Artificial Intelligence In Managing The Electronic Customer Relationship And Enhancing The Level Of Satisfaction With Electronic Services

  • Mohammad Ali Alqudah Fellow of Ph.D., Department of Computer Science, Khazar University, Azerbaijan
  • Leyla Muradkhanli Associate Professor, Department of Computer Science, Khazar University, Azerbaijan
Keywords: customer, relationship management, information technology

Abstract

The customer is the backbone of the process of using the applications in the government institution. In a way that ensures the creation of its information about the change of his desires and opinions about the products and applications that are developed by electronic governments, and even his reactions and complaints within a marketing strategy that artificial intelligence sought with its embodied tools for information technology to provide it, and the result was to manage the relationship with the customer using the technological developments that help to do so. Throughout this article, we try to find out the following questions: What The role of artificial intelligence in managing a government institution's customer relationship? For this, we proposed three objectives, how expert systems embody the mechanisms of artificial intelligence within the government institution, while the mechanism of customer relationship management within the government institution is represented, how artificial intelligence has contributed to the success of customer relationship management to the e-government

References

Al-Ma’aitah, M. (2019). Drivers of E-Government Citizen Satisfaction and Adoption: The Case of Jordan. International Journal of E-Business Research (IJEBR), 15(4), 40–55.

AL-MA’AITAH, M. A., & AL-HASHEM, A. O. (2019). THE ROLE OF E-TRUST IN ACHIEVING E-LOYALTY: AN EXPLORATORY STUDY ON JORDANIAN CUSTOMERS USING SHOPPING WEBSITES. Journal of Theoretical and Applied Information Technology, 97(5).

Al-Weshah, G. A., Al-Manasrah, E., & Al-Qatawneh, M. (2019). Customer relationship management systems and organizational performance: Quantitative evidence from the Jordanian telecommunication industry. Journal of Marketing Communications, 25(8), 799–819.

Alawneh, A., Al-Refai, H., & Batiha, K. (2013). Measuring user satisfaction from e-Government services: Lessons from Jordan. Government Information Quarterly, 30(3), 277–288.

Aljawarneh, N., & Al-Omari, Z. (2018). The role of enterprise resource planning systems ERP in improving customer relationship management CRM: An empirical study of Safeway company of Jordan. International Journal of Business and Management, 13(8), 86–100.

Allahverdi, N. (2014). Design of fuzzy expert systems and its applications in some medical areas. International Journal of Applied Mathematics Electronics and Computers, 2(1), 1–8.

Badwan, J. J., Al Shobaki, M. J., Abu-Naser, S. S., & Abu Amuna, Y. M. (2017). Adopting technology for customer relationship management in higher educational institutions.

Baharon, B. M., Yap, C. S., Ashar, S. F. E., Hanafi, M. H. H. M., & Hazmi, M. S. R. M. (2017). Citizen Satisfaction with E-Government Portals in Malaysia. International Journal of Business & Information, 12(3).

Bejarbaneh, B. Y., Bejarbaneh, E. Y., Fahimifar, A., Armaghani, D. J., & Abd Majid, M. Z. (2018). Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77(1), 345–361.

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

Delanoy, N., & Kasztelnik, K. (2020). Business Open Big Data Analytics to Support Innovative Leadership and Management Decision in Canada. Business Ethics and Leadership, 4(2), 56–74.

Dewnarain, S., Ramkissoon, H., & Mavondo, F. (2019). Social customer relationship management: An integrated conceptual framework. Journal of Hospitality Marketing & Management, 28(2), 172–188.

Drexler, N., & Lapré, V. B. (2019). For better or for worse: Shaping the hospitality industry through robotics and artificial intelligence. Research in Hospitality Management, 9(2), 117–120.

Egrioglu, E., Aladag, C. H., & Yolcu, U. (2013). Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Systems with Applications, 40(3), 854–857.

Garcelon, N., Neuraz, A., Salomon, R., Faour, H., Benoit, V., Delapalme, A., Munnich, A., Burgun, A., & Rance, B. (2018). A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse. Journal of Biomedical Informatics, 80, 52–63.

