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This study examines whether engagement with Sustainable Development Goal (SDG)-focused specialized training programs enable senior public officials (focal actor) to collectively deliver on public services that have a transformational societal impact over time. Further, the study explores the factors that impede and facilitate the delivery of such services. The authors do so by using service mechanics theorization and drawing on the lens of actor and collective engagement.

This study undertakes a longitudinal exploratory qualitative study design. SDG-focused training programs were delivered, as interventions, for two cohorts of senior public officials from Bangladesh in an Australian University in 2017 and 2019. In-depth interviews were conducted upon the training's completion and then after 8- and 12-month intervals to assess the short- and long-term impact respectively.

An empirical framework is proposed from the study findings. It shows that engagement – cognitive, emotional and behavioral – with SDG-focused specialized training programs enables focal actors (i.e. senior public officials) to engage other actors (other public officials, community members) in networks, facilitated the delivery of SDG-aligned public services. Such engagement results in a transformative impact that spans micro (individual), meso (organizational) and macro (societal) levels over time. Factors that impede and facilitate SDG-aligned delivery of public services are also identified.

Theoretically, the authors contribute to the literature that relates to actor and collective engagement, SDG-focused capacity-building training programs and service mechanics. Practically, this study informs organizations about the ways that they can effectively engage their senior employees with capacity-building training programs that focus on sustainability.

This study is one of the few that connects the interface between public service delivery for enacting societal changes and SDG-focused capacity-building training programs through service mechanics theorization and using the lens of actor and collective engagement.

This paper aims to explore the concept of “actor engagement” within the context of the sharing economy, a novel and dynamic business model. Specifically, it investigates the formation of actor engagement and its relationship with value creation within this business model.

Drawing on Storbacka et al. (2016)actor engagement framework and service-dominant logic service ecosystem model, unstructured data (text and images) from the Airbnb platform in seven countries and text- and image-mining techniques such as machine learning are used to measure the research variables and test the model by PLS-SEM.

The results indicate that affective engagement has a more significant impact on behavioural engagement than cognitive engagement for multidimensional actor engagement. Service providers’ engagement – directly, and through customer engagement – influences value creation for service providers (i.e. performance). Moreover, national-level moderator (i.e. economic, competitiveness, technology, social and political factors) plays a significant moderating role in our model.

This study encourages future research to explore how actor engagement leads to value creation for all actors on the different sharing economy platforms.

The findings provide practical insights for service providers to engage their customers and platform managers, especially in an international context, on managing their relationships with both customers and service providers in different countries.

This study advances the current literature on actor engagement and its role in value creation by providing a better understanding of the role of the national context in this process through unstructured data analysis.

Oppositional brand loyalty poses a challenge to the management of virtual communities. This study aims to categorize these loyalty behaviors into positive (willingness to pay a price premium and brand evangelism) and negative (schadenfreude and anti-brand actions) dimensions. It then explores how customer engagement and moral identity influence these dimensions in the context of brand competition.

Structural equation modeling was conducted to analyze the main and moderating effects, using survey data obtained from 498 valid responses out of a total of 636 responses from Xiaomi's virtual communities.

The results indicate that customer engagement significantly influences all four dimensions of oppositional brand loyalty. The relationship between customer engagement and brand evangelism is notably stronger among customers with a strong moral identity. Conversely, the effects of customer engagement on schadenfreude and anti-brand actions are attenuated for these customers.

Anchored in theories of brand tribalism, social identity and brand polarization, this study bifurcates oppositional brand loyalty into directions of preference and antagonism, empirically showcasing moral identity's moderating effect. It contributes to the literature on antagonistic loyalty and moral identity, offering strategic insights for companies to navigate schadenfreude and anti-brand actions in online communities.

This study aims to perform the bibliometric analysis of the customer engagement (CE) literature, highlights the major research themes and classifies the subdomains. The study also identifies antecedents and consequences, as well as dimension evolution, and suggests future research directions.

This study used a comprehensive bibliometric approach using Scopus data from 2002 to January 2024. Advanced analytical techniques, including bibliometric and cocitation analysis using R and bibexcel, were used. In addition, machine learning (ML)-based Latent Dirichlet Allocation (LDA) was used to extract latent themes.

