Artificial intelligence adoption for pharmaceutical salesforce performance: a case study in Kuwait
Bibliographic record
Abstract
Purpose The research aims to investigate the factors influencing the adoption of artificial intelligence (AI) in pharmaceutical industry companies, specifically focusing on its impact on salesforce performance. Design/methodology/approach We collected research data from pharmaceutical industry companies, specifically the salesforce teams, using a valid and reliable questionnaire. Descriptive data analysis was used to summarize the participants’ demographic characteristics, and inferential data analysis employed partial least squares structural equation modeling (PLS-SEM) to assess the reliability and validity of the measurement model and the relationships between variables. Findings About 176 questionnaires were completed, and only 169 were deemed complete and used for data analysis. This study reveals a significant relationship between pharmaceutical salesforce performance and AI adoption (a path coefficient of 0.647, a p-value of 0.000 and an R2 of 0.584). Supervisory support and career development emerged as the essential variables of pharmaceutical salesforce performance, with the strongest relationship to AI adoption, as indicated by a path coefficient of 0.434 and a p-value of 0.000. Practical implications Based on the results of this study, supervisory support drives AI adoption, underscoring the importance of leaders during technological transformations. Supervisors help minimize employees’ opposition to change by providing concise direction, training and persuasion. By providing clear guidance, training and persuasion, supervisors decrease employee resistance to change. This supports community socioeconomic growth by enhancing patient care and access to healthcare, especially in underserved areas. Originality/value This paper is the first to link salesforce performance and AI adoption in pharmaceutical companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".