Selected work in organizational communication, human–machine communication, collaboration, and digital life.
The figures and tables below show questions, relationships, and findings from papers I coauthored.
Open either figure to see the labels at full size. The visual on small screens scrolls horizontally.
Barley, Dinh, Workman, and I distinguished the similarity of collaborators’ expertise from their familiarity with one another’s language. The lower-right quadrant is the difficult but promising case: different expertise with enough linguistic familiarity to make knowledge usable.
In a study of offshore IT professionals, team identification and shared vision were associated with job satisfaction. The fitted lines show that high shared vision especially helps when identification with the team is low.
Kim, Zhang, and I identified three ways participants drew the human–AI boundary. The table keeps the boundary claims and corresponding practices together.
| Boundary | How GenAI is framed | Common practice |
|---|---|---|
| Complementary | Different from human intelligence, yet useful for capabilities such as retrieval and efficiency. | Active, relatively unrestricted engagement across tasks. |
| Competitive | Different from humans and seen as lacking capacities such as intuition, creativity, or empathy. | Non-use or calls for regulation. |
| Co-evolving | The line between human and AI intelligence is understood as changing through interaction. | Reflective collaboration, including sequencing, compartmentalizing, and training. |
Adapted in shorter form from Table 1 in Kim, Zhang, & Fang (2025), Computers in Human Behavior: Artificial Humans. Read the article ↗
In our study of legal work, professional roles drew on different institutional logics when assessing intelligent technologies. The table shows the main patterns in this sample, from lawyers’ emphasis on expertise to law librarians’ concern with access and legal staff members’ attention to efficiency.
| Role | Salient logic | View of legal work | Orientation to intelligent technologies |
|---|---|---|---|
| Lawyers | Expertise | Complex and subjective work grounded in experience, ethics, and professional judgment. | Generally resistant in this sample. |
| Law librarians | Accessibility | Information seeking and making legal knowledge accessible. | Supportive while attentive to benefits and limits. |
| Legal staff & students | Efficiency | Clerical work and effective use of legal research tools and processes. | Generally supportive in this sample. |
Condensed from Table 3 in Fang, Wilkenfeld, Navick, & Gibbs (2023), Management Communication Quarterly. Read the article ↗
These selected papers link to their journal pages or open-access versions.
Frontiers in Psychology · with Ceciley Xinyi Zhang and Ronald E. Rice
Treats a person’s media as an interconnected environment to examine how its configuration relates to digital stress.
Computers in Human Behavior: Artificial Humans · with Young Ji Kim and Ceciley Xinyi Zhang
Examines how people draw and revise boundaries between human and machine capacities as they use generative AI.
Management Communication Quarterly · with J. Nan Wilkenfeld, Nitzan Navick, and Jennifer L. Gibbs
Shows how distinct occupational roles in legal work draw on expertise, accessibility, and efficiency to make sense of AI.
Journal of International Management · with Jennifer L. Gibbs, Jessica Eisenberg, and J. Nan Wilkenfeld
Finds that team identification and shared vision are associated with greater satisfaction among offshore IT professionals.
Communication Research · with William C. Barley, Ly Dinh, and Hallie Workman
Explains why the collaborators who bring different knowledge together may be least familiar with one another’s expertise.
Human-Machine Communication · with Jennifer L. Gibbs, Gavin L. Kirkwood, and J. Nan Wilkenfeld
Develops a framework for studying how people and intelligent machines negotiate control in organizing.