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This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

In software development (SD), practitioners have realized the importance of lean thinking. A new term “Leagile” is coined, which is an integrated approach of traditional lean and agile thinking to managing the operations. The study aims to investigate the application of the Leagile principles in a new sector and establish a relationship between the adoption of Leagile approach and operational performance (OP) in SD organizations.

Empirical research is conducted to investigate the linkage between the Leagile principles and operational measures. Data is collected through surveys from 256 SD industries located in 11 states of India and analysed using the structural equation modelling approach.

The practitioners envisage a positive impact of adoption of Leagile principles on OP of SD organizations, but one of the principles, i.e. perfection, is unable to influence the performance.

The study contributes by authorizing the contribution of Leagile principles towards OP of SD organizations. The outcomes will motivate the practitioners to enhance the adoption of Leagile principles in SD organizations.

This study aims to explore the interrelationships among human-related lean practices, lean production shop floors, process quality, inventory management, operational productivity and business productivity.

This study used a cross-sectional survey approach, and quantitative data were collected from 324 Indian auto-component manufacturing firms. Confirmatory factor analysis was used, followed by structural equation modelling techniques for the conceptual model, which incorporated a complete set of 11 hypotheses.

The results confirmed that human-related lean practices trigger lean production shop floors and improve process quality. Furthermore, the study revealed the positive impact of a lean production shop floor on process quality and inventory management and the positive impact of process quality on both operational and business productivity. Finally, inventory management is of the utmost importance in achieving better operational and business productivity, and operational productivity positively leads to business productivity.

The findings of this study can benefit auto-component manufacturing firms by elucidating the complex relationships between human-related lean practices, lean production shop floors, process quality, inventory management, operational productivity and business productivity. Better knowledge of these relationships will enable firms to enhance efficiency levels, reduce costs and resource wastage and improve their overall performance. This study provides a good understanding of the interplay between lean and quality factors and their influence on inventory management and business performance.

In this study, the authors assess the current state of lean product development and the lean production shop floor, along with the impact of the former on process quality and the latter on product quality and customer complaint reduction. The interplay between process and product quality and customer complaint reduction is assessed, along with their impacts on business performance.

Data were collected from 377 managers working at auto-component manufacturing firms in India. Confirmatory factor analysis was used for scale validation, and structural equation modelling was employed to test the research hypotheses.

The results of the statistical analyses reveal the positive influence of a lean production shop floor on process quality and lean product development on product quality and customer complaint reduction, and thereby on business performance.

The findings of this research provide insights into the interplay between lean and quality factors and their influence on customer complaint reduction and business performance. Practitioners can use the proposed model to strategically design unique products and improve the efficiency and effectiveness of the production shop floor, which can help enhance the product and process quality. This can reduce customer dissatisfaction and improve the business performance.

Few studies have simultaneously investigated the influence of lean product development and lean production shop floors in the Indian manufacturing context. To the best of our knowledge, this study is one of the first attempts to include customer complaint reduction as a construct in a lean model. It helps identify and prioritise the enablers of business performance and provides valuable insights for practitioners to strengthen lean implementation to attain a competitive edge.

In keeping with global developments rendering online shopping as an emerging trend among consumers, the present study extends the unified theory of use and acceptance of technology (UTAUT2) comprising the digital payment mode (DPM) as a new driver of online shopping and with the mediation of attitudes toward technology (ATTs) to gauge a better and deeper understanding of behavioral intention (BI).

This study used a survey instrument with snowball sampling from 600 consumers in northern India. Partial least squares structural equation modeling was used to find the association between drivers using UTUAT2, along with DPM and ATTs. The data were divided into a test group (20%) and validated through a training group (80%).

DPM was shown to be directly associated with BI. The mediation of ATTs was also validated through the model. The predictability of the model was 67.5% for the test group (20%) and 69.6% for the training group (80%). The results also indicated that facilitating conditions is a critical driver of BI.

This study enhances the understanding of the roles that DPM and ATTs play in BI during online shopping, suggesting that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The study examined a wide range of proactive supply chain practices to demonstrate a cross-linkage among them and to understand their effects on both practitioners of previous decision-making models, frameworks, strategies and policies. Here, six supply chain practices are empirically evaluated based on 28 constructs to investigate a comprehensive model and confirm the connections for achieving performance and competence. The study presents a conceptual model and examines the influence of many crucial factors, i.e. supply chain collaboration, knowledge, information sharing, green human resources (GHR) management and lean-green (LG) practices on supply chain performance.

Structural equation modeling (SEM) examines the conceptual model and allied relationship. A sample of 175 respondents' data was collected to test the hypothesized relations. A resource based view (RBV) was adopted, and the questionnaires-based survey was conducted on the Indian supply chain professionals to explore the effect of LG and green human resource management (GHRM) practices on supply chain performance.

