Search results

This study aims to integrate Delone and McLean’s information system success (DMISS) model with the innovation resistance model to evaluate the relationship between behavioural intention to use (BIU) and innovation resistance in the context of neo-banking. The primary objective of this study is to identify the drivers of neo-banking adoption and the barriers to its adoption and incorporate constructs such as e-trust (ETR) and personal innovativeness (PIV) to provide a more comprehensive understanding of the factors influencing neo-banking adoption.

A structured survey-based questionnaire was used to gather data from a diverse sample population in India. The Partial Least Squares Structural Equation Modeling (PLS-SEM) model was employed to further examine the adoption of neobanking services and users' intention to use neobanking services.

This study reveals a significant correlation between BIU and the uptake of neobanking services, demonstrating the value of consumers' readiness to embrace these offerings. However, resistance to usage has emerged as a major obstacle for consumers concerned about data security, technology reluctance and perceived risks associated with digital-only neobanks.

Analysing the driving and restraining factors will provide substantial information on the formation of consumers' decision-making processes in the Indian banking industry, which is undergoing rapid digital transformation. This information is of great importance to scholars, practitioners and policymakers, as it highlights the factors that may facilitate or impede the adoption of neobanking in India. The outcomes of this analysis will be of particular interest to researchers, experts and stakeholders in the field as they will provide valuable insights into the dynamics of consumer behaviour in the Indian banking sector.

This study represents an initial effort to examine BIUs and usage resistance within the rapidly developing neobanking sector in India. The findings of this study build on the existing research in this area and contribute to the ongoing discussion on the adoption of neo-banking.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

This study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial intelligence (AI) and robotics process automation [RPA]). It investigates the adoption and use of these technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey.

The study investigates the adoption and use of emerging technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey. This study considers the said nature and characteristics of emerging technologies and proposes a model using the factors that have been found to be significant and most commonly investigated by existing prior technology-organization-environment (TOE)-related technology adoption studies. This survey applies the TOE framework and examines the influence of significant and most commonly known factors on Canadian firms’ intention to adopt the said emerging technologies.

Study results indicate that Canadian accounting professionals’ self-assessed knowledge (about these emerging technologies) is more theoretical than operational. Cloud computing is highly used by Canadian firms, while the use of other technologies, particularly blockchain and RPA, is reportedly low. However, firms’ intention about the future adoption of these technologies seems positive. Study results reveal that only the relative advantage and top management commitment are found to be significant considerations influencing the adoption intention.

Study findings confirm some results presented in earlier studies but provide additional insights from a new perspective, that of accounting professionals in Canada. The first limitation relates to the respondents. Although accounting professionals provided valuable insights, their responses are personal views and do not necessarily represent the views of other professionals within the same firm or the official position of their accounting departments or firms. Therefore, the exclusion of diverse viewpoints from the same firm might have negatively impacted the results of this study. Second, this study sample is limited to Canada-based firms, which means that the study reflects only the situation in that country. Third, considering the research method and the limit on the number of questions the authors could ask, respondents were only asked to rate the impact of these five technologies on the accounting field and to clarify which technologies are used.

This study’s findings confirm that the organizational intention to adopt new technology is not primarily based on the characteristics of the technology. In the case of emerging technology adoption, the decision also depends upon other factors related to the internal organization. Furthermore, although this study found no support for the effect of environmental factors, it fills a gap in the literature by including the factor of vendor support, which has received little attention in prior information technology (IT)/ information system (IS) adoption research. Moreover, in contrast to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study findings may serve as a guide for researchers, practitioners, firms and other stakeholders, particularly technology providers, interested in learning about emerging technologies’ adoption and use in Canada and/or in a relevant context. Contrary to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study provides insights into the said technologies’ actual adoption and improves the awareness of firms and stakeholders to the effect of some constructs that influence the adoption of these emerging technologies in accounting.

This study aims to investigate the influence of environmental factors on individual personality traits associated with mobile paymens (MP) adoption using the technological personal environment (TPE) theory as a framework for the proposed theoretical model.

A total of 736 feedback from respondents was used to validate the proposed model using structural equation modeling. The model comprises Trust and Self-efficacy to explain MP adoption from a personal trait perspective. Meanwhile, environmental aspects are represented by social influence, vendor regulations and network externalities.

