While being integrated together conceptually and practically, the literature on game theory in the context of financial markets lacks a cohesive understanding. This study aims to systematically scrutinize and analyse the literature of game theory in the context of financial markets, through a systematic literature review.
A systematic literature-based approach, through the theories, context, characteristics and methodology (TCCM) framework has been applied to 97 articles, extracted and filtered from two databases, Scopus and Web of Science, for a comprehensive review and understanding of the intellectual development in the domain of game theory and financial markets.
The review highlighted the most utilitarian theories within the literature, the context of research in terms of countries and industries, four themes which characterize the literature, as well as the methodologies and research designs used in this research domain. The paper also uncovered certain essential areas that present scope for further research.
While two of the largest indexation databases have been used, some relevant articles may have been excluded due to the restriction of databases and screening criteria, which may lead to the less exploration of several facets of the domain.
Practically, the paper has implications for multiple stakeholders including traders, businesses and governments. For traders, this paper acts as a guide to entering and understanding the dynamics of financial markets. The review also covers decision-making from the perspective of firms, including venture capitalists. This paper would allow firms to understand how game theory can help influence or analyze the strategic interactions between their stakeholders in terms of information disclosure, or consumers and their behavior to stimuli from the firm’s actions.
To the best of the authors’ knowledge, this study is the first of its kind that attempts to comprehensively provide an overview of the literature on game theory in financial markets. In doing so, this study shall help assess the current state of knowledge in the said field and locate gaps in the literature to propose new research directions.
This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.
The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.
The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.
This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.
Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).
This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.
The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.
This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.
This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.
The study aims to explore the organizational benefits of positive employee experience and to provide a framework for measuring it. The positive employee experience has a profound impact on employees’ attitudes; thus, it is particularly important to what extent an organization can create the conditions supporting this.
The study is based on literature review and the framework needs to be empirically tested to draw final conclusions.
Organizational performance and success are influenced by employees’ well-being, commitment, job satisfaction and the high level of individual performance. However, this grouping of variables is not exhaustive, but in practice, it is often not necessary to fully understand the complex and complicated relationships among the organizational variables. However, a positive employee experience has an impact on all of these variables. According to our understanding and experience, the task of management is not to strengthen the variables describing employee attitudes individually, based on the knowledge of specific relations presented in the management literature and selected for the sake of a single research, but to create an acceptable level of the positive employee experience, which is able to strengthen these variables in a way that is useful for the organization.
In this study, the authors introduce the concept of the positive employee experience and the ways and steps to measure it. The authors review the methodology of predictive analytics, the main principles of data collection and the types of data with their possible applications. Finally, the limitations of the framework and the risks of enhancing the positive employee experience are also discussed.
The ever-evolving landscape of financial technology (Fintech) has revolutionised payment methods and raised questions about what drives user behaviour in adopting these innovative solutions. This study, using narrative transportation theory as an underpinning theory, aims to investigate into the dynamics of green user behaviour in adopting Fintech payments.
This study used a deductive approach, and with data obtained from 635 respondents through the purposive sampling technique, partial least squares structural equation modelling was employed to yield significant insights.
The study found a positive association between green brand positioning and product differentiation. However, it unexpectedly didn't impact user attitudes towards Fintech payments. Green brand image and perceived performance positively influenced product differentiation. Perceived product differentiation fully mediated the association between green brand positioning and user attitudes. The study introduced fear of missing out's (FOMO) moderating role, enriching eco-conscious marketing insights and user behaviour understanding.
This study reveals crucial implications for marketers, policymakers and user experience (UX) designers operating within the Fintech industry. It emphasises green brand positioning's impact on product differentiation, user attitudes and its mediating role. It advocates for sustainability integration, innovation, strategic messaging and user-centric improvements to optimise user perceptions and competitiveness in the evolving Fintech landscape. The study's cross-sectional design may limit the ability to establish causal relationships over time and overlook temporal changes in green Fintech adoption dynamics; thus, longitudinal studies are warranted to better understand the evolving nature of user attitudes and behaviours towards green Fintech payments.
This study adds novelty to the existing body of literature by introducing the dimension of innovation appeal to green brand positioning and employing narrative transportation theory in the Fintech realm. The findings also add novelty by highlighting the moderating impact of fear of missing out in predicting the association between green brand positioning and product differentiation in the realm of green Fintech and green use behaviour.
This paper aims to deal with intra and inter-cell layout problems in cellular manufacturing systems. The model is organized to minimize the total handling cost, i.e. intra and inter-cell handling costs in a continuous environment.
The research was conducted by developing a mixed integer mathematical model. Due to the complexity and NP-hard nature of the cellular manufacturing layout problem, which mostly originated from binary variables, a “graph-pair” representation is used for every machine set and cells each of which manipulates the relative locations of the machines and cells both in left-right and below-up direction. This approach results in a linear model as the binary variables are eliminated and the relative locations of the machines and cells are determined. Moreover, a genetic algorithm as an efficient meta-heuristic algorithm is embedded in the resulting linear programming model after graph-pair construction.
Various numerical examples in both small and large sizes are implemented to verify the efficiency of the linear programming embedded genetic algorithm.
Considering the machine and cell layout problem simultaneously within the shop floor under a static environment enabled managers to use this concept to develop the models with high efficiency.
The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination.
We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver.
Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach.
The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner.
This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem.
Supply chain (SC) research has boomed over the past two decades. Significant contributions have been made to the field from various analytical and decision-making perspectives. This paper, a comprehensive bibliometric study, aims to identify the key research contributors, institutions and themes.
A comprehensive knowledge domain visualization of over 1,000 articles, published between 2000 and 2022, is carried out to construct a bird’s eye view of the field in terms of research production, key authors, main publication outlets, geographic disparity of the contributions and emerging research trends. Additionally, collaboration patterns among researchers and institutions are mapped to highlight the communication networks underlying research initiatives.
Results show an explosive growth in the number of articles tackling supply chain optimization (SCO) issues with a significant concentration of the contributions in a relatively small cluster of authors, journals, institutions and countries. Among the many important findings, our analysis indicates that mixed-integer linear programming is the most commonly used model, while robust optimization is the method of choice for handling uncertainty. Furthermore, most SC models are developed at only one level of the organizational hierarchy and consider only one planning horizon. The importance of developing integrated SCO systems is key for future research.
The study fills the optimization techniques gap that exists in SC management bibliometric studies and presents a thematic map for the SCO research highlighting the various research foci.
Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.
The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.
The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.
The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.
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