Demystification of readiness, security, and technological enhancements in the adoption of a cashless economy

Susanto, Heru et al. Article Demystification of readiness, security, and technological enhancements in the adoption of a cashless economy Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Susanto, Heru et al. (2024) : Demystification of readiness, security, and technological enhancements in the adoption of a cashless economy, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 11, pp. 1-35, https://doi.org/10.3390/economies12110285 This Version is available at: https://hdl.handle.net/10419/329212 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Demystification of Readiness, Security, and Technological Enhancements in the Adoption of a Cashless Economy. Economies 12: 285. https://doi.org/10.3390/ economies12110285 Academic Editor: Weixin Yang Received: 21 June 2024 Revised: 16 August 2024 Accepted: 22 August 2024 Published: 24 October 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Demystification of Readiness, Security, and Technological Enhancements in the Adoption of a Cashless Economy Heru Susanto 1,2,3 , Noor Tamtini 1,2,*, Fahmi Ibrahim 1, Alifya Kayla Shafa Susanto 2,4, Desi Setiana 5,6 and Leu Fang Yie 2,3 1School of Business, University Technology Brunei, Bandar Seri Begawan BE1410, Brunei; [email protected] or [email protected] (H.S.) 2Center for International Research Collaboration of Graph Theory and Combinatory, Members: BRIN (National Research and Innovation Agency), Jakarta Pusat 10340, Indonesia; UTB (University of Technology Brunei), Jalan Tungku Link Gadong BE1410, Brunei; ITB (Institute of Technology Bandung), Bandung 40132, Indonesia; UI (University of Indonesia), Depok 16424, Indonesia; THU (Tunghai University), Taichung 40704, Taiwan; IOU (International Open University), Jakarta 12780, Indonesia; [email protected] (A.K.S.S.); [email protected] (L.F.Y.) 3Center for Artificial Intelligent and Cyber Security, National Research and Innovation Agency, Bandung 40135, Indonesia 4School of Computing and Informatics, University Technology Brunei, Bandar Seri Begawan BE1410, Brunei 5Digital Psychology Research Group, Institute of Brunei Studies, University Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei 6The Southern Correctional Center, BAPAS Jakarta Selatan, Ministry of Law and Human Rights, Jakarta 12940, Indonesia *Correspondence: noor[email protected] Abstract: The adoption of a cashless economy was accelerated globally by the devastating impact of the COVID-19 pandemic. Brunei Darussalam was not excluded from this trend, as pandemic-related restrictions were implemented to ensure the safety of its population. In light of the COVID-19 crisis, this research paper examines the factors influencing the readiness and acceptance of a cashless economy among working society in Brunei Darussalam. The integrated concepts of the Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) are applied to examine perceptions impacting their acceptance and readiness to continue adopting a cashless economy. The methodology includes a literature review and the use of secondary data from government reports and industry publications. A quantitative approach is employed, utilizing an online survey to collect non-probability samples from 212respondents. The main instruments used in the survey are structured questionnaires. The study’s findings show that factors such as the assessment of payment modes, technological development, digital literacy, knowledge, regulatory policies, and security concerns significantly affect working society’s perceptions, readiness, and acceptance of a cashless economy. These results provide insights for policymakers and stakeholders on the key factors influencing continued cashlessness adoption and shaping societal behavior towards cashless payments. Keywords: adoption; cashless payments; perceptions; technology readiness index; technology acceptance model; COVID-19 pandemic; working society; cashless economy 1. Introduction A cashless economy refers to an economic system where transactions are primarily conducted using cards, digital payment methods, and other non-physical forms of money, moving away from cash as the main mode of payment. This concept has gained significant global traction and has been widely anticipated by economic experts as monetary and payment systems have evolved over the years (Srouji 2020). With technological advancements rapidly transforming the financial industry, banking structures and practices have shifted Economies 2024,12, 285. https://doi.org/10.3390/economies12110285 https://www.mdpi.com/journal/economies Economies 2024,12, 285 2 of 35 from traditional cash-based transactions to modern cashless banking, including digital banking services and card payments. In the context of Brunei, the Digital Economy Masterplan 2025 outlines strategies to transform the nation into a Smart Nation, as envisioned under Wawasan Brunei 2035. A key component of this transformation is the adoption of digital banking and payment systems. In line with Brunei’s Smart Nation vision, digital transformation in the banking sector was fully implemented by 2019 (Digital Economy Council 2020). A robust digital ecosystem is a key enabler for a cashless economy, requiring the active participation of stakeholders, including government bodies, financial institutions, and businesses. These stakeholders must play a pivotal role in creating awareness, promoting digital payment methods, and ensuring the security of digital transactions. The success of a cashless economy is also heavily dependent on the societal adoption of digital payments. While the term “cashless economy” does not imply the complete elimination of cash, it highlights a shift towards non-cash transactions. For certain informal sectors, cash remains a critical form of payment, especially when interacting with formal sectors (Srouji 2020). This dynamic is one of the key areas to explore when assessing society’s readiness and willingness to embrace cashless transactions as a primary payment method. The public perception of a cashless economy is a crucial factor in its acceptance. Attitudes and perceptions are often shaped by the level of knowledge and understanding people have on the subject. Studies have shown that individuals with higher levels of digital literacy and awareness are more likely to adopt and continue using cashless payments (Avirutha et al. 2020). In Brunei, the COVID-19 pandemic accelerated the adoption of digital payments due to the necessity for convenience and compliance with social distancing measures. However, as the country moves into the endemic phase, changes in attitudes towards cashless payments may occur, making it essential to study the factors that influence continued acceptance and the intention to use these payment methods. Stakeholders in the digital ecosystem play a crucial role in the ongoing adoption of cashless payments. While this study focuses on individual respondents, their responses will reflect the actions and measures taken by these stakeholders in providing, marketing, and maintaining cashless payment systems. For example, the security of digital payments is a key factor influencing user confidence. Ensuring robust protection against card fraud and digital scams will boost the confidence of cashless payment users. In addition to regularly updating platforms with necessary security measures, stakeholders must also raise awareness about how to safely perform digital transactions, which further enhances user confidence (Anshari et al. 2021). This is closely linked to digital literacy and competency. Studies have shown that digital literacy is a critical factor in the adoption of cashless payments, as it directly influences user acceptance and readiness (Salman and Saleem 2017). People with lower levels of digital literacy are generally more hesitant to embrace and use cashless payments compared to those with higher levels of knowledge. This issue will be addressed in more detail in the study. The primary goal of this research is to assess the level of acceptance and readiness among Brunei’s working population and income earners in the wake of the COVID-19 pandemic, as well as to identify the key factors that influence the continued adoption of a cashless economy in the country. The following objectives are outlined to achieve this goal: • To investigate the perceptions of Brunei’s working population regarding the transition to a fully cashless economy. • To determine the factors that influence the acceptance of a cashless economy among Brunei’s working society. • To explore the relationship between acceptance and readiness for cashless payments among income earners. Economies 2024,12, 285 3 of 35 In support of these research objectives, the study will propose a framework for advancing the cashless economy based on the readiness and acceptance of Brunei’s working society. This framework will offer recommendations to help facilitate the continued adoption of cashless payments in Brunei Darussalam. The findings from this study are expected to provide valuable insights into the relationship between acceptance and readiness for cashless payments following the COVID-19 crisis, as well as address the factors influencing their continued use. These insights will benefit cashless payment users by offering a deeper understanding of its potential impacts, enabling them to make informed decisions when adopting digital payment methods. Additionally, the results will assist stakeholders—such as government bodies, the banking industry, and businesses—in determining the extent of measures needed to foster continued adoption of cashless payments. By influencing societal behavior, these stakeholders can help shape a more cashless society. The findings will also guide the development of action plans to support Brunei’s digital economy strategies, further accelerating the growth of the