The goal of this research is to test the mediating effect of product symbolism on the relationship between conspicuous consumption and social identity formation in the branded clothing sector of young adults. Young adults between the ages of 20-35 who wear branded clothes in Sri Lanka were considered the target population. The data was collected through a survey to find answers to the identified research questions through a quantitative approach. The Snowball sampling method was used as the sampling method for this study. The Sobel Test was carried out using an online calculator to measure the statistical significance of the mediation. The results showed that the effect of conspicuous consumption on social identity formation mediated through product symbolism could be judged as statistically significant. The findings can help with market segmentation and brand positioning. Knowing these symbolic value attachments to branded clothes, marketers can create their marketing mix strategies to provide solutions for self-image enhancement and sustainable competitive advantage from both consumer and company points of view.

With the rapid advancement of blockchain technology, SMEs face an opportunity to leverage decentralized ledger systems to address longstanding challenges related to financial transparency. This study aims to assess the implications of blockchain adoption for SMEs operating in the Jordanian context, focusing on its potential to improve accountability, trust, and efficiency in financial operations. Drawing on quantitative research methods, this paper examines the current state of financial transparency using a structural equation modeling approach of 215 surveys. The findings indicated that there is a positive impact of block-chain technology on enhancing financial transparency. The findings of this research contribute to both academic understanding and practical implications for policymakers, regulators, and SMEs in Jordan seeking to enhance financial transparency through blockchain technology. By shedding light on the positive impact of blockchain on financial transparency in the Jordanian SME sector, this paper aims to inform strategic decision-making and stimulate further research in this emerging field. Ultimately, it underscores the transformative potential of block-chain technology in promoting accountability, trust, and eco-nomic development among SMEs in Jordan and beyond.

The global investment landscape has undergone a paradigm shift, focusing on Environmental, Social, and Governance (ESG) factors as the major determinants of financial sustainability in investment decisions worldwide. This study uses predictive modeling to analyze the complex link between ESG variables and investment decisions. Focusing on three key sectors: IT, FMCG, and BFS, the study adopts a predictive modeling approach, recognizing the distinct characteristics and challenges within each sector. The information depends on the data obtained from different places like ESG details, and previous financial performance pointers – EBITDA, EPS, ROE, and P/E for 2018 to 2022, inclusive of general investor behavior. We can do this by working with relevant sources of data together with machine learning methods which show what happens in the Indian market in terms of ESG influencing the market thus leading to sustainable investment outcomes. This article seeks to comprehend why investors may favor sustainability as opposed to their conventional monetary units.

This study was, therefore, set to achieve the following objective: to explore the impact of social media on consumers’ buying decisions. The current research was conducted on online consumers through luxury perfume brands' websites. The phenomenon of online sales has recently spread in Saudi Arabia, where the authorized number of those stores reached 1,800 sites at the end of 2021. The target population was online purchasing consumers from luxury perfume sellers. The appropriate sample size in unlimited populations is 385 responses. The research hypotheses were tested using the SEM (Structural Equation Modeling) method, which allows for the evaluation of the degree of dependence of consumers buying decisions on social media marketing. The results of the study demonstrated that social media marketing influences the consumer purchasing decision process. The outcome can be credited to the effectiveness of marketing efforts in the chosen retail via social media.

This study aims at exploring the effect of four elements of an integrated marketing mix. The mix consists of both elements of the 4Ps and SIVA marketing models. These elements are product-solution, promotion-information, place-access, and price-value. A questionnaire was used to collect the required data from a sample of retailing market customers in Saudi Arabia. The total number of the questionnaires used in data analysis was 378. The study found that product-solution and place-access from customers’ perspective had significant effects on customer retention. On the other hand, price-value had a negative significant effect on customer retention, while promotion-information had no effect on customer retention. Hence, companies are called for considering products as solutions, promotion as a source of information for customers, place as an access point for such a solution, and price must be appropriate to the value that the customer gets.

Slow fashion is a new movement in the textile industry, where slow production mode and more ethical business processes are highly considered. This movement is an alternative to buying fast fashion productsto achieve a sustainable pattern. The theory of planned behavior also includes attitudes, subjective norms, and perceived behavior control, which are commonly used to analyze the patterns of green attitudinal variables through other additional principles, namelythe willingness to pay a premium, consumer effectiveness, and environmental knowledge. Therefore, this study analyzed factors influencing purchase intention of slow-fashion products. In this analysis, a randomized questionnaire was implemented and distributed to 140 Generation Z people in West Java Province, Indonesia. Structural equation modelingwas also used to test the fit model and path analysis of attitudes mediating green products knowledge on the intensity of buying slow-fashion products. The results showed that the three main variables of TPB and other influential/significant expanding principles were observed, except consumers' perceived effectiveness did not affect purchase intention. The limitations also prioritized the need for more experimental loci capable of being developed at different points. Moreover, the results obtained were beneficial for both academic and managerial purposes. This proved that green product purchasing behavior analysis needs to be academically improved, specifically for slow-fashion in developing countries. Managerial suggestions also increased green knowledge of fashion products through descriptive analysis. These suggestions enhanced consumers' understanding of the effective reduction of textile waste by purchasing fashion products.

