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The ongoing COVID-19 pandemic highlights the importance of destination marketing and media profiling to re-engage international tourists. However, potential crisis-induced nation brand (NB) deficits must be addressed to re-ignite tourism demand. The study examines the possible intervening effect of the contemporary NB in the international destination marketing and media-travel motives nexus.

A deductive quantitative study was undertaken with an online Amazon Mechanical Turk sample of n = 454 respondents. Hypotheses were tested using PROCESS Macro, Model 4.

The results show that the NB [people and negative events] had a practically significant partial mediating effect in the destination marketing – nature-cultural oriented travel motivation nexus.

New insights are provided via a practical model which facilitates the measurement of potential nuances in the influence of destination marketing and media profiling on leisure tourists' travel motives amid crises. The intervening effect implies that a better understanding of the NB as an indirect antecedent to travel motivation may result in more effective crisis communications and tourism recovery-oriented marketing.

The study is amongst the first to extend marketing and behavioural theory to explore the interplay between the marketing and media profile, a nation's brand and tourists' travel behaviour amid a crisis. The study addresses a discernible dearth of knowledge related to the influence of the NB on tourist behaviour from an emerging market perspective.

With the advent of the COVID-19 and increased access to Internet technologies, students tend to use e-learning technologies for improved academic results, and since then, improving student engagement in a virtual space became a difficult task for educational institutions globally. The present study aims to examine the effect of key e-learning factors (i.e. perception, hedonic motivation (HM), usefulness, empowerment and attitude) on student engagement among open and distance learners (ODLs) of the central region of India.

Simple random sampling was applied to collect data from ODLs of Chhattisgarh. ODLs who are using e-learning platforms for at least one year were chosen to be the participant in the study. The authors collected 1,137 responses using both online and offline modes of collection.

The test results indicated that usefulness, HM and attitude factors were found to have a significant relationship with student engagement in e-learning, while perception and empowerment variables did not contribute to the engagement of students.

The present study is novel in its approach. It clarifies the key factors to student engagement which might increase the students' level of involvement in e-learning technologies, if these factors are addressed tactfully by the educational institutions or concerned administration.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

This study analyses the relationship between the use of work–family benefits and job satisfaction (JS). Furthermore, it proposes that work-to-family conflict (WFC) and work-to-family enrichment (WFE) play a mediating role in this relationship. The purpose of this paper is to address these issues.

Data are gathered from 1,051 employees of Colombian organisations. Partial least squares path modelling is used.

The results show that the perception of WFE to a greater extent and the WFC perception, to a lesser extent, are significant mediators in the relationship between the use of benefits and JS.

This study justifies investments and initiatives on the adoption and promotion of work–family benefits. Moreover, it provides practical clues on how to boost JS: WFC and WFE are variables to be considered.

This study proposes a multiple mediation model to analyse the relationship between the actual use of work–family benefits and JS from a family perspective. It contributes to the literature in examining antecedents of JS, highlighting the role of WFE.