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BACKGROUND: During the last decades, much research has been accomplished on the antecedents of bridge employment. Nevertheless, there is limited investigation into the influence of person-environment fit levels on retirement intentions. OBJECTIVE: This study’s purpose was to examine the interactive effect of person-environment fit levels on the intention to pursue bridge employment. More specifically, the authors investigated the mediator and moderator roles of person-organization fit on the relationship between person-vocation fit and the intention to pursue bridge employment within the same organization. METHODS: Data from 246 older executives working in the French banking sector were used to test the hypotheses. RESULTS: The results indicated the existence of a positive and significant relationship between person-vocation fit and the intention to pursue bridge employment within the same organization. Further, the findings confirmed the mediating and moderating roles of person-organization fit on this relationship. CONCLUSIONS: The results of this study have theoretically and empirically extended person-environment fit theory, continuity, and role theories to shed light on the underlying mechanisms through which person-vocation fit impact bridge employment intention. The findings of this study can be used by career counsellors and managers to design more effective actions to retain older workers.

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

Lots of work has been conducted to explore and explain inter-organizational relations between supply chain partners. However, we have noticed that there is no accurate agreement between authors. Therefore, to better understand this disparity, the authors have studied supply chain inter-organizational relationship dynamics in different industries to bring out an analytical framework that allows a better understanding of such an issue. Further, the framework is subjected to expert’s opinion and ranked using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. These case studies instruct professionals and researchers so that they bring up their level of abstraction that remains appropriate to catch this dynamic in order to guide decision-making and future research and studies.

PurposeAs COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.Design/methodology/approachThe present study has combined data analytics with theK-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By usingK-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. AfterK-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.FindingsAfter successful iterative training, the model is programmed usingRfunctions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.Research limitations/implicationsUsing probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.Practical implicationsThe study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.Originality/valueFurther, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.

With smart sensors and embedded drivers, today’s automotive industry has taken a giant leap in emerging technologies like Machine learning, Artificial intelligence, and the Internet of things and started to build data-driven decision-making strategies to compete in global smart manufacturing. This paper proposes a novel design framework that uses Federated learning-Artificial intelligence (FAI) for decision-making and Smart Contract (SC) policies for process execution and control in a completely automated smart automobile manufacturing industry. The proposed design introduces a novel element called Trust Threshold Limit (TTL) that helps moderate the excess usage of embedded equipment, tools, energy, and cost functions, limiting wastages in the manufacturing processes. This research highlights the use cases of AI in decentralised Blockchain with smart contracts, the company’s trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. The developed model supported by real-time cases incorporated cost functions, delivery time and energy evaluations. Results spotlight the use of FAI in decision accuracy for the developed smart contract-based Automobile Assembly Model (AAM), thereby qualitatively limiting the threshold level of cost, energy and other control functions in procurement assembly and manufacturing. Customisation and graphical user interface with cloud integration are some challenges of this model.

Real-time monitoring, is now the integral component in smart manufacturing with the rapid application of Artificial Intelligence (AI) in manufacturing. Machine Learning (ML) algorithms and Intern...

Societal emergence and sustainability are results of human actions and practices which can either imbalance it with maximum exploitation or retain it through responsible utilization of resources. Based on theories on institutional and resource-based views, the study explores the enablers of green sustainable practices of procurement, logistics, product and process design and regulatory frameworks for low carbon performance .The study employs a Hybrid approach of step-by-step empirical process to examine the impact of sustainable practices on low carbon performance which further affects the sustainable manufacturing and societies. A theoretical model developed based on hypothesis is tested first using modified Dillman’s approach. Then it is tested in in the PLS-SEM package using 380 data responses collected from the various manufacturers. Further robustness of proposed model is validated using different ML (machine learning) followed by post hoc analysis using Item Response Theory to validate the scale and efficacy of the measurement model. The study validates the positive relationships of sustainable practices on the low carbon performance which eventually is responsible for sustainable societies. The area of sustainable manufacturing is found relatively lacking and requires further attention of leadership for better societal establishments. The study hopes to further enrich the literature with its unique Hybrid approach of SEM/PLS Machine Learning and IRT which is used to presents carbon performance as a central entity deriving from green practices and driving sustainable manufacturing and societies.

PurposeA huge number of events can affect the operations of a long and complicated supply chain. This paper deals with the development of a supply chain risk mitigation index (SCRMI) based on a risk mitigation maturity framework. A comprehensive list of supply chain risks has been ascertained and segregated into risks faced at various supply chain echelons through a detailed literature review.Design/methodology/approachThis paper is based on an extensive literature review and questionnaire to identify risks. order of magnitude analytic hierarchy process (OM-AHP) was used as the methodology to assess the prioritization of supply chain risks under two clusters, viz., Probability and severity leading to risk were tested in a distillery.FindingsSCRMI was determined and used to categorize their maturity level in facing supply chain risks. Thus, organization can focus on improvements for their specific needs.Research limitations/implicationsThe model was tested in the distillery industry. It should be tested in other contexts with other methods to provide generalizability.Practical implicationsThis research provides direction to managers for choosing risk mitigation strategies based on the global supply chain environment. SCRMI can be a performance metric for the supply chain managers.Originality/valueThe manufacturer's readiness to take action in the face of disruptions in the supply chain is a critical challenge in today's complex business environment and SCRMI framework is instrumental in such business environment.

