Cybersecurity challenges in Saudi Arabia’s service and manufacturing sectors are escalating due to increased digital adoption, highlighting the need for robust security measures and awareness in SMEs. Therefore, this research is significant due to the increasing reliance on digital technologies and the unique cybersecurity challenges faced by SMEs in these vital economic sectors. With rapid technological advancements, IT capabilities and cybersecurity have become paramount, particularly in the post-COVID-19 era. The service and manufacturing sectors in Saudi Arabia have seen significant shifts towards digital operations. This study aimed to explore the impact of organizational cybersecurity systems on organizational resilience and sustainable business performance in Saudi Arabia’s service and manufacturing sectors, examining the mediating and moderating effects of organizational resilience and culture. A quantitative research method was employed, combining a thorough literature review with empirical data from a sample of 394 respondents in Saudi Arabia, split evenly between the service and manufacturing sectors. Smart PLS 3.3.3 was used to test the proposed hypotheses. The findings suggested a positive effect of the factors of organizational cybersecurity systems on organizational resilience. Organizational cybersecurity systems also significantly influenced sustainable business performance; however, organizational resilience and culture did not play mediating and moderating roles. This study is one of the first to offer a nuanced analysis of IT capabilities and cybersecurity within Saudi Arabia’s service and manufacturing sectors, especially in a post-COVID-19 context. The insights gleaned contribute to the academic discourse and have pivotal managerial implications for organizations navigating the digital era in Saudi Arabia.

Big data analytics technology offers significant opportunities for innovation and performance improvement for small- and medium-sized enterprises (SMEs) operating in competitive environments. However, reaping these benefits requires the adoption of such technologies by SMEs. This study investigates the factors influencing the adoption of big data and analytics in Saudi Arabian SMEs in the service and manufacturing sectors, with a particular focus on the role of facilitating sustainable technology in enabling sustainable business performance. Data were collected from managers of SMEs in Saudi Arabia using a quantitative method. The proposed hypotheses were tested using structural equation modeling with SmartPLS 4.0. The findings reveal that big data security and management support significantly influence the perceived ease of use and usefulness of big data analytics in SMEs. Perceived ease of use significantly influences the adoption of big data analytics. Furthermore, facilitating sustainable technology was a significant predictor of sustainable business performance. Additionally, the study revealed that the adoption of big data analytics significantly influenced business performance. The insights obtained from this study can be useful for the service and manufacturing industries operating in Saudi Arabia, particularly regarding the key influencing factor of perceived ease of use that determines the adoption of big data analytics in the Saudi Arabian SME market.

This study explores green work engagement in response to the global demand for sustainability in businesses and the shift toward green-oriented agendas. Specifically, this study aims to examine how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-efficacy (TSE) in the AIA→JS relationship and trust in leadership (TIL) in the GWE→JS relationship. A PLS-SEM analysis was conducted on 392 valid replies from full-time employees of five-star hotels in Egypt using WarpPLS 7.0. The findings indicated that artificial intelligence awareness (AIA) negatively affects employees’ green work engagement (GWE) and positively affects job stress (JS). In addition, GWE is negatively affected by JS. Moreover, TSE negatively moderates the AIA→JS relationship, while TIL negatively moderates the JS→GWE relationship. The study also found a significant mediating effect of JS on the AIA→GWE relationship. The study enhances research on AIA’s impact on JS and GWE, addressing a gap in existing empirical studies on the relationship between these elements in hotels. Overall, the study of green work engagement has the potential to be a valuable contribution to the growing field of sustainable business practices.

The authors would like to make the following correction to the published paper

Artificial intelligence (AI) tools are becoming part of how students think, not only what they use. Whether a learner engages with such a tool or delegates to it is a decision about how their own cognitive resources are deployed, which places that decision within the realm of study of human intelligence. Investment accounts of intellectual development hold that ability is built through motivated engagement with cognitively demanding material, yet technology acceptance frameworks emphasize utility judgments and underrepresent the motivational processes that drive such engagement. This study tests an extended Technology Acceptance Model in which self-efficacy shapes perceived usefulness through motivation and enjoyment. Survey data from 185 undergraduate School of Business students at a Saudi university who used generative AI tools during coursework were analyzed using partial least squares structural equation modeling. All nine hypotheses were supported. Self-efficacy strongly predicted both motivation and enjoyment, but motivation was the dominant mediating pathway to perceived usefulness (β = 0.295, p < 0.001), with enjoyment a weaker affective route (β = 0.086, p < 0.05), alongside a significant serial pathway from self-efficacy through motivation to enjoyment (β = 0.113, p < 0.001). The model explained 54.4% of the variance in behavioral intention. The findings indicate that beliefs about one’s own technical competence, rather than appraisal of the tool alone, govern how students commit to intelligent systems, with implications for whether AI use develops or displaces learners’ own intellectual capacity.