Chatbot research in the fields of business and information systems: a systematic review and bibliometric analysis

Sharma N., Arora M., Tandon U., Mittal A.

Purpose This study aims to conduct a comprehensive analysis of the current body of existing literature on chatbots and online shopping. Additionally, this study identifies and emphasize the future research agenda and emerging trends within this domain. Design/methodology/approach A thorough investigation was conducted on a set of 147 publications sourced from the Scopus database spanning the years 2016 to 2023 by using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. The analysis included bibliometric techniques through VOSviewer, including science mapping and performance analysis of the literature under investigation. Findings The findings of the study indicate a systematic impression of prevailing scientific research on integration of Chatbot in online shopping. A majority of publications were contributed by developing countries specifically Asian regions. There has been a notable rise in research collaborations over the course of time. Further, themes were identified through keyword co-occurrence for exploration of future trends in the domain. Practical implications This study identifies and analyzes the patterns in the existing literature on chatbot and online shopping, with the objective of enhancing e-retailers comprehension of this particular topic area. The research findings hold significance for both researchers and organizations in their efforts to enhance strategy design. Originality/value This study uses bibliometric analysis to examine the literature on chatbots and online shopping, aiming to develop a systematic comprehension of the research field. This study makes a valuable contribution to the current scholarly discourse and provides support for future scholars in their investigations.

Chang T., Hsiao W.

PurposeThe rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.Design/methodology/approachIn this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.FindingsThe results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.Originality/valueThis study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

Pham H.C., Duong C.D., Nguyen G.K.

The recent surge in AI technologies, like ChatGPT, has sparked significant interest in their potential to revolutionize various industries, with the travel and tourism sector at the forefront. AI-driven chatbots now handle various tasks, from processing orders to providing tailored recommendations within hospitality and tourism. However, understanding what drives tourists to adopt ChatGPT for travel services has remained limited. Drawing on the Stimulus-Organism-Response model, a sample of 606 participants recruited in the crowded tourist destinations in Vietnam using a systematic sampling approach, the findings indicate the impact of anthropomorphic stimuli (perceived warmth, communication speed, and perceived competence) on tourists' cognitive organisms (trust in ChatGPT and attitude towards ChatGPT), which, in turn, influence their behavioral responses (satisfaction and continuance usage intentions of ChatGPT for travel services). Simultaneously, it also reveals the negative moderating effect of technology anxiety on the satisfaction-continuance usage intentions relationship. From a practical standpoint, these findings can potentially guide practitioners and marketers in leveraging ChatGPT's advantages within the hospitality and tourism industry.

Sarraf S., Kar A.K., Janssen M.

Chatbots are radically redefining the customer service landscape. With the advent of AI-enabled chatbots, like ChatGPT, organizations are adopting chatbots to provide better customer services; however, the user experience has been given less attention. Building on IS success model and cognitive absorption theory, we posit that system and user characteristics enhance cognitive absorption amongst users, such that the relationship varies between anthropomorphic (e.g., human-like) and non-anthropomorphic chatbots. We undertook a cross-sectional comparative study, which was analyzed using PLS-SEM and fsQCA. Where PLS-SEM provided limited inferential insights about the differences between anthropomorphic and non-anthropomorphic chatbots, the FsQCA analysis resulted in three configurations of attributes for non-anthropomorphic and two configurations for anthropomorphic chatbots, which lead to higher cognitive absorption. The findings extend the existing literature, suggesting that anthropomorphic and non-anthropomorphic chatbots impact cognitive absorption through separate system and user characteristics configurations.

Yu S., Zhao L.

The prevalence of chatbots in human–computer communication has significantly increased. Emojis, as a form of emotional disclosure, have gained significant attention for their potential to boost chatbot service satisfaction. However, how and when emoji usage can increase satisfaction toward chatbots is not fully examined. This paper aims to fill this gap and contribute to the rapidly evolving field of human-chatbot communication research. Through three experiments, this paper investigates and explores the role of emojis in enhancing chatbot interactions. The results reveal that emojis heighten chatbot's perceived warmth but do not necessarily augment their competence. This warmth promoting effect leads to boosted service satisfaction and is more apparent when chatbots serve hedonic purposes and are pre-programmed rather than highly autonomous. However, the warmth upshot of emojis is not as potent for chatbots as it is for humans. While this study unravels the intricate pathway of how emojis augment service satisfaction, it also extends the dialogue of the Stereotype Content Model (SCM) and propels the new wave of the Computers Are Social Actors (CASA) paradigm. Thus, this research lays down pathways for further studies in understanding the role of emotionally simulated interactions in automated technologies.

Alsharhan A., Al-Emran M., Shaalan K.

Niu B., Mvondo G.F.

