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The purpose of the study is to investigate and analyze the dynamics of the government-enterprise grain joint storage mechanism, particularly, focusing on profit-driven speculative behaviors exhibited by enterprises within this context. The study aims to understand the various factors influencing the behavior of stakeholders involved in grain storage, including government storage departments, agent storage enterprises and quality inspection agencies.

The study employs a tripartite evolutionary game model to investigate profit-driven behaviors in government-enterprise grain joint storage. It analyzes strategies of government departments, storage enterprises and quality inspection agencies, considering factors like supervision costs and speculative risks. Simulation analysis examines tripartite payoffs, initial probabilities and the impact of digital governance levels to enhance emergency grain storage effectiveness.

The study finds that leveraging digital governance tools in government-enterprise grain joint storage mechanisms can mitigate risks, enhance efficiency and ensure the security of grain storage. It highlights the significant impact of supervision costs, speculative risks and digital supervision levels on stakeholder strategies, offering guidance to improve the effectiveness of emergency grain storage systems.

The originality of this study lies in its integration of digital governance tools into the analysis of the government-enterprise grain joint storage mechanism, addressing profit-driven speculative behaviors. Through a tripartite evolutionary game model, it explores stakeholder strategies, emphasizing the impact of digital supervision levels on outcomes and offering insights crucial for enhancing emergency grain storage effectiveness.

Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

This study aims to propose a new conforming tax measure based on the work of Badertscher et al. (2019).

This study divides total tax avoidance/management (TM) into nonconforming and conforming portions through a regression. The residual of the regression is treated as the conforming tax measure. In addition, the new conforming tax measure is validated via three approaches. Then, this study examines the moderating effect of nonconforming earnings management (EM) on the relationship between conforming TM and firm performance.

The empirical results show that the model has stronger explanatory power than the model proposed by Badertscher et al. (2019). Additionally, the validation results show that the mean value of the conforming tax measure is lower in quasi-private corporations (financially constrained companies) than in matched public corporations (nonfinancially constrained companies), and firms under high market capital pressure are less motivated to engage in conforming tax practices. Furthermore, nonconforming EM positively moderates the conforming tax–ROA association, implying that nonconforming EM can reduce financial reporting costs resulting from conforming tax practices.

This study contributes to conforming tax research in the following ways. First, this study proposes a new conforming tax measure by substituting the cash book tax difference (BTD) for the BTD in the model of Badertscher et al. (2019) (“BKRW”). Second, this study demonstrates theoretically why the cash BTD should outperform the BTD in computing the BKRW conforming tax measure and confirm this empirically. Third, this study presents a three-way conceptual schema that divides corporations into two groups along each of three tax-relevant dimensions. The group of firms that use both conforming and nonconforming tax strategies have different characteristics compared to the other group. This study also validates the conforming tax measure across the two-group dichotomies.

Mega construction projects (MCPs), characterized by their vast scale, numerous stakeholders and complex management, often face significant uncertainties and challenges. While existing research has explored the complexity of MCPs, it predominantly focuses on qualitative analysis and lacks systematic quantitative measurement methods. Therefore, this study aims to construct a complexity measurement model for MCPs using fuzzy comprehensive evaluation and grey relational analysis.

This study first constructs a complexity measurement framework through a systematic literature review, covering six dimensions of technical complexity, organizational complexity, goal complexity, environmental complexity, cultural complexity and information complexity and comprising 30 influencing factors. Secondly, a fuzzy evaluation matrix for complexity is constructed using a generalized bell-shaped membership function to effectively handle the fuzziness and uncertainty in the assessment. Subsequently, grey relational analysis is used to calculate the relational degree of each complexity factor, identifying their weights in the overall complexity. Finally, the weighted comprehensive evaluation results of project complexity are derived by combining the fuzzy evaluation results with the grey relational degrees.

To validate the model’s effectiveness, the 2020 Xi’an Silk Road International Conference Center construction project is used as a case study. The results indicate that the overall complexity level of the project is moderate, with goal complexity being the highest, followed by organizational complexity, environmental complexity, technical complexity, cultural complexity and informational complexity. The empirical analysis demonstrates that the model can accurately reflect the variations across different dimensions of MCP complexity and can be effectively applied in real-world projects.

This study systematically integrates research on MCPs complexity, establishing a multidimensional complexity measurement framework that addresses the limitations of previous studies focusing on partial dimensions. Moreover, the proposed quantitative measurement model combines fuzzy comprehensive evaluation and grey relational analysis, enhancing the accuracy and objectivity of complexity measurement while minimizing subjective bias. Lastly, the model has broad applicability and can be used in MCPs across different countries and regions, providing a scientific and effective basis for identifying and managing MCP complexity.

Organizational performance (OP) is one of the most important constructs in management research and all functions are evaluated by their contribution toward OP. This paper evaluates the current state of the research on OP in strategic human resource management (SHRM) literature. The study intends to generate new ideas for ongoing research in the field, facilitate the strategic alignment of HR operations and provide practical guidance on adopting OP measurement metrics for evidence-based decision-making at both organizational and individual levels. Consequently, a dual-method systematic review methodology was employed to achieve the stated objectives. This research underpins its theoretical argument on a resource-based view.

