Investigating the Role of Artificial Intelligence Enablers and Companies' Readiness in Adopting an Artificial Intelligence-integrated Customer Relationship Management System

Document Type : Research Paper

Authors

1 MSc., Department of Business Management, Faculty of Economics and Management, Urmia University, Urmia, Iran.

2 Assistant Prof., Department of usiness Management, Faculty of Economics and Management, Urmia University, Urmia, Iran.

Abstract

Objective
Escalating competition and environmental uncertainty drive business owners towards adopting agile and intelligent communication functions with customers. Also, due to the vital importance of the customer in the success or failure of businesses, it is necessary to pay special attention to agility and flexibility in the communication activities with the customers. The use of new technologies such as artificial intelligence, especially when faced with a huge amount of data, with high data processing capacity, rapid response speeds, and the potential for personalization and customization, helps organizations in achieving efficiency and effectiveness of processes. Artificial intelligence, as one of the top technological trends of the fourth industrial revolution and fifth-generation marketing creates extensive capacity and benefits for businesses by integrating into customer relationship management systems. A fundamental aspect in this domain revolves around the acceptance of technology. Considering the novelty of using artificial intelligence in customer relationship management, the purpose of this research is to investigate the effect of some organizational factors on the adoption of intelligent customer relationship management systems and to provide appropriate solutions for improving these factors for the successful adoption, deployment, and institutionalization of intelligent technologies.
 
Methodology
To align with the research topic and ensure compatibility with the common characteristics of the studied community, data collection involved surveying a sample of 384 businesses specializing in the sale of goods. The selection was made using simple random sampling, and the investigation was conducted by administering a standard questionnaire. To test the research hypotheses, structural equations were tested using a partial least squares approach, employing the third version of SMART-PLS software.
 
Findings
The results showed that AI empowerment (including technology roadmap, attitude, and professional expertise) and AI readiness (including awareness, infrastructure, and technical level) affect adopting artificial intelligence integrated with customer relationship management systems. Based on these findings, business organizations should prioritize certain factors within the organization, particularly in the context of artificial intelligence enablers or activators. These include developing a technology roadmap, fostering a positive attitude toward technology, and acquiring the necessary expertise through training or leveraging experts in the field. Also, in terms of organizational readiness, increasing the level of awareness, creating the required infrastructure, and organizational understanding regarding the technical level and complexity of these systems are required for establishing a solid foundation for the successful adoption and deployment of intelligent systems. Business owners can enjoy the extensive benefits of intelligentizing customer communication processes with a conscious decision.
 
Conclusion
According to the obtained results, AI readiness with a path coefficient of 0.641 has a positive and significant effect on the acceptance of customer relationship management systems integrated with artificial intelligence. It was also confirmed that AI enablers with a path coefficient of 0.315 have a positive and significant effect on the adoption of these systems. Business organizations can enhance their readiness for artificial intelligence adoption and implementation by prioritizing two main strategies. Firstly, they can focus on strengthening their organizational readiness for artificial intelligence by improving their understanding of the technical aspects and complexity of AI systems, establishing the necessary infrastructure, and raising awareness among stakeholders. Secondly, they can concentrate on empowering their organization with artificial intelligence capabilities by fostering a positive attitude towards technology, developing a technology roadmap, and acquiring the necessary expertise to successfully implement and manage AI systems. By integrating customer relations with artificial intelligence, businesses can leverage its extensive benefits to take proactive action and derive value. In terms of existing limitations such as time and financial limitations as well as human resources required for quick response and establishing online communication, the use of communication and response programs based on artificial intelligence seems to be an efficient and effective alternative that can improve the financial and non-financial performance of the organization.

