Abstract
Knowledge exchange among employees in organizations is critical to employees’ ability to solve problems and innovate. However, the possibility that employees may have different perceptions regarding the existence of exchanges between them and the factors that may reduce these differences have not been considered in the literature. Based on a socio-cognitive approach, we argue that misalignments in perceptions of knowledge transfer are likely to be common in organizations. We also propose that different forms of mutual familiarity with exchange partners will be associated with the alignment of perceptions of dyadic knowledge transfer. Our results show that misalignment in perceptions of complex knowledge transfers is more common than alignment. It is a pervasive phenomenon in the organization we studied. Based on an in-depth sociometric research design and exponential random modelling we further find that only one form of familiarity (mutual trust) contributes to increasing the alignment of dyadic knowledge transfer perceptions. We discuss the implications of our results for practice, highlighting the implications of misalignment in knowledge transfer in organizations. We also suggest actions that managers can take to diminish the risk of misalignments and facilitate the transfer of complex knowledge in their teams.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
We focus on complex knowledge because it is difficult to codify and the observability and traceability of its transfer between actors is not clearly observable and objectively verifiable because it usually occurs by means of face-to-face interaction or observation/imitation (Von Krogh et al., 2000). Transfers of complex knowledge are more exposed to perceptual processes of parties involved in the transfer.
- 3.
We empirically established high correlation among the three knowledge contents, which provided support for their aggregation.
- 4.
We binarized matrices above 0 in order to capture even weaker advice giving and seeking relationships and to avoid that our measure of alignment and misalignment relies on differences in the strength of the relationship.
- 5.
Conceptually, misalignment and alignment are the opposite of each other. Empirically, this is more complex due to the fact that alignment in not sending and receiving knowledge has no practical relevance (especially for dyads without any work interaction); while conceptually it still represents an alignment in perceptions. Therefore, in our data we define three mutually exclusive states for each dyad: (1) the dyad has an aligned perception of knowledge transfer (covered by our Alignment outcome variable), (2) the dyad has a misaligned perception of knowledge transfer (covered by our Misalignment outcome variable), or (3) the dyad has no perception of knowledge transfer (null value in our data). Moreover, we control for work interaction in the dyad (Work Tie) in all empirical models.
- 6.
Goodness of fit of the models is not reported but is available from the authors. The goodness of fit is assessed by simulating 1,000,000 graphs based on the model and comparing the features of 10,000 graphs selected randomly to the observed data. The features of the graphs are compared across more than 50 indices. The models presented here had very good fit for all but 3 indices that represent degree distribution. Hence, our models capture only partially the degree distribution of the networks. Because modelling completely/perfectly the degree distribution of these networks is not a main aim of this paper and it does not affect the other results (all of which have an excellent fit), we prefer to present these simpler models.
References
Alexopoulos, A. N., & Buckley, F. (2013). What trust matters when: The temporal value of professional and personal trust for effective knowledge transfer. Group & Organization Management, 38(3), 361–391. https://doi.org/10.1177/1059601113488939
Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review, 13(3), 219–235. https://doi.org/10.1177/1088868309341564
Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: Logit models for social networks. Social Networks, 21(1), 37–66. https://doi.org/10.1016/S0378-8733(98)00012-4
Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169. https://doi.org/10.1006/obhd.2000.2893
Baker, W. E., & Bulkley, N. (2014). Paying it forward vs. rewarding reputation: Mechanisms of generalized reciprocity. Organization Science, 25(5), 1493–1510. https://doi.org/10.1287/orsc.2014.0920
Bandura, A. (1965). Vicarious processes: A case of no-trial learning. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 2, pp. 1–55). Academic Press.
Bernieri, F. J. (2001). Toward a taxonomy of interpersonal sensitivity. In J. A. Hall & F. J. Bernieri (Eds.), Interpersonal sensitivity: Theory and measurement (pp. 3–20): Taylor & Francis.
