The Impact of AI-Based Customer Relationship Management on Organizational Performance and Competitive Advantage: A Study in the B2B Context

Document Type : Research Paper

Authors

1 1. Assistant Prof., Department of Management, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran.

2 2. Assistant Professor, Department of Management, Binaloud Institute of Higher Education, Mashhad, Iran.

3 MSc.Business Administration, Marketing, Department of Business Management, Binaloud Institute of Higher Education, Mashhad, Iran.

Abstract

The main objective of this study is to examine the impact of Artificial Intelligence-based Customer Relationship Management (AI-CRM) on organizational performance and competitive advantage in the B2B context. The study is applied in nature and descriptive-survey (field) in methodology. The research population comprised all senior managers and experts of small and medium-sized enterprises (SMEs) operating in the Binalood and Kavian industrial parks, estimated at 500 individuals. Using Cochran’s formula, the sample size was determined as 215 participants, and sampling was conducted using a random selection method.Data were collected through a researcher-developed questionnaire based on the literature, consisting of 36 items across 9 variables. The content and face validity of the questionnaire were confirmed by experts, and its reliability was verified with a Cronbach’s alpha coefficient of 0.890, indicating a high level of internal consistency. For data analysis, the model’s adequacy was first assessed using Structural Equation Modeling (SEM) with LISREL, followed by hypothesis testing using path coefficients and t-statistics. The findings of the study indicated that implementation process quality (β = 0.74, t = 7.23), employee-person fit (β = 0.37, t = 4.41), and organizational fit (β = 0.42, t = 6.42) have a positive and significant impact on the outcomes of AI-based customer relationship management (AI‑CRM) implementation in the industrial companies under study. Furthermore, the implementation of AI‑CRM positively and significantly affects industry-to-industry interactions (β = 0.89, t = 6.46), employee experience (β = 0.67, t = 5.80), and information processing capabilities of the companies (β = 0.72, t = 6.12). The results show that effective B2B interactions (β = 0.35, t = 6.01), employee experience in industrial relationship management (β = 0.35, t = 6.01), and information processing capability (β = 0.84, t = 5.94) play a significant role in improving organizational performance.

