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Multimodal Knowledge Sharing Networks: A Research Agenda

Multimodal Knowledge Sharing Networks: A Research Agenda. Maryam Alavi Goizueta Business School Emory University. Agenda *. Background Perspectives on knowledge sharing in organizations The proposed multimodal perspective on knowledge sharing

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Multimodal Knowledge Sharing Networks: A Research Agenda

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  1. Multimodal Knowledge Sharing Networks: A Research Agenda Maryam Alavi Goizueta Business School Emory University

  2. Agenda* • Background • Perspectives on knowledge sharing in organizations • The proposed multimodal perspective on knowledge sharing • A field study of multimodal knowledge sharing networks • Findings and future direction for research * With Gerald Kane, Boston College

  3. Study of Knowledge Sharing (KS) in Organizations • Existing KS research adopts two distinct perspectives: Social and technological • Social perspective focuses on nature and structure of interpersonal relationships for KS. Uses social networks as a framework for study of relations • Technological perspective focuses on information systems and tools that can be leveraged for KS. Capture and sharing codified knowledge

  4. The Challenge • Both perspectives are indispensable for understanding KS performance • Can have a technological infrastructure in place, but if people not inclined to use it, then ineffective. • Can have strong social knowledge sharing in place, but if given inadequate tools, then also ineffective.

  5. Key aspects of knowledge sharing in large and complex organizations may be overlooked if either the social or the user-system interactions are examined independently • Multimodality: Fusing digital and social networks

  6. Individuals IS Multimodal Knowledge Networks • Combining the social network perspective with the technological perspective on knowledge sharing

  7. Knowledge Sharing in Multimodal Networks • Knowledge is shared through Interpersonal exchanges among individuals as well as direct use of IS nodes comprising the network

  8. Social Network Perspective • Conceptualizes individuals as “nodes” and the relationships among them as “ties” • Strength of “ties” and location of nodes impact knowledge sharing in social networks • Tie strength: Frequency and depth of interactions between two nodes

  9. Research on Relationship Between Tie Strength and KS

  10. In a multimodal network, as multiple individuals come together to share knowledge, the overall tie strength among individuals enhances knowledge sharing. • Overall tie strengths in a network is referred to as “density”

  11. Density • The ratio of actual ties to the number of possible ties in a network (Brass, 1995)

  12. Hypothesis 1 • The density of the interpersonal connections in a multimodal knowledge sharing network is positively related to outcomes

  13. Knowledge Sharing in Multimodal Networks • Knowledge is shared through interpersonal interactions among individuals as well as direct use of ISnodes comprising the network

  14. Technological Perspective on KS • IS tools enable individuals to store, search and retrieve vast amounts and types of knowledge • Whether and how individuals interact and use the systems impact knowledge sharing

  15. IS Use • Frequency of user interactions with information system (e.g., Devaraj et al. 2003) • Depth (features and functionality) of user-system interactions ( Griffith 1999, Jasperson et al. 2005)

  16. How multiple systems are used together (Vertegaal 2003 & Sambamurthy et al. 2003) • Need to consider the frequency and depth of use between users and all systems together to study multimodal network performance • An example

  17. Hypothesis 2 • Average tie strength (frequency and depth) between users and systems in a multimodal network is positively related to outcomes

  18. Individuals IS • Social network perspective on knowledge sharing: H1

  19. Individuals IS • User-system (IS) perspective on knowledge sharing: H2

  20. Individuals IS Multimodality: Fusing Social and Digital Networks • Combining the social network perspective with the IS perspective on knowledge sharing

  21. Individuals IS IS Nodes Centrality • A function of user-system relationship, and of user with other network members

  22. Hypothesis 3 • The centrality of IS in a multimodal network is positively related to knowledge sharing outcomes

  23. Research Setting and Method • Conducted in a regional division of a national health maintenance organization in the U.S. • Studied multimodal networks consisting of healthcare provider teams and the IS at their disposals • Each team: 4-6 physicians and 8-10 clinical and administrative support • 40 teams participated in the study

  24. Research Setting and Method • Each team had a risk adjusted panel of about 2000 patients • The teams were independent of each other (they were at different physical locations) • They had similar tasks (providing primary care) • Each team had access to the same six IS provided by the company.

  25. Recall the Hypotheses • H1: The density of the interpersonal connections in a multimodal knowledge sharing network is positively related to outcomes • H2: Average tie strength (frequency and depth) between users and systems in a multimodal network is positively related to outcomes • H3: The centrality of IS in a multimodal network is positively related to KS outcomes

  26. Research Variables • Independent variables • Interpersonal network density (the average frequency and depth of interactions among team members) • User-system tie strength (frequency and depth of interactions between user and system aggregated across all systems) • IS centrality (eigenvector centrality of each system averaged across all systems within the team) • Eigenvector was calculated using UCINet 6.97 (Borgatti et al. 2002)

  27. Sample Survey Questions to Assess Multimodal Network Structure • How frequently do you interact with this person? 1 – Never 2 – Rarely 3 – A few times per month 4 – Weekly 5 – Daily 6 – A few times a day 7 – Hourly or more • How frequently do you interact with this system (i.e., personally use with keyboard and/or mouse)? 1 – Never 2 – Rarely 3 – A few times per month 4 – Weekly 5 – Daily 6 – A few times a day 7 – Hourly or more

  28. Research Variables • Dependent variables • Efficiency of care: the time it takes a patient to see a doctor after he signs in at the office • Quality of care: if a team’s patients are getting the required tests and treatments recognized as “best practices” within the industry (e.g.,% breast cancer screening) • Chronic care outcomes: whether a patient’s chronic disease is under control according to test results

  29. Research Variables Control variables • Physician level: age, tenure, gender, position (team leader) • Patient level (for chronic care outcome): eye exam, cholesterol screening, nephropathy screening, insurance plan • Team level: average age, diversity, average tenure, composition

  30. Results • Efficiency of care: • The density of the interpersonal exchanges is negatively related to patient wait time (t = -2.938, p<.01), H1 supported • The average tie strength between systems and users is also negatively related to patient wait time (t = - 2.909, p<.01), H2 supported • The centrality of the IS within the team is also negatively related to patient wait time (t = -2.174, p<.01), H3 supported

  31. Results • Quality of care: • Density of interpersonal interactions is not significant (H1 is not supported) • Average tie strength is significant, but in the opposite direction! (contradicts H2) • Centrality of IS is significant in terms of quality of care (H3 supported)

  32. Results • Chronic care outcomes: • Density of interpersonal interactions is not significant , H1 is not supported • Average tie strength with systems is significant, but in the opposite direction! H2 contradicted • Centrality of IS is significant (z = 2.53, p<.01), H3 is supported

  33. Results & Discussion

  34. Limitations • Low generalizablity, beyond healthcare delivery teams • A snapshot of a network structure, versus evolution of networks over time

  35. Future Research • Study of performance implications of how centrality of individuals in different roles can impact performance (e.g., physicians) • Study of cognitive and relational dimensions of multimodal networks on performance • Developing the theoretical mechanisms that drive multimodal network formations and performance

  36. Summary • How to incorporate both social and technological perspectives into a single framework • Multimodal knowledge networks address the interrelationship between humans and IS in organization • Drawing upon social network analysis (SNA) from management/sociology literature to explore structural features leading to knowledge sharing

  37. The End

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