Text

theses

Over the decades, practitioners and researchers alike have increasingly focused on how organization members can effectively share knowledge in an effort to create and maintain knowledge-intensive services. The growing interest in knowledge sharing is due in part to the increased digitalization and specialization of work practices. For example, the advance of computer-aided design, 3D printing, programming languages, financial regulation, and algorithmic stock trading places an increasing requirement on organization members to keep up with changes in their environment. Rapid technological and regulatory changes drastically impact and change how knowledge-intensive services must be approached. Organization members are unable to independently develop the expertise needed to create, maintain, and deliver complex services on their own. Knowledge sharing allows organization members to rely on others to provide services.

Effective knowledge sharing increases organizational member’s performance, and in turn benefits organizations. However, organization members are faced with challenges that hinder knowledge sharing. Organization members become experts by repeatedly engaging in their area of expertise. Repeated engagement in an area limits the ability to generate expertise in other areas. The way organization members approach problems, the solutions they see, and the way they communicate is impacted and grounded by their repeated engagement. Organization members with different expertise have unique vocabulary, interpretations, and work practices.

This dissertation examines how awareness of differences and the development of common ground between organization members can ease knowledge sharing. In doing so it is tested whether awareness of difference is sufficient for knowledge sharing compared to the existence of common ground between organization members. A mixed methods approach, blending social network analysis with observations and interviews, is used to answer the primary research question and hypotheses. Observations, interviews, and social network data is used to map the communicative relationships between organization members and identify the statistical likelihood of their co-occurrence in three organizations. The observations and interviews are analyzed using a grounded theory approach and content analysis, while the social network survey data is analyzed using descriptive statistics, quadratic assignment procedures, and exponential random graph modeling. In aggregate, this dissertation examines the type of communication and relational mechanisms that ease knowledge sharing between organization members.

ETD_10483

Ph.D.

Includes bibliographical references

doi:10.7282/t3-rzdj-3q23

ETD doctoral

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