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This article aims to trace the historical development of the behavioral strategy (BS) field, which implements psychology in strategic management. Mainly, it provides a contextual understanding of how this stream of research has historically evolved and what relevant future trajectories are. This work is part of the “over half a century of Management Decision” celebrative and informal Journal section.

We consider BS literature produced in management decision (MD), the oldest and longest-running scholarly publication in management, as a proxy for the evolution of management thought. Through a Systematic Literature Review (SLR) process, we collected – via the MD website and Scopus – a sample of 97 BS articles published in MD from its foundation (1967) until today (2024). Regarding the analysis, we adopted a Reflexive Thematic Analysis approach to synthesize the main BS topics, then read from a historical perspective regarding three “eras” over which the literature developed. Selected international literature outside the Journal’s boundaries was considered to complement this historical analysis.

Historically, within the BS field, the interest passed from the rules to rationally govern strategic decision-making processes, to studying what causes cognitive errors, to understanding how to avoid biases and to being prepared for dramatic changes. The article also identifies six future research trajectories, namely “positive heuristics,” “context-embedded mental processes,” “non-conventional thinking,” “cognitive evolutionary triggers,” “debiasing strategies” and “behavioral theories for new strategic challenges” that future research could investigate.

The limitation of the study lies in its exclusive focus on MD for investigating the historical evolution of BS, thereby overlooking critical contributions from other journals. Therefore, MD’s editorial preferences have influenced results. A comprehensive SLR on the BS field is still needed, requiring broader journal coverage to mitigate selection biases and enhance field appraisal.

This contribution is the first to offer a historical evolutionary view of the BS field, complementing the few other reviews on this stream of research. This fills a gap in the study of the evolution of management thought.

This paper aims to propose an accurate and efficient decision-making process adapted to the specific context of Claim Management situations, implying partners engaged in a high-involvement relationship.

We used a three-step approach: first, an inductive phase based on 12 past case studies. Second, a theory-building phase. Third, a theory-testing phase based on an ongoing case study to observe and test our propositions.

Proposal 1: Partner’s Strategic Value is an influential decision parameter that must be incorporated into Claim Management-related decision-making processes in high-involvement relationships. Proposal 2: The Fast-and-Frugal Heuristic is adapted to the intense, interactive and iterative nature of the Claim Management context. Our final proposal combines these two findings, i.e. a Fast-and-Frugal Heuristic incorporating the Partner’s Strategic Value and based on using decision criteria as a sequence, not simultaneously.

In the context of high-involvement business relationships and Claim Management, this study introduces the importance of selecting an appropriate decision methodology and integrating a strategic decision parameter (Partner’s Strategic Value) into an operational decision-making context. Furthermore, the principle of considering decision parameters in a specific sequence corresponds to the iterative and interactive nature of the Claim Management processes.

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.

Theory is an essential prerequisite in the development and maturation of any scholarly discipline. This study offers insight into theory development in reverse logistics (RL) studies, provides a synopsis of the theories employed in RL studies, and presents a comprehensive framework for choosing and applying theories in RL studies.

Using the systematic literature review approach, 265 various RL articles were analysed to discover the trend in using theories in RL studies and classify the individual theories employed. The analysis of the theoretical classification is presented to explain the type and frequency of the usage of theories.

The analysis discovered 52 specific theories from the sample. These theories were categorised under various categories: competitive, inventory, economic, decision, etc. The institutional, stakeholder, transaction cost economies, resource-based view, natural resource-based view, dynamic capability, agency and theory of planned behaviour were some of the key theories discovered. Finally, a comprehensive framework is provided to aid researchers in choosing and utilising theories.

This study gives authors, reviewers and editors perspectives on utilising theories in RL studies. It will give them the impetus to develop theories in RL and limit the borrowing or extension of theories from other disciplines to RL studies.

To the best of the researcher's knowledge, this is the first attempt to comprehensively provide an anatomical perspective into theory usage in RL studies. Besides, this study's proposed framework for selecting and using theories is a novelty in the domain of RL.

The practices of sustainable and responsible investment (SRI) among Islamic financial institutions (IFIs) nowadays still rely on the existing environmental, social and governance (ESG) criteria. However, based on observation, some of the existing criteria listed by the reports of certain authorities and organizations do not seem to be aligned with Shariah principles. Therefore, this study aims to investigate those criteria to help develop Islamic-SRI (i-SRI) criteria based on the ESG concept.

