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This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.

Despite the widespread recognition of the significance of entrepreneurial innovations in driving financial success and building firms' competitive advantage, there is a lack of empirical investigation into their impact on organizations. This research paper aims to address this gap by exploring how entrepreneurial innovations can contribute to firms' competitive advantage and the mediating role of entrepreneurial thinking in this relationship. To answer these research questions, this paper utilizes the resource-based view (RBV) of the firm and the dynamic capabilities view (DCV).

The study employed a quantitative research design, and survey data were collected from 102 chief executive officers (CEOs) of Jordanian manufacturing companies in various industries. Structural equation modeling was used to test the hypothesized relationships.

The findings of this study suggest that there is a positive and significant impact of entrepreneurial innovation and thinking on the competitive advantage of firms. The authors also found that entrepreneurial thinking positively mediates this relationship. This study’s results support the RBV and DCV perspectives, which suggest that firms need to leverage unique resources and capabilities and develop dynamic capabilities to achieve a sustained competitive advantage.

The significance of this study lies in its contribution to the literature on the relationship between entrepreneurial innovation, thinking and competitive advantage. While previous research has explored these concepts separately, this study integrates them into a comprehensive framework that highlights the mediating role of entrepreneurial thinking in the relationship between entrepreneurial innovation and competitive advantage.

This study aims to analyse the way the internal resources and their attributes contribute to the competitive advantages in an intensive organisation in knowledge of the pharmaceutical industry in an emerging market.

This is a qualitative case study focused where the NVivo software was used for information analysis and thematic analysis.

The outcomes showed that from the VRIO framework (value, rarity, imitability and organisation), the plant and equipment and the technical knowledge of its workers are the resources that, due to their attributes, especially the rare, those that grant an advantage competitive position compared to other companies in its sector. Those findings highlight that the resource-based view (RBV) is a good approximation to explain the construction of competitive advantage (CA) and, in addition, the relevance of rare attribute in pharmaceutical companies was confirmed.

The study points out empirical evidence on the relevancy of RBV, from the VRIO framework and the competitive profile matrix (CPM) for the analysis of the management of organisations from the emerging market (economy) perspective. The study also provides competitive advantage analysis tools with which managers can identify strategic resources for their companies.

The VRIO framework and CPM were integrated in the study to analyse the role of internal resources and their attributes in achieving CAs. This integration is the first time that it has been carried out in companies in the context of an emerging market.

This paper aims to examine the effect of R&D laboratories on the perceived performance of MNE subsidiaries during recession.

Employing resource-based view and knowledge-based theory, the authors investigate a unique sample of 171 technologically heterogenous foreign MNE subsidiaries located in Greece over the period of recession 2009–2016. The sample subsidiaries operate different types of R&D laboratories.

The authors find that MNE subsidiaries with advanced R&D laboratories such as locally integrated laboratories (LILs) and internationally interdependent laboratories (IILs) perform better in recession than subsidiaries with support laboratories (SLs) or subsidiaries without R&D laboratories. Overall, the authors find an asymmetric performance contribution of R&D laboratories at subsidiary level.

The study provides useful insights into the environmentally derived “knowledge-based - performance” context, so filling an important research gap, since little is known about the performance impact of the input-side of technological activity at MNE subsidiary level, especially as regards R&D facilities/infrastructure. Based on the findings the authors identify important managerial implications.

The efficiency of each of an organization’s individual workers determines its effectiveness. The study aims to explore the relationship between human resource management (HRM) practices and organizational effectiveness with employee performance as a mediating variable.

Data were collected from 800 police officers in the Greater Accra and Tema regions. The data were supported by the hypothesized relationship. Construct reliability and validity was established through confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling.

The results show that career planning and employee performance were significantly related. Self-managed teams and employee performance were shown to be nonsignificantly related. Similarly, performance management and employee performance were shown to be nonsignificantly related. Employee performance significantly influenced organizational effectiveness. The results further indicate that employee performance mediates the relationship between HRM practices and organizational effectiveness.

The generalizability of the findings will be constrained due to the research’s police service focus and cross-sectional data.

The study’s findings will serve as valuable pointers for the police administration in the adoption, design and implementation of well-articulated and proactive HRM practices to improve the abilities, skills, knowledge and motivation of officer’s to inordinately enhance the effectiveness of the service.

By evidencing empirically that employee performance mediates the relationship between HRM practice and organizational effectiveness, the study extends the literature.

This study aims to assess if the voluntary reporting of adjusted earnings before interest, taxes, depreciation and amortization (EBITDA), a widely used non-generally accepted accounting principles (GAAP) measure, has effects on information asymmetry and value relevance and how the adjustments to GAAP earnings made to derive it contribute to these effects. This study focuses on firms from two countries with contrasting institutional settings, Canada and France.

