I want to embrace it: how diffusion of innovation drives employee attitude and intention toward data analytics adoption?
Bibliographic record
Abstract
Purpose Drawing upon the diffusion of innovation perspective, this study examines the effects of the diffusion of innovation characteristics (i.e. observability, compatibility, relative advantage, complexity and trialability) on employees' intention to adopt data analytics tools. In addition, the mediating role of attitude toward data analytics tools has also been examined in the above relationships. Design/methodology/approach Using a time-lagged field survey, data were collected from 211 managerial-level employees working in the information technology and banking sectors. The statistical analyses were conducted using a bootstrapping mediation technique. Findings The findings indicated a positive relationship between observability, relative advantage, trialability and intention to adopt data analytics tools. Complexity was found to have a negative relationship with the intention to adopt data analytics tools, but no direct effect on attitude toward the adoption of data analytics tools was found. Further, the diffusion of innovation factors had an indirect relationship with intention to adopt data analytics tools through attitude toward the adoption of data analytics tools. Originality/value The findings of this research add value by providing insights into the comparative effects of various diffusion of innovation factors on the attitudinal and intentional change experiences of employees. Unlike focus of most of the literature on organizational-level changes resulting from the use of new analytical tools, this research offers a holistic understanding of individual-level change experiences of employees toward the adoption of data analytics tools.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".