The study aims to analyze behavioral intentions influenced by customer engagement, experience, and identification moderated by competitive choices in the granite sector of Pakistan. The study has been carried out through primary data analysis of cross-sectional approach in the transition to a sustainable economy. In total, 400 questionnaires were distributed, for which only 216 were filled and usable with a response rate of 54%—collected data from the production managers and units. In contrast, missed mine holders and labor analyzed the data in SPSS and AMOS to run various tests, i.e., reliability, correlation analysis, regression, moderation regression, and confirmatory factor analysis. The study findings indicate a positive and significant relationship and effect among the variables. The reviews might contain some biases. Therefore, this study recommended adopting a probability sampling technique for future studies. The study results in a positive manner indicating customer service involvement as a significant factor in behavioral intention despite competitive options.

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.