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One of the most marginalized communities in India is the Lesbian, Gay, Bisexual and Transgender (LGBT) community which commonly experiences discrimination. Many studies have countered that the LGBT community faces high discrimination in the banking and financing industry. As a result, this study concentrates on this marginalized community and its acceptance and continuation habit regarding mobile wallets. Consequently, this study has considered continuance intentions as a response to confirm the progress of the mobile-wallet industry. Also, this study tried to study the relationship between behavioral intention (BI) and continuous intention (CI) which is seriously lacks in the library of literature. The research operationalized the stimulus–organism–response (SOR) framework for the conceptual model and surveyed 100 self-proclaimed members of the LGBT community in India. The analysis has been done using the partial least structure (PLS). The findings demonstrate that variables like perceived trust (PT) directly influence the BI. On the other hand, variables like perceived ease of use (PEoU), social influence (SI), and satisfaction (S) doesn’t influence BI of the LGBT Community. The main outcome was a favorable association between BI and CI. It will help the stakeholders to understand how important this new market avenue is and how it can be explored. To ensure safe and secure transactions, a group think tank composed of important parties (financial institutions, mobile-wallet providers, the government, security specialists, etc.) should make recommendations. Mobile-wallet providers will attain benefit from this study’s understanding of user categories and ability to tailor their service offers as per the community.

The aim of this study was (1) to investigate the relationship between destination image (DI), destination personality (DP) and behavioral intention (BI) in the context of ski tourism and (2) especially the role of DP in the relationship between DI and BI among ski tourists.

We collected data using WJX.CN (N = 400) to test the hypothesized model. Confirmatory factor analysis (CFA) was used to examine the psychometric properties of the measurement model and partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses.

The results show that DI directly affects DP and partially affects BI, while DP directly affects ski tourists' BI. In addition, the indirect effect of DP between affective image and BI was significant, showing full mediation, and the indirect effect of DP between cognitive image and BI was significant, showing a partial mediation effect.

The findings enrich the ski tourism literature, contribute to the development of ski tourism in destination cities and the strategic marketing of ski resorts and provide recommendations for ski tourism researchers and marketers.

Although wearable health-monitoring technology (WHMT) has become a stimulus for public health, women’s acceptance rate of this technology appears to be low. Thus, this study intends to investigate the factors affecting women’s adoption of WHMT.

The unified theory of acceptance and use of technology–2 model has been used in this study as a research framework that has been extended to include lifestyle and attitude. The proposed extended framework is validated using primary data (n = 314) collected from female respondents using a structured questionnaire; the partial least square-based structural equation modeling technique is subsequently used to test the proposed hypothesis.

The results show that effort expectancy, social influence, price value, habit, attitude and lifestyle have significant positive effects on women’s behavioral intention to use WHMT and accelerate actual usage behavior. Notably, effort expectancy and habit exhibit the largest impact on behavioral intention. However, performance expectancy, facilitating conditions and hedonic motivation are not significantly associated with behavioral intentions.

The findings of this study are important for healthcare practitioners and service providers to comprehensively understand the factors that affect women’s behavioral intentions in line with their actual usage behavior. This insight will help policymakers design viable strategies regarding WHMT to promote its sustainable usage in least developed countries.

This study contributes novelty by using an extended model that links women’s attitudes and lifestyles to their adoption of WHMT. This study also fills the gaps in the existing literature on women’s behavioral intentions in the context of WHMT by showing novel associations in the domain of WHMT uptake.

In keeping with global developments rendering online shopping as an emerging trend among consumers, the present study extends the unified theory of use and acceptance of technology (UTAUT2) comprising the digital payment mode (DPM) as a new driver of online shopping and with the mediation of attitudes toward technology (ATTs) to gauge a better and deeper understanding of behavioral intention (BI).

This study used a survey instrument with snowball sampling from 600 consumers in northern India. Partial least squares structural equation modeling was used to find the association between drivers using UTUAT2, along with DPM and ATTs. The data were divided into a test group (20%) and validated through a training group (80%).

DPM was shown to be directly associated with BI. The mediation of ATTs was also validated through the model. The predictability of the model was 67.5% for the test group (20%) and 69.6% for the training group (80%). The results also indicated that facilitating conditions is a critical driver of BI.

This study enhances the understanding of the roles that DPM and ATTs play in BI during online shopping, suggesting that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience.

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

This paper develops and tests a theoretical model that draws on the Diffusion Contingency Model and the Theory of Human Behavior to explain the factors influencing users’ acceptance of accrual accounting in terms of two distinct dimensions: behavioral intention and usage behavior.

