SUSTAINABLE ARTIFICIAL INTELLIGENCE TOOL STRATEGY AND CUSTOMER EXPERIENCE IN EYE WEAR RETAIL CHAIN STORES
Keywords:
Customer Experience, E-Satisfaction, NLP (Natural Language Processing), Online Customer Experience (OCE), CECoR frameworkAbstract
Artificial intelligence tools and processes have hugely impacted the retail industry and the satisfaction of online customers. With technology largely pervading all facets of our lives, people want meaningful experiences. Artificial intelligence has the ability to deliver positive experiences for customers that help build brand trust and customer satisfaction. Whether you are using your smartphone, laptop, or voice assistants such as Alexa or Siri, service on the internet is gaining new ground. This paper does a literature review of the various technological advances that optimize thecustomer experience to evoke esatisfaction (i.e., satisfaction while shopping online). E-satisfaction asa construct will be reviewed with its impact on customer purchase intention. The main results of the documentation were used to substantiate the conceptual framework introduced by the paper. The research revealed a variety of advanced solutions, benefits, but also risks that AI generates in retail, in different segments of the value chain, abbreviated CECoR, from improving the customer experience (Customer Experience, CE) with the help of virtual agents (chatbots, virtual assistants, etc.), tocost reductions (Cost, Co) by using smart shelves, and to increasing revenues (Revenue, R) due to product recommendations and personalized offers or discounts. The proposed conceptual framework is focused on customer profiles and includes recommendations on AI implementations in a retail company, from the perspective of CECoR drivers. The results of the research can be capitalized by practitioners and researchers in the field, who are presented with concrete examples of benefits, challenges, and risks generated by AI technologies. The CECoR framework could be a useful tool for both retail and AI specialists, providing common and clearguidelines for initiating and overseeing projects for integrating AI in a company’s informationsystems. This review will provide businesses and other researchers a frame of reference to conduct empirical studies in the area of Aland technology-enabled retail.
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References
Ahmeda, R. A. E., Shehaba,M. E., Morsya, S., & Mekawiea,N.(2015). PerformanceStudyofClassification Algorithms for Consumer Online Shopping Attitudes and Behaviour Using Data Mining. In 2015 Fifth International Conference on Communication Systems and Network Technologies. IEEE.
doi:10.1109/CSNT.2015.50
Anderson, J. R. (1985). Cognitive Psychology and Its Implications (2nd ed.). WH Freeman/Times Books/HenryHolt & Co.
Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology and Marketing,20( 2), 123-138. doi:10.1002/mar.10063
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001, NovemberDecember). Recentadvances in augmented reality. IEEE Computer Graphics and Applications, 27(6),34-47. doi:10.1109/38.963459
Bala, K., Kumar, M., Hulawale, S., & Pandita, S. (2017). Chat-Bot for College Management System Using^/.International Research Journal of Engineering and Technology.
Balabanis, G., Reynolds, N., & Simintiras, A. (2006). Bases of e-store loyalty: Perceived switching barriers and satisfaction. Journal of Business Research, 59(2), 214— 224.doi:10.1016/j.jbusres.2005.06.001
Ballantine, P. W. (2005). Effects of interactivity and product information on consumer satisfaction in an online retail setting. International Journal of Retail & Distribution Management, 33(6 ), 461^471. doi:10.1108/09590550510600870
Bauer, H. H., Falk, T., & Hammerschmidt, M. (2006). ETransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59(1), 866-875. doi:10.1016/j.jbusres.2006.01.021
Bhandari, A., Rama, K., Seth, N., Niranjan, N., Chitalia, P., & Berg, S. (2017). Toward an Efficient Method of Modelling “ Next Best Action” for Digital Buyer’s Journey in B2B. In International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, Lecture Notes in Computer Science.Springer.
Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings on customers and employees. Journal ofMarketing,56( 2 ), 57-71. doi:10.1177/002224299205600205
Brakus, J., Schmitt, B. H., & Zarantonello, L. (2009). Brand Experience: What Is It? How Is It Measured? Does ItAffect Loyalty? Journal ofMarketing,73(3 ), 52-68. doi:10.1509/jmkg.73.3.052
Burke, R. R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store? Journal of the Academy of Marketing Science, 30(4 ), 411— 432. doi:10.1177/009207002236914
Cao, L., & Li, L. S. (2015). 2015: The impact of cross-channel integration on retailers’ sales growth. Journal of Retailing, 91(2 ), 198— 216. doi:10.1016/j.jretai.2014.12.005
Chen, M. Y., & Ching, I. T. (2013). A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electronic Commerce Research, 13( 1 ), 1-23. doi:10.1007/s10660-013-9104-5
Chen, Z., & Dubinsky, A. J. (2003). A conceptual model of perceived customer value in E- commerce: A preliminary investigation. Psychology and Marketing, 20(4 ), 323-347. doi:10.1002/mar.10076
Chintagunta, P. K., Chu, J., & Cebollada, J. (2012). Quantifying transaction costs in online/ offline grocery channel choice. Marketing Science,31( 1), 96-114. doi:10.1287/mksc. l110.0678
Cosbey, S. (2001). Clothing interest, clothing satisfaction, and self-perceptions of sociability, emotional stability, and dominance. Social Behavior and Personality, 29(2), 145-152.doi:10.2224/sbp.2001.29.2.145
Crittenden, W. F., Biel, I. K., & Lovely, W. A. III. (2018). Embracing Digitalization: Student Learning and New Technologies. Journal of Marketing Education, 27(1), 5-14.doi:10.1177/0273475318820895
Devika, P., Jisha, R. C., & Sajeev, G. P. (2016). A Novel Approach for Book Recommendation Systems. In 2016IEEE International Conference on Computational Intelligence Computing Research (ICCIC). IEEE. doi:10.1109/ICCIC.2016.7919606
Djurica, D., & Figl, K. (2017). The Effect of Digital Nudging Techniques on Customers’ Product Choice and Attitudestowards E-Commerce Sites. Twenty-third Americas Conference on Infonnation Systems, 1-5.
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