Search results

This study aimed to examine consumers’ impulse purchases of luxury products in the metaverse, specifically by exploring how consumers’ motivational and emotional experiences affect virtual luxury product purchases in the metaverse.

An online survey was administered to a total of 230 users of various metaverse platforms in China. The data were analysed using partial least squares structural equation modelling (PLS-SEM) disjoint two-stage approach.

The findings indicated that motivational experience (goal importance and goal interest) and positive emotion (fantasy, feeling and fun) have positive effects on impulse buying of luxury products in the metaverse, but none for negative emotions (loneliness and isolation).

This study indicated that understanding and leveraging consumers’ motivational experiences and positive emotions can drive their impulse buying behaviour of luxury products in the metaverse, hence providing virtual and brand retailers with a testbed for their products before they launch in the physical market.

This study enriches our comprehension of consumers’ metaverse luxury purchases by delving into the impacts of motivational and emotional experiences on impulse buying behaviours.

The aim of this research is to explore how religiosity (RG) could influence the Islamic apparel brand personality (IABP) dimensions, and to determine the degree to which IABP, attitude (ATT), subjective norms (SN) and purchase intention (PI) are influenced by RG. In addition, this research attempts to investigate the significant relationship between IABP and the components of the theory of planned behavior in the apparel industry in Egypt.

This research adopts a quantitative research method to provide insights relating to relationships between variables. The research data were collected through a conducted survey of Muslim females in Egypt. A convenience nonprobability sampling technique for data collection was used. To achieve the research purposes, confirmatory factor analyses, reliability and validity tests and structural equation modeling were adopted.

The research results show that RG has a positive significant relationship with ATT, SN and PI of Islamic apparel. Moreover, it was that only ATT has a positive significant influence over the PI of Islamic apparel unlike SN and Perceived behavioral control (PBC). Results also found that there is a positive relationship between IABP with ATT and SN. However, the relationship between RG and IABP was not statistically supported.

The research provides practical implications for brand managers, designers and producers in the Islamic apparel sector on how to increase PIs by extending IABP as well as for Egyptian policymakers. The practical implications include the possible approaches that stakeholders of Islamic apparel brands need to address while promoting, and this will influence marketing strategies in general and branding specifically.

This study extends our understanding of consumers’ Islamic apparel purchasing intentions using TPB to determine its rationale. Unlike other studies, this study operated RG and IABP to assess their influence on Islamic apparel PI in Egypt.

This chapter draws from bibliometric data and secondary sources to explore the interrelation between sustainable development and organic agricultural practices. The study contends that the adoption of organic farming methods holds the potential to address multiple Sustainable Development Goals (SDGs), thereby contributing to the well-being of all living organisms. The analysis of current trends in organic agriculture research has revealed prevalent themes pursued by academics. Notably, themes such as biotechnology, biodegradation, soil conversion, soil restoration, environmental sustainability, health, alternative agriculture and community development emerge as significant and burgeoning areas of study within the field. Additionally, other themes have come to the forefront in the findings, including agricultural emissions, cultivation practices, environmental regulations, erosion control, agricultural policy and sustainable agriculture. These themes present vital areas for further investigation and exploration, indicating areas where more research is warranted. This qualitative piece through content analysis provides a deep insight on the fact that organic farming in relation to sustainability is a very under researched area. Researchers and practitioners can explore this area as a tool to achieve the goals of sustainability in diverse dimensions. The study suggests some under-researched areas as future research agendas which include supply chain and distribution management, market development and consumer behaviour in relation to organic farming, investigating and creating novel commercial strategies for organic farming, financial and investment mechanisms in the field of organic farming, technological developments in organic agriculture, analysing rules and regulations to see how they affect the development and sustainability of organic farming and social impact and stakeholder engagement for organic farming as tool to promote sustainability.

This study aims to investigate the neurocognitive status of people with different moods (depressed and non-depressed) both in a typical urban environment (TUE) and in an urban green environment (UGE).

The study was conducted with the participation of 210 individuals in a real environment. Heart rates of the participants were measured together with EEG and eye-tracking measurements while they were walking in a real urban environment (TUE and UGE). The participants were divided into depressed and non-depressed groups according to different moods. Movements within the city based on these two distinctions and different cognitive moods were investigated, together with the effects of the city plan on these people.

As a result, the green urban fabric was found to have a positive effect even on people with depression. Yet, it was found that the non-depressed group also spent more time in a green urban environment.

The study shows that urban planners and designers should assume an important role in the design of green spaces, which have a more intense visual and cognitive impact than structures.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The Indonesian Waqf Board and United Nation Development Program are developing green waqf in Indonesia. Development of green waqf in Indonesia as a response to save the earth from environmental damage and other social impacts. This paper aims to measure public perceptions about willingness to participate in making donations to the green waqf program in Indonesia.

Modifying the Theory of Reasoned Action framework, questionnaire data were collected from 311 Muslims in Indonesia. Data were analyzed using the partial least squares structural equation modeling method.

The findings show that attitude, subjective norm, product knowledge and trust influence the Muslims in Indonesia to donate to green waqf. In contrast, Islamic religiosity harms the intention to donate in green waqf but is insignificant.

