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.

The surge in digital transactions has led to a notable spike in identity theft cases, incurring enormous expenses for e-commerce companies and users. The safety of online businesses and their users depends on addressing identity theft. We utilise natural language processing to examine Reddit posts related to identity theft to determine the elements influencing individuals' concerns about identity theft when transacting online. We match these factors with theoretical lexicons to create a unique framework. Further, to extend the study, we intend to validate these factors through statistical analysis to understand their impact on users' fear of identity theft during online transactions.

The proliferation of fake news across the internet has become a significant area of concern globally. The COVID-19 pandemic highlights that the propagation of fake news can jeopardize public health and heighten irrational behavior amongst consumers, like panic buying. However, the existing literature has not explored its impact on the supply chain. This study uses reactance and cognitive load theories to examine a model for fake news propagation causing supply chain disruption. Our research employed a computationally intensive big data-driven method across three studies to demonstrate misinformation's impact on supply chain disruption, identify the factors creating this impact, and validate an inferential analysis model to explain this phenomenon. Results highlight the relationship between unverified information sharing (UIS) and perceived threat, perceived scarcity, fear appeal, and information overload with panic buying. The paper dwells more profoundly on fake news disrupting the supply chain.

eXplainable Artificial Intelligence (XAI) has attracted researchers in various domains over the last few years. Explainable AI includes the explainability in the AI systems which capable of explaining their decisions. This study performs a systematic literature review on XAI. In the first phase, we collected 78 high-quality web of science research journal papers from the Scopus data. It revealed that IEEE access and Expert systems with applications are the main targeted journals for researchers for XAI. Our study applies an Apriori algorithm and network analysis to get the dominant theme and check the connectivity among the methods/techniques respectively. The analysis showed that Robotics, Financial Services, Healthcare, Banking, Security, and business are the most dominant areas where XAI provides an explainability to the artificial intelligence (AI) systems. Based on our analysis, this literature review provides a future direction for researchers, academicians, and industrialists.

The customers use social media platforms to share their grievances and unresolved concerns about a product or service. This behaviour was rampant during the ongoing pandemic, COVID-19. The airline industry could not handle the uncertainties and manage the customer distress. The extant research on how airlines could address social media grievances needs further enrichment. The present paper presents a model of low-cost carriers (LCCs) response to social media customer complaints. It uses content analysis, followed by logistic regression for the model verification. Results highlighted that the type of complainer, emotions, lockdown situation, complain text, and complain concerns can impact the firm's response. The paper contributes to understanding firms' responses to social media customer complaints.