cashless economy and contributing to the realization of the Smart Nation initiative under Wawasan Brunei 2035. A cashless economy refers to an economic system where transactions are primarily conducted using cards, digital payment methods, and other non-physical forms of money. This concept has gained significant popularity worldwide, with experts predicting its continued growth alongside advancements in monetary and payment systems (Srouji 2020). Technological expansion in the financial sector has rapidly transformed banking structures, shifting from traditional, cash-based transactions to modern cashless services, including card payments and digital banking. In Brunei Darussalam, the Digital Economy Masterplan 2025 outlines strategies to transform the nation into a Smart Nation as part of Wawasan Brunei 2035. A key aspect of this transformation is the implementation of digital banking and payment systems, which was fully achieved by 2019 (Digital Economy Council 2020). Building a robust digital ecosystem is essential for the success of a cashless economy, with stakeholders such as government agencies, financial institutions, and businesses playing pivotal roles in expanding and strengthening this ecosystem. This involves creating awareness, promoting digital payment options, and ensuring the security of digital transactions. The COVID-19 pandemic significantly accelerated the adoption of cashless payments globally, including in Brunei, where public safety restrictions were introduced. This research aims to explore the factors influencing the readiness and acceptance of a cashless economy among Brunei’s working population. The study integrates the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI) to assess the perceptions shaping individuals’ willingness to continue using cashless transactions. The methodology includes a literature review and analysis of secondary data from government reports and industry publications. A quantitative approach is utilized, with data collected via an online survey from a non-probability sample of 212 respondents.. The primary instrument for data collection is a structured questionnaire. The findings suggest that factors such as the evaluation of payment methods, technological advancements, digital literacy, regulatory policies, and security concerns significantly influence the perceptions, readiness, and acceptance of a cashless economy among Brunei’s working society. These results provide valuable insights for policymakers and stakeholders, highlighting the key drivers of continued adoption and helping to shape public attitudes toward digital transactions. The main objective of this research is to assess the level of acceptance and readiness among working individuals and income earners in Brunei following the COVID-19 pandemic, as well as to identify the factors impacting the continued growth of the cashless economy. The specific aims are to: Economies 2024,12, 285 4 of 35 1. Investigate the perceptions of Brunei’s working population toward transitioning to a fully cashless economy. 2. Identify the factors that influence the acceptance of a cashless economy among the working society. 3. Examine the relationship between acceptance and readiness for cashless payments among income earners. The findings from this study are expected to clarify the relationship between acceptance and readiness for cashless payments post-COVID-19 while addressing the contributing factors influencing their ongoing use. These insights will benefit users of cashless payments by helping them make informed decisions based on enhanced knowledge. Furthermore, the results will guide stakeholders, including government bodies, the banking industry, and businesses, in identifying necessary actions to support continued adoption, align with digital economy strategies, and ultimately contribute to the Smart Nation vision of Wawasan Brunei 2035. 2. Literature Review 2.1. Global Shift Towards Cashless Economies A wealth of studies worldwide has examined the shift toward a cashless economy, with a particular focus on user readiness and adoption. In Brunei, the move toward a cashless society gained significant momentum during the COVID-19 pandemic, as the need to shift from contact-based to contactless activities became essential for daily life. 2.2. Historical Development of Cashless Payment Systems According to Fabris (2019), cashless societies have existed since the early days of human civilization, when livelihoods relied on barter and other forms of exchange that did not involve currency. However, the modern concept of a cashless society represents a more advanced stage, where physical money is replaced by digital alternatives, enabling transactions to occur electronically. The first cashless payments emerged in the 1950s, and since then, a variety of cashless instruments have been developed (Jain and Jain 2017). Recent trends show that cashless transactions are now common not only in large financial exchanges but also in everyday small-scale transactions. 2.3. Technological Advancements Technological advancements have been instrumental in driving the shift toward cashless societies, with innovations in digital payment systems and mobile banking playing a key role. Countries with strong e-commerce infrastructures and supportive government policies, such as China, Sweden, and Finland, have led this transition. In China, rapid urbanization and strategic government initiatives have propelled the widespread adoption of cashless payments, making the country a global leader in e-commerce and digital payment usage (Thomas 2013;Filipiak 2020). Sweden’s aggressive policies have reduced cash transactions to just 20% of all transactions, while Finland excels in card payment frequency and Internet banking penetration (Filipiak 2020). 2.4. Impact of COVID-19 on Cashless Economy The COVID-19 pandemic had a profound impact on daily life, particularly in how people made payments. Brunei Darussalam’s Ministry of Health emphasized the ease with which COVID-19 could spread through physical contact, prompting a shift to contactless methods, including cashless payments (Abdul-Halim et al. 2022). Globally, movement restrictions and safety concerns accelerated the adoption of cashless transactions. The G20 Italian Presidency (2021) noted that the potential of cashless payments to enhance financial inclusion became more apparent during the pandemic as governments promoted digital payments to protect vulnerable populations. However, Kotkowski and Polasik (2021) cautioned that this shift might exacerbate financial inclusion challenges, as prepandemic cashless users continued with digital payments while others still relied on cash. Economies 2024,12, 285 5 of 35 Wisniewski et al. (2021) suggested that the pandemic reshaped payment habits, as fear and apprehension about handling physical cash led many to adopt cashless methods, a trend likely to persist even after the pandemic. 2.5. Theoretical Foundations on the Continued Use of Cashless Payments Many studies utilize the Technology Acceptance Model (TAM) to understand users’ intentions to adopt new technology. TAM, derived from the Theory of Reasoned Action (TRA), evaluates Technology Acceptance based on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) (Davis 1989). Routray et al. (2019) highlighted the importance of these factors and extended TAM by incorporating additional elements such as information quality, system quality, and service quality. Ahuja and Joshi (2018) identified Ease of Use, Benefits, Trust, and Self-Efficacy as key factors influencing customer perceptions of e-wallets, though they acknowledged limitations in their study due to a small sample size. Similarly, Maqableh et al. (2015) found that perceived trust—encompassing reputation, security, privacy, and transaction size—plays a significant role in the adoption of cashless payments. The Technology Readiness Index (TRI) assesses users’ readiness to adopt new technologies. Mick and Fournier (1998) noted that users often experience both positive and negative emotions when engaging with new technologies. Parasuraman (2000) further argued that the intensity of these emotions varies among individuals, reflecting their openness to embracing innovations. Humbani and Wiese (2018) applied TRI to examine consumer readiness for mobile payment services, identifying both drivers and barriers to adoption. Karim and Muhammad (2022) found that Technology Readiness, Expectation Confirmation, User Satisfaction, and Perceived Security are critical factors influencing the continued use of cashless payments. 2.6. Theoretical Framework Davis (1989) developed the Technology Acceptance Model (TAM) to analyze user behavior and predict technology adoption. TAM focuses on users’ perceptions, emphasizing that a technology’s Perceived Usefulness and Ease of Use are key factors influencing its acceptance. Parasuraman (2000) introduced the concept of Technology Readiness, which refers to an individual’s willingness to adopt and use new technologies. The Technology Readiness Index (TRI) assesses this readiness by evaluating four dimensions: Optimism, Innovativeness, Discomfort, and Insecurity. • Optimism: a positive belief that technology offers increased control, efficiency, and flexibility. •Innovativeness: a tendency to be a technology pioneer and thought leader. • Discomfort: a perceived lack of control over technology and feeling overwhelmed by it. •Insecurity: a distrust of technology and skepticism about its ability to work properly. 