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As sustainability teaching and learning rises in importance, an increasing number of higher education institutions (HEIs) are assessing the effectiveness of their approach to sustainability education. However, most assessments fall short in determining the impacts of curriculum plans on learning outcomes. Therefore, this study aims to assess the impact of curricula on undergraduate sustainability knowledge and assess opportunities for improving sustainability education in HEIs.

A campus-wide survey deployed at California Polytechnic State University, San Luis Obispo, (Cal Poly) solicited data identifying students’ sustainability knowledge score (SKS). The survey collected responses from undergraduate student groups enrolled in different curriculum plans under different academic settings.

This study reveals that Cal Poly honors students enrolled in a structured sustainability curriculum have significantly higher SKS than general students (i.e. nonhonors students) enrolled in random sustainability courses. Further, taking at least three sustainability-related courses significantly distinguishes SKS for general students. The results also show that SKS does not significantly differ across colleges, suggesting that additional sustainability education can benefit all students.

Findings of this study provide statistical evidence to justify institutional efforts to integrate sustainability into existing courses, with the minimum requirement of three sustainability-related courses to make an impact on SKS for the general student population. Such efforts could represent the first steps toward developing sustainability education at a HEI and improving sustainability learning outcomes.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.

To determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.

This study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.

This study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.

According to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

The purpose of this paper is to evaluate the interplay of various measures used by different governments around the world in combatting COVID-19.

The research uses the interpretative structural modelling (ISM) for assessing the powerful measures amongst the recognized ones, whereas to establish the cause-and-effect relations amongst the variables, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used. Both approaches utilized in the study aid in the comprehension of the relationship amongst the assessed measures.

According to the ISM model, international support measures have the most important role in reducing the risk of COVID-19. There has also been a suggestion of a relationship between economic and risk measures. Surprisingly, no linkage factor (unstable one) was reported in the research. The study indicates social welfare measures, R&D measures, centralized power and decentralized governance measures and universal healthcare measures as independent factors. The DEMATEL analysis reveals that the net causes are social welfare measures, centralized power and decentralized government, universal health coverage measure and R&D measures, while the net effects are economic measures, green recovery measures, risk measures and international support measures.

The study includes a list of numerous government measures deployed throughout the world to mitigate the risk of COVID-19, as well as the structural links amongst the identified government measures. The Matrice d'Impacts croises-multiplication applique and classment analysis can help the policymakers in understanding measures used in combatting COVID-19 based on their driving and dependence power. These insights may assist them in employing these measures for mitigating the risks associated with COVID-19 or any other similar pandemic situation in the future.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

The purpose of this paper is to explore the motives of Indian firms for engaging with corporate social responsibility (CSR) practices and their interplay by using interpretive structural modelling methodology (ISM) and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis.

The research uses ISM and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis to find the structural relationship among the CSR motives of the Indian firms identified from the past literature and agreed upon by the experts.

The ISM model indicates that firms primarily engage in CSR either because of top management commitment to certain values, to meet the legal mandate or of the pressure from the NGOs. The top management commitment gives a strategic orientation to CSR, which results in community engagement by the firm as one of the important components of the strategy. The community engagement helps in engaging with its employees and investors along with finding sources of innovations, which, in turn, help the firm in engaging its customers, managing corporate reputation and getting a cost advantage. Collectively, these help them in improving their financial performance. However, the model highlights two autonomous sources, meeting legal mandate and pressure from NGOs also motivate firms to engage in CSR without having any strategic thought or engagement with its strategic system.

The study provides a comprehensive listing of CSR motives of Indian firms along with the structural relationships among the identified CSR motives. The model developed provides CSR professionals and policymakers an understanding of the primary CSR motives along with their driving power and dependence. This insight will help them in manipulating these motives for better CSR engagement by the Indian firms.