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Suppliers and marketers have started planning toward postpandemic scenarios where logistics and retail will happen in a new way with the help of technological advances. This shift means new challenges for manufacturers, suppliers and retailers, and there is a need for strategic sourcing decisions for a robust supply chain system, logistics and on-time delivery system, as consumers have shown a positive change in online buying behavior. Furthermore, with digital transformation, customers are expected to not return to traditional buying. Hence, it becomes essential to identify the factors acting as enablers of online purchase behavior for sustainable digital business. This study aims to analyze the positive shifts in online purchasing by consumers, identify and model the enablers of positive transformations in online purchasing by consumers.

The interpretative structural modeling (ISM) technique is used to draw the interrelationships among the variables and their impact on online buying. A context-oriented relationship among the factors has been set up through the expert opinion technique. A total of 40 specialists have been approached for this. ISM with Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis was used to prioritize these drivers, identify the most critical factors and establish a driver-dependence relationship among these drivers.

Several significant categories of enablers like health, trust, convenience, work from home, referral buying, panic purchase and overstocking possess a strong influence on the shift to online due to the pandemic. The results will help the policymakers, suppliers, retailers, managers and practitioners with insights to plan, prepare for challenges and make decisions toward preparation and shifting to the emergent digital world. In addition, the study provides academicians scope for further research in the related area.

Consumer behavior significantly impacts retail and supply chain business, as it is an interface with the customer and links between a manufacturer and a customer. This study provides an insight into the shift in purchase behavior which can help suppliers in this transition phase to be better prepared for tomorrow to achieve sustainable competitive advantage.

This study assists practitioners and researchers in understanding the interrelationships among the factors using ISM-MICMAC analysis in a realistic way rather than daydreaming with overambitious goals.

The purpose of this paper is to empirically examine the impact of supply chain risks (SCRs) and demand management strategies (DMSs) on the company performance in order to study the use of DMSs in delivering improved results even in the presence of SCRs. The SCRs considered under the study are as follows: demand variability, constrained capacity and quality of services delivery, and competitive performance, customer satisfaction and financial performance are the measures considered for company performance.

This study is based on a survey of 439 businesses in India representing 10 groups of services industries (information technology/IT enabled services, business process outsourcing, IT infrastructure, logistics/transportation, healthcare, hospitality, personal services, consulting, education and training, consumer products and retail), using structural equation modeling (SEM) methods.

The findings reveal that presence of demand variability risk has significant influence upon the use of demand planning and forecasting, controlling customer arrival during peaks and shifting demand to future. Mismatch of capacity against demand (unused capacity) leads to the use of techniques to influence business during lean periods, thereby resulting in enhanced supply chain (SC) and financial performance. Controlling customer arrival during peaks to shift the demand to lean periods leads to enhanced financial performance. Presence of delivery quality risk does not significantly influence the use of DMS. Also, short-term use of customer and business handling techniques does not exert significant influence on company performance.

The study has limitations as follows: (1) respondents are primarily from India while representing global organizations, (2) process/service redesign to relieve capacity as a DMS is not considered and (3) discussion on capacity management strategies (CMSs) is also excluded.

SC managers can be resourceful in shifting the peak demand to future with the application of techniques to control customer arrival during peaks. The managers can also help enhance business by influencing business through offers, incentives and promotions during lean periods to use available capacity and improve company performance.

This study is one of the first empirical works to explore how presence of SCRs influences the use of DMS and impacts the three types of company performance. The study expands current research on demand management options (DMOs) by linking three dimensions of company performance based on the data collected from ten different groups of service industry.

The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

This study aims to provide a causal framework for teacher burnout (BO) and work engagement (WE) by examining the factors that contribute to it and evaluating how progressive education (PE) affects teachers' performance in Higher education institutions (HEIs).

This study uses a multi-stage sampling technique with the help of computer random generation data from a selected list of teachers. The survey has two sections; the first consisted of a questionnaire of PE, BO, WE and organizational outcomes and the second contained four items to measure the demographic variables. The researcher contacted 745 teachers and asked them to fill up the questionnaire but the authors received only 498 useable responses.

The results of the study confirmed that moderating role PE reduces the BO of the teachers of HEIs and increases WE. The job demand-resource (JD-R) model was also validated in the Indian context and the model was found suitable for the Indian sample.

The study has been conducted to manage BO and teachers' engagement in HEIs and the result suggests that the Management of HEIs should value PE characteristics as a crucial component of the educational process. PE encourages academic engagement among professors and students in HEIs.