2.7. Current Trends and Future Directions Current trends reveal a significant rise in cashless transactions, driven by technological advancements and shifting consumer behaviors, particularly in the wake of COVID-19. Future research should investigate the long-term sustainability of these trends, the impact of emerging technologies like blockchain and cryptocurrencies, and the role of government policies in fostering financial inclusion through cashless systems. Additionally, research should address potential challenges associated with a cashless society, such as data privacy concerns and the digital divide, to provide a comprehensive understanding of the evolution of the cashless economy. Globally, numerous studies have explored the cashless economy and user readiness and adoption. In Brunei, the momentum toward a cashless society accelerated due to the COVID-19 pandemic, which necessitated a shift from contact-based to contactless daily activities. Economies 2024,12, 285 6 of 35 The development of cashless societies has deep historical roots. According to Fabris (2019), cashless societies have existed since early human history when bartering and other non-currency exchange methods were used. However, the modern concept of a cashless society represents an advanced stage where physical money is replaced by digital equivalents, facilitating transactions in electronic form. The first cashless payments were introduced in the 1950s, and since then, various cashless instruments have been developed (Jain and Jain 2017). Recent trends indicate that cashless transactions are prevalent not only in large financial transactions but also in smaller everyday transactions. Thomas (2013) noted that countries with widespread adoption of cashless solutions and high usage rates are considered advanced. His research highlighted China as a leading example, driven by rapid urbanization and supportive government policies that promote cashless payments. China has emerged as a global leader in e-commerce and cashless technology, alongside other advanced countries like Sweden and Finland (Filipiak 2020). In Sweden, aggressive policies and widespread adoption by both businesses and society have reduced cash transactions to just 20% of the total (Filipiak 2020). Similarly, Finland is advancing toward a cashless society, leading in both card payment frequency and Internet banking penetration. Impact of COVID-19 pandemic on cashless economy This study examines a crucial aspect of daily life—payment methods—which have undergone significant changes due to the COVID-19 pandemic. The Brunei Darussalam Ministry of Health highlighted that COVID-19 spreads easily through contact with respiratory droplets from an infected person’s cough, sneeze, or exhalation. These droplets can land on objects and surfaces, potentially infecting others who touch these surfaces and then touch their eyes, nose, or mouth (Abdul-Halim et al. 2022). Consequently, there was a global shift toward contactless activities, including cashless payments, to enhance safety and minimize physical contact. This transition accelerated financial inclusion as governments promoted digital payments to protect vulnerable populations during the pandemic (G20 Italian Presidency 2021). While the pandemic spurred widespread adoption of cashless transactions, Kotkowski and Polasik (2021) raised concerns about financial inclusion, noting that those already using cashless payments continued to do so, while some persisted with cash transactions despite the pandemic. Wisniewski et al. (2021) suggested that the pandemic changed payment habits, driving users away from physical cash due to fear and apprehension, which may sustain the adoption of cashless transactions even as conditions improve. Theoretical foundations for understanding the continued use of cashless payments include the Technology Acceptance Model (TAM). Developed by Davis (1989), TAM is based on the Theory of Reasoned Action (TRA) and focuses on Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as key factors influencing Technology Acceptance. Routray et al. (2019) emphasized these factors’ importance in consumer behavior but expanded the model by integrating dimensions of quality, such as information quality, system quality, and service quality. Ahuja and Joshi (2018) examined key factors affecting perceptions of e-wallets, including Ease of Use, Benefit, Trust, and Self-Efficacy, though they noted limitations due to a small sample size. Maqableh et al. (2015) found that perceived trust, encompassing reputation, security, privacy, and transaction size, significantly impacts the adoption of cashless payments. The Technology Readiness Index (TRI), developed by Mick and Fournier (1998), measures users’ readiness for new technology. TRI assesses factors such as Optimism, Innovativeness, Discomfort, and Insecurity, reflecting the overall mindset and inclination toward technology adoption (Parasuraman 2000). Studies by Humbani and Wiese (2018) applied TRI to examine readiness for mobile payments, identifying drivers and inhibitors of adoption behaviors. Similarly, Karim and Muhammad (2022) found that Technology Readiness, along with Confirmation Expectation, User Satisfaction, and Perceived Security, influences the continued use of cashless payments. Economies 2024,12, 285 7 of 35 Theoretical Framework: Davis developed the Technology Acceptance Model in 1989 to study user behavior regarding information technology and predict adoption. TAM emphasizes user perceptions and the belief that technology must be both useful and userfriendly to gain acceptance. The model focuses on two main factors: Perceived Usefulness and Perceived Ease of Use. Parasuraman (2000) defined Technology Readiness as an individual’s propensity to embrace and use new technologies for personal and professional goals. The Technology Readiness Index (TRI) measures overall Technology Acceptance based on four factors: Optimism, Innovativeness, Discomfort, and Insecurity (Figure 1). Economies 2024, 12, x FOR PEER REVIEW 7 of 37 Theoretical Framework: Davis developed the Technology Acceptance Model in 1989 to study user behavior regarding information technology and predict adoption. TAM emphasizes user perceptions and the belief that technology must be both useful and userfriendly to gain acceptance. The model focuses on two main factors: Perceived Usefulness and Perceived Ease of Use. Parasuraman (2000) defined Technology Readiness as an individual’s propensity to embrace and use new technologies for personal and professional goals. The Technology Readiness Index (TRI) measures overall Technology Acceptance based on four factors: Optimism, Innovativeness, Discomfort, and Insecurity (Figure 1). Figure 1. Technology Acceptance Model (Davis 1989). In Technology Readiness (TR), Parasuraman (2000) identifies several key factors. Optimism reflects a positive belief in technology, with users confident that it enhances control, efficiency, and flexibility. Innovativeness describes a tendency to be an early adopter and a thought leader in technology. Discomfort encompasses feelings of a lack of control over technology and being overwhelmed by its complexities. Lastly, Insecurity involves a distrust of technology and skepticism about its reliability and effectiveness (Figure 2). Figure 2. Technology Readiness Index (Parasuraman 2000). 3. Hypothesis Formulation Seven hypotheses have been developed to test the primary objectives of the research. 3.1. Innovativeness H1: Personal Innovativeness with technology leads to high perceived ease of use towards the continued adoption of cashless payments. H2: Personal Innovativeness with technology leads to high perceived usefulness towards the continued adoption of cashless payments. Figure 1. Technology Acceptance Model (Davis 1989). In Technology Readiness (TR), Parasuraman (2000) identifies several key factors. Optimism reflects a positive belief in technology, with users confident that it enhances control, efficiency, and flexibility. Innovativeness describes a tendency to be an early adopter and a thought leader in technology. Discomfort encompasses feelings of a lack of control over technology and being overwhelmed by its complexities. Lastly, Insecurity involves a distrust of technology and skepticism about its reliability and effectiveness (Figure 2). Economies 2024, 12, x FOR PEER REVIEW 7 of 37 Theoretical Framework: Davis developed the Technology Acceptance Model in 1989 to study user behavior regarding information technology and predict adoption. TAM emphasizes user perceptions and the belief that technology must be both useful and userfriendly to gain acceptance. The model focuses on two main factors: Perceived Usefulness and Perceived Ease of Use. Parasuraman (2000) defined Technology Readiness as an individual’s propensity to embrace and use new technologies for personal and professional goals. The Technology Readiness Index (TRI) measures overall Technology Acceptance based on four factors: Optimism, Innovativeness, Discomfort, and Insecurity (Figure 1). Figure 1. Technology Acceptance Model (Davis 1989). In Technology Readiness (TR), Parasuraman (2000) identifies several key factors. Optimism reflects a positive belief in technology, with users confident that it enhances control, efficiency, and flexibility. Innovativeness describes a tendency to be an early adopter and a thought leader in technology. Discomfort encompasses feelings of a lack of control over technology and being overwhelmed by its complexities. Lastly, Insecurity involves a distrust of technology and skepticism about its reliability and effectiveness (Figure 2). Figure 2. Technology Readiness Index (Parasuraman 2000). 3. Hypothesis Formulation Seven hypotheses have been developed to test the primary objectives of the research. 3.1. Innovativeness H1: Personal Innovativeness with technology leads to high perceived ease of use towards the continued adoption of cashless payments. H2: Personal Innovativeness with technology leads to high perceived usefulness towards the continued adoption of cashless payments. Figure 2. Technology Readiness Index (Parasuraman 2000). 3. Hypothesis Formulation Seven hypotheses have been developed to test the primary objectives of the research. 