Abstract
Purpose
Digital platform users not only consume but also produce communication related to their experiences. Although service research has explored users' motivations to communicate and focused on outcomes such as electronic word-of-mouth (eWOM), it remains largely unexplored how users iteratively interact with communication artifacts and potentially create value for themselves, other users and service providers. The purpose of this paper is, thus, to introduce communicative affordances as a framework to advance user-created communication (UCC) in service.
Design/methodology/approach
Drawing from the literature in communication, service research and interactive marketing, an affordance perspective on UCC in service is introduced.
Findings
Three UCC affordances for the service context are presented – interactivity, visibility and anonymity – opportunities and challenges for service providers associated with these affordances are discussed and, finally, affordance-specific research questions and general recommendations for future research are offered.
Research limitations/implications
By conceptualizing UCC in service from an affordances perspective, this paper moves beyond the traditional sender–receiver communication framework and emphasizes opportunities and challenges for service research and practice.
Practical implications
Instead of focusing separately on specific technologies or user behaviors, it is recommended that service managers adopt a holistic perspective of user goals and motivations, use experiences and platform design.
Originality/value
By conceptualizing UCC as an augmenting, dialogical process concerning users’ experiences, and by introducing communicative affordances as a framework to advance UCC in service, an in-depth understanding of the diverse and ever-evolving landscape of communication in service is offered.
Keywords
Citation
Ciuchita, R., Medberg, G., Penttinen, V., Lutz, C. and Heinonen, K. (2022), "Affordances advancing user-created communication (UCC) in service: interactivity, visibility and anonymity", Journal of Service Management, Vol. 33 No. 4/5, pp. 688-704. https://doi.org/10.1108/JOSM-10-2021-0407
Publisher
:Emerald Publishing Limited
Copyright © 2022, Robert Ciuchita, Gustav Medberg, Valeria Penttinen, Christoph Lutz and Kristina Heinonen
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.
Introduction
Significant advancements in information and communication technology have led to the proliferation of digital platforms, allowing their users to connect with each other in unprecedented ways, and surrounding them with customer experiences from various sources (Heller et al., 2021; Huh, 2020). For instance, as of 2022, TripAdvisor's community of travelers has published over one billion reviews and opinions that support the decisions of millions of travelers each year (TripAdvisor, 2022). Concurrently, YouTube and Amazon have become the most important channels for discovering new products and services (Statista, 2021). Consequently, the communication created by digital platform users strongly influences other customers' perceptions and behaviors (Babić Rosario et al., 2020). For firms, this communication represents a valuable source of customer insights (De Luca et al., 2021): it has, like customer co-creation practices and initiatives (Gustafsson et al., 2012), the potential to create value for service users and service providers.
Traditionally, firms send professionally created and managed content to their customers and other stakeholders (Colicev et al., 2019). However, with the expansion of digital platforms, users started developing their own content and broadcasting it online, leading to an increase in communication created by users (Hollebeek and Macky, 2019). This kind of communication involves different types of actors (e.g. other customers, employees and chatbots) (Chung et al., 2020), assumes different communication artifacts (e.g. posts and ephemeral videos) (Wakefield and Wakefield, 2018) and is shared through various technologies (e.g. mobile social media applications) (Larivière et al., 2013), activities (e.g. posting and replying) (Muntinga et al., 2011) and formats (e.g. text and video) (Grewal et al., 2021).
It is against this background that research on communication in the service literature has been developing (Finne and Grönroos, 2017). Studies have examined service users' motivations to create communication on digital platforms (primarily social media) (Smith et al., 2012), frequently from a uses-and-gratifications perspective (Shao, 2009), and have focused on outcomes such as electronic word-of-mouth (eWOM) (Bacile et al., 2020). However, to understand the complex communication among users, studying users and their attitudes and the technology and its features in isolation is insufficient.
This research explains how users, aiming to potentially create value for themselves, other users and service providers, interact with communication artifacts to exchange information. In doing so, we offer an in-depth understanding of the diverse and ever-evolving landscape of communication in service. First, we conceptualize user-created communication (UCC) as an augmenting, dialogical process concerning user experiences. Then, drawing from the communication literature, we introduce communicative affordances (Evans et al., 2017) as a relational framework to advance UCC in service. We spotlight three affordances particularly relevant in a service context: interactivity, visibility and anonymity. Finally, we highlight promising ways forward for future research on the topic – for example, the increasing importance of artificial intelligence (AI)-based technologies (e.g. chatbots) in service encounters.
Conceptual background
Communication in service research
Service research acknowledges the customer as the creator and consumer of communication about offerings, brands and experiences (Beh et al., 2020). However, it also distinguishes other actors in the communication landscape: from service provider representatives – which can be human (Lechermeier et al., 2020) or nonhuman (e.g. AI conversational agents; Van Pinxteren et al., 2020) – to independent parties, such as other customers (Jacobson et al., 2020). While the focus of research has traditionally been on human actors, recent service research has increasingly addressed nonhuman communication (Zhao et al., 2020).
Service research has also recognized user communication on digital platforms, including social media (Jacobson et al., 2020), e-commerce websites (Diwanji and Cortese, 2020), email (Lechermeier et al., 2020), virtual communities (Bacile et al., 2020) and crowdsourcing platforms (Lang et al., 2022). Such communication typically revolves around customers' purchases and consumption experiences in the form of eWOM (Babić Rosario et al., 2020) and communication between different users (Heinonen, 2011). Generally, eWOM reflects customer experiences with service providers (e.g. staying at an Airbnb) as outcomes (Zhao et al., 2020). Meanwhile, communication with other service users shapes customers' perceptions and experiences (Betzing et al., 2020).
Moreover, service research has primarily focused on user communication in textual format (e.g. Mathwick and Mosteller, 2017), paying less attention to verbal and nonverbal communication (Diwanji and Cortese, 2020). However, with the rise of digital media and various features on digital platforms (Rangaswamy et al., 2020), service users have been frequently exchanging images, video and audio recordings instead of plain text (Grewal et al., 2021). Service users make use of interactive platform features to contribute to online conversations with various reactions and animations, such as likes, emoticon functions, shares or comments (Jacobson et al., 2020).
Service research hints at the role of technology in communication, highlighting how technological features allow service users to create, share, modify and access different types of communication (Betzing et al., 2020). For instance, technology plays an important role in how customers engage in activities related to reviews (Mariani et al., 2019): customers can use their smartphones to share their experiences with a service provider through a livestream (e.g. on Instagram) or a recorded video (e.g. on YouTube).
