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This study examines the dynamics of the modesty-conscious market within the global fashion industry. Specifically, the research aims to understand and analyse the preferences of consumers in this market segment and provide guidance for fashion companies seeking to engage with this sizable audience consumer demand for modest fashion, such as loose-fitting garments and headscarves for women.

Employing a mono-method quantitative research approach, this study uses a comprehensive big data analytics framework to analyseashion data sets obtained from e-commerce websites.

The findings highlight a persistent and growing demand for modest fashion; that demand proved resilient in the face of challenges presented by the COVID-19 pandemic. Modest shoppers demonstrate price sensitivity, and their preference for premium brands over affordable ones varies considerably. Crucial factors contributing to the success of modest clothing as best-sellers include price, retailer, colour and fabric, while the discount feature is less important.

While this research provides significant insights, it is important to acknowledge its limitations. This study relies on data gathered from certain e-commerce websites, and specific nuances of consumer behaviour may not be fully captured. In addition, the scope is limited to a specific timeframe and may not account for long-term market shifts.

Fashion companies could use the results of this study to customise their strategy for engaging the modesty-conscious demographic. Comprehending the significance of elements such as price, retailer, colour and fabric can allow firms to enhance their product offerings and marketing strategies.

This study highlights the social ramifications of the modesty-focused industry, stressing the changing tastes and requirements of customers within this sector. By aligning their strategies with these societal shifts, fashion companies can contribute to a more inclusive and diverse industry landscape.

This research contributes to the academic literature on modest fashion by using a unique combination of exploratory data analysis and machine learning techniques with fashion e-commerce data sets. This study addresses a gap in the use of big data within this field, and provides novel insights into consumer demand for modest garments. This study challenges the prevailing assumption that consumers of modest fashion prioritise premium pricing, and offers fresh insights into their price sensitivity across both mass-market and luxury segments. It contributes to the literature on consumer behaviour in niche fashion markets and introduces a theoretical framework for understanding the intersection of fashion, culture and consumer economics within the context of modest fashion.

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

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.

Social exclusion is a complicated psychological phenomenon with behavioral ramifications that influences consumers' lifestyles and behaviors. In contrast, anthropomorphism is a phenomenon that marketing strategists employ and that occurs in customers' lives as a result of social isolation. The literature discusses these two complicated structures as ones that require investigation based on consumer judgments. The purpose of the current study is to understand the fundamental motivations that underlie the propensity for anthropomorphizing in people who suffer social isolation through their pets.

To look into the motivations driving these themes, a study technique with three distinct components was created. Cyberball was employed as a technique to manipulate social exclusion in the initial stage of this research methodology. Two scenarios, one of which had an anthropomorphizing tendency and the other of which did not, were presented to participants who had suffered social exclusion and advanced to the second phase in order to determine the anthropomorphizing tendency. The Attachment to Pets Scale (LAPS), which Johnson et al. (1992) created based on the social support provided by pets, was utilized while creating the scenarios. The Zaltman method was applied as an interviewing technique in the third stage of the research design, with the interviewees being guided by visuals that reflected their emotions and thoughts.

The results of the data analysis were evaluated in light of social psychology. A more thorough expression of the complex relationship between anthropomorphism and those who experience social exclusion has been made. The findings showed that when people anthropomorphize their pets in response to feelings of social exclusion, the motivations that emerge include pure love, loyalty, animals' need for a human, living creature and embracing. The study emphasizes that these ideas will be helpful in customers' interactions with anthropomorphic objects.

As a contribution to the literature, the study findings offer the five major motivations underpinning these beliefs. These findings may help marketing scientists comprehend social exclusion and anthropomorphism, thereby benefiting the individual and society.

The majority of research in the literature (Chen et al., 2017; Epley et al., 2008; Eyssel and Reich, 2013; Waytz et al., 2019) verified that people who were socially excluded would use anthropomorphism, but no studies were discovered about the motivations outlined in the current study. The results of this investigation should add to the body of knowledge in this area. The pet was employed as an anthropomorphism tool in the current study because it is the object that a person chooses to anthropomorphize deliberately and independently. It adds to the study's originality by explaining in the individual's own terminology how he will feel as a result of his social isolation, how he will make up for it and potential responses he may have. In addition to all of these contributions, the study's primary goal of analyzing the motivations behind anthropomorphism yields significant findings that are relevant to both industry and academic research.