Based on surveyed data from financial departments and directorates of different Greek general government entities, the paper uses factor analysis to build a theoretical model that assesses the factors influencing behavioral intention to adopt and usage behavior of accrual accounting. Then, it tests the relationship between behavioral intention and usage behavior through structural equation modeling.

The theoretical model suggests that the expected improvement of the quality of financial information and political and financial support are the most important determinants of behavioral intention. Usage behavior is mainly influenced by the compatibility between the existing legal framework and the new accounting system. The structural equation modeling identifies a statistically significant positive influence of behavioral intention on usage behavior.

The study provides valuable insights regarding the timing and focus of the actions taken by policymakers when designing accounting reforms. Special attention is drawn to the factors influencing behavioral intentions, as these are found to influence usage behavior significantly.

The study extends prior research on the diffusion of accounting innovations by breaking down the diffusion process into intentions-oriented actions aiming to promote accrual accounting and increase acceptance and implementation-oriented actions aiming to facilitate successful implementation.

Financial technology (FinTech) is experiencing transformation because artificial intelligence has become the new norm to enrich the experiences of individuals in this modern era of technological advancement. The article utilizes the stimuli-organism-response (SOR) framework to investigate how individual attitudes and behavioral intentions influence the adoption of FinTech, particularly in mobile banking.

433 respondents participated in the self-administered survey to answer questions related to demographic profiles and items to assess the variables adopted in the conceptual framework. The study applied “partial least squares structural equation modeling” PLS-SEM to analyze the data.

A structural equation model indicates that perceived usefulness and ease of use significantly affect attitude and behavioral intention. Moreover, the outcomes show that perceived value and social influence significantly influence, while perceived risks and performance expectancy insignificantly affect behavioral intention. Further, the outcomes also confirm that attitude and behavioral intention substantially influence mobile banking adoption.

The article provides insights for practitioners to improve and assess the quality of mobile banking services by using proposed antecedents that may increase the actual use of FinTech services, which serves as a valuable resource for stakeholders.

The new research model adds to the existing literature by offering empirical evidence of mobile banking adoption by considering three theories. Further, the study builds upon the S-O-R framework that incorporates FinTech attributes to explain the antecedents of the actual use of FinTech towards mobile banking adoption.

Research on technology acceptance of online entertainment with age, gender and cultural factors as moderator, is rarely conducted. Previous research predominantly focused on age or gender as moderator, neglecting the influence of cultural factors. Therefore, this study aims to investigate acceptance of online entertainment technology, incorporating age, gender and cultural factors as moderator.

Data were collected through a survey comprising 1,121 individuals aged 14–24 years from three cities in Indonesia. The proposed theoretical model examined the causal effect of acceptance and moderating effects due to individual gender, age, power distance, individualism, feminism and uncertainty avoidance (AU). Subsequently, structural equation modeling was used to evaluate the theoretical model, and the results confirmed several findings from previous research.

The findings confirmed the positive direct impact of habit and price value (PV) on behavioral intention and hedonic motivation, as well as social influence on habit. The recent findings derived from the moderating effect analysis showed that age, individualism and feminism played a moderating role in the effects on individual intention due to habit. Additionally, gender and AU moderated the effects on individual habits due to hedonic motivation.

This research contributes to the limited knowledge of technology acceptance of online entertainment, and also integrates the causal effects of individual intention due to habit, PV, hedonic motivation and social influence, considering the moderating role of culture, age and gender. Consequently, the investigation provides valuable insights into the literature by presenting evidence of age, gender and cultural differences in acceptance. Furthermore, it offers practical guidance to online entertainment application developers on designing applications to satisfy consumers of different ages, genders and cultures.

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.

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.

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.

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.

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.

The opening of government data is high on the policy agenda of governments worldwide. However, data release faces barriers due to limited support of civil servants, whereas the literature neglects civil servants' role in opening data. This paper aims at understanding why civil servants can be reluctant to support the disclosure of data. The authors developed a model to explain civil servants' behavioral intention to open data.

The authors test a series of hypotheses by collecting and analyzing survey data from 387 civil servants and by applying multivariate hierarchical regression.

The results indicate the factors influencing the behavior of civil servants. Social influences, performance expectancy, data management knowledge and risks have a significant influence. Personal characteristics control these effects.

Caution is needed to generalize the findings towards the support to open data provision by civil servants. Though the analyzed sample was limited to Brazil, other countries and cultures might yield different outcomes. Larger and more diversified samples might indicate significant effects on variables not found in this research.

The insights can be used to develop policies for increasing the support of civil servants towards governmental data disclosure.

This study suggests factors of influence to civil servants' behavior intentions to disclose governmental data. It results in a model of factors, specifically for their behavioral intention at the individual level.