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The purpose of the perspective article is to review relevant literature on family business and ethnic fashion and establish links across identity (defined as culture, tradition, heritage and status) and fashion (color, design, pattern and fabric/texture) and internationalization (foreign market entry), and develop a conceptual model using the identity theory and qualitative method.

This is a qualitative study by design. We used a systematic research and ethnographic method for this study. Specifically, the author used the participant observation aspect of ethnography to collect information and images relating to ethnic fashion. Ethnography is a well-established methodology widely used in social sciences research, including fashion.

The study's conceptual model proposes that (1) ethnic fashion mediates the identity-internationalization relationship, (2) knowledge transfer moderates the identity-ethnic fashion relationship and (3) family business size moderates the ethnic fashion-internationalization relationship. It is also revealed that a person's status can be judged by their dress and fashion in under two minutes.

This study is limited to the African continent, though it has 54 countries with a current population of about 1.5 billion people, which is expected to be roughly 2.5 billion by 2050.

Implications of the study for the entrepreneurs and family businesses are that they should realize the opportunities presented by ethnic African fashion and tap into the most crucial key to success—local design, color, fabric and patterns associated with meaning and messages. Cross-cultural collaborations and digital innovations can help the internationalization of African fashion while preserving local heritage and identity. Another implication is that quality and consistency in branding are equally essential to be on par with intentional luxury brands.

The social implication of the study is that culture and fashion are correlated and influence designers' creations, reflecting and conveying identity, status and societal values. Fashion allows people to express their identity, individuality and values. The proper fashion and outfit can boost mood, self-esteem and confidence, resulting in healthy social interaction and mental health. Fashion can also raise social issues (e.g. inclusiveness, diversity and gender by featuring various models and designs) and environmental issues (e.g. sustainable practices local and ethical production).

The paper synthesizes ethnic fashion in the context of family businesses in Africa, highlights specific examples of ethnic fashion of African people with the potential for internationalization and proposes future fashion perspectives for family businesses. It adds value in that it focuses on fashion family businesses in the African continent.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.