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Rice is an essential determinant of food security in some developing countries as it has become the state’s staple food. Due to its essential role, rice supplies have been studied over the years. For this reason, it is essential to ensure quantity and quality availability, safety, distribution and affordability from input suppliers, farmers and milling industries to consumers. This study aims to assess and evaluate the relationship between sustainability risk factors for each rice supply chain actor to minimise uncertainty and losses in the supply chain and achieve a sustainable rice supply chain (SRSC).

A total of 50 sub-risk factors for the rice supply chain, divided into four sustainability dimensions, were derived through a literature review. Next, it was identified through interviews with 12 experts in 2 stages: the first stage, reviewing the literature review results, and the second stage, finalising with Pareto analysis. Each stage produces 28 and 21 sub-risk factors. Fuzzy-decision-making trial and evaluation analysis was used to evaluate the relationship between sub-risk factors in the context of SRSC.

The sub-risk factors that need to be managed to achieve SRSC are climate change risk (floods and rainfall) from the environmental dimension (case group) and operational risk (loss of low-quality results) from the process dimension (impact group). These practical findings provide actionable insights for supply chain actors and contribute to a deeper understanding of the complexities of the rice supply chain.

This study underscores the urgent need for a comprehensive understanding of the risks faced by all actors in the rice supply chain. Such an understanding is crucial for future research and practical applications, and it is the key to ensuring the sustainability and security of the rice supply chain.

To the best of the authors’ knowledge, this is the first comprehensive study in the context of SRSC that evaluates the relationship between risk factors to achieve food security in developing countries.

A circular economy (CE) is an economic system that tries to eliminate waste and continually use resources. Due to growing environmental concerns, supply chain (SC) design should be based on the CE considerations. In addition, responding and satisfying customers are the challenges managers constantly encounter. This study aims to improve the design of an agile closed-loop supply chain (CLSC) from the CE point of view.

In this research, a new multi-stage, multi-product and multi-period design of a CLSC network under uncertainty is proposed that aligns with the goals of CE and SC participants. Recycling of goods is an important part of the CLSC. Therefore, a multi-objective mixed-integer linear programming model (MILP) is proposed to formulate the problem. Besides, a robust counterpart of multi-objective MILP is offered based on robust optimization to cope with the uncertainty of parameters. Finally, the proposed model is solved using the e-constraint method.

The proposed model aims to provide the strategic choice of economic order to the suppliers and third-party logistic companies. The present study, which is carried out using a numerical example and sensitivity analysis, provides a robust model and solution methodology that are effective and applicable in CE-related problems.

This study shows how all upstream and downstream units of the SC network must work integrated to meet customer needs considering the CE context.

The main goal of the CE is to optimize resources, reduce the use of raw materials, and revitalize waste by recycling. In this study, a comprehensive model that can consider both SC design and CE necessities is developed that considers all SC participants.

Understanding consumer behaviour towards ethanol-blended fuel is crucial for assessing the adoption of alternate fuel vehicles (AFVs). By examining factors influencing purchase decisions, such as cost considerations, environmental concerns and perceptions of vehicle performance, researchers can elucidate patterns of consumer acceptance and identify barriers to widespread adoption.

The study uses the theory of planned behaviour (TPB) and stimulus organism response (S-O-R). The current research aims to bridge the gap by focusing on consumers' intention to buy ethanol-blended fuel. Data were collected from 303 survey respondents and analysed using partial least square structural equation modelling (PLS-SEM).

The study finds that adopting motivation, policy incentives, risk perception and sustainable practices significantly influence the intention to purchase ethanol-blended fuel. Surprisingly, cost perception and infrastructure readiness do not have a significant impact on purchase intention.

The study proposes four innovative policy implications to amplify the adoption of ethanol-blended fuel. These policies are (a) flexible fuel vehicle incentive schemes, (b) renewable fuel mandate and certification, (c) ethanol pricing and infrastructure development policy and (d) ethanol urban mobility and public transportation initiatives.

This study provides novel insights into the factors influencing ethanol-blended fuel adoption in India, contributing to the literature on sustainable transportation solutions.

This study aims to assess the efficacy of nano-alumina (nano-Al₂O₃) in improving the performance of epoxy adhesives used to assemble archaeological glass. The conservators face a significant problem in assembling this type of artifact. Therefore, the assembling process is considered one of the important stages that must be taken care of to preserve these artifacts from damage and loss.

