APPROACHES TO DESIGNING A WEB-BASED CUSTOMER SUPPORT SYSTEM FOR THE AGRICULTURAL SECTOR
DOI:
https://doi.org/10.32718/agroengineering2025.29.218-224Keywords:
customer support systems, approaches, design, web technologies, agricultural sectorAbstract
This paper examines and analyzes approaches to the design, development, and operation of web-based customer support systems for agricultural enterprises. It examines prevailing practices and methodologies in the design and implementation of website-based user support solutions, identifying technologies that promote the advancement of customer support services in the agricultural sector. It has been established that the primary forms of support systems for producers and the population engaged in agriculture are digital platforms, cooperative systems, and advisory services. Furthermore, the study outlines the most common models of customer support systems, including the self-service model, interactive support model, and hybrid model, and evaluates their respective advantages and limitations. Finally, it highlights the key challenges encountered by developers in creating web-oriented customer support systems.
Arguments are presented for the feasibility of using generative artificial intelligence technologies to improve the personalized approach to customer support. The roles and tasks of “live” agents and digital assistants as the most common forms of support for users of commercial Internet resources, are clarified. The features of using digital assistants are considered in comparison to such traditional forms of customer support as telephone lines, messengers, and consultants. The importance of segmenting agricultural enterprise customers as an important element of the support system planning and design process is substantiated. The key components of the customer support system are analyzed, and the main functions and options for their implementation are considered.
The findings highlight the necessity of a comprehensive approach to designing Internet-based customer support systems for agricultural producers, taking into account the specific nature of their activities, the potential of modern technologies, the frequency and volume of user requests, and the available resources.
References
Atwell, E. (2024). Customer self-service: what it is, why it's important, and how to get it right, 2024. Retrieved from: https://www.zendesk.com/blog/customer-self-service-guide-helping-customers-help/ (Accessed January 05, 2024).
Brady, M. K., & Cronin, J. J. (2001). Customer Orientation: Effects on Customer Service Perceptions and Outcome Behaviors. Journal of Service Research, 3(3), 241–251. https://doi.org/10.1177/109467050133005
Chaturvedi, R., & Verma, S. (2023). Opportunities and Challenges of AI-Driven Customer Service. In: Sheth J. N., Jain V., Mogaji E., Ambika A. (eds) Artificial Intelligence in Customer Service. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-33898-4_3
Chen, J.-Sh., & Tsou, H.-T. (2012). Performance effects of IT capability, service process innovation, and the mediating role of customer service. J. Eng. Technol. Manag, 29, 71–94. https://doi.org/10.1016/j.jengtecman.2011.09.007
Gorry, A., & Westbrook, R. (2011). Once more, with feeling: Empathy and technology in customer care. Business Horizons, 54 (2), 125–134. https://doi.org/10.1016/j.bushor.2010.10.003.
Guerola-Navarro, V., Gil-Gomez, H., & Oltra-Badenes, R. (2024). Customer relationship management and its impact on entrepreneurial marketing: a literature review. Int Entrep Manag J, 20, 507–547. https://doi.org/10.1007/s11365-022-00800-x
Hevko, V. L. (2013). Klasyfikatsiia informatsiinykh system upravlinnia vzaiemovidnosynamy z kliientamy. Sotsialno-ekonomichni problemy i derzhava, 2 (9), 44–57.
Lui, T.-W., & Piccoli, G. (2016). The Effect of a Multichannel Customer Service System on Customer Service and Financial Performance. ACM Trans. Manage. Inf. Syst. 7, 1, Article 2, 1–15. https://doi.org/10.1145/2875444
Marhita, M. V., Vitovskyi, O. I., Davyda, N. M., Savchuk, R. O., & Tsybrukh, A. I. (2024). Customer service system digitalization strategy. Scientific Notes of Lviv University of Business and Law, 40, 507–516. Retrieved from https://nzlubp.org.ua/index.php/journal/article/view/1179 (Accessed June 6, 2024)
Markovitch D., Stough R., & Huang D. (2024). Consumer reactions to chatbot versus human service: An investigation in the role of outcome valence and perceived empathy. Journal of Retailing and Consumer Services, 79, 2024, 103847. https://doi.org/10.1016/j.jretconser.2024.103847.
Martins, De Andrade, I., & Tumelero, C. (2022). Increasing customer service efficiency through artificial intelligence chatbot. Revista de Gestão, 29 (3).
Negash, S., Ryan, T., & Igbaria, M. (2003). Quality and effectiveness in Web-based customer support systems, Information & Management, 40 (8), 757–768. https://doi.org/10.1016/S0378-7206(02)00101-5
Piccoli, G., Anglada, L. D., & Watson, R. T. (2005). Using Information Technology to Improve Customer Service: Evaluating the Impact of Strategic Opportunities. Journal of Quality Assurance in Hospitality & Tourism, 5 (1), 2005, 3–26. https://doi.org/10.1300/J162v05n01_02
Piccoli, G., Brohman, M., Watson, R., & Parasuraman, A. (2004). Net-Based Customer Service Systems: Evolution and Revolution in Web Site Functionalities. Decision Science, 35 (3), 423–455. https://doi.org/10.1111/j.0011-7315.2004.02620.x
Ping, N. L., Hussin, A. R., & Ali, N. B. (2019). Constructs for Artificial Intelligence Customer Service in E-commerce, 6th International Conference on Research and Innovation in Information Systems (ICRIIS), Johor Bahru, Malaysia, 1–6. DOI: 10.1109/ICRIIS48246.2019.9073486.
Selber, St., Johndan, J., & Brad, M. (1996). Online Support Systems. ACM Comput. Surv, 28, 197–200. 10.1145/234313.23437. Retrieved from: https://www.researchgate.net/publication/220566330_Online_Support_Systems (Accessed January 05, 2024).
Xintong, Y., & Yipeng, X. (2024). Pathways linking expectations for AI chatbots to loyalty: A moderated mediation analysis. Technology in Society, 78, 102625. https://doi.org/10.1016/j.techsoc.2024.102625.