Keywords

Main Subjects

References

 
Abdoulaye, T., Abass, A., Maziya-Dixon, B., Tarawali, G., Okechukwu, R., Rusike, J. & Ayedun, B. (2014). Awareness and adoption of improved cassava varieties and processing technologies in Nigeria. Journal of Development and Agricultural Economics 6(2), 67-75.
Aboelmaged, M.G. (2014). Predicting E-Readiness at Firm-Level: An Analysis of Technological, Organizational and Environmental (TOE) Effects on E-Maintenance Readiness in Manufacturing Firms. International Journal of Information Management (34:5), 639-651.
Abugabah, A., Sanzogni, L., Houghton, L., AlZubi, A. A. & Abuqabbeh, A. (2021). RFID adaption in healthcare organizations: An integrative framework. Computers, Materials and Continua, 70, 1335.
Ahani, A., Rahim, N. Z. A. & Nilashi, M. (2017). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in human behavior, 75, 560-578.
Ahmadi, H., Nilashi, M. & Ibrahim, O. )2015(. Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals. International journal of medical informatics, 84(3), 166-188.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 (2), 179–211. https://www.sciencedirect.com/science/article/pii /S0743016717303959
Alam, M., Masum, A., Beh, L. & Hong, C.S. )2016(. Critical factors influencing decision to adopt human resource information system (HRIS) in hospitals. PloS one, 11(8), 1-22.
Alsheibani, S., Cheung, Y. & Messom, C. (2018). Artificial Intelligence Adoption: AI-readiness at Firm-Level. PACIS, 4, 231-245.
Alsheibani, S., Cheung, Y. & Messom, C. (2020). Re-thinking the competitive landscape of artificial intelligence. In Proceedings of the 53rd Hawaii international conference on system sciences.
Alsheikh, L. & Bojei, J. (2014). Determinants affecting customer’s intention to adopt mobile banking in Saudi Arabia. Int. Arab. J. e Technol., 3(4), 210–219.
Alsudairi, M. & Dwivedi, Y. K. (2010). A multi-disciplinary profile of IS/IT outsourcing research. Journal of Enterprise Information Management, 23(2), 215–258.
Alvin Yau, K., Mat Saad, N. & Chong, Y. (2021). Artificial Intelligence Marketing (AIM) for Enhancing Customer Relationships. Applied Sciences, 11(18), 5861-5870.
Arab Mazar Yazdi, M., Ahmadi, A. & Abdoli, M. (2016). Intelligent and accounting systems. Accountant's Monthly, 21(4), 53-59. (in Persian)
Baabdullah, A., Alalwan, A., Louise Slade, E., Raman, R. & Khatatneh, K. (2021). SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management 98, 255–270.
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.
Bangalore Seetharam, S. (2020). Developing a digital AI roadmap for retail. Master’s thesis. Metropolia University of Applied Sciences.
Cao, G., Duan, Y., Edwards, J. & Dwivedi, Y. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.
Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for efective AI_CRM integration. The Bottom Line, 32(2), 144–157.
Chatterjee, S., Nguyen, B., Ghosh, S.K., Bhattacharjee, K. K. & Chaudhuri, S. (2020). Adoption of artifcial intelligence integrated CRM system: An empirical study of Indian organizations. The Bottom Line, 33(4), 359–375.
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.
Chiu, Y., Zhu, Y. & Corbett, J. (2021). In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations, International Journal of Information Management, 60, 102379. https://doi.org/10.1016/j.ijinfomgt.2021.102379
Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13.
Daradkeh, M. K. (2019). Determinants of visual analytics adoption in organizations: Knowledge discovery through content analysis of online evaluation reviews. Information Technology & People, 32(3), 668–695.
Dastjerdi, M. & Keramati, A. (2023). A Novel Framework for Investigating Organizational Adoption of AI-integrated CRM Systems in the Healthcare Sector; Using a Hybrid Fuzzy Decision-Making Approach, Telematics and Informatics Reports, 11, 100078.
Davis, F. D., )1989(. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–339.
Esfahani, A., Ahmadi, H., Nilashi, M. & Alizadeh, M. (2018). An evaluation model for the implementation of hospital information system in public hospitals using multi-criteria-decision-making (MCDM) approaches. International Journal of Engineering and Technology (IJET), 7(1), 1-18.
Ghobakhloo, M. & Ching, N.T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, 100107.
Gotteland, D., Shock, J., & Sarin, S. (2020). Strategic orientations, marketing proactivity and frm market performance. Industrial Marketing Management, 91, 610-620. https://doi.org/10.1016/j. indmarman.2020.03.012
Halabi, O., El-Seoud, S. A., Aljaam, J. M. & Alpona, H. (2017). Design of immersive virtual reality system to improve communication skills in individuals with autism. International Journal of Emerging Technologies in Learning, 12(5), 50–64.
Hasani, T., Bojei, J. & Dehghantanha, A. )2017(. Investigating the antecedents to the adoption of SCRM technologies by start-up companies. Telematics and Informatics, 34(5), 655-675.
Hu, S., Liou, J.J., Lu, M., Chuang, Y. & Tzeng, G. )2018(. Improving NFC technology promotion for creating the sustainable education environment by using a hybrid modified MADM Model. Sustain 10(5): 1-24.
