Purpose When it comes to how people accept and use technology, trust is crucial. To comprehend users’ attitudes and intentions toward adopting technology, the technology acceptance model (TAM) has been extensively used. Through a thorough study of the available research, this study aims to investigate the trust element in the TAM framework. Design/methodology/approach A Web of Science and Scopus bibliographic data set containing 892 journal articles was evaluated using descriptive and network analytics. To trace the various intellectual linkages, bibliographical coupling of journals and authors’ affiliated nations was performed. Using keyword cooccurrence analysis, prominent themes were visualized. Findings The findings indicate that trust research in the TAM has gathered steam in recent years and is currently expanding. Global interest in this topic is growing, with participation from academics in 153 different geographical areas, with China at the forefront. Leading publications, journals and nations are also disclosed. The subjects with the most research are TAM and trust. Emerging topics, such as “e-commerce,” “motivation” and “perceived risk” point to a corporate domain that is taking an interdisciplinary approach. Originality/value This systematic literature review is unique in that it takes a thorough approach to examine the trust element in the TAM framework, integrating bibliometric analysis to identify research trends and offering insights that might guide future research and practical solutions.

PurposeBlockchain is a nascent technology that has the ability to revolutionize the workings of the educational landscape. However, there are several barriers to the adoption of blockchain technology (BCT). So, this paper aims to determine, rank and analyse the interdependent contextual relationship among the BCT adoption barriers within the education management system (EMS) of higher education institutes.Design/methodology/approachThe present research principally uses the technology-organization-environment model for the classification of BCT adoption barriers in the EMS. An integrated dual phase best-worst method and interpretive structural modelling – cross-impact matrix multiplication applied to classification (BWM-ISM-MICMAC) analysis is used for the identification, prioritization and analysis of the contextual relationships among the BCT barriers.FindingsThe findings of the research show that the environmental barrier among the primary barriers and the lack of government initiatives among the sub-barriers are crucial barriers to BCT adoption in the EMS.Research limitations/implicationsCertain barriers might not be selected during the literature review and expert opinions might be biased. Future studies should use structural equation modelling to validate the relationships between BCT barriers and conduct similar research across other business sectors to provide valuable insights for scholars and practitioners alike.Practical implicationsThe study facilitates a better perspective of BCT barriers and explores possible solutions more comprehensively for policymakers and field managers. Consequently, it offers viable suggestions for the successful and effective implementation of BCT within the EMS.Originality/valueThe concept of using the blockchain in academics is a novel one. This study establishes a crucial research base for the smooth adoption of BCT in EMS.

Purpose This study introduces a multi-vaccine multi-echelon supply chain (MVMS) framework designed to ensure sustainable vaccine distribution during outbreaks. The framework aims to minimize the total costs of vaccine distribution and reduce greenhouse gas (GHG) emissions to mitigate environmental impacts while maximizing job opportunities within the network. Design/methodology/approach Our proposed appraoch employs a multi-objective mixed-integer linear programming model. Findings The findings indicate that incorporating uncertainties related to demand and inspection errors significantly facilitates timely responses to unexpected shortages, fulfills the requirements of healthcare facilities, and enhances the supply chain’s resilience against future uncertainties. This study also explores managerial implications and suggests avenues for future research to further advance this field. Originality/value Existing literature on MVMS often relies on simplifying assumptions of perfect vaccines and primarily focuses on demand uncertainty. However, real-world supply chains are typically marked by imperfections, disruptions, and a variety of uncertainties beyond demand. In this work, we address several sources of parameter uncertainty, including demand variability, inspection errors, vaccine waste, and defective treatments rates to enhance the robustness of our model.

PurposeThis study aims to investigate the pricing dynamics within a triple-channel supply chain. The publisher can sell printed books (p-books) through bookstores or online direct sales, and electronic books (e-books) are sold directly through the internet. The primary objectives include determining optimal wholesale and final prices for p-books, assessing the profitability of introducing e-books, comparing profits across channels and supply chain modes and identifying optimal demand volumes.Design/methodology/approachThe research uses first-order derivatives and the Stackelberg game to analyze the pricing strategies. Two supply chain modes, centralized and decentralized, are considered, and various parameters are examined to understand their impact on prices, demand volumes and final sales profit.FindingsThe results indicate that the e-book is either not published or is introduced simultaneously with the printed version in both modes. In the decentralized mode, the wholesale price of a p-book is equivalent to the final price in the bookstore channel in the centralized mode. One channel among the three selling channels is used to maximize the total profit in the centralized supply chain, whereas all demand should be fulfilled through either online direct sales or e-book channels in the decentralized mode.Originality/valueThis paper introduces a comprehensive triple-channel book supply chain model, considering cross-price sensitivities and lag times for e-books. The study provides insights into the dynamics of the book industry and compares them with existing literature, contributing to a broader understanding of the pricing strategies in a triple-channel context.

