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This article aims to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) model to understand the factors affecting the usage behavior of Blockchain from accountants' and auditors’ perspectives and its impact on their performance.

A quantitative research approach employing a web-based questionnaire was applied, and the empirical data were gathered from 329 potential and current users of Blockchain in the accounting and auditing profession in Jordan. The analytical model was based on structural equation modeling (SEM) using AMOS 25.0.

The experimental findings of the structural path confirmed that performance expectancy (PE), social influence (SI), Blockchain transparency (BT) and Blockchain efficiency (BE) were significantly affecting individuals’ behavioral intention (BI) toward the use of Blockchain-based systems and helped to explain (0.67) of its variance. Also, BE has a positive significant impact on PE. Whereas, in contrast to what is anticipated, the influence of effort expectancy (EE) on BI was not supported. Additionally, users’ intentions were found to affect the actual usage (AU) behavior and helped to explain (0.69) of its variance. The outcome variables proposed in this study: knowledge acquisition (KACQ) and user satisfaction (USAT) were significantly influenced by the AU of Blockchain technology.

This study outlines practical implications for government, policymakers, business leaders and Blockchain service providers aiming to exploit the advantages of Blockchain technology (BCT) in the accounting and auditing context.

To the best of the authors’ knowledge, this article is one of the few studies that offer an evidence-based perspective to the discussions on the effect of disruptive and automated information and communication technologies (ICTs), on the accounting and auditing profession. It applies an innovative approach to analysis through the integration of UTAUT, contextual factors: BT and BE, besides two outcome factors: KACQ and USAT within its theoretical model. This study extends and complements the academic literature on information technology/information systems acceptance and use by providing novel insights into accountants' and auditors’ views.

The use of e-health applications has remained popular during pandemic for medical treatments. Nevertheless, adoption of e-health applications among individuals is considerably low in post-COVID-19 world. To address this issue, the current research integrates three renowned theories, namely unified theory of acceptance and use of technology model, diffusion of innovation and DeLone and McLean model and investigates individual intention to adopt e-health application.

Research design is grounded in quantitative and cross-sectional methods and uses a survey questionnaire. Survey questionnaire is administered toward smartphone users. Overall, 238 valid responses were analyzed with structural equation modeling approach.

Results indicate that altogether performance expectancy, effort expectancy, information quality, system quality, service quality, facilitating condition and social influence explained substantial variance (R² = 76.5%) in user intention to adopt e-health applications. Similarly, effect size analysis has revealed substantial impact of social influence on user intention to adopt e-health applications. Geiser and Stone’s Q² analysis discloses that research model has substantial power to predict user intention to adopt e-health applications.

This study integrates three known information system models to investigate individual behavior toward adoption of e-health applications. In practice, it suggests that managers should pay attention in improving performance expectancy, social influence, facilitating condition, system quality, service quality and information quality which develop positive behavior and encourage smartphone users to adopt e-health applications.

This study is original as it integrates three models and investigates individual intention to adopt e-health applications. To the best of the author’s knowledge, this is the first study to test the moderating impact of patient health value between user intention to adopt and actual use of e-health applications. It has established that compatibility and innovativeness are essential antecedents of performance and effort expectancy and hence make this research more unique and valuable.

This paper aims to investigate the variables that could contribute to facilitating or hindering FinTech adoption in Jordan and how that will affect human well-being (quality of life [QoL]).

A conceptual framework was formulated through the integration of “the unified theory of acceptance and use of technology” (UTAUT), “task-technology fit” (TTF) model and two additional factors, namely, “financial literacy” (FL) and “quality of life” (QoL). A cross-sectional online survey was used to obtain data from 378 FinTech users employing a quantitative method. AMOS 26.0 was utilized to analyse the data based on “structural equation modelling” (SEM).

The analysis of the structural path found that UTAUT constructs including “performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI)”, and TTF were significant determinants of FinTech adoption. Only technology characteristics (TECH) was a significant predictor of TTF. Also, the analysis of empirical data revealed a significant mediating impact of FinTech adoption on the association between FL and QoL, underlining the important role of digital FL in digitalizing societies. Likewise, FL affected the QoL directly.

This research will be beneficial for “FinTech service providers” (FSPs) and policymakers to offer thorough insights regarding the current relatively low acceptance rates of FinTech, contributing to strategies’ formulation that could promote FinTech usage by Jordanian customers, where FinTech is still considered an innovative technology. In addition, FL needs to integrate digital literacy to utilize state-of-the-art technologies for more effective financial management. This is with being able to make decisions facilitating the management of life outcomes which could result in better QoL.

To the best of the author’s knowledge, this research is the first research paper that integrates the UTAUT and TTF models and also adds two additional constructs, namely, FL and QoL, to investigate the FinTech in the Jordanian setting. This study could contribute to the literature on IT adoption by considering FinTech usage and incorporation into individuals’ life in Jordan.

Autonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.

This study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.

This research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.

This study was conducted only in specific Auckland areas.

This study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.

This study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.

Recognizing the importance of Robo-advisors in digital financial services, this paper aims to analyse the users’ perception and acceptability of artificial intelligence (AI) in digital investment solutions using an extended “Technology Acceptance Model” (TAM).

The model is tested using 454 online valid responses received from Indian Fintech users via direct path analysis, mediation and moderation.

The study’s findings show that trust, perceived usefulness and perceived risk all significantly impact users’ attitudes towards Robo-advisors. In contrast, ease of use and social influence did not impact users’ attitudes statistically. Furthermore, the results indicate that their attitudes and ease of use influence users’ intentions to adopt Robo-advisors. Moreover, the moderation effect of gender partly supports the overall model. Specifically, in the path between attitudes and their antecedents, gender plays a role in influencing the relationships among these variables. This aligns with preliminary research in the field, providing additional insight into how gender may moderate the factors influencing users’ attitudes and intentions regarding Robo-advisory services.