Hung, S.Y., Chang, C.M. & Kuo, S.R. )2013(. User acceptance of mobile e-government services: an empirical study. Government Information Quarterly, 30 (1), 33–44.
Jeffrey, H., Sedgwick, J., & Robinson, C. (2013). Technology roadmaps: An evaluation of their success in the renewable energy sector. Technological Forecasting and Social Change, 80(5), 1015–1027.
Johnk, J., Weißert, M. & Wyrtki, K. (2021). Ready or not, AI comes – An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63, 5–20.
Kosasi, S., Vedyanto, V. & Yuliani, I. )2018(. Appropriate Sets of Criteria for Innovation Adoption of IS Security in Organizations. In 2018 5th Int Confer on Electrical Eng, Computer Science and Informatics (EECSI), 608-613. IEEE.
Kros, J. F., Glenn Richey, R., Chen, H. & Nadler, S. S. (2011). Technology emergence between mandate and acceptance: An exploratory examination of RFID. International Journal of Physical Distribution and Logistics Management, 41(7), 697–716.
Lee, J. H., Kim, H. I. & Phaal, R. (2012). An analysis of factors improving technology roadmap credibility: A communications theory assessment of roadmapping processes. Technological Forecasting and Social Change, 79(2), 263–280.
Lichtenthaler, U. (2020). Extremes of acceptance: employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39-45. https://doi.org/10.1108/JBS-12-2018-0204
Lin, H. F. & Lee, G. G. (2005). Impact of organizational learning and knowledge management factors on e-business adoption. Management Decision, 43(2), 171–188.
Lin, H. F. & Lin, S. M. (2008). Determinants of e-business diffusion: A test of the technology diffusion perspective. Technovation, 28(3), 135–145.
Masum, A., Mamnun, A., Islam, M. & Beh, L.S. )2020(. The Impact of eHRM Practice on Organizational Performance: Investigating the Effect of Job Satisfaction of HRM Professionals. Journal of Computational Science, 16(7), 983-1000.
Nassoura, A. (2020). Critical Success Factors for Adoption of Cloud Computing In Jordanian Healthcare Organizations. International Journal of Scientific & Technology Research, 9(4), 2798-2803.
Newby, M., Nguyen, H.T. & Waring, T. (2014). Understanding customer relationship management technology adoption in small and medium-sized enterprises: An empirical study in the USA. Journal of Enterprise Information Management, 27(5), 541-560. https://doi.org/10.1108/JEIM-11-2012-0078.
Paschen, J., Kietzmann, J. & Kietzmann, T. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing, 34(7), 1410-1419.
Peng, M. W., Lebedev, S., Vlas, C. O., Wang, J. C. & Shay, J. S. (2018). The growth of the frm in (and out of) emerging economies. Asia Pacifc Journal of Management, 35(4), 829–857.
Rahimi, G. H., Hojjati, S.A & Sadati, M.H. (2017). Evaluation of the impact of technology infrastructure in the implementation of knowledge management components (Study case: Urmia Municipality). Public Policy in Management, 7 (Special Issue 2015), 169-183.
(in Persian)
Ramdani, B., Duan, B. & Berrou, I. )2020(. Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study. JMIR medic informat., 8(7), 1-17.
Richey, R. G., Daugherty, P. J. & Roath, A. S.) 2007(. Firm Technological Readiness and Complementarity: Capabilities Impacting Logistics Service Competency and Performance. Journal of Business Logistics, 28(1), 195-228.
Rogers, E.M. (1995). Diffusion of Innovations, (4th ed.). The Free Press, New York, NY.
Salah, O., Yusof, Z. & Mohamed, H. )2021(. The determinant factors for the adoption of CRM in the Palestinian SMEs: The moderating effect of firm size. Plos one, 16(3), 1-25.
Salisu, I., Bin Mohd Sappri, M. & Bin Omar, M.F. )2021(. The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review. Cogent Business & Management, 8(1), 1-22.
Sarmah, B., Kamboj, S. & Phookan, N. )2022(. Determinants of RFID Adoption Intention in the Healthcare Industry for Patient Monitoring: A Special Reference to COVID-19. In Handbook of Research on Emerging Business Models and the New World Economic Order. 197-213. IGI Global.
Shahzad, K., Jianqiu, Z., Zia, M.A., Shaheen, A.  & Sardar, T. )2018(. Essential factors for adopting hospital information system: a case study from Pakistan. International Journal of Computer Applications, 43(1), 26-37.
Shahzad, K., Jianqiu, Z., Zubedi, A. & Xin, W.  )2020(. DANP-based method for determining the adoption of hospital information system. International Journal of Computer Applications in Technology, 62(1): 57-70.
Sigala, M. (2006). Mass customisation implementation models and customer value in mobile phones services: Preliminary findings from Greece. Managing Service Quality: An International Journal, 16(4), 395–420.
Sohn, K. & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics, 47, 101324.
Soltani, Z. & Jafari Naoimipour, N. (2016). The impact of cost, technology acceptance and employees' satisfaction on the effectiveness of the electronic customer relationship management systems, Computers in Human Behavior, 55, 1052-1066. (in Persian)
Tornatzky, L.G., Fleischer, M. & Chakrabarti, A.K. (1990). Processes of technological innovation. Lexington books, Rowman & Littlefield.
Journal of Business Management
Volume 16, Issue 1
2024
Pages 34-58

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