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The present work investigated the effect of corporate social responsibility and top management support on employee ecological behaviour (EEB) with the mediating role of green culture and green commitment. Social identity theory (SIT) was used to describe the association between green culture, green commitment and EEB. Further, a conceptual model that summarises the interaction between perceived corporate social responsibility, top management support, green commitment, green culture and the adoption of ecological behaviour was developed.

The paper opted for a quantitative design using convenience sampling by collecting the data through a structured questionnaire gathered from 308 academics working in five Malaysian higher education institutions.

Corporate social responsibility and top management support positively influence green culture and commitment. Moreover, green commitment positively influenced EEB and fully mediated the relationship between corporate social responsibility and EEB and between top management support and EEB.

The academic staff of universities was the target population of this research. Nevertheless, universities have a diverse population with complex activities that can affect the implementation of a sustainable workplace within the campus. Future research should also examine non-academic staff, including administrative, technical and operational staff, due to different employees' perceptions.

As far as the authors know, this is the first study to assign the mediator role to green culture in a relationship between top management support and EEB amongst academic staff in the Malaysian context. Future research should consider other intervening variables that influence adopting ecological behaviour.

The purpose of this study is the hydrothermal analysis of the natural convection phenomenon within the heat exchanger containing nanofluids using the lattice Boltzmann method (LBM).

The thermal conductivity as well as dynamic viscosity of the CuO–water nanofluid is estimated using the Koo-Kleinstreuer-Li model. The LBM has been used with unique modifications to make it flexible with the curved boundaries. The local as well as total entropy generation assessment, local Nusselt variation, as well as heatline visualization are used.

The solid volume percentage of the CuO–water nanofluid, a range of Rayleigh numbers (Ra) and thermal settings of internal operational fins and bodies are all factors that have been thoroughly researched to determine their effects on entropy production, heat transfer efficiency and nanofluid flow.

The originality of this work is using a novel numerical method (i.e. curved boundary LBM) as well as the local/volumetric second law analysis for the application of heat exchanger hydrothermal analysis.

Studies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.

The authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.

The results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.

This study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.

Prior work underscores the important role of customer advocacy for brands. The purpose of this study is to explore the critical role customers can play as brand heroes. The authors developed and validated a measurement scale composed of properties that are derived from distinct brand hero motivational mechanisms.

The authors conducted one exploratory pilot, using semi-structured interviews, with industry and academic experts, and employed three main studies across varying brands and market settings.

This study explores and empirically demonstrates how the brand hero scale (BHS) is related to, yet distinct from, existing scales of opinion leaders, market mavens, attachment and customer advocacy. The six-item BHS demonstrates convergent, discriminant, nomological and predictive validity across several different brand contexts.

This research extends the extant body of work by identifying and defining brand heroes, developing and validating a parsimonious BHS, and demonstrating how its predictive validity extends both to a range of key advocacy and loyalty customer behaviors.

The study provides provocative insights for marketing researchers and brand managers and ascertains the important role heroes may play for brands in terms of strong customer advocacy and loyalty behaviors.

Building on the theory of meaning, this study shows that identifying and working with brand heroes is of great managerial importance and offers critical avenues for future research.

Assistive technology has been developed to assist the visually impaired individuals in their social interactions. Specifically designed to enhance communication skills, facilitate social engagement and improve the overall quality of life, conversational assistive technologies include speech recognition APIs, text-to-speech APIs and various communication tools that are real. Enable real-time interaction. Using natural language processing (NLP) and machine learning algorithms, the technology analyzes spoken language and provides appropriate responses, offering an immersive experience through voice commands, audio feedback and vibration alerts.

These technologies have demonstrated their ability to promote self-confidence and self-reliance in visually impaired individuals during social interactions. Moreover, they promise to improve social competence and foster better relationships. In short, assistive technology in conversation stands as a promising tool that empowers the visually impaired individuals, elevating the quality of their social engagement.

The main benefit of assistive communication technology is that it will help visually impaired people overcome communication barriers in social contexts. This technology helps them communicate effectively with acquaintances, family, co-workers and even strangers in public places. By enabling smoother and more natural communication, it works to reduce feelings of isolation and increase overall quality of life.

