1,720,986 research outputs found
An introduction to socially responsible sustainable consumption: issues and challenges
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Negatively-valenced influencing behaviour: forms, triggers and impacts
No full textThis thesis was previously held under moratorium from 3rd October 2018 until 3rd October 2020.Influencing behaviour, as a form of Customer Engagement Behaviour (CEB), has the potential to impact other actors within a network and likewise the value and performance of firms in different ways, depending on its valence. However, despite its potentially detrimental effect, negatively-valenced influencing behaviour (NVIB) remains relatively poorly studied in terms of empirical work, specifically regarding its forms, triggers and impact. This thesis adopts mixed methods research, specifically, a sequential exploratory design so that the qualitative study provides findings that inform the quantitative study. The first study which is qualitative used netnography to explore how customers engage in NVIB and what triggers customers to engage in NVIB on review sites. The findings include six forms of NVIB classified as direct and indirect, based on the way customers use each form in their online reviews. The findings also contain five triggers of NVIB and indicate relationships between forms and triggers of NVIB.;The second study which is quantitative used three experiments to measure the impact of NVIB on other actors' attitude and behavioural intentions towards service providers using the forms conceptualised in the first study. The results from the three experiments showed the negative impact of NVIB on other actors' attitudes and behavioural intentions, with a relative strength of direct over indirect NVIB displayed. The thesis contributes to understanding NVIB by providing a typology of six distinct forms of NVIB and five triggers and also by showing that customers may directly or indirectly address other actors when engaging in NVIB; however, both direct and indirect forms of NVIB negatively impact other actors' attitudes and behavioural intentions. Finally, this thesis contributes to studies in this area with empirical results that show the impact of specific forms of NVIB on other actors' attitude and behavioural intentions towards service providers.Influencing behaviour, as a form of Customer Engagement Behaviour (CEB), has the potential to impact other actors within a network and likewise the value and performance of firms in different ways, depending on its valence. However, despite its potentially detrimental effect, negatively-valenced influencing behaviour (NVIB) remains relatively poorly studied in terms of empirical work, specifically regarding its forms, triggers and impact. This thesis adopts mixed methods research, specifically, a sequential exploratory design so that the qualitative study provides findings that inform the quantitative study. The first study which is qualitative used netnography to explore how customers engage in NVIB and what triggers customers to engage in NVIB on review sites. The findings include six forms of NVIB classified as direct and indirect, based on the way customers use each form in their online reviews. The findings also contain five triggers of NVIB and indicate relationships between forms and triggers of NVIB.;The second study which is quantitative used three experiments to measure the impact of NVIB on other actors' attitude and behavioural intentions towards service providers using the forms conceptualised in the first study. The results from the three experiments showed the negative impact of NVIB on other actors' attitudes and behavioural intentions, with a relative strength of direct over indirect NVIB displayed. The thesis contributes to understanding NVIB by providing a typology of six distinct forms of NVIB and five triggers and also by showing that customers may directly or indirectly address other actors when engaging in NVIB; however, both direct and indirect forms of NVIB negatively impact other actors' attitudes and behavioural intentions. Finally, this thesis contributes to studies in this area with empirical results that show the impact of specific forms of NVIB on other actors' attitude and behavioural intentions towards service providers
Innovation and Tradition: Customers and GenAI in Service Recovery
Full text linkThis study delves into the dynamics among customers and GenAI in services focusing on service failure recovery. It reveals that customers accept GenAI’s apologies and how such apologies resemble/differ from human apologies. It also offers a model of an apology that companies can use based on the severity of the situation. Finally, the study provides tactics for service providers to enhance GenAI-driven service recovery
COVID-19 vaccination : engagement behavior patterns and implications for public health service communication
Full text linkPurpose: COVID-19 vaccinations face a backdrop of widespread mistrust in their safety and effectiveness, specifically via social media platforms which constitute major barriers for the public health sector to manage COVID-19 (and future) pandemics. This study provides a more nuanced understanding of the public's engagement behavior toward COVID-19 vaccinations. Design/methodology/approach: Using Netnography, this study explores the public's interactions with vaccine communications by the WHO via Facebook. From WHO posts about the COVID-19 vaccination 23,726 public comments on Facebook were extracted and analyzed. Findings: Building on crisis communication, health and engagement literature, this paper identifies and conceptualizes seven patterns of engagement behavior toward the COVID-19 vaccination and develops the first framework of relationships between these patterns and the extant vaccine attitudes: vaccine acceptance, hesitancy and refusal. Practical implications: This paper helps policymakers identify and adapt interventions that increase vaccine confidence and tailor public health services communications accordingly. Originality/value: This research offers the first typology of patterns of engagement behavior toward COVID-19 vaccinations and develops a framework of relationships between these patterns and the existing understanding in health literature. Finally, the study provides data-driven communication recommendations to public health service organizations
Actor Engagement: Former Customers’ Role in Online Social Networks
