As firms struggle to regroup their business efforts after Covid-19, significant focus turns to strategically using their resources to competitively outmanoeuvre their rivals through utilizing their human capital, social capital, and brand reputations in their performance. This research scrutinizes the relationship between the intangible resources of human resources practices, brand reputation and sustainable competitive advantage of SMEs through the lens of the RBV as a tool for strategic marketing management – an underexplored area in marketing research – using SEM model. The analysis of primary data collected from 128 SMEs operating in Lebanon reveals the existence of a positive impact of social capital on competitive advantage and shows that competent human capital has a positive impact on both competitive advantage and reputation of Lebanese SMEs. In addition, findings indicate that reputation leads to enhancing both competitive advantage and performance. Finally, the results show that the competitive advantage has a positive impact on SMEs performance during uncertain periods. These findings provide valuable insights into RBV, supporting the belief that human and social capital resources are key success factors for SMEs during uncertain periods, which can be of great significance for strategic marketing managers to help them strengthen the position of their SMEs in emerging markets during difficult times.

This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock’s trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.

Purpose: This study contributes to examining the factors that drive the adoption of cryptocurrencies for financial transactions in the tourism and hospitality industries. This is crucial to develop tourism and hospitality and stimulate financial inclusion in developing and developed countries.Design/methodology/approach: This research paper employs the SEM model and bootstrapping method on a sample of 417 French participants involved in tourism and hospitality industries to reveal the causal pathway between a set of independent factors and the willingness to adopt cryptocurrencies for financial transactions.Findings: The empirical findings reveal that ease of use, perceived usefulness, social influence, and financial literacy increase the willingness to use cryptocurrencies. French hotels need to have a strategic orientation, to deal with customers, competitors and changing technological environment. The study also reveals that social influence and financial literacy reduce the level of perceived financial risk and thus, leads to increase the intention to adopt the new type of decentralized currencies.Originality/value: In contrast to previous studies that focused on the volatility and risk of cryptocurrencies, this research employs a human-centric approach covering different factors that could lead to the adoption of the new type of currency for financial transactions in tourism and hospitality industries.

Purpose: The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.Design/methodology/approach: This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.Findings: This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.Practical implications: This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.Originality/value: This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

Purpose: This research addresses the critical challenge of fostering customer loyalty within the highly competitive landscape of the insurance industry. The study investigates the interplay between customer satisfaction, loyalty, and the influence of technology and service quality in the context of insurance services and in periods of crisis. Design/methodology/approach: A quantitative research approach was employed, utilizing a structured questionnaire distributed among diverse insurance customers in Lebanon during crisis conditions. The data were analyzed using SPSS-Amos, incorporating descriptive statistics, correlation analysis, and structural equation modeling (SEM). Findings: This research emphasizes the crucial role of customer satisfaction in fostering loyalty in the insurance sector, especially during crises. High satisfaction levels, influenced by user-friendly online platforms, positively correlate with increased customer loyalty. Technology plays a vital role in maintaining and improving satisfaction, making it a key driver during challenging times. Positive interactions between service quality and satisfaction further highlight the multifaceted impact of technology on shaping customer loyalty. Practical implications: The research findings provide valuable insights with practical implications for insurers aiming to boost customer loyalty. The study recommends strategic investments in critical areas like claims processing, customer service, communication strategies, digitalization initiatives, and employee training. The study provides insights applicable particularly to insurance companies navigating crisis conditions. Originality/value: This research contributes both to academic understanding and practical applications by shedding light on the distinctive challenges and opportunities faced by insurers in cultivating customer loyalty within the insurance industry during crisis. The elucidations provided serve as a foundation for developing targeted strategies to address these challenges and to leverage opportunities for enhanced customer loyalty.

Purpose: The purpose of this paper is to extend the current literature on international entrepreneurial careers by employing the structural equation modeling (SEM) technique. It mainly aims at identifying determinants of international entrepreneurial intentions among young people and after their first employment experience in Lebanon.Design/methodology/approach: The paper uses SEM on a sample of 150 young Lebanese employees to analyze the relationships among the model constructs.Findings: The SEM broadly holds and adequately fits the data. The entrepreneurship context and the motivation of young employees are the variables that positively affect entrepreneurial intentions. Entrepreneurship education and social norms have no statistically significant impact. Finally, risk awareness and professional experience are the variables that negatively affect entrepreneurial intentions. The level of international entrepreneurial intentions among young Lebanese employees is relatively low, which may be justified by the bad economic and political situation.Research limitations/implications: Data were drawn from organizations located in a specific geographical area, namely, Lebanon. Thus, this may constrain the generalizability as well as causality of the results.Originality/value: This study provides an empirical explanation of having an international entrepreneurial career after the first employment experience, with a sample of 150 young employees in Lebanon. By applying the SEM, this study developed a multi-perspective framework covering various factors that may affect the choice of an international career in entrepreneurship, especially after the first job experience. Even though this study's results reflect a particular case, its recommendations could facilitate the achievement of better learning outcomes.