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In structural fire engineering, the design of columns is done either by prescriptive approaches or by empirical equations derived from experimental research. Performance-based design is the emerging methodology for designing structures under fire, which is case-specific. There is a need to develop design equations from first principles to design/find the residual strength of the column at elevated temperatures. The present study aims to develop equations from stress block parameters to find the residual strength of reinforced concrete (RC) columns subjected to elevated temperatures.

The stress-strain variation across the cross-section of the RC column is determined at elevated temperatures. Based on the updated stress distribution diagram, stress block parameters are derived for various depths of neutral axis (NA) and different temperatures. Using updated stress block parameters, Pu-Mu interaction curves are generated for elevated temperatures. The results are verified against conventional methods and experimental results.

The load-carrying capacity calculated from the proposed methodology is analogous to the experimental results. The methodology can be utilized to estimate the residual strength of RC columns subjected to elevated temperatures.

The work done here attempts to develop the equations to estimate the residual strength of the column. The work involves calculating the strength of columns subjected to fire curves.

This study seeks to investigate the ability of blockchain technology (BCT) to increase circular economy (CE) practices in the electronics industry, emphasising India and Taiwan.

The study adopts a mixed-methods approach. Initial qualitative semi-structured interviews examined how BCT could inform CE practice. The qualitative aspects were followed by the Analytical Hierarchy Process (AHP), integrating qualitative and quantitative approaches, and fuzzy-set Qualitative Comparative Analysis (fsQCA), to analyse data from 391 industry experts surveyed.

The results show that the BCT has great potential to promote CE processes by improving the aspects of security, transparency and traceability. BCT adoption is driven by a conducive regulatory regime, stakeholder collaboration, and the availability of required technology.

By identifying key drivers and requisite requirements for successful BCT adoption in CE practices, this research offers critical guidance for policymakers, practitioners and researchers. It adds to the wider conversation about how emerging technologies can support sustainability and efficiency in industry.

This study contributes to the literature by providing a new lens to study BCT and CE practices intersectionality, particularly in the context of the emerging electronics industry and in countries such as India and Taiwan. Unlike previous research that studied either BCT or CE independently, this study uniquely illustrates how the principles underpinning these concepts, when implemented together, can positively impact sustainability outcomes within a resource-intensive industry notorious for generating highly significant waste streams.

This paper aims to analyse the fiscal performance of non-special category states of India.

This study applied a global Malmquist productivity index (GMPI) to measure the state fiscal performance, using eight fiscal indicators to measure the fiscal stance of 17 non-special category states of India over the period 2000–2020.

The findings reveal significant inter-state variations in all the fiscal indicators over the period. Goa, Haryana, Kerala, Chhattisgarh, Odisha and Jharkhand have been the best fiscal performers in recent years. The states Chhattisgarh, Jharkhand and Odisha present an interesting case; their fiscal performance index (productivity) score increased more than 1% during the sample period as compared to other states. This improvement is primarily associated with revenue efficiency, expenditure quality and size of capital outlay.

The conventional Malmquist productivity index uses a geometric mean form of two contemporaneous measures of productivity change and this index faces a potential linear programming infeasibility problem in measuring cross-period directional distance functions. Therefore, productivity growth measured using indexes should be interpreted with caution. Therefore, the results obtained based on the GMPI might be missing the sensitive check.

This study is a pioneering initiative in India, constructing a fiscal performance index to measure and rank the non-special category states of the country based on GMPI, thus addressing limitations in previous studies on index formation techniques and selecting fiscal indicators.

This study aims to propose mindfulness as a key factor in merchants’ adoption of mobile payments through the trust and perceived value pathways in an unorganized sector.

Data were collected physically via a structured questionnaire from a random sample of 350 merchants from the unorganized retail sector in India. Partial least squares-based structural equation modeling was used to analyze the data.

Mindfulness plays a key role in the formation of trust and perceived value, which further influences merchants’ intentions to adopt mobile payments. Furthermore, moderating effects of technology anxiety and skepticism were also observed.

To the best of the authors’ knowledge, this study is the first to illustrate mindfulness in merchants of the unorganized sector in adopting mobile payments to influence the adoption intentions of mobile payments in emerging markets.

The purpose of this paper is to provide an incisive commentary on the role of replication in advancing knowledge accumulation in organizational science. For research to be credible, it must be replicable. However, replication studies are seldom published in high-quality management research journals. Drawing on insights from other disciplines, this essay offers a critical analysis of the replication process. The central conclusion of the essay is that replication should be firmly integrated into the scientific process within management and related fields. Doing so will not only advance robust and reliable theories but also yield valuable lessons for managerial practice and inform sound policymaking.

To structure the argument that replication ensures the validation of theories, refinement of methodologies and greater applicability of research findings, this essay uses Simon Sinek’s Why, How and What framework to highlight the essential role of replication research in advancing management studies. By addressing why replication is critical, how it can be effectively conducted and what the authors can do to promote replication research in organizational studies, the essay underscores its importance in building a reliable and credible foundation for knowledge.

Replication serves as a cornerstone of academic rigor, promoting the cumulative growth of knowledge. This essay outlines the essential steps for conducting robust replications of prior research through three approaches: literal, operational and constructive replication. Furthermore, it proposes actionable strategies for individual, doctoral programs and institutions (i.e. journals and conferences) to enhance transparency and accountability in management research by leveraging replication as a critical tool.

The originality of this essay lies in addressing the underrepresentation of replication research in management, despite its critical role in ensuring scientific rigor. Drawing insights from different disciplines, it provides a framework for conducting replication studies, emphasizing their value in validating theories and bridging the gap between research and practice. By proposing actionable steps for fostering a culture of replication, the essay advocates for increased transparency and accountability in management research. Its main contribution lies in highlighting the necessity of replication as a foundation for robust theory-building, practical insights and evidence-based policymaking, enhancing the credibility of the field.

