PurposeThis research clarifies the connotations and dimensions of artificial intelligence (AI) technology stimulation and establishes a stimulus scale to explain the relationship between AI technology stimuli and smart customer experience.Design/methodology/approachThis is an empirical study that uses SPSS 24.0 software to perform hypothesis testing on the path relationships between model elements.FindingsTwo dimensions of AI technology stimuli (i.e. passion and usability) have a significant, positive impact on smart customer experience; the moderating effects of contrasting dimensions of technology readiness (i.e. optimism and discomfort) are significantly different; smart customer experience has a significant, positive impact on the word-of-mouth (WOM) intentions of consumers.Research limitations/implicationsThere are several limitations. Most importantly, the data collected in this study are only from consumers who use intelligent customer service robots in the catering industry. Future research can consider exploring relevant AI technologies in other sectors.Practical implicationsThis study has several implications that guide catering companies to develop various positioning and strategies for remaining competitive effectively.Originality/valueBased on arousal theory, customer experience theory and WOM marketing theory, this is the first novel research project that empirically discusses the dimensions of AI technology stimuli, smart customer experience and WOM intentions with regard to the moderating effect of the technology readiness of consumers toward AI technology.

Whether cities can attain a win-win situation with simultaneous environmental protection and economic growth is a compelling issue in current urban development. It will be of great practical significance to comprehensively evaluate the implementation effect of the multi-dimensional goals of an urban development from the perspective of a green total factor productivity (GTFP) evaluation. The paper places pollution reduction and carbon reduction into the research framework of GTFP, introduces the SBM-DDF model to evaluate the urban GTFP of 58 cities in the Yellow River Basin (YRB) from 2006 to 2020, and employs the panel regression model to empirically study the factors influencing the urban GTFP of the region. The results are obtained as follows: (1) from the perspective of time range, the urban GTFP in the basin displays an evolutionary trend of first declining and then mounting, demonstrating the highest GTFP in the downstream, the second-highest in the midstream and the lowest in the upstream; (2) regarding the spatial distribution characteristics, the urban GTFP in the basin presents obvious spatial differences, showing the regional differences by increasing from the upstream to the downstream; (3) from the perspective of the whole basin, the advancement of economic development, urbanization processes, environmental regulations and the ecological background have significantly positive effects in improving the urban GTFP, while the improvement of the industrial structure, opening-up and energy intensity affects the urban GTFP of the basin negatively; and (4) from the perspective of the regional heterogeneity of the effects of the various influencing factors, the improvement of the opening-up and industrial structure expedites the growth of the urban GTFP of the downstream, the advancement of urbanization process restrains the urban GTFP in the upstream and the impact of the ecological background on the urban GTFP in different regions is relatively complex. This study is of great importance to improve the urban GTFP and boost the high-quality development of the cities in the basin.