Businesses heavily depend on social media to engage with customers, utilizing various platforms for interaction, feedback, and promoting products. The influence of social media on customer trust, loyalty, and satisfaction is a prominent subject. This study seeks to comprehend how businesses leverage social media to attain these objectives, utilizing both qualitative and quantitative methods. The initial exploratory phase collected qualitative data from 24 business enterprises, employing grounded theory techniques such as open, axial, and selective coding to pinpoint the primary factors affecting customer trust, satisfaction, and loyalty. Building on the insights from the exploratory study, the research proposes a model and hypotheses. The subsequent confirmatory study employs a quantitative approach, collecting data from 300 respondents in Jordan and utilizing Structural Equation Modeling (SEM) for analysis. Results underscore the pivotal roles of personalization, user-generated content, communications, word-of-mouth, emotions, promotions, and customer support as social media directions in shaping customer trust, satisfaction, and loyalty. This research provides valuable insights into the dynamics of how social media shapes customer relations, offering guidance to businesses navigating this ever-evolving landscape.

Nowadays, companies offer product bundles with special discounts in order to sell more products. However, it is important to note that customers show different levels of loyalties to companies, and each segment of the market has unique features, which influences the customers’ buying patterns. The primary purpose of this paper is to propose a novel model for product bundling in e-commerce websites by using market segmentation variables and customer loyalty analysis. RFM model is employed to calculate customer loyalty. Subsequently, the customers are grouped based on their loyalty levels. Each group is then divided into different segments based on market segmentation variables. The product bundles are determined for each market segment via clustering algorithms. Apriori algorithm is also used to determine the association rules for each product bundle. Classification models are applied in order to determine which product bundle should be recommended to each customer. The results demonstrate that the silhouette coefficient, support, confidence, and accuracy values are higher when both customer loyalty level and market segmentation variables are used in product bundling. Accordingly, the proposed model increases the chance of success in direct marketing and recommending product bundles to customers.

The objective of this study was to examine the drivers of consumers’ attitudes towards mobile advertisement. It also sought the relationship between consumers’ attitudes towards mobile advertisement and their willingness to accept mobile advertising. Confirmatory factor analysis was used to assess the measurement model while structural equation was conducted to assess the goodness-fit of the overall model. The findings indicate that entertainment, credibility and personalization had positive effects on consumers’ attitudes toward mobile advertising. Furthermore, the results show that, consumers’ attitude determines their willingness to accept mobile advertising.

One of existing challenges in personalization of the web is increasing the efficiency of a web in meeting the users' requirements for the contents they require in an optimal state. All the information associated with the current user behavior following in web and data obtained from pervious users’ interaction in web can provide some necessary keys to recommend presentation of services, productions, and the required information of the users. This study aims at presenting a formal model based on colored Petri nets to identify the present user's interest, which is utilized to recommend the most appropriate pages ahead. In the proposed design, recommendation of the pages is considered with respect to information obtained from pervious users' profile as well as the current session of the present user. This model offers the updated proposed pages to the user by clicking on the web pages. Moreover, an example of web is modeled using CPN Tools. The results of the simulation show that this design improves the precision factor. We explain, through evaluation where the results of this method are more objective and the dynamic recommendations demonstrate that the results of the recommended method improve the precision criterion 15% more than the static method.