Abstract:Consumer resistance is one of the major causes of failure of any innovation. Despite rising academic interest, the non-adoption of digital innovation or consumer resistance has received less scholarly attention as compared to the factors driving the adoption of digital products and services. The existing research on consumer resistance is also in siloes, running across multiple verticals, spanning from resistance to green products to the Internet of things (IoT). The current study provides a systematic review … Show more
“…As it is often the case with new and emerging technologies, individuals continue to have important reservations due to the significant levels of risk and uncertainty associated with those novelties (Seegebarth et al, 2019). Resistance, anxiety, and ambivalence toward new technologies represent important aspects of the success and adoption of those technologies (Talwar et al, 2020). Research showed that some people tend to prefer human recommendations over those made by statistical models (Castelo et al, 2018; Dietvorst et al, 2015; Longoni et al, 2019).…”
Section: Theoretical Framework and Predictionsmentioning
When do consumers trust artificial intelligence (AI)? With the rapid adoption of AI technology in the field of marketing, it is crucial to understand how consumer adoption of the information generated by AI can be improved. This study explores a novel relationship between number presentation details associated with AI and consumers' behavioral and evaluative responses toward AI. We theorized that consumer trust would mediate the preciseness effect on consumer judgment and evaluation of the information provided by AI. The results of five studies demonstrated that the use of a precise (vs. imprecise) information format leads to higher evaluations and behavioral intentions. We also show mediational evidence indicating that the effect of number preciseness is mediated by consumer trust (Studies 2, 4, and 5). We further show that the preciseness effect is moderated by the accuracy of AI‐generated information (Study 3) and the objective product quality of the recommended products (Study 4). This study provides theoretical implications to the AI acceptance literature, the information processing literature, the consumer trust literature, and the decision‐making literature. Moreover, this study makes practical implications for marketers of AI businesses including those who strategically use AI‐generated information.
“…As it is often the case with new and emerging technologies, individuals continue to have important reservations due to the significant levels of risk and uncertainty associated with those novelties (Seegebarth et al, 2019). Resistance, anxiety, and ambivalence toward new technologies represent important aspects of the success and adoption of those technologies (Talwar et al, 2020). Research showed that some people tend to prefer human recommendations over those made by statistical models (Castelo et al, 2018; Dietvorst et al, 2015; Longoni et al, 2019).…”
Section: Theoretical Framework and Predictionsmentioning
When do consumers trust artificial intelligence (AI)? With the rapid adoption of AI technology in the field of marketing, it is crucial to understand how consumer adoption of the information generated by AI can be improved. This study explores a novel relationship between number presentation details associated with AI and consumers' behavioral and evaluative responses toward AI. We theorized that consumer trust would mediate the preciseness effect on consumer judgment and evaluation of the information provided by AI. The results of five studies demonstrated that the use of a precise (vs. imprecise) information format leads to higher evaluations and behavioral intentions. We also show mediational evidence indicating that the effect of number preciseness is mediated by consumer trust (Studies 2, 4, and 5). We further show that the preciseness effect is moderated by the accuracy of AI‐generated information (Study 3) and the objective product quality of the recommended products (Study 4). This study provides theoretical implications to the AI acceptance literature, the information processing literature, the consumer trust literature, and the decision‐making literature. Moreover, this study makes practical implications for marketers of AI businesses including those who strategically use AI‐generated information.
“…Hence, the concept of an AI‐powered avatar will provide a competitive edge to the gamer in terms of CUST, interactivity, and enjoyment. IDT can further offer innovative characteristics to gamers who wish to adopt new technology (Rogers, 2010; Talwar et al, 2020). This study of AI‐powered avatar with IDT can give consumers insights into how they perceive this phenomenon and how ready they are to adopt it.…”
Section: Literature Review and Theoretical Frameworkmentioning
Artificial intelligence (AI) tools have altered the gaming industry, thanks to their newly incepted functionalities, which have enhanced the consumer experience. Building on innovation diffusion theory, technology acceptance model, and flow theory, this study highlights an AI‐powered avatar concept. This study explores the roles of perceived easiness, usefulness, advantage, compatibility, enjoyment, customization, and interactivity in forming the gamers' intention to play with AI‐powered avatars. A survey data of 500 respondents from China having experience playing online video games is used to test the proposed hypotheses. The results offer significant support to the proposed relationships related to adopting an AI‐powered avatar and the consumers' psychological association with its adoption. Consequently, the results imply that AI‐powered avatars should allow gamers to customize, interact, and take assistance to move up levels with an enjoyable experience. Moreover, this study also suggests that digital technologies such as AI could be integrated into the gaming environment for a more pleasing and immersive experience.
“…The present study aims to address the research gaps in the literature on the use of BDA in healthcare by conducting a systematic literature review (SLR) (Dhir et al 2020;Tandon et al 2020;Talwar et al 2020). SLRs have been recognised for their ability to summarise valuable knowledge about a topic of importance (Dhir et al 2020;Talwar et al 2020) and to guide future research on the topic (Dhir et al 2020;Talwar et al 2020). This SLR aims to address four research questions (RQs) as follows: RQ1.…”
The current study performs a systematic literature review (SLR) to synthesise prior research on the applicability of big data analytics (BDA) in healthcare. The SLR examines the outcomes of 41 studies, and presents them in a comprehensive framework. The findings from this study suggest that applications of BDA in healthcare can be observed from five perspectives, namely, health awareness among the general public, interactions among stakeholders in the healthcare ecosystem, hospital management practices, treatment of specific medical conditions, and technology in healthcare service delivery. This SLR recommends actionable future research agendas for scholars and valuable implications for theory and practice.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.