Bondonio, D. (1998). Predictors of accuracy in perceiving informal social networks. Social Networks, 20(4), 301–330. https://doi.org/10.1016/S0378-8733(98)00007-0
Bernard, H. R., Killworth, P. D., & Sailer, L. (1979). Informant accuracy in social network data IV: A comparison of clique-level structure in behavioral and cognitive network data. Social Networks, 2(3), 191–218.
Bernard, H. R., Killworth, P. D., & Sailer, L. (1981). Summary of research on informant accuracy in network data and the reverse small world problem. Connections, 4(2), 11–25.
Bernard, H. R., Killworth, P. D., & Sailer, L. (1982). Informant accuracy in social-network data V. An experimental attempt to predict actual communication from recall data. Social Science Research, 11(1), 30–66.
Bogenrieder, I. (2002). Social architecture as a prerequisite for organizational learning. Management Learning, 33(2), 197–212. https://doi.org/10.1177/1350507602332003
Brands, R. A. (2013). Cognitive social structures in social network research: A review. Journal of Organizational Behavior, 34(S1), S82–S103. https://doi.org/10.1002/job.1890
Brands, R. A., & Kilduff, M. (2013). Just like a woman? Effects of gender-biased perceptions of friendship network brokerage on attributions and performance. Organization Science, 25(5), 1530–1548. https://doi.org/10.1287/orsc.2013.0880
Brands, R. A., Menges, J. I., & Kilduff, M. (2015). The leader-in-social-network schema: Perceptions of network structure affect gendered attributions of Charisma. Organization Science, 26(4), 1210–1225. https://doi.org/10.1287/orsc.2015.0965
Cannella, A. A., & McFadyen, M. A. (2016). Changing the exchange: The dynamics of knowledge worker ego networks. Journal of Management, 42(4), 1005–1029. https://doi.org/10.1177/0149206313511114
Cannon-Bowers, J. A., Salas, E., & Converse, S. (1993). Shared mental models in expert team decision making. In N. J. Castellan (Ed.), Individual and Group Decision Making (pp. 221–246). Lawrence Erlbaum Associates.
Carnabuci, G., & Diószegi, B. (2015). Social networks, cognitive style and innovative performance: A contingency perspective. Academy of Management Journal, 58(3), 881–905. https://doi.org/10.5465/amj.2013.1042
Carley, K. M., & Krackhardt, D. (1996). Cognitive inconsistencies and non-symmetric friendship. Social Networks, 18(1), 1–27. https://doi.org/10.1016/0378-8733(95)00252-9
Carlile, P. R., & Rebentisch, E. S. (2003). Into the black box: The knowledge transformation cycle. Management Science, 49(9), 1180–1195. https://doi.org/10.1287/mnsc.49.9.1180.16564
Casciaro, T. (1998). Seeing things clearly: Social structure, personality, and accuracy in social network perception. Social Networks, 20(4), 331–351. https://doi.org/10.1016/S0378-8733(98)00008-2
Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-process theories in social psychology. Guilford Press.
Chowdhury, S. (2005). The role of affect-and cognition-based trust in complex knowledge sharing. Journal of Managerial Issues, 17(3), 310–326.
