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Industry 4.0 is driving an incremental shift in paradigms for the construction industry. Current research in the built environment is limited to exploring the exponential technological prowess of Industry 4.0 with very little work on its implications to the construction business model, strategy and competitive advantage. There arises a challenge for researchers to understand how appropriate technologies can be assembled to assist in achieving the goals of construction businesses. The overarching aim of this research is to develop a construction Business Model Transformation Canvas (BMTC) to map the transformation of construction enterprises in Industry 4.0.

The research was carried out by conducting an expert forum with academics from nine universities across Australia and New Zealand. The study employed purposive sampling, and the academics were selected in a strategic manner in order to provide data that are relevant to the research.

The research identifies that technology-based partnerships supporting strategy and capability building, platforms enabling enterprises to conceive, design, manufacture and assemble buildings and competition with stakeholders having superior capabilities not in building but in other areas of business are fundamental to Industry 4.0 transformation.

The results present state-of-the-art development of business model research in construction that intends to support the strategic planning of construction enterprises in Industry 4.0. This research is the first and only research that uses a business model canvas (BMC) for strategy-reformulation in incumbent construction enterprises to maintain a competitive advantage in Industry 4.0. Merits of the construction BMTC lie in its holistic approach, visual representation and simplicity.

Industry 4.0 is characterised by systemic transformations occurring exponentially, encompassing an array of dynamic processes and technologies. To move towards a more sustainable future, it is important to understand the nature of this transformation. However, construction enterprises are experiencing a capacity shortage in identifying the transitional management steps needed to navigate Industry 4.0 better. This paper presents a maturity model with the acronym “Smart Modern Construction Enterprise Maturity Model (SMCeMM)” that provides direction to construction enterprises.

It adopts an iterative procedure to develop the maturity model. The attributes of Industry 4.0 maturity are obtained through a critical literature review. The model is further developed through knowledge elicitation using modified Delphi-based expert forums and subsequent analysis through qualitative techniques. The conceptual validity of the model is established through a validation expert forum.

The research defines maturity characteristics of construction enterprises across five levels namely ad-hoc, driven, transforming, integrated and innovative encompassing seven process categories; data management, people and culture, leadership and strategy, automation, collaboration and communication, change management and innovation. The maturity characteristics are then translated into assessment criteria which can be used to assess how mature a construction enterprise is in navigating Industry 4.0.

The results advance the field of Industry 4.0 strategy research in construction. The findings can be used to access Industry 4.0 maturity of general contractors of varying sizes and scales and generate a set of recommendations to support their macroscopic strategic planning.

Industry 4.0 is characterised by the exponential pace of technological innovations compelling organisations to transform or be displaced. Industry 4.0 transformation of construction enterprises lacks systematic guidance and notable earlier studies have utilised maturity models to map transformation of enterprises. This paper proposes a conceptual maturity model for construction enterprises for business scenarios leading to Industry 4.0.

The requirements for designing maturity models, including comparison with existing models and scientifically documenting the design process, make Systematic Literature Reviews (SLR) appropriate. Two systematic literature reviews (SLRs) are conducted to shortlist a total of 95 papers, which are subjected to subsequent content analysis.

The first SLR identifies the following process categories as critical levers of industry 4.0 maturity; data management, people and culture, leadership and strategy, collaboration and communication, automation, innovation and change management. The second SLR ascertains that the existing maturity models in construction literature do not adequately correspond to Industry 4.0 business scenarios with limited emphasis on data management, automation, change management and innovation. The findings are assimilated to propose a conceptual Smart Modern Construction Enterprise Maturity Model (SMCeMM).

The paper systematises the transformation of construction enterprises in Industry 4.0 and leads to state-of-the-art development of Industry 4.0 and maturity model research in construction. The proposed conceptual model addressed both the demands of the construction industry as well as what is required to navigate Industry 4.0 better.

After completion of the case study, students will be able to analyze the marketing mix strategy of a firm, discuss the importance of a generic strategy to gain a competitive advantage, analyze the basis of consumer segmentation in furniture and highlight the importance of positioning in influencing the overall marketing mix strategy of a firm.

It was April 18, 2022. Puneet Singh Seehra (Seehra), the owner and director of Shearling Skins Private Limited (Shearling), was visibly worried as he was looking at the recent sales report. Shearling was in the business of manufacturing premium-quality furniture for corporate clients. Seehra was happy about the growth of his company. However, he was lately concerned about the declining sales figures. Some important questions were troubling Seehra. Was competition eating into his business? How could he differentiate Shearling from competition? What was the right marketing strategy for a market dominated by unorganized competitors and a few major players? His head spinning, he leaned back on his chair as he looked out of his office window.

The case study can be taught in a graduate-level course in marketing or strategy.

Teaching notes are available for educators only.

CSS: 8 Marketing

This study aims to investigate the level of empowerment of rural women organic farmers through the Total Observed Score of a Statement-Revised scale (TOSS-R).