To evaluate the stability of adhesives, the samples were subjected to artificial aging under varying environmental conditions. Some investigative techniques and mechanical testing were used in this study to evaluate the selected materials. It includes a transmission electron microscope, X-ray diffraction, visual assessment, digital microscope, scanning electron microscopy (SEM), color change and tensile strength test.

The visual evaluation and the digital microscope results showed that the epoxy/nano-Al₂O₃ greatly resisted artificial aging. Although slight yellowing was present, it did not significantly affect the general appearance of the samples. On the other hand, the pure epoxy sample showed cracks of different sizes on its surface due to aging, as evidenced by SEM examination. Furthermore, epoxy/nano-Al₂O₃ has a better tensile strength (11.27 MPa) and slight color change (ΔE = 2.06).

The main objective of the experimental study was to identify appropriate adhesive materials that possess key properties such as non-yellowing and improved tensile strength by conducting various tests and evaluations. Ultimately, the goal was to identify materials that could serve as effective adhesives for assembling the archaeological glass.

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.

Recognition of the complexity of job embeddedness in the work environment has grown, highlighting the need for a deeper understanding of the factors that contribute to this phenomenon. This study analyzed how and when job crafting and leisure crafting are linked to job embeddedness by investigating employee resilience as a mediator and employee adaptivity as a moderator.

Data were gathered from 568 Taiwanese hotel employees. The PROCESS macro was used to verify all hypotheses.

Both job crafting and leisure crafting increased job embeddedness. Employee resilience mediated the impacts of job and leisure crafting on job embeddedness. The positive relationship between employee resilience and job embeddedness was stronger when employee adaptivity was high. Employee adaptivity moderated the indirect impacts of job and leisure crafting on job embeddedness through employee resilience.

Hotel managers should foster a workplace culture that encourages employees to engage in job crafting. Additionally, managers can offer employee assistance programs to proactively encourage workers to participate in leisure crafting. Providing training and wellness programs to strengthen employee resilience, along with allocating resources and designing learning programs to enhance employee adaptability, can further promote job embeddedness.

This research contributes to the literature through the construction of a moderated mediation model that explored how and when job and leisure crafting affect job embeddedness.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

The purpose of this paper is to investigate user satisfaction with digital health solutions by identifying and prioritizing different service attributes on the basis of their impact on improving user satisfaction.

Through a literature review and interviews with health professionals and patients, 20 attributes of digital health services provided in Italy have been identified. User satisfaction with these attributes has been evaluated by adopting the Kano model’s continuous and discrete analyses.

The findings reveal the essential attributes of digital health services that meet users' expectations, identify the attributes that users appreciate or dislike having and highlight unexpected attributes that lead to a significant boost in satisfaction when provided.

This study demonstrates the efficacy of the Kano model in assessing the nonlinear correlation between user satisfaction and the quality of digital health services, thus contributing to fill a gap in the literature in this area. The main limitation of this work is the use of a non-probabilistic sampling method.

This research suggests healthcare institutions and organizations consider user preferences when designing digital health solutions to increase their satisfaction. The results indicate different effects on user satisfaction and dissatisfaction for different categories of attributes in the Italian context.

Previous works studied customer satisfaction with digital health, assuming a linear relationship with service quality, or investigated consumer adoption intentions focusing on the technological factors. This work advances available knowledge by analyzing the nonlinear relationship between digital health attributes and users’ satisfaction and dissatisfaction.

The Kingdom of Saudi Arabia’s dedicated pursuit of technological modernization positions it as a forefront leader in integrating advanced systems, aligning smoothly with the ambitious goals outlined in Vision 2030. The purpose of this study is to investigate the influence of integrating enterprise resource planning (ERP) and business intelligence (BI) systems on decision-making processes within the industrial sector of Saudi Arabia.

Using a quantitative research design, this study uses a bootstrapping approach and partial least squares structural equation modeling to meticulously analyze data collected from Saudi industrial firms.

The research reveals favorable relationships among infrastructure readiness, data quality, security and access control, user capabilities, user training and the integration of ERP and BI. These positive associations collectively affirm the overarching positive impact of ERP and BI integration on decision-making processes within the industrial sector.

The study underscores the strategic imperative of aligning organizational practices with the identified characteristics to fully unlock the potential benefits of ERP and BI integration in the Saudi Arabian industrial sector.

This study contributes significantly to the existing literature by delving into the integration of ERP and BI in the industrial sector and its nuanced impact on decision-making processes, specifically in the context of the Kingdom of Saudi Arabia – an area that has not been extensively studied.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.