Search results

The purpose of this paper is to explore the impact of five dimensions of green supply chain management (GSCM) practices (i.e. internal environmental management, green purchasing, cooperation with customers, eco-design, and investment recovery) on three dimensions of organizational performance (i.e. environmental, economic and operational performance).

Data were collected based on a cross-sectional survey of managers of 160 manufacturing firms in India. Structural equation modelling was used to test the influence of GSCM practices on each of the organizational performance outcomes.

Except for internal environmental management and green purchasing, all other GSCM dimensions are found to significantly impact at least one of the performance dimensions, either directly or indirectly. The results highlight that investment recovery practice is a key predictor of environmental performance, whereas eco-design is a key predictor of operational performance. The structural equation modeling result also suggests that GSCM do not directly affect economic performance, but can improve it indirectly.

Since data was collected from managers of manufacturing firms on the basis of their subjective evaluations, future research studies should employ objective performance indicators for analysis. Also, the study did not consider some dimensions of GSCM practices, which can be included in future studies.

Manufacturing firms should implement GSCM practices not just because of the pressure from regulatory bodies but also to elevate their environmental, operational and economic performance. The proposed model in this paper suggests practitioners which GSCM factors are driving these performance changes and supports the building of a roadmap for GSCM implementation in their organization.

Manufacturing firms from four different operating sectors, namely automotive, electrical and electronics, process and machinery sectors are the respondents. The originality of the paper lies in testing the influence of GSCM practices on organizational performance in a novel context, where most GSCM initiatives fail for one reason or another. Furthermore, the assessment of the interaction between five constructs of GSCM practices and three constructs of organizational performance in the Indian manufacturing context offers unique conceptual contribution to the researches in the GSCM field.

This study aims to address the adoption issues of green and sustainable practices in the hotel industry. The study identifies critical performance indicators (CPIs) and utilizes Hotel Carbon Management Initiative (HCMI) framework to prioritize CPIs for achieving a robust adoption framework for green and sustainable practices.

The hotel industry is driven by changing ecological degradation, and it is necessary to achieve feasible development goals. This research article formulates the CPIs derived from HCMI and decision-making model is created using the Analytic Hierarchy Process (AHP).

In this research, CPIs of HCMI are considered and aim to formulate five major CPIs of HCMI, namely air pollution, energy efficiency, water conservation, noise pollution and waste management. The study identifies the need for better control and sustainable growth in the Indian hotel industry with minimum carbon emissions coupled with the green approach adoption.

The CPIs work on minimization of risks and maximizing optimality of return on investment. The development of the hotel industry will be improved and immensely welcomed by capping the carbon emission with the green initiatives. This research is limited as urban hotels are surveyed in this study.

This work makes a valid argument to establish HCMI as a model initiative for environment quality improvement and further extension of other activities in the hospitality sector and scale-up sustainable practices for future-ready circular economies.

The purpose of this paper is to investigate the impact of organizational size on adoption of green supply chain management (GSCM) practices for the Indian industry. It also evaluates the impact of GSCM practices on organizational performance.

This study aims to empirically test the GSCM model to investigate the present orientation of the Indian industry toward GSCM practices using a pre-tested structured questionnaire. The statistical inferences were drawn using the data provided by 161 Indian firms. This has compared the GSCM practice implementations among small-sized, medium-sized and large-sized organizations.

The study reveals that Indian organizations have shown a satisfactory implementation of majority of the environmental practices, except supplier ISO:14001 certification and Tier-II supplier evaluation. Out of 21 practices, medium-sized organizations have adopted GSCM practices at a similar level compared with large organizations, with three exceptions: existing environmental management systems, support from mid-level and top management and supplier evaluation for environmental practice. It was found that GSCM adoption can lead to equal improvements in operational performance for both large-size and medium-size organizations.

This paper makes two major contributions in the domain of green supply chain practices in India. First, it investigates the adoption of GSCM practices in organizations of different sizes (small, medium and large) and the impact of GSCM practices on the performance of organizations of different sizes. Second, it identifies the key areas for improvement and recommends a set of measures for improving the implementation of GSCM practices in Indian organizations.

Sustainable supply chain management (SSCM) ensures integration of socially, environmentally and economically feasible practices in entire supply chain. SSCM principles can be implemented to improve efficiency and productivity of a system by different attributes of the system. The purpose of this article is to identify the most appropriate existing (SSCM) framework that can be implemented suitably in Indian smart manufacturing industries.