3.1. Innovativeness H1: Personal Innovativeness with technology leads to high perceived ease of use towards the continued adoption of cashless payments. Economies 2024,12, 285 8 of 35 H2: Personal Innovativeness with technology leads to high perceived usefulness towards the continued adoption of cashless payments. Individuals who possess high innovativeness with technology typically have a stronger intrinsic motivation to explore and use new technology. Those highly motivated by innovation are not worried about whether it is user-friendly and may still attempt to try and use it (Dabholkar and Bagozzi 2002). 3.2. Optimism H3: Personal Optimism about technology significantly leads to high perceived ease of use towards the continued adoption of cashless payments. H4: Personal Optimism about technology significantly leads to high perceived usefulness towards the continued adoption of cashless payments. According to Parasuraman (2000), a technology optimist is someone who believes that new technologies offer increased control, flexibility, and efficiency in their daily lives. This Optimism means that individuals with a positive outlook are likely to view new technology favorably, even if they have not yet used it. Their pre-determined positive attitude influences their readiness to embrace and adopt new technological advancements. 3.3. Discomfort H5: Personal Discomfort with technology significantly leads to low perceived ease of use towards the continued adoption of cashless payments. H6: Personal Discomfort with technology significantly leads to low perceived usefulness towards the continued adoption of cashless payments. Discomfort involves a perception of having limited control over technology, which can lead to feelings of being overwhelmed (Lin et al. 2007). According to Parasuraman (2000), individuals who experience discomfort with new technology often believe that it will dominate their lives rather than serve them. They may also feel that technology is designed for those with advanced technical knowledge rather than for the average user. 3.4. Insecurity H7: Personal Insecurity about technology significantly leads to low perceived ease of use towards the continued adoption of cashless payments. H8: Personal Insecurity about technology significantly leads to low perceived usefulness toward the continued adoption of cashless payments. Insecurity refers to a lack of trust in technology and skepticism about its reliability and effectiveness (Lin et al. 2007). Research suggests that individuals who feel insecure about technology are more likely to focus on potential risks rather than the benefits. This apprehension often leads them to avoid adopting new technology altogether (Blut and Wang 2020). 3.5. Perceived Ease of Use and Perceived Usefulness H9: There is a significant positive relationship between Perceived Ease of Use and Perceived Usefulness regarding the continued adoption of cashless payments. Economies 2024,12, 285 15 of 35 Table 6. Details of demographic profile. Demographic Profile Gender •Male •Female Age group •Below 20 years •21–30 years •31–40 years •41–50 years •51–60 years •Above 60 years Level of education •Certificate •Undergraduate degree •Postgraduate degree •Professional qualification •Others Monthly income •Below BND1000 •BND1000–BND2000 •BND2001–BND3000 •BND3001–BND4000 •BND4001–BND5000 •Above BND5000 Do you use cashless payments (Debit Cards/Credit Cards/Internet Banking/Mobile Banking/E-wallet QR Codes) when making or transferring payments? •Yes •No. Two (2) cashless payment modes mostly used •Credit and/or Debit Card •Internet Banking •Mobile Banking •E-wallet QR Codes Measurement Model Assessment Measurement model assessment analyses the quality of measurement to improve its usefulness and accuracy. With the quantitative approach, the constructs of the instrument will be assessed for factor loading, validity, and reliability. Considering the reliability and validity of data-collection instruments is critical when conducting and discussing research. However, it is also crucial to begin the measurement model assessment by examining the factor analysis of the variable items to determine how well the item represents the underlying factor. Reliability and Validity Analysis For this study, confirmatory factor analysis will be performed to confirm the validity of the hypotheses and measurement instruments. This will be performed through SmartPLS 4. To ensure how well the applied method of the questionnaire can measure the responses, reliability and validity analysis will be performed. Reliability refers to the consistency of a measure. Studies by Heale and Twycross (2015), in measuring behaviors relating to nursing practices, explain that participants completing a quantitative instrument would result in providing the same response each time it is completed. When analyzing reliability for this study, internal consistency will be assessed to determine the extent to which hall the items on a scale measure a construct. In this sense, Cronbach’s Alpha, being the most common test, will be used to determine the internal consistency of the questionnaire with a measurement of test or scale between 0 and 1. According to Tavakol and Dennick (2011), alpha value increases when items correlate. However, it is also important to note that a high alpha coefficient would not represent a high level of internal consistency if there were a lack in the length of the test. Therefore, depending on the assessment Economies 2024,12, 285 16 of 35 conducted for indicator reliability, composite reliability may also be applied to testing for its reliability. Validity analysis refers to the accuracy of a measure in a quantitative study. In other words, it determines whether results obtained accurately represent the desired measure. In measuring for validity, construct validity and criterion validity will be measured by examining homogeneity and convergent validity, respectively. Discriminant validity will also be measured to determine whether the items measure something else unexpectedly. The heterotrait–monotrait (HTMT) ratio of correlations approach will be applied to determine it. Studies by Henseler et al. (2015) found it to be of superior performance when compared to the Fornell–Larcker criterion. Structural Model Assessment Prior to examining the hypothesis proposed, the structural model will be tested. Structural moment assessment focuses on analyzing the relationship between independent variables, dependent variables, the mediator connection between two variables, or moderation analysis with an additional variable affecting the relationship, that include the path coefficient (ß), the coefficient of determination (R 2 ), and the effect size (F 2 ) in the assessment of the structural model. For this study, the path coefficient and the coefficient of determination will be analyzed to validate the structural model. The bootstrapping process will be applied to determine whether the path coefficient is statistically significant or otherwise. Assessment will be done through PLS-SEM software, which can evaluate the significance and relevance of path coefficients, upon which the model’s explanatory and predictive power can be assessed. It is crucial to note that the initial step in the evaluation of structural model constructs is the assessment of whether there are issues of collinearity. Structural models showing high multicollinearity can affect the path coefficient and change the sign of these coefficient. Therefore, a test for collinearity will be performed prior to structural model assessments which is shown in Figure 4. Economies 2024, 12, x FOR PEER REVIEW 16 of 37 else unexpectedly. The heterotrait–monotrait (HTMT) ratio of correlations approach will be applied to determine it. Studies by Henseler et al. (2015) found it to be of superior performance when compared to the Fornell–Larcker criterion. Structural Model Assessment Prior to examining the hypothesis proposed, the structural model will be tested. Structural moment assessment focuses on analyzing the relationship between independent variables, dependent variables, the mediator connection between two variables, or moderation analysis with an additional variable affecting the relationship, that include the path coefficient (ß), the coefficient of determination (R 2 ), and the effect size (F 2 ) in the assessment of the structural model. For this study, the path coefficient and the coefficient of determination will be analyzed to validate the structural model. The bootstrapping process will be applied to determine whether the path coefficient is statistically significant or otherwise. Assessment will be done through PLS-SEM software, which can evaluate the significance and relevance of path coefficients, upon which the model’s explanatory and predictive power can be assessed. It is crucial to note that the initial step in the evaluation of structural model constructs is the assessment of whether there are issues of collinearity. Structural models showing high multicollinearity can affect the path coefficient and change the sign of these coefficient. Therefore, a test for collinearity will be performed prior to structural model assessments. Figure 4 below shows the structural model assessment procedure according to Joseph F. Hair et al. (2020). Figure 4. Structural model assessment performed. 