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The digital direct-to-consumer (D2C) approach has seen widespread adoption across many industries, but its application to regional food products has been slower. This study aims to identify and rank the most significant challenges to the widespread use of digital D2C for regional food products.

A multi-criteria decision-making method called a fuzzy analytic hierarchy process (FAHP) was used to determine the order in which these obstacles were evaluated. Thirty-five barriers were identified and categorized into six categories named technological, operational, sociocultural, financial, market and institutional and regulatory barriers.

Market barriers ranked as the top barrier, and the technological barrier ranked the least significant amongst the main barrier categories for the adoption of digital D2C model for the regional food products. Lack of consumer awareness ranked number one globally, and lack of government subsidies ranked the least amongst the thirty-five identified barriers. Operational barriers came out to be second most significant barrier followed by institutional and regulatory barrier, sociocultural barrier, financial barrier and technological barrier.

The findings of this research were derived through a numerical examination of data gathered from the Indian setting. It follows that the technological, sociocultural, financial, market, operational and institutional constraints, among others, outlined here are all unique to India. Because of the unique nature of the Indian setting, the results of this study can only be used there and not elsewhere. It is possible that future research will broaden the aims of this one and refine its methodology. Digital D2C adoption for regional food products may be prioritized and ranked using quantitative and qualitative data sources like ANP and TOPSIS. It is possible that similar studies may be conducted in nations which have a different set of operational, technological, sociocultural market and financial and regulatory barriers. Conceptual framework can be formed by integrating TAM and TPB to understand the buying behavior of regional food products via digital D2C.

This research is the first to identify challenges to the widespread use of the digital D2C model for regional food products. Policymakers and other interested parties can use this information to better understand the difficulties of expanding the distribution of regional food products beyond their immediate regions.

It is essential to track the development of resource and pollution intensive industries such as textile, leather, pharmaceutical, etc., under burgeoning pressure of environmental compliance. Therefore, the purpose of this paper is to analyze the progress of Indian leather industry in terms of individual factors and total factor productivity.

This study applies and examines the various concepts of productivity such as labor productivity, capital productivity, material productivity and energy productivity. Further, it assesses and compares the performance of Indian leather industry in Tamil Nadu (TN), West Bengal (WB) and Uttar Pradesh (UP) based on productivity analysis, spatial variations determinants in productivity and technology closeness ratio.

The findings suggest that as per the productivity analysis, WB leather clusters have performed remarkably better in terms of partial factor productivity and technical efficiency (TE), followed by TN and UP. This can be attributed to shifting of leather cluster of WB to a state-of art leather complex with many avenues for resource conservation. Further, the findings reveal that the firm size and partial factor productivities have significant positive correlation with TE which supports technological theory of the firm.

The results of this study can be useful for the policy makers associated with the Indian leather industry especially to design interventions to support capacity building at individual firm level as well as cluster level to enhance the efficiency and productivity of overall industry.

The findings also support the resource dependence theory of firm according to which the larger size firms should reflect on resource conservation practices, for instance the concept of prevention is better than cure based upon 3R (reduce, recycle and reuse) principles.

The paper gives an explanation of the productivity in the leather industry in terms of its factor productivity and TE.

On 20 March 2020, the four adult convicts of the 2012 Delhi rape case were executed after a long debate regarding the punishment for their crime. The Delhi rape case, unlike others, was also given to the fast track court because of the worldwide outrage India received in its aftermath. Otherwise, most rape survivors rarely speak out and if they do, their lives are often endangered and threatened, depending on the severity of the case itself and the perpetrator's rank in the society. Through the analysis of Aniruddha Roy Chowdhury's, 2016 film Pink, and Ajay Bahl's film Section 375 (2019), this chapter explores the different ways in which mainstream Hindi cinema deals with such questions, especially in its depictions of courts. Both these films foreground India's contemporary cultural systems of fear that silence the rape survivors. They also imply that in the court cases, unless the specific court case faces intense global publicity, as was the case of the Delhi gang rape, rape survivors will never want to speak out. Moreover, the rape survivors will also hesitate to file a First Information Report (FIR) – a document that records crimes by the police against their perpetrators – limiting any possibility for justice for them. The laws surrounding rape cases are obscure and complex and finding justice for a rape victim (unless it is on a global level) is not an easy venture in India. At the time of the #metoo movement, the rape laws in India are not designed in such a way to arguably encourage victim-survivors to speak up. Instead, if rape survivors do decide to confront their perpetrators, they not only face ostracisation from society but also the danger of losing loved ones and endanger their lives as well.

This chapter shifts contemporary debates on Eurocentrism from its focus on European social theory to an analysis of its moorings in non-Atlantic sociological traditions and especially those within ex-colonial countries. It discusses the sociological/anthropological visions of two first generation sociologists/anthropologists from India, G. S. Ghurye (1893–1983) and D. P. Mukerji (1894–1961), within Orientalist-Eurocentric positions and explores how these are reinvented in the work of contemporary sociologist T. N. Madan (1933–). It suggests that colonial processes and its institutions together with “derivative” nationalist ideas have played and continue to play important mediatory role in organizing these Orientalist-Eurocentric visions.