Grewal, D., & Roggeveen, A. L. (2020). Understanding retail experiences and customer journey management. Journal of Retailing, 96(1), 3–8.

Hossain, M. S., Zander, P., Kamal, M. S., & Chowdhury, L. (2015). Belief‐rule‐based expert systems for evaluation of e‐government: a case study. Expert Systems, 32(5), 563–577.

Hou, S., Fei, J., Chen, C., & Chu, Y. (2019). Finite-time adaptive fuzzy-neural-network control of active power filter. IEEE Transactions on Power Electronics, 34(10), 10298–10313.

Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, 57–71.

Kaya, T. (2019). Artificial intelligence driven e-government: the engage model to improve e-decision making. ECDG 2019 19th European Conference on Digital Government, 43.

Khrais, L. T. (2020). Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce. Future Internet, 12(12), 226.

Kramer, O. (2017). Genetic algorithms. In Genetic algorithm essentials (pp. 11–19). Springer.

Kumar, V. (2010). Customer relationship management. Wiley International Encyclopedia of Marketing.

Küpper, T., Jung, R., Lehmkuhl, T., & Wieneke, A. (2014). Features for social CRM technology–An organizational perspective.

Lee, S., & Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41(6), 3041–3046.

Magoutas, B., Schmidt, K.-U., Mentzas, G., & Stojanovic, L. (2010). An adaptive e-questionnaire for measuring user perceived portal quality. International Journal of Human-Computer Studies, 68(10), 729–745.

Murmu, S., & Biswas, S. (2015). Application of fuzzy logic and neural network in crop classification: a review. Aquatic Procedia, 4, 1203–1210.

Papadomichelaki, X., & Mentzas, G. (2009). A multiple-item scale for assessing e-government service quality. International Conference on Electronic Government, 163–175.

Reis, J., Santo, P. E., & Melão, N. (2019). Artificial intelligence in government services: A systematic literature review. World Conference on Information Systems and Technologies, 241–252.

Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130–157.

Salinas, S. O., & Lemus, A. C. N. (2017). Data warehouse and big data integration. Int. Journal of Comp. Sci. and Inf. Tech, 9(2), 1–17.

Sigala, M. (2018). Implementing social customer relationship management. International Journal of Contemporary Hospitality Management.

Sigwejo, A., & Pather, S. (2016). A citizen‐centric framework for assessing e‐government effectiveness. The Electronic Journal of Information Systems in Developing Countries, 74(1), 1–27.

Silva Araújo, V. J., Guimarães, A. J., de Campos Souza, P. V., Rezende, T. S., & Araújo, V. S. (2019). Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Machine Learning and Knowledge Extraction, 1(1), 466–482.

Siskos, E., Askounis, D., & Psarras, J. (2014). Multicriteria decision support for global e-government evaluation. Omega, 46, 51–63.

Soltani, Z., Zareie, B., Milani, F. S., & Navimipour, N. J. (2018). The impact of the customer relationship management on the organization performance. The Journal of High Technology Management Research, 29(2), 237–246.

Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596–615.

Wong, C., Guo, Z. X., & Leung, S. Y. S. (2013). Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Elsevier.

Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018). Big Data-enabled customer relationship management: A holistic approach. Information Processing & Management, 54(5), 818–846.

Ulugbek Hamdam. Combinations of different colors. "Uzbekiston adabiyoti va sanati" newspaper. 2000.29-September.

Meliboev Akhmadzhon. Tree share (Checklist for yourself). www.yozuvchi.uz

Published
2021-04-12
How to Cite
Mohammad Ali Alqudah, & Leyla Muradkhanli. (2021). Artificial Intelligence In Managing The Electronic Customer Relationship And Enhancing The Level Of Satisfaction With Electronic Services. International Journal on Orange Technologies, 3(4), 4-20. https://doi.org/10.31149/ijot.v3i4.1595
Section
Articles