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This research aimed to measure the influence of resource dependency (trading and/or shareholder's dependencies) squad age structure by building archetypes to identify strategic dominant schemes.

Based on the Ligue 1 football clubs from the 2009/2010 season to the 2018/2019 data, the authors use the k-means classification to build archetypes of resource dependency and squad structure variables. The influence of resource dependency on squad structure is then analysed through a table of contingency.

Firstly, the authors identify archetypes of resource dependency with some clubs that are dependent on the transfer market and others that do not count on sales to balance their account. Secondly, they provide different archetypes of squad structure choices. The contingency between those archetypes allows to identify three main strategic schemes (avoidance, shaping and adaptation).

The research tests an original relationship between resource dependency of clubs and their human resource strategy to respond to it. This paper can help to provide detailed profiles for big clubs looking for affiliate clubs to know which clubs have efficient academy or player development capacities.

The purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.

This study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.

Asymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.

This paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.

This empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.

This paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

Stakeholder theory understands business in terms of relationships among stakeholders whose interests are mainly joint but may be occasionally conflicting. In the latter case, managers may need to make trade-offs between these interests. The purpose of this paper is to explore the nature of managerial decision-making about these trade-offs.

This paper draws on the ordonomic approach which sees business life to be rife with social dilemmas and locates the role of stakeholders in harnessing or resolving these dilemmas through engagement in rule-finding and rule-setting processes.

The ordonomic approach suggests that stakeholder interests trade-offs ought to be neither ignored nor avoided, but rather embraced and welcomed as an opportunity for bringing to fruition the joint interest of stakeholders in playing a better game of business. Stakeholders are shown to bear responsibility for overcoming the perceived trade-offs through the institutional management of social dilemmas.

For many stakeholder theorists, the nature of managerial decision-making about trade-offs between conflicting stakeholder interests and the nature of trade-offs themselves have been a long-standing point of contention. The paper shows that trade-offs may be useful for the value creation process and explicitly discusses managerial strategies for dealing with them.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

Interest rate derivatives (IRDs) are the essential components of financial risk management and are used across various industry sectors. The objective is to analyze the differences in approach to managing interest rate risks between the Indian corporates that execute IRDs and the ones that do not.

Interest rate fluctuations require Indian corporates to hedge their exposures in foreign currency interest rates. This is all the more true for mid-sized corporates because of their balance sheet exposures. Additionally, they may not have the resources to formulate risk management policies. This paper analyzes data collected from financial statements of a diverse set of companies that use IRD and helps in formulating such a strategy.

The results indicate significant differences for some of the parameters like information asymmetry and agency cost between users and non-users of IRDs. The study further suggests causality between users of derivatives and parameters like size, growth and debt.

The study compares users and non-users of IRDs, thereby identifying factors unique to users of IRDs. It also studies causality relations which explain the motivation to do IRDs. Thus, it enables risk managers to use this as a reference point to decide on their strategies.

While there are multiple studies across geographies and sectors including commercial banks in India on the usage of interest rate swaps, this study focuses on Indian mid-tier corporates. Furthermore, the study distinguishes between users and non-users based on financial parameters, which in turn would provide a framework for decision-hedging strategies.

The dissemination of misleading and false information through media can jeopardize a company’s reputation, thus posing a threat to its stock and performance. Institutional investors are known to influence capital markets. Therefore, this paper investigates whether institutional investors engage in shaping the media sentiment stock nexus, stabilize company stocks and enhance performance.

We first investigate the effect of media sentiment on market reactions by using panel regression models. To examine the role of institutional investors, we design a quasi-experiment by exploiting the Financial Crisis of 2008 and go further by examining the heterogeneity across levels of institutional ownership. Due to risk-averse, investors may respond asymmetrically to pessimistic and positive sentiment. Accordingly, we split the sample into two sub-types, good news and bad news, based on keywords representing positive or negative content.

We find supportive evidence that institutional investors have impacts on how the markets react to media news, and the impacts are heterogeneous in the face of bad and good news. We conjecture that institutional investors act as a stabilizer of stock prices through media sentiment management.

