A barrier, not a bridge: why Global Brand Presence hinders CBDC adoption plans in an emerging economy

This research looks into the factors affecting Central Bank Digital Currency (CBDC) adoption intentions among households in India. Extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study integrates perceived trust and perceived security as mediators and attitude as a moderator. After collecting data from 386 respondents, the proposed model is validated through Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-performance Map Analysis (IPMA). The results indicate that the intention to use CBDC is most affected by performance expectancy and facilitating conditions. Moreover, perceived trust substantially affects the association between perceived security and behavioral intention. Also, it is found that attitude significantly moderates the path from performance expectancy to behavioral intentions, whereas it exerts an insignificant moderating effect on the relationships involving effort expectancy, social influence, perceived security, and behavioral intentions. Finally, this study contributes uniquely to the existing body of literature on CBDC by focusing on households’ perspective, providing an insightful information on customer-specific adoption factors and other such information for policymakers and practitioners.

Unveiling the influencing mechanism underlying users’ adoption and recommend intentions of central bank digital currency: A behavioral reasoning theory perspective

<abstract> <p>Central Bank Digital Currencies (CBDC) are being researched in academia and piloted by central banks around the world. Initial research highlights the importance of privacy concerns on adoption intention in CBDC. We took one step further and investigated the link between privacy concerns and adoption using the Chinese CBDC and digitalized version of the Yuan, the e-CNY. We integrated and applied the established Antecedent Privacy Concerns and Outcomes (APCO) model with the Task-Technology Fit model in a quantitative online-questionnaire with 682 Chinese participants to study the influence of privacy concerns on CBDC usage. The data was analyzed using partial least squares structural equation modeling (PLS-SEM) to identify significant path coefficients and effects in the developed model. The findings demonstrated that several antecedents significantly influenced privacy concerns, which in turn influenced e-CNY usage. In particular, perceived vulnerabilities impacted privacy concerns, while soft and hart trust factors were found to neither impact concerns or usage. When compared to prior research, the distinction between intention to use and usage of CBDC, under consideration of privacy concerns, seemed to be negligible. The often discussed 'privacy-paradox' could not be observed for CBDC. Observed differences in antecedents and other factors may have been due to cultural, political, and demographic factors, as well as different CBDC design choices. For practitioners, the results further emphasized the need for a privacy-friendly implementation of retail CBDC, which efficiently communicated user benefits while rebutting perceived vulnerabilities.</p> </abstract>

Background and Purpose: As central banks globally explore the implementation of Central Bank Digital Currencies (CBDC), India has initiated a phased rollout of its Digital Rupee. While technological and regulatory dimensions of CBDC have received attention, empirical understanding of its perceived impact on monetary policy transmission remains limited. This study aims to evaluate public awareness, usage patterns, and perceptions of the Digital Rupee, with a focus on implications for India's monetary policy framework. Methods: A cross-sectional, quantitative survey was conducted among 320 Indian adults using a structured online questionnaire. The instrument measured CBDC awareness, adoption intent, perceived benefits, risks (including policy transmission, disintermediation, financial inclusion, and privacy), and views on key technological features. Descriptive statistics, ANOVA, and Partial Least Squares Structural Equation Modeling (PLS-SEM) were used for analysis. Results: Our findings revealed that respondents exhibited high awareness of the Digital Rupee (85%) but limited direct pilot usage (28%). They strongly supported using CBDC for faster and more targeted fiscal transfers (mean = 5.2/7) and endorsed key features such as offline capability (mean = 6.1) and programmability (mean = 5.8). However, concerns about potential bank disintermediation (mean = 4.8) and systemic stability risks (mean = 4.5) were also prominent. Views on cross-border use and legal safeguards indicated moderate interest alongside notable knowledge gaps. Discussion: The study highlights both the enthusiasm and reservations surrounding CBDC adoption in India. Key insights include the need for seamless UPI integration, tiered wallet caps, and public trust mechanisms to preserve financial stability. Strategic policy design balancing innovation with inclusion and privacy is essential to ensure the Digital Rupee complements India’s existing monetary tools without destabilizing its banking ecosystem.

Central bank digital currency (CBDC) serves as a transformative instrument for advancing digital strategies within enterprises, increasingly becoming an integral component of organizational digital initiatives. Nevertheless, the current adoption rate of CBDC among firms remains notably low, highlighting the significant practical importance of examining the factors that influence the intention to utilize CBDC from the corporate perspective. This article focuses on CBDC as the subject of investigation and, utilizing the Technology-Organization-Environment (TOE) framework in conjunction with managerial capabilities, develops a theoretical model to understand the intention to adopt CBDC. Furthermore, the Partial Least Squares Structural Equation Modeling (PLS-SEM) method is employed to empirically analyze the determinants influencing firms’ intentions to adopt the e-CNY, using data collected from pilot cities involved in China’s CBDC initiative. The findings reveal that the adoption of CBDC issued by central banks is primarily driven by organizational factors, such as organizational readiness and top-management support, while environmental factors, including vendor partnerships and market uncertainty, exert a secondary influence. Technical considerations appear to have minimal impact on the intention to adopt CBDC, with complexity identified as the sole factor negatively associated with this intention. Although managerial attributes do not directly affect a firm’s propensity to use CBDC, both technical and organizational elements can exert indirect influence. This study provides valuable insights to assist firms in making informed decisions and strategically allocating resources in the context of utilizing central bank digital currency.