5. Finding The study employed partial least-squares structural equation modeling (PLS-SEM) to rigorously assess the measurement model, a robust technique well suited to complex predictive modeling and theory-building. PLS-SEM is particularly advantageous for handling intricate research models and smaller sample sizes, providing flexibility in managing multiple dependent constructs and accommodating non-normal data distributions. This methodology was chosen to explore the causal relationships between constructs related to the cashless economy, including readiness, security, and technological enhancement. PLS-SEM is ideal for exploratory research where theoretical frameworks may still be developing. To ensure the reliability and validity of the constructs, several measures were implemented. Reliability was evaluated using Cronbach’s Alpha and composite reliability (CR), while convergent validity was confirmed through average variance extracted (AVE) values exceeding the 0.50 threshold. Discriminant validity was assessed using the Fornell– Larcker criterion and the heterotrait–monotrait (HTMT) ratio to ensure that each construct was distinct. For constructs like “Insecurity”, which may show lower reliability coefficients, potential inconsistencies were addressed by reviewing item wording, the conceptual domain, and contextual factors of the study. The structural model analysis was comprehensive, with significant path coefficients clearly linked to the study’s hypotheses or research questions. A concise summary of results was presented in tabular format for easy reference, enhancing clarity. The interpretation of findings emphasized their implications for broader research questions, discussing unexpected results or deviations from prior literature. Possible explanations for these Step 1: Assess collinearity issues in the structural model Step 2: Assess the significance and relevance of the structural model relationships Step 3: Assess the model’s explanatory power Step 4: Assess the model predictive power Figure 4. Structural model assessment performed. 5. Finding The study employed partial least-squares structural equation modeling (PLS-SEM) to rigorously assess the measurement model, a robust technique well suited to complex predictive modeling and theory-building. PLS-SEM is particularly advantageous for handling intricate research models and smaller sample sizes, providing flexibility in managing multiple dependent constructs and accommodating non-normal data distributions. This methodology was chosen to explore the causal relationships between constructs related to the cashless economy, including readiness, security, and technological enhancement. PLS-SEM is ideal for exploratory research where theoretical frameworks may still be developing. To ensure the reliability and validity of the constructs, several measures were implemented. Reliability was evaluated using Cronbach’s Alpha and composite reliability (CR), while convergent validity was confirmed through average variance extracted (AVE) values exceeding the 0.50 threshold. Discriminant validity was assessed using the Fornell–Larcker criterion and the heterotrait–monotrait (HTMT) ratio to ensure that each construct was distinct. For constructs like “Insecurity”, which may show lower reliability coefficients, potential inconsistencies were addressed by reviewing item wording, the conceptual domain, and contextual factors of the study. Economies 2024,12, 285 17 of 35 The structural model analysis was comprehensive, with significant path coefficients clearly linked to the study’s hypotheses or research questions. A concise summary of results was presented in tabular format for easy reference, enhancing clarity. The interpretation of findings emphasized their implications for broader research questions, discussing unexpected results or deviations from prior literature. Possible explanations for these anomalies, such as sample characteristics or measurement issues, were explored, and directions for future research were suggested. Results were integrated with the study’s overarching research questions, demonstrating how each statistical outcome contributes to understanding the dynamics of the cashless economy, readiness, security, and technological advancement. This synthesis provided a cohesive narrative, highlighting the study’s contributions to theory, practice, and policy. For example, insights on security may inform regulatory changes, while findings on technological enhancement could guide improvements in user interfaces for cashless payment systems. In conclusion, the Data Analysis section critically evaluated the study’s limitations and suggested areas for future research, enhancing the study’s scholarly rigor and contributing to ongoing academic discourse. Practical implications were discussed in detail, offering recommendations for policymakers, industry practitioners, and researchers to advance cashless economy initiatives. Strategies for improving security in digital transactions and increasing public readiness for a cashless economy were particularly emphasized (Table 7). Table 7. Summary of hypotheses and results. Hypothesis Path Coefficient Significance (p-Value) Supported (Yes/No) H1 0.35 <0.01 Yes H2 −0.12 0.05 No H3 0.45 <0.01 Yes . . . . . . . . . . . . Demographic Analysis A total of 219 responses (N = 219) were collected over 4 weeks through an online survey, forming the quantitative foundation of this study. Initially, the survey was distributed via WhatsApp and email to friends and family, ensuring a broad reach within known networks. To further expand the respondent base, the distribution was later extended to include random participants. To maintain data integrity, all survey questions were set as mandatory, ensuring that each respondent provided complete answers. As a result, all responses received are considered valid for analysis. Tables 7–9present the demographic profile of the respondents (=219). According to Table 7, the sample consists predominantly of female respondents, with 144 participants (64.86%), compared to 75 male respondents (33.78%). This gender distribution highlights a higher female representation in the study. Table 8. Demographic profile by gender and age (N = 219). Respondent Profiles Frequency % Cumulative % Gender Male 75 33.78% 34.2% Female 144 64.86% 100% Age Group Below 20 Years 12 5.41% 5.50% 21–30 Years 21 9.46% 15.1% 31–40 Years 49 22.07% 37.4% 41–50 Years 112 50.45% 88.6% 51–60 Years 23 10.36% 99.1% Above 60 Years 2 0.90% 100% Economies 2024,12, 285 18 of 35 Table 9. Demographic profile by education, employment, and income level (N = 219). Respondent Profiles Frequency % Cum % Education Level Certificate/Diploma Level 77 34.68% 35.2% Undergraduate Degree 61 27.48% 63.0% Postgraduate Degree 68 30.63% 94.1% Professional Qualification 11 4.95% 99.1% Others 2 0.90% 100% Employment Status Employed (Government) 113 50.90% 51.6% Employed (Private) 78 35.14% 87.2% Self-employed 6 2.70% 90.0% Unemployed 16 7.21% 97.3% Retired 6 2.70% 100% Monthly Gross Income Below BND1000 31 13.96% 14.2% BND1000–BND2000 33 14.86% 29.2% BND2001–BND3000 36 16.22% 45.7% BND3001–ND4000 44 19.82% 65.8% BND4001–BND5000 55 24.77% 90.9% Above BND5000 20 9.01% 100% In terms of age distribution, most respondents fall within the 41–50 years age group, accounting for over half of the sample (50.45%). The next largest age group is 31–40 years, representing 22.07% of respondents. Smaller percentages of participants are spread across other age groups: 21–30 years (9.46%), 51–60 years (10.36%), below 20 years (5.41%), and above 60 years (0.90%). This demographic breakdown provides a comprehensive overview of the respondent characteristics, indicating a diverse range of ages, with a concentration in the middle-age categories. These demographic insights will be essential in contextualizing the findings of the study and understanding how different age and gender groups perceive the issues explored. Table 8offers a detailed breakdown of respondents’ demographic profiles, categorized by education level and employment status. The data shows that most respondents have attained either a Certificate/Diploma or a Postgraduate Degree, representing 34.68% and 30.63% of the sample, respectively. Those with an Undergraduate Degree constitute 27.48% of the respondents. These statistics indicate a highly educated participant pool, with a significant proportion having achieved advanced education. Regarding employment status, government employees are the largest group, accounting for 50.9% of the respondents. This is followed by those working in the private sector, who make up 35.14% of the sample. A smaller segment, 2.70%, are self-employed, while 9.91% are currently unemployed, totaling 21 individuals. Despite their unemployment status, these individuals are considered valuable for the study as they may still have other income sources and represent potential future members of the workforce. In terms of income distribution, most respondents fall within the monthly income bracket of BND4001 to BND5000, making up 24.77% of the sample. This is followed by those earning between BND3001 and BND4000, who represent 19.82% of respondents. Including unemployed respondents in the data analysis provides a more comprehensive view of income dynamics and economic participation across various segments of society, highlighting potential future earnings and contributions to the workforce. To address the research questions, the study focuses on individual customers who use cashless payment methods, using this segment as the sample for further analysis. As illustrated in Table 9, out of the 219 respondents who initially participated, 14 individuals were identified as non-cashless payment users and were therefore excluded from the analysis. The remaining 205 respondents, who use cashless payment methods, provide the basis for the subsequent analysis (Table 10). Economies 2024,12, 285 19 of 35 Table 10. Respondents based on cashless payment user (N = 219). Respondent Profiles Frequency % Cashless Payment User No 14 6.31% Yes 205 92.34% Among these 205 cashless payment users, a significant portion, 34.10%, reports using cashless payments 3 to 5 times a day. In contrast, an equal percentage of respondents, 27.30%, use cashless payments either once or twice a day. This distribution indicates a relatively high frequency of cashless payment usage among a substantial number of respondents. Regarding the modes of cashless payments preferred by respondents, the majority show a strong preference for credit and debit cards, with 95.10% using