Search Results (5)

Recommendation systems are popular information systems that help consumers manage information overload. Whilst personality has been recognised as an important factor influencing consumers’ choice, it has not yet been fully exploited in recommendation systems. This study proposes a restaurant recommendation approach that integrates customer personality traits, opinions and preferences, extracted either directly from online review platforms or derived from electronic word of mouth (eWOM) text using information extraction techniques. The proposed method leverages the concept of venue personality grounded in personality–brand congruence theory, which posits that customers are more satisfied with brands whose personalities align with their own. A novel model is introduced that combines fine-tuned BERT embeddings with linguistic features to infer users’ personality traits from the text of their reviews. Customers’ preferences are identified using a custom named-entity recogniser, while their opinions are extracted through structural topic modelling. The overall framework integrates neural collaborative filtering (NCF) features with both directly observed and derived information from eWOM to train an extreme gradient boosting (XGBoost) regression model. The proposed approach is compared to baseline collaborative filtering methods and state-of-the-art neural network techniques commonly used in industry. Results across multiple performance metrics demonstrate that incorporating personality, preferences and opinions significantly improves recommendation performance. Full article

The extensive electronic word-of-mouth (eWOM) data generated by consumers encapsulates authentic product experience information. By leveraging advanced data analysis technologies, enterprises can extract sustainable consumer behavior preference knowledge, thereby supporting the optimization of their marketing and management strategies. However, existing data-driven product ranking processes predominantly focus on single-source eWOM data and rarely mine product insights from a multi-source perspective. Moreover, the quality of eWOM data cannot be overlooked. Consequently, this study uses automobile products as a case example and integrates rating eWOM data, complaint eWOM data, and safety test data to construct a multi-source data-driven personalized product ranking recommendation algorithm. Specifically, an evaluation index system is established for each of the three data types. To model information quality, these data are transformed into basic uncertain information (BUI), which incorporates scoring information and credibility metrics. The XLNet model is employed to convert complaint text data into scoring data, and three targeted credibility evaluation models are developed to assess the reliability of the three data types. Subsequently, BUI is aggregated using the BUI ordered weighted average (BUIOWA) aggregation operator. Based on this, a personalized product ranking method aligned with user preferences is proposed, offering consumers recommendation results that match their preferences. Finally, using automobile products as an illustrative example, this study elucidates the multi-source data-driven personalized product recommendation process and provides managerial implications for enterprises. Full article

Considering the expanding e-commerce in the social media landscape and the increasing importance of brand management in the online sphere, our primary goal was to comprehensively review existing research on consumer-based brand equity in digital brands. The current post-pandemic environment has seen a significant surge in digital presence, particularly on social networks and e-commerce platforms. Although the available literature provides an overview of brand equity in general, digital brands have taken center stage in consumer interactions on social media, becoming highly commercialized in virtual environments and, recently, gaining significant value in financial markets. However, there is still a lot to uncover regarding the research trajectory for these brands. Using the PRISMA protocol, a corpus of 258 articles was obtained from the Web of Science and Scopus databases, with Journal Impact Factor and CiteScore impact factors. The bibliometric analysis for mapping the production was performed using SciMat, VosViewer, and Biblio-metrix software. According to the results, we found that consumer-based brand equity in digital brands is strongly linked to online consumer behavior variables, particularly engagement, electronic word-of-mouth, communication effects (such as social media advertising), impacts on various metrics, and applications in specific contexts. Overall, our research shows that the brand equity of digital brands is studied similarly to non-digital brands. Still, their virtual origin and their exposure on social media have increased consumer appreciation for them. The main studies and trending topics were discussed, providing a foundation for a research agenda regarding new challenges and approaches of consumer-based brand equity in the digital market. Full article

This study explores the impact of artificial intelligence (AI) on customer perceptions and behavior in restaurants, airline companies, and hotel sectors within the hospitality industry of Iran. The primary objective is to analyze how AI affects customer trust, brand engagement, electronic word-of-mouth (eWOM), and tourists’ readiness to use AI technologies. Using a comparative analysis approach and surveys, this research tests hypotheses about the effects of artificial intelligence on various dimensions of customer interaction. The findings highlight significant relationships between the quality of artificial intelligence and customer engagement metrics, such as trust and brand loyalty, which are crucial for understanding and predicting customer behavior in response to technological advancements. This study lays the groundwork for theoretical assumptions about sustainability in these sectors in developing countries, providing a basis for future empirical research that could validate these assumptions and explore broader implications of AI integration in enhancing sustainable practices within the hospitality industry. Full article

The goal of this research was to build a model that evaluates the influence of affective commitment, high-sacrifice commitment, and satisfaction on the customers’ word-of-mouth concerning an online retailer. Two word-of-mouth dimensions were considered: volume and valence. A survey was administered to 282 respondents and structural equation modeling techniques were used to process the data and test the hypotheses. Our findings show that satisfaction and high-sacrifice commitment have an important impact on both word-of-mouth volume and valence, while affective commitment only influences word-of-mouth valence. This paper offers detailed explanations of these results in light of other theories and studies in the field. Full article