The result indicates that self-efficacy has the most significant direct effect on user intention to use MP, followed in decreasing order of significance by social influence, trust, vendor regulations and network externalities. Furthermore, social influence is the most contributing aspect from the environmental area that influences user intention directly and indirectly through trust and self-efficacy as mediators. Meanwhile, the moderating effect analysis also found that gender moderates the effect of user self-efficacy on MP adoption.

This study fills the gap by comparing trust and self-efficacy and exploring how those factors are developed and affected by the environmental aspect of MP usage. It was discovered that self-efficacy was the most influential construct influencing the adoption of MP. Social influence was identified as the primary environmental factor that directly impacts user intention regarding MP usage. Furthermore, gender was shown as a moderator, as males place a higher value on self-efficacy as a factor affecting their intention to embrace MP in comparison to females.

Emerging literature on fintech has shown that consumers have been slow to adopt fintech-based products and services. However, limited literature is available regarding the factors associated with consumers' adoption of these products and services. This study aims to investigate the factors that are associated with consumer adoption of fintech-based products and services.

Data on the usage and perception of smartphone financial apps by US residents ages 18–70 was collected in the fall of 2020. Based on the Extended Post-Acceptance Model (EPAM) framework, Structural Equation Modeling and Confirmatory Factor Analysis were applied to inspect how financial capability, perceived security and perceived usefulness affect fintech adoption.

Fintech proficiency, investment risk tolerance and perceived safety are positively associated with the frequency of fintech application use upon adoption. Consumers are more likely to feel safer if they are more financially capable and technologically proficient. Consumers with higher risk tolerance tend to believe fintech apps are safe to use. Consumers with higher fintech proficiency are more likely to recognize the usefulness of fintech services.

The study introduces a revised EPAM framework with antecedent factors, fintech proficiency and risk tolerance to investigate the factors associated with consumer adoption of fintech-based products and services. The key findings of this study validate the EPAM in the American context. Additionally, this research is among the first to have confirmed the direct relationship between perceived security and fintech adoption. The results have practical implications for existing fintech companies, banks and financial institutions, policymakers and financial advisory practices considering adopting fintech-based services for their clients.

With the rise of cryptocurrency and its influence on the financial industry, this paper aims to explore cryptocurrency affordances that lead to approach–avoidance behavioral intentions for non-users (potential) and the intention to continue use for users (actual), drawing upon affordance theory and chasm theory.

The authors collected data from 480 potential and actual users in Germany and used maximum likelihood structural equation modeling (ML-SEM) to analyze it. In particular, the data consisted of 301 cryptocurrency users in Germany\ the authors used ML-SEM to test the post-adoption model. Additionally, logistic regression was utilized to determine the dominant actual usage method (store of value or medium of exchange) for various cryptocurrency coins.

According to the study's results, the perceived value benefits have a positive impact on the behavioral intention of potential users to adopt cryptocurrency, and they influence the intention of actual users to continue using it. However, both perceived volatility and financial risk tolerance are the most crucial factors hindering cryptocurrency adoption, whether in the pre-adoption or the post-adoption stage.

This is the first study to reveal cryptocurrency affordances and examine their effect on behavioral intentions toward cryptocurrency adoption based on the differences between non-users (potential) and users (actual). Furthermore, the authors explore how cryptocurrency holders perceive and invest in different coins (e.g. NFTs), which sheds light on factors such as financial risk tolerance that affect their decision making.

Powered by artificial intelligence, voice assistants (VAs), such as Alexa, Siri and Cortona, are at early-stage adoption rates in service contexts. Customers express hesitance in using the technology. Furthermore, the effect of a relevant variable (VA empathy) as a determinant of VAs is not widely researched. This study aims to extend the unified theory of acceptance and use of technology (UTAUT) and social response theory (SRT) to propose and test a conceptual model of the role of customer perceptions of VA empathy and risk on VA adoption and usage intensity.

In this study, data were collected from 387 VA users in the USA using a survey administered through Amazon MTurk. Data cleaning retained a final n = 318 for structural equation modeling analysis.

Findings show that perceived VA empathy enhances customers’ attitude toward VA and drives adoption, thereby increasing VA usage intensity. Perceived risk is a moderator; users with high perceptions of VA empathy have greater VA adoption rates when they have high (vs low) risk perceptions of using VA.

