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 increasing amount of IoT devices increases the size of network traffic data, causing an increase in the incidence of security breaches in IoT networks. Cybercriminals have developed malware to compromise the security of sensitive data, among other cyber threats. In the presence of inadequate and robust security mechanisms, sensitive data is prone to vulnerability. Hence, protecting data in the IoT environment is becoming a mandatory task. Various approaches have addressed malware detection using network data features. However, there is still room for improvement in developing superior techniques and utilizing more comprehensive datasets. This paper presents a novel lightweight ensemble voting classifier to detect malware traffic by deploying the best possible network data. The merits of the correlation coefficient and Opposition-Based Crow Search Algorithm (OCS) have been leveraged to compute the best possible features. Another advantage of this proposed experiment is its focus on a dataset tailored to malware traffic features. This focus enables highly accurate malware detection. After feature selection using OCS, the proposed malware classifier is trained and validated with both 5-fold and 10-fold cross-validation techniques. The tested results confirm that the presented malware classifier performs best using a minimal feature set, which is highly advantageous for IoT networks due to resource constraints.

The purpose of this study is to explore how various online marketing tools, including Content Marketing, Email Marketing, Social Media Marketing, and SEO, affect customers' decisions to purchase Internet services. The study utilized a survey distributed through simple random sampling, with 410 questionnaires given to Zain Telecom customers in Zarqa city. After excluding incomplete questionnaires, 385 were analyzed using multiple regressions, descriptive analysis, and reliability tests. Results showed that all independent variables had a significant impact on customers' purchasing decisions of Internet services, with Email Marketing having the strongest relationship. The study recommends further investment in Email Marketing strategies to improve customer retention and acquisition.