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This study aims to provide the first longitudinal examination of the relationship between affective, calculative, normative commitment and customer loyalty by using longitudinal panel survey data.

Repeated measures for 269 customers of a large financial services provider are employed. Two types of segmentation methods are compared: predefined classes and latent class models and predictive power of different models contrasted.

The results reveal that the impact that different dimensions of commitment have on share development varies across segments. A two-segment latent class model and a managerially relevant predefined two-segment customer model are identified. In addition, the results demonstrate the benefits of using panel survey data in models that are designed to study how loyalty develops over time.

This study illustrates the benefits of including both baseline level information and changes in the dimensions of commitment in models that try to understand how loyalty unfolds over time. It also demonstrates how managers can be misled by assuming that everyone will react to commitment improvement efforts similarly. This study also shows how different segmentation schemes can be employed and reveals that the most sophisticated ones are not necessarily the best.

This research provides the first examination of models for change in customer loyalty by employing survey panel data on the three-component model of customer commitment (affective, calculative, and normative) and considers alternative segmentation methods.

There is general agreement among researchers and practitioners that satisfaction is relative to competitive alternatives. Nonetheless, researchers and managers have not treated satisfaction as a relative construct. The result has been weak relationships between satisfaction and share of wallet in the literature, and challenges by managers as to whether satisfaction is a useful predictor of customer behavior and business outcomes. The purpose of this paper is to explore the best approach for linking satisfaction to share of wallet.

Using data from 79,543 consumers who provided 258,743 observations regarding the brands that they use (over 650 brands) covering 20 industries from 15 countries, various models such as the Wallet Allocation Rule (WAR), Zipf-AE, and Zipf-PM, truncated geometric model, generalization of the WAR and hierarchical regression models are compared to each other.

The results indicate that the relationship between satisfaction and share of wallet is primarily driven by the relative fulfillment customers perceive from the various brands that they use (as gauged by their relative ranked satisfaction level), and not the absolute level of satisfaction.

The findings provide practical insight into several easy-to-use approaches that researchers and managers can apply to improve the strength of the relationship between satisfaction and share of wallet.

This research provides support to the small number of studies that point to the superiority of using relative metrics, and encourages the adoption of relative satisfaction metrics by the academic community.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

Mobile marketing, the two- or multi-way communication and promotion of an offer between a firm and its customers using a mobile medium, device, platform, or technology, has made rapid strides in the past several years. Mobile marketing has entered its second phase or Mobile Marketing 2.0. The surpassing of desktop by mobile devices in digital media consumption, diffusion of wearable devices among customers, and an overall integration and interconnectedness of devices characterize this phase. Against this backdrop, we present a synthesis of the most recent literature in mobile marketing. We discuss three key advances in mobile marketing research relating to mobile targeting, personalization, and mobile-led cross-channel effects. We outline emerging industry trends in mobile marketing, including mobile app monetization, augmented reality, data and privacy, wearable devices, driverless vehicles, the Internet of Things, and artificial intelligence. Within each extant and emerging area, we delineate the future research opportunities in mobile marketing. Finally, we discuss the impact of mobile marketing on customer, firm, and societal outcomes.

The purpose of this paper is to assess how the study of consumer behavior can benefit from the presence of Big Data.

This paper offers a conceptual overview of potential opportunities and changes to the study of consumer behavior that Big Data will likely bring.

Big Data have the potential to further our understanding of each stage in the consumer decision-making process. While the field has traditionally moved forward using a priori theory followed by experimentation, it now seems that the nature of the feedback loop between theory and results may shift under the weight of Big Data.

A new data culture is now represented in marketing practice. The new group advocates inductive data mining and A/B testing rather than human intuition harnessed for deduction. The group brings with it interest in numerous secondary data sources. However, Big Data may be limited by poor quality, unrepresentativeness and volatility, among other problems.

Managers who need to understand consumer behavior will need a workforce with different skill sets than in the past, such as Big Data consumer analytics.

To the authors ' knowledge, this is one of the first articles to assess how the study of consumer behavior can evolve in the context of the Big Data revolution.

The objective of this research is to explore the relationship between brand experience and customer equity (value equity, brand equity, and relationship equity). We examine the impacts of different contact points’ experiences (media contact, physical environment contact, people contact, and product usage contact) and different dimensions of brand experience on customer equity. Further we investigate the possible moderating effects of different brand positioning and strategies – hedonic and utilitarian, on this relationship. The data which are collected via online survey includes 410 observations with brand experience and 83 without brand experience, 493 valid samples in total. We found that positive and strong brand experience is the key factor for building strong customer equity. Although the impacts of all four contact points’ brand experiences are significant, product usage contact has the most powerful influence on customer equity and its individual drivers. The results also indicate that the different brand positioning strategies do have moderating effects. For utilitarian brand, only brand experience at product usage contact point has significant impact on customer equity and its three drivers. For hedonic brand, all four contact points’ experiences have significant relationships with customer equity. Finally, the four experience dimensions (sensory, affective, intellectual, and behavioral) have different impacts on customer equity and its three drivers at different experience contact points.