With the emergence of artificial intelligence (AI), the technological revolution has transformed human lives and processes, empowering the products and services in today’s marketplaces. AI introduces new ways of doing jobs and business, and of exploring new global market opportunities. However, on the other hand, it provides many challenges to comprehend. Therefore, our study’s main objective was to examine the behavioral, cultural, ethical, social, and economic challenges of AI-enabled products and services in consumer markets and discuss how businesses might shape their approaches to address AI-related ethical issues. AI offers numerous social, ethical, and behavioral difficulties for people, organizations, and societies that endanger the sustainable development of economies. These fundamental challenges due to AI technologies have raised serious questions for the sustainable development of electronic markets. Based on this, the current study presents a framework highlighting these issues. Systematic reviewing was our research method; we looked for explicit information and methods that indicate the credibility of research or reduce biases. This paper is of great importance, as it highlights several behavioral, societal, ethical, and cultural aspects in electronic markets which were not presented in previous studies. Some key issues are the security and privacy of consumers, AI biases, individual autonomy, wellbeing, and issues of unemployment. Therefore, companies that use AI systems need to be socially responsible and make AI systems as secure as possible to promote the sustainable development of countries. The results suggest that AI has undoubtedly transformed life and has both positive and negative effects. However, the main aim regarding AI should be to use it for the overall goals of humanity. Moreover, authorities operating in e-business environments need to create proper rules and regulations and make the systems as secure as possible for people.

Purpose: This study aims to focus on peer-to-peer accommodation services in the sharing economy. Adopting construal level theory as the theoretical foundation, this study investigates the main and interaction effects of social and spatial distances on guest loyalty toward peer-to-peer accommodation. Design/methodology/approach: This study uses a secondary source of online observational data archived on Xiaozhu, a leading peer-to-peer accommodation sharing platform in China. It consists of 2,612 observations of 1,304 unique travelers who stayed at 559 listings managed by 281 hosts in four major metropolitan areas of China over four years from August 2012 to August 2016. Non-linear binary choice panel models of probability regressions were used to estimate the effects of psychological distances (social and spatial) between hosts and guests on the likelihood of repeat purchase. The software used for the econometric analyses is STATA 14. Findings: The results indicate that social distance negatively affects guest loyalty toward the listing hosts, while spatial distance has a positive influence on guest loyalty. The results also show significant interactions between the two psychological distance dimensions in influencing loyalty. The findings provide important insight into the influences of psychological distances on travelers’ repeat purchase behavior toward peer-to-peer accommodation providers. Originality/value: This study contributes to the literature by providing empirical evidence that supports the importance of psychological distances in forming a loyal relationship between hosts and guests in the peer-to-peer accommodation sector of the sharing economy.

Under information asymmetry, electronic word of mouth (eWOM) is crucial to understanding customer experience in peer-to-peer (P2P) accommodation. However, the topics of what dimensions are included in eWOM and how these dimensions determine sales performance from a contingency perspective remain starkly underinvestigated. Drawing upon trust transfer theory and signaling theory, we identify two eWOM dimensions (i.e., listing-based eWOM and host-based eWOM) and examine their interactions with price and responsiveness on sales performance. Using objective data from Xiaozhu.com, a leading accommodation-sharing platform in China, we apply machine learning algorithms and economic analysis to illustrate customer behavior. Our results indicate that the valence of listing-based eWOM and that of host-based eWOM are positively associated with sales performance. Compared with listing-based eWOM, host-based eWOM exerts a more decisive influence on sales performance. Notably, the relationship between the valence of listing-based eWOM (host-based eWOM) and sales performance is weakened (strengthened) by listing price (host’s responsiveness). These findings provide helpful managerial insights for service strategies and policy actions to improve the accommodation-sharing economy.