Purpose The purpose of this study is to examine the determinants that impact the inclination to embrace financial technology (fintech) among Islamic banking customers in Sri Lanka by using the extended Unified Theory of Acceptance and Use of Technology (UTAUT). Design/methodology/approach This study used a quantitative, self-administered questionnaire that applied a convenience sampling strategy to collect 393 valid responses from customers who used Islamic banking products in Sri Lanka. The data were analysed in two stages: Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 4 and fuzzy-set qualitative comparative analysis (fsQCA). Findings The PLS-SEM results indicate that performance expectancy, effort expectancy, perceived value, social influence, privacy enablers, system quality, personal innovativeness and facilitating conditions have positive and significant impacts. By contrast, perceived risk negatively and significantly impacts the adoption intention of fintech among Islamic banking customers. Personal innovativeness moderated only the relationship between performance expectancy and adoption intention. By contrast, the results obtained using fsQCA offer a distinct explanation and enhanced understanding of the propensity of Islamic banking customers to adopt fintech services by uncovering the intricate connections among various combinations of factors that precede adoption intention. This facilitates comprehension of the factors influencing the intention of such customers to adopt fintech in Sri Lanka. Practical implications This study provides significant insights for Islamic financial institutions offering fintech service strategies to enhance fintech adoption among their customers. This assists these institutions in determining their objectives to promote fintech adoption among customers in Sri Lanka. In addition, regulators can gain an understanding of their role in establishing user-friendly and adaptable fintech services for individual users. Originality/value This study contributes to the expanding body of research on fintech adoption in the Islamic banking sector, specifically in Sri Lanka. Sri Lanka has been slow to adopt fintech services as a developing country for Islamic banking. This study successfully demonstrates the effectiveness of the proposed theoretical framework in identifying the factors underlying slow adoption. To the best of the authors’ knowledge, this study is the first to use fsQCA with PLS-SEM to examine Sri Lanka’s intention to adopt fintech.

This study aims to uncover key factors influencing adoption process of BIM technology in an engineering consultant firm, Penta Rekayasa. The research employs mixed-methods approach combining quantitative analysis and qualitative input from industry experts and user judgement. A comprehensive of reviewing existing literature used to design a conceptual framework. A modified Technology Acceptance Model (TAM) of Unified Theory of Acceptance and Use of Technology (UTAUT) chosen as a main landing theory to design conceptual framework. Additionally, seven main constructs identified to influence usage behavior along with seven hypotheses proposed: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Organizational Support (OS), Resistance to Change (RC), Facilitating Condition (FC) and Behavioral Intention (BI). Primary data is collected using questionnaire and semi-structure interview, further analysis is carried out utilizing SmartPLS software’s Partial Least Squares – Structural Equation Modelling (PLS-SEM) and thematic analysis for interview analysis. The findings demonstrate that four major constructs are regarded as influencing factors in the Penta BIM adoption process: Performance Expectancy (PE), Effort Expectancy (EE), Resistance to Change (RC), and Behavioral Intention (BI). Other three are neither supported as the path coefficient, p-values and t-values threshold is exceed. The result of thematic analysis using Braun & Clarke’s six-phase framework show three main problems arises that negatively influence the adoption process: Social influence and organizational support – unsupportive figure; Resistance to change – Penta’s employee resist to change; and facilitating condition – socialization and training importance. Based on user judgement, theories or practical article, and expert opinion; business solutions are proposed: Develop training and development system that can be used for BIM and future training; budgeting external education include seminar or bootcamp; Held internal meeting with stakeholders to increase the employee engagement and aligned goals with high management profile figure. For a long-term goal, Penta may also consider to develop BIM division in order to increase the speed of project completion. Lastly, the actionable schedule of implementation plan is proposed based on internal recommendation, project design life cycle, and working schedule to ensure the effectiveness of the program.

