Along with the UN Sustainable Development Goals, cities of the future aim to increase the quality of life for citizens. Thus, public service providers leverage exponential technologies, such as artificial intelligence, to develop data-driven services for an inclusive and economically thriving environment. However, these services need to rely on large databases and to date cities struggle to mobilize personal data of citizens. Smart cities can only evolve from a co-production with citizens who are willing to share their data. Herewith, data donations provide the possibility to collect data by emphasizing the promotion of social welfare (Hillebrand & Hornuf, 2021). With the increasing importance of data for public services, the question arises how public service providers can motivate consumers to donate personal data.

We introduce data donations as a distinct form of data disclosure that is rather a philanthropic act in which individuals provide data voluntarily and without the direct benefits in return (Kirkpatrick, 2013). Therefore, data donations may be distinguished from sole data disclosure as that the donor holds higher control on when and where to share his data. We thus assume that asking for a data donation in the collection of personal data increases an individual’s perception of control over the data. Because individuals who are more likely to disclose data also express the need for greater privacy controls, we infer that framing the disclosure of personal data as a data donation may increase consumer engagement in giving personal data (Prince, 2017). We thus propose to answer the following questions in our research: (1) What motivates consumers to donate personal data for public services that leverage big data, (2) does the act of donating data increase perceived control over personal data and (3) how can service providers to design interventions that increase these data donations?

Using a multi-method design, qualitative in-depth interviews and subsequent focus groups shed light on the consumers’ willingness to engage in data donations for public services. Framing the process of disclosing data as a donation results in a high acceptance of mobilizing personal data for public services. We further find evidence to support the assumption that data donation increases perceived control over personal data and conclude that a greater control perception and the warm glow of contributing to social welfare influences consumers’ willingness to donate data. Drawing on privacy calculus theory, donation behaviour and human-computer interaction, we investigate in an experimental setting the design of a stimuli on the decision to donate data for the public service sector.

To our knowledge, we are the first to introduce and propose interventions to increase data donations in public service settings. Our interdisciplinary study contributes to the literature on service innovation in the public service sector by applying data donation as a lever to greater engagement in the co-production of public services. From a practical perspective, we provide solutions for cities on how to mobilize personal data for the development of smart services while maximizing value in the co-production process.

Compelling research on engagement and value creation discuss aspects on destinations as examples of engagement objects (e.g., Rather et al., 2019; Villamediana-Pedrosa et al., 2020). Preliminary findings of an on-going research project investigating visitor patterns at rural destinations in Sweden have pointed at a need for considering the influence of the individual self and social roles (Solomon, 1983) in connection with the matter of synchronizing collective engagement (Kleinaltenkamp et al., 2021). As a case in point, imagine the following:

A family is making their first visit to a small coastal town, known for its picturesque architecture. In preparing for the trip, the father (a project manager by profession) is as always taking the role of the “tour guide”, researching what to see and do. The family mother, a history teacher, is happy to finally see the Viking-age excavations of the area. The two young ones enjoy being on holiday – even though they don’t share each other’s interests: shopping and videogaming, respectively. In this case, the father has to balance his expectations of the visit in his role as a tour guide with his role as a father and spouse in keeping the family in a happy mood, considering the known differences in interests. In addition, it is on his shoulders to trigger any engagement at all from his youngest.

The primary object of engagement for the father is to create a synchronised collective engagement in his family, through influencing his family member’s engagement behaviour (Jaakkola & Alexander, 2014) towards the secondary object: the destination per se. The father’s ultimate mission is to get his small group of visitors – his family – to enjoy the holiday destination, through triggering and facilitating engagement.

Answering calls for furthering engagement research (Ng et al., 2020) and inspired by initial research findings from an ongoing research project, the aim of this study is to increase our understanding of the influence of social roles, and any role conflict/correspondence, in synchronizing collective engagement.

A fundamental premise of actor engagement is that it is formed through interactions with social roles of others (Chandler & Lusch, 2015; Alexander et al., 2019; Brodie et al., 2019), dependent on and linked by these individual social roles (Kleinaltenkamp et al., 2019). The role an actor plays guides the individual’s behaviour in a social setting, where role-taking helps the individual to estimate how others will react (Solomon, 1983). An actor takes on social roles such as scholars, parents or friends, in different service systems, and simultaneously serving different sets of actors can result in role conflicts (Chandler & Lusch, 2015).

Engagement is a process involving various touchpoints and interactions with a range of actors, and there is a need for further insights on the dynamic process through which engagement evolves and manifests over time (Ng et al., 2020). Furthering a discussion on the influence of social roles to engagement, this paper contributes to understanding the process of synchronizing individual actor dispositions and behaviours towards collective engagement (cf. Kleinaltenkamp et al., 2021).

