Supply chain social responsibility is increasingly a pressing concern to organizations of all sizes.Justified by its impact on the bottom line, various measures were adopted for resolution and prevention. However, despite the abundance of literature on the topic, there continues to be a lack of evidence on which of the measures are the most prolific, that is, how the concern of interest is actually handled. Such evidence would highlight missed opportunities and set the stage for future research. Toward this goal, the authors conducted a mapping study analyzing 590 articles. The findings reveal that corporate social responsibility, sustainable reporting, and social life cycle assessment are the most used methods whereas systems thinking ranks far behind. This work is original in that it is the first of its kind to reveal such findings scientifically. Practical implications of this work include reducing the supply chain's social footprint, ameliorating stakeholder quality of living, and mitigating social risk.
Given its material impact on bottom lines, social responsibility became essential to supply chain sustainability strategies. The authors of this paper reviewed the relevant literature and discovered its reliance on approaches like corporate social responsibility and its marginalization of systems thinking. This situation undermines the contributions of this literature, as it could be reductionist from a systemic perspective. Reductionism limits solutions to suboptimizations incapable of achieving multifinal and holistic outcomes. To assess this literature, the authors conducted a mapping study to analyse its distribution over four systemic paradigms: functionalism, interpretivism, emancipation, and postmodernism. The results showed that it clustered unevenly around these paradigms and lacked pluralism in perspective. This paper is significant as it revealed the innate inability of most of the supply chain social responsibility literature in offering creative and holistic solutions. Therefore, this literature can only resolve some social responsibility factors allowing the persistence and resurfacing of social responsibility messes.
The purpose of this paper is to categorize articles on social sustainability in supply chains based on the methodology used to address the topic. By reading all articles one by one, the paper considers the use of text mining as an alternative to manual classification. This work reduces the time it takes to find information in a body of literature from months to minutes.Practitioners should ensure that the data language used to describe the topic is correct and complete. The findings of the paper assist researchers and postgraduate students in completing time-consuming tasks such as literature reviews.The current work could be improved by incorporating contextual information classification rather than frequency classification.
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