To summarize, research on communication in service has so far examined in isolation who is communicating, where and what they are communicating and how they are communicating.
Introducing user-created communication (UCC)
In communication research, communication represents an inherently social process and can be defined as the “sharing of elements of behavior, or modes of life, by the existence of sets of rules” (Cherry, 1966, p. 6). In human communication, these rules are often based on language, and communication happens through spoken and written words as well as nonverbal means (e.g. gestures and symbols). Marketing research (e.g. Heinonen, 2011; Smith et al., 2012) has examined user-generated content (UGC) as the means through which consumers share information, connect with each other and express themselves online (e.g. on social media). However, the element of reciprocity is central to understanding communication (Lewis, 2015): the created content must be received and acted upon to count as communication. Though it is the object around which sociality can occur (Smith et al., 2012), UGC focuses on developing and disseminating content. Thus, while it may be the starting point of communication, UGC does not emphasize its potential for reciprocal value.
Similarly, communication in service research is largely considered an informational interaction (i.e. persuasive message-making) or communicational interaction (i.e. informing and listening), leaving its dialogical mode (i.e. learning) less explored (Ballantyne and Varey, 2006). Such dialogical interaction is multidirectional, altruistic, open-ended and discovery-oriented (Ballantyne and Varey, 2006). Traditionally, communication in this dialogical mode occurs between the customer and provider (i.e. face-to-face), and technology, if considered, has a mediating role (Spence, 2019). However, over the past two decades, communication has shifted from communicating via technology to communicating with technology (Sundar, 2020), with technological advancements equipping digital platforms with more and more advanced social features that allow users to create communication artifacts, paving the way for an enhanced communication mode between users (i.e. augmenting). This augmenting mode involves extending the scope, range and speed of users' interactions with diverse communication artifacts to facilitate reciprocal, value-creating outcomes for users (e.g. accomplishing user goals) (Sundar, 2020). Inspired by these developments and aiming to advance communication in service, we define User-Created Communication (UCC) as an augmenting, dialogical process concerning user experiences. Next, we offer a framework for UCC based on the communicative affordances literature, which is an ideal perspective since communicative affordances explain how the complex coevolution of users and technology alters communication outcomes (Schrock, 2015).
An affordances perspective on UCC in service
The affordance(s) concept was originally developed to describe the properties of an environment in relation to an animal: “The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or ill” (Gibson, 1977, p. 127). The reciprocal nature of the concept is apparent in this definition. An affordance is not simply an object/environment and its properties or a living organism/subject and its perceptions – it exists at their intersection. The idea of affordances was adopted in design research as the “fundamental properties that determine just how the thing could be used. A chair affords (is for) support and therefore affords sitting” (Norman, 1988, p. 9). Since affordances invite or trigger people to intuitively engage in certain actions, the design aspect is important. For example, a well-designed button that makes the user want to click it spontaneously has a clickability affordance.
Research on affordances has emerged in recent decades, especially with the increasing prevalence of digital technologies, going beyond design and human–computer interaction to disciplines including media and communication (Bucher and Helmond, 2017), sociology (MacKenzie et al., 2017), science and technology studies (Davis and Chouinard, 2017), information systems (Mettler and Wulf, 2019) and management (Leonardi and Vaast, 2017). Thus, affordances research is multidisciplinary, with the potential for cross-disciplinary stimulation and fertilization. However, service research has only recently begun to adopt the affordances perspective (e.g. Azzari et al., 2021; Vink and Koskela-Huotari, 2021).
The affordances literature, with its vague conceptual boundaries and a plethora of typologies, is complex. In this article, we rely on the communication literature and Evans et al.'s (2017) well-established conceptualization of affordances, which has three threshold criteria for identifying an affordance: (1) it cannot be an object itself or a feature (e.g. a smartphone or a search window are not affordances); (2) it cannot be an outcome (e.g. knowledge-sharing as an outcome of affordances is not an affordance itself) and (3) it must have variation (e.g. ubiquity is not an affordance). A concept that fails to adhere to at least one of these criteria is not an affordance.
To apply affordances for UCC in service, we adopt Schrock's (2015) definition of communicative affordances as the interactions between the subjective perceptions of utility and the objective qualities of the technology that influence users' communicative practices. UCC affordances are dynamic since they emerge at the intersection of users, an artifact and the outcomes of such communication (Bucher and Helmond, 2017). Particularly, the intersection of users and artifacts constitutes the users’ experience, the intersection of users and outcomes constitutes the goals and motivations and the intersection of the artifact and outcomes constitutes the design (see Figure 1). Appendix illustrates how this framework can be employed in a service setting.
Next, we elaborate three key UCC affordances for service research – interactivity, visibility and anonymity – that comply with the affordance criteria (Evans et al., 2017). We also discuss the opportunities and challenges for service providers. The selection criteria for these three affordances included parsimony (given space constraints, we opted for a small number of affordances to give sufficient space for each), breadth (each affordance should apply across different digital technologies), prominence in the communication and affordances literature (each affordance is established) and, above all, relevance to the service context. All of these affordances substantially shape service-relevant outcomes.
Interactivity
Interactivity is defined as “a communication that offers individuals active control and allows them to communicate both reciprocally and synchronously” (Liu, 2003, p. 208). It refers to establishing opportunities for communication anywhere and anytime so that users can feel engaged in two-sided communication (Labrecque, 2014). Notably, Beh et al. (2020) discussed “conversationality” as an affordance that allows information exchange between users, which we consider to be only a part of interactivity because it neglects the reciprocity of UCC beyond information exchange.
Interactivity presents service providers with opportunities, mainly by using employees and/or conversational agents to respond to UCC reactively or proactively without geographical and time constraints (Chung et al., 2020). For instance, interactivity allows providers to respond to customers who communicate their experiences in public channels (e.g. review platforms) with appreciation messages and/or apologies (Liu et al., 2017). Such an iterative exchange, by developing customer understanding and satisfying customer needs, strengthens providers' connections with their customers (Ni and Sun, 2018). In turn, interactivity can improve customer satisfaction, increase purchase intentions and boost customer loyalty (Sanchez et al., 2020).
