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This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

This paper aims to assess the factors favoring the adoption of the challenges faced and support mechanism, which will lead to the proliferation of glass fiber-reinforced gypsum (GFRG) technology in India.

Semi-structured interviews with 35 experts, including construction developers, architects, contractors, government officials and design consultants, were conducted. This qualitative data was analyzed using thematic analysis and matrix analysis.

GFRG-based buildings produce much less carbon footprints as compared to traditional ones and can be safely recommended as a promising, environmentally sensitive technology of the future. The major drivers in its adoption are its efficient construction capability, energy and soil conservation and significant waste reduction. Some of the challenges in implementation are long planning time, lack of skilled labor, lack of awareness about green building technologies and myopic perception of high cost incurred in green building adoption in people’s minds.

This study establishes that the construction industry has the potential to contribute toward creating a sustainable and green planet. It does so by evaluating and then positively positioning GFRG as an environmentally friendly building system.

The harmful effects of continuous environmental manipulation by humans leading to its degradation is a critical discussion agenda for most nations of the world. The issue has been taken up seriously by developing countries, and now, developing countries are also becoming sensitised to it. Several policies toward the attainment of this goal have been formulated and are being implemented by government and private bodies. Although some authors have studied the issues and challenges related to the adoption of green buildings, their attempts mostly focused on developed countries. Moreover, research that investigated the evaluation of the GFRG building system as a successful green technology of the future is inadequate.

The mental healthcare is experiencing an ever-growing surge in understanding the consumer (e.g., patient) engagement paradox, aiming to vouch for the quality of care. Despite this surge, scant attention has been given in academia to conceptualize and empirically investigate this particular aspect. Thus, drawing on the Stimulus-Organism-Response (S-O-R) paradigm, the study explores how patients engage with healthcare service providers and how they perceive the quality of the healthcare services.

Data were collected from 279 respondents, and the derived conceptual model was tested by using Smart PLS 3.2.7 and PROCESS. To complement the findings of partial least squares (PLS)-based structural equation modeling (SEM), the present study also applied fuzzy set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions to explore substitute conjunctive paths that emerge.

Findings show that patients’ perceived intimacy (PI), cohesion and privacy enhance the quality of mental healthcare service providers. The results also suggest that patients’ PI, cohesion and privacy have indirect effects on the perceived quality of care (PQC) by the service providers through consumer engagement. The fsQCA results derive that the relationship among conditions leading to patients’ perception of the quality of care in regard to mental healthcare service providers is complex and is best reflected as multiple and conjectural causation configurations.

The findings from this research contribute to the advancement of studies on patients’ experiences by empirically examining the unique dynamics of interaction between consumers (patients) and mental healthcare service providers, thereby enriching both the literature on social interactions and the understanding of the consumer–provider relationship.

The results of this study provide practical implications for mental healthcare service providers on how to combine the study variables to enhance the quality of care and satisfy more patients.

A significant research gap has ascertained the inter-relationship between PI, cohesion, privacy, engagement and PQC from the perspective of mental healthcare service providers. This research is one of the primary studies from a managerial and methodological standpoint. The study contributes by combining symmetric and asymmetric statistical tools in service marketing and healthcare research. Furthermore, the application of fsQCA helps to understand the interactions that might not be immediately obvious through traditional symmetric methods.

This study aims to propose a conceptual model indicating the impact of Industry 4.0 (I4.0) technologies on lean tools. Additionally, it prioritizes I4.0 technologies for the digital transformation of lean plants.

The authors conducted a questionnaire-based survey to capture the perception of 115 experts of manufacturing industries from Germany, India, Taiwan and China. The impact of I4.0 on lean tools, using analysis of variance (ANOVA). Further, the authors drew a prioritization map of I4.0 on the employment of lean tools in manufacturing, using the Best–Worst Method (BWM).

The findings indicate that cloud manufacturing, simulation, industrial internet of things, horizontal and vertical integration impact 100% of the lean tools, while both cyber-security, big data analytics impact 93% of the lean tools and advanced robotics impact 74% of the lean tools. On the other hand, it is observed that augmented reality and additive manufacturing will impact 21% and 14% of the lean tools, respectively.

The results of this study would help practitioners draw up a strategic plan and roadmap for implementing lean 4.0. The amalgamation of lean with I4.0 technologies in the right combination would enhance speed productivity and facilitate autonomous operations.

Studies exploring the influence of I4.0 on lean manufacturing lack comprehensiveness, testing and validation. Importantly, no studies in the recent past have explored mapping and prioritizing I4.0 technologies in the “lean” context. This study thereby attempts to establish a conceptual model, indicating the influence of I4.0 technologies on lean tools and presents the hierarchy of all digital technologies.

The purpose of this paper is to present a comprehensive and useful insight into the knowledge management (KM) in the supply chain (SC) research and provide gaps and future research implications. The literature review includes a metadata analysis of KM in SC research along a number of dimensions, the existing dimensions as well as dimensions which are previously not been presented within the current area to predict the future role of KM in SC.

The study is to examine the status of KM in SC within academic and industrial research in the last 14 years. The systematic literature review of KM in SC is based on 176 peer reviewed articles selected through a multi-stage process, from 1,063 studies during January 2001-June 2014.

An increase in the interest level of incorporating KM in SC is clearly visible while studying available literature. The statistics prove that research in the field of KM in SC is overwhelmingly empirically oriented. Another major finding shows that the survey model score highest among all the research method used in the discipline. In general, the research is more interpretive in nature and provides a frame for theoretical critique. The result indicates that there is a need to use of advanced data analysis techniques with traditional data analysis technique.

The review simply provides statistical summary which is easy to understand for practitioner and provide increased insights on the current state of research for the KM in SC.