Relying on multivariate analyses and using Heckman’s procedure to address the sample self-selection issue, this study first estimates the likelihood of a firm to report adjusted EBITDA. Then, this study examines if adjusted EBITDA, as well as the adjustments made to GAAP earnings to derive adjusted EBITDA (adjustments), affect a firm’s information asymmetry and its value. These adjustments are essentially GAAP-grounded items that are discarded by management to derive non-GAAP adjusted EBITDA. The dependent variables are share price volatility, as a proxy for information asymmetry, alongside market-to-book and stock market return as indicators of value.

In terms of the used sample, results suggest that Canadian firms are much more likely to report adjusted EBITDA than French firms. Chief executive officer (CEO) attributes (CEO power) appears to increase such likelihood. Moreover, for both Canadian and French firms, adjusted EBITDA is associated with reduced stock market volatility, an indication of lower information asymmetry, as well as higher market-to-book and returns, suggesting value relevance. The results also indicate that investors view the adjustments to GAAP earnings made by management to derive adjusted EBITDA as not value relevant (similar to noise). The GAAP-grounded elements that management discard to derive adjusted EBITDA actually increase information asymmetry.

This study adds to prior research on the interface between a CEO attributes and governance and non-GAAP reporting. This study also provides evidence that, despite very different institutional settings, non-GAAP reporting conveys relevant information to capital markets’ participants in both France and Canada. Hence, a country’s institutional setting may have a differential impact on the disclosure choice but not on the resulting value relevance of such disclosure. Finally, this study extends the non-GAAP literature by examining the value relevance of a widely used yet under-researched measure, adjusted EBITDA.

Designing and implementing effective strategies for managing heritage resources throughout the world has become critically important as the impacts of climate change and human-caused destruction are increasingly felt. Of particular importance is the ability to identify and track fast- and slow-moving processes associated with weathering, erosion and the movement or removal of heritage objects by natural and human agents. In this paper, the authors demonstrate how 3D laser scanning can be used to detect and monitor changes to the Okotoks Erratic “Big Rock” Provincial Historic Resource in Alberta, Canada, over a period of 7 years.

Terrestrial laser scanning surveys of the Okotoks Erratic “Big Rock” Provincial Historic Resource were undertaken in 2013, 2016 and 2020. Registration was used to place the three epochs of point clouds into a unique datum for comparison using the cloud-to-cloud distance function in Cloud Compare.

The movement/repositioning of rocks around the base of the erratic, the emergence of “unofficial” paths and changes to interpretive trails and fencing were all identified at the site over the time period of the study.

Current conservation at the Okotoks Big Rock focus primarily on the rock art panels that are scattered over the erratic. The results of this study indicate they should be broadened so that the geological integrity of the site, which is intrinsically linked to its cultural value, can also be maintained.

This is the first study the authors are aware of that utilizes terrestrial laser scanning + change detection analysis to identify and track changes to a heritage site over a period as long as 7 years.

This article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.

The model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.

The findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.

The proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.

Deep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?

This paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.

The findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.

The findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.

Very few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.

The present study aims to investigate the non-linear relationship between trade and income inequality to address goal 10 of sustainable development goals (SDGs) using the Kuznets Curve (KC) framework for major emerging countries during 1991–2020.

For this purpose, recent econometric techniques, such as Common Correlated Effect (CCE) and Dynamic Common Correlated Effect (DCCE) estimators have been employed to deal with the cross-section dependence (CD) that arises in panel data, while the robustness of the study is checked through Driscoll–Kraay standard errors method.

The empirical results of the study confirm the existence of inverted “U-shaped” relationship between trade and income inequality suggesting evidence for the trade-led KC in the panel of emerging countries. Along with the non-linear model, the threshold value is estimated to be between 3.5 and 4% of gross domestic product (GDP).

The authors' findings support that trade contributes significantly toward reducing income inequality and helps in achieving goal 10 of SDGs. Hence, trade policies appear to be more egalitarian. The results widen the scope for further research and provide insights for regulators and policymakers in modeling trade policies and changing the status quo trade policy framework accordingly.

The present study is a pioneering attempt to examine the non-linear relationship between trade and income inequality under the KC framework in light of the Agenda 2030 for sustainable development. The study also considers other explanatory factors that have an impact on income inequality. Furthermore, the study considers other explanatory factors that have an impact on income inequality, and the attempt to estimate the threshold value for the trade-led KC is novel and interesting.