Digital servitization describes a change of market offerings to smart service solutions enabled by digitization. Smart service offerings in the form of platform business models, outcome-based contracts and integrated solutions gain market attraction, not least because of increasing digitalization and pledged sustainability outcomes. Such transformation to smart service is challenging product oriented industries like manufacturing.
Building on service logic-based view, the concept of value-in-use (i.e., emergent value throughout customer usage of a value proposition) is the nucleus of consideration. Especially when it comes to the (co)-production of service offerings, value-in-use monitoring is mentioned as an important capability for suppliers today. Research so far has focused on customers' value-in-use of solutions and integrated products and services. However, due to the described transformational shift and timely sustainability debates, it can be assumed that value-in-use assessments are changing. So far, an investigation of the value-in-use of smart services has not been carried out. Therefore, that is the aim of this study.
To examine individual value-in-use concepts regarding smart services, we conducted 22 in-depth expert interviews with repertory grid technique. 13 within one German manufacturer of rotating equipment and 9 with their customers, each lasted 90 to 120 minutes. Recorded unconscious value preferences of participants were then analyzed. Therefore named intuitive personal constructs of participants were clustered, quantified, put in relation, and graphically processed. Overall, our results show that respondents agree on what constitutes an ideal service. Their perception of captured constructs like service tomorrow, smart service, and remote service are closer to this understanding of ideal service, which stands in constrast to service from the past, as well as service today. The results also show that the respondents can be grouped into four clusters, 1) maximum relief, 2) individual solutions, 3) proximity to customer, 4) co-production. However, the internal and external respondents differ in their focus topics. While internally, emphasis is placed on the process of customer involvement in co-production, customers look more closely at the result e.g., of how, today personal delivered, services might be automated. Further, traditional product sales with reactive service behavior no longer meet expectations. Customers expect manufacturers to provide professional solutions that take the pressure off them and to deal with problems in a spirit of partnership. In addition, customers see a value-in-use in predictive intelligent systems, which supports them proactively and individually.
Overall, it is apparent that smart services are viewed positively. Smart services appeal to the values of suppliers and customers but differ from today's service experiences, which may indicate smart service to remain a relevant research field. Further, substantial insights can be derived from the analysis e.g., a delineated view between providers and customers regards co-production of service offerings. The concept is considered important on the supplier side. Customers, on the other hand, actually expect the best possible solutions without investing heavily their own resources. A more differentiated view of the customer's role could be important here. Our results are based on a small number of interviews, to validate findings further studies should be conducted.

Introduction & Literature Review
A plethora of new media channels provides consumers with numerous opportunities for engagement (Blasco-Arcas et al. 2016; Azer and Alexander 2020). Photo-based social media platforms, such as Facebook and Instagram, are key means of self-expression and brand-related posts are shared publicly by a myriad of customers including 1.8 billion photos uploaded and shared daily, receiving 200% more attention and interaction than textual posts (Perez-Vega et al. 2018; Argyris et al. 2020). Despite the acknowledged influence of visual compared to textual manifestations in online contexts (cf. Townsend and Kahn 2014; Akpinar and Berger 2017; Villarroel et al. 2019; Filieri et al. 2021), research investigating other forms of communication in social media beyond text and numerical rating remains rare (Rosario et al. 2020). Although VME is likely to create value for the brands, the extant research exploring the visual brand-related content generated by users on social media has mainly focused on content type rather than users’ behavioral manifestations of engagement they enact using visual modality which are likely to differ in their impact on brands in line to studies with a predominant focus on the textual modality of engagement.

Methodology/approach
Netnography is selected as an appropriate method to guide this study. As a marketing research technique, netnography uses publicly available online information to explore the activities of relevant online consumer groups (Kozinets, 2010). To ensure the diversity of contexts and the robustness of findings, Instagram and Facebook were selected. 30,000 Facebook and Instagram pictorial posts created by customers on the official pages of top four brands (identified based on their total analytics scores across social visibility, web visibility, sentiments, growth, and search visibility according to Brandwatch.com (2020)): Amazon, Apple, American Airlines, and Nike. Pictorial analysis was conducted following the theory of visual rhetoric, where visual images are viewed as communicative artifacts, symbols used to perform communication (Bakri et al., 2020).

Findings
This study presents a typology of seven forms of VME and classified them into positive (evidential, experiential, inspirational, and devotional) and negative (disparaging, mocking, and dissuasive) forms. The study shows that customers engage in these distinct forms of behavior using four types of pictures: Brand selfie, brand only, emotional accessory, and logo parodies. By running an NVIVO pro query matrix, the study reveals what type of pictures they use when engaging in each form and the percentage of frequency of using each type of picture and the frequency of engaging in each form of visual engagement.

Implications
This study contributes to the engagement and visual content literature with the first typology of VME that reveals the importance of understanding modality as well as the context for engagement. Practically, we offer valuable insights for managers to build the brand in social media by leveraging VME in their engagement marketing activities.