To explore the formation mechanism of college students' adoption intention of generative artificial intelligence (GAI)—i.e., the dynamic paths of direct/indirect effects of antecedent variables and interaction effects of moderating variables—this study integrates and extends the traditional Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The integration is necessary because traditional TAM focuses on rational cognition, while UTAUT2 lacks the emotional dimension in educational scenarios and the integration of multi-level contexts. A theoretical framework incorporating the “individual-family- institution-region” four-dimensional moderating and collaborative perspective was constructed, and an empirical analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). A multi-stage stratified sampling method was adopted, with a sample of 842 college students from five universities in eastern and western China. The scales for UTAUT2 core variables and extended TAM variables in the questionnaire were adapted from previous studies that had undergone reliability and validity verification. Reliability was tested using Cronbach's α and Composite Reliability, while validity was tested using Average Variance Extracted, the Fornell-Larcker criterion, and Heterotrait-Monotrait Ratio. Results showed that the measurement model had acceptable reliability and validity, with good explanatory power (R 2 = 0.743) and predictive validity of the structural model. Specifically, perceived comfort (β = 0.112, p < 0.005), perceived security (β = 0.109, p < 0.05), and emotional dependence (β = 0.497, p < 0.005) all exerted positive effects on perceived usefulness and perceived ease of use. Performance expectancy (β = 0.216, p < 0.005), social influence (β = −0.064, p < 0.05), facilitating conditions (β = 0.143, p < 0.005), and perceived ease of use (β = 0.469, p < 0.005) directly drove adoption intention, whereas the direct effect of perceived usefulness was not significant (β = −0.031, p = 0.523). This result challenges the core assumption of TAM—which emphasizes “priority of rational utility” and confirms the “de- instrumentalization” characteristic of generative AI adoption, meaning user decisions rely more on emotional experience and interactive fluency.Regarding moderating effects: gender negatively moderated the relationship between performance expectancy and adoption intention (β = −0.207, p < 0.05); family structure negatively moderated the relationship between habit and adoption intention (β = −0.228, p < 0.05); university type positively moderated the relationship between performance expectancy and adoption intention (β = 0.251, p < 0.05); and regional differences negatively moderated this relationship (β = −0.251, p < 0.05).In practice, it is suggested that educational authorities strengthen the construction of digital infrastructure in western universities, universities develop differentiated guidance strategies for students majoring in humanities/social sciences and science/engineering, and developers optimize emotional interaction design. This study provides theoretical support for context-adapted strategies for the educational application of generative AI.

In this study, we examined the influence of users’ experiences with the unified payments interface (UPI) system on the usage behavior of central bank digital currency (CBDC) in India. Our research developed a novel conceptual framework that investigated the relationships between technology, cognitive factors, and behavioral intentions towards CBDC use. The framework integrated UPI usage experience as a moderator within existing models of behavioral intentions and use behaviors. We collected data through a survey conducted in major Indian cities during the pilot launch of CBDC. By utilizing a partial least squares structural equation model (PLS-SEM), we analyzed the proposed model and the relationships between the constructs. Our findings revealed the significant impact of hedonic motivation and performance expectancy on users’ behavioral intentions towards CBDC. Social influence also played a significant role in CBDC usage. Furthermore, we identified that prior UPI usage negatively moderated the relationship between performance expectancy and behavioral intention, as well as the relationship between social influence and use behavior. However, prior UPI usage did not significantly moderate the relationships between perceived risk, hedonic motivation, behavioral intention, and use behavior. These findings contribute to our understanding of the factors influencing CBDC adoption and usage behavior in India.

Central Bank Digital Currencies (CBDCs) have gained significant attention as potential innovations in the global financial landscape. The incorporation of artificial intelligence (AI) in the financial sector has brought transformative changes, particularly in the adoption and usage of CBDCs. Therefore, this study explores the impact of artificial intelligence (AI) on consumers’ intentions toward adopting CBDCs in the Chinese banking sector through digital technology awareness, addressing privacy concerns, and ease of use with the moderating role of government support. This research study examined the relationships of a sample of 420 employees in the Chinese banking sector. The current study uses the partial least squares structural equation modeling (PLS-SEM) method to assess these parameters. The findings show that artificial intelligence positively impacts consumers’ willingness to use CBDCs in the Chinese banking sector through digital technology awareness, addressing privacy concerns, and ease of use. Furthermore, government support significantly influences the link between AI, digital technology awareness, addressing privacy concerns, and ease of use. This study contributes to the literature on digital currency adoption by highlighting the critical role of AI in enhancing user experiences and trust in financial innovations. It also provides practical insights for policymakers and financial institutions to leverage AI technologies to strategically foster CBDCs adoption in China.

