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The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

The recent COVID-19 pandemic affects all segments of the population in all possible fields of life. To encounter this critical situation and to safeguard the health of the library users & library staff, the library services have been forced to adapt and accept the “new normal” and give a greater use and reliance on virtual space than ever before with physical spaces. This paper aims to demonstrate an artificial intelligence (AI)-driven solution that can be practically implemented in the form of a “InfoBot” or “Chatbot” to fulfil user needs 24/7 with minimal or without human intervention.

To give a general overview of the use of AI Chatbot in libraries and its multitasking features followed by the practical implementation, a versatile AIchatbot service, Engati is used as a target bot with its basic free plan.

The study findings reveal that AI Chatbots offer a dependable solution for the libraries to initiate virtual assistance, thus augmenting the reference service while adding a newer dimension to virtual reference service.

Innovative and intelligent information service would be overcoming the time and location barriers reaffirming the concept of “library without walls” while falling in line with the five laws of library science as propounded by Dr S.R. Ranganathan, the father of library science in India.

Though the implementation of bots and chatbots is not a purely novel concept to explore, no such research has been conducted focusing particularly on its exceptional benefits in the library in reference to the recent pandemic.

Purpose: The main aim is to explore and recognize productive ways to create human-centred designs (HCDs) for employee experience (EX). HCD is a concept that prioritizes the needs, preferences, and behaviours of humans using the product or service. EX refers to all interactions an employee has with their employment lifespan – from recruitment to retirement. By taking the HCD approach to EX design, companies can create a work environment tailored to their employees’ needs and preferences.

Design / Methodology: The explorative research design to apply journey maps. By mapping out the employee journey, designers can identify pain points and areas for improvement.

Findings: The findings highlight that artificial intelligence and robotics are core components of designing HCD and can be applied to EX design. By prioritizing EX, companies can attract and retain top talent, increase employee engagement and productivity, and gain a competitive advantage.

Research Limitations: The study is developing and involves detailed insights from different companies, making it difficult and time-consuming to prepare a comprehensive report.

Practical Implications: The findings of the study will add value to other organizations to follow and develop policies and practices that make the employees cherish their work.

Originality: The chapter’s originality lies in providing a comprehensive understanding of HCD and EX. It emphasizes leveraging the strengths of both humans and bots for enhanced workforce experience and business growth. Exploring future automation and technology integration trends adds depth to the chapter’s contribution.

This study defines a three-angled research plan to intensify the knowledge and development undergoing in the retail sector. It proposes a theoretical framework of the customer journey to explain the customers' intent to adopt artificial intelligence (AI) and machine learning (ML) as a protective measure for interaction between the customer and the brand.

This study presents a research agenda from three-dimensional online search, ML and AI algorithms. This paper enhances the readers' understanding by reviewing the literature present in utilizing AI in the customer journey and presenting a theoretical framework.

Using AI tools like Chatbots, Recommenders, Virtual Assistance and Interactive Voice Recognition (IVR) helps create improved brand awareness, better customer relationships marketing and personalized product modification.

This study intends to identify a research plan based on investigating customer journey trends in today's changing times with AI incorporation. The research provides a novel model framework of the customer journey by directing customers into different stages and providing different touchpoints in each stage, all supported with AI and ML.

Social media is becoming a hub of fake content, be it political news, product reviews, business promotion or any other sociocultural event. This study aims to provide a comprehensive review of the emerging literature to advance an understanding of misinformation on social media platforms, which is a growing concern these days.

The authors curate and synthesize the dispersed knowledge about misinformation on social media by conducting a systematic literature review based on the preferred reporting items for systematic reviews and meta-analyses framework. The search strategy resulted in 446 research articles, out of which 33 relevant articles were identified for this research.

Misinformation on social media spreads swiftly and may result in negative consequences. This review identifies 13 intrinsic predictors of the dissemination, 11 detection approaches and 10 ways to combat misinformation on social media.

The study adds to the present knowledge of spread and detection of misinformation on social media. The results of this study will be beneficial for researchers and practitioners and help them in mitigating the harmful consequences of the spread of misinformation.

Generative AI and more specifically ChatGPT has brought a revolution in the lives  of people by providing them with required knowledge that it has learnt from an exponentially large knowledge base. In this viewpoint, we are initiating the debate and offer the first step towards Generative AI based knowledge management systems in organizations.

This study is a viewpoint and develops a conceptual foundation using existing literature on how ChatGPT can enhance the KM capability based on Nonaka’s SECI model. It further supports the concept by collecting data from a public sector univesity in Hong Kong to strenghten our argument of ChatGPT mediated knowledge management system.

We posit that all four processes, that is Socialization, Externalization, Combination and Internalization can significantly improve when integrated with ChatGPT. ChatGPT users are, in general, satisfied with the use of ChatGPT being capable of facilitating knowledge generation and flow in organizations.

