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The Kingdom of Saudi Arabia is in the midst of a transformative economic vision, aiming to diversify its economy and reduce dependency on oil revenues. Adopting modern technological solutions like Cloud Accounting Information Systems (AIS) is pivotal in this regard. The purpose of this study is to explore the impact of Cloud AIS on achieving Sustainable Development Goal (SDG) 8.

This study uses a quantitative research strategy and bootstrapping to analyze data from the Kingdom of Saudi Arabia (KSA) industrial sector using partial least squares structural equation modeling.

The study findings provide substantial evidence supporting the positive impact of Cloud AIS on achieving SDG 8. Significant relationships were identified between infrastructure readiness and Cloud AIS utilization, the critical role of data quality in driving Cloud AIS utilization and the significance of security compliance in influencing Cloud AIS utilization. In addition, the quality of service providers was demonstrated to be a crucial factor for effective Cloud AIS implementation. This study also established that infrastructure readiness significantly influences the attainment of SDG 8.

The practical implications of this study provide actionable insights for decision-makers in the KSA industrial sector. Leveraging Cloud AIS effectively will enable organizations to enhance operational efficiencies, promote inclusive economic growth and contribute significantly to achieving SDG 8.

This study contributes significantly to the existing literature by delving into the impact of Cloud AIS on achieving SDG 8, specifically in the context of the KSA – an area that has not been extensively studied.

The Kingdom of Saudi Arabia’s dedicated pursuit of technological modernization positions it as a forefront leader in integrating advanced systems, aligning smoothly with the ambitious goals outlined in Vision 2030. The purpose of this study is to investigate the influence of integrating enterprise resource planning (ERP) and business intelligence (BI) systems on decision-making processes within the industrial sector of Saudi Arabia.

Using a quantitative research design, this study uses a bootstrapping approach and partial least squares structural equation modeling to meticulously analyze data collected from Saudi industrial firms.

The research reveals favorable relationships among infrastructure readiness, data quality, security and access control, user capabilities, user training and the integration of ERP and BI. These positive associations collectively affirm the overarching positive impact of ERP and BI integration on decision-making processes within the industrial sector.

The study underscores the strategic imperative of aligning organizational practices with the identified characteristics to fully unlock the potential benefits of ERP and BI integration in the Saudi Arabian industrial sector.

This study contributes significantly to the existing literature by delving into the integration of ERP and BI in the industrial sector and its nuanced impact on decision-making processes, specifically in the context of the Kingdom of Saudi Arabia – an area that has not been extensively studied.

This study aims to empirically investigate the impact of adopting big data and data analytics (BD&A) on audit quality (AQ).

A questionnaire was distributed among audit practitioners working at audit firms in Egypt and 205 responses were collected. Partial least square structural equation modeling (PLS-SEM) was used to analyze and test research hypotheses.

The results reveal that BD&A has a direct significant positive effect on the audit process (AP) and auditor competence (AC). However, an insignificant impact of BD&A is found on audit fees (AF). In addition, the results indicate that BD&A has significant positive direct and indirect impacts on AQ.

The results of this study will benefit several auditing stakeholders, such as audit firms, audit regulators, novice financial auditors and academic scholars.

This research is one of the earliest to empirically address the role of BD&A in enhancing AQ. It incorporates AP, AC and AF as mediators into a single model to explain the impact of BD&A on AQ. Also, it attempts to provide empirical evidence from a developing country with a less-regulated audit environment.

This paper aims to examine the antecedents of the Automated VAT Solution (AVS) and its eventual consequence on value-added tax (VAT) compliance costs among the small and medium enterprises (SMEs) in Gulf Cooperation Countries (GCC), with the United Arab Emirates (UAE) as context.

A quantitative research design was deployed through a survey of 576 SMEs in the UAE. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The findings revealed that technological factors (IT complexity and IT competency) and organizational factors (management support and size of SME) significantly influence AVS adoption. However, only consumer pressure was found to be significant among the environmental factors, and AVS adoption was found to have a significant negative effect on the VAT compliance cost.

A lower coefficient of determination for the effect of AVS adoption on VAT compliance cost meant that there may be other accounting-related technologies that improve operational efficiency and process automation and, in the long run, lower the cost of VAT compliance. These technologies should be included in future studies.

The findings imply that the adoption of AVS among SMEs is highly desirable, as it reduces VAT compliance costs. Increased regulatory pressure by the UAE’s policymakers is also desirable to accelerate AVS adoption for enhanced cost reduction and revenue maximization from the perspectives of both the government and SMEs.

To the best of the authors’ knowledge, this study could be the first to expand the Technology-Organization-Environmental (TOE) Framework through the integration of determinants of AVS adoption and VAT compliance costs among SMEs in GCC countries.

This study aims to investigate the causal relationships among environmental, social and governance reporting (ESGR), stakeholder sustainability awareness, use of artificial intelligence (AI), sustainability culture, innovation performance and climate resilience of organizations across diverse sectors in Sri Lanka.

A survey was conducted among 327 respondents, including senior accounting professionals, operations managers and functional heads to gather company-level data in various industries in Sri Lanka. A disjoint two-stage approach validated the measurement model, and the partial least squares structural equation model (SEM) was used to test the proposed hypotheses.

The analysis evidences the mediating role of stakeholders' sustainability awareness on the relationship between ESGR and sustainability culture. Furthermore, it emphasizes the role of sustainability culture in driving climate resilience. Innovation performance acts as a moderator, strengthening the relationship between the use of AI and sustainability culture.

The study suggests that organizations should strategically use ESGR, integrate AI and prioritize stakeholder engagement to strengthen their commitment to sustainability. These provide insight for decision-making in organizations seeking to align with sustainable business practices.

It explores the use of AI to enhance ESGR and sustainability culture, providing a broader understanding of how organizations manage AI and stakeholders in sustainability issues.

This research paper aims to explore the concerns and determinants of travelers’ behavior toward ChatGPT in the hospitality and tourism context. It also examines the weight of risk factors versus that of motivation and innovation characteristics influencing travelers’ approach behaviors toward ChatGPT.

A cumulative prospect theory was used to determine travelers’ responses to ChatGPT. This study, using a fuzzy-set qualitative approach, explored risk, motivation and innovation factors as determinants of approach behaviors for ChatGPT.

Findings revealed that risk, motivation and innovation factors were the key triggers of approach behaviors for ChatGPT. An intricate combination effect of the perceived risk, motivation and innovation characteristics was found, and the necessary predictors were determined.

The findings of this study will expand our current knowledge and offer practical insights for the development of ChatGPT in the hospitality and tourism sector.

This study makes a significant contribution to the existing literature by providing a nuanced understanding of the intricate interplay between the various factors that shape customer behavior in the context of technology adoption in hospitality and tourism studies.