Research findings include successful activity recognition, aligning with activities on which the VGG-16 model was trained, such as hugging, shaking hands, talking, walking, waving and more. The originality of this study lies in its approach to address the challenges faced by the visually impaired individuals in their social interactions through modern technology. Research adds to the body of knowledge in the area of assistive technologies, which contribute to the empowerment and social inclusion of the visually impaired individuals.

The purpose of this study is to identify the critical readiness factors (CRFs) that mainly affect the implementation of Lean Six Sigma (LSS) in an organization and their interactions, and to develop a model that allows the management to assess the Lean Implementation Readiness Level.

The methodology is separated into two main parts: the literature review and the assessment model development. In the literature review, the main CRFs and their interactions for LSS implementation in Scopus Data Base were identified. The second part of the methodology is the model development. It was built on a stepwise framework that considers the relations among the CRFs and their importance. Moreover, it was used fuzzy-based linguistic variables given by the experts working in the company to consider the actual performance rating of each CRF. The model has been validated in the healthcare sector in nine hospitals.

From the model application, it is possible to note that the most frequent level among the nine hospitals interviewed is “Average Ready”. Also, the most extreme level of readiness occurred ones while the most extreme level of not readiness never occurred. Results show that in 78% of the cases, there would have been a high probability of implementation failure. Also, it was possible to identify for each hospital if the CRFs are good, if they are weak and need to change or if another factor needs to be improved before it and what this factor is.

This work proposes a new methodology that eliminates the negative aspects and limitations of the total interpretive structural modeling methodology and the fuzzy logic approach currently applied to evaluate the LSS readiness of a company. The present methodology lies in the fact that it provides a solution not only by defining the weak CRFs but also by giving an indication of priority as it identifies the weak antecedent factors that inhibit the preparedness of the depending factors.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

This study aims to develop and test an original conceptual framework that examines the role of various factors borrowed from three theories (i.e. Institutional Theory, Resource-Based View and Diffusion of Innovation) in adopting Human Resource Analytics (HRA).

A new conceptual framework (research model) is developed based on previous research and coherent theoretical arguments. Its factors are classified using the Technology–Organization–Environment (TOE) framework. Research hypotheses are tested using primary data collected from 152 managers of Greek organizations. Empirical data are analyzed using the “Structural Equation Modelling” (SEM) technique.

The technological and organizational context proved extremely important in enhancing Organizational Analytics Maturity (OAM) and HRA adoption, while the environmental context did not. Relative advantage and top management support were found to significantly impact the adoption of HRA, while Information Technology (IT) infrastructure, human resource capabilities and top management support are crucial for increasing OAM. Overall, the latter is the most important factor in enhancing HRA adoption.

This study contributes to the limited published research on HRA adoption while at the same time it can be used as a guideline for future research. The novel findings offer insights into the factors impacting OAM and HRA adoption.

This study examines the effect of occupational stress on turnover intention of employees working in the banking industry. The authors examine the mediating effects of service climate and emotional regulations of the employees in the relationship between occupational stress and intention to leave the organization.

This study followed stratified sampling technique for data collection from employees of ten commercial banks based on the banks' financial performance of top 5 and bottom 5 out of 27 banks. Data were collected at 2 stages, first from 465 employees for occupational stressors and second from 408 employees among the participants in the first stage for turnover intention, service climate and emotional regulation. Harman’s one-factor test was conducted to examine the common method bias. Confirmatory factor analysis (CFA), regression analysis and Preacher and Hayes Process Macro approach were used to examine mediation effect.

Three factors, namely workload (WL), role ambiguity and growth opportunity expectations were identified as the occupational stressors in the banking industry, predicting a positive relation of overall occupational stress to the intention to turnover. Service climate and the employees’ emotion regulation ability mediate the relationship between stress and turnover intention. Results also revealed no significant role of control variables in predicting occupational stress and turnover intention.

This study implies that the WL, role ambiguity and growth opportunity expectations of the employees cause stress in employees which may lead to have turnover intention. In order to get success in competitive environment, managers of banking industry can address stressors by enhancing service climate and formulating policies and programs to strengthen the emotion regulation which is evidence to strengthen the reciprocity approach of social exchange theory in employees’ commitment.

This study contributes to the social exchange theory and attempts to fulfill the gaps in empirical research on personnel psychology, human capital\ and organization management in developing countries.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.