Knowledge sharing processes and the role of attachment patterns
Bibliographic record
Abstract
Purpose Past research on the motivational processes underpinning knowledge sharing has assumed that the sharing processes are similar for all individuals. Yet, sharing is a fundamental affiliative behavior, and the sharing processes can differ between people. This study aims to propose and test a model of the moderating influence that employee attachment patterns have on the theory of reasoned action (TRA)-defined knowledge sharing processes. Design/methodology/approach The authors administered a questionnaire to 1,103 employees from a range of industries who participated in an online Qualtrics survey. Advanced forms for structural equation modeling and latent profile analysis were used to assess the proposed model. Findings The results revealed that participants in the study exhibited the latent profiles corresponding to secure, dismissive, preoccupied and fearful patterns. The preoccupied cohort had the lowest knowledge sharing behavior, yet the strongest links within the sharing process. Secure, dismissive and fearful had similar sharing levels, but the strength of the TRA-defined processes differed. These findings underscore equifinality: although sharing may be approximately equal across different attachment patterns, the fundamental processes underpinning sharing differ. Research limitations/implications The authors used self-report data, given that sharing attitudes, norms and intentions may not be overly amenable to ratings even from well-acquainted others. Further, the use of advanced analytical methods helps to minimize common method concerns. Additionally, causal mechanisms underscoring the TRA have been demonstrated (Ajzen and Fishbein, 2005), allowing us to explore the moderating role of attachment patterns. Practical implications This study speaks to the importance of considering employees’ attachment patterns, and developing comprehensive intra-organizational norms, policies and systems that support and encourage knowledge sharing from employees with a variety of attachment patterns. Originality/value This study uniquely contributes to knowledge sharing literatures by incorporating attachment patterns as moderators within the TRA-defined sharing processes. The authors provide important insights on the role of individuals’ attachment patterns have for knowledge sharing behaviors, but also highlight how structure of knowledge sharing differed across subgroups of employees, determined based on their dispositional attachment pattern.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".