The chapter presents three sets of arguments. First it suggests that in order to understand postcolonialism it is imperative to lay out the organic links between Orientalism and Eurocentrism. Eurocentrism and its mirror Orientalism mediated to frame social science language in terms of the binaries of universal (the West) and particular (the East). The particular was represented in India through the discipline of anthropology. The latter studied “traditions” through the themes of religion, caste, and family and kinship. When sociology emerged as a discipline in India in the early twentieth century, it continued to use the language organized by anthropology to analyze the particular cultural traditions of the country. Second, I suggest that these binaries also framed nationalist thought and the latter mediated in framing the sociological ideas of G. S. Ghurye and D. P. Mukerji which were embedded in Eurocentric-Orientalist principles. Third, I analyze the ideas of the contemporary social theorist T. N. Madan to indicate how his perspective continues to derive its positions from Orientalist-Eurocentric positions and ignores an engagement with critics who have questioned Orientalist Eurocentrism. Disregarding these arguments implies the legitimation of the latter perspective derived from the disciplines of sociology/anthropology.

The chapter contends that a decolonized critique of colonial social science has existed in other regions of the world including India, and that this perspective needs to be retrieved by social theorists to reformulate the sociological discourse as a study of modern India. It also suggests that contemporary analysis of Eurocentrism needs to move out from within the circuits of knowledge defined by received colonial geopolitical enclaves in order to assess the way production, distribution, and consumption of Orientalist-Eurocentric perspectives have organized sociological traditions across the world including the Global South.

The success of SMEs' financial and market performance (MAP) depends on the firms' level of blockchain technology adoption (BCA) and identifying the crucial antecedents that influence SMEs' adoption. Therefore, this research attempts to develop an integrated model to understand and predict the determinants of BCA and its effect on SMEs' performance. The purpose of this paper is to address this issue.

The theoretical foundations are the technology–organization –environment (TOE) framework and the resource-based view (RBV) perspective. The authors distributed a survey to SMEs in South Africa and received 311 responses. The covariance-based structural equation modeling (CB-SEM) followed by the artificial neural network (ANN) technique was used for the data analysis.

The SEM results showed that SMEs' relative advantage, compatibility, top management support (TMS), organizational readiness (ORD), competitive pressures (COP), external support, regulations and legislation significantly influence SMEs' BCA. However, complexity negatively impacts SMEs' BCA. The analysis results also revealed that SMEs' BCA significantly influences the financial performance of the firms, followed by MAP. Furthermore, model determinants were input to an ANN modeling. The ANN results showed that TMS is the most critical predictor of SMEs' BCA, followed by ORD, COP, external support, and regulations and legislation.

The results provide valuable information for SMEs when maneuvering their adoption strategies in the scope of blockchain technology. Additionally, from the perspective of an emerging market, the study has successfully contributed the TOE framework and the RBV.

This study is the first work to explore the determinants of BCA in the context of SMEs from a developing country. This paper is also one pioneer in attempts to develop a causal and predictive statistical model for predicting the determinants of BCA in SMEs' performance.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.

COVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.

We adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.

An AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.

As the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.

Supply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.

The present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

Quality 4.0 is being presented as the new stage of quality development. However, its overlying concept and rationale are still hard to define. To better understand what different authors and studies advocate being Quality 4.0, a systematic literature review was undertaken on the topic. This paper presents the results of such review, providing some avenues for further research on quality management.

The documents for the systematic literature review have been searched on the Scopus database, using the search equation: [TITLE-ABS-KEY (“Quality 4.0”) OR TITLE-ABS-KEY (Quality Management” AND (“Industry 4.0” OR “Fourth Industr*” OR i4.0))]. Documents were filtered by language and by type. Of the 367 documents identified, 146 were submitted to exploratory content analysis.

The analyzed documents essentially provide theoretical discussions on what Quality 4.0 is or should be. Five categories have emerged from the content analysis undertaken: Industry 4.0 and the Rise of a New Approach to Quality; Motivations, Readiness Factors and Barriers to a Quality 4.0 Approach; Digital Quality Management Systems; Combination of Quality Tools and Lean Methodologies and Quality 4.0 Professionals.

It was hard to find studies reporting how quality is actually being managed in organizations that already operate in the Industry 4.0 paradigm. Answers could not be found to questions regarding actual practices, methodologies and tools being used in Quality 4.0 approaches. However, the research undertaken allowed to identify in the literature different ways of conceptualizing and analyzing Quality 4.0, opening up avenues for further research on quality management in the Industry 4.0 era.

This paper offers a broad look at how quality management is changing in response to the affirmation of the Industry 4.0 paradigm.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.

The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.

In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.

The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.

The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.

The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.