them for transactions. Mobile banking follows as the second most popular method, used by 76.60% of respondents. These preferences highlight the dominance of traditional card payments and the growing role of mobile banking in cashless transactions. Figures 5and 6provide visual representations of these findings. Figure 5depicts a bar graph illustrating the frequency of daily cashless payment usage among the 205 respondents, while Figure 6presents a bar graph showing the percentage of each cashless payment method utilized. These figures offer a clear overview of the usage patterns and preferences among cashless payment users, facilitating a deeper understanding of the data. Economies 2024, 12, x FOR PEER REVIEW 19 of 37 illustrated in Table 9, out of the 219 respondents who initially participated, 14 individuals were identified as non-cashless payment users and were therefore excluded from the analysis. The remaining 205 respondents, who use cashless payment methods, provide the basis for the subsequent analysis (Table 10). Among these 205 cashless payment users, a significant portion, 34.10%, reports using cashless payments 3 to 5 times a day. In contrast, an equal percentage of respondents, 27.30%, use cashless payments either once or twice a day. This distribution indicates a relatively high frequency of cashless payment usage among a substantial number of respondents. Regarding the modes of cashless payments preferred by respondents, the majority show a strong preference for credit and debit cards, with 95.10% using them for transactions. Mobile banking follows as the second most popular method, used by 76.60% of respondents. These preferences highlight the dominance of traditional card payments and the growing role of mobile banking in cashless transactions. Figures 5 and 6 provide visual representations of these findings. Figure 5 depicts a bar graph illustrating the frequency of daily cashless payment usage among the 205 respondents, while Figure 6 presents a bar graph showing the percentage of each cashless payment method utilized. These figures offer a clear overview of the usage patterns and preferences among cashless payment users, facilitating a deeper understanding of the data. Table 10. Respondents based on cashless payment user (N = 219). Respondent Profiles Frequency % Cashless Payment User No 14 6.31% Yes 205 92.34% Figure 5. Frequency % of cashless payment use in a day (N = 205). 2.40% 27.30% 27.30% 34.10% 8.80% 0% 5% 10% 15% 20% 25% 30% 35% 40% Not used daily Once Twice Three to Five times Six times and more Frequency of Cashless Payment Use in a Day Figure 5. Frequency % of cashless payment use in a day (N = 205). Economies 2024, 12, x FOR PEER REVIEW 20 of 37 Figure 6. Percentage of cashless payment modes used (N = 205). Preliminary Data Analysis Before conducting any statistical analyses, a thorough data screening process was undertaken to ensure the validity and completeness of the collected data. Out of the initial 219 responses, 14 were excluded from the dataset. This exclusion was based on the identification of non-response bias, particularly related to questions concerning indicators central to the study. These 14 respondents were classified as non-cashless payment users, and their responses did not meet the study’s criteria for valid data, rendering them unusable for the research. Extreme Values/Outliers: The accuracy and reliability of statistical analysis depend heavily on the quality of the data, which must be screened for missing values and extreme outliers (Pallant 2020). In this study, data cleaning was performed using SPSS software to identify and address outliers and extreme values, with the goal of achieving a normal distribution of the dataset. Marcoulides and Saunders (2006) emphasize that removing extreme values and outliers is crucial before conducting statistical tests, as they can skew results and affect the validity of the findings. During the data-cleaning process, a normality test was conducted using SPSS, which identified three cases with extreme values. These cases were removed to ensure that the final dataset adhered to normality assumptions. Consequently, the revised dataset included 203 valid responses. Table 11 illustrates the responses and constructs associated with the extreme values and outliers identified during this process. This careful screening and cleaning of the data ensures that the remaining dataset is robust and suitable for subsequent statistical analysis. Table 11. Extreme values identified and excluded. Respondent No. OPT INN DIS INS PEOU PU CA 36 1 1 2 2 1 1 1 77 2 2 3 2 1 1 2 189 1 1 3 3 2 1 1 Note: One (1) = Strongly Disagree, Two (2) = Disagree, Three (3) = Neutral, Four (4) = Agree, Five (5) = Strongly Agree. Normality of Data A normality test is commonly utilized to determine whether the data are collected from a normally distributed population. Research data verified to be normally distributed will enable the study to apply parametric tests such as a t-test, ANOVA, correlation, and 95.10% 40.50% 76.60% 13.20% 4.90% 59.50% 23.40% 86.80% 0% 25% 50% 75% 100% Credit or Debit Card Internet Banking Mobile Banking E-wallet / QR Code Yes No Figure 6. Percentage of cashless payment modes used (N = 205). Economies 2024,12, 285 20 of 35 Preliminary Data Analysis Before conducting any statistical analyses, a thorough data screening process was undertaken to ensure the validity and completeness of the collected data. Out of the initial 219 responses, 14 were excluded from the dataset. This exclusion was based on the identification of non-response bias, particularly related to questions concerning indicators central to the study. These 14 respondents were classified as non-cashless payment users, and their responses did not meet the study’s criteria for valid data, rendering them unusable for the research. Extreme Values/Outliers: The accuracy and reliability of statistical analysis depend heavily on the quality of the data, which must be screened for missing values and extreme outliers (Pallant 2020). In this study, data cleaning was performed using SPSS software to identify and address outliers and extreme values, with the goal of achieving a normal distribution of the dataset. Marcoulides and Saunders (2006) emphasize that removing extreme values and outliers is crucial before conducting statistical tests, as they can skew results and affect the validity of the findings. During the data-cleaning process, a normality test was conducted using SPSS, which identified three cases with extreme values. These cases were removed to ensure that the final dataset adhered to normality assumptions. Consequently, the revised dataset included 203 valid responses. Table 11 illustrates the responses and constructs associated with the extreme values and outliers identified during this process. This careful screening and cleaning of the data ensures that the remaining dataset is robust and suitable for subsequent statistical analysis. Table 11. Extreme values identified and excluded. Respondent No. OPT INN DIS INS PEOU PU CA 36 1 1 2 2 1 1 1 77 2 2 3 2 1 1 2 189 1 1 3 3 2 1 1 Note: One (1) = Strongly Disagree, Two (2) = Disagree, Three (3) = Neutral, Four (4) = Agree, Five (5) = Strongly Agree. Normality of Data A normality test is commonly utilized to determine whether the data are collected from a normally distributed population. Research data verified to be normally distributed will enable the study to apply parametric tests such as a t-test, ANOVA, correlation, and regressions for its statistical analysis. Essentially, for parametric tests, the assumption of normality needs to be checked, as the validity of the test depends on it. The normality of data is achieved when the significant critical values are more than 0.05 (p-values> 0.05) (Ghasemi and Zahediasl 2012). One of the techniques in checking for normality of data is the Kolmogorov–Smirnov test performed using the SPSS software. A previous study by Mohd Sapian and Norziah Ismail (2021) applied this test method based on the number of responses collected exceeding50 responses applied this test. Table 12 illustrates the result of the Kolmogorov–Smirnov test performed where all the variables indicate that normal distribution cannot be assumed, as significant p-values are less than 0.05, therefore rejecting the null hypothesis that the data are normally distributed. As the normality of data cannot be assumed, it can be concluded that data analysis will be further tested using the non-parametric techniques of SmartPLS version 4. Economies 2024,12, 285 21 of 35 Table 12. Result of normality for all constructs. Variables Kolmogorov–Smirnov Statistic Sig. Optimism (OPT) 0.146 0.000 Innovativeness (IN) 0.095 0.000 Discomfort (DIS) 0.080 0.003 Insecurity (INS) 0.117 0.000 Perceived Ease of Use (PEOU) 0.248 0.000 Perceived Usefulness (PU) 0.152 0.000 Continued Adoption of Cashless Payments (CA) 0.117 0.000 H0 = Data are normally distributed Descriptive Analysis Descriptive analysis was constructively used to describe and summarize significant data points as well as identify patterns within the variables to gain accessible insights prior to performing further data analysis (Bush 2020). Utilizing SPSS software, descriptive statistics of the mean, standard deviation, and frequency were used to describe the obtained data. Based on six independent variables and one dependent variable developed for the study, the mean and standard deviation were computed to determine the median and central tendencies. Table 13 shows the measurement of the central tendencies for both the independent and dependent constructs. The analysis was factored based on a5-point Likert scale for each question with a scale from one (strongly disagree) to five (strongly agree). Based on the calculations, independent variables Discomfort, with a low mean of 2.971 and standard deviation of0.74, and Insecurity, with a low mean of 3.02 and deviation of 