This paper confirms the distinctive effects of institutional investors on capital markets, and uncovers the behind-the-scenes intervention and possible causal link running from institutional investors to media sentiment management. It contributes to the broad field of institutional investors' behavior, media news involvement in capital markets and market efficiency.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

Although earlier research suggests a positive relationship exists between engaging in entrepreneurial marketing activities and firm performance, there may be contingent issues that impact the association. This investigation unpacks the relationship between entrepreneurial marketing behaviour and firm performance under the moderating role of coopetition, in an immediate post-COVID-19 period.

A resource-based theoretical lens, alongside an outside-in perspective, underpins this study. Following 20 field interviews, survey responses via an online survey were obtained from 306 small, passive exporting wine producers with a domestic market focus in the United States. The data passed all major robustness checks.

The statistical findings indicated that entrepreneurial marketing activities positively and significantly influenced firm performance, while coopetition provided a non-significant moderation effect. Field interviews suggested that entrepreneurs’ attemps to scale up from passive to more active export activities in an immediate post-pandemic period helped explain the findings. Owner-managers rejoined trustworthy and complementary pre-pandemic coopetition partners in the immediate aftermath of coronavirus disease 2019 (COVID-19) for domestic market activities. In contrast, they had to minimise risks from dark-side/opportunistic behaviour when joining coopetition networks with partners while attempting to scale up export market activities.

Unique insights emerge to unpack the entrepreneurial marketing–performance relationship via the moderation effect of coopetition, namely, with the temporal setting of an immediate post-COVID-19 period. Firstly, new support arises regarding the likely performance-enhancing impact of owner-managers’ engagement in entrepreneurial marketing practices. Secondly, novel findings emerge in respect of the contrasting role of coopetition in both domestic and export market activities. Thirdly, new evidence arises in relation to a resource-based theoretical lens alongside an outside-in perspective, whereby, strategic flexibility in pivoting facets of a firm’s business model needs effective management following a crisis.

This paper aims to identify the antecedents and postcedents of customer satisfaction, including utilitarian, social and emotional factors, in a fair trade (FT) coffee consumption context.

This paper is based on a broad range of 177 consumers of FT coffee in Spain, the data analysis used structural equation modeling (SEM) with SPSS/AMOS 26.0 software.

This paper supports that both customer social value and quality affect perceived value (PV). PV in turn has effects on customer satisfaction and the latter influences loyalty. Conversely, both customer emotional value and customer expectations were not confirmed as antecedents of PV.

The consumer satisfaction analysis conducted differs substantially from those of conventionally traded coffee, as social and emotional factors were considered along with utilitarian factors.

Practitioners, retailers and relevant institutions should design strategies to manage efficiently channel efforts to improve the consumer satisfaction and its loyalty.

This paper contributes to a substantial improvement in the understanding of consumer satisfaction and its consequences, in FT coffee consumption contexts. A new integrated theoretical model on customer satisfaction has been provided, which includes social and emotional perception factors, along with cognitive perception (quality and expectations) factors.

研究旨在確認在公平貿易咖啡消費的課題上，顧客滿意的誘因及其後因，這包括實用的因素，社會的因素和情感的因素。

研究之數據廣泛來自在西班牙177名公平貿易咖啡消費者; 分析則以結構方程模型，並以SPSS Amos 26軟件來進行。

研究結果證實，顧客社會價值和質量是會影響認知價值的;認知價值繼而影響顧客滿意度，而顧客滿意度又進而影響他們的忠誠。相反的，顧客情緒價值或他們的期望、均未能證實是認知價值的先決條件。

本研究所進行的消費者滿意度分析，與其它以傳統方法銷售的咖啡之相關研究有很大的分別，這是因為本研究除了考慮實用的因素外，還納入了社會因素和情感因素。

從業人員、零售商和有關的機構應制訂適切的策略，以能有效地管理各個管道，來提升消費者的滿意度和忠誠。

本研究的貢獻在於它幫助我們在公平貿易咖啡消費的課題上，對消費者滿意及其效果有更深入的認識。研究亦提供了一個探討顧客滿意度的嶄新、綜合的理論模型，而這個理論模型，除了涵蓋知覺認知 (質量和期望) 的因素外，還納入了社會的和情感的知覺因素。