AI adoption and perceived organizational performance in Chinese pharmaceutical sales: Efficiency and motivation pathways

  • 8 Views
  • 1 Downloads

Creative Commons License DMCA.com Protection Status
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
Artificial intelligence is increasingly embedded in pharmaceutical sales, but evidence remains limited on how AI adoption translates into performance in compliance-sensitive sales contexts. This study aims to examine whether perceived organizational AI adoption is associated with perceived organizational performance in pharmaceutical sales through two parallel pathways: operational efficiency and employee motivation. A cross-sectional survey was conducted among 335 pharmaceutical sales professionals in China from October 2025 to November 2025. Respondents were recruited through purposive sampling via professional networks, WeChat workgroups, industry forums, and sales-related communities because they had direct experience using AI tools in daily sales-related activities. The sample covered prescription drug sales, OTC/channel sales, hospital/institutional sales, key account/market access roles, and sales or marketing management. The data were analyzed using PLS-SEM in SmartPLS 4 with 10,000 bootstrap resamples. The results show that AI adoption is positively associated with perceived organizational performance (β = 0.196, t = 3.383, p = 0.001), operational efficiency (β = 0.459, t = 7.611, p < 0.001), and employee motivation (β = 0.385, t = 6.748, p < 0.001). Operational efficiency (β = 0.212, t = 3.355, p = 0.001) and employee motivation (β = 0.230, t = 3.879, p < 0.001) are positively associated with performance. Mediation tests confirm significant indirect effects through operational efficiency (β = 0.097, p = 0.003) and employee motivation (β = 0.089, p = 0.001), indicating partial mediation. The findings suggest that AI creates performance value when embedded in sales workflows and accompanied by motivational support.

view full abstract hide full abstract
    • Figure 1. Conceptual model
    • Table 1. Respondent characteristics and AI-use profile (N = 335)
    • Table 2. Descriptive statistics and correlations
    • Table 3. Measurement model results (Loadings, CR, and AVE)
    • Table 4. Discriminant validity assessment (HTMT ratios)
    • Table 5. Structural model results and hypothesis tests (direct paths)
    • Table 6. Mediation analysis (indirect effects)
    • Table 7. Data-quality, common method bias, and robustness diagnostics
    • Table A1. Measurement items and sources
    • Conceptualization
      Yunlu Cai, Siti Rohaida Mohamed Zainal
    • Data curation
      Yunlu Cai
    • Formal Analysis
      Yunlu Cai
    • Investigation
      Yunlu Cai
    • Methodology
      Yunlu Cai, Siti Rohaida Mohamed Zainal
    • Resources
      Yunlu Cai
    • Visualization
      Yunlu Cai
    • Writing – original draft
      Yunlu Cai
    • Writing – review & editing
      Yunlu Cai, Siti Rohaida Mohamed Zainal
    • Project administration
      Siti Rohaida Mohamed Zainal
    • Supervision
      Siti Rohaida Mohamed Zainal
    • Validation
      Siti Rohaida Mohamed Zainal