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The primary objective of this research is to explore the impact of the repeated two-syllable communication strategy on the interaction effectiveness between AI and customers.

This study adopts an experimental research methodology to investigate the role of the repeated two-syllable communication strategy employed by AI customer service agents.

Study 1 shows that AI agents using the repeated two-syllable strategy enhance the interaction effectiveness between AI and customers. Study 2 identifies humanization perception as a key factor linking the strategy to better interaction effectiveness. Study 3 highlights how consumer materialism moderates this effect, while Study 4 examines how the type of agent (AI vs. human) influences the results.

This study extends the application of AI communication strategies in interactive marketing, specifically how AI agents enhance consumer interaction through repeated two-syllable communication. It pioneers the exploration of AI-human interaction, enriching the humanization theory by revealing how AI can evoke emotional responses. The study also integrates consumer materialism as a moderating factor, offering new theoretical and practical insights for brands to optimize AI-customer service interactions and improve engagement in real-world marketing contexts.

The study aims to examine the influence of Unified Theory of Acceptance and Use of Technology (UTAUT) and anthropomorphic design cues in determining the level of satisfaction among banking chatbot users. It also tests the moderating impact of the localization of content on the relationship. The study also encompasses expectation confirmation, elucidating the significance of perceived trust in maintaining intention.

The study conducted a comprehensive online survey, collecting 667 questionnaires from users of conversational chatbots in both public and private sector banks. We analyse the data using Partial Least Squares Structural Equation Modelling and fuzzy-set Qualitative Comparative Analysis.

Performance and effort expectancy, perceived interestingness of interaction and perceived empathy were identified as significant indicators, whereas facilitating conditions, social influence and perceived intelligence were not significant in explaining satisfaction. Perceived trust was a significant mediator, while localization was a significant moderator in all the cases except social influence and satisfaction.

To improve perceived intelligence and empathy, tech developers should focus on improving the chatbot’s ability to maintain contextual understanding within a conversation where it can remember and reference previous interactions. Future studies might explore the development of banking chatbots that incorporate advanced levels of anthropomorphic characteristics, whether visual or intuitive.

The work is unique in that it integrates UTATUT, anthropomorphism and expectation confirmation model in the context of conversational banking chatbots, which is not achievable in a single theory-based model. The study also underlined the necessity of localizing chatbot content, recommending that banks engage localized native speakers to help with chatbot training and content creation, where specialists can fine-tune the conversational features.

The purpose of this paper is to improve the air movement operations planning heuristic in the literature to generate better solutions in a shorter time period.

Through a rigorous design of experiments (DOEs), we make significant heuristic improvements by evaluating alternative modular methodologies and tuning heuristic parameters for two scenarios. This includes a new approach to considering refueling operations.

We find the fine-tuned heuristic averages a 33% objective improvement and 70% reduction in computation time over the heuristic with original parameters for one of the scenarios. Additionally, we analyze the heuristic's quality of solution over time.

Further analysis is required to generalize heuristic settings, which would require significant access to operational data or a portfolio of scenarios of interest.

Tuned heuristic parameters reduce the computation time from hours to minutes. This also makes it practically feasible to adjust parameters in the objective function to generate multiple courses of action (COAs) for a given instance.

This research provides novel vehicle assignment and routing heuristic improvement alternatives and demonstrates a DOEs-based heuristic tuning procedure.

The purpose of this study is to examine the effect of trust on user adoption of artificial intelligence-generated content (AIGC) based on the stimulus–organism–response.

The authors conducted an online survey in China, which is a highly competitive AI market, and obtained 504 valid responses. Both structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis.

The results indicated that perceived intelligence, perceived transparency and knowledge hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in platform. Both cognitive trust and affective trust in platform lead to trust in AIGC. Algorithm bias negatively moderates the effect of cognitive trust in platform on trust in AIGC. The fsQCA identified three configurations leading to adoption intention.

The main limitation is that more factors such as culture need to be included to examine their possible effects on trust. The implication is that generative AI platforms need to improve the intelligence, transparency and empathy, and mitigate knowledge hallucination to engender users’ trust in AIGC and facilitate their adoption.

Existing research has mainly used technology adoption theories such as unified theory of acceptance and use of technology to examine AIGC user behaviour and has seldom examined user trust development in the AIGC context. This research tries to fill the gap by disclosing the mechanism underlying AIGC user trust formation.

In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots.

For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk

The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria.

Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study.

The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly.

Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base.

As an unhealthy dependence on social media platforms, social media addiction (SMA) has become increasingly commonplace in the digital era. The purpose of this paper is to provide a general overview of SMA research and develop a theoretical model that explains how different types of factors contribute to SMA.

Considering the nascent nature of this research area, this study conducted a systematic review to synthesize the burgeoning literature examining influencing factors of SMA. Based on a comprehensive literature search and screening process, 84 articles were included in the final sample.

Analyses showed that antecedents of SMA can be classified into three conceptual levels: individual, environmental and platform. The authors further proposed a theoretical framework to explain the underlying mechanisms behind the relationships amongst different types of variables.

The contributions of this review are two-fold. First, it used a systematic and rigorous approach to summarize the empirical landscape of SMA research, providing theoretical insights and future research directions in this area. Second, the findings could help social media service providers and health professionals propose relevant intervention strategies to mitigate SMA.

Chatbots are increasingly embodied in business and IS contexts to enhance customer and user experience. Despite wide interest in chatbots among business and IS academics, surprisingly, there are no current comprehensive reviews to reveal the knowledge structure of chatbot research in such areas.

This study employed a mixed-method approach that combines systematic review and bibliometric analysis to provide a comprehensive synthesis of chatbot research. The sample was obtained in December 2023 after searching across six databases: EBSCOhost, PsycINFO, Web of Science, Scopus, ACM Digital Library and IEEE Computer Society Digital Library.

This study reveals the major trend in publication trends, countries, article performance and cluster distribution of chatbot research. We also identify the key themes of chatbot research, which mainly focus on how users interact with chatbots and their consequences, such as users’ cognition and behavior. Moreover, several important research agendas have been discussed to address some limitations in the current chatbot research in business and IS fields.

The present review is one of the first attempts to systematically reveal the ongoing knowledge map of chatbots in business and IS fields, which makes important contributions and provides useful resources for future chatbot research and practice.