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Purpose ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools. Design/methodology/approach This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals. Findings This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs. Practical implications These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT. Originality/value This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions.
Purpose ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools. Design/methodology/approach This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals. Findings This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs. Practical implications These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT. Originality/value This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions.
Purpose This study aims to empirically examine and analyze the factors that influence the adoption of blockchain technology, particularly within small and medium-sized enterprises (SMEs). The study also predicts how adopting blockchain technology may affect SMEs’ market and financial performance. Design/methodology/approach The research is grounded in the theoretical frameworks of the “technology–organization–environment (TOE) framework” and the “resource-based view (RBV)” perspective. The researchers collected 407 responses from a survey conducted on SMEs in India. The statistical package for social science, followed by the “partial least square structural equation modeling (PLS-SEM)” technique, was applied for the data analysis. Findings This paper offered a robust research framework for blockchain technology adoption in which one of the two proposed technological factors (relative advantage), one organizational factor (top management support) and two environmental factors (competitive pressure and market dynamics) significantly influence blockchain technology adoption. Similarly, there is a substantial association between blockchain technology adoption and both market and financial performance. More specifically, the complexity and perceived investment cost have been recognized as barriers to SMEs adopting blockchain technology. Research limitations/implications The primary focus of this research lies in examining the adoption of blockchain technology among SMEs in India. Consequently, there exists an opportunity to broaden the scope of this study to include various other countries. Such an expansion holds the potential to yield more precise and comprehensive results, enabling a comparative analysis across diverse international contexts. Practical implications The outcomes have practical significance for SMEs as they navigate their strategies for adopting blockchain technology. Moreover, policymakers and practitioners can use these findings to enact specific measures targeting barriers, fostering the adoption of blockchain in Indian SMEs and creating a more supportive environment for technological integration and growth. Originality/value This study introduces a novel theoretical framework focusing on the impact of blockchain adoption on SMEs. Its distinctive contribution lies in investigating the mediating role of blockchain adoption in the relationship between market and financial performance, specifically within emerging economies. By addressing this gap, the study enhances the understanding of how blockchain adoption shapes SME performance in evolving economic landscapes.
PurposeThe study explores the impact of social media-induced social comparison on impulse travelling, drawing upon social comparison theory. It thoroughly examines the intermediary functions of fear of missing out (FoMO) and compulsive use of social media, alongside exploring the moderating impacts of self-esteem and self-control within this dynamic process.Design/methodology/approachTo meet the objective, we conducted a survey of 382 social media users among Indian millennial tourists. The analysis has been done using SPSS (AMOS 24) and Process macro (model 1) for moderation effect. Purposive and snowball sampling techniques have been employed for data collection.FindingsThe results indicate a positive influence of social comparison on impulsive travel. Additionally, the findings suggest that FoMO and the compulsive use of social media serve as serial mediations on the link between social comparison and impulse travelling. Moreover, self-esteem has shown a negatively significant relationship between social comparison and FoMO. Furthermore, self-control has also been found to have a negatively significant effect on the relationship between FoMO and the compulsive use of social media.Practical implicationsThe study’s findings offer valuable guidance for destination administrators. It suggests that administrators should refrain from engaging in aggressive and overly tailored marketing tactics. Instead, they should focus on sharing real and authentic stories that resonate with travellers, and administrators can mitigate the effects of social comparison and discourage impulsive travelling.Originality/valueThis study delves into an unexplored realm in digital marketing literature, shedding light on how social comparison on social media influences the impulsive travelling of Indian millennial tourists. This study is an inaugural attempt to formulate a theoretical framework within the scope of the tourism sector.
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