Validity and reliability analysis on the existing SSCM frameworks was carried out with the help of empirical data collected using questionnaire survey methodology from various Indian smart manufacturing organizations. The empirical data were gathered from various experts from top- and middle-level management in different smart manufacturing organizations across the country. Further, factor analysis was carried on the collected data to estimate the unidimensionality of each SSCM frameworks. Cronbach's alpha value was used to assess reliability of each framework. Subsequently, the frequency distribution analysis was done to obtain familiar elements in the segregated frameworks based on validity and reliability analysis.

The work observed that only five SSCM frameworks have shown unidimensionality in terms of the elements or constructs. The work further found that these segregated frameworks have not shown sufficiently high level of reliability. Additionally, this work attempted frequency distribution analysis and observed that there were very few elements which were being repeatedly used in numerous frameworks proposed by researchers. Based on the findings of this work, the work concluded that there is acute need of a new SSCM framework for Indian smart manufacturing industries.

This study gathered empirical data from 388 Indian smart manufacturing organizations. Thus, before generalizing the findings of the study across the sectors, there is a possibility of some more explication.

The main purpose of this article is to explore the feasibility of the existing SSCM frameworks in Indian smart manufacturing sector. The study also assumes that the manufacturing managers and executives may have the complete understanding on the existing sustainable manufacturing frameworks and a chance to executing proper suitable framework in the respective manufacturing organization.

This research looks into the revolutionary potential of Industry 5.0, healthcare, sustainability and the metaverse, with a focus on the transformation of healthcare firms through cutting-edge technologies such as artificial intelligence (AI) and Internet of Things (IoT). The study emphasizes the significance of sustainability, human-machine collaboration and Industry 5.0 in the development of a technologically advanced, inclusive and immersive healthcare system.

The study surveyed 354 medical professionals and used structural equation modeling (SEM) to investigate healthcare sustainability, Industry 5.0 and the metaverse, emphasizing the integration of modern technology while maintaining ethical issues.

The findings highlight Industry 5.0’s and the metaverse’s transformational potential in healthcare firms. The study finds that human centricity (HC) has only a minor direct impact on healthcare sustainability, whereas intelligent automation (IA) and innovation (INN) play important roles that are regulated by external factors.

Utilizing IA inside healthcare organizations can result in significant industrial advancements. However, these organizations must recognize the importance of moderating factors and attempt to find a balance between INN and thesev restraints.

This study makes a substantial contribution to the field by investigating the potential of Industry 5.0, healthcare, sustainability and the metaverse. It discusses how these advances can transform healthcare firms, with an emphasis on patient-centered treatment, environmental sustainability and data ethics. The study emphasizes the importance of having a thorough awareness of these trends and their implications for healthcare practices.

Traceability is predicted to usher in a fundamental shift in the way transactions in supply chains (SCs) are carried out. By reducing the negative aspects of trust-related issues in a SC, traceability enables improved visibility and transparency.

We advance research on traceability adoption in the perishable products supply chain by developing and validating an integrated model that combines the technology acceptance model (TAM), the technology readiness index (TRI) and the theory of planned behavior (TPB). A quantitative approach was employed, collecting data through an online survey of 174 supply chain professionals in major Indian cities using a five-point Likert scale. Participants were selected via LinkedIn, each with at least two years of SCM experience. Nonresponse bias was assessed by comparing early and late respondents, revealing no significant differences. Structural equation modeling (SEM) was used to test various research hypotheses derived from literature. Composite reliability and discriminant validity of constructs were verified before examining the relationships among the constructs within the structural model.

The study found that the TRI components of optimism and innovation did not impact perceived ease of use or perceived utility. Additionally, behavioral intention is shaped by perceived utility, attitude and perceived behavioral control.

This research provides valuable insights for managers aiming to adopt traceability in supply chains (SCs). It helps identify critical factors for effective traceability adoption, showing that perceived ease of use (PEU) and perceived usefulness are pivotal in shaping practitioners’ intentions. Managers should prioritize developing intuitive, user-friendly traceability applications that demonstrate clear value in optimizing SC efficiency. The study also reveals that while practitioners are generally optimistic about traceability, they may feel indifferent or lack a sense of control over it. Therefore, companies should focus on marketing strategies that empower decision-makers, highlighting the ease of use and practical benefits of traceability. Additionally, the findings suggest that perceived behavioral control, combined with intention, can effectively predict traceability adoption. By understanding these dynamics, managers can better guide their firms in successfully implementing traceability, ensuring both technological acceptance and operational efficiency.