Cross, R., & Cummings, J. N. (2004). Tie and network correlates of individual performance in knowledge-intensive work. Academy of Management Journal, 47(6), 928–937. https://doi.org/10.5465/20159632
Cross, R., & Sproull, L. (2004). More than an answer: Information relationships for actionable knowledge. Organization Science, 15(4), 446–462. https://doi.org/10.1287/orsc.1040.0075
Černe, M., Nerstad, C., Dysvik, A., & Škerlavaj, M. (2014). What goes around comes around: Knowledge hiding, perceived motivational climate, and creativity. Academy of Management Journal, 57(1), 172–192. https://doi.org/10.5465/amj.2012.0122
De Jong, B. A., & Dirks, K. T. (2012). Beyond shared perceptions of trust and monitoring in teams: Implications of asymmetry and dissensus. Journal of Applied Psychology, 97(2), 391–406. https://doi.org/10.1037/a0026483
DeRue, S. D., Nahrgang, J. D., & Ashford, S. J. (2015). Interpersonal perceptions and the emergence of leadership structures in groups: A network perspective. Organization Science., 26(4), 1192–1209. https://doi.org/10.1287/orsc.2014.0963
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
Espinosa, J. A., Slaughter, S. A., Kraut, R. E., & Herbsleb, J. D. (2007). Familiarity, complexity, and team performance in geographically distributed software development. Organization Science, 18(4), 613–630. https://doi.org/10.1287/orsc.1070.0297
Evans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. https://doi.org/10.1146/annurev.psych.59.103006.093629
Eysenck, M. W., & Keane, M. T. (2005). Cognitive psychology: A student’s handbook. Taylor & Francis.
Fang, R., Landis, B., Zhang, Z., Anderson, M. H., Shaw, J. D., & Kilduff, M. (2015). Integrating personality and social networks: a meta-analysis of personality, network position, and work outcomes in organizations. Organization Science, 26(4), 1243–1260. https://doi.org/10.1287/orsc.2015.0972
Fiske, S. T., & Neuberg, S. L. (1990). A continuum of impression formation, from category-based to individuating processes: Influences of information and motivation on attention and interpretation. Advances in Experimental Social Psychology, 23, 1–74. https://doi.org/10.1016/S0065-2601(08)60317-2
Fiske, S. T., & Taylor, S. E. (1991). Social cognition. Mcgraw-Hill Book Company.
Fiske, S. T., & Taylor, S. E. (2013). Social cognition: From brains to culture. Sage.
Freeman, L. C., Romney, K. A., & Freeman, S. C. (1987). Cognitive structure and informant accuracy. American Anthropologist, 89(2), 310–325. https://doi.org/10.1525/aa.1987.89.2.02a00020
Gerstner, C. R., & Day, D. V. (1997). Meta-Analytic review of leader–member exchange theory: Correlates and construct issues. Journal of Applied Psychology, 82(6), 827–844. https://doi.org/10.1037/0021-9010.82.6.827
Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(Special issue), 109–122. https://doi.org/10.1002/smj.4250171110
Gray, P. H., & Meister, D. B. (2004). Knowledge sourcing effectiveness. Management Science, 50(6), 821–834. https://doi.org/10.1287/mnsc.1030.0192
Haldin-Herrgard, T. (2000). Difficulties in diffusion of tacit knowledge in organizations. Journal of Intellectual Capital, 1(4), 357–365. https://doi.org/10.1108/14691930010359252
Hall, J. A., & Bernieri, F. J. (2001). Interpersonal sensitivity: Theory and measurement. Taylor & Francis.
Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111. https://journals.sagepub.com/doi/10.2307/2667032
Hansen, M. T. (2002). Knowledge networks: Explaining effective knowledge sharing in multiunit companies. Organization Science, 13(3), 232–248. https://doi.org/10.1287/orsc.13.3.232.2771
Healey, M. P., Vuori, T., & Hodgkinson, G. P. (2015). When teams agree while disagreeing: Reflexion and reflection in shared cognition. Academy of Management Review, 40(3), 399–422. https://doi.org/10.5465/amr.2013.0154
Jacoby, L. L., et al. (1989). Becoming famous overnight: Limits on the ability to avoid unconscious influences of the past. Journal of Personality and Social Psychology, 56(3), 326–338. https://doi.org/10.1037/0022-3514.56.3.326
Johnson-Laird, P. N. (1983). Mental models. Cambridge University Press.
Jones, E. E. (1990). Interpersonal perception. WH Freeman/Times Books/Henry Holt & Co.
Kashy, D. A., & Kenny, D. A. (1990). Do you know whom you were with a week ago friday? A re-analysis of the Bernard, Killworth, and Sailer studies. Social Networks, 53(1), 55–61. https://doi.org/10.2307/2786869
Kenny, D. A. (1994). Interpersonal perception: A social Relations Analysis. Guilford Press.