However, interactivity also poses challenges. To respond to rising UCC quickly and efficiently service providers can use AI communication agents on their owned media, such as websites and social media accounts (Van Pinxteren et al., 2020). Though these agents can be helpful, they mainly react to direct customer inquiries rather than proactively interact with the UCC on third-party digital platforms. Some customers may also find communicating with AI agents uncomfortable, and since not all inquiries can be addressed by automated agents, customers can get frustrated (Luo et al., 2019). Finally, monitoring and responding to UCC outside the service providers' control – such as on paid (e.g. influencer marketing) and earned (e.g. review websites) platforms – presents volume and resource challenges.
Visibility
Visibility refers to whether communication between users can be seen by other users and the ease of finding this communication (Evans et al., 2017). The visibility of communication has increased drastically with the advent of new technologies, such as social media and sharing platforms (Sutherland and Jarrahi, 2018).
Visibility offers several opportunities for service providers. The high visibility of social media allows service providers to engage with users at a lower cost and superior efficiency than traditional communication technologies (Leeflang et al., 2014). This is especially relevant for small- and medium-sized service providers with limited resources (Chatterjee and Kumar Kar, 2020). Furthermore, publicly displayed UCC allows service providers to monitor brand mentions and user sentiments (Misopoulos et al., 2014). Ideally, any negative communication can be addressed publicly and directly by service providers (Zhao et al., 2020). Additionally, insights from visible UCC can stimulate service innovation and assist future decision-making (Jeong et al., 2019).
The visibility of UCC also creates challenges for service providers. Particularly, visibility might empower users more than service providers (Haji, 2014) – negative UCC that may reach large audiences can harm service providers (Babić Rosario et al., 2020; Rust et al., 2021). Negative UCC, in large, closed discussion groups on social media platforms is especially problematic because of its potential reach and the inability of service providers to access these groups and explain their service failures (Taylor et al., 2020).
Anonymity
Anonymity refers to the degree to which a user perceives the source of a message as unknown or unspecified (Evans et al., 2017). Digital platforms represent varying degrees of anonymity: some require users to disclose their identities (e.g. Facebook), while others (e.g. Reddit) do not. Even if anonymity is possible, some users may reveal their identities intentionally (e.g. by verifying accounts) or unintentionally (e.g. by adding self-disclosing multimedia, such as audio or video).
Anonymity, while underrepresented in service research, offers opportunities for service providers. For example, perceived anonymity can increase satisfaction with a service complaint (Beh et al., 2020). Studies in other fields have focused on communicative outcomes when digital platforms offer varying levels of anonymity. For instance, when the anonymity of online hotel reviews is not ensured, users are less likely to complain (Dyussembayeva et al., 2020). For service providers, anonymity can thus entail more honest, direct and helpful feedback, overcoming social desirability. In organizational settings, as Wagenknecht et al. (2018) suggested, anonymity may be welcomed in corporate debates since it separates the person from the communication. However, it may also lead to more polarizing discussions or even incite the use of foul language.
For service providers, affording anonymity may come with challenges. High anonymity may enable unintended and negative outcomes, such as cyberbullying, fake reviews and misinformation. Restaurant reviewers who can post anonymous online reviews are likelier to provide lower ratings and express more negative emotions, which may lead to negativity bias: the more a user is exposed to negative anonymous communication, the more likely they are to also leave negative anonymous communication (Deng et al., 2021). Perceived anonymity combined with frequent social media use can also lead to cyberbullying (Lowry et al., 2016). This can translate into brand trolling, resulting in negative consequences for service providers (Demsar et al., 2021). Anonymity can also enable nonhuman actors (e.g. social bots) to disseminate misleading communication (Liu, 2019).
Discussion
In this article, we discussed how service research has approached the separate facets of communication among users. We introduced UCC as an augmenting, dialogical process reflecting the interaction of users, artifacts and outcomes of communicative encounters. The UCC affordances framework offers an integrative conceptualization of UCC by moving away from the independent exploration of users' communication behaviors and attitudes, the adoption and use of different technologies and formats (artifacts) and the outcomes of communicative behavior. We, then, presented three key UCC affordances for service: interactivity, visibility and anonymity. By drawing on UCC examples, we developed future research questions related to each UCC affordance (Table 1). These questions reflect underresearched areas in the service literature related to the UCC affordances, with a specific focus on connecting service and communication research.
While the suggested UCC affordances reflect the state of the art in the communication literature (Evans et al., 2017), it is by no means an exhaustive typology. In addition to our affordance-specific research agenda in Table 1, we, thus, present four more general lines of future inquiry.
First, we encourage service researchers to identify other UCC service–relevant affordances based on Evans et al.’s (2017) three criteria for identifying an affordance (i.e. an affordance is not a feature, not an outcome and must have variation). In doing so, we encourage the identification and conceptualization of potential service affordances beyond UCC (e.g. De Luca et al., 2021). For example, Cabiddu et al. (2014) suggested engagement as an affordance; future research must explore whether and how it complies with the three criteria for identifying affordances.
Second, although we introduced the three UCC affordances in isolation, multiple affordances may be present simultaneously. How do multiple affordances, each with different variability, interact and enable UCC simultaneously? The variability of each UCC affordance matters because different users interact with the same features of an artifact to achieve different outcomes (Evans et al., 2017). The outcomes may differ depending on the study approach. Affordances have been studied quantitatively with surveys, as well as with qualitative and ethnographic methods, such as case studies and netnography (Beh et al., 2020; Cabiddu et al., 2014; McKenna, 2020). We, further, recommend configurational approaches (Fiss, 2007) that go beyond relating individual attributes to an outcome (e.g. through a regression model) and aim to uncover the different patterns of attributes that may lead to that outcome (e.g. through categorical principal components analysis). Such configurations could better capture the complex interplay of attributes related to the user, the artifact and their interaction.
Third, the UCC landscape is constantly evolving with emerging technologies, trends and changes in customer demands and expectations, as well as geopolitics (Gartner, 2020). Also evolving are service encounters empowered by new technologies, such as AI or augmented reality (Kunz et al., 2019). We, thus, recommend that service scholars consider how the interactions between service users and novel technology artifacts push the boundaries of UCC. The future of UCC is increasingly dynamic and multimodal (Grewal et al., 2021). Future research should determine what bids (e.g. requests and demands) novel artifacts’ place on service users and how novel artifacts respond to users' desired actions (e.g. encouragement, discouragement and refusal; Davis and Chouinard, 2017). Such research could take inspiration from the field of human–machine communication (Spence, 2019; Sundar, 2020), where the interest lies in the communication between humans and smart technologies as actors themselves (e.g. chatbots, social robots and algorithms) rather than as media through which communication between humans takes place. Particularly, this research invites contributions concerning the functional, relational and metaphysical aspects of AI as a communicator (Guzman and Lewis, 2020). The functional aspects could be researched to evaluate “how people interpret the human and machine-like traits and characteristics of communicative technologies” (Guzman and Lewis, 2020, p. 76), leading to service design improvements. The relational aspects could be studied to explore how (different types of) users integrate AI-based technologies into their personal and social lives and doing so can generate knowledge for service delivery. Moreover, the metaphysical aspects – for example, the blurring of the boundaries between humans and machines – point to ethical questions (e.g. around overtrust and deception; Aroyo et al., 2021) from which service researchers should not shy away.
