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Due to increasing socio-ecological concerns, manufacturers are paying ample attention to the strategic decision-making for enhancing customer satisfaction considering Industry 4.0 requirements. Customers' preferences are being considered vital into the decisions related to sustainable supplier selection for building competitive gain. Thus, the objective of this study is to develop a real-case-based empirical approach for evaluating performance of suppliers based on customers' feedback, an area not explored much in literature. The novelty of current study lies in the development of an integrated supplier evaluation and selection model involving three phases: (1) identifying sustainable criteria according to Industry 4.0 requirement through customers' feedback, (2) calculating relative scores of criteria using Z-numbers and (3) determining evaluation weights of suppliers using fuzzy-TOPSIS. The contribution of the study lies in effective validation of the model by considering the case of a manufacturing firm, which aids the firm in evaluating performance of suppliers based on customers' socio-ecological expectations while considering reliable information provided by decision-makers (DMs).

The purpose of this paper is to develop a framework for the assessment of service quality of bus fleet services based on the service quality influencing factors. The paper also tries to evolve a quantitative measure for fleet service quality in the form of a fleet service quality index.

A graph theoretical approach is employed in this paper for bus fleet service quality assessment. Modelling of fleet service quality factors and their interrelations with due attention towards their structure is achieved through graph theory. A directed graph (digraph) of the service quality is developed, where its nodes represent factors influencing the quality while its edges show the degree of interrelationships. A matrix, which is equivalent to the digraph, is established that will generate a service quality function that will result in the development of a fleet service quality index (FSQI).

Attaining customer satisfaction through assurance of quality is the cornerstone of the existence and survival of any business organization, and bus fleet services are no exception to this. Several influential factors are there for the bus fleet service quality. This research paper has identified factors such as fleet management practices, operational characteristics, safety and reliability features, travel comfort, bus maintenance and environmental concerns that affect fleet service quality. Every factor is composed of distinct sub-factors. Furthermore, these factors are linked with one another. A higher value of the fleet service quality index indicates the adequate performance of the bus fleet service organization.

The methodology is useful for not only evaluating but also for comparison of service quality of different fleet agencies or organizations. The perceptions would be useful to the fleet service managers to create procedures and arrangements for improving the service quality.

The paper identifies various service quality factors of the bus fleet and an evaluation scheme for those factors has been developed. Based on these, a framework had been developed for the assessment of the service quality of different fleet service providers.

Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

This study aims to comprehend the smart city–built environment’s (SCBE) effect on citizens’ quality of life in the smart cities of a developing country such as India. This study also sought to investigate the moderating effect of technological anxiety on the association, as mentioned earlier.

This study’s hypotheses were tested using data from 494 sample respondents who lived in the top five smart cities of India using partial least square-based structural equation modeling.

This study validated five dimensions of an SCBE, including smart governance, smart economy, smart people, smart transportation, smart environment and smart living, and demonstrated a favorable connection between a SCBE quality of life. This study confirmed that technology anxiety strongly moderates the relationship between the built environment of smart cities and citizens’ quality of life.

This study is a pioneering effort that explores the relationship between SCBE and the quality of life of its residents and the moderating effect of technology anxiety on this relationship.

This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface.

The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects.

The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface.

Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces.

The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools.

Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration.

Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/

The Covid-19 virus badly affected working patterns in almost every sector. The purpose of this paper is to analytically substantiate how work and life integration impacts the exhaustion and work–life balance among employees of academic institutions and IT companies.

Current study is empirical in nature based on the survey of 500 respondents taken from academic (250) and IT companies (250) from Ahmedabad and Gandhinagar, Gujarat. Structural equation modelling (SEM) was used to test the hypothesis with the application of the software Smart-PLS. Two surveys were conducted to collect the data separately for academic institutions and IT organizations.

Findings revealed the facts that during Covid-19, the employee’s work and life integration affected the work–life balance and exhaustion in academic institutions highly. The relationship was positively significant. But, for IT employees, it was identified as non-significant.

The current study highlighted the issues which employees faced during Covid-19 severe spread while managing work and family; how it varied due to the nature of work performed by the employees, for example, academics being more exposed to transformation from offline to complete online mode posed more challenges to teaching staff. This study also disclosed the scenario created and how it was handled in the deadly phase.

This study presents the social contribution in understanding the importance of work and life balance and problems related to it, especially when everyone everywhere is scared of going out. The study provides insight into how it became difficult for employees to maintain their payroll successfully.

The current study contributes to the existing body of knowledge by testing statistically that the integration between work and life is important for work–life balance and prohibiting emotional exhaustion. The current paper extends the theoretical contribution by offering suggestions to companies on why to synchronize positive balance between work and life while keeping boundaries relatively strict between family and work to gain employee well-being and competitive advantages.

Cavitation plays a significant role in the performance of textured journal bearings. Furthermore, because of the usage of low-viscosity lubricants and the high working speed of machines, it is pertinent to consider the lubricant inertia while analyzing the operating characteristics of bearings. This paper aims to investigate the influence of lubricant inertia in the case of a spherically textured journal bearing, considering both protrusion and dimple texturing and implementing the mass-conserving (JFO) boundary conditions.

A novel modified Reynolds equation has been developed to accommodate the effects of lubricant inertia and cavitation. The cavitation is treated by using mass-conserving (Jakobsson−Floberg−Olsson [JFO]) boundary conditions. The governing equation is solved by the Gauss−Seidel method with successive over-relaxation. To enhance computational efficiency and expedite the convergence process, the progressive mesh densification (PMD) method has been integrated into the solution process.

The current study indicates that the JFO boundary conditions result in higher load-carrying capacity and lesser friction variables for heavily loaded bearings, whereas the flow coefficient is reduced due to the application of JFO boundary conditions. The lubricant inertia effect enhances the flow coefficients for lightly loaded and protrusion-textured bearings.

It is crucial to understand the combined effects of lubricant inertia and cavitation for the effective design of textured journal bearings. The findings from this work will help in designing textured journal bearings more effectively and accurately, particularly when low-viscosity oil is used.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0276/

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

The objective of this paper is to investigate how Industry 4.0 technologies can contribute to solve the problems of managing materials and medicines in the hospital supply chain, identifying opportunities for their adoption, evaluating their potential and impacts on this segment. This paper also plans to investigate the challenges involving change management, financial impacts and major changes in the process with the introduction of new technologies.

The research carried out a literature review using the CAPES Periodicals portal, which includes renowned scientific bases, like Scopus and Web of Science. The intention was to identify problems in the logistics of materials and medicines in hospital pharmacy. In addition, a second search was carried out in papers related to Industry 4.0 and the supply chain. Subsequently, it was listed how Industry 4.0 technologies could influence the management of the hospital supply chain, on materials and medicines.

The new technologies of Industry 4.0 identified in the research can contribute to the improvement of the hospital management supply chain, benefiting from a higher level of automation, control and security presented in the research. Thus, the article addresses a new perspective in the management of materials and medications in hospital pharmacy, adding value to the topic, as new technologies can provide more safety to patients, savings for hospital management, reducing waste and environmental impacts.

The automation of the supply chain, in the materials and medicines segment, in hospital environments, adopting Industry 4.0 techniques, will make it possible to differentiate hospital management, generating great added value, benefiting the entire chain. The combination of technologies such as IoT, BigData and artificial intelligence, applied to the management of materials and medicines, will allow real-time management; consumption and stock estimates; more agile and reliable decision-making process; greater economic efficiency, in addition to contributing to patient safety.