKM-05-2013-0195.

    Article  Google Scholar 

  • Serenko, A., and J. Dumay. 2015. Citation classics published in knowledge management journals. Part I: Articles and their characteristics. Journal of Knowledge Management 19 (2): 401–431. doi:10.1108/JKM-06-2014-0220.

    Article  Google Scholar 

  • Shank, G. 1993. Qualitative research? Quantitative research? What’s the problem? Resolving the dilemma via a postconstructivist approach. In Proceedings of Selected Research and Development Presentations at the Convention of the Association for Educational Communications and Technology Sponsored by the Research and Theory Division, New Orleans, LA. (pp. 0–27).

  • Shannon, C.E. 1948. A mathematical theory of communication. The Bell System Technical Journal 27: 379–423. doi:10.1145/584091.584093.

    Article  Google Scholar 

  • Skelton, O. 2015. Exploring knowledge management practices in service-based small business enterprises.

  • Skyrms, B. 2010. Signals: Evolution, learning and information, 1st ed. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Sternberg, R.J. 2005. The theory of successful intelligence. Interamerican Journal of Psychology 39 (2): 189–202. doi:10.1177/1069072703011002002.

    Google Scholar 

  • Szulanski, G., D. Ringov, and R.J. Jensen. 2016. Overcoming stickiness: How the timing of knowledge transfer methods affects transfer difficulty. Organization Science 27 (2): 304–322. doi:10.1287/orsc.2016.1049.

    Article  Google Scholar 

  • Venkitachalam, K., and H. Willmott. 2016. Determining strategic shifts between codification and personalization in operational environments. Journal of Strategy and Management 9 (1): 2–14. doi:10.1108/MRR-09-2015-0216.

    Article  Google Scholar 

  • Wang, S., and R.A. Noe. 2010. Knowledge sharing : A review and directions for future research. Human Resource Management Review 20 (2): 115–131. doi:10.1016/j.hrmr.2009.10.001.

    Article  Google Scholar 

  • Witherspoon, C.L., J. Bergner, C. Cockrell, and D.N. Stone. 2013. Antecedents of organizational knowledge sharing: A meta-analysis and critique. Journal of Knowledge Management 17 (2): 250–277. doi:10.1108/13673271311315204.

    Article  Google Scholar 

  • Wolpert, D. 2006. Information Theory–The Bridge Connecting Bounded Rational Game Theory and Statistical Physics. In Complex Engineered Systems (pp. 262–290). Berlin: Springer. Retrieved from http://www.springerlink.com/index/W4J386VN72018J34.pdf.

  • Zhang, Xiao, and J.Y. Jiang. 2015. With whom shall I share my knowledge? A recipient perspective of knowledge sharing. Journal of Knowledge Management 19 (2): 277–295. doi:10.1108/JKM-05-2014-0184.

    Article  Google Scholar 

  • Xue, Y., J. Bradley, and H. Liang. 2011. Team climate, empowering leadership, and knowledge sharing. Journal of Knowledge Management 15 (2): 299–312. doi:10.1108/13673271111119709.

    Article  Google Scholar 

  • Zhu, Z. 2008. Knowledge, knowing, knower : What is to be managed and does it matter ? Knowledge Management Research & Practice 6: 112–123. doi:10.1057/palgrave.kmrp.8500173.

    Article  Google Scholar 

  • Zwicky, F. 1969. Discovery, invention, research—through the morphological approach, 1st ed. London: Macmillan.

    Google Scholar 

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Correspondence to S. Sudhindra.

