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This study aims to understand the social cooperative model's empowerment and social cohesion impacts based on the case of a cooperative with the partnership of local and refugee women in southern Türkiye to give evidence for the potential and challenges of women cooperatives.

The authors conducted surveys and focus group interviews with both members and board members. The authors grounded the findings in dimensions extracted from literature on the impact of cooperatives on their members and the wider community.

The social cooperative economically empowers women through employment and income generation, and skill training while enhancing them socially with increased decision-making power, autonomy, self-esteem and respect. It fosters social cohesion between local and refugee members by building trust and peace, solidarity, knowledge sharing and collective action. However, the cooperative faces challenges in managing sustainable business models, and cooperative membership does not ensure a steady income, social security, economic independence or a fairer division of domestic work.

The challenges and limited outcomes of social cooperatives are primarily due to resource scarcity. Hence, these needs must be considered by policymakers and sponsors of women empowerment programmes so that they can offer response actions to empower social women cooperatives. During the research period, the COVID-19 pandemic posed a significant threat to the survival of the cooperative. Moreover, the restrictions imposed by the pandemic made it impossible to engage Syrian women in focus group discussions. Consequently, the focus group interactions were limited to two Palestinian members, while Syrian members were included in survey interviews.

This study is one of the few attempts to examine the social cooperative model’s impact on women’s empowerment and social cohesion in the context of a mixed membership of local and refugee women in Türkiye. Fieldwork evidence on cooperatives that improve gender equality and inclusive growth can contribute to the advocacy of support for women’s cooperatives in the context of refugees.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.