The study provides a conceptual foundation to further the knowledge on how ChatGPT can be integrated within organizations to enhance the knowledge management capability of organizations. Further, it develops an understanding on how managers and executives can use ChatGPT for effective knowledge management through improving the four processes of Nonaka’s SECI model.

This is one of the earliest studies on the linkage of knowledge management with ChatGPT and lays a foundation for ChatGPT mediated knowledge management system in organizations.

This paper aims to document how AI has changed the way consumers make decisions and propose how that change impacts services marketing, service research and service management.

A review of the literature, documentation of sales and customer service experiences support the evolution of bot-driven consumer decision-making, proposing the bot-driven service platform as a key component of the service experience.

Today the focus is on convenience, the less time and effort, the better. The authors propose that AI has taken convenience to a new level for consumers. By using bots as their service of choice, consumers outsource their decisions to algorithms, hence give little attention to traditional consumer decision-making models and brand emphasis. At the moment, this is especially true for low involvement types of decisions, but high involvement decisions are on the cusp of delegating to AI. Therefore, management needs to change how they view consumers’ decision-making-processes and how services are being managed.

In an AI-convenience driven service economy, the emphasis needs to be on search ranking or warehouse stock, rather than the traditional drivers of brand values such as service quality. Customer experience management will shift from interaction with products and services toward interactions with new service platforms such as AI, bots. Hence, service marketing, as the authors know it might be in decline and be replaced by an efficient complex attribute computer decision-making model.

The change in consumer behavior leads to a change in the service marketing approach needed in the world of AI. The bot, the new service platform is now in charge of search and choice for many purchase situations.

The importance of mental wellbeing and the need for organizations to address it is increasing in the post-pandemic context. Although Artificial Intelligence (AI) is increasingly being adopted in HRM functions, its adoption and utility for enabling mental wellbeing is limited. Building on the Open System Theory (OST) and adopting the technology-in-practice lens, the authors examined the roles of human and technology agencies in enabling mental wellbeing.

The study was conducted in two stages; in Stage 1, the authors adopted a case methodology approach to examine the feasibility of a technology company's offerings to assess mental wellbeing. In Stage 2, the authors followed the grounded theory approach and interviewed 22 key stakeholders and HR leaders of diverse organizations. The authors used Gioia's approach to analyze the data.

The study demonstrates the interdependence and inseparability of human activity, technological capability and structured context. Specifically, the authors observe that AI adoption is pushing the boundaries of how organizations could support employees' mental health and wellbeing. These technological advancements and adoption are likely to facilitate the evolution of agentic practices, routines and structures.

This study carries two important implications. While the advent of cutting-edge technologies appears to affect employees' mental wellbeing, the study findings indicate the assistive role of technology in supporting mental wellbeing and facilitating changes in organizational practices. Second, the ontology of technology-in-practice shows how human–machine agencies gain newer relevance from the interactions that unite them. Specifically, per OST, technology (from an external context) can potentially change how mental wellbeing practices in organizations are managed. The authors extend the existing literature by suggesting that both human agents and internal contexts effectively limit the potential of technology agents to change existing structures significantly.

The authors address the need for more research on the technology-management interface, and the boundaries of technology-enabled wellbeing at work. While AI-HRM scholarship has primarily relied on micro-level psychological theories to examine impact and outcomes, the authors borrow from the macro-level theories, such as the OST and the technology-in-practice to explain how AI is shifting the boundaries of human and machine agencies for enabling mental wellbeing.

This viewpoint paper calls in to question the current design approach to personal artificial intelligence (AI) assistance in support of everyday professional communications, where a bot emulates a human in this role. It aims to stimulate fresh thought among designers and users of this technology. It also calls upon scholars to more widely share incidental insights that arise in their own encounters with such new AI.

The paper employs a case of an email exchange gone wrong to demonstrate the current failings of personal AI assistance in support of professional communications and to yield broader insights into bot design and use. The viewpoint is intended to provoke discussion.

From the case, it is indicated that industrial-strength personal AI assistance is not here yet. Designing a personal AI assistant to emulate a human is found to be deeply problematic, in particular. The case illuminates what might be called the problem of blinded agency, in performative contexts where human, robotic and organizational identities are at least partially masked and actions, inactions and intentions can too easily disappear in a thick fog of digital exchange. The problem arises where parties must act in contexts not known to each other, and where who is responsible for what in a mundane exchange is obscured (intentionally or not) by design or by actions (or inactions) of the parties. An insight is that while humans act with a sense of agency to affect outcomes that naturally invoke a corresponding sense of responsibility for what transpires, bots in social interaction simply act and feign responsibility as they have no sense of it beyond their code and data. A personal AI assistant is probably best designed to communicate its artificiality clearly. Missing today are distinctive social conventions for identifying machine agency in everyday interactions as well as an accepted etiquette for AI deployment in these settings.

As a viewpoint contribution, the paper's value is as a stimulant to discussion of alternate approaches to design and use of personal AI assistance in professional communications and where we should be going with this. The presented case of an email exchange gone wrong is simple on the face of it but reveals in its examination a number of complexities and broader insights.