AI adoption and perceived organizational performance in Chinese pharmaceutical sales: Efficiency and motivation pathways
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DOIhttp://dx.doi.org/10.21511/ppm.24(2).2026.50
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Article InfoVolume 24 2026, Issue #2, pp. 743–758
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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.
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JEL Classification (Paper profile tab)M15, M31, O33, L65
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References52
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Tables8
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Figures1
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- Figure 1. Conceptual model
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- 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
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- Abulela, M. A. A., & Khalaf, M. A. (2024). Does the number of response categories impact validity evidence in self-report measures? A scoping review. SAGE Open, 14(1).
- Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), Article 103168.
- Bullemore-Campbell, J., Díaz Tautiva, J. A., & Cristobal-Fransi, E. (2025). AI in sales: Environmental, behavioral, and technological drivers of adoption in an emerging market. Social Sciences & Humanities Open, 12, Article 102161.
- Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652-661.
- Chatterjee, S., Chaudhuri, R., Vrontis, D., & Kadić-Maglajlić, S. (2023). Adoption of AI integrated partner relationship management (AI-PRM) in B2B sales channels: Exploratory study. Industrial Marketing Management, 109, 164-173.
- Cheah, J.-H., Magno, F., & Cassia, F. (2024). Reviewing the SmartPLS 4 software: The latest features and enhancements. Journal of Marketing Analytics, 12(1), 97-107.
- Cheng, Q., Goh, B. W., & Kim, J. B. (2018). Internal control and operational efficiency. Contemporary Accounting Research, 35(2), 1102-1139.
- Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745-783.
- Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, Article 101257.
- Del Ponte, A., Li, L., Ang, L., Lim, N., & Seow, W. J. (2024). Evaluating SoJump.com as a tool for online behavioral research in China. Journal of Behavioral and Experimental Finance, 41, Article 100905.
- Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994.
- Edwards, M. R., Zubielevitch, E., Okimoto, T., Parker, S. K., & Anseel, F. (2024). Managerial control or feedback provision: How perceptions of algorithmic HR systems shape employee motivation, behavior, and well-being. Human Resource Management, 63(4), 691-710.
- Fehrenbach, D., Herrando, C., & Österle, B. (2026). Artificial intelligence applications in the B2B sales funnel. Journal of Business-to-Business Marketing, 33(1), 1-24.
- Fischer, H., Seidenstricker, S., Berger, T., & Holopainen, T. (2022). Artificial intelligence in B2B sales: Impact on the sales process. Artificial Intelligence and Social Computing, 28, 135-142.
- Gagné, M., Parent-Rocheleau, X., Bujold, A., Gaudet, M.-C., & Lirio, P. (2022). How algorithmic management influences worker motivation: A self-determination theory perspective. Canadian Psychology / Psychologie Canadienne, 63(2), 247-260.
- Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(7), 378-392.
- George, B., Walker, R. M., & Monster, J. (2019). Does strategic planning improve organizational performance? A meta-analysis. Public Administration Review, 79(6), 810-819.
- Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: A review of empirical research. Academy of Management Annals, 14(2), 627-660.
- Gonzalez, G. R., Habel, J., & Hunter, G. K. (2026). AI agents, agentic AI, and the future of sales. Journal of Business Research, 202, Article 115799.
- Granulo, A., Caprioli, S., Fuchs, C., & Puntoni, S. (2024). Deployment of algorithms in management tasks reduces prosocial motivation. Computers in Human Behavior, 152, Article 108094.
- Hair, J. F. Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110.
- Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), Article 100027.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hall, K. R., Harrison, D. E., Ajjan, H., & Marshall, G. W. (2022). Understanding salesperson intention to use AI feedback and its influence on business-to-business sales outcomes. Journal of Business & Industrial Marketing, 37(9), 1787-1801.
- Hu, P., Zeng, Y., Wang, D., & Teng, H. (2024). Too much light blinds: The transparency-resistance paradox in algorithmic management. Computers in Human Behavior, 161, Article 108403.