The study tests the moderating role of PE with the JD-R and the JD-R model in the higher education system in India, which is rarely tested. The study's integrated approach to BO and WE, which provide insight into both viewpoints and aids in employees' poor health.

With ever-increasing global concerns over environmental degradation and resource scarcity, the need for sustainable manufacturing (SM) practices has become paramount. Industry 5.0 (I5.0), the latest paradigm in the industrial revolution, emphasizes the integration of advanced technologies with human capabilities to achieve sustainable and socially responsible production systems. This paper aims to provide a comprehensive analysis of the role of I5.0 in enabling SM. Furthermore, the review discusses the integration of sustainable practices into the core of I5.0.

The systematic literature review (SLR) method is adopted to: explore the understanding of I5.0 and SM; understand the role of I5.0 in addressing sustainability challenges, including resource optimization, waste reduction, energy efficiency and ethical considerations and propose a framework for effective implementation of the I5.0 concept in manufacturing enterprises.

The concept of I5.0 represents a progressive step forward from previous industrial revolutions, emphasizing the integration of advanced technologies with a focus on sustainability. I5.0 offers opportunities to optimize resource usage and minimize environmental impact. Through the integration of automation, artificial intelligence (AI) and big data analytics (BDA), manufacturers can enhance process efficiency, reduce waste and implement proactive sustainability measures. By embracing I5.0 and incorporating SM practices, industries can move towards a more resource-efficient, environmentally friendly and socially responsible manufacturing paradigm.

The findings presented in this article have several implications including the changing role of the workforce, skills requirements and the need for ethical considerations for SM, highlighting the need for interdisciplinary collaborations, policy support and stakeholder engagement to realize its full potential.

This article aims to stand on an unbiased assessment to ascertain the landscape occupied by the role of I5.0 in driving sustainability in the manufacturing sector. In addition, the proposed framework will serve as a basis for the effective implementation of I5.0 for SM.

Ti6Al4V alloy patient-customized implants (PCI) are often fabricated using laser powder bed fusion (LPBF) and annealed to enhance the microstructural, physical and mechanical properties. This study aims to demonstrate the effects of annealing on the physio-mechanical properties to select optimal process parameters.

Test samples were fabricated using the Taguchi L9 approach by varying parameters such as laser power (LP), laser velocity (LV) and hatch distance (HD) to three levels. Physical and mechanical test results were used to optimize the parameters for fabricating as-built and annealed implants separately using Grey relational analysis. An optimized parameter set was used for fabricating biological test samples, followed by animal testing to validate the qualified parameters.

Two optimized sets of process parameters (LP = 100 W, LV = 500 mm/s and HD = 0.08 mm; and LP = 300 W, LV = 1,350 mm/s and HD = 0.08 mm) are suggested suitable for implant fabrication regardless of the inclusion of annealing in the manufacturing process. The absence of any necrosis or reaction on the local tissues after nine weeks validated the suitability of the parameter set for implants.

To help PCI manufacturers in parameter selection and to exclude annealing from the manufacturing process for faster implant delivery.

To the best of the authors’ knowledge, this is probably a first attempt that suggests LPBF parameters that are independent of inclusion of annealing in implant fabrication process.

This study aims to identify enablers of belief and ethics-based marketing practices, establish relationships among the factors and present them in a hierarchical model to derive critical insights. This paper emphasizes interpretations of the in-depth interviews to decipher the market pervasiveness of the evolved model.

In-depth interviews were conducted with individuals and small groups of informed and elite respondents pursuing marketing guided explicitly by ethics and led by belief. The interview data further corroborated with the related literature contributed to specific factors. Finally, interpretive structural modeling has been implemented step by step to develop a systematic model for enablers.

This paper contributes a structural relationship of morality and ethics, strengthening faith and belief through philosophical understanding, which traverses into the actions related to societal benefits with the support of market opportunity development while bringing in value, enhancing the demand in return and establishing market pervasiveness. The crux of this paper is that the foundation of belief will reduce the hierarchy of other related factors while strengthening their interdependencies with equity to contribute to the development of the pervasiveness of the market for such organizations.

To the best of the authors’ knowledge, this is the first study exploring and examining the enablers contributing to belief and ethics-based organizations’ pervasiveness along with their interrelationships. The initial intrigue that led to the inquiry was evidence of the market pervasiveness of such organizations’ products and services across various streams.