This study reveals the domain’s past trend and present research scenario. The thematic analysis of CE is classified into three phases. Document cocitation analysis provided four broad clusters: conceptualization and operationalization, value creation through engagement, building relationships with brands and engagement-social media interface. The antecedents and consequences are categorized and presented along with the evolution of the multidimensional nature of CE.

This study adds to the literature in two key ways. First, the entire scholarly production has been compiled into one frame. Second, multiple methods were used to unravel citation, cocitation and textual data. Furthermore, ML-based LDA was used to extract latent themes from clusters and future research directions were proposed.

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.

Because brand addiction is one of the most intense forms of consumer–brand relationships, this type of behavior has recently attracted the attention of researchers in the field of consumer behavior management. The purpose of this paper is to investigate the antecedents and consequences of brand addiction among Iranian consumers.

This research used a cross-sectional study based on a questionnaire to collect data from a sample of 401 people in Iran. Structural equation modeling was used to test the hypotheses.

This study found that brand love and brand involvement significantly influence brand addiction. Brand addiction was also positively associated with contingent self-esteem (CSE), compulsive shopping and resilience to negative information. Additionally, the results revealed a positive and significant effect of brand involvement on brand love and a similar positive and significant effect of CSE on compulsive shopping.

By examining the antecedents and consequences of brand addiction in an Islamic country, this research offers valuable insights that can enrich the literature on brand addiction in consumer behavior and brand management.

This study proposes a conceptual model and aims (1) to understand how co-creation experience influences guest engagement, (2) to analyse the effect of satisfaction and affective image between co-creation experience and guest engagement, and (3) to analyse the effect of guest engagement on behavioural intentions.

A questionnaire-based online survey was conducted targeting U.S.-based hotel customers. Developed on Qualtrics and distributed via Mechanical Turk, the survey garnered 465 useable responses from individuals who had visited international chain hotels within the last year. Data analysis was performed using Structural Equation Modelling (SEM) in IBM SPSS Amos.

The findings revealed significant direct effects of co-creation experience on affective image and guest satisfaction, with guest satisfaction notably influencing guest engagement, and guest engagement directly influencing behavioural intention. However, both co-creation experience and affective image showed minimal direct impact on guest engagement. The study also highlighted the importance of indirect effects and total effects in understanding the dynamics between the constructs analysed.

The findings revealed significant direct effects of co-creation experience on affective image, with affective image notably influencing guest engagement, and guest engagement directly influencing behavioural intention. Although satisfaction had direct effects on guest engagement, co-creation experience showed minimal direct impact on guest satisfaction and on guest engagement.

This study advises managers to use co-creation primarily to enrich guest experiences and establish emotional connections rather than as a tool for directly enhancing engagement. It recommends that managers invest in strategies to enhance guest engagement beyond co-creation given the direct link between guest engagement and behavioural intentions, and the minimal direct impact between co-creation, guest engagement and satisfaction.

This study demonstrates that affective image is a pivotal mediator between co-creation experience and guest engagement. The findings provide valuable implications for hospitality practitioners in designing and managing co-creation experiences, emphasizing the importance of fostering a positive affective image in the value co-creation process to attain positive behavioural outcomes.

In banking services, how customers interact with their bank – whether it’s through the physical branch or online – plays a significant role in how they feel about the experience. It’s not just about performing the service; it’s also about building trust, keeping them committed and getting them engaged so that a long-lasting relationship is developed. While there’s abundant research about trust and commitment in banking, not many studies have looked at how customers see both the online and offline sides of banking, especially in credit unions. Credit unions emphasize proximity with members, so it’s important to understand how these different ways of interacting affect how much people trust the credit union and stay committed to it and how engaged they feel as members. This study aims to explore this issue.

A quantitative survey was conducted using partial least squares structural equation modeling (PLS-SEM). The survey was answered by 195 members of one of the most traditional credit unions in Brazil.

The results suggest that both face-to-face and digital channels’ dimensions impact trust, which in turn influences commitment. Engagement is influenced by both trust and commitment, providing inputs to the value co-creation process.

The research presents relevant contributions to academia by deepening the understanding of the role of different service channels in value co-creation and customer engagement. It also offers significant contributions to the cooperative, which can improve the member experience in interaction channels to consequently develop lasting relationships and stimulate the engagement of its members.