This research was conducted using a quantitative approach with a limited sample of several communities, so the results cannot be generalized. Further investigation needs to be carried out by involving a more diverse sample to get better results. However, the results of this study can be used as an illustration of how Muslims behave in donating green waqf.

These results imply that to develop green waqf in Indonesia, especially in attracting waqf candidates willing to make donations, waqf institutions must carry out engineering to shape the attitude of prospective donors through various activities such as socialization and education of the green waqf program. Waqf institutions must also build public trust by involving public figures to campaign for the green waqf program. This will likely increase prospective donors’ active participation in donating their money to develop green waqf in Indonesia.

Waqf is a severe concern for the Indonesian Government, including waqf for the environment. To the bets of the authors’ knowledge, this paper is the first attempt to look at the behavior of the green waqf model in Indonesia. Thus, the acceleration of waqf development can be realized and is expected to impact the community significantly.

This paper presents the findings from the assessment of the determinants of cloud computing (CC) deployment by construction organisations. Using the technology-organisation-environment (TOE) framework, the study strives to improve construction organisations' project delivery and digital transformation by adopting beneficial technologies like CC.

This study adopted a post-positivism philosophical stance using a deductive approach with a questionnaire administered to construction organisations in South Africa. The data gathered were analysed using descriptive and inferential statistics. Also, the fusion of structural equation modelling (SEM) and machine learning (ML) regression models helped to gain a robust understanding of the key determinants of using CC.

The study found that the use of CC by construction organisations in South Africa is still slow. SEM indicated that this slow usage is influenced by six technology and environmental factors, namely (1) cost-effectiveness, (2) availability, (3) compatibility, (4) client demand, (5) competitors' pressure and (6) trust in cloud service providers. ML models developed affirmed that these variables have high predictive power. However, sensitivity analysis revealed that the availability of CC and CC's ancillary technologies and the pressure from competitors are the most important predictors of CC usage in construction organisations.

The paper offers a theoretical backdrop for future works on CC in construction, particularly in developing countries where such a study has not been explored.

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.

The purpose of this paper is to investigate the vulnerability of smallholder farmers to climate change in northwest Ethiopia.

To achieve this aim, data was collected from a survey of 352 households, which were stratified into three groups: Lay Gayint (138 or 39%), Tach Gayint (117 or 33%) and Simada district (97 or 28%). To gain a deeper understanding of the vulnerability of these households, two approaches were used: the livelihood vulnerability index (LVI), consisting of 32 indicators, and the socioeconomic vulnerability index (SeVI), containing 31 indicators. Furthermore, qualitative data was obtained through focus group discussions conducted in six randomly chosen groups from the three districts, which were used to supplement the findings.

Both methods indicate that Simada is the most vulnerable district, followed by Tach Gayint and Lay Gayint. According to the SeVI approach, Simada district showed the highest level of sensitivity and exposure to climate-related hazards, as well as the lowest score for adaptive capacity. However, using the LVI approach, Simada district was found to have the highest sensitivity to climate effects and exposure to climate-related hazards, along with a higher adaptive capacity than both Lay Gayint and Tach Gayint districts.

Although there are numerous studies available on the vulnerability of farmers to climate change, this particular study stands out by using and contrasting two approaches – the LVI and the SeVI – to assess the vulnerability of households in the study area. Previous research has indicated that no single approach is sufficient to evaluate climate change vulnerability, as each approach has its own strengths and limitations. The findings of this study have significant implications for policymakers and development practitioners, as they can use the results to identify the households that are most vulnerable to climate change. This will enable them to design adaptation options that are tailored to the specific needs of each community and that will effectively address the risks of current and future climate change.

The purpose of this paper is to develop the theory of environmentally responsible behavior by extending the theory of planned behavior to empirically examine waste reduction behaviors among young coastal tourists in Bangladesh.

Through review of literature, in-depth interviews and survey, the study is conducted. Initial survey collects 355 data, which is used for exploratory factor analysis to develop constructs and its measurement items. After preparing the formal survey questionnaires, another survey is conducted and collected 403 usable responses. The subsequent valid responses are used for confirmatory factor analysis through structural equation modeling, using Smart PLS 3.3.3.

The results of this study reveal that waste reduction behavior is significantly affected by waste reduction intention, and then subsequently waste reduction intention is significantly affected by environmental concern, climate change concern and healthy environmental norms of young coastal tourists in Bangladesh.

The findings of the study will benefit industry operators and policymakers by understanding the factors that are crucial for influencing young costal tourists’ environmentally responsible behaviors (i.e. waste reduction behaviors). Eventually, the findings may assist industry operators and policymakers to develop strategies to attract more young tourists to the coastal tourist destinations in Bangladesh.

The importance of environmentally responsible behavior has received a significant attention in present decade. Consistent with this phenomenon, this study uniquely develops the theory of environmentally responsible behavior and establishes the link between environmental concern, climate change concern, healthy environmental norms, waste reduction intention and waste reduction behavior in coastal tourism domain in Bangladesh.