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Blockchain technology is one of the major contributors to supply chain sustainability because of its inherent features. However, its adoption rate is relatively low due to reasons such as the diverse barriers impeding blockchain adoption. The purpose of this study is to identify blockchain adoption barriers in sustainable supply chain and uncovers their interrelationships.

A three-phase framework that combines machine learning (ML) classifiers, BORUTA feature selection algorithm, and Grey-DEMATEL method. From the literature review, 26 potential barriers were identified and evaluated through the performance of ML models with accuracy and f-score.

The findings reveal that feature selection algorithm detected 15 prominent barriers, and random forest (RF) classifier performed with the highest accuracy and f-score. Moreover, the performance of the RF increased by 2.38% accuracy and 2.19% f-score after removing irrelevant barriers, confirming the validity of feature selection algorithm. An RF classifier ranked the prominent barriers and according to ranking, financial constraints, immaturity, security, knowledge and expertise, and cultural differences resided at the top of the list. Furthermore, a Grey-DEMATEL method is employed to expose interrelationships between prominent barriers and to provide an overview of the cause-and-effect group.

The outcome of this study can help industry practitioners develop new strategies and plans for blockchain adoption in sustainable supply chains.

The research on the adoption of blockchain technology in sustainable supply chains is still evolving. This study contributes to the ongoing debate by exploring how practitioners and decision-makers adopt blockchain technology, developing strategies and plans in the process.

Peer support has been identified as an important protective factor for mental health and overall well-being. The purpose of this study is to examine the feasibility of implementing an online peer support group and its impact on measures of well-being.

A mixed-methods randomized controlled trial design was used to examine the feasibility and impact of online peer support. Comparisons in well-being were made between the online peer support group and an in-person peer support group and control group. Participants were randomly assigned to a control group or either a six-week in-person or online peer support group. All participants completed an online survey measuring constructs of well-being pre- and post-condition. Additionally, qualitative data regarding the benefits of peer support and in particular the efficacy of the online format were collected from participants. Analysis of variance and post hoc tests determined significant differences within and between the groups.

Both the online and face-to-face peer support groups scored significantly higher on post-test measures of well-being than pre-test scores and control group scores. Qualitative narratives and significant quantitative findings supported the feasibility of peer support offered online. Post-condition outcomes showed that online peer support is as effective as in-person peer support for improving well-being.

To the best of the authors’ knowledge, this study is the first of its kind to compare online and in-person peer support programs for students in higher education. The results have direct implications for higher education students and practitioners, especially at times when face-to-face support is not feasible.