Finally, expanding the notions of communication as an element of service value (Heinonen and Strandvik, 2005) and dialogical interaction (Ballantyne and Varey, 2006), we argue that UCC has the potential to create value for service users as well as service providers. For example, by guiding service users throughout the customer experience, UCC can make service users better off (e.g. by supporting decision-making at different stages along the customer journey). Similarly, by offering customer insights to service providers, UCC can help firms improve their offerings (e.g. identify opportunities for touchpoint innovation along the customer journey). According to the service perspective on marketing and business, such improvements in the well-being or performance of individuals or organizations form the essence of value creation (Grönroos, 2008). Value is created “through the integration of resources, provided by many sources, including a full range of market-facing, private and public actors” (Vargo and Lusch, 2016, p. 9). We, thus, encourage service and communication researchers to undertake future research in this area that aims to substantiate the value-creating properties of UCC and, ultimately, generate implications for service and communication theory and practice.
For service managers, our UCC affordance framework offers practical guidance. Rather than focusing separately on technologies or user behaviors, the framework shifts attention to UCC as an integrative phenomenon of user experiences, platform design, and user goals and motivations. Service managers should develop an in-depth understanding of both user experiences and user goals and motivations through user-centered methods, such as netnography (Kozinets, 2020) or interviews where users comment on their own trace data (Latzko-Toth et al., 2017). The insights gained can then be used iteratively to improve communication and service design.
Furthermore, by recognizing the opportunities and challenges of the three suggested UCC affordances – interactivity, visibility and anonymity – service managers can better develop successful communication practices. These UCC affordances raise issues that service providers should address now (e.g. deciding whether to invest in nonhuman communication through chatbots, dealing with public negative feedback on social media, engaging or abstaining from sociopolitically charged communication, for example, in terms of social justice issues) and in the future (e.g. shift from textual to video communications, augmented and virtual realities, and metaverse environments). Taken together, this article guides service managers on how to better understand and navigate the complex, diverse and ever-evolving UCC landscape.
Figures
Future research agenda for UCC affordances in service
| UCC Affordance | Definition | UCC examples | Potential future research questions |
|---|---|---|---|
| Interactivity | The extent to which users perceive they are a part of or engaged in two-sided UCC | Through social media posts, UCC can offer consumers’ increased interactivity, such as by providing consumers direct links to the service provider's websites so that they can make quick purchases | How can service providers create more seamless purchase experiences through UCC (e.g. when AI and/or human agents share relevant links in response to user inquires on different digital platforms)? |
| Service providers can improve the perceived interactivity of UCC by approaching the users who add negative comments (e.g. on review websites) | When should service providers reply to UCC that is not directly addressed to them (e.g. paid and earned media)? | ||
| Service providers typically react to UCC as comments on owned social media by either replying or reacting (e.g. liking) to them. However, users increasingly prefer communications in other forms (e.g. audio recordings, videos), some of which are more interactive (e.g. you can pause/speed audio recordings and videos) | Considering the increasing popularity of non-textual UCC formats, how should service providers adapt their approaches to responding to UCC (e.g. should they respond to video with video, should they use ephemeral communication, and so on)? | ||
| Some service providers employ humans to reply to user inquires (i.e. interact with customers) during office hours, while chatbots take over outside of office hours | How should service providers distribute communication tasks with users between human and nonhuman service provider representatives on different platforms (e.g. social media and websites)? | ||
| Users can create online reviews that include different integrated interactive features (e.g. by adding a tab that asks them to provide answers to multiple-choice questions in Instagram Stories) | How does service provider engagement with UCC in nonverbal forms (e.g. answering to a multiple-choice question) influence perceptions and behaviors of UCC creators/other users? | ||
| Some service providers use interactive features on their websites to allow users to “experience” clothes/furniture virtually and get recommendations from professionals | How can service providers interact with users to support their decision-making in the context of mixed reality spaces? | ||
| While the use of AI can be of great help to service providers (e.g. offer responses quickly and efficiently), it may also make UCC feel less personal and, consequently, less interactive | How should service providers leverage the different dimensions of AI (e.g. magnitude, autonomy and so on) to respond to the different types of user inquires (e.g. questions about opening hours vs a complaint)? |
| UCC Affordance | Definition | UCC examples | Potential future research questions |
|---|---|---|---|
| Visibility | The extent to which UCC can be seen by others and the relative ease for users to find and access it | Loyalty program members often provide direct but private negative feedback to service providers (e.g. hotels) via apps, which means that other users are unlikely to be affected by this negative content | To what extent should service providers encourage users to share negative feedback directly with them (vs on public digital platforms)? |
| Users can write hotel reviews on public platforms (e.g. TripAdvisor) but record voice messages to share their experiences with a limited number of other users (e.g. friends on Facebook) | Under what circumstances does the same user communicate about the same experience with different audiences on different platforms (e.g. public vs private) using different formats? | ||
| Users typically share reviews on public platforms (e.g. Yelp) after their experiences with service providers. However, on their personal social media accounts, users can share information about the experience with their friends (followers) while it is happening (e.g. while staying at a resort for a week) | How does the presence or absence of synchronicity impact the visibility of how users share their experiences? | ||
| Both overly negative and overly positive feedback is posted by users on public platforms, and they may thus need to be addressed publicly by service providers, which can be challenging. For overly negative UCC, service providers may need to defend themselves against untruthful accusations. Moreover, when embracing positive UCC very enthusiastically, they risk appearing complacent or self-righteous | How should service providers address publicly available overly negative or overly positive UCC through both verbal (a textual or video reply) and nonverbal (e.g. likes, reposts and so on) communication means? | ||