Central bank digital currencies (CBDC) have been implemented by some countries and trialled by many more. The consumer has an increasing range of financial services to choose from including decentralised blockchain-based cryptocurrencies. A CBDC may use blockchain technology, but it is centralized, so the institutions that support it play an important role. Despite the centralised top-down nature of this financial technology, it still needs to be adopted so the consumer’s perspective, particularly their trust in it, is very important. Each CBDC implementation can be different, and each country’s context can be different, therefore it is important to understand each case separately. This research models the Brazilian consumer’s trust in their two-tier CBDC, where the central bank and the retail banks retain their current role. The six ways to build trust in a CBDC, identified by previous research in a different region, are supported for this case also. These are: (a) Trust in government and central bank offering the CBDC, (b) expressed guarantees for those using it, (c) the favourable reputation of other active CBDCs, (d) the CBDC technology, the automation and limited human involvement necessary, (e) the trust building features of the retail bank’s CBDC wallet app, and (f) the privacy features of the retail bank’s CBDC wallet app and back-end processes.

Central Bank Digital Currency (CBDC) is a digital currency issued by a government-controlled central bank of a particular country. Bank Indonesia as the central bank in Indonesia has been trying to develop a CBDC known as digital rupiah. Digital rupiah is expected to complement and perform same function as the fiat money in Indonesia. Research is needed to identify people’s views on the use of CBDCs. This study seeks to understand the factors that influence intention to use CBDC by adapting Technology Acceptance Model. Data were collected through questionnaires from 565 valid respondents. Partial Least Square-Structural Equation Modeling (PLS-SEM) was used to evaluate the proposed model. This study verifies that perceived usefulness, perceived ease of use, hard trust, and soft trust can influence intention to use CBDC in Indonesia, while personal innovativeness is known to influence perceived usefulness and perceived ease of use.

Purpose The present research aims to study the behavioural intention to use the digital currencies issued by the central bank through the lens of technology acceptance and switching behaviour perspective. The study also proposes to analyse the role of financial constructs to explain the adoption intention. Design/methodology/approach The current study develops a model by integrating the unified theory of acceptance and use of technology (UTAUT) and the push–pull–mooring (PPM) theory of switching behaviour. It amends the same by including financial literacy, financial inclusion and trust. A sample data of 419 respondents has been collected through a structured questionnaire and the PLS-SEM approach has been used for data analysis. Findings The findings suggest that UTAUT and PPM models can significantly predict individuals' readiness to adopt the central bank digital currency (CBDC). More precisely, performance expectancy, social influence, government support, relative advantage and task-technology fit jointly determine the adoption behaviour. Besides, the financial constructs also affect the intention to use CBDC. Research limitations/implications The study is largely based on a quantitative approach with cross-sectional data from an Indian sample. Thus, the findings may benefit from a longitudinal approach with mixed-method data analysis. However, the study elaborates on several implications for policymakers and research scholars. Originality/value The present study uniquely integrates the technology adoption perspective with switching behaviour applied to the migration studies. Given the nascent stage of CBDC implementation in many countries, the current study uses a triangulation approach to enhance the understanding of its adoption behaviour.

The rise of Central Bank Digital Currency (CBDC) is a crucial development in the evolution of financial systems. The purpose of this study was to investigate Gen-Z’s behavioural intention to adopt CBDC in northern India, using the Unified Theory of Acceptance and Use of Technology (UTAUT). The study specifically examined the roles of perceived trust, performance expectancy, and effort expectancy as mediators in the relationships between perceived security, social influence, and attitude. The data was collected from 258 Gen-Z digital payment users in the northern part of India with the help of a well-structured survey questionnaire, using convenience and snowball sampling techniques. In addition, the data analysis was conducted using statistical tools such as SmartPLS 4.0 and SPSS. The results show that despite a non-significant negative influence of effort expectancy on attitude, the overall positive influence of attitude on the behavioural intention to use CBDC was established. The theoretical implications emphasize the contribution of this study to FinTech adoption literature, particularly in the context of CBDC among Gen-Z. Moreover, the study recommends refining theoretical frameworks to accommodate the evolving financial landscape. This study also suggests tailored strategies for policymakers, financial institutions, and businesses to align with Gen-Z’s preferences, fostering CBDC acceptance. A few limitations, including the focus on a specific age group and potential selection bias, should be considered when interpreting the results.

Electric vehicles (EV) adoption in Indonesia is still lagging, with current sales volumes far below the government's target, particularly for battery electric vehicles (BEVs). This research aims to uncover the diffusion of BEVs by analyzing factors that influence their acceptance through adoption intention and willingness to pay, using an integrated model of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which incorporates perceived risk, environmental concerns, and government support. Data from 329 respondents in major Indonesian cities were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that effort expectancy, habit, perceived risk, environmental concerns, and government support have an impact on EV adoption intention. Additionally, performance expectancy, price value, and adoption intention were found to positively influence willingness to pay for EV. These findings confirm the applicability of some factors of UTAUT2, perceived risk, environmental concern, and willingness to pay in examining EVs adoption in Indonesia, contributing to the literature on technology adoption in the automotive sector. This study also discusses implications that offer valuable insights for stakeholders in the EV market, thereby helping to promote electric vehicle adoption in Indonesia.