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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

Multimodal Knowledge Sharing Networks: A Research Agenda

Maryam Alavi

Goizueta Business School

Emory University


Agenda
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


Study of knowledge sharing ks in organizations
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


The challenge
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.


Multimodal knowledge sharing networks a research agenda


Multimodal knowledge networks

Individuals organizations may be overlooked if either the social or the user-system interactions are examined independently

IS

Multimodal Knowledge Networks

  • Combining the social network perspective with the technological perspective on knowledge sharing


Knowledge sharing in multimodal networks
Knowledge Sharing in Multimodal Networks organizations may be overlooked if either the social or the user-system interactions are examined independently

  • Knowledge is shared through Interpersonal exchanges among individuals as well as direct use of IS nodes comprising the network


Social network perspective
Social Network Perspective organizations may be overlooked if either the social or the user-system interactions are examined independently

  • 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


Research on relationship between tie strength and ks
Research on Relationship Between organizations may be overlooked if either the social or the user-system interactions are examined independently Tie Strength and KS


Multimodal knowledge sharing networks a research agenda


Multimodal knowledge sharing networks a research agenda

  • Density together to share knowledge, the overall tie strength among individuals enhances knowledge sharing.

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


Hypothesis 1
Hypothesis 1 together to share knowledge, the overall tie strength among individuals enhances knowledge sharing.

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


Knowledge sharing in multimodal networks1
Knowledge Sharing in Multimodal Networks together to share knowledge, the overall tie strength among individuals enhances knowledge sharing.

  • Knowledge is shared through interpersonal interactions among individuals as well as direct use of ISnodes comprising the network


Technological perspective on ks
Technological Perspective on KS together to share knowledge, the overall tie strength among individuals enhances knowledge sharing.

  • 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


Is use
IS Use together to share knowledge, the overall tie strength among individuals enhances knowledge sharing.

  • 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)


Multimodal knowledge sharing networks a research agenda


Hypothesis 2
Hypothesis 2 Sambamurthy et al. 2003)

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


Multimodal knowledge sharing networks a research agenda

Individuals Sambamurthy et al. 2003)

IS

  • Social network perspective on knowledge sharing: H1


Multimodal knowledge sharing networks a research agenda

Individuals Sambamurthy et al. 2003)

IS

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


Multimodality fusing social and digital networks

Individuals Sambamurthy et al. 2003)

IS

Multimodality: Fusing Social and Digital Networks

  • Combining the social network perspective with the IS perspective on knowledge sharing


Is nodes centrality

Individuals Sambamurthy et al. 2003)

IS

IS Nodes Centrality

  • A function of user-system relationship, and of user with other network members


Hypothesis 3
Hypothesis 3 Sambamurthy et al. 2003)

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


Research setting and method
Research Setting and Method Sambamurthy et al. 2003)

  • 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


Research setting and method1
Research Setting and Method Sambamurthy et al. 2003)

  • 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.


Recall the hypotheses
Recall the Hypotheses Sambamurthy et al. 2003)

  • 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


Research variables
Research Variables Sambamurthy et al. 2003)

  • 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)


Sample survey questions to assess multimodal network structure
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


Research variables1
Research Variables Structure

  • 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


Research variables2
Research Variables Structure

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


Results
Results Structure

  • 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


Results1
Results Structure

  • 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)


Results2
Results Structure

  • 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



Limitations
Limitations Structure

  • Low generalizablity, beyond healthcare delivery teams

  • A snapshot of a network structure, versus evolution of networks over time


Future research
Future Research Structure

  • 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


Summary
Summary Structure

  • 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


Multimodal knowledge sharing networks a research agenda

The End Structure