This research paper follows a systematic review of 127 empirical studies conducted in the last three decades, in which, the selection of OP as a dependent variable is evaluated. This systematic review followed the integrated and systemic review of literature combining Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis. The tools used for bibliometric analysis are Biblioshiny package from R software and VOSviewer software.

Out of selected 127 empirical studies, none of the studies provide a comprehensive measuring framework. As a result, the current literature review provides an expanded list of OP indicators and a measurement framework with 10 different performance perspectives based on Business Process Measurement literature.

Performance is a complex concept that requires understanding its relationships and the impact of context and measures. Objective measures alone may not capture this, so research is needed to determine the best combinations of subjective and objective measures. This systematic literature review identifies gaps in existing literature on performance measurement indicators in management and provides an exhaustive list of 161 indicators. These indicators can be used by practitioners and researchers to choose appropriate ones based on their needs. Future research should focus on case studies to validate results and enhance performance measurement systems in SHRM research.

The study emphasizes the importance of examining the multidimensionality of OP, which is linked to stakeholders affected by performance measures, the assessment environment, and the time frame when gauging performance.

This review analyzed the intellectual structure of SHRM and OP research field and identified various research fronts. This study added to the literature a measurement framework with 10 perspectives in which 161 performance indicators were categorized.

This paper aimed to shed light on the relationship between blockchain technology intensity and tax evasion and whether this relationship is moderated by good governance.

Data from a sample of 50 European companies selected from the STOXX 600 index between 2010 and 2019 were used to test the model via panel data and multiple regression. Here, we used the generalized least squares method estimated on panel data. A multivariate regression model was used to analyze the moderating effect of good governance on the association between blockchain technology intensity and tax evasion. For the robustness analyses, we included the comparative study of legal systems. We performed an additional analysis by testing the dynamic dimension of the data set using the generalized method of moments to control for the endogeneity problem.

Expectedly, the results showed a negative relationship between blockchain technology intensity and tax evasion. Furthermore, the findings suggest that the moderating variable negatively affects the relationship between blockchain technology and tax evasion.

To our knowledge, this study supports the existing literature. Firstly, it expands the scientific debate on tax evasion. Secondly, it extends the scope of the agency theory, which is used to explain the phenomena associated with tax evasion. This study is one of the first to examine the moderating effect of good governance on the association between blockchain technology intensity and tax evasion.

The planning of massive vaccination campaigns often falls to nongovernmental organizations that have to face the critical challenge of vaccinating the largest number of people in the shortest time. This study aims to provide an easy tool for minimizing the duration of mass vaccination campaigns in rural and remote areas of developing countries.

This paper presents a linear mathematical model that combines location, scheduling and routing decisions that allows determining where to locate the vaccination centers, as well as the schedule/route that each medical team must follow to meet the target demand in the shortest time possible. In addition, the paper proposes an heuristic approach that can be integrated in a spreadsheet.

As the numerical experiments show, the proposed heuristic provides good solutions in a short time. Due to its simplicity and flexibility, the proposed approach allows decision-makers to analyze and evaluate several possible scenarios for decision-making by simply playing with input parameters.

The integration of the heuristic approach in a spreadsheet provides a simple and efficient tool to help decision-makers while avoiding the need for large investments in information systems infrastructure by user organizations.

Motivated by a real-life problem and different from previous studies, the objective of the planning is to reduce the length of the vaccination campaigns with the available resources and ensure a target coverage instead of planning for minimizing costs or maximizing coverage. Furthermore, for helping implementation to practitioners, the heuristic can be solved in a spreadsheet.

This systematic review aims to identify the key determinants of knowledge transfer (KT) activities among researchers.

This study systematically reviewed KT literature in academic settings from 1995–2023. The authors searched Web of Science and Scopus using predefined keywords, following PRISMA guidelines for screening and eligibility assessment. From 158 selected articles, the authors extracted data and conducted a descriptive analysis to map KT activities’ evolution. A narrative synthesis approach categorized determinants of researchers’ KT activities.

The systematic review findings revealed a general conceptual framework that categorizes the identified determinants of KT into four categories. At the individual level, the factors are related to the sociodemographic characteristics of the researcher (e.g. gender, age, experience), their psychological aspects (e.g. attitude, intrinsic motivation, intention) and personal characteristics (e.g. self-efficacy, communication skills). At the research team level, leadership style and team dynamics. At the organizational level, the findings emphasize university characteristics (e.g. size, structure and ranking), KT culture installed and university resources. At the inter-organizational level, the key determinants were funding sources, network strength and trust.

The studies included in our database were different in terms of contexts, country of the study, the disciplines of KT and the types of KT activities examined. This variety restricts the direct comparison of research findings thus the generalizability of our conclusions. Future research should focus on specific contexts, disciplines, countries or types of KT activities to provide generalizable findings.

A better understanding of all the factors influencing KT among university researchers is essential for several reasons. First, it will enable the government to develop effective policies to promote KT ecosystems. Second, universities can create strategies, policies and programs to support researchers’ engagement in KT activities. Finally, researchers can be more strategic in their KT efforts.

This systematic review contributes to the literature by providing a comprehensive conceptual framework that identifies KT determinants at different levels and fills a gap in the existing literature that only addresses specific aspects of KT determinants. This framework can be a theoretical reference for future empirical studies. Furthermore, it practically provides recommendations for different actors including, government, universities and researchers.

We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).