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The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

This paper considers the role of economic sentiment and economic policy uncertainty (both domestic and European) in explaining the changes in the contemporaneous and future travel and leisure stock index returns in top European Union (EU) tourism destinations, namely, in France, Germany, Spain and the UK.

The authors conducted the ordinary least square (OLS) regression estimations to investigate the impact of changes in economic sentiment and economic policy uncertainty on travel and leisure stock returns. Furthermore, the authors used predictive regressions to determine whether economic sentiment and economic policy uncertainty are useful predictors over the short- or medium-term for travel and leisure stock returns.

Empirical results revealed that, in France and Spain, the changes in regional economic sentiments predominantly and positively affected travel and leisure stock index returns. Also, results indicated that changes in European economic sentiment have a strong positive effect on the future travel and leisure stock returns in Spain and the UK over the short run, while in France, changes in European economic policy uncertainty have a weak negative effect on the future travel and leisure stock returns over the medium-term.

This paper provides valuable practical implications for investors who trade travel and leisure stocks. Traders can use economic sentiment and economic policy uncertainty to establish arbitrageur strategies.

This study is the first to examine the effects of economic sentiment and economic policy uncertainty (both domestic and European) on contemporaneous and future travel and leisure stock returns in a top European tourism destination.

This paper focuses on the role of sentiment and monetary policy (both domestic and the United States (US)) in explaining the changes in the Mexican tourism firms' stock returns for the period 1998M03–2019M12.

The authors conducted the ordinary least square regression estimations using various models to investigate the impact of sentiment and monetary policy changes on tourism firms' stock returns. Furthermore, to provide a robust check, the authors run all regression models based on the capital asset pricing model by regressing the excess returns of tourism firms' stocks on all independent variables.

Empirical findings reveal that the changes in Mexican consumer sentiment have a stronger positive effect on tourism firms' stock returns than Mexican business sentiment changes. However, the US consumer and business sentiment are irrelevant to tourism firms' stock returns. Moreover, this study’s results indicate that changes in the US interest rates positively influence tourism firms' stock returns. This study’s findings show that as the monetary divergence between Mexico and the US (differential real interest rates) widens, the lower is the tourism firms' stock returns.

This study is the first to extend the prior studies by examining the effects of sentiment and monetary policy (both domestic and US role) on Mexican tourism stock return.

The purpose of this paper is to investigate the effect of context and culture on leadership and decision‐making styles of Lebanese‐born executives working in the USA, the Gulf Cooperation Council countries, and Lebanon.

Using a semi‐structured questionnaire, 76 successful Lebanese executives were interviewed in three regions of the world. Comparisons among the three groups are made on three elements: early ingredients for success particularly during childhood and educational years, emotional intelligence (EI) leadership styles, and decision‐making styles.

Although successful leaders, born and raised in Lebanon, share the early ingredients for success, they differ significantly in their decision making and EI leadership styles when working outside Lebanon with multicultural and diverse followers.

The research findings strongly suggest that future research on cross‐cultural leadership will be more fruitful when context and culture are taken into account, and if researchers use a non‐Western conceptualization of culture, and when the research is conducted by multicultural and interdisciplinary researchers.

The study lends support to the notion that successful leaders adapt to their new culture and context, learning from adversity and experience, and mastering the cultural context.

The evaluation of readiness provides insight into the readiness of its individual components for successful accomplishment of tasks. This study aims to evaluate readiness in leagility of supply chains based on the design and analysis of fuzzy cognitive maps (FCM) and interpretive structural modeling (ISM).

On the basis of the purpose of this study, data are gathered via the Delphi method. Moreover, FCM and ISM are also used to evaluate readiness.

Findings initially demonstrate a categorization of factors influencing leagility into static and dynamic variables according to the degree of their influence derived from the resultant behavior of FCM and ISM. It is also found that evaluating readiness in leagility of supply chains with ISM and FCM was done with respect to the type and role of the study variables, which were determined within the minimum and maximum ranges of 20 to 100 per cent, respectively.

The evaluation of the readiness using the FCM and ISM is proved to be more efficient than other classical methods. Experimental results of the study contribute to improve readiness of leagility of supply chain as well as develop functional areas of business.

This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.

The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.

The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.

The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.

The purpose of the study is to assess the implementation of integrated information systems in UK higher education institutions (HEIs) via multiple internal stakeholders.