| Users search for UCC in a variety of contexts, such as trying to find the best restaurant to eat within an hour vs choosing a hotel for a trip that is months away | What is the influence of temporality on how users react and perceive UCC visibility? | ||
| Users are increasingly concerned about their privacy in digital spaces and also when approached by service providers | When should service providers use less visible/invisible backstage channels, such as direct messages, and when should they be visible on the frontstage? | ||
| Users can create content that remains visible for an unlimited amount of time (e.g. Facebook posts), as well as content that disappears after a certain amount of time (e.g. Facebook stories that are visible for only 24 h) | How does the limited visibility of UCC (e.g. due to ephemerality) impact the experience of users who are exposed (vs not exposed) to the UCC? |
| UCC Affordance | Definition | UCC Examples | Potential Future Research Questions |
|---|---|---|---|
| Anonymity | The degree to which users perceive the source of UCC to be unknown or unspecified | Platform service providers (e.g., Facebook) are often blamed for ignoring cyberbullying, which users can engage in anonymously | What is the role of platform providers in dealing with unintended or negative outcomes of anonymous UCC (e.g., to what extent can AI be leveraged to prevent cyberbullying)? |
| Digital platforms collect user data and use it to improve their services and gain revenue (e.g., from advertisers) | How does anonymity affect the business model of platform service providers (e.g., to what extent does user anonymity impact their analytics and market research)? | ||
| Users who share textual reviews are more likely to remain anonymous compared to users who share video reviews (e.g., users can often see and hear reviewers in video reviews) | How does the anonymity of UCC in different formats change the degree to which other users are influenced? | ||
| More reputable and socially acceptable platforms (e.g., Instagram) encourage users to share their personal information, while others (e.g., 9gag) are likely to do the opposite | How do different platforms’ characteristics (e.g., reputation, theme, focus, and so on) influence user anonymity? | ||
| Some users create “throw-away” (anonymous) accounts they do not want to be associated with their “main” account | Why does the same user need to behave differently on the same (anonymous) platform? | ||
| Anonymous platform users (e.g., Reddit) accuse the media platform employees of posing as regular anonymous users to get inspiration for content (e.g., media articles) | What are the ethical implications of service provider anonymity (e.g., under what circumstances can / should service providers remain anonymous)? | ||
| There is an increasing number of fake reviews and other untruthful information shared by users (often from fake/anonymous accounts) on various platforms that may create a distorted image about service providers | How can platform providers or other users fight against fake reviews and other untruthful information posted by users from fake/anonymous accounts (e.g., with the help of AI and/or user policies)? |
Appendix An illustration of the UCC framework in a service setting
Let us assume Twitter user Rebecca tweets about her positive experience when visiting a new restaurant in her neighborhood, mentioning the name of the restaurant in the tweet. In this case, Rebecca is the user, Twitter is the artifact and outcomes include positive eWOM for the restaurant, satisfaction on Rebecca's part when other users like or retweet her tweet – or simply with the act of remembering her positive experience –, and informational benefits on the part of her followers who see the tweet. “Use” describes Rebecca's specific activity of tweeting, “design” involves Twitter's structural properties (e.g. 280-character limit, possibility to easily embed pictures, hashtags and algorithmic curation of the feed) that foster the outcomes described above (e.g. the use of a specific hashtag in Rebecca's tweet might mean that non-followers people notice the restaurant, creating positive word of mouth) and “goals” refer to Rebecca's motives for tweeting (e.g. altruism and social gratification). A particularly relevant affordance in this example is visibility: Twitter's design choices (e.g. most accounts are public, it is easy to follow other users and tweets can be shared from multiple devices) afford seeing Rebecca's tweet relatively easily on the part of her followers (or even strangers through the hashtag or Twitter's search function), thus creating attention.
References
Aroyo, A.M., De Bruyne, J., Dheu, O., Fosch-Villaronga, E., Gudkov, A., Hoch, H. and Tamò-Larrieux, A. (2021), “Overtrusting robots: setting a research agenda to mitigate overtrust in automation”, Paladyn, Journal of Behavioral Robotics, Vol. 12 No. 1, pp. 423-436, doi: 10.1515/pjbr-2021-0029.
Azzari, C.N., Anderson, L., Mende, M., Jefferies, J.G., Downey, H., Ostrom, A.L. and Spanjol, J. (2021), “Consumers on the job: contextualization crafting in expert services”, Journal of Service Research, Vol. 24 No. 4, pp. 520-541, doi: 10.1177/10946705211012474.
Babić Rosario, A., de Valck, K. and Sotgiu, F. (2020), “Conceptualizing the electronic word-of-mouth process: what we know and need to know about eWOM creation, exposure, and evaluation”, Journal of the Academy of Marketing Science, Vol. 48 No. 3, pp. 422-448, doi: 10.1007/s11747-019-00706-1.
Bacile, T.J., Krallman, A., Wolter, J.S. and Beachum, N.D. (2020), “The value disruption of uncivil other-customers during online service recovery”, Journal of Services Marketing, Vol. 34 No. 4, pp. 483-498, doi: 10.1108/JSM-05-2019-0209.
Ballantyne, D. and Varey, R.J. (2006), “Creating value-in-use through marketing interaction: the exchange logic of relating, communicating and knowing”, Marketing Theory, Vol. 6 No. 3, pp. 335-348, doi: 10.1177/1470593106066795.
Beh, Y.S., Sajtos, L. and Cao, J.T. (2020), “Complainers' resource investment and mobilization in digital environments using Conservation of Resources theory”, Journal of Service Management, Vol. 31 No. 3, pp. 509-534, doi: 10.1108/JOSM-10-2018-0344.
Betzing, J.H., Kurtz, M. and Becker, J. (2020), “Customer participation in virtual communities for local high streets”, Journal of Retailing and Consumer Services, Vol. 54, p. 102025, doi: 10.1016/j.jretconser.2019.102025.
Bucher, T. and Helmond, A. (2017), “The affordances of social media platforms”, in Burgess, J., Marwick, A. and Poell, T. (Eds), The SAGE Handbook of Social Media, Sage, London, pp. 233-253.
Cabiddu, F., De Carlo, M. and Piccoli, G. (2014), “Social media affordances: enabling customer engagement”, Annals of Tourism Research, Vol. 48, pp. 175-192, doi: 10.1016/j.annals.2014.06.003.
Chatterjee, S. and Kumar Kar, A. (2020), “Why do small and medium enterprises use social media marketing and what is the impact: empirical insights from India”, International Journal of Information Management, Vol. 53 No. 8, pp. 1-13, doi: 10.1016/j.ijinfomgt.2020.102103.
Cherry, C. (1966), On Human Communication, MIT Press, Cambridge, MA.
