References
- The references that follow are cited in the above text. Additional references for the papers filtered from results of the searches over the selected databases are listed at https://figshare.com/s/4ec9f1d08f6a1a362b7c
- Aarthi, N. G. (2020). Chatbot for retail shop evaluation. International Journal of Computer Science and Mobile Computing, 9(3), 69–77.
- Ameen, N., Hosany, S., & Tarhini, A. (2021). Consumer interaction with cutting-edge technologies: Implications for future research. Computers in Human Behavior, 120, 106761.
- Chaves, A. P., & Gerosa, M. A. (2020). How should My Chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 0(0), 1–30.
- Chen, S., Li, C., Ji, F., Zhou, W., & Chen, H. (2019). Review-driven answer generation for product-related questions in E-commerce. Proceedings of WSDM ‘19. ACM, New York, NY, USA, 411–419.
- Cheng, Y., & Jiang, H. (2020). How Do AI-driven chatbots impact user experience? Examining gratifications, perceived privacy risk, satisfaction, loyalty, and continued Use. Journal of Business Economics and Management, 64(4), 592–614.
- Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595.
- Colombi, C., Kim, P., & Wyatt, N. (2018). Fashion retailing “tech-gagement”: engagement fueled by new technology. Research Journal of Textile and Apparel, 22(4), 390–406.
- Copulsky, J. (2019). Do conversational platforms represent the next big digital marketing opportunity? Applied Marketing Analytics, 4(4), 311–316.
- De Carolis, B., De Gemmis, M., & Lops, P. (2015). A multimodal framework for recognizing emotional feedback in conversational recommender systems. ACM International Conference Proceeding series, 2015-September, 11–18.
- De Carolis, B., de Gemmis, M., Lops, P., & Palestra, G. (2017). Recognizing users feedback from non-verbal communicative acts in conversational recommender systems. Pattern Recognition Letters, 99, 87–95.
- Diederich, S., Brendel, A. B., & Kolbe, L. (2019). On conversational agents in Information Systems research: Analyzing the past to guide future work. Wirtschaftsinformatik, 85, 1550–1564.
- Eisman, E. M., Navarro, M., & Castro, J. L. (2016). A multi-agent conversational system with heterogeneous data sources access. Expert Systems With Applications, 53, 172–191.
- Gao, S., Zhao, D., Ren, Z., Yin, D., Zhao, Y., & Yan, R. (2019). Product-aware answer generation in e-commerce question-answering. WSDM, 2019, 429–437.
- Go, E., & Sundar, S. S. (2019). Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions. Computers in Human Behavior, 97, 304–316.
- Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6.
- Jurafsky, D., & Martin, J. H. (2020). Speech and language processing (3rd ed. Draft). Upper Saddle River, NJ: Prentice Hall.
- Jusoh, S. (2018). Intelligent conversational agent for online sales. Proceedings of ECAI 2018, 1–4.
- Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280.
- Lai, P., & Westland, S. (2020). Machine learning for colour palette extraction from fashion runway images. International Journal of Fashion design. Technology and Education, 13(3), 334–340.
- Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2016). A diversity-promoting objective function for neural conversation models. Proceedings of NAACL-HLT2016, 110-119.
- Li, L., Li, C., & Ji, D. (2021). Deep context modeling for multi-turn response selection in dialogue systems. Information Processing & Management, 58(1), 102415.
- Liao, L., Zhou, Y., Ma, Y., Hong, R., & Chua, T. S. (2018). Knowledge-aware multimodal fashion chatbot. Proceedings of ACM MM 2018, 1265–1266.
- Liu, C., Jiang, J., Xiong, C., Yang, Y., & Ye, J. (2020). Towards Building an Intelligent chatbot for customer service: Learning to respond at the appropriate time. ACM SIGKDD KDD, 3377–3385.
- Marulli, F., Pota, M., & Esposito, M. (2018). A comparison of character and word embeddings in bidirectional LSTMs for POS tagging in Italian. KES-IIMSS-18, 14–23.
- Mauldin, M. L. (1994). Chatterbots, tinymuds, and the turing test: Entering the loebner prize competition. AAAI, 94, 16–21.
- Merrilees, B., & Miller, D. (2019). Companion shopping: The influence on mall brand experiences. Marketing Intelligence & Planning, 37(4), 465–478.
- Moriuchi, E., Landers, V. M., Colton, D., & Hair, N. (2020). Engagement with chatbots versus augmented reality interactive technology in e-commerce. Journal of Strategic Marketing, 0(0), 1–15.
- Morotti, E., Donatiello, L., & Marfia, G. (2020, March). Fostering fashion retail experiences through virtual reality and voice assistants. 2020 IEEE VRW (pp. 338-342). IEEE.
