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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.

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.

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.

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.

This study aims to explore the impact of financial technology (fintech) and behavioral intention on financial inclusion, specifically focusing on the role of digital marketing as a mediator.

Using a quantitative research design, this study collected data from 638 respondents in the province of Punjab, Pakistan to investigate the relationship between variables.

The results indicate that both behavioral intention and fintech have a positive and favorable effect on financial inclusion. Furthermore, the study reveals that digital marketing acts as a mediating factor between financial inclusion and both behavioral intention and fintech. These findings underscore the significance of using effective digital marketing strategies to facilitate financial inclusion through fintech platforms. Policymakers should prioritize the adoption of fintech innovations and supportive regulatory frameworks while implementing comprehensive digital marketing strategies to promote financial inclusion.

This research contributes to the existing body of literature by presenting empirical evidence that highlights the interconnectedness of fintech, behavioral intention, digital marketing and financial inclusion. By harnessing the potential of fintech and digital marketing, financial institutions can bridge the gap between underserved populations and formal financial services, thereby promoting economic growth and reducing inequality.

This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.

Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.

The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.

This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.

First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.

Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve creating customized ChatGPT solutions and actively engaging in training and development programs to empower employees in effectively using ChatGPT’s capabilities. Such initiatives can contribute to improved customer service and overall operations within the tourism industry.

This study integrates TPB with perceived risks in ChatGPT, thus providing empirical evidence. It highlights the importance of considering perceived risks in tourists’ intentions and contributes to the sustainable development of generative AI in tourism. As such, it provides valuable insights for practitioners and policymakers.

本研究旨在调查游客对使用ChatGPT获取旅游信息的态度和意向。此外, 通过将与ChatGPT相关的感知风险与计划行为理论（TPB）相结合, 本研究探讨了三种感知风险（隐私风险、准确性风险和过度依赖风险）对游客行为意向的影响。

本研究通过两个在线调查平台收集了536名受访者的数据。在线调查问卷评估了游客对ChatGPT使用的感知风险、态度、主观规范、感知行为控制、行为意向以及与其使用ChatGPT相关的人口统计信息。

结构方程建模分析显示, 游客对使用ChatGPT搜索旅游信息的相关风险表示关切, 特别是隐私风险、准确性风险和过度依赖风险。发现感知风险显著影响游客对使用ChatGPT的态度和意向, 与先前有关游客对ChatGPT感知风险的文献中提出的假设一致。

本研究将TPB与ChatGPT中的感知风险相结合, 提供了实证证据。它强调了在考虑游客意向时考虑感知风险的重要性, 并为旅游中生成AI的可持续发展提供了贡献。因此, 它为从业者和政策制定者提供了宝贵的见解。

This study aims to investigate the impact of service quality (SQ), perceived value (PV) and consumer satisfaction on Thai street food, with customer satisfaction (CS) as a mediator for customer loyalty and repurchase intention (RI). It also explores how consumer trust (CT) in Thai street food safety moderates these relationships.

Structural equation modelling (SEM) was utilised to analyse the complex interrelationships between various constructs. Multi-group analyses were conducted to investigate the moderating effects of CT on the structural model, considering two distinct groups based on trust levels: low and high.

The findings revealed that SQ and PV significantly influenced CS and behavioural intention, while the perceived quality of Thai street food had no significant impact on post-COVID-19 consumer satisfaction. The study highlighted the critical role of CT in moderating the relationships between SQ, PV and CS, with distinct effects observed in groups with varying trust levels.

The research emphasises the importance of enhancing SQ and delivering value to customers in the context of Thai street food, which can contribute to increased CS, RI and positive word-of-mouth. Furthermore, the study underscores the critical role of building CT in fostering enduring customer relationships and promoting consumer satisfaction and loyalty.

This research offers valuable insights into consumer behaviour and decision-making processes, particularly within the realm of Thai street food. It underscores the significance of understanding and nurturing CT, especially in the post-COVID-19 landscape, emphasising the need for effective business strategies and consumer engagement.

Zakat has succeeded in becoming one of the safety nets for welfare during the crisis. As a result, continuous improvement is a necessity, especially through strengthening technology adaptation. This study aims to explore the factors determining Muslim behavior on their intention to pay zakat by taking into consideration the adoption of digital technology using the modified Unified Theory of Acceptance and Use of Technology (UTAUT).

The data collected were 265 respondents who live in urban and suburban areas. They were processed using the partial least square structural equation modeling (PLS-SEM) design. Furthermore, the multigroup analysis (MGA) was conducted to capture the difference results between urban and suburban.

The findings show that performance expectancy, social influence, facilitating conditions, perceived security and privacy and zakat literacy significantly increase the intention of Muzakki to adopt financial technology. Perceived security and privacy has succeeded in being an important predictor of digital payment adoption for Muzakki. This paper provides a specific description of the adoption of Muzakki living in urban and suburban areas by using MGA. The research findings illustrate that there is a different urgency between the related variables. Suburban communities have more significant results regarding the research model used.

This research provides new component variables that can drive individuals’ intentions to use digital services to pay zakat online by using the redesigned UTAUT model. Further research can explore more variables related to zakat digitalization, such as social media interaction, by conducting in-depth interviews with stakeholders to improve zakat performance in this digital era.

The result of this research recommends that zakat institutions enhance their zakat literacy and education among the Muslim population to improve zakat performance. The government should pay attention to the digital ecosystem to attract the community to use a digital platform.

This research modified the UTAUT model by integrating several other important constructs to produce more comprehensive findings in investigating the factors that can influence an individual's intention to pay zakat through an online digital platform. This study also examined the indirect effect to obtain significant results by positioning perceived security and privacy as an intervening variable. The implementation of the MGA was conducted to divide research respondents into two categories (urban and suburban) and compare the test results.