Chung, M., Ko, E., Joung, H. and Kim, S.J. (2020), “Chatbot e-service and customer satisfaction regarding luxury brands”, Journal of Business Research, Vol. 117, pp. 587-595, doi: 10.1016/j.jbusres.2018.10.004.
Colicev, A., Kumar, A. and O'Connor, P. (2019), “Modeling the relationship between firm and user generated content and the stages of the marketing funnel”, International Journal of Research in Marketing, Vol. 36 No. 1, pp. 100-116, doi: 10.1016/j.ijresmar.2018.09.005.
Davis, J.L. and Chouinard, J.B. (2017), “Theorizing affordances: from request to refuse”, Bulletin of Science, Technology and Society, Vol. 36 No. 4, pp. 241-248, doi: 10.1177/0270467617714944.
De Luca, L.M., Herhausen, D., Troilo, G. and Rossi, A. (2021), “How and when do big data investments pay off? The role of marketing affordances and service innovation”, Journal of the Academy of Marketing Science, Vol. 49 No. 4, pp. 790-810, doi: 10.1007/s11747-020-00739-x.
Demsar, V., Brace-Govan, J., Jack, G. and Sands, S. (2021), “The social phenomenon of trolling: understanding the discourse and social practices of online provocation”, Journal of Marketing Management, Vol. 37 Nos 11-12, pp. 1058-1090, doi: 10.1080/0267257X.2021.1900335.
Deng, L., Sun, W., Xu, D. and Ye, Q. (2021), “Impact of anonymity on consumers' online reviews”, Psychology and Marketing, Vol. 38, pp. 2259-2270, doi: 10.1002/mar.21565.
Diwanji, V.S. and Cortese, J. (2020), “Contrasting user generated videos versus brand generated videos in ecommerce”, Journal of Retailing and Consumer Services, Vol. 54, p. 102024, doi: 10.1016/j.jretconser.2019.102024.
Dyussembayeva, S., Viglia, G., Nieto-Garcia, M. and Invernizzi, A.C. (2020), “It makes me feel vulnerable! the impact of public self-disclosure on online complaint behavior”, International Journal of Hospitality Management, Vol. 88, p. 102512, doi: 10.1016/j.ijhm.2020.102512.
Evans, S.K., Pearce, K.E., Vitak, J. and Treem, J.W. (2017), “Explicating affordances: a conceptual framework for understanding affordances in communication research”, Journal of Computer-Mediated Communication, Vol. 22 No. 1, pp. 35-52, doi: 10.1111/jcc4.12180.
Finne, Å. and Grönroos, C. (2017), “Communication-in-use: customer-integrated marketing communication”, European Journal of Marketing, Vol. 51 No. 3, pp. 445-463, doi: 10.1108/EJM-08-2015-0553.
Fiss, P.C. (2007), “A set-theoretic approach to organizational configurations”, Academy of Management Review, Vol. 32 No. 4, pp. 1180-1198, doi: 10.5465/amr.2007.26586092.
Gartner (2020), “Tech providers 2025: strategic transformation drives growth”, available at: https://tinyurl.com/yzcd7tpb (accessed 28 October 2021).
Gibson, J.J. (1977), “The theory of affordances”, in Shaw, R.E. and Bransford, J. (Eds), Perceiving, Acting, and Knowing: toward an Ecological Psychology, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 67-82.
Grewal, D., Herhausen, D., Ludwig, S. and Ordenes, F.V. (2021), “The future of digital communication research: considering dynamics and multimodality”, Journal of Retailing, doi: 10.1016/j.jretai.2021.01.007 (In press).
Grönroos, C. (2008), “Service logic revisited: who creates value? And who co-creates?”, European Business Review, Vol. 20 No. 4, pp. 298-314, doi: 10.1108/09555340810886585.
Gustafsson, A., Kristensson, P. and Witell, L. (2012), “Customer co-creation in service innovation: a matter of communication”, Journal of Service Management, Vol. 23 No. 3, pp. 311-327, doi: 10.1108/09564231211248426.
Guzman, A.L. and Lewis, S.C. (2020), “Artificial intelligence and communication: a human–machine communication research agenda”, New Media and Society, Vol. 22 No. 1, pp. 70-86, doi: 10.1177/1461444819858691.
Haji, M.N. (2014), “A study of the impact of social media on consumers”, International Journal of Market Research, Vol. 56 No. 3, pp. 387-404, doi: 10.2501/IJMR-2014-025.
Heinonen, K. (2011), “Consumer activity in social media: managerial approaches to consumers' social media behavior”, Journal of Consumer Behaviour, Vol. 10 No. 6, pp. 356-364, doi: 10.1002/cb.376.
Heinonen, K. and Strandvik, T. (2005), “Communication as an element of service value”, International Journal of Service Industry Management, Vol. 16 No. 2, pp. 186-198, doi: 10.1108/09564230510592306.
Heller, J., Chylinski, M., de Ruyter, K., Keeling, D.I., Hilken, T. and Mahr, D. (2021), “Tangible service automation: decomposing the technology-enabled engagement process (TEEP) for augmented reality”, Journal of Service Research, Vol. 24 No. 1, pp. 84-103, doi: 10.1177/1094670520933692.
Hollebeek, L.D. and Macky, K. (2019), “Digital content marketing's role in fostering consumer engagement, trust, and value: framework, fundamental propositions, and implications”, Journal of Interactive Marketing, Vol. 45, February, pp. 27-41, doi: 10.1016/j.intmar.2018.07.003.
Huh, J. (2020), “Smart communication for a digital world”, Journal of Interactive Advertising, Vol. 20 No. 3, pp. 240-243, doi: 10.1080/15252019.2020.1849693.
Jacobson, J., Gruzd, A. and Hernández-García, Á. (2020), “Social media marketing: who is watching the watchers?”, Journal of Retailing and Consumer Services, Vol. 53, p. 101774, doi: 10.1016/j.jretconser.2019.03.001.
Jeong, B., Yoon, J. and Lee, J.M. (2019), “Social media mining for product planning: a product opportunity mining approach based on topic modeling and sentiment analysis”, International Journal of Information Management, Vol. 48, pp. 280-290, doi: 10.1016/j.ijinfomgt.2017.09.009.
Kozinets, R.V. (2020), Netnography: The Essential Guide to Qualitative Social Media Research, Sage, London.
Kunz, W.H., Heinonen, K. and Lemmink, J.G.A.M. (2019), “Future service technologies: is service research on track with business reality?”, Journal of Services Marketing, Vol. 33 No. 4, pp. 479-487, doi: 10.1108/JSM-01-2019-0039.
