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This study aims to test the impact of digital skills on the entrepreneurial intentions of last-year undergraduate students in Jordanian universities, especially after the Coronavirus disease pandemic and the digital transformation in education and business patterns. In addition, it aims to assess the role of entrepreneurial alertness as a mediator and entrepreneurship education as a moderator in the relationship between the independent and dependent variables.

The quantitative study used a questionnaire distributed to 401 students from different Jordanian universities. The data was collected over 2 months and two structural equation models were developed using AMOS 25 to examine the relationship.

A significant negative relationship was found between digital skills and entrepreneurial intentions of last-year undergraduate students in Jordanian universities post-coronavirus disease 2019 (COVID-19) pandemic. A fully mediating role of the alertness variable has appeared in addition to a significant moderating role of entrepreneurship education.

This is the first study that attempts to investigate the impact of digital skills on students' entrepreneurial intentions in Jordan after the COVID-19 pandemic, In addition, it is one of the few studies that assess the mediator's and moderator's effects on the same conditions. Finally, the study provided a review of the definitions and models used as part of the contribution to upcoming reviews.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Perceptions of employment histories are important insofar as they influence future job prospects. Critically, in light of the current pandemic, wherein many individuals are likely to have unanticipated employment gaps and/or temporary work experiences, this exploratory study aims to seek a better understanding of the signal associated with temporary employment histories, which is particularly germane to individuals' employment trajectories and a successful labour market recovery.

Drawing primarily on signalling theory and using a simulated hiring decision experiment, the authors examined the perceptions of temporary employment histories, as well as the period effect of COVID-19, a major exogenous event, on the attitudes of fictitious jobseekers with standard, temporary and unemployment histories.

The authors find that prior to COVID-19 unemployed and temporary-work candidates were perceived less favourably as compared to applicants employed in a permanent job. During the COVID-19 pandemic, assessments of jobseekers with temporary employment histories were less critical and the previously negative signal associated with job-hopping reversed. This study’s third wave of data, which were collected post-COVID, showed that such perceptions largely dissipated, with the exception for those with a history of temporary work with different employers.

The paper serves as a reminder to check, insofar as possible, preconceived biases of temporary employment histories to avoid potential attribution errors and miss otherwise capable candidates.

This paper makes a unique and timely contribution by focussing and examining the differential effect of economic climate, pivoted by the COVID-19 pandemic, on perceptions of temporary employment histories.