Kilduff, M., & Brass, D. J. (2010). Organizational social network research: Core ideas and key debates. Academy of Management Annals, 4(1), 317–357. https://doi.org/10.5465/19416520.2010.494827
Kilduff, M., & Krackhardt, D. (2008). Interpersonal networks in organizations: Cognition, personality, dynamics, and culture. Cambridge University Press.
Klimoski, R., & Mohammed, S. (1994). Team mental model: Construct or metaphor? Journal of Management, 20(2), 403–437. https://doi.org/10.1016/0149-2063(94)90021-3
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397. https://doi.org/10.1287/orsc.3.3.383
Korsgaard, M. A., Brower, H. H., & Lester, S. W. (2015). It isn’t always mutual a critical review of dyadic trust. Journal of Management, 41(1), 47–70. https://doi.org/10.1177/0149206314547521
Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134. https://doi.org/10.1016/0378-8733(87)90009-8
Krackhardt, D. (1992). The strength of strong ties: The importance of philos in organizations. In R. Cross, A. Parker, & L. Sasson (Eds.), Networks and organizations: Structure, form, and action (pp. 216–239).
Lane, P. J., Koka, B. R., & Pathak, S. (2006). The reification of absorptive capacity: A critical review and rejuvenation of the construct. Academy of Management Review, 31(4), 833–863. https://doi.org/10.5465/amr.2006.22527456
Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477–1490. https://doi.org/10.1287/mnsc.1030.0136
Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential random graph models for social networks: Theory, methods and applications. Cambridge University Press.
Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85(2), 273–283. https://doi.org/10.1037/0021-9010.85.2.273
Marsden, P. V. (1987). Core discussion networks of Americans. American Sociological Review, 52, 122–131. https://doi.org/10.2307/2095397
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792
McCallister, L., & Fischer, C. (1978). A procedure for surveying personal networks. Sociological Methods and Research, 7(2), 131–148. https://doi.org/10.1177/004912417800700202
McCroskey, J. C., & Richmond, V. P. (1995). Fundamentals of human communication: An interpersonal perspective. Waveland Press Inc.
Mohammed, S., & Dumville, B. C. (2001). Team mental models in a team knowledge framework: Expanding theory and measurement across disciplinary boundaries. Journal of Organizational Behavior, 22(2), 89–106. https://doi.org/10.1002/job.86
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242–266. https://doi.org/10.5465/amr.1998.533225
Neeley, T. (2021). Remote work revolution: Succeeding from anywhere. Harper Collins.
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. https://doi.org/10.1287/orsc.5.1.14
Osterloh, M., & Frey, B. S. (2000). Motivation, knowledge transfer, and organizational forms. Organization Science, 11(5), 538–550. https://doi.org/10.1287/orsc.11.5.538.15204
Payne, B. K. (2012). Control, awareness, and other things we might learn to live without. In S. T. Fiske & C. N. Macrae (Eds.), Sage handbook of social cognition (pp. 12–30). Sage.
Quintane, E. (2013). Comparing networks: structural correspondence between behavioral and recall networks. Exponential random graph models for social networks: Theory, methods, and applications (pp. 272–283). Cambridge University Press.
Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly, 48(2), 240–267. https://doi.org/10.2307/3556658
Ren, Y., & Argote, L. (2011). Transactive memory systems 1985–2010: An integrative framework of key dimensions, antecedents, and consequences. The Academy of Management Annals, 5(1), 189–229. https://doi.org/10.5465/19416520.2011.590300
Ringberg, T., & Reihlen, M. (2008). Towards a socio-cognitive approach to knowledge transfer. Journal of Management Studies, 45(5), 912–935. https://doi.org/10.1111/j.1467-6486.2007.00757.x
Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173–191. https://doi.org/10.1016/j.socnet.2006.08.002
Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100(3), 349–363. https://doi.org/10.1037/0033-2909.100.3.349
Rouse, W. B., Cannon-Bowers, J., & Salas, E. (1992). The role of mental models in team performance in complex systems. IEEE Transactions on Systems, Man and Cybernetics, 22(6), 1296–1308. https://doi.org/10.1109/21.199457
Smith, E. R. (1984). Model of social inference processes. Psychological Review, 91(3), 392–413. https://doi.org/10.1037/0033-295X.91.3.392
Smith, E. B., Menon, T., & Thompson, L. (2012). Status differences in the cognitive activation of social networks. Organization Science, 23(1), 67–82. https://doi.org/10.1287/orsc.1100.0643
Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17, 27–43. https://doi.org/10.1002/smj.4250171105
Szulanski, G. (2000). The process of knowledge transfer: A diachronic analysis of stickiness. Organizational Behavior and Human Decision Processes, 82(1), 9–27. https://doi.org/10.1006/obhd.2000.2884
Tasselli, S., Kilduff, M., & Menges, J. I. (2015). The microfoundations of organizational social networks: A review and an agenda for future research. Journal of Management, 41(5), 1361–1387. https://doi.org/10.1177/0149206315573996
Tsai, W. (2001). Knowledge transfer in intraorganizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Academy of Management Journal, 44(5), 996–1004. https://doi.org/10.2307/3069443
Tsai, W. (2002). Social structure of “Coopetition” within a multiunit organization: Coordination, competition, and intraorganizational knowledge sharing. Organization Science, 13(2), 179–190. https://doi.org/10.1287/orsc.13.2.179.536
Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm networks. Academy of Management Journal, 41(4), 464–476. https://doi.org/10.5465/257085
Van der Vegt, G. S., de Jong, S. B., Bunderson, J. S., & Molleman, E. (2010). Power asymmetry and learning in teams: The moderating role of performance feedback. Organization Science, 21(2), 347–361. https://doi.org/10.1287/orsc.1090.0452
Van Wijk, R., Jansen, J. J., & Lyles, M. A. (2008). Inter-and intra-organizational knowledge transfer: A meta-analytic review and assessment of its antecedents and consequences. Journal of Management Studies, 45(4), 830–853. https://doi.org/10.1111/j.1467-6486.2008.00771.x
Von Krogh, G., Ichijo, K., & Nonaka, I. (2000). Enabling knowledge creation: How to unlock the mystery of tacit knowledge and release the power of innovation. Oxford University Press.
Walsh, J. P. (1988). Selectivity and selective perception: An investigation of managers’ belief structures and information processing. Academy of Management Journal, 31(4), 873–896. https://doi.org/10.5465/256343
Weick, K. E. (2001). Making sense of the organization. Blackwell.
Zheng, Y., & Yang, H. (2015). Does familiarity foster innovation? The impact of alliance partner repeatedness on breakthrough innovations. Journal of Management Studies, 52(2), 213–230. https://doi.org/10.1111/joms.12112
Yang, L., Holtz, D., Jaffe, S., Suri, S., Sinha, S., Weston, J., … & Teevan, J. (2022). The effects of remote work on collaboration among information workers. Nature Human Behaviour, 6, 43–54. https://doi.org/10.1038/s41562-021-01196-4
Zou, X., & Ingram, P. (2013). Bonds and boundaries: Network structure, organizational boundaries, and job performance. Organizational Behavior and Human Decision Processes, 120(1), 98–109. https://doi.org/10.1016/j.obhdp.2012.09.002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kaše, R., Quintane, E. (2023). In the Mind of the Beholder: Perceptual (Mis)alignment About Dyadic Knowledge Transfer in Organizations. In: Gerbasi, A., Emery, C., Parker, A. (eds) Understanding Workplace Relationships. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-16640-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-16640-2_9
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-031-16639-6
Online ISBN: 978-3-031-16640-2
eBook Packages: Business and ManagementBusiness and Management (R0)