Modelling the enablers of industry 4.0 in the Indian manufacturing industry

Developing an evidence-based TISM: an application for the success of COVID-19 Vaccination Drive

The study illustrates an application of evidence data for performing Total Interpretive Structural Modeling (TISM). TISM is widely used to analyze the critical success factors or inhibitors and their interlinkages. This study uses learning from evidence data, specifically social media analytics, to identify the relationship between the elements. Thus, it leads to the advancement of the TISM-P methodology with evidence-based TISM (TISM-E). This study uses Twitter as a source of evidence data. Further, 2,60,297 tweets were used to illustrate the process of TISM-E. The paper demonstrates the application of TISM-E for the success of the COVID-19 vaccination drive. The pandemic effects are long-term; therefore, the hierarchical model developed shows a sustainable approach for vaccinating maximum population. Further, the framework developed will ensure the distribution efficacy of vaccines. In addition, it will aid practitioners in developing future vaccination policies. The enhanced model provides evidence for polarity (positive/negative) of relationships and can help to build propositions for theory development. The study contributes to healthcare, modeling research, and graph-theoretic literature.

Modelling the factors affecting Quality of Life among Indian police officers: a novel ISM and DEMATEL approach

Background

This study examines quality of life (QOL) of a sample of police officers in India. The concept of QOL includes well-being, social obligations, relationships of a person, and his association with environment. The study examines the factors on which QOL of police officers depend and their relationships with each other. The issues linked with QOL are important because they directly affect the individual's ability to maintain a healthy lifestyle and affect organizational performance in the long run. This study explores relevant factors that have an impact on the QOL of the employees of police department in India.

Method

In this paper, literatures review, ISM, MICMAC, and DEMATEL methodology have established eleven factors that impact the QOL of police officers in India. Mutual relations between factors have been established using the ISM approach to develop a model to represent these relationships. DEMATEL methodologies were used to analyze these factors.

Results

Results indicate that "fair compensation, work overload, workplace safety, and job stress" are the top-level factors that affect QOL of police officers.

Conclusion

The identification of factors and their mutual relationships that affect QOL are important for police officers and have to be dealt with according to their order of importance. The research model developed in this study shows how the factors of police officers' QOL are interrelated and presents the interrelationships among these factors. A comprehensive model depicting the relationships among these factors has been established, so that the QOL of police officers can be improved.

Performance measurement and management system 4.0: an action research study in investee NPOs by local government

Purpose

The Fourth Industrial Revolution is increasing the use of digital technologies for delivering products and services. A performance measurement and management system (PMMS) is recognised as a useful system to guide era 4.0; however, the literature has not adequately addressed this challenge in public government and not-for-profit organisations (NPOs). This research aims to investigate the evolution of the PMMS adopted by a local government for the control of investee NPOs in era 4.0.

Design/methodology/approach

Through action research, the authors investigate an Italian municipality and its main investee NPOs. The project involved 4 researchers and 25 managers of the local government and NPOs.

Findings

This paper describes the design of a PMMS developed for a local government to control its investee NPOs. Considering the regulations and managerial needs, the designed system evolved from a fragmented set of indicators based on legitimacy and economic perspectives to a holistic set of indicators based on a comprehensive set of perspectives to consider the changing business environment.

Originality/value

This study sheds light on the design of a PMMS adopted by local governments for controlling investee NPOs in era 4.0. The paper contributes (1) to identify the main control needs for the design of a PMMS in a public network and (2) to capture the evolution of a PMMS in light of era 4.0 by developing two conceptual propositions.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

Implementing Industry 4.0 through Cleaner Production and Social Stakeholders: Holistic and Sustainable Model

Although Industry 4.0 has received much attention in recent years due to the possibility of increasing companies’ productivity, the implementation process is complex. The aim of this study is to present a holistic model for implementing Industry 4.0 based on cleaner production as a fundamental tool for the development of production systems that meet the Sustainable Development 04026-002Goals (SDGs), and social stakeholders that cooperate with this implementation process, helping to develop sustainable infrastructure, processes and technologies to increase the sustainable transformation of these companies towards Industry 4.0. The method used was literature research, and the Delphi technique was used to ask specialists to contribute with their experience to evaluate and propose improvements to the model, in the form of a consensus. The model contributes to the Sustainable Development Goals 9, 12 and 15. This holistic and sustainable model is a contribution to theory and practice, helping executives, technicians, entrepreneurs and those involved with Industry 4.0 to base the implementation process in the needs and specificities of each company, avoiding the “one fits all” models, considering the peculiarities of each company and the complexity of the implementation process in a more efficient and collaborative digital production ecosystems base, seeking to reduce inequalities, through the joint effort of social stakeholders to find ways to restore and/or improve social harmony, impacted by Industry 4.0.

An assessment of government measures in combatting COVID-19 using ISM and DEMATEL modelling

Purpose

The purpose of this paper is to evaluate the interplay of various measures used by different governments around the world in combatting COVID-19.

Design/methodology/approach

The research uses the interpretative structural modelling (ISM) for assessing the powerful measures amongst the recognized ones, whereas to establish the cause-and-effect relations amongst the variables, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used. Both approaches utilized in the study aid in the comprehension of the relationship amongst the assessed measures.

Findings

According to the ISM model, international support measures have the most important role in reducing the risk of COVID-19. There has also been a suggestion of a relationship between economic and risk measures. Surprisingly, no linkage factor (unstable one) was reported in the research. The study indicates social welfare measures, R&D measures, centralized power and decentralized governance measures and universal healthcare measures as independent factors. The DEMATEL analysis reveals that the net causes are social welfare measures, centralized power and decentralized government, universal health coverage measure and R&D measures, while the net effects are economic measures, green recovery measures, risk measures and international support measures.

Originality/value

The study includes a list of numerous government measures deployed throughout the world to mitigate the risk of COVID-19, as well as the structural links amongst the identified government measures. The Matrice d'Impacts croises-multiplication applique and classment analysis can help the policymakers in understanding measures used in combatting COVID-19 based on their driving and dependence power. These insights may assist them in employing these measures for mitigating the risks associated with COVID-19 or any other similar pandemic situation in the future.