0.77,indicatethat respondents neither agree nor disagree about having lack of control and feelings of distrust towards cashless payments. Independent variables of Optimism, Innovativeness, Perceived Ease of Use, and Perceived Usefulness showed higher means, ranging from 3.43 to 4.28,with a standard deviation of 0.703 to 0.870, which indicates that most respondents agreed with the questions under each variable. Perceived Ease of Use showed the highest mean of 4.28, giving a further indication that respondents agree that cashless payments are easy to use rather than respondents being disagreeable towards them. Table 13. Central tendencies for all constructs. Constructs Mean SD Optimism 3.978 0.870 Innovativeness 3.438 0.753 Discomfort 2.971 0.740 Insecurity 3.023 0.772 Perceived Ease of Use 4.281 0.708 Perceived Usefulness 4.046 0.703 Continued Adoption of Cashless Payments 4.061 0.767 In terms of the dependent variable, Continued Adoption of Cashless Payments had a high mean value of 4.06 with a standard deviation of 0.76. This further shows that most respondents are agreeable to continuing to adopt and use cashless payments. Dissecting it further into each item of the construct provides further insight into the respondents’ level of agreement with the questions posed in the survey distributed. This will be further discussed in Section 5. Table 14 shows the central tendencies according to the question items under each construct. Economies 2024,12, 285 22 of 35 Table 14. Central tendencies for each indicator. Constructs Items Mean SD. Innovativeness INN1 I find cashless payments to be mentally stimulating 3.41 0.839 INN2 I can usually figure out how to use cashless payments without help from others 3.82 0.923 INN3 I am among the first in my circle of friends to adopt cashless payments when it is introduced 3.39 1.134 INN4 I feel that other people come to me for advice on how to use cashless payments 3.13 0.972 Optimism OPT1 Cashless payments give me flexibility in making payments 4.24 0.954 OPT2 Cashless payments fit my lifestyle 4.06 0.913 OPT3 Cashless payments make me more productive in my personal life 3.91 0.996 OPT4 Cashless payments make me more efficient in my profession 3.79 0.996 OPT5 I feel confident that cashless payments will follow through with what I instruct them to do 3.88 0.963 Discomfort DIS1 I feel it is not safe to do transactions online 2.73 0.870 DIS2 Sometimes, I think that cashless payments are not designed for use by ordinary people 2.65 0.986 DIS3 It is embarrassing when I have trouble with cashless payments while other people are watching 3.15 1.125 DIS4 I feel that cashless payments have risks that are not known until after people have used them 3.35 0.986 Insecurity INS1 I feel cashless payments expose my financial information online 2.93 1.005 INS2 I do not feel confident buying or doing business with a place that only accepts cashless payments 2.51 0.998 INS3 Any cashless payment transaction should be confirmed later with a separate communication 3.63 1.145 Perceived Ease of Use PEOU1 I use cashless payments based on my own personal wants 4.20 0.780 PEOU2 In my opinion, the use of cashless payments is flexible (can be used anytime and anywhere) 4.29 0.812 PEOU3 Overall, cashless payments are easy to use 4.36 0.777 Perceived usefulness PU1 I feel cashless payments enable me to make payments effectively 4.17 0.787 PU2 I feel cashless payments help to manage my financesbetter 3.69 0.990 PU3 I find cashless payments make it easier to accomplish my payment activities 4.13 0.776 PU4 I feel cashless payments area practical option formaking payment 4.20 0.726 Continued Adoption of Cashless payments CA1 I have been an active user of cashless payments for some time 4.13 0.856 CA2 I intend to continue using cashless payments in the future 4.25 0.805 CA3 I intend to increase thefrequency of cashless payments in my daily life 3.86 0.915 CA4 I will always recommend that others use cashless payments 4.00 0.855 Economies 2024,12, 285 23 of 35 The measurement model evaluates the relationship between latent variables and their corresponding measurements. As outlined in Section 3, the assessment of construct validity and Cronbach’s Alpha will be conducted to ensure the quality of these measurements before proceeding to hypothesis testing. To perform this assessment, both SPSS software and PLS-SEM through SmartPLS V4.0 will be employed. Previous research by Zamil et al. (2022) has demonstrated that PLS-SEM is effective in addressing complex modeling issues, including non-normal data distributions. This dual approach will facilitate a comprehensive evaluation of the measurement model’s robustness and validity. Reliability and Validity Test Validity and reliability are crucial elements when evaluating an instrument for measurement quality (Kimberlin and Winterstein 2008). As the study implements a questionnaire as the instrument to obtain data for measurement and analysis, its ability to measure consistently needs to be examined. For this study, the assessment begins with evaluating the outer model using PLS-SEM and testing for indicator reliability. Assessment of the outer model supports validity by proving how well each item represents the underlying constructs (Joeseph F. Hair et al. 2014). Items with high outer loadings indicate that the items are more in common and, therefore, support the validity of the construct. Studies by Hair et al. (2014) further recommend that factor loading should be at least 0.708 for the acceptable reliability of its item. Using PLS-SEM, the result of the outer loadings can be found in Table 15, which shows that all 11 items of the dependent variables meet the recommended values of over 0.708, deducing indicator reliability for Continued Adoption, Perceived Ease of Use, and Perceived Usefulness. Loadings for items of the independent constructs, however, showed 5 items with weaker loadings below the cut-off point of 0.708. The weaker indicators are those under constructs of Innovativeness (INN1, INN4), Discomfort (DIS3, DIS4), and Insecurity (INS3). Out of the weak loadings, item INS3 showed an unacceptable loading of − 0.071, therefore prompting the removal of this item from the construct before proceeding with further analysis (Joeseph F. Hair et al. 2011). The numbers in blue indicated as higher score of Reliability and Validity Test of indicators. Though SPSS’s Cronbach’s Alpha was initially applied for the pilot test performed in Section 3, studies by Haji-Othman and Yusuff (2022) suggest using PLS-SEM, which prioritizes the items according to their individual reliability. This also aligns with the assessments recommended by Hair et al. (2020) based on the results of outer loadings. Table 15 shows further reliability after discarding item INS3 from the constructs and running the test using PLS-SEM. All constructs adopted in the questionnaires are found to be acceptably reliable with Cronbach’s Alpha. However, based on composite reliability values, there was a lack of reliability found under the Insecurity construct, as composite reliability values are acceptable for above 0.70 (Joeseph F. Hair et al. 2020). Nevertheless, for this study, the reliability value for Insecurity is accepted based on Cronbach’s Alpha. However, it is also vital to acknowledge the possibility of the redundancy of the items should internal consistency result in values higher than 0.95. For the constructs assessed, the highest reliability values are the Optimism constructs at 0.939, which could be due to the high number of items for the construct. Nevertheless, the reliability of all the constructs is found to be acceptable, and items are usable for further testing. In assessing validity, convergent validity was conducted using the SmartPLS Software to find out how far measures that are expected to be theoretically related correlate with one another in practice. Average variance extracted (AVE) is one of the common measurements to evaluate convergent validity. Hair et al. (2014) state that convergent validity is supported when each item has outer loadings above 0.70 and when each construct’s average variance extracted (AVE) is 0.50 or higher. As shown in Table 16, validity testing results in all variables with AVE values reaching above the 0.5 cut-off point, meaning that these constructs have passed the validity test. High AVE values of 0.792 for Optimism and 0.778 for Continued Adoption imply that both constructs revealed more variance instead of errors in constructs. Economies 2024,12, 285 24 of 35 Table 15. Outer loadings results for indicator reliability. INN OPT DIS INS PEOU PU CA INN1 0.674 0.434 −0.043 −0.088 0.398 0.429 0.357 INN2 0.803 0.482 −0.157 −0.285 0.507 0.463 0.438 INN3 0.843 0.396 −0.15 −0.333 0.364 0.452 0.509 INN4 0.697 0.351 −0.062 −0.289 0.253 0.39 0.413 OPT1 0.477 0.868 −0.092 −0.221 0.593 0.5 0.559 OPT2 0.491 0.885 −0.231 −0.313 0.517 0.517 0.598 OPT3 0.516 0.916 −0.187 −0.291 0.537 0.57 0.547 OPT4 0.497 0.879 −0.163 −0.321 0.455 0.497 0.513 OPT5 0.491 0.902 −0.176 −0.307 0.49 0.516 0.537 DIS1 −0.203 −0.209 0.742 0.475 −0.07 −0.139 −0.243 DIS2 −0.071 −0.195 0.85 0.4 −0.102 −0.161 −0.248 DIS3 −0.043 0.02 0.65 0.213 −0.039 −0.109 −0.025 DIS4 −0.05 −0.065 0.618 0.406 0.088 −0.056 −0.086 INS1 −0.222 −0.252 0.504 0.796 −0.154 −0.224 −0.302 INS2 −0.315 −0.286 0.458 0.882 −0.219 −0.302 −0.357 INS3 0.023 0.037 0.182 −0.071 0.121 0.115 0.057 PEOU1 0.476 0.469 −0.059 −0.145 0.803 0.55 0.543 PEOU2 0.387 0.489 −0.059 −0.23 0.896 0.658 0.577 PEOU3 0.49 0.565 −0.125 −0.287 0.91 0.643 0.674 PU1 0.534 0.461 −0.063 −0.322 0.631 0.793 0.603 PU2 0.453 0.459 −0.253 −0.254 0.367 0.743 0.489 PU3 0.447 0.532 −0.161 −0.289 0.64 0.9 0.666 PU4 0.472 0.481 −0.154 −0.275 0.66 0.857 0.64 CA1 0.509 0.558 −0.224 −0.385 0.625 0.648 0.879 CA2 0.466 0.6 −0.203 −0.348 0.678 0.682 0.933 CA3 0.518 0.492 −0.196 −0.309 0.512 0.566 0.8 CA4 0.518 0.533 −0.253 −0.377 0.607 0.682 0.912 Table 16. Reliability and validity