No full textAlthough former customers can potentially be highly beneficial to firms in terms of influence value, the extant literature concentrates predominantly on current customers, while the role of former customers remains under-investigated. This research utilizes the conceptual lens of actor engagement (AE) to understand the role of former customers focusing on their influence on others in online social networks. The results of an online survey and two experimental studies demonstrate that former customers engage in actor influencing behavior (AIB) via e-WOM about products and services they no longer use, although their motives for doing so differ from those of current customers. Further, actors with small networks and strong social ties have the highest influence on other actors; followed by those with large networks and utilitarian ties. Practically, this research highlights the need to move beyond a focus on the dyadic firm–customer relationships and embrace network relationships among high influence actors
Beyond the Algorithm: Decoding Human-Machine Engagement
No full textThe rise of artificial intelligence (AI) technologies in service and marketing contexts has fundamentally altered how customers engage with businesses and each other. While current research has extensively explored AI's adoption and the characteristics of AI-driven tools, less attention has been paid to engagement with AI itself. The engagement literature has predominantly focused on how AI facilitates engagement with brands or services rather than examining the direct engagement of individuals with the AI itself as the primary object of interaction. Addressing this gap, this research introduces the concept of Human-Machine Engagement (HME), offering a typology and a nomological network to conceptualize, measure, and understand engagement with generative AI (GenAI) technologies
GenAI as a Service Failure Recovery Provider: A Linguistics Approach
No full textThe integration of AI is transforming the service industry; 90% of businesses reported faster complaint resolution of AI compared to human employees. While firms are investing in AI for service recovery, research on how GenAI can facilitate recovery is still in its early stages. The level of sophistication and uniqueness of GenAI compared to earlier AI models make it a powerful tool for problem-solving in service failure recovery. The current literature focuses more on human employees as recovery providers, while GenAI steps into a role that, within the conceptual confines of communication, has been restricted to humans. Therefore, exploring the role of GenAI as a recovery provider may bring with it the opportunity to develop new categories for interactions between people and technologies and reflect upon and rethink the boundaries of human communication.
This paper broadens the scope of inquiry into service recovery by investigating symbolic recovery facilitated by GenAI using a linguistics approach. The apology has been recognized as a fundamental symbolic recovery of service failure. Service research has focused primarily on whether an apology is present rather than how it is said. According to linguistics theories, how the apology is said expresses the apologizer's intention, which in turn influences the recipient's behavior. Therefore, building on service recovery, linguistics, and Speech Acts (SA) theories, this paper uses a combination of four studies to (a) explore GenAI’s apology strategies in severe and minor service failure situations, (b) examine their impact on customers and service providers, (c) capture human service providers’ evaluation of GenAI’s apologies and (d) examine the impact of GenAI vs. human employees.
This paper contributes to service recovery, linguistics, and SA theories by providing the first empirical proof of how GenAI differs from humans in apologizing as a recovery provider and offering its customer- and service-provider-related outcomes. Furthermore, this paper establishes a model of a favorable apology and tests its outcomes on both customers and service providers. Finally, the paper contributes with new knowledge about customers’ responses to human employees vs. GenAI as recovery providers. Informed by the results, service providers will find a detailed guide to enhance the efficacy of service recovery using GenAI
Human–machine engagement (HME) : conceptualization, typology of forms, antecedents, and consequences
Full text linkArtificial intelligence (AI) applications in customer-facing settings are growing rapidly. The general shift toward robot- and AI-powered services prompts a reshaping of customer engagement, bringing machines into engagement conceptualizations. In this paper, we build on service research around engagement and AI, incorporating computer science, and socio-technical systems perspective to conceptualize human-machine engagement (HME), offering a typology and nomological network of antecedents and consequences. Through three empirical studies, we develop a typology of four distinct forms of HME (informative, experimenting, praising, apprehensive), which differ in valence and intensity, underpinned by both emotional (excitement) and cognitive (concern, advocacy) drivers. We offer empirical evidence which reveals how these HME forms lead to different cognitive and personality-related outcomes for other users (perceived value of HME, perceived risk, affinity with HME) and service providers (willingness to implement in services, perceived value of HME). We also reveal how outcomes for service providers vary with the presence and absence of competitor pressure. Our findings broaden the scope of engagement research to include non-human actors and suggest both strategic and tactical guidance to service providers currently using and/or seeking to use generative AI (GenAI) in services alongside an agenda to direct future studies on HME
Negative customer engagement behaviour: the interplay of intensity and valence in online networks
Full text linkRecent marketing and service research highlights the detrimental impact of negative customer engagement behaviour (CEB) in online social networks. Nevertheless, the extant literature captures the impact of what customers say about service providers in their negative reviews and fails to provide any understanding of different intensity levels of negative engagement. This article marks the first attempt to provide a more nuanced view of negative CEB by investigating the impact of six forms of negatively valenced influencing behaviour (NVIB) using two online experiments. Our results provide new insights into intensity levels of NVIB and how they are moderated by positive reviews. Practically, this paper addresses one of the challenges for service providers in managing NVIBs, centred on understanding the heterogeneity of its forms. The results suggest that managers use semantic tools to detect the intensity levels of NVIB and to prioritise handling and/or mitigating the more intense NVIBs when they occur
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