This research offers a novel and in-depth exploration of traceability as an emerging concept in supply chains, particularly in India, where adoption remains limited. It highlights that while SC practitioners recognize traceability’s potential, they lack practical expertise, often driven by curiosity about decentralized databases. It underscores the critical role of artificial intelligence, IoT devices and big data in ensuring precise data collection and analytics, essential for successful traceability. The research also introduces a predictive model combining TAM, TRI and TPB constructs, identifying perceived usefulness, attitude and perceived behavioral control as key factors influencing traceability adoption.

The purpose of this study is to examine the impact of the shift towards a circular economy and marketing strategies on the collaborative purchasing behaviour of consumers in India. The study uses the theory of planned behaviour (TPB) and the marketing mix to understand the factors affecting a consumer’s intention to participate in collaborative consumption (CC).

A Web-based survey was conducted, and 349 valid responses were analysed using AMOS (Analysis of Moment Structures) structural equation modelling. The study emphasised the impact of price, promotion and perceived behaviour control on CC and provided direction and advice for companies that rent and swap apparel.

According to the study, promotion and perceived behaviour control are the two key characteristics that significantly impact a consumer’s willingness to participate in CC in India. The study also found that perceived behaviour control plays a significant direct role in behavioural usage. These findings emphasise the impact of price, promotion and perceived behaviour control on CC and offer direction and advice for companies that rent and swap apparel.

This article can be used to evaluate the business in different countries and can be developed further. It does, however, have some restrictions. Because most respondents are from northern and central India, in addition, some respondents are from the southwestern and southern regions, especially in the Mumbai and Chennai locales. Hence, the geographical sample was not diverse in terms of demographics. Furthermore, the gender identity of the respondents might essentially affect how the authors interpret customer buying behaviour, but the study missed this. Researchers could enhance this by using various sampling techniques and ensuring that other demographic characteristics are considered in the future. Furthermore, the survey could not distinguish between online and in-person transactions.

The study provides practical advice for companies that rent and swap apparel, emphasising the impact of price, promotion and perceived behaviour control on consumer willingness to participate in CC. The findings suggest that companies can improve consumer participation by focusing on promotion and perceived behaviour control. In addition, the significance of perceived behaviour control on behavioural usage highlights the importance of empowering consumers to control their decisions to participate in CC.

To the best of the authors’ knowledge, this study is one of the first to examine the factors influencing consumer willingness to participate in CC in the context of the shift towards a circular economy in India. By examining the impact of the TPB and the marketing mix on consumer intention, the study provides valuable insights for companies that rent and swap apparel. The findings highlight the importance of promotion and perceived behaviour control in shaping consumer behaviour and provide practical direction for companies to promote and market their products effectively. The study adds to the existing knowledge on the circular economy and the role of CC in reducing waste and promoting sustainability.

The purpose of this study is to examine the impact of sustainable supply chain management (SSCM) practices on both competitive advantage (CA) and organizational performance (OP) in the manufacturing sector in Ethiopia.

Data for the study were collected from a sample of 221 manufacturing companies operating in the four manufacturing groups/sectors in Ethiopia. In addition, data analysis was performed using the partial least squares method, which is a variance-based Structural Equation Modeling approach in the Smart-PLS software version (SmartPLS 4.0).

Based on the statistical analysis of the collected data, it demonstrates that SSCM has a significant and positive impact on both competitive advantage and organizational performance. Furthermore, statistical findings offer proof of the clear connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly mediates the relationship between SSCM and OP.

The primary limitation of this research is its reliance on a cross-sectional design. The generalizability of the findings obtained from the present study may be hindered. The variable under investigation in this research assessed organizational performance, a concept that is widely acknowledged to be extremely dynamic.

The study provides managers and researchers with valuable information on Sustainable Supply Chain Management strategies and how they influence competitive advantage and organizational performance in commercial and industrial environments.

This paper adds to the body of knowledge by providing new data and empirical insights into the relationship between SSCM practices and the performance of manufacturing companies in Ethiopia.