Labrecque, L.I. (2014), “Fostering consumer–brand relationships in social media environments: the role of parasocial interaction”, Journal of Interactive Marketing, Vol. 28 No. 2, pp. 134-148, doi: 10.1016/j.intmar.2013.12.003.
Lang, B., Kemper, J., Dolan, R. and Northey, G. (2022), “Why do consumers become providers? Self-determination in the sharing economy”, Journal of Service Theory and Practice, Vol. 32, pp. 132-155, doi: 10.1108/JSTP-09-2020-0220.
Larivière, B., Joosten, H., Malthouse, E.C., van Birgelen, M., Aksoy, P., Kunz, W.H. and Huang, M. (2013), “Value fusion: the blending of consumer and firm value in the distinct context of mobile technologies and social media”, Journal of Service Management, Vol. 24 No. 3, pp. 268-293, doi: 10.1108/09564231311326996.
Latzko-Toth, G., Bonneau, C. and Millette, M. (2017), “Small data, thick data: thickening strategies for trace-based social media research”, in Sloan, L. and Quan-Haase, A. (Eds), The SAGE Handbook of Social Media Research Methods, Sage, London, pp. 199-214.
Lechermeier, J., Fassnacht, M. and Wagner, T. (2020), “Testing the influence of real-time performance feedback on employees in digital services”, Journal of Service Management, Vol. 31 No. 3, pp. 345-371, doi: 10.1108/JOSM-10-2018-0341.
Leeflang, P.S.H., Verhoef, P.C., Dahlström, P. and Freundt, T. (2014), “Challenges and solutions for marketing in a digital era”, European Management Journal, Vol. 32 No. 1, pp. 1-12, doi: 10.1016/j.emj.2013.12.001.
Leonardi, P.M. and Vaast, E. (2017), “Social media and their affordances for organizing: a review and agenda for research”, Academy of Management Annals, Vol. 11 No. 1, pp. 150-188, doi: 10.5465/annals.2015.0144.
Lewis, S.C. (2015), “Reciprocity as a key concept for social media and society”, Social Media + Society, Vol. 1 No. 1, pp. 1-2, doi: 10.1177/2056305115580339.
Liu, Y. (2003), “Developing a scale to measure the interactivity of websites”, Journal of Advertising Research, Vol. 43 No. 2, pp. 207-216, doi: 10.1017/S0021849903030204.
Liu, X. (2019), “A big data approach to examining social bots on Twitter”, Journal of Services Marketing, Vol. 33 No. 4, pp. 369-379, doi: 10.1108/JSM-02-2018-0049.
Liu, X., Burns, A.C. and Hou, Y. (2017), “An investigation of brand-related user-generated content on Twitter”, Journal of Advertising, Vol. 46 No. 2, pp. 236-247, doi: 10.1080/00913367.2017.1297273.
Lowry, P.B., Zhang, J., Wang, C. and Siponen, M. (2016), “Why do adults engage in cyberbullying on social media? An integration of online disinhibition and deindividuation effects with the social structure and social learning model”, Information Systems Research, Vol. 27 No. 4, pp. 962-986, doi: 10.1287/isre.2016.0671.
Luo, X., Tong, S., Fang, Z. and Qu, Z. (2019), “Frontiers: machines vs. humans: the impact of artificial intelligence chatbot disclosure on customer purchases”, Marketing Science, Vol. 38 No. 6, pp. 937-947, doi: 10.1287/mksc.2019.1192.
MacKenzie, R., Marks, A. and Morgan, K. (2017), “Technology, affordances and occupational identity amongst older telecommunications engineers: from living machines to black-boxes”, Sociology, Vol. 51 No. 4, pp. 732-748, doi: 10.1177/0038038515616352.
Mariani, M.M., Borghi, M. and Gretzel, U. (2019), “Online reviews: differences by submission device”, Tourism Management, Vol. 70, pp. 295-298, doi: 10.1016/j.tourman.2018.08.022.
Mathwick, C. and Mosteller, J. (2017), “Online reviewer engagement: a typology based on reviewer motivations”, Journal of Service Research, Vol. 20 No. 2, pp. 204-218, doi: 10.1177/1094670516682088.
McKenna, B. (2020), “Creating convivial affordances: a study of virtual world social movements”, Information Systems Journal, Vol. 30 No. 1, pp. 185-214, doi: 10.1111/isj.12256.
Mettler, T. and Wulf, J. (2019), “Physiolytics at the workplace: affordances and constraints of wearables use from an employee's perspective”, Information Systems Journal, Vol. 29 No. 1, pp. 245-273, doi: 10.1111/isj.12205.
Misopoulos, F., Mitic, M., Kapoulas, A. and Karapiperis, C. (2014), “Uncovering customer service experiences with Twitter: the case of airline industry”, Management Decision, Vol. 52 No. 4, pp. 705-723, doi: 10.1108/MD-03-2012-0235.
Muntinga, D.G., Moorman, M. and Smit, E.G. (2011), “Introducing COBRAs: exploring motivations for brand-related social media use”, International Journal of Advertising, Vol. 30 No. 1, pp. 13-46, doi: 10.2501/IJA-30-1-013-046.
Ni, W. and Sun, H. (2018), “The trustworthiness of internet-based quality signals: an interactivity perspective”, Journal of Services Marketing, Vol. 32 No. 2, pp. 195-210, doi: 10.1108/JSM-07-2015-0235.
Norman, D.A. (1988), The Psychology of Everyday Things, Basic Books, NY.
Rangaswamy, A., Moch, N., Felten, C., van Bruggen, G., Wieringa, J.E. and Wirtz, J. (2020), “The role of marketing in digital business platforms”, Journal of Interactive Marketing, Vol. 51, pp. 72-90, doi: 10.1016/j.intmar.2020.04.006.
Rust, R.T., Rand, W., Huang, M.H., Stephen, A.T., Brooks, G. and Chabuk, T. (2021), “Real-time brand reputation tracking using social media”, Journal of Marketing, Vol. 85 No. 4, pp. 21-43, doi: 10.1177/0022242921995173.
Sanchez, J., Abril, C. and Haenlein, M. (2020), “Competitive spillover elasticities of electronic word of mouth: an application to the soft drink industry”, Journal of the Academy of Marketing Science, Vol. 48 No. 2, pp. 270-287, doi: 10.1007/s11747-019-00683-5.
