Spanning Time, Distance and Diversity with Technology A Program of Research

1. Spanning Time, Distance and Diversity with TechnologyA Program of Research Laku Chidambaram Traci Carte Michael F. Price College of Business The University of Oklahoma Norman, OK 73072, USA

2. Agenda • Theory Development • Stream of Studies • Synopsis of Study 1 (focused on Quantitative Analysis) • Synopsis of Study 2 (focused on Qualitative Analysis)

3. 1. Theory Development

4. Prior Research • Relational Demography (Tsui & O’Reilly, 1989) • Social Categorization Theory (Turner, 1987) • Similarity/Attraction Paradigm (Byrne, 1971)

5. The Group Formation Process • Many theorists (i.e., Gersick, 1989; McGrath 1991) have suggested that groups alternate between focusing on: • Relational activities (i.e., group well-being, member support, relational development) • Production activities (i.e., task performance, project deliverables, work outcomes) • Some of Gersick’s work suggests that relational development may take precedence in early stages and production in later stages of group’s history.

6. Collaborative Technologies (CTs)

7. Impact of CTs

8. Challenging Conventional Wisdom Conventional wisdom and existing work on technology support for collaborative work suggests this for teams in general We propose that, depending on the degree of diversity, the opposite may be more appropriate

9. 2. Stream of Studies

10. Stream of Studies

11. 3. Synopsis of Study 1(Quantitative Analyses Focused on the Interactions and Performance of Diverse Teams)

12. Research Question • Do the effects of collaborative technologies differ over time between diverse and homogeneous teams?

13. Research Design • Conducted field experiment using students at OU, Salisbury State, and Michigan Tech • 22 virtual teams collaborated on a semester-long database project and used Yahoo! Groups exclusively (for communication and task-related exchanges) • 22 collocated teams collaborated on same project communicating primarily in face-to-face settings and using Yahoo! Groups for task-related exchanges • Team assignments were made in each treatment so that half the teams were diverse and half homogeneous

14. Project Details • Phase 1: Conceptual model (rough draft) • Phase 2: Conceptual model (final version) • Phase 3: Logical design (normalized) • Phase 4: Implementation (queries, forms, reports) • Phase 5: Debriefing

15. Data Collected • A variety of perceived and actual demographic data • Surveys administered after each phase of the deliverable capturing • Perceived diversity (surface and deep) • Relational conflict • Cohesion • Outcome satisfaction • Grade assigned by course instructor used as an “actual” measure of performance

16. Sample Demographics

17. Survey Response Rates: Actual (Percent)

18. Summary of Findings • Diverse groups without technology support seemed to start off well (in contrast to the literature on diversity), but then encountered steep drop offs in cohesion, task-based conflict and outcome satisfaction (in line with the literature) • In contrast, diverse groups with technology support started off poorly (in contrast to our expectations) but then gained ground, especially in terms of improvements in cohesion and outcome satisfaction (in line with our expectations)

19. Summary of Findings (contd.) • Surprisingly, technology had little impact on the task performance of any group—all started out poorly, improved and then flattened out • Also, remarkably similar profiles along most dimensions for homogeneous groups (with and without technology support) • Overall, technology seemed to act as a brake for the dysfunctional processes of diverse teams, rather than as an accelerator of their inherent value

20. 3. Synopsis of Study 2(Qualitative Analyses Focused on Coordination in Virtual Teams)

21. Coordination • Technology coordination refers to the integration of the available technological tools with the task deliverables (Montoya-Weiss et al., 2001) • Conflicting results from the literature, based on whether technology coordination is emergent (Malhotra et al., 2001) or imposed (Piccoli & Ives, 2003) • Temporal coordination refers to the synchronization of these task deliverables with member schedules and team deadlines (Sutanto et al., 2005) • Again mixed results: direct (Maznevski & Chudoba, 2000) vs. indirect effects (Massey et al., 2002); individual vs. group mechanisms (Sutanto et al., 2005); imposed vs. emergent rules

22. Coordination and Capabilities • Coordination occurs through the two sets of capabilities—reductive and additive—provided by collaboration technologies (Herbsleb, 2002; Brander et al., 2000) • We suggest that technology coordination predominantly occurs through reductive capabilities, while temporal coordination predominantly occurs through additive capabilities of CTs—an idea drawn from the Task-Technology Fit model (Zigurs & Buckland, 1998)

23. Intertwining Strands of Coordination Reductive Capabilities (Structure) Additive Capabilities (Structure) Temporal Coordination (Content) Technology Coordination (Content)

24. Main Thesis • A single type of coordination—termed “monochordic” coordination—is unlikely to provide the requisite means for a virtual team to succeed • Both types of coordination—termed “dichordic” coordination—representing intertwining strands (over the life of the team) are likely to provide the means for success in such settings • No coordination, termed “non-chordic,” refers to the absence of any significant coordination—i.e., no (or little) coordination content in either technological capability—and is likely to be the least successful approach of all

25. Operationalizing Constructs • A total of about 5,000 messages • Coding for coordination • Two coders coded two teams independently; used for training • All differences discussed and resolved for consensus • Remaining 20 teams were split between the two coders • One more team was done together to check consistency; inter-rater reliability was 93.5% • Counts for each coordination category split by session • Coding for technology capabilities just completed (by two different coders)

26. Summary of Results • All teams, regardless of coordination type, started off—not surprisingly—at about the same place in terms of their performance (as indicated by F2,19 = .074, p=.929) • However, by the last session, significant performance differences emerged (F2,19 = 3.341, p=.057) • These differences were consistent with our expectation: Teams that engaged in dichordic coordination outperformed those teams that engaged in non-chordic coordination; and, those that engaged in monochordic coordination fell in between

27. Performance of Virtual Teams 0.9250 0.9050 0.8850 0.8650 Coordination Types 0.8450 Non-chordic SCORES 0.8250 Monochordic Dichordic 0.8050 0.7850 0.7650 0.7450 0.7250 P1 P4 TIME PERIODS Coordination and Performance

28. Conclusions • Embracing asynchroniety: Teams that interwove temporal and technology coordination to stretch time performed the best • Replicating familiarity: In contrast, those that tried to overload time—typically by meeting together simultaneously—performed the worst • One or the other: Teams that relied on one form or another of coordination fell in between

29. Questions?