Keywords

References

References:
Alnofeli, K. K., Akter, S., & Yanamandram, V. (2025). Unlocking the power of AI in CRM: A comprehensive multidimensional exploration. Journal of Innovation & Knowledge, 10(3), 100731. https://doi.org/10.1016/j.jik.2025.100731
Bag, S., Gunasekaran, A., & Kumar, S. (2020). Big data analytics in supply chain management: Insights from industrial marketing. Industrial Marketing Management, 87, 96 114. https://doi.org/10.1016/j.indmarman.2020.02.003
Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92, 178‑189. https://doi.org/10.1016/j.indmarman.2020.12.001
Balouchi, H. (2025). Examining the Impact of Toxic Leadership on Export Performance with Organizational Silence as a Mediator and the Moderating Role of Dispersion in Organizational International Marketing Capabilities. Organizational Culture Management, 23 (2), 121-138. http//doi.org/10.22059/jomc.2024.379889.1008688 [in Persian]
Balouchi, H. , Mehrasa, H. and Moloudian, H. (2023). Investigating the Moderating Role of the Organization's Dynamic Environment in the Relationship Between Dynamic Organizational Capabilities, Performance, and Competitive Advantage. System Engineering and Productivity, 3(3), 1-32.  doi: 10.22034/msb.2023.711488. [in Persian]
Bansal, J. (2024). Effectiveness of AI powered CRM tools in enhancing customer experience. International Journal of Research in Marketing Management and Sales, 6(2), 318‑320.
Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology, 87(5), 875‑884. https://doi.org/10.1037/0021-9010.87.5.875
Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations. The Bottom Line, 33(4), 359-375. https://doi.org/10.1108/BL-08-2020-0057
Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2023). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25(4), 1299-1313. https://doi.org/10.1007/s10796-021-10181-1
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205-219. https://doi.org/10.1016/j.indmarman.2021.07.013.
Chen, L., Jiang, M., Jia, F., & Liu, G. (2022). Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework. Journal of Business & Industrial Marketing, 37(5), 1025-1044. https://doi.org/10.1108/JBIM-09-2020-0448.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021.
Jabbar, A., Akhtar, P., & Dani, S. (2020). Real-time big data processing for instantaneous marketing decisions: A problematization approach. Industrial Marketing Management, 90, 558-569. https://doi.org/10.1016/j.indmarman.2019.09.001.
Johnston, W. J., & Cortez, R. M. (2018). Unit pricing and its implications for B2B marketing research. Industrial Marketing Management, 69, 32-39. https://doi.org/10.1016/j.indmarman.2018.01.026.
Keegan, B. J., Dennehy, D., & Naudé, P. (2024). Implementing artificial intelligence in traditional B2B marketing practices: an activity theory perspective. Information systems frontiers, 26(3), 1025-1039.  DOI:10.1007/s10796-022-10294-1.
Keramati, A., Mehrabi, H., & Mojir, N. (2010). A process-oriented perspective on customer relationship management and organizational performance: An empirical investigation. Industrial Marketing Management, 39(7), 1170-1185. https://doi.org/10.1016/j.indmarman.2010.02.001
Kreye, M. E., & Perunovic, Z. (2020). Performance in publicly funded innovation networks (PFINs): The role of inter-organisational relationships. Industrial Marketing Management, 86, 201-211. https://doi.org/10.1016/j.indmarman.2019.11.018
Kristof-Brown, A. L., Zimmerman, R. D., & Johnson, E. C. (2005). Consequences of individuals’ fit at work. Personnel Psychology, 58(2), 281‑342. https://doi.org/10.1111/j.1744-6570.2005.00630.x
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155. https://doi.org/10.1177/000812561985931.
Laaksonen, A. (2020). The use of artificial intelligence in customer relationship management (Bachelor's thesis).
Lacka, E., Chan, H. K., & Wang, X. (2020). Technological advancements and B2B international trade: A bibliometric analysis and review of industrial marketing research. Industrial Marketing Management, 88, 1-11. https://doi.org/10.1016/j.indmarman.2020.04.007.
Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51(1), 44-56. https://doi.org/10.1016/j.intmar.2020.04.002.
Lin, W. L., Yip, N., Ho, J. A., & Sambasivan, M. (2020). The adoption of technological innovations in a B2B context and its impact on firm performance: An ethical leadership perspective. Industrial Marketing Management, 89, 61-71. https://doi.org/10.1016/j.indmarman.2019.12.009.
Massi, M., Rod, M., & Corsaro, D. (2020). Is co-created value the only legitimate value? An institutional-theory perspective on business interaction in B2B-marketing systems. The Journal of Business and Industrial Marketing. https://doi.org/10.1108/ JBIM-01-2020-0029.
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), 103434.  https://doi.org/10.1016/j.im.2021.103434
Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management, 98, 80-92. https://doi.org/10.1016/j.indmarman.2021.08.003.
Nadler, D. A., & Tushman, M. L. (1977). A congruence model for diagnosing organizational behavior, research paper no. 40A. Graduate School of Business, Columbia University.
Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403-414.  https://doi.org/10.1016/j.bushor.2020.01.003
Peltier, J. W., Dahl, A. J., & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: A conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54-90. https://doi.org/10.1108/JRIM-01-2023-0030.
Rahimi, R., Nadda, V. K., & Wang, H. (2018). CRM in tourism: Customer relationship management (CRM). In Digital Marketing and Consumer Engagement: Concepts, Methodologies, Tools, and Applications (pp. 928-955). IGI Global.
Rahman, M. S., Bag, S., Gupta, S., & Sivarajah, U. (2023). Technology readiness of B2B firms and AI-based customer relationship management capability for enhancing social sustainability performance. Journal of Business Research, 156, 113525. https://doi.org/10.1016/j.jbusres.2022.113525
Saura, J. R., Palos-Sanchez, P., & Blanco-González, A. (2019). The importance of information service offerings of collaborative CRMs on decision-making in B2B marketing. Journal of Business & Industrial Marketing, 35(3), 470-482. https://doi.org/10.1108/JBIM-12-2018-0412.
Sin, L. Y., Tse, A. C., & Yim, F. H. (2005). CRM: conceptualization and scale development. European Journal of marketing, 39(11/12), 1264-1290. https://doi.org/10.1108/03090560510623253
Wang, C., Zhang, X., Ghadimi, P., Liu, Q., Lim, M. K., & Stanley, H. E. (2019). The impact of regional financial development on economic growth in Beijing–Tianjin–Hebei region: A spatial econometric analysis. Physica A: Statistical Mechanics and its Applications, 521(3), 635-648.‏
Yoo, J. W., Park, J., & Park, H. (2024). The impact of AI-enabled CRM systems on organizational competitive advantage: A mixed-method approach using BERTopic and PLS-SEM. Heliyon, 10(16). https://doi.org/10.1016/j.heliyon.2024.e36392.
 Zhang, C., Wang, X., Cui, A. P., & Han, S. (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management, 91, 483-494. https://doi.org/10.1016/j.indmarman.2020.10.012.
Zhang, Y., Xu, H., & Li, S. (2020). Big data analytics capabilities and firm performance in B2B marketing. Journal of Business & Industrial Marketing, 35(7), 1169‑1183. https://doi.org/10.1108/JBIM-09-2019-0384
Innovation Economic Ecosystem Studies
Volume 5, Issue 3
July 2026
Pages 73-93

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