At present, consumers in emerging economies are becoming more conscious about environmental well-being Therefore, organizations compete to make their products and practices more eco-friendly Several studies have tried to explain the relationship between green consumerism and an individual's buying behaviour using traditional theories However, there is quite a challenge in understanding the influence of green self-concept (GSC) and green self-identity (GSI) in predicting the green purchase intention (GPI) of consumers Therefore, the authors developed six hypotheses to assess the relation between self-concept and the GPI The survey was conducted, and the responses were evaluated through the partial least square (PLS) method The authors analysed the measurement model results (n = 717) and the direct and indirect mediating effect of the latent variable contributing to GPI The measurement model results show that a significant relationship exists in the proposed model, namely, GSCs ? green purchasing intentions, product self-concept (PSC) ? green purchasing intentions and GSI ? green purchasing intentions Further, the GSI acted as a mediator for the measurement model The implications of the study can be used to understand the green consumer behavior in developing new strategies and policies for the organizational practice in emerging economies

PurposeIn this paper, the authors examine the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance.Design/methodology/approachData collection was carried out through a multinational survey with 318 respondents from different manufacturing companies located in 15 countries. Multivariate data techniques were applied to analyze the collected data. Diffusion of innovations theory (DIT) was the adopted theoretical lens for our research.FindingsThe authors’ findings indicate that I4.0 technologies that aim to process information to support decision-making and action-taking directly affect maintenance performance. Technologies oriented to sensing and communicating data among machines, people, and products seem to moderate the relationship between TPM practices and maintenance performance. However, the extent of such moderation varies according to the practices involved, sometimes leading to negative effects.Originality/valueWith the advances of I4.0, there is an expectation that several maintenance practices and performance may be affected. Our study provides empirical evidence of these relationships, unveiling the role of I4.0 for maintenance performance improvement.

The dynamics of the supply chain has been changing since the past two decades. The different view of the supply chain is proposed by the researchers to achieve sustainability in the system. With consideration of the environment and cleaner production concerns, lean and green closed-loop supply chain aspect is crucial to enhance the sustainability of an organization. In this context, this chapter analyzes the barriers for the implementation of lean and green closed-loop supply chain in the Indian small and medium enterprises (SMEs). A total of 35 barriers have been identified from the literature survey; out of which, 15 potential barriers have been shortlisted by using the best–worst method (BWM). After finalizing the top 15 barriers, interpretive structural modeling (ISM) has been adopted to analyze the relationship among the barriers. The proposed framework provides systematic approach for analyzing the barriers, and also creates roadmaps for the implementation of lean and green closed-loop supply chain in the SMEs.

Novel coronavirus disease (COVID-19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision-making model for disruptive events in Morocco. The study develops a three-phase evaluation approach. The approach involves fuzzy logic (

Purpose Industrial symbiosis (IS) is basically a synergistic association between two or more industries or businesses wherein the waste or by-product of one industry becomes the raw material or immediate material of another industry. IS is believed to bring in significant benefits to the organizations. Thus, the purpose of this paper is to evaluate the competitiveness attained through IS. Design/methodology/approach Analytic hierarchy process was adopted to analyze the various organizational competitiveness of IS implementation. The conceptual model was developed to understand the interrelationship between 14 outcomes and 5 organizational competitiveness which are identified from the literature review. The attainment of these five organizational competitiveness was evaluated by computing the global priority score of the outcomes. Findings The global priority score suggests that the organizational collaboration (0.19) is the important output. Further, collective learning and growth (44 percent) have been identified as the important competitiveness attained through the implementation of IS. Practical implications The relationship matrix developed can be used by the practicing managers/researchers to understand the various interactions. Thus, systematic decision making and guidance for future implementation studies will be ensured. Originality/value In the past, few authors discussed the conceptualization of IS; however, the impact of IS on the organizational performance was not extensively studied. Therefore, a conceptual model was proposed to analyze the attainment of various competitiveness through the participation of industrial symbiosis network. Further, based on the computed scores, the relationship matrix developed between outcomes and organizational competitiveness is one of the significant contributions of this work.

