Threats or Opportunities? Enhancing Business Performance in the Era of Generative AI
Bibliographic record
Abstract
Despite Generative AI’s emerging prominence in the business community, its nascent nature presents uncertainties that impede its broader adoption as a disruptive technology. This paper investigates two pertinent conundrums with theoretical and managerial implications. The first pertains to the potential tension surrounding the adoption of Generative AI: is it financially viable for organizations to adopt Generative AI? Organizations might be reluctant to invest without clear financial benefits. The second concerns the role of complementary assets in amplifying the financial benefits brought about Generative AI. In response to these concerns, we conducted a randomized field experiment with a global online tutoring and learning platform. The study yielded three primary findings. First, we not only find a positive spillover effect of Generative AI on existing services, dampening the concern that Generative AI may supplant a firm’s existing services but also support the presence of complementary assets such as an idiosyncratic proprietary database amplifying the beneficial impact of Generative AI. Second, Generative AI increases advertising impression revenues, an effect that is further magnified when used in conjunction with the unique proprietary database. Third, our analysis shows that these effects are enduring, which implies that the proprietary database is an integral, socially complex, and causally ambiguous resource, instrumental in fostering a sustainable competitive edge. We conclude by affirming that our findings align with and contribute to the resource-based view of the firm and the dynamic capabilities perspective.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".