The integration of AI in DM is reshaping strategies to promote sustainability-oriented outcomes. AI-powered capabilities, including PA and PS, offer opportunities for firms to improve the MP while addressing the environmental responsibilities. This research examines the straight impacts of AIA on advertising presentation and examines the part of PA, PS, and SDM as key mechanisms through which AI enhances sustainable DM outcomes. A structural method was developed and tested using SPSS 26.0 and SmartPLS-SEM 4.0 based on data gathered from 312 marketing professionals. AIA was treated as the primary construct, hypothesized to positively influence PA and PS, which subsequently enhanced the SDM. The structural method was assessed to test the significance of direct relations and assess the explanatory power of the constructs. The analysis indicates that the proposed hypotheses were well supported. AIA significantly improved PA (β = 0.78), PS (β = 0.63), and SDM, enhancing overall marketing outcomes. The results emphasize the central role of SDM in leveraging AI insights to enhance campaign effectiveness, customer engagement, and brand loyalty in sustainability-focused marketing. This research demonstrates that AIA is strategically leveraged to improve MP while advancing environmental responsibility. The proposed method offers practical guidance for managers and policymakers aiming to integrate AI in sustainable DM, balancing ecological imperatives with competitive growth.

In today's digitally enabled world, banks use ChatGPT to handle customer inquiries, expedite service encounters, and provide intelligent, human-like responses; thus, ChatGPT has grown in popularity within the banking sector. The purpose of this study is to investigate the key factors of ChatGPT that influence consumer banking decision-making at Jordanian commercial banks. To accomplish the study's goals, the researcher employed a descriptive-analytical approach. The study sample consists of 445 customers with valid accounts at commercial banks in Amman city. The total number of valid and completed questionnaires was 419 and included in the final analysis. The reported results demonstrate a significant impact of ChatGPT factors that include credibility, informativeness, interaction, content suitability, perceived trustworthiness, and personalization on consumers' banking decision-making. The results can help banks develop a digital plan to enhance consumer awareness and boost benefits, which allows them to overcome challenges when utilizing AI for transactions. ChatGPT banking services are still in their early phases in Jordan. Few empirical studies have examined actual user behavior, and this study may give valuable insights to scholars and practitioners.

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.