Antecedents of intention to use chatbots in service encounters: A meta-analytic review

The trend of using artificial intelligence based chatbots for service encounters has seen a significant spike in recent times. Many studies have attempted to explain the adoption of the technology for various service applications; this study synthesizes the literature through a meta-analysis, for which data from 70 independent studies, with a combined sample size of 20,026 was considered, to understand the influencers of the attitude and intention to use chatbots for service encounters. In addition, this study also sought to understand the moderating role of the culture, the technological characteristics captured through the variables transactional/non-transactional, and informative/entertaining of the hypothesized relationships. Meta structural equation modelling (meta-analytic SEM) was used to analyse the data. The results indicate playfulness, attitude, usefulness, facilitating condition and social influence of the chatbots are the key factors in influencing user adoption. Further, technological characteristics and culture were found to significantly moderate certain relationships in the model. Practical and theoretical implications have also been discussed.     

Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behavior

The COVID-19 pandemic has created a new reality for consumers all around the globe. To cope, users of digital technologies have faced the necessity of adopting and using specific technologies practically overnight. They are doing this under the condition of social isolation, all while facing the fear of catching the disease. The purpose of the paper is to study the way unexpected circumstances cause disruptions in existing theoretical models and their implications for the post-COVID-19 era. Therefore, the paper examines the unified theory of acceptance and use of technology (UTAUT) model under the circumstances of the COVID-19 pandemic and social isolation, and it identifies herd behavior as a possible new mechanism affecting behavioral intention under these unique decision-making circumstances. Behavioral intention toward online shopping was analyzed using data from 420 individuals aged 60 and older who present an increasingly important potential market for electronic commerce and who are particularly affected by COVID-19. The main results show that performance expectancy still has the most important influence on behavioral intention, whereas the impact of social influence was not supported under these conditions. Rather, herd behavior was identified as particularly influential for behavioral intention. Based on the study results, the option to reconsider the social influence factor in the UTAUT model and its possible complementary mechanisms are discussed.     

The role of mobile value and trust as drivers of purchase intentions in m-servicescape

Building on the premises of the unified theory of acceptance and use of technology (UTAUT), this study introduces the concept of mobile servicescape (m-servicescape) and explores the drivers of purchase intentions in the mobile service environment. Data were collected from a sample of 284 service mobile users and analyzed using structural equation modeling. Results show that the dimensions of m-servicescape (i.e., aesthetic appeal, perceived security, and layout and functionality) generate mobile value (i.e., hedonic and utilitarian), which in turn, leads to user purchase intentions. Utilitarian value was found to have a higher effect on purchase intentions than hedonic value and trust was found to enhance this effect. We highlight theoretical contributions and offer managerial insight for mobile marketers and designers on the specificities of consumer behavior in the service mobile environment.     

基于UTAUT模型的高校学生E-learning采纳影响因素分析

在UTAUT模型基础上,对我国高校学生对于e-learning的采纳意向进行了实证研究,分析其影响因素,提出改进意见。结果表明,绩效期望会显著影响高校学生的采纳意向;社会影响、促进条件对行为意向具有正向影响,但相对绩效期望较小;高校学生对e-learning服务的满意程度对以后的使用意向和使用行为有正面影响,且满意对使用行为的直接影响大于其对采纳意向的影响。     

Which food delivery platforms are winning the restaurant food delivery wars? Analysis from a consumer perspective

The emergence of the COVID-19 pandemic dramatically lowered the foodservice industry's income overnight. Conversely, the practical measure of remaining at home to deal with the pandemic's impact has boosted the online food delivery business. In this study, a consumer perspective was adopted and an adapted version of the extended unified theory of acceptance and use of technology (UTAUT2) was used alongside multi-attribute decision-making methods (DEMATEL, DANP and modified VIKOR) to construct a model for evaluating and selecting a food delivery platform (FDP). The results of the INRM (influential network relation map) revealed that the first dimension to be improved upon and adjusted should be security, followed by effort expectancy, performance expectancy, social influence, facilitating conditions, hedonic motivation and habit. The DANP influential weights suggested that habits were the most important dimension, followed by hedonic motivation, while performance expectancy was the least important. According to the results of the gap analysis, the first dimension that required improvement was performance expectancy, followed by effort expectancy, facilitating conditions, security, social influence, habits and hedonic motivation. It is expected that the findings of this study can serve as a reference for consumers selecting FDPs to better satisfy their dining needs. The novel model is discussed in terms of theoretical, practical and managerial implications.