This research is one of the first known studies to provide empirical evidence of the role of customer perceptions of VA empathy and risk on VA adoption in service delivery. It goes beyond VA adoption research to provide empirical evidence of the impact of VA adoption on actual usage intensity. By extending the UTAUT and SRT, this research adds to the theoretical foundation for research on VA adoption, offering practical insights for firms regarding empathetic VA design to enhance customer service delivery.

Using the technology acceptance model (TAM), this study investigates factors influencing the adoption of cryptocurrency in Bangladesh.

Data were collected from 346 members of the general public through a structured web survey using snowball sampling. Structural equation modeling was used to analyze the data and assess the reliability and validity of the measurement model.

The results show that knowledge of cryptocurrency, benefits of use (perceived usefulness), attitude and challenges all have a significant impact on the adoption of cryptocurrency.

This study was conducted in a single country, relied on self-reported data and used a cross-sectional design, which limits the ability to draw causal inferences. Future research could explore the factors that influence the adoption of cryptocurrency in different countries and regions and incorporate additional variables to provide a more comprehensive understanding of the drivers of intention to use cryptocurrency.

This study contributes to understanding the factors driving the adoption of and intention to use technology-based services, providing insights that can inform the design and implementation of future technology-based services.

Recently, the Government of India has emphasized digital financial inclusion for promoting cashless transactions with a vision to transform India from a traditional cash-based economy into a cashless economy. Technology-driven payment apps are facilitated greater access to cashless financial services and improve the speed, efficiency, accuracy and effectiveness of financial transactions. This study aims to explore the determinants of quick response (QR) code mobile payment (m-payment) adoption intention among marginalized street vendors in India.

The proposed research model was tested using 320 responses from QR code m-payment users. An interview schedule was performed using the structured questionnaire from marginalized street vendors by adopting a purposive sampling technique. The proposed research framework of this study developed on the Unified Theory of Acceptance and Use of Technology (UTAUT). In addition to the existing variables proposed in the UTAUT model, three more variables have been added, namely, digital financial literacy (DFL), personal innovativeness (PI) and perceived trust (PT). Besides, the study used confirmatory factor analysis and structural equation modeling techniques to analyze the data.

This study confirms that factors such as performance expectancy, effort expectancy, facilitating conditions, PT and customers’ DFL are significant determinants of street vendors’ intention to use QR code m-payment services. However, social influence and PI have shown an insignificant relationship with adopting a QR code m-payment system.

The results provide insights for policymakers and service providers. Specifically, government and bankers design promotional campaigns emphasizing the ease of use, perceived benefits, security and faster business transactions to accept and use the QR code m-payment system to encourage prospective users to achieve a cashless economy.

Many prior studies have widely concentrated on m-payment adoption intention in India. However, only a few studies have attempted to examine the factors influencing the adoption of QR code m-payment services among merchants from emerging economies. There is a dearth of studies on QR code adoption from an unorganized sector perspective, specifically marginalized street vendors. Therefore, this study explicitly examines the extent to which the determinants of adoption intention toward QR code-based m-payment services among marginalized street vendors within the framework of the extended UTAUT model by incorporating DFL, PI and PT. The findings of this study contribute, theoretically and practically, to the existing literature.

This study aims to understand the learner behaviour of millennials for Massive Open Online Courses (MOOCs) in the post-adoption stage by extending the theory of Unified Theory of Acceptance and User Technology 2 (UTAUT2) with expectancy confirmation model (ECM) along with personal innovativeness as the exogenous, satisfaction as a mediating and continued intention as an endogenous construct.

This study applied a cross-sectional research design by using a survey method to collect primary data with a structured questionnaire. Convenience sampling was used to collect data from millennial MOOC users, and partial least square structural equation modelling method was applied for data analysis.

The results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation influence satisfaction. Similarly, performance expectancy, hedonic motivation, personal innovativeness and satisfaction influence the continued intention for MOOCs.

In terms of limitations, the study applied a cross-sectional research design that could lead to data collection bias. Similarly, the study used convenience sampling as the authors did not have access to the participant list of users from MOOC platforms.

The research highlights various insights to all the stakeholders on improving MOOC satisfaction and enhance the continued intention for millennial learners.

The findings of this research bridge this gap by examining the post-adoption usage behaviour of MOOCs by extending the baseline model of UTAUT2 with personal innovativeness and integrating it with ECM.