PurposeThis study aims to develop and apply a process model for prioritizing and selecting basic research projects in developing countries.Design/methodology/approachBasic research is mainly funded by governments and since, unlike technological research, it does not have clear business goals, its prioritization is one of the complicated issues in formulating science and technology policy. Adopting a design science research methodology, the authors chose a general framework for project portfolio selection as an appropriate artifact for solving this problem. By customizing it for two specific features of this study, i.e. national scale of the problem and the basic nature of research proposals, the authors developed the proposed framework for solving the problem of priority setting.FindingsThe process for selecting basic research proposals consists of several steps, which can be categorized into eight steps including strategic decisions, preparation, pre-screening, evaluating individual proposals, screening, portfolio selection and monitoring. This study emphasizes the necessity of defining goals that can be evaluated for the national basic research portfolio, as a key strategic decision. Evaluating individual proposals is a peer-review-based process. In contrast, portfolio selection is done through a zero-one linear programming model. The validity of the proposed framework has been confirmed based on the data obtained from the Iran National Science Foundation.Originality/valueTo the best of the authors’ knowledge, in this research, for the first time, a mathematical model for prioritizing basic research at the national level has been presented, which effectively contributes to policymaking regarding the development of an optimum national research portfolio.

PurposeThe aim of this paper was to delve into the underlying mechanism of the relationship between environmental knowledge and green purchase intentions, using an extended model based on the knowledge-attitude-behaviour (KAB) theory.Design/methodology/approachThe parallel and serial mediation effects of environmental concern, green perceived value and green attitude were examined using PROCESS macro (Models 4 and 6). Data were collected from 395 youth in three different cities in India using a purposive sampling method.FindingsThe study’s findings revealed that environmental concern, green perceived value and green attitude act as parallel and sequential mediators between environmental knowledge and green purchase intentions. However, the direct impact of environmental knowledge on green purchase intentions was deemed insignificant. In essence, environmental knowledge, along with environmental concern and green perceived value, significantly contributes to the formation of attitudes conducive to green purchase intentions.Originality/valueThe present study theoretically contributes to green behaviour research by proposing and testing an extended model of KAB theory with parallel and serial mediations in the Indian context. The model explores the underlying mechanism of the relationship between environmental knowledge and green purchase intentions in detail.

PurposeThe purpose of this study is to analyze the operations and performance dynamics of a supply chain (SC) subject to disruptions. The preparedness of Moroccan responders in handling emergencies could be enhanced significantly, by devising digital twin-based decision support systems (DSSs).Design/methodology/approachThe authors create a discrete-event simulation model to investigate proactively risks and resilience of a Moroccan basic-items SC (BISC). In this study, the authors analyze the effects of catastrophe-related disruptions (CRDs) on the Moroccan BISC, by the use of a simulation-based decision-supporting quantitative method.FindingsIn the disruption-free simulation experiment, the outcome was a satisfactory 100% coverage. By implementing CRDs, inventory levels have dropped, service levels decreased, lead time raised and there was an increase in backlogged products and late orders numbers. The highest impact was observed for the shutdown of paths linking suppliers to warehouses, whereas the increase in demand had a comparatively minor effect. The risk analysis approach helps to identify critical products for which the time-to-recover is longer and requires more commitment to enhance their resilience.Practical implicationsThe model serves to deduce quantitative resilience assessment from simulation, streamline the selection of recovery strategies and enable the best-informed reactive decision-making to minimize the impact.Originality/valueThe research brings organizing solutions to catastrophe-related emergencies in Morocco. It would contribute significantly by visualizing, examining and unveiling the effects of disruptions on a BISC and offering actionable recommendations for remedial measures.

PurposeThis 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThis paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

PurposeGiven 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).Design/methodology/approachThis 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.FindingsThe 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.Research limitations/implicationsThis 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.Originality/valueThis 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.

PurposeThis paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices.Design/methodology/approachThe conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework.FindingsThe findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity.Practical implicationsThis study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC.Originality/valueThis study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.