SOCIAL, TECHNICAL, AND ORGANIZATIONAL DETERMINANTS OF EMPLOYEESâ PARTICIPATION IN ENTERPRISE SOCIAL TAGGING TOOLS: A CONCEPTUAL MODEL AND AN EMPIRICAL INVESTIGATION
Bibliographic record
Abstract
Organizations are attempting to leverage their knowledge resources by integrating knowledge sharing systems, a key and new form of which are social computing tools. A large number of these initiatives fail, however, due to employees' reluctance to use, contribute content to, and share knowledge through such tools. Although research regarding one's motivation to share knowledge is extensive, there has been little research examining social computing systems, especially from the seeking and contributory perspectives—the two distinct, but closely interrelated facets of knowledge sharing. Motivated by such concerns, and by incorporating knowledge-seeking and knowledge- contribution perspectives in a single study, this research develops and empirically examines a theoretical model to explain what motivates employees to seek, contribute and share social tags using Enterprise Social Tagging Tools (ESTTs). \n\nTwo research phases were employed to address the research objective. The goal of the first phase of the study was to explore factors affecting users’ tagging behavior in online social tagging tools. An extensive literature review was synthesized and a preliminary theoretical model emerged. A pilot study was conducted yielding 184 responses featuring eight different online social tagging tools. Mostly, the preliminary theoretical model showed positive influence on users’ tag behavior with a special focus on the newly developed concepts of information retrievability, information refindability.\n\nThe goal of the study’s second phase was combining the results from the first phase with motivational theories to build and validate a belief-based and socio-organizational model that can explain employees’ tag seeking, contributing, and sharing behavior in ESTTs. The model was developed by employing theories such as Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), and social exchange theory. Through a large-scale survey (n=481) in two large Information Technology (IT) companies, the model was validated. The results speak to the importance of the three newly developed factors impacting employees’ tag seeking, contributing and sharing behavior. These factors are uniquely context-specific reflecting actual features of social tagging tools and potentially social media in general. Particularly, the results reveal that employees' tag seeking behavior is affected by their perception of the ESTTs in terms of enjoyment, information retrievability, ease of use, and managerial influence. In the context of tag contribution and sharing, the results show that employees contribute and share tags because of their perception of information refindability, ease of use, altruism, and pro-sharing norms. Differences among the seeking, contributing and sharing model have implications for future research and practice.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".