This research study also reveals that trust, perceived risk, ease of use and demographic factors influence the adoption of Robo-advisory services. It is functional, but its sample selection is not probabilistic and overly emphasizes gender. Future research should use probabilistic sampling, other demographic factors and experience and situational factors. Also, it is necessary to examine how convenient and satisfying it is to communicate with service providers. Filling these gaps will improve the knowledge of consumer behaviour in the context of Fintech adoption and develop the current research.

This study posits that perceived usefulness, trust, perceived risk and ease of use remain core determinants of adopting Robo-advisory services. So, to improve the level of trust of users, it is necessary to develop security measures, data clarity and quality and customer support. Enhancing ease of use by incorporating better interface gestures is always beneficial for increasing the number of users and their level of satisfaction. As identified in previous studies, practical solutions will be achieved by pursuing the increased use of technology while leveraging AI for personal services and minimizing perceived risks, which will strengthen more advanced security measures as well as sufficiently clear communication.

The paper aims to extend the TAM by incorporating measures of trust and social influence to identify the factors that drive the adoption of Robo-advisors. In doing so, the paper may contribute to developing a more comprehensive understanding of the factors that shape consumers’ attitudes and intentions towards these technologies. Moreover, the paper appears to examine the moderating effect of gender on attitude and its predictors, which could provide insights into how gender characteristics may impact the adoption of Robo-advisors.

This study delves into how perceived ease of use and perceived usefulness impacts consumer attitude and intention to adopt digital technologies in Ghana.

We utilized covariance-based structural equation modeling with a dataset consisting of 204 respondents to evaluate the model. The analysis was conducted using R (version 0.6–12).

The results confirm that both perceived ease of use and perceived usefulness are key predictors of the intention to adopt technology. Additionally, factors such as attitude, subjective norms and perceived behavioral control also play a significant role in influencing adoption intent. Notably, the research indicates that perceived ease of use not only impacts peer influence among consumers, but also shapes their confidence in effectively using the technology. Furthermore, in contrast to previous studies, this investigation suggests that while perceived behavioral control impacts behavioral intent, it does not predict attitude in the Ghanaian context. The study advocates for development of user-centric technologies, promotion of digital literacy and cultivation of a digital culture.

Our use of a single country data restricts generalizability of the study. For a more comprehensive and profound understanding, we recommend a broader study across multiple sub-Saharan African countries with a mixed method or qualitative approach.

The significance of this research lies in its original contribution to the field in providing crucial insights for governments and industry players. It sheds light on factors influencing user readiness to adopt technology and makes a substantial contribution to the ongoing discussion on digital technology adoption in literature.

This study explores how customers' individual characteristics and perceptions affect acceptance of service robots as a hotel workforce. The Interactive Technology Acceptance Model (iTAM) has inspired us to investigate effects of customers' technological self-efficacy, perceived interactivity, sense of utility, and enjoyment-level of acceptance related to hotel-service robots as staff.

Data were collected from 224 customers via an online questionnaire conducted in the period April–June 2022 by convenience sampling, and then analyzed by using partial least squares – structural equation modeling (PLS-SEM).

The findings show that customers' technological self-efficacy and perceived interactivity with service robots enhances perceived usefulness and perceived enjoyment, serving as functional and emotional value components of service robots. They also demonstrate that robot's interactivity outweighs other robot's value components, such as perceived usefulness and perceived enjoyment for acceptance of service robots as employees in hotels.

While empirically validating the iTAM, this study emphasizes service robot interactivity as the most important aspect for customers' acceptance, and it adds a new perspective regarding the underexplored role of the customer-robot interface. Combining specific dimensions from different technology acceptance models (functional/socio-emotional/relational; utilitarian/hedonic) the study contributes to the service robot literature currently missing a more holistic understanding of consumers' experience and adoption drivers, and it provides managerial guidance on how to successfully implement service robots in hotel environments.

The main purpose of this study is to determine the accounting analytics technology (AAT) adoption among manufacturing small and medium-sized enterprises (SMEs) based on the extended technology acceptance model (TAM).

The quantitative research approach with online surveys was used to collect data from 219 accounting managers among manufacturing SMEs in Jordan. To test the suggested research model, partial least squares structural equation modeling was used.

The findings indicated that all direct paths were found to be significant in the hypothesized directions. Ultimately, the results also revealed that perceived usefulness has mediated the relationship between perceived ease of use and intention to use AAT, and hence all direct and indirect hypotheses were accepted.

This research has successfully extended the TAM model in the context of AAT adoption among Jordanian manufacturing SMEs by including new factors along with the original factors of the TAM model, particularly in the postpandemic era.

This study examines the relationship between the writing anxiety experienced by English second language learners and their intention to employ ChatGPT for their academic writing as an automated writing evaluation tool. This research integrates writing anxiety as an additional variable to understand how much writing anxiety affects the perceived usefulness of ChatGPT as an automated writing evaluation tool, perceived ease of use of ChatGPT, and attitude towards using ChatGPT as an automated writing evaluation tool for their academic writing with the technology acceptance model (TAM) as a theoretical framework.

This is a cross-sectional study, with SEM PLS to analysis data collected from 639 undergraduate students.

This study found that writing anxiety significantly affects perceived ease of use of ChatGPT as an automated writing evaluation tool, and attitude towards using ChatGPT. Altogether they both influence students’ intention to use the ChatGPT as an automated writing evaluation tool.

This study contributes to the understanding of students intention to use ChatGPT as an automated writing evaluation tool when they suffer from writing anxiety.

The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.

An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.

Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.

Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.

This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.