The Role of Organizational Involvement Facilitation in Enhancing Employees’ Innovative Work Behaviour under Technological Uncertainty: The Mediating Effect of IT Self-Efficacy
Bibliographic record
Abstract
This study investigates how technological uncertainty pressure affects employees’ innovative work behaviour (IWB), with a particular focus on the mediating role of IT self-efficacy and the moderating role of organizational involvement facilitation. As organizations continually adopt new technologies, employees are increasingly challenged to adapt, thereby creating a context of heightened technological uncertainty. We argue that technological uncertainty pressure negatively influences IWB, but that IT self-efficacy—defined as employees’ belief in their ability to manage technological demands—serves as a mediator in this relationship. Moreover, we hypothesize that involvement facilitation, referring to organizational practices that encourage employee participation in technology-related decisions, moderates the impact of technological uncertainty on IWB. Data collected from a survey of 350 employees across various industries were analysed using structural equation modelling (SEM). The results indicate that technological uncertainty pressure has a detrimental effect on IWB, with IT self-efficacy partially mediating this relationship. Additionally, involvement facilitation moderates the effect of technological uncertainty on IWB, such that employees who perceive higher levels of organizational support through involvement practices experience weaker negative impacts. This study contributes to the literature by elucidating the mechanisms through which technological uncertainty affects employee innovation. Theoretically, it advances research on self-efficacy and IWB in the context of digital transformation. Practically, it underscores the importance of enhancing IT self-efficacy and fostering organizational involvement in technology decisions to support innovation in environments characterized by rapid technological change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".