- Nazir, A., Khan, M. Y., Ahmed, T., Jami, S. I., & Wasi, S. (2019). A novel approach for ontology-driven information retrieving chatbot for fashion brands. International Journal of Advanced Computer Science and Applications, 10(9), 546–552.
- Nie, L., Wang, W., Hong, R., Wang, M., & Tian, Q. (2019). Multimodal dialog system: Generating responses via adaptive decoders. MM 2019, 1098–1106.
- Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37(1), 43.
- Palma, M. D. C. O., Seeger, A. M., & Heinzl, A. (2020). Mitigating information overload in e-commerce interactions with conversational agents. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. Randolph, & T. Fischer (Eds.), Information Systems and neuroscience (pp. 221–228). Cham: Springer.
- Pantano, E., Passavanti, R., Priporas, C. V., & Verteramo, S. (2020). The use of new technologies for corporate marketing communication in luxury retailing: Preliminary findings. Qualitative Market Research, 23(3), 503–521.
- Pantano, E., & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, 55, 102096.
- Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). Bleu: A method for automatic evaluation of machine translation. Proceedings of the ACL’02, 311–318.
- Park, S., & Choi, J. Y. (2020). Malware detection in self-driving vehicles using machine learning algorithms. Journal of Advanced Transportation, 2020, 1–9.
- Paul, A., Haque Latif, A., Amin Adnan, F., & Rahman, R. M. (2019). Focused domain contextual AI chatbot framework for resource poor languages. Journal of Information and Telecommunication, 3(2), 248–269.
- Prajwal, S. V., Mamatha, G., Ravi, P., Manoj, D., & Joisa, S. K. (2019). Universal semantic web assistant based on sequence to sequence model and natural language understanding. ICACC, 2019, 110–115.
- Pricilla, C., Lestari, D. P., & Dharma, D. (2018). Designing interaction for chatbot-based conversational commerce with user-centered design. ICAICTA, 2018, 244–249.
- Qiu, M., Li, F. L., Wang, S., Gao, X., Chen, Y., Zhao, W., … Chu, W. (2017). Alime chat: A sequence to sequence and rerank based chatbot engine. ACL 2017 - (long papers), 2, 498–503.
- Rese, A., Ganster, L., & Baier, D. (2020). Chatbots in retailers’ customer communication: How to measure their acceptance? Journal of Retailing and Consumer Services, 56, 102176.
- Roh, J. S., Chi, Y. S., & Kang, T. J. (2010). Wearable textile antennas. International Journal of Fashion design. Technology and Education, 3(3), 135–153.
- Sanny, L., Susastra, A., Roberts, C., & Yusramdaleni, R. (2020). The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia. Management Science Letters, 10(6), 1225–1232.
- Sapna, C. R., Anagha, M., Vats, K., Baradia, K., Khan, T., Sarkar, S., & Roychowdhury, S. (2019). Recommendence and fashionsence online fashion advisor for offline experience. ACM International Conference Proceeding series, 256–259.
- Skjuve, M., Følstad, A., Fostervold, K. I., & Brandtzaeg, P. B. (2021). My Chatbot Companion-a study of human-chatbot relationships. International Journal of Human-Computer Studies, 149, 102601.
- Statista. (2019). Leading ten areas retailers are using Artificial Intelligence (AI) in their business in the United Kingdom (UK) as of 2019*. Retrieved from https://www.statista.com/statistics/1026052/artificial-intelligence-retailers-area-of-use-in-the-united-kingdom-uk/. Accessed April 13th, 2021
- Sun, Y., & Zhang, Y. (2018). Conversational recommender system. The 41st International ACM SIGIR ‘18. ACM, New York, NY, USA, 235–244.
- Tan, S. M., & Liew, T. W. (2020). Designing embodied Virtual agents as product specialists in a multi-product Category E-commerce: The roles of source credibility and social presence. International Journal of Human–Computer Interaction, 36(12), 1136–1149.
- Vaccaro, K., Agarwalla, T., Shivakumar, S., & Kumar, R. (2018). Designing the future of personal fashion. Conference on human factors in Computing systems - proceedings, 2018-April, 1–11.
- Wintersberger, P., Klotz, T., & Riener, A. (2020, October). Tell Me more: Transparency and time-fillers to optimize chatbots’ waiting time experience. Proceedings of NordiCHI-2020 (pp. 1-6).
- Yan, R. (2018). “Chitty-Chitty-Chat Bot”: Deep Learning for conversational AI. IJCAI, 18, 5520–5526.
- Yu, J., Qiu, M., Jiang, J., Huang, J., Song, S., Chu, W., & Chen, H. (2017). Modelling domain relationships for transfer Learning on retrieval-based question answering systems in E-commerce. Proceedings of WSDM ‘18. ACM, New York, NY, USA, 682–690.