The study presented five constructs for supply chain capabilities (SCCA), five constructs for supply chain collaboration and integration (SCIN), four constructs for supply chain knowledge and information sharing (SCKI), five constructs for GHR, five constructs for LG practices (LGPR) and four constructs for lean-green SCM (LG-SCM) firm performance to be utilized for validation by the specific industry, company size and operational boundaries for attaining sustainability. The outcome emphasizes that SCCA positively influence GHRM, LG practices and LG supply chain firm performance. However, LG practices do not influence LG-SCM firm performance, particularly in India.

The study exploited multiple practices in a conceptual model to provide a widespread understanding of decision-making to assist in developing a holistic approach based on different practices for attaining organizational sustainability. The study stimulates the cross-pollination of ideas between many supply chain practices to better understand SCCA, SCIN, SCKI, GHRM and LG-SCM under a single roof for retaining organization performance.

This paper aims at developing an improved method, based on binary search algorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy electric conversion systems. The BSA is also compared with linear search algorithm (LSA) to bring out the merits of BSA over LSA.

All the parameters of SEIG, including the varying core loss of the machine, have been considered to ensure accuracy in the predetermined performance values of the set up. The nodal admittance method has been adopted to simplify the equivalent circuit of the generator and load. The logic and steps involved in the formulation of the complete procedure have been illustrated using elaborate flowcharts.

The proposed approach is validated by the experimental results, obtained on a three-phase 240 V, 5.0 A, 2.0 kW SEIG, which closely match with the corresponding predicted performance values. The analysis is shown to be easy to implement with reduced computation time.

A novel improved and simplified technique has been formulated for estimating the per unit frequency (a), magnetizing reactance (X_m) and core loss resistance (R_m) of the SEIG using the nodal admittance of its equivalent circuit. The accuracy of the predetermined performance is enhanced by considering the SEIG’s varying core loss. Only simple MATLAB programming has been used for adopting the algorithms.

Food not only satisfies the need and nourishes positive experiences but also enhances involvement with the cultural, social and environmental attributes of a destination. As urban tourism is embracing sustainable consumption practices (SCP), this study aims to explore tourist’s responsible behaviour by embracing traditional gastronomic delicacies. More specifically, it pinpoints the driving forces behind why people choose traditional gastronomic delights.

The study adopted the triangulation method involving the grounded theory approach (GTA) attained through a series of focus group discussions followed by the survey method taking an emerging economy’s perspective (India and Bangladesh). This study accords equal importance to both the demand and supply perspectives of gastronomic tourism and its stakeholders.

Critical dimensions such as travel motivation, tourist expectations, socio-economic perspectives, mindful consumption, sustainable marketing efforts and community awareness were identified as major influencers towards traditional gastronomic delicacies.

The present study bears significance to the urban developers, policymakers, marketers, regional tourism bodies and tour operators in promoting urban gastronomic cultures through marketing traditional delicacies for sustainable development of the evolving gastronomic industry in India and Bangladesh.

This study makes a novel attempt in exploring critical dimensions in an evolving gastronomic industry by blending an innovative qualitative research methodology like GTA supported by the empirical validation process (quantitative). It proposes a theoretical framework for further advancement of gastronomic and urban tourism towards a SCP.

The purpose of this paper is to identify the challenges surrounding the implementation of digital technology (DT) agri-food supply chain (AFSC) and explore how these challenges relate to the various sustainability dimensions. Additionally, it aims to assess how these challenges are interconnected in relation to achieving sustainable development goals (SDGs).

The study employs a mixed-method approach utilizing the EFA-ISM-Fuzzy DEMATEL technique. To support and validate the findings, exploratory factor analysis (EFA) categorized 12 critical challenges in sustainable dimensions from 141 participants' responses. Furthermore, interpretive structural modeling (ISM) and decision-making trial and evaluation (DEMATEL) methods were used to obtain the interrelationship and hierarchical structure of the challenges.

The study identified 12 critical challenges while adopting DT in AFSC. These challenges were categorized into four sustainable dimensions: technological, economic, environmental and social. These challenges hinder the achievement of SDGs as well. Lack of regulatory and policy framework with security and privacy issues were the key challenges faced while adopting DT. These observations emphasize the necessity for government and policymakers to prioritize tackling the identified challenges to successfully endorse and execute DT initiatives in AFSC while also fulfilling the SDGs.

The implication underscores the need for collaboration among various stakeholders, such as governments, policymakers, businesses and researchers. By collectively addressing these challenges, DT can be leveraged optimally, fostering sustainable practices and making progress toward achieving the SDGs within the AFSC.

The study uses a combination technique of EFA and ISM-DEMATEL to identify the challenges faced in Indian AFSC while adopting DT and categorizes the interrelation between the challenges along with fulfilling the SDGs.