Schrock, A.R. (2015), “Communicative affordances of mobile media: portability, availability, locatability, and multimediality”, International Journal of Communication, Vol. 9, pp. 1229-1246, available at: https://ijoc.org/index.php/ijoc/article/view/3288/1363.
Shao, G. (2009), “Understanding the appeal of user-generated media: a uses and gratification perspective”, Internet Research, Vol. 19 No. 1, pp. 7-25, doi: 10.1108/10662240910927795.
Smith, A.N., Fischer, E. and Yongjian, C. (2012), “How does brand-related user-generated content differ across YouTube, Facebook, and Twitter?”, Journal of Interactive Marketing, Vol. 26 No. 2, pp. 102-113, doi: 10.1016/j.intmar.2012.01.002.
Spence, P.R. (2019), “Searching for questions, original thoughts, or advancing theory: human-machine communication”, Computers in Human Behavior, Vol. 90, pp. 285-287, doi: 10.1016/j.chb.2018.09.014.
Statista (2021), Digital Advertising in the U.S. 2021 Report, available at: https://tinyurl.com/bdeanphr (accessed 22 February 2022).
Sundar, S.S. (2020), “Rise of machine agency: a framework for studying the psychology of Human–AI Interaction (HAII)”, Journal of Computer-Mediated Communication, Vol. 25 No. 1, pp. 74-88, doi: 10.1093/jcmc/zmz026.
Sutherland, W. and Jarrahi, M.H. (2018), “The sharing economy and digital platforms: a review and research agenda”, International Journal of Information Management, Vol. 43 December, pp. 328-341, doi: 10.1016/j.ijinfomgt.2018.07.004.
Taylor, C.R., Kitchen, P.J., Sarkees, M.E. and Lolk, C.O. (2020), “Addressing the Janus face of customer service: a typology of new age service failures”, European Journal of Marketing, Vol. 54 No. 10, pp. 2295-2316, doi: 10.1108/EJM-12-2019-0916.
TripAdvisor (2022), Travelers Push TripAdvisor Past 1 Billion Reviews & Opinions!, available at: https://tinyurl.com/mrxf2emw (accessed 11 February 2022).
Van Pinxteren, M.M.E., Pluymaekers, M. and Lemmink, J.G.A.M. (2020), “Human-like communication in conversational agents: a literature review and research agenda”, Journal of Service Management, Vol. 31 No. 2, pp. 203-225, doi: 10.1108/JOSM-06-2019-0175.
Vargo, S.L. and Lusch, R.F. (2016), “Institutions and axioms: an extension and update of service-dominant logic”, Journal of the Academy of Marketing Science, Vol. 44 No. 1, pp. 5-23, doi: 10.1007/s11747-015-0456-3.
Vink, J. and Koskela-Huotari, K. (2021), “Building reflexivity using service design methods”, Journal of Service Research, doi: 10.1177/10946705211035004.
Wagenknecht, T., Teubner, T. and Weinhardt, C. (2018), “A Janus-faced matter–the role of user anonymity for communication persuasiveness in online discussions”, Information and Management, Vol. 55 No. 8, pp. 1024-1037, doi: 10.1016/j.im.2018.05.007.
Wakefield, L.T. and Wakefield, R.L. (2018), “Anxiety and ephemeral social media use in negative eWOM creation”, Journal of Interactive Marketing, Vol. 41 No. 2, pp. 44-59, doi: 10.1016/j.intmar.2017.09.005.
Zhao, Y., Wen, L., Feng, X., Li, R. and Lin, X. (2020), “How managerial responses to online reviews affect customer satisfaction: an empirical study based on additional reviews”, Journal of Retailing and Consumer Services, Vol. 57, p. 102205, doi: 10.1016/j.jretconser.2020.102205.
Acknowledgements
The authors would like to thank the guest editors and the anonymous reviewers for their constructive feedback.
Funding statement: Christoph Lutz received funding from the Research Council of Norway under grant agreements 275347 Future Ways of Working in the Digital Economy and 299178 Algorithmic Accountability: Designing Governance for Responsible Digital Transformations. Valeria Penttinen received funding from the OP Group Research Foundation and Bergsrådet Tekn. och Ekon. dr h.c. Marcus Wallenbergs Stiftelse för Företagsekonomisk Forskning.
Corresponding author
About the authors
Robert Ciuchita is an assistant professor at CERS, Centre for Relationship Marketing and Service Management, at the Department of Marketing at Hanken School of Economics in Helsinki, Finland. His research and teaching interests lie in the areas of digital marketing, service innovation and customer experience management. His work has appeared in Journal of Service Research, Journal of Business Research and Journal of the Association for Consumer Research.
Gustav Medberg is a postdoctoral researcher and lecturer at CERS, Centre for Relationship Marketing and Service Management, at the Department of Marketing at Hanken School of Economics in Helsinki, Finland. His main research interests include financial services, service marketing and management, value creation and destruction and qualitative research methods. Dr. Medberg has received Emerald Literati Awards for his publications on these topics in journals such as Journal of Services Marketing, Journal of Service Theory and Practice and International Journal of Bank Marketing. He also regularly serves as a reviewer of several marketing and service research journals.
Valeria Penttinen is a fourth-year PhD candidate at CERS, Centre for Relationship Marketing and Service Management, at the Department of Marketing at Hanken School of Economics in Helsinki, Finland. Her research and teaching focus on the use of data in marketing decisions making, social media marketing and user engagement. Valeria's work reflects her personal interests along with academic and business relevance.
Christoph Lutz (Dr. oec., University of St. Gallen, Switzerland) is an associate professor in the Department of Communication and Culture, BI Norwegian Business School, Norway. He is also the current codirector of the Nordic Centre for Internet and Society, a leading Internet and Society research center in Europe. His research interests include digital inequality, social media, privacy, the sharing economy and social robots. Dr. Lutz has published widely on these topics in top-tier journals such as New Media and Society, Information, Communication and Society, Journal of Business Research, Journal of Management Information Systems and Science and Engineering Ethics.
Kristina Heinonen is a professor of service and relationship marketing and director of CERS, Centre for Relationship Marketing and Service Management at Hanken School of Economics in Helsinki, Finland. She is a Fulbright Alumni (University of Massachusetts) and holds an adjunct professorship at University of Tampere, Finland. Dr. Heinonen's research interests include service marketing and management and particularly, service innovation, value creation, digitalization and customer experience. She has authored more than 70 articles in leading scientific journals and edited books.