The paper aimed to examine the impact of corporate governance on the financial performance of commercial banks in Jordan. The variables used to measure corporate governance were the board of directors' size, independent members of the board of directors, and the number of audit committee members, while those used to measure financial performance were return on investment, return on equity and earnings per share. The study used a quantitative approach based on the data of 12 commercial banks in Jordan during the period 2005-2022. The panel data were analyzed using the EViews software based on the ordinary least squares time series technique. The paper found the effect of all corporate governance variables on both return on investment and return on equity. However, it indicated that the board of directors' size and the independent members of the board of directors had an impact on earnings per share. This study highlighted corporate governance variables in one of the significant sectors of developing economies. Moreover, it recommended the need to review the principles used in selecting members of the audit committee for commercial banks in Jordan, due to their importance in developing long-term financial performance.

Spectral methods are among the most powerful approaches to recovering global structure in network data, but the vast majority of existing theory is developed for crisp graphs, in which vertices and ties observed are unambiguous. Both the vertices and edges are uncertain in terms of existence, strength, and reliability in many network-science contexts, such as social-media interaction data (friendship networks etc), trust networks, recommendation systems and partially observed relational databases. This research introduces a mathematically principled spectral theory for undirected fuzzy graphs, which is based on vertex-normalized fuzzy adjacency matrix and the corresponding Laplacian operators. The construction has symmetry, obeys the fuzzy constraint μ_ij ≤ min(σ_i, σ_j) and when all vertices belong to one membership class reduces down to classical weighted-graph adjacency and Laplacian. Under this paradigm several structural outcomes are proven: the fuzzy Laplacian is positive semidefinite, its nullity matches the number of connected components of the support graph, and the second Laplacian eigenvalue measures fuzzy algebraic connectivity, along with explicit upper and lower spectral bounds. A cut-based inequality and perturbation theorem are subsequently obtained to characterize community separability, as well as robustness against membership noise. The theory is exemplified on a six-node fuzzy network, and then the new method is applied to the well-known Zachary karate club benchmark after equitable fuzzification of vertices and edges. In the empirical study, we present that with this method the canonical split is recovered with 94.12 % accuracy, a fuzzy modularity of 0.3645 is produced and bridge-like actors are identified based on Perron fuzzy centrality score along with very high stability under multiplicative perturbations of edge memberships. The paper thus provides a rigorous spectral toolkit for uncertainty-aware network analysis, filling an important bridge between fuzzy mathematics and modern data and network science.

EEG is an important source of information about the activity of the brain and finds broad applications in brain-computer interface and cognitive state analysis. This study focuses on the classification of eye states using EEG signals recorded from multiple scalp electrodes. Several machine learning methods were used to differentiate between eye open and eye closed conditions using EEG recordings. The models were tested using various classification measures to have a complete measure of predictive power. The K-Nearest Neighbors model is the highest performing in terms of classification with an accuracy of 0.9609 and ROC-AUC of 0.9927. CatBoost, XGBoost, and Random Forest were also found to be good predictors by ensemble methods. To enhance the transparency of the model, explainable artificial intelligence (XAI) models, including SHAP analysis, Partial Dependence Plot (PDP) and Individual Conditional Expectation Plot (ICE) plots were used to analyze features contribution and model performance. The results of the interpretability show that the signals of certain EEG electrodes, especially in frontal and occipital brain areas are important in the process of classifying the eye state. The results indicate that machine learning with explainable algorithms can be successfully used to assist EEG-based eye state detection in addition to providing insightful information on the decision-making of the model.

It is essential to early detect skin cancer, especially melanoma since it significantly affects patients' survival. However, an experienced dermatologist still has difficulty distinguishing between a healthy lesion and a tumor; there are fine distinctions between benign and malignant tumors. In this research, we have developed a combined deep learning system, which uses Convolutional Neural Networks (CNNs) in combination with Vision Transformers (ViTs), to develop an automated diagnostic tool for detecting skin cancer based upon skin images. In developing this hybrid model, we utilized EfficientNet-B4 as a local feature extractor and a Vision Transformer as a global feature extractor. To combine these two feature extractors, we employed a special fusion module. This module used concatenation to merge the feature sets from each branch into a single layer and then processed them through a multi-layer perception. We were able to train and test the model using the ISIC 2020 data set, which contains 33,126 skin images, with successively improved training methodologies using a technique called 5-fold cross-validation. On the test set, the proposed hybrid model had an accuracy of 95.4%, a sensitivity of 90.7%, a specificity of 95.1%, and an AUC-ROC of 0.982. The above results show that the hybrid CNN-Transformer design performed better than the previous state-of-the-art EfficientNet-B4 + Attention model (accuracy of 92.1%) and had significant increases in both sensitivity (+1.8%) and specificity (+1.0%). These findings indicate that a hybrid CNN-Transformer design can provide a hopeful means of assisting physicians in diagnosing skin cancer, thus potentially improving physician decision making.