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Scaling up smart city infrastructure projects will require a large financial investment. Using public–private partnerships is one of the most effective ways to address budget constraints. Numerous factors have varying degrees of influence on the performance of Public private partnerships (PPP) projects; certain PPP factors are more crucial to the success of a smart city infrastructure project than others, and their influence can be greatly increased when they are fulfilled collectively. This study aims to find out what factors are unique to smart city PPP initiatives, as well as how these factors work together, so that successful smart city infrastructure PPP projects can be scaled up.

The methodology included three sequential stages: identifying the critical success factors (CSF) of PPP for smart cities based on an extensive literature review, collecting data from a sample of 90 PPP practitioners using a Likert scale questionnaire and estimating interrelationships among the CSF and their emergent clusters using structural equation modelling.

The best fit model developed in this study demonstrated the significance of each factor and their interrelationships within their categories in enhancing the performance of PPPs in smart city infrastructure projects. Five categories of critical success factors for PPPs in smart city infrastructure projects have been established: partnership and collaboration; financial sustainability; contractual duties and outsourcing; smart integration; and contract governance.

The proposed model represented the causal interrelationships among relevant critical success factors derived from literature, which may help in directing the organization’s attention and resources to more critical areas, leading to the effective fulfilment of the smart city infrastructure project’s objectives. In addition to the theoretical and methodological contributions, this study produced a usable and readily adaptable list and clusters of critical success factors for research in the area of the implementation of PPP in smart city infrastructure projects.

To the best of the authors’ knowledge, this is the first study to identify PPP critical success factors and their themed clusters for smart city infrastructure projects.

The main purpose of this study is to determine the antecedent factors of smart government service apps intention and its impact on actual use by extending the unified theory of acceptance and use of technology (UTAUT) in the Jordanian context.

To achieve the main purpose, a quantitative method was used to test collected data, and hypotheses testing through using statistical package for social sciences (SPSS) and smart partial least squares software.

The findings indicate that social media advertising has a positive effect on both social influence and peer influence. Furthermore, it demonstrated a significant effect of social influence on performance expectancy. In addition, there is a direct correlation between the government capacity, effort expectancy, facilitating conditions and the intention to use e-government services. Lastly, the results mainly show that the actual use of e-government services is significantly and positively influenced by intention and self-isolation. Next, as expected, self-isolation moderated the relationship between intention to use and actual use of e-government services via the Sanad application, and hence the related hypothesis was supported.

This study provides practical recommendations for the policy-makers in the Jordanian e-government and Ministry of Digital Economy and Entrepreneurship (MoDEE) in Jordan.

The purpose of this paper is to examine whether artificial intelligence (AI) increases data and information quality in the accounting and disclosure context.

Data were collected from financial managers, who are working in listed Jordanian firms in the Amman Stock Exchange. SmartPLS software based on the Partial Least Squares Structural Equation Modeling approach was used to test hypotheses.

The empirical results reached the acceptance of all hypotheses, and this means that all hypothesized relationships were positive, as the impact of AI was positive on data and information quality in the accounting and disclosure context, and also the adoption of digital disclosure mediated the relationship between AI and the quality of financial data and information, and hence, all hypotheses were statistically supported in the context of Jordan.

This study broadened the literature by proposing a research model that defines some of the main factors for determining financial managers’ perceptions of issues with digital financial disclosure adoption and its impact on financial data and information quality. By illuminating the relevance of these issues in the presence of the mandate of digital disclosure, this study sheds some light on digital disclosure regulators in making future policies for digital disclosure adoption among the different sectors such as financial, service and industrial in the Jordanian context.

The purpose of this study is to examine the factors that may have an influence on the continuous intention to use mobile money as one of the Financial Technology (FinTech) products beyond the COVID-19 pandemic lockdown period.

This study has empirically tested the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) to explain the mobile money adoption in Jordan. Data collected were analyzed through partial least squares–structural equation modeling (PLS-SEM).

The results mainly showed that the quality of administrative services and trust significantly impact confirmation and perceived usefulness. In addition, perceived security and knowledge of mobile money as one of the FinTech services significantly influence users’ confirmation and perceived usefulness. Also, usefulness and satisfaction influence continuous intention. Significant relationships were noted among confirmation, perceived usefulness, satisfaction and continuous intention to use mobile money.

This paper integrates two key theories: the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) in the post-adoption behavior of mobile money. Therefore, this study attempts to fill a literature gap by examining the antecedent factors that influence the continuous intention to use mobile money services in the post-consumption stage.

The main purpose of this study is to determine the factors affecting the adoption of telemedicine services as mobile health apps in the Jordanian context by extending the technology acceptance model (TAM).

A quantitative research approach with an online survey was used to collect data from 306 Jordanian respondents to achieve the main purpose. The partial least squares structural equation modeling (PLS-SEM) approach was used to analyze collected data.

The results mainly confirmed that the intention to use mobile health apps (otherwise known as AMAN apps in Jordan) is significantly influenced by social influence, perceived usefulness and perceived ease of use, trust in government and social media awareness. Next, contrary to what is expected, culture does not moderate the association between perceived ease of use and behavioral intention to use the AMAN app, and hence the related hypothesis was not supported. To conclude, the results show that the actual use of the AMAN app is significantly influenced by behavioral intention, and hence, the final hypothesis was supported.

The current research contributed to the literature on information technology (IT)/information systems (IS) acceptance and use by switching the investigation efforts from the acceptance of IT/IS to the adoption of protection technology in the crisis era.

Digital health or digital healthcare, is a broad, multidisciplinary concept that includes concepts from an intersection between technology and healthcare. Digital health applies digital transformation to the healthcare field, incorporating software, hardware and services. The usefulness of digital health technologies such as digital contact tracing apps (CTAs) depends deeply on the acceptance to use them. Accordingly, the main purpose of this study is to explore the usage of CTAs by integrating the protection motivation theory (PMT) and technology acceptance model (TAM) in the Jordanian context.

Partial least squares structural equation modelling was employed to test empirically the integrated research model against data collected from 420 respondents, leading to the identification of the important determinants for the usage of CTAs.

The results reveal that perceived severity and perceived vulnerability have a significant positive impact on the perceived need for CTAs, which, in turn, affects the individuals’ intention to use CTAs. It also reveals that perceived trust in government, perceived privacy and perceived usefulness have a significant positive impact on individuals’ intention to use CTAs, which, in turn, affects the usage of CTAs. In contrast, perceived ease of use did not find any significant relationship, and hence hypothesis H7 was not supported. Lastly, social media awareness has a significant positive impact on perceived usefulness and ease of use.

This study is considered among the first studies in the Jordanian context that integrate the PMT and TAM to gain a better understanding of the critical determinants for the acceptance and usage of CTAs in the context of pandemics.