results (after exclusion of indicator INS3). Constructs Cronbach’s Alpha CR AVE Innovativeness 0.768 0.842 0.574 Optimism 0.939 0.950 0.792 Discomfort 0.729 0.810 0.519 Insecurity 0.708 0.620 0.472 Perceived Ease of Use 0.846 0.904 0.759 Perceived Usefulness 0.846 0.895 0.681 Continued Adoption of Cashless Payments 0.904 0.933 0.778 Fornell and Larcker (2016) suggest that discriminant validity is established if a latent variable reveals more variance in its items instead of other constructs within the same model. For this study, the heterotrait–monotrait (HTMT) ratio of correlation was relied on in accordance with the criteria developed by Henseler et al. (2015) to further assess for discriminant validity. Numerous studies suggest HTMT is the superior method in performing the analysis with higher sensitivity rates to detect discriminant validity when compared to the Fornell and Larcker method. Hair et al. (2020) recommend the cut-off points of 0.85 and 0.90 as acceptable values when interpreting the results of HTMT. As shown in Table 17 for HTMT results, all values are below the recommended value of 0.90, which confirms that all constructs have acceptable levels of discriminant validity. Economies 2024,12, 285 31 of 35 maintain and boost this trend, service providers should focus on increasing awareness and understanding of cashless payments, ensuring robust security measures, and addressing users’ concerns about fraud and identity theft. By improving consumer knowledge and confidence, providers can support the continued growth and acceptance of cashless payment systems. 7. Conclusions and Recommendations 7.1. Conclusions This research sought to evaluate the acceptance and readiness of Brunei’s working population toward cashless payments following the COVID-19 pandemic and to examine the trends influencing the continued adoption of cashless transactions in the country. As Brunei transitions to an endemic stage, the emphasis on technology adoption has shifted from health-related “push factors” to user-driven “pull factors” based on preferences and perceived benefits. The study’s findings indicate that different dimensions of Technology Readiness affect Perceived Usefulness and Perceived Ease of Use in distinct ways. Innovativeness positively impacts both Perceived Usefulness and Ease of Use, while Optimism influences Perceived Ease of Use alone. In contrast, the dimensions of Discomfort and Insecurity do not significantly impact Perceived Usefulness or Ease of Use, which challenges previous research findings. In conclusion, understanding how personality traits influence Technology Acceptance is crucial. Service providers should factor these relationships into the development and implementation of cashless payment systems. Strategies should be devised to enhance or maintain Technology Readiness according to users’ personalities, as these traits significantly impact adoption and usage. Consumer acceptance is essential for planning and investing in new technologies, given the substantial time and cost involved for service providers. The ongoing adoption of cashless payments brings notable benefits, particularly for the banking sector, such as reduced operating costs. The growth of cashless branches in Brunei reflects increasing consumer engagement with cashless transactions and underscores the broader shift towards a cashless economy. 7.2. Recommendations It is important to recognize that some consumers in Brunei remain hesitant about the current cashless payment options available in the market. Understanding individuals’ ability to learn, accept, and eventually adapt to new technology is crucial. During the COVID-19 pandemic, the Brunei government accelerated the shift towards a cashless economy due to movement restrictions and standard operating procedures (SOPs) implemented to protect the population from the infectious disease. Various organizations, including government-linked companies, banks, service providers, and businesses, adopted different strategies to raise awareness about cashless payments, helping users gradually learn and adapt to these systems through campaigns and informational efforts. From two perspectives—organizations and consumers—the adoption of cashless payments presents different considerations. For consumers, the use of physical credit or debit cards remains a fundamental option for cashless transactions, but it may not be universally preferred. Concerns about security and the potential compromise of private and confidential information persist, affecting the continued adoption of cashless payments. Recognizing that even basic cashless payment modes like credit or debit cards can evoke insecurity, it is crucial for major providers such as banks and fintech companies to enhance the safety and efficiency of financial transactions. This can be achieved by reducing consumer insecurity through robust fraud detection, secure authentication processes including biometrics, and protective measures such as transaction limits. Employing reliable third-party secure payment systems can also bolster consumer confidence. Awareness and knowledge about the benefits of cashless payments—such as Ease of Use and Usefulness—are essential in not only reassuring existing users but also encouraging Economies 2024,12, 285 32 of 35 those who have not yet adopted these methods. By combining increased security measures with educational efforts, organizations can enhance consumer confidence and drive greater adoption of cashless payments. To support this, a proposed framework for the continued adoption of cashless payments, based on user acceptance and readiness, is suggested (see Figure 8). This framework could serve as a reference for future studies and guide organizations in fostering the continued use of cashless payments in Brunei. With ongoing initiatives by the government and various organizations, Brunei’s cashless transaction market is poised for significant growth, contributing to the realization of the Smart Nation Agenda and Wawasan 2025. Economies 2024, 12, x FOR PEER REVIEW 33 of 37 ongoing initiatives by the government and various organizations, Brunei’s cashless transaction market is poised for significant growth, contributing to the realization of the Smart Nation Agenda and Wawasan 2025. Continued Adoption of Cashless Economy Consumer Acceptance 1) Consumer protection 2) Security levels of the cashless payments service provider on the confidentiality of personal information and confident delivery of consumer payment instruction Consumers Readiness 1) Knowledge and education on cashless payments (awareness, notification, marketing promotion, or campaigns) Governance - key regulatory and legislative Initiatives fostering the continued transition to cashless or digital payments - Role of stakeholders in enabling cashless societies - Focusing on Consumer protection, innovation, resilience, secured Technology Integral cashless payment experiences and systems Culture (cash and cashless practices) - Brunei demographic - Population Habits and traditions (such as e-zakat vs. physical zakat) - Population trust in the government initiatives for digitalization - Financial inclusion (including unbanked individuals) Figure 8. Suggested cashless economy framework. Author Contributions: Conceptualization, methodology, funding acquisition, H.S.; software, validation, N.T.; formal analysis, F.I.; investigation, resources, data curation, A.K.S.S.; writing—original draft preparation, writing—review, and editing, D.S.; visualization, supervision, project administration, L.F.Y. All authors have read and agreed to the published version of the manuscript. Funding: Universiti Teknologi Brunei (UTB) Special Research Grant Reference Number: UTB/GSR/1/2024 (10). Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author due to confidentiality privacy concerns, ethical restrictions, and proprietary information reasons. Acknowledgments: Heru Susanto (H.S.) is the Main Contributor and Lead Author. The remaining are contributors: Noor Tamtini (N.T.), Fahmi Ibrahim (F.I.), Alifya Kayla Shafa Susanto (A.K.S.S.), Desi Setiana (D.S.), and Leu Fang Yie (L.F.Y). We would like to thank other parties that directly and indirectly supported this research through the Center for International Research Collaboration, including the School of Business, Universiti Teknologi Brunei, Brunei Darussalam, the Cyber Security Figure 8. Suggested cashless economy framework. Author Contributions: Conceptualization, methodology, funding acquisition, H.S.; software, validation, N.T.; formal analysis, F.I.; investigation, resources, data curation, A.K.S.S.; writing—original draft preparation, writing—review, and editing, D.S.; visualization, supervision, project administration, L.F.Y. All authors have read and agreed to the published version of the manuscript. Funding: Universiti Teknologi Brunei (UTB) Special Research Grant Reference Number: UTB/GSR/1/ 2024 (10). Informed Consent Statement: Not applicable. Economies 2024,12, 285 33 of 35 Data Availability Statement: The data presented in this study are available on request from the corresponding author due to confidentiality privacy concerns, ethical restrictions, and proprietary information reasons. Acknowledgments: Heru Susanto (H.S.) is the Main Contributor and Lead Author. The remaining are contributors: Noor Tamtini (N.T.), Fahmi Ibrahim (F.I.), Alifya Kayla Shafa Susanto (A.K.S.S.), Desi Setiana (D.S.), and Leu Fang Yie (L.F.Y). We would like to thank other parties that directly and indirectly supported this research through the Center for International Research Collaboration, including the School of Business, Universiti Teknologi Brunei, Brunei Darussalam, the Cyber Security Research Group, the National Research and Innovation Agency, Indonesia and Tunghai University, Taiwan. Conflicts of Interest: The authors declare no conflict of interest. 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