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 This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots. Practical implications This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.

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.

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PurposeHuman resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.Design/methodology/approachThis study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.FindingsThis research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.Practical implicationsThis paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.Originality/valueThis research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.