Appendix 1: Derivations

Appendix 1: Derivations

Entropy of knowledge entity with discrete characteristics (expression 6)

Consider a knowledge entity with k discrete characteristics all of them having different numbers of possible values. For example, the mth characteristic (say, the colour of the entity) has d m number of options. If all the options for this characteristic are equiprobable, the entropy is given by

$$H_{m} = \left( {\frac{1}{{d_{m} }}{ \log }d_{m} } \right) + \left( {\frac{1}{{d_{m} }}{ \log }d_{m} } \right) + \cdots + \left( {\frac{1}{{d_{m} }}{ \log }d_{m} } \right) = { \log }d_{m}.$$
(10)

From MTC, the joint entropy for k random variables that are mutually independent is given by

$$H = H_{1} + H_{2} + \cdots + H_{k}.$$
(11)

Thus, the joint entropy for an entity with k discrete characteristics is given by

$$H = \left[ {\log \left( {d_{1} } \right) + \log \left( {d_{2} } \right) + \cdots + \log \left( {d_{k} } \right)} \right].$$
(12)

Entropy of knowledge entity with characteristics: normal distribution case (expression 7)

Let θ =  1 , θ 2,\(\ldots\), θ k} be a vector of k random variables all following normal distribution with μ as the mean vector and Σ as the covariance matrix. The joint probability distribution function of the multivariate normal distribution is given by

$$f\left(\varvec{\theta}\right) = f\left( {\theta_{1} ,\theta_{2} , \ldots ,\theta_{k} } \right) = \frac{1}{{\sqrt {\left( {2\pi } \right)^{k} \left|\varvec{\varSigma}\right|} }}e^{{ - \frac{1}{2}\left( {\varvec{\theta}- {\varvec{\upmu}}} \right)^{T}\varvec{\varSigma}^{ - 1} \left( {\varvec{\theta}- {\varvec{\upmu}}} \right)}}.$$
(13)

Hence entropy of this vector is given by

$$H\left(\varvec{\theta}\right) = - \mathop \int \nolimits f\left(\varvec{\theta}\right)\log f\left(\varvec{\theta}\right){\text{d}}\varvec{\theta}$$
$$H\left(\varvec{\theta}\right) = \mathop \int \nolimits f\left(\varvec{\theta}\right)\left[ {\frac{1}{2}\left( {\varvec{\theta}- {\varvec{\upmu}}} \right)^{T}\varvec{\varSigma}^{ - 1} \left( {\varvec{\theta}- {\varvec{\upmu}}} \right){ \log } e + \frac{1}{2}{ \log }\left( {\left( {2\pi } \right)^{k} \left|\varvec{\varSigma}\right|} \right)} \right]{\text{d}}\varvec{\theta}$$
(14)
$$H\left(\varvec{\theta}\right) = \frac{1}{2}\log e\left[ {E\left\{ {\left( {\varvec{\theta}- {\varvec{\upmu}}} \right)^{T}\varvec{\varSigma}^{ - 1} \left( {\varvec{\theta}- {\varvec{\upmu}}} \right)} \right\} + \log \left( {\left( {2\pi } \right)^{k} \left|\varvec{\varSigma}\right|} \right)} \right]$$
(15)
$$E\left\{ {\left( {\varvec{\theta}- {\varvec{\upmu}}} \right)^{T}\varvec{\varSigma}^{ - 1} \left( {\varvec{\theta}- {\varvec{\upmu}}} \right)} \right\} = k.$$
(16)

Substituting (16) in (17),

$$H\left(\varvec{\theta}\right) = \frac{1}{2}\log e\left[ {k + \log (\left( {2\pi } \right)^{k} \left|\varvec{\varSigma}\right|)} \right]$$
(17)
$$H\left(\varvec{\theta}\right) = \frac{1}{2}\log \left( {\left( {2\pi e} \right)^{k} \left|\varvec{\varSigma}\right|} \right).$$
(18)

If the random variables are independent of each other, the determinant of the covariance matrix will be

$$\left|\varvec{\varSigma}\right| = \sigma_{1}^{2} \sigma_{2}^{2} , \ldots ,\sigma_{k}^{2}.$$
(19)

Hence,

$$H\left(\varvec{\theta}\right) = \frac{1}{2}\log \left( {\left( {2\pi e} \right)^{k} \sigma_{1}^{2} \sigma_{2}^{2} , \ldots ,\sigma_{k}^{2} } \right).$$
(20)

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Sudhindra, S., Ganesh, L.S. & Arshinder, K. Knowledge transfer: an information theory perspective. Knowl Manage Res Pract 15, 400–412 (2017). https://doi.org/10.1057/s41275-017-0060-z

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