Many local high streets in the UK have been facing unprecedented challenges over recent years, including the rise of online retailing, out-of-town shopping and leisure, and the coronavirus pandemic. The closure of many shops led to worries about the decline of retails and footfalls of high streets. The need for novel solutions for reviving high streets has never been greater to ensure the survival of our high streets. Digital platforms, including websites and mobile applications, can help bridge online commerce and traditional brick-and-mortar shops in town centres. For example, York, Leeds, Twickenham, and Portobello Road are among are among high streets and towns in the UK that are evolving rapidly towards a mobile-enabled future using digital platforms. Such digital platforms help residents and visitors digitally engage with their towns and high streets. However, the presence of many existing mobile apps for e-commerce (e.g., John Lewis), catering (e.g., Deliveroo), and sales vouchers (e.g., Wowcher) makes developers struggle for securing visibility and adoption by consumers. For instance, the extant research shows that over 70% of mobile apps received less than one thousand downloads, with only 1% reaching one million downloads. Although previous studies on the adoption of e-commerce platforms and loyalty programs provide insights regarding antecedents and inhibitors of mobile app adoption, little is known about how to encourage consumers to adopt and engage with mobile apps developed by city councils in order to increase footfalls to UK high streets.

Our study aims to uncover how adding unique features and benefits drive consumers to adopt and engage with high street mobile apps. We employed a multi-step mixed-method approach by first conducting qualitative interviews to unpack features and benefits consumers expect from a high street mobile app. Insights from interviews show that while consumers appreciate a mobile app to inform them about local social events, discount offers, and council services and facilities in their local high street, they expect unique benefits that are not available on comparable mobile apps. Drawing on gamification and customer engagement literature (e.g., Berger et al., 2018; Bitrian et al., 2021; Hofacker et al., 2016), we developed and examined a theoretical framework using multiple experiment-based studies. We examined how gamification allows consumers to enjoy using a high street mobile app that provides information about social events, discount offers, and high street facilities while earning rewards for visiting local events (e.g., farmer markets) and city council facilities (e.g., museums, library) and using eco-friendly services (e.g., electric city bikes). Our findings advance the literature by showing that not all gamification mechanics (e.g., rewards based on point collection vs. activity bingo game completion) drive consumers’ intrinsic and extrinsic motivations and engagement. We show that simplified gamification mechanics that are points collection-based and that involve achieving hierarchical level/status decrease the consumer’s skepticism and positively drive intrinsic motivation, engagement, and willingness to visit high streets.

Social media has empowered small independent service sectors firms, such as restaurants, to more easily market their offerings. For the firm, customer engagement with their social media content (e.g., likes, comments) has become crucial. This is because content that receives more engagement is elevated in social media ranking algorithms, thus extending its visibility and reach (Carbone, 2019). Although advancements in artificial intelligence have allowed firms to better facilitate, measure, and understand the factors that influence customer engagement over social media (Li & Xie, 2020; Rietveld et al., 2020), this requires an extensive amount of expertise and resources that are out of reach for smaller firms (DeLuca et al., 2021). This current study demonstrates how firms can use the user-friendly image classification machine learning algorithm of Google Cloud Vision to understand what types of social media posts within their industry are more effective at facilitating customer engagement.

We demonstrate this method in the context of restaurants. Social media in the food services industry is vital, as the vast majority of customers use it (e.g., checking reviews, viewing photos) before visiting a restaurant (Lepkowska-White, 2017). Additionally, restaurants are able to use it at low cost (DiPietro et al., 2012) and engage more directly with their customers (Simon & Tossan, 2018). When it comes to how restaurants use social media, a recent trend has been to post and offer increasingly elaborate foods designed to be more “Instagrammable” (An, 2018; Fantozzi, 2017). This is consistent with word-of-mouth and virality research that suggests unique and entertaining content garners more engagement (Berger, 2014 for review). However, this idea directly contradicts conventional psychological research on processing fluency, where stimuli that are standard, typical, and familiar are liked more (e.g., Mayer & Landwehr, 2018; Pancer et al., 2019).

We test these competing possibilities by analyzing 5,689 Instagram posts from 871 restaurants with Google Cloud Vision to understand how the visual characteristics of posted product offerings (i.e., a restaurant’s food) relates to customer engagement. Results support that food images that are more confidently recognized by Google Vision (a validated proxy for item typicality) is positively associated with engagement (β-likes = .585, p < .001; β-comments = .175, p < .05). Results remain significant after controlling for a variety of control variables. A follow-up experiment shows that exposure to more typical (vs. atypical) appearing foods elevates positive affect, suggesting they are easier to mentally process, which subsequently drives engagement. Therefore, contrary to current restaurant industry trends, the more typical a food appears, the more customer engagement it receives on social media.

Using Google Cloud Vision to identify what visual characteristics of product offerings relates to customer engagement presents an accessible method for marketers to understand their industry and inform their own social media marketing strategies. This makes it possible for practitioners, who may not have the resources or knowledge to train their own machine learning algorithms (DeLuca et al., 2021), to easily identify what types of product content posted on social media within their industry relates to customer engagement.