To attain a competitive edge, it is essential to realize the significant critical success factors (CSFs) that contribute to the adoption of Quality 4.0 (Q4.0) in manufacturing organizations. Therefore, the study aimed to analyze CSFs for Q4.0 implementation in manufacturing small and medium-sized enterprises (SMEs) using multi-criteria decision-making (MCDM) tool.

The present study begins with a systematic literature review of past studies about Q4.0 implementation in manufacturing, followed by the identification of CSFs. Further, a case study was conducted wherein 42 CSFs identified were grouped into five dimensions. Best–worst method is a MCDM tool applied as a solution methodology for the analysis of CSFs based on expert opinion and priority order of CSFs attained.

The priority order of CSFs is obtained. Based on the findings, significant CSFs are “Data prediction and Analytics,” “Organizational culture towards Quality 4.0” and “Machine to Machine communication.”

The shifting market dynamics incorporate Q4.0 inclusion for realizing zero defects and high traceability in automotive SMEs. The present study offers implications for industry managers and practitioners by delivering insights on how Q4.0 could be serving automotive systems and CSFs that industry authorities need to pay attention to effectively adopt Q4.0 in the current quality systems. The study will facilitate industry practitioners to meticulously examine CSFs for Q4.0 toward the improvement of SME performance.

The identification of CSFs for Q4.0 adoption in manufacturing SMEs, along with the prioritization of CFSs using the MCDM tool, is the original contribution by the authors.

To achieve changing customer demands, organizations are striving hard to embrace cutting-edge technologies facilitating a high level of customization. Industry 4.0 (I4.0) implementation aids in handling big data that could help generate customized products. Lean six sigma (LSS) depends on data analysis to execute complex problems. Hence, the present study aims to empirically examine the key operational characteristics of LSS and I4.0 integration such as principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 technologies and performance measures.

To stay competitive in the market and quickly respond to market demands, industries need to go ahead with digital transformation. I4.0 enables building intelligent factories by creating smart manufacturing systems comprising machines, operators and information and communication technologies through the complete value chain. This study utilizes an online survey on Operational Excellence professionals (Lean/Six Sigma), Managers/Consultants, Managing Directors/Executive Directors, Specialists/Analysts/Engineers, CEO/COO/CIO, SVP/VP/AVP, Industry 4.0 professionals and others working in the field of I4.0 and LSS. In total, 83 respondents participated in the study.

Based on the responses received, reliability, exploratory factor analysis and non-response bias analysis were carried out to understand the biasness of the responses. Further, the top five operational characteristics were reported for LSS and I4.0 integration.

One of the limitations of the study is the sample size. Since I4.0 is a new concept and its integration with LSS is not yet explored; it was difficult to achieve a large sample size.

Organizations can utilize the study findings to realize the top principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 tools and performance measures with respect to LSS and I4.0 integration. Moreover, these operational characteristics will help to assess the organization's readiness before and after the implementation of this integration.

The authors' original contribution is the empirical investigation of operational characteristics responsible for I4.0 and LSS integration.

The adoption of metaverse in manufacturing holds significant benefits, but there are several barriers to its seamless integration. This paper aims to identify such barriers and prioritize them in a manner that allows industrial leaders to strategize for smooth adoption.

In this study, we applied two-stage methods, first the identification and validation of barriers through an empirical study applied to Exploratory Factor Analysis (EFA). A purposive sampling technique and snowball sampling facilitated data collection from these expert sources. Through snowball sampling, additional contacts working in the metaverse field were reached, resulting in 235 possible respondents; the survey yielded 104 completed responses. Thereafter, the best-worst method (BWM) was used to measure and rank the barriers.

The study results show that the two most critical barriers are “Lack of data security and privacy” and “Lack of integration compatibility with existing systems.” Such findings inform industry leaders of specific recommendations for structural changes, training programs, necessary technological investments and collaborative efforts to overcome these barriers.

This work adds significantly to academic discussion by prioritizing barriers towards integrating metaverse technology in manufacturing. In addition, this strategic methodology aids in critical appraisal and ranking of barriers for successful adoption. This study also identifies key barriers but acknowledges that other unexamined factors might be lurking in the background, such as virtual economy, financial risks and cross-border legal issues.

The study’s conclusions cannot be generalized to the other sectors, thus indicating the necessity of carrying out a comparative multi-sector study in the future.

To the best of the authors’ knowledge, the study on systematic prioritization of barriers to adopting metaverse technology in manufacturing is the original contribution of the authors.

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.

The research on financial satisfaction has risen substantially in recent years due to its importance in personal financial planning and individuals’ subjective well-being. Hence, this study aims to map the existing literature on financial satisfaction to present the current state of knowledge and identify substantial gaps.

The present review uses 109 articles published between 1985 and March 2024 and retrieved from the Scopus database. The study deploys a systematic literature review (SLR), bibliometric analysis and content analysis to attain the objectives. Through bibliometric analysis, the present study highlights the most influential authors, journals, countries and affiliations, augmenting the literature on financial satisfaction. Moreover, the study presents the detailed antecedents and consequences of financial satisfaction through content analysis.

The study outlines that most studies in the financial satisfaction area revolve around its antecedents and consequences. The review details multiple antecedents affecting financial satisfaction, such as socioeconomic, psychological, social, personality, religious, financial literacy, financial behavior and technological factors. The prominent consequences of financial satisfaction include subjective well-being, life satisfaction, happiness, emotional and financial well-being, relationship quality, work engagement and sustainable growth.

The present research is an inaugural SLR that comprehensively maps the existing intellectual structure on financial satisfaction. In addition, it offers future research directions for further developments on the subject.