This research aims to explore the determinants of users' satisfaction and loyalty towards ChatGPT while also investigating ethical concerns related to the usage of the artificial intelligence (AI) chatbot. For this purpose, the study develops a framework based on five models and theories (information system success, technology acceptance model, affinity theory, coolness theory, and posthumanism) as well as other important constructs (user ethical perceptions and user ethical beliefs). Analysis of data collected from 456 actual ChatGPT users in the US reveals several key findings. First, information quality significantly and positively affects users' satisfaction, perceived usefulness, and coolness. Second, perceived usefulness, coolness, technology affinity, and posthuman ability also have a positive impact on users' satisfaction, which subsequently influences their loyalty to the AI chatbot. Furthermore, the findings demonstrate that user ethical perceptions and beliefs negatively moderate the relationship between satisfaction and loyalty. The main implication of this research is that brand managers and programmers should regularly assess the chatbot's performance to ensure that the information provided is relevant, reliable, concise, and delivered promptly. This is because users highly value the quality of information delivered by the AI chatbot. Additionally, they should prioritize the ethical aspect, as it directly influences users' satisfaction and loyalty towards the chatbot services.

Sharma S., Singh G., Islam N., Dhir A.

Developments in artificial intelligence (AI) have led to the emergence of new technologies offering unique business opportunities. This article examines the factors influencing AI-based chatbot implementation by small and medium enterprises (SMEs). We grounded the article's conceptual model in the technology–organization–environment (TOE) framework. Employing a quantitative research methodology, we collected data from 292 SME respondents via an online survey. We then utilized covariance-based structural equation modeling to analyze the data. The empirical results reveal that perceived employee capability, perceived availability of financial support, perceived top management support, perceived cost, perceived complexity, and perceived relative advantage are positively associated with SMEs' AI-based chatbot adoption intention. This article, thus, contributes to the scarce literature on the adoption of AI-based chatbots for SMEs in developing small island countries. The findings provide meaningful insights to developers, marketers, and SMEs to enhance firms’ performance and competitiveness by increasing the adoption of AI-based chatbots.

Martijn G., Van Hooijdonk C., Kunneman F., Hoeken H.

De Freitas J., Uğuralp A.K., Oğuz‐Uğuralp Z., Puntoni S.

AbstractChatbots are now able to engage in sophisticated conversations with consumers. Due to the ‘black box’ nature of the algorithms, it is impossible to predict in advance how these conversations will unfold. Behavioral research provides little insight into potential safety issues emerging from the current rapid deployment of this technology at scale. We begin to address this urgent question by focusing on the context of mental health and “companion AI”: applications designed to provide consumers with synthetic interaction partners. Studies 1a and 1b present field evidence: actual consumer interactions with two different companion AIs. Study 2 reports an extensive performance test of several commercially available companion AIs. Study 3 is an experiment testing consumer reaction to risky and unhelpful chatbot responses. The findings show that (1) mental health crises are apparent in a non‐negligible minority of conversations with users; (2) companion AIs are often unable to recognize, and respond appropriately to, signs of distress; and (3) consumers display negative reactions to unhelpful and risky chatbot responses, highlighting emerging reputational risks for generative AI companies.

Chang J.Y., Cheah J., Lim X., Morrison A.M.

Park A., Lee S.B.

Heyder T., Passlack N., Posegga O.

AI-based technologies have changed the nature of the symbiosis between humans and AI, and so strategic management of human-AI interaction in organizations requires deeper ethical considerations. Aligning AI with human values requires a systematic understanding of the ethical management of human-AI interaction. We conduct a theoretical review, from a sociotechnical perspective, and analyze ethical management of human-AI interaction through the lens of sociomateriality. Our systematic approach helps explain and clarify the interdependencies between two ethical perspectives – duty and virtue ethics – in sociotechnical systems. We also provide a theoretical framework that leads to seven avenues for future research.

Hyun Baek T., Kim M.

Few studies have examined user motivations to use generative artificial intelligence (AI). This research aims to address this gap by examining how user motivations for ChatGPT usage affect perceived creepiness, trust, and the intention to continue using AI chatbot technology. The findings of an online survey (N = 421) reveal a negative relationship between personalization and creepiness, while task efficiency and social interaction are positively associated with creepiness. Increased levels of creepiness, in turn, result in decreased continuance intention. Furthermore, task efficiency and personalization have a positive impact on trust, leading to increased continuance intention. The results contribute to the field of human–computer interaction by investigating the motivations for utilizing generative AI chatbots and advancing our comprehension of AI creepiness, trust, and continuance intention. The practical ramifications of this research can inform the design of user interfaces and the development of features for generative AI chatbots.

Lee S.E., Ju N., Lee K.

This study identified the research trends and intellectual structure of chatbots, through chatbot-related articles to suggest a future research agenda. Systematic literature reviews were performed on 386 articles from the Web of Science database. The intellectual structure investigated major articles and research topics, wherein the research gap and agenda were identified by analyzing keywords. Research on chatbots has been rapidly increasing since 2021, and is being conducted based on the theory of technology adoption. Althrough the bias of chatbots as well as issues related to ethics and security were treated as important topics in newspaper articles, studies were found to be insufficient. As a research variable, there have been many studies verifying the effect of chatbot humanness. However, studies on individual factors and strategies that influence the adoption and proliferation of chatbots are insufficient.