Purpose In recent years, a new transformation known as Industry 4.0 has drawn much attention throughout the world, and numerous investigations have fundamentally looked into the uprising concept. However, few have concentrated on the literature review. Taking this as motivation, the authors endeavored to assess the different attributes of Industry 4.0. The paper aims to discuss these issues. Design/methodology/approach This review concentrated on linking the articles from different database and distinguishing the attributes of Industry 4.0. The authors assessed 85 articles from scholarly database and peer-reviewed journals on Industry 4.0 through the selection of the Mayring (2004) procedure. The examination included a four-advance process, in particular, material gathering, engaging investigation, classification of the choice and material assessment. Findings The study backs experts and readers to comprehend the spread of Industry 4.0 in various ventures. For academicians, this gives an extensive rundown of Industry 4.0 attributes. Besides, the writing review gives a chance to comprehend the idea of Industry 4.0 in detail. Further, the outcome aggregates the success factor, failure factor, business model, potential and difficulties in the context of Industry 4.0. Research limitations/implications Many areas such as supply chain, circular economy and healthcare have started exploring Industry 4.0. However, few specific cases have reported about it, and no generic model is found for real-time application. Practical implications The study serves as reference material for researchers and practitioners to understand the spread of Industry 4.0 and utilize the concept of Industry 4.0 for real-life application. Originality/value The study focuses on reporting the potential, challenges, business model and pre-requites of Industry 4.0.

Purpose Nowadays, firms are keen on improving the quality culture in the organizations. The proven success of Lean and Six Sigma has given rise to the synergetic Lean Six Sigma (LSS) approach that has been catching fire in the past one decade. However, there exists a gap between the understanding and implementation of LSS in the organizations, especially in the emerging economies. Taking this as a valuable opportunity, the purpose of this paper is to present a development of LSS training module. Design/methodology/approach This study starts with a literature review of LSS to reinforce the understanding of the research subject in scope of manufacturing sector. Then, an online questionnaire was designed and used to collect responses from 181 companies located in the Indian sub-continent. Subsequently, the results obtained from the survey were analyzed using COARSE approach. Findings This study reveals two key findings and associated contributions. First, it was found that the overall awareness of LSS within the responded manufacturing firms is about 70.4 percent. Second, there is no single standard training module that exists in any of the sampled firms to cater to their quality programs. Hence, in order to improve the LSS awareness which could subsequently help managers as a resource for creating an efficient workplace, this paper presents a structured LSS training framework. Research limitations/implications Although this paper presents the importance of LSS and associated awareness level among the responded firms, more empirical evidence is required to generalize the model findings. Second, this study is scoped to firms that work out of the Indian sub-continent, and this provides a future opportunity to expand the scope of this research toward a global study for a comparison between emerging and developed economies. Third, this study is limited to manufacturing firms and hence paves an opportunity to research on a similar theme in services context as well. Practical implications Before embarking on an LSS journey, an organization can use the LSS training module proposed in this study to assess the employee awareness on LSS. Furthermore, organizations that already have a mature LSS practice can incorporate the LSS training module for periodic evaluation of the employees for effective change management. Originality/value The training module presented in this paper is the original contribution by the authors. This is no association to any single identifiable organization or associated funding. The direct practical implication of its application in real time is the value that managers could derive from the proposed LSS training framework.

Purpose The purpose of this paper is to identify major healthcare service quality (HSQ) dimensions, their most preferred service levels, and their effect on HSQ perceptions of patients using a Taguchi experiment. Design/methodology/approach This study adopted a sequential incidence technique to identify factors relevant in HSQ and examined the relative importance of different factor levels in the service journey using Taguchi experiment. Findings For HSQ, the optimum factor levels are online appointment booking facility with provision to review and modify appointments; a separate reception for booked patients; provision to meet the doctor of choice; prior detailing of procedures; doctor on call facility to the room of stay; electronic sharing of discharge summary, an online payment facility. Consultation phase followed by the stay and then procedures have maximum effect on S/N and mean responses of patients. The appointment stage has a maximum effect on standard deviations. Research limitations/implications Theoretically, this study attempted to address the dearth of research on service settings using robust methodologies like Taguchi experiment, which is popular in the manufacturing sector. The study implies the need for patient-centric initiatives for better HSQ through periodic experiments that inform about the changing priorities of patients. Practical implications The trade-off between standardization and customization create challenges in healthcare. Practically, a classification of processes based on standardization vs customization potential is useful to revamp processes for HSQ. Originality/value This study applied the Taguchi approach to get insights in re-designing a patient-centric healthcare servicescapes.

Bitcoin is a crypto-currency-based open-source technology that operates in the peer-to-peer grid as a private payment mechanism. Bitcoin works on sophisticated cryptography supported by a local community in a peer-to-peer network. This study reviews scholarly articles to understand how bitcoin is addressed in the literature. The study reports on the attributes of bitcoin through a systematic literature review. The paper is based on primary data from existing literature and secondary data from relevant case studies in the public domain. Unlike other currencies, Bitcoin seems to have faced many hurdles and with many applications in day-to-day life, created unique challenges for the end user community. When Bitcoin came into existence, it seemed to signal hope for a better future, but the growth of bitcoin is hard to predict. Bitcoin opens up an entirely new world for both practitioners and academicians. Further, this study presents an idea about the “potential” of bitcoin, highlights the prerequisites, needs, implications, and challenges faced by bitcoin in processing business transactions.

Purpose Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue. Design/methodology/approach This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization’s readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review. Findings To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts’ ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE. Research limitations/implications The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process. Practical implications The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment. Originality/value The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.

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