Sociotechnical systems design: coordination of virtual teamwork in innovation
Bibliographic record
Abstract
Purpose This paper aims to report on a qualitative comparative case study of coordination in three ongoing research and development projects, each conducted by teams working virtually across multiple, geographically dispersed sites and involving varying degrees of task uncertainty at differing stages on an innovation continuum, from basic fundamental research to scale-up and commercial development. Design/methodology/approach This study investigated characteristics of effective virtual innovation teamwork, primarily using structured interviews, observation and a limited number of surveys. The analysis was based upon Pava’s (1983) methodology of sociotechnical systems (STS) for non-linear work and was used to assess the influence of virtuality and task uncertainty on the quality of team deliberations and the knowledge development barriers experienced at the various stages on the innovation continuum. Findings The study identified different technical and social coordination mechanisms and their impact in mitigating knowledge barriers for differing levels of task uncertainty. Technical elements, many based in digital information technology, appeared most significant for coordination where task uncertainty and ambiguity were low. However, with high task uncertainty, the most significant mechanisms were closely tied to the formal and informal social systems of virtual organization. Research limitations/implications The key implication for future research is the development of further applications to evaluate this coordination model for modern teamwork in virtual contexts. Practical implications The findings extend previous theory about coordination of innovation to include fundamental research and virtual collaboration. Based on the results, a four-step STS methodology for design of virtual team coordination mechanisms was developed and piloted successfully by scientific teams at a prominent North American research laboratory. Originality/value This research project has shown that modern STS methodology, updated for non-routine work in a virtual context, can provide a way to assess and mitigate “coordination costs” associated with virtual teamwork. Further, it has identified clear categories of coordination mechanisms that are most effective when teams are working at different stages in the innovation process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".