- Jarotschkin, V., Soykoth, M. W., & Chaker, N. N. (2025). Artificial intelligence in sales research: Identifying emergent themes and looking forward. Journal of Business Research, 198, Article 115383.
- Jazairy, A., Shurrab, H., & Chedid, F. (2025). Impact pathways: Walking a tightrope-unveiling the paradoxes of adopting artificial intelligence (AI) in sales and operations planning. International Journal of Operations & Production Management, 45(13), 1-27.
- Kanfer, R., Frese, M., & Johnson, R. E. (2017). Motivation related to work: A century of progress. Journal of Applied Psychology, 102(3), 338-355.
- Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.
- Kock, F., Berbekova, A., & Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, Article 104330.
- Ledro, C., Nosella, A., & Vinelli, A. (2022). Artificial intelligence in customer relationship management: Literature review and future research directions. Journal of Business & Industrial Marketing, 37(13), 48-63.
- Lee, M. T., & Raschke, R. L. (2016). Understanding employee motivation and organizational performance: Arguments for a set-theoretic approach. Journal of Innovation & Knowledge, 1(3), 162-169.
- Leiner, D. J. (2019). Too fast, too straight, too weird: Non-reactive indicators for meaningless data in internet surveys. Survey Research Methods, 13(3), 229-248.
- Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration. International Review of Social Psychology, 32(1), 1-10.
- Liu, W., Ganbaatar, B., & Wang, Z. (2024). Sales innovation and synergy strategy of Chinese pharmaceutical enterprises in the context of digital transformation: A perspective from the affordance theory. Journal of Infrastructure, Policy and Development, 8(15), Article 8352.
- Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
- Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), Article 103434.
- Mikalef, P., Islam, N., Parida, V., Singh, H., & Altwaijry, A. (2023). Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective. Journal of Business Research, 164, Article 113998.
- Ministry of Science and Technology of the People’s Republic of China. (2023). Notice on issuing the Measures for Ethical Review of Science and Technology (Trial) (In Chinese).
- Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176-1197.
- Moradi, M., & Dass, M. (2022). Applications of artificial intelligence in B2B marketing: Challenges and future directions. Industrial Marketing Management, 107, 300-314.
- National Health Commission of the People’s Republic of China. (2023). Notice on issuing the Measures for the Ethical Review of Life Science and Medical Research Involving Humans. (In Chinese).
- Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), Article 100838.
- Podsakoff, P. M., Podsakoff, N. P., Williams, L. J., Huang, C., & Yang, J. (2024). Common method bias: It’s bad, it’s complex, it’s widespread, and it’s not easy to fix. Annual Review of Organizational Psychology and Organizational Behavior, 11, 17-61.
- Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210.
- Rangarajan, D., Westphal, J., Habel, J., & Rutherford, B. (2026). Artificial intelligence (AI) in sales organizations: The role of trust. Journal of Personal Selling & Sales Management, 46(1), 1-6.
- Sarstedt, M., & Moisescu, O.-I. (2024). Quantifying uncertainty in PLS-SEM-based mediation analyses. Journal of Marketing Analytics, 12(1), 87-96.
- Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., Luqman, A., & Zahid, R. (2021). Impact of big data analytics on sales performance in pharmaceutical organizations: The role of customer relationship management capabilities. PLOS ONE, 16(4), Article e0250229.
- Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49.
- Van den Broeck, A., Howard, J. L., Van Vaerenbergh, Y., Leroy, H., & Gagné, M. (2021). Beyond intrinsic and extrinsic motivation: A meta-analysis on self-determination theory’s multidimensional conceptualization of work motivation. Organizational Psychology Review, 11(3), 240-273.
- Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, Article 102544.
- Wang, N., Luan, Y., & Ma, R. (2024). Detecting causal relationships between work motivation and job performance: A meta-analytic review of cross-lagged studies. Humanities and Social Sciences Communications, 11, Article 595.


