Purpose: This paper aims to highlight the importance of Smart Human Resources 4.0 (Smart HR 4.0) and its role as a catalyst in the disruption process in the human resource domain. This paper illustrates the advantages of Smart HR 4.0 in the HR domain by using the example of Credit Suisse, which has extensively used people analytics to reduce employee attrition. Design/methodology/approach: The paper discusses the role of Smart HR 4.0 as a disruptor in the human resource domain. With the help of the Smart HR 4.0 conceptual framework, this paper illustrates how Smart HR 4.0 disrupts the talent on-boarding, talent development, and talent off-boarding process. Findings: An organization would require a successful Smart HR 4.0 strategy to cope up with the challenges of Industry 4.0 transformation. Emerging technologies such as Internet-of-Things, Big Data, and artificial intelligence will automate most of the HR processes, resulting in efficient and leaner HR teams. Both organization structure and leadership style changes would be required for efficient Smart HR 4.0 implementation that would allow HR departments to play a more strategic role in the overall organization growth. Originality/value: This paper contributes to the existing literature and body of knowledge in the HR domain by developing a Smart HR 4.0 conceptual framework. This paper discusses how Smart HR 4.0 acts as a catalyst in the disruption of talent ion-boarding, talent development, and talent off-boarding process with the help of emerging technologies and change in the employee generation.

Purpose: This study aims to investigate the actual usage (AU) of digital payment systems by the consumers during the period of demonetization (from November 9, 2016 to December 30, 2016) in India.Design/methodology/approach: The conceptual frame work for this study is based on the unified theory of acceptance and use of technology (UTAUT 2) and innovation resistance theory. A total of 766 sample respondents were surveyed using a pre-tested questionnaire. The empirical validation of the framework and analysis was done using partial least squares (PLS)-structural equation modeling (SEM) technique.Findings: The results suggest that the behavioral intention (BI) to use and innovation resistance (IR) affect the usage of digital payment systems. The relation between BI to use digital payment systems and the AU of digital payment systems is moderated by the stickiness to cash payments.Research limitations/implications: This cross-sectional study is limited by geographic constraints and highlights the AU of digital payment systems by using the UTAUT 2 and IR theory only during the demonetization period.Practical implications: This study offers valuable insights to the economists, policymakers and digital payment service providers regarding the usage of digital payment systems by consumers during demonetization.Originality/value: This study assumes importance as it empirically examines the influence of BI and IR on the AU of digital payment systems during the demonetization period in India. This study empirically validates the moderating influence of stickiness to cash payments on the AU of digital payment systems.

Purpose: 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.Design/methodology/approach: 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.Findings: 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.Practical implications: 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.Originality/value: 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.

Purpose: AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.Design/methodology/approach: The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.Findings: This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.Originality/value: This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.