The approach analyses the implementation strategy of two HEIs and assesses the impact of new systems on working practices. This involves interviews with various stakeholder groups from the HEIs, capturing 35 interviews.

Results indicate that growth of alternative power bases emerge within both HEIs, as well as new roles and responsibilities for administrative staff, and different working practices for academics. Varying levels of importance are given to people and culture, management support, user involvement and clarity of communication and systems' requirements at project pre-implementation, implementation and post-implementation stages.

This study provides lessons of HEIs planning to undertake significant change by implementing integrated information systems. Challenges emerge around fit, complexity, training, communication and consultation. Benefits gained and emerging challenges show some commonality between the two case HEIs, pointing the way forward for other “large” (student number determined) HEIs embarking on similar change.

The UK HEI sector is experiencing major change emphasising cost reduction and operational efficiency. Understanding challenges relating to significant systems change in complex settings with varying stakeholder demands has considerable sectoral value.

This study aims to investigate the use of Artificial Intelligence (AI) applied to vertical farms to evaluate whether disrupting technology supports sustainability and increases strategic business model choices in the agricultural sector. The study responds through empirical analysis to the gap on the subject of AI-driven business models present in the growing sector literature.

The paper analyzes the case of “ZERO”, a company linked to the strategy innovation ecosystem of the Ca’ Foscari University of Venice, Italy. The empirical data were collected through a semi-structured questionnaire, interviews and the analysis of public news on the business model available in the analyzed case study. The research is empirical and uses exploratory, descriptive analysis to interpret the findings. The article focuses on the evaluation of AI impact on the agricultural sector and its potential to create new business models.

The study identified how AI can support the decision-making process leading to an increase in productivity, efficiency, product quality and cost reduction. AI helps increase these parameters through a continuous learning process and local production, and the possible decrease in prices directed toward the goal of zero km food with fresh products. AI is a winning technology to support the key elements of the vertical farm business model. However, it must be coupled with other devices, such as robots, sensors and drones, to collect enough data to enable continuous learning and improvement.

The research supports new research trends in AI applied to agriculture. The major implication is the construction of ecosystems between farms, technology providers, policymakers, universities, research centers and local consumer communities.

The ZERO case study underlines the potential of AI as a destructive technology that, especially in vertical farms, eliminates external conditions by increasing productivity, reducing costs and responding to production needs with adequate consumption of raw materials, boosting both environmental and social sustainability.

The study is original, as the current literature presents few empirical case studies on AI-supporting business models in agriculture. The study also favors valuable strategic implications for the policies to be adopted in favor of new business models in agriculture.

This study aims to examine the necessity effects of big data analytics capabilities (BDAC) on decision-making performance (DMP), particularly in the public sector.

The authors used the combined methods of partial least square structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to test the hypothesized relationships.

The findings show that the presence of all three BDAC (infrastructure, management and personnel) is significant and necessary to achieve higher levels of DMP. Specifically, the results revealed big data management capabilities to be of higher necessity to achieve the highest possible DMP. The findings provide public-sector practitioners with insights to support the development of their BDAC.

Time-sensitive domains such as the public sector require insight and quality decision-making to create public value and achieve competitive advantage. This study examined BDAC in light of the combined methods of (PLS-SEM) and NCA to test the hypothesized relationships in the public sector context.

The emergence the fourth industrial revolution, known as well as industry 4.0, and its applications in the manufacturing sector ushered a new era for the business entities. It not only promises enhancement in operational efficiency but also magnify sustainable operations practices. This current paper provides a thorough bibliometric and network analysis of more than 600 articles highlighting the benefits in favor of the sustainability dimension in the industry 4.0 paradigm.

The analysis begins by identifying over 1,000 published articles in Scopus, which were then refined to works of proven influence and those authored by influential researchers. Using rigorous bibliometric software, established and emergent research clusters were identified for intellectual network analysis, identification of key research topics, interrelations and collaboration patterns.

This bibliometric analysis of the field helps graphically to illustrate the publications evolution over time and identify areas of current research interests and potential directions for future research. The findings provide a robust roadmap for mapping the research territory in the field of industry 4.0 and sustainability.

As the literature on sustainability and industry 4.0 expands, reviews capable of systematizing the main trends and topics of this research field are relevant.