This study adopted the qualitative method via content analysis of documents and interviews with experts.

Based on the analysis, a set of i-SRI criteria is developed based on the ESG concept, of which 33 elements are environmental, 50 elements are social and 26 elements are governance issues. Overall, this study finds that there is no obvious contradiction with the Islamic philosophy in the existing ESG criteria, with the exception of four criteria, i.e. promoting human rights, freedom of expression, freedom of censorship and freedom of association under social criteria. These four existing criteria are not aligned with Islamic teaching and not appropriate with Islamic ESG criteria.

The creation of Islamic ESG criteria can assist relevant authorities to improve the current ESG criteria and to embed an Islamic perspective within it.

This study developed a set of i-SRI criteria, which may be suitable as a source of reference to relevant parties.

The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.

The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.

The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.

This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

The cyber-physical system (CPS) is a well proven technology for improving system performance, resilience, and interconnectivity. In emerging nations like India, the logistics industry lacks practices connecting logistical equipment with cyberspace. This paper aims to bridge this gap by identifying and evaluating the performance metrics of connectivity solutions. Its goal is to establish an appropriate infrastructure that enables seamless connectivity within the CPS-enabled logistics ecosystem.

A novel integrated decision method is employed to classify the optimal connectivity solution for CPS. It integrates Regret Theory (RT) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-1) method in a Hesitant Fuzzy (HF) environment. This method considers the psychological traits of decision-makers and effectively incorporates their hesitancy for the classification.

The findings highlight security (c10) as the foremost critical performance metric, followed by cost (c6), scalability (c9), traceability (c2) and trustworthiness (c1) to build connective infrastructure for CPS. For extensive coverage scenarios, like freight transportation, cellular connectivity (a2) emerges as the most suitable connectivity solution.

This study provides a roadmap to logistics managers for selecting a suitable connectivity infrastructure to enhance seamless connectivity in logistics operations and processes. Technology providers can utilize the findings to develop the CPS infrastructure for effective freight logistics management.

This research introduces a novel decision-making tool for making choices related to advanced technology assessment. It holds significant value in facilitating well-informed decisions in the digital transformation era.

By combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity.

We used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms.

Our findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions.

The insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm’s adoption of corporate actions (ESG practices).

To the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms.

This study aims to assess National Health Service (NHS) decision-making protocols during the pandemic, with two primary objectives: (1) to establish whether decision-making protocols changed during the pandemic and (2) to evaluate if these changes could inform future decision-making strategies beyond the pandemic. By focusing on the shift from traditional to emergency decision-making processes, this research seeks to derive actionable insights for enhancing policy and practice in crisis conditions.

We employ a mixed-methods approach, gathering data through an online survey targeted at senior NHS decision-makers involved in the pandemic response. Our survey collected quantitative and qualitative data to assess changes in decision-making protocols. The analysis included statistical techniques to quantify changes and thematic analysis to explore their implications, providing a detailed understanding of decision-making adaptations during the crisis and their potential future impact.

Our findings clarify the role of the NHS values and constitution, which prioritize patient welfare, dignity and equitable access to healthcare, guiding all decision-making. During the pandemic, the urgency to respond swiftly necessitated modifications to these guiding principles. Traditional processes were adapted, allowing for more rapid decision-making while still aligning with the core values, effectively balancing immediate response needs with long-term healthcare commitments.

Our research contributes to decision-making under crisis conditions within a healthcare context and brings together a theoretical background which has accommodated the development of models and approaches that can be utilized by both service and manufacturing organizations. In addition, we have sought to bring together the importance of decision-making protocols under crisis conditions using observations from respondents who experienced decision-making at a senior level prior, during and beyond the period of the COVID-19 pandemic, which has assisted in the models developed in this paper. In addition, our empirical research demonstrates the importance that the values of the organization have upon decision-making and how such values need to be adjusted in the light of crisis operations.

Our research provides insightful observations relating to the pressures upon decision-making protocols under crisis conditions and provides senior decision-makers with an approach to realigning values to cope with unusual and highly pressurized operating environments. Notably, there is a clear requirement for decision-makers to communicate clearly to staff the need to temporarily alter the modus operandi to reflect crisis operations.

To the best of the authors’ knowledge, this is the first study to explore decision-making in the NHS during a pandemic and to clearly demonstrate how such decision-making needs to be adapted to reflect the nature and scope of delivering a complex healthcare service under crisis conditions.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.