Search results

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.

How to identify the critical success factors (CSFs) of public health emergencies (PHEs) is of great practical significance to carry out a scientific and effective risk assessment. The purpose of this paper is to address this issue.

In this paper, the authors propose a new approach to identify the CSFs by hesitant fuzzy linguistic set and a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. First, a larger group of experts are clustered into three groups according to similarity degree. Then, the weight of each cluster is determined by the maximum consensus method, and the overall direct influence matrix is obtained by clustering with hesitant fuzzy linguistic weighted geometric (HFLWG) operators. Finally, the overall direct influence matrix is transformed into the crisp direct impact matrix by the score function, and 11 CSFs of PHEs are identified by using the extended DEMATEL method.

In addition, an example of PHEs shows that the approach has good identification applicability. The approach can be used to solve the problems of fuzziness and subjectivity in linguistic assessments, and it can be applied to identify the customer service framework with the linguistic assessments process in emergency management.

This paper extends the above DEMATEL method to study in the hesitant fuzzy linguistic context. This proposed hybrid approach has a wider application in the high-risk area where disasters frequently occur.

This research study aims to introduce a maturity model based on capability maturity model integration (CMMI) that can assess the digital manufacturing maturity level of manufacturing companies.

A CMMI model for the manufacturing industry is designed to assess the digitalisation level of manufacturing industries. The model is developed exclusively for the process area “organisational process focus” (OPF), and the digitalisation level is quantified using fuzzy logic by employing a case study approach.

The CMMI is successfully employed to assess the digitalisation level of a manufacturing organisation using the fuzzy logic approach. The triangular fuzzy number of the Fuzzy CMMI Measure Index (FCMI) is obtained as (6.08, 7.33, 8.52). The transformation of FCMI into linguistic terms discloses the digitalisation level of the manufacturing organisation as “Capability Maturity Level 4” (CML 4).

The authors tested the suitability of CMMI in the manufacturing sector. The operational concept introduced in this research sets forth a unique framework to quantify the digitalisation level of manufacturing industries.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

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 examine the effect of perceived value of recommended product on consumer’s e-loyalty, based on the proposition of expectation confirmation theory. Vendors’ reputation is tested as the mediator in the perceived value of recommended product and e-loyalty relationship, whereas shopping enjoyment is predicted as the moderator that conditions the perceived value of recommended product and e-loyalty relationship through vendors reputation.

Data were collected via an online survey platform and through a QR code. Partial least squares analysis, confirmatory factor analysis and structural equation modeling were used to verify the research proposed model.

The findings revealed that the perceived value of recommended product had a significant positive effect on E-loyalty; in addition, the perceived value of the recommended product and e-loyalty link was partly explained by e-shopper’s confidence in vendor reputation. Therefore, the study established that the direct and indirect relationship between the perceived value of the recommended product and e-loyalty was sensitive and profound to shopping enjoyment.

This study has established that the perceived value of a recommended product can result in consumer loyalty. This has successively provided the e-shop manager and other stakeholders with novel perspectives about why it is necessary to understand consumers’ pre- and postacquisition behavior before recommending certain products to the consumer.

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.