In doing so, exploratory factor analysis is used to investigate the factorial structure of the 8-dimensional TOSS scale. Further, first and second-order confirmatory factor analysis is used to confirm the construct reliability and model adequacy of TOSS-R. The data has been collected from 314 women organic farmers from four selected districts (Nayagarh, Khurda, Boudh, and Ganjam) of Odisha based on 2022 records from the Directorate of Horticulture.

The results showed that the TOSS-R is showing better model adequacy compared (CMIN/df = 2.031, RMSEA = 0.073, SRMR = 0.064) to the earlier TOSS scale (CMIN/df = 2.697, RMSEA = 0.840, SRMR = 0.096). Further in the analysis of the overall empowerment, the TOSS-R scale determined that 49.60% of women are highly empowered, 44.58% are moderately, and 5.73% are less empowered.

The study emphasizes that the policymakers should establish a local capacity to promote gender equity in land titling such that women irrigators will be officially labelled as “farmers”, thereby bringing them under government scheme that is exclusively granted to women farmers.

The study’s novelty lies in a more comprehensive model of determining the empowerment of women organic farmers which has the capability to determine the constraints of the women failing to be empowered in the farming sector.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2023-0693

Corona Virus Disease (Covid-19) is a global pandemic that emerged at the end of 2019 and caused disruptions in global supply chains, particularly in the food supply chains that exposed the vulnerability of today’s food supply chain in a major disruption which provided a unique research opportunity. This review explores the current research direction for food supply chain resilience and identifies gaps for future research in preparing for future major global pandemics.

This article presents a review of food supply chain resilience followed a systematic literature review of the business and management-based studies related to the food supply chain in Covid-19 published between December 2019 and December 2021 to identify the immediate issues and responses that need to be addressed in the event of future disruptions in food supply chains due to new global health threats.

The study revealed the need for more literature on food supply chain resilience, particularly resilience to a major global pandemic. The study also uncovered the sequence of events in a major pandemic and identified some strategies for building resilience to potential future risks of such an event.

The limitations of this study are apparent. Firstly, the selection of databases is not comprehensive. Due to time limitations, authoritative publishers such as Springer, Emerald, Wiley and Taylor & Francis were not selected. Secondly, a single author completed the literature quality testing and text analysis, possibly reducing the credibility of the results due to subjective bias. Thirdly, the selected literature are the studies published during the immediate event of Covid-19, and before January 2022, other research studies may have been completed but were still in the state of auditing at this time.

This paper is the first study that provides a detailed classification of the immediate challenges to the food supply chain faced in both upstream and downstream nodes during a major global disruption. For researchers, this clearly shows the immediate difficulties faced at each node of the food supply chain, which provides research topics for future studies.

A direct correlation exists between waste disposal, disease spread and public health. This article systematically reviewed healthcare waste and its implication for public health. This review identified and described the associations and impact of waste disposal on public health.

This paper systematically reviewed the literature on waste disposal and its implications for public health by searching Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), PubMed, Web of Science, Scopus and ScienceDirect databases. Of a total of 1,583 studies, 59 articles were selected and reviewed.

The review revealed the spread of infectious diseases and environmental degradation as the most typical implications of improper waste disposal to public health. The impact of waste includes infectious diseases such as cholera, Hepatitis B, respiratory problems, food and metal poisoning, skin infections, and bacteremia, and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife.

Infectious diseases such as cholera, hepatitis B, respiratory problems, food and metal poisoning, skin infections, bacteremia and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife are some of the public impacts of improper waste disposal.

Healthcare industry waste is a significant waste that can harm the environment and public health if not properly collected, stored, treated, managed and disposed of. There is a need for knowledge and skills applicable to proper healthcare waste disposal and management. Policies must be developed to implement appropriate waste management to prevent public health threats.

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

In the last 10 years, the global financial services industry has significantly benefited from fintech. As the Indian entrepreneurial ecosystem continues to change, more fintech-use case-driven firms are created, and more investors are supporting these enterprises. India is acknowledged as a powerful fintech centre internationally.

The goal of the current research is to comprehend the revolutionary landscape of the Indian financial system.

Methodology: The research methodology entails a thorough review of several research papers and government reports better to understand fintech's role in the Indian financial system. This requires examining the trends, regulations and technical breakthroughs driving the fintech ecosystem to present a comprehensive picture of its influence.

The present chapter indicates that the fintech industry is flourishing in India. Over the following years, technological improvements will fuel the market's continuous expansion and change how financial products and services are produced, distributed and used.

This study assesses the SDG-related well-being of indigenous communities in India using bibliometric analysis and the ADO-TCM framework. It provides insights into their alignment with sustainable development objectives.

This study analysed 74 high-impact journals using bibliometric analysis to evaluate the well-being of India’s indigenous peoples about the SDGs.

This study analyses the well-being of tribal communities in India using existing scholarly articles and the ADO-TCM framework. It emphasises the importance of implementing Sustainable Development Goals (SDGs) to promote the well-being of indigenous populations.

This study uses bibliometric analysis and the ADO-TCM framework to investigate factors impacting tribal community welfare. It proposes theoretical frameworks, contextual considerations and research methodologies to achieve objectives.