Generative artificial intelligence (GenAI) revolution: A deep dive into GenAI adoption
Bibliographic record
Abstract
• We examine the reasons for/against B2B managers’ intent to adopt generative AI (GenAI). • Need for uniqueness, information completeness, and convenience boost adoption intention. • Conversely, deceptiveness and information overload reduce adoption intention. • Managers’ intent to adopt GenAI is found to boost firm performance. • Ethical leadership acts as a moderator between intent to adopt GenAI and firm performance. This study examines key reasons (for and against) that influence business-to-business (B2B) managers’ intention to adopt generative artificial intelligence (GenAI). We also investigate how GenAI adoption influences firm performance, along with the moderating effect of ethical leadership. Study 1 undertakes a series of in-depth interviews, yielding a set of hypotheses that are tested in Study 2. A total of 277 responses was collected from respondents in the USA, the UK, Canada, India, Australia, Malaysia, and Japan to test the proposed model using structural equation modeling. The findings highlight that need for uniqueness, information completeness, convenience, and deceptiveness significantly impact GenAI adoption. The results also highlight that GenAI adoption boosts firm performance. Finally, ethical leadership was found to moderate the effect of GenAI adoption on firm performance. This study enriches the GenAI, technology adoption, and behavioral reasoning theory literatures while also providing pertinent insights for firms intending to adopt GenAI.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".