Service ecosystems have gained increasing traction in theory and practice. Underlying is the idea that organizations, or actors in general, do not operate solely, but rather in constellation of actors who co-create value through the integration of resources (Vargo & Lusch, 2016).

Literature predominantly paid attention to amplifying our understanding of service ecosystems from a metatheoretical perspective (Brodie et al., 2019). As a result, our knowledge on a micro foundational understanding of service ecosystems looks rather pale. This creates a serious concern as managerial support and midrange levels, that is, bridging the theory with empirics, are underdeveloped (Vargo et al., 2017; Vargo et al., 2018). Furthermore, service ecosystems are mainly investigated “as is”, but its emergence remains opaque.

In response, this article adopts the notion of actor engagement to extricate how actor engagement takes place in emerging ecosystems and explain what emergence entails in such context. In this, actor engagement can be understood as “a group of actors' (collectives or organizations) exchange-based and non-exchange-based resource contributions, that are facilitated by dispositions, formed partly by actor specific characteristics and partly by the institutional and organizational arrangements prevalent in the context in which the resource contributions occur” (Storbacka, 2019, p. 8). Emergence, on the other hand, is the process that results in new properties that are more than the sum of their constituent parts alone (Taillard et al., 2016).

This research is interpretive in nature and draws on four case studies of service ecosystem development as part of an EU Horizon 2020 project, involving four unique rural regions across Europe. Each of them contains idiosyncrasies in terms of culture and sector. Through ethnographic data collection techniques, we were able to collect rich data over a period of 13 months. A multiplicity of actors, ranging from service providers, (end)users and ancillary partners, were involved in the understanding and development of each regional ecosystem in which the co-authors engaged with them in practice as researchers and participants.

We describe actor engagement according to the principles of Storbacka et al. (2016), which involves elements of actor engagement in terms of engaging actor, engagement platform, actor disposition, engagement properties and resource integration patterns. The findings characterize in detail different actor engagement profiles that lay out mechanisms of situational action formation and emergence of ecosystems.

The current study contributes to service research literature by offering a detailed empirical description of actor engagement in premature phases of service ecosystems development. Moreover, business leaders and managers may enjoy our findings in maneuvering throughout the complex landscapes of service ecosystems.

In the context of Service Dominant Logic and with treatises on Customer Experience the construct of Customer Engagement (CE) emerged and is assigned to Customer (Relationship) Management. CE can be defined as a customer’s observable extra-role behavior towards a company or an organization beyond purchase that occurs in interactions between two parties (C2C or B2C). This extra-role behavior manifests itself in the voluntary expansion of the customer’s classically assigned field of activity from buying and consuming to taking on actual employee activities such as advertising-and-acquisition-activities, customer-support-activities and/or new-product/service-development-activities. The customer's motives for this and the long-term nature of the engagement vary depending on the corporate context, situation and individual consumer personality. Customer Engagement is embedded in a nomological network with other constructs of Customer Relationship Management.
While some researchers have agreed on several characteristics to define Customer Engagement in recent years, some others have been controversial about them – both, the characteristics’ specifications as well as their existence at all. It seems reasonable to assume that there is some truth to be found in all the collected individual reflections of the various researchers, so that Customer Engagement should not be viewed in a one-track, overly definitive manner and that there are different forms of CE that vary. Therefore, the overall aim and overriding research question of my investigation is:
"How can Customer Engagement be classified?"
In order to fill the research gap about how to classify CE, I have first conceptually created a CE-Map, drawing on the theories of Customer-Dominant-Logic and Service-Logic. The CE-Map is used to visualize the importance of CE in the ecosystem between customers and providers. Part of the CE-Map is the so-called engagement spiral, which relates CE to other constructs of Relationship Management. With the help of this, I was able to derive the basic thesis "Customer Engagement can be classified with the help of the customer’s level of Involvement" since only the construct of Involvement can already be present before the first consumption/purchase, i.e., even before a relationship is established between customers and providers.
Based on this, qualitative interviews were conducted in accordance with the Grounded-Theory-Approach to answer the overarching research question. In addition, the following research questions were investigated: "In which behaviors does Customer Engagement manifest itself?", "How does Customer Engagement evolve over time?" and "What motives lead to Customer Engagement?” In order to quantify the subjective assessments of the interview participants with regard to Involvement, the selected examples were evaluated using Zaichkowsky's Involvement-scale.
As a result of the qualitative study, I developed the "Customer-Engagement-Classification-Matrix", which classifies the Customer Engagement shown depending on the intensity of the internal state of arousal and the duration of Involvement. Divided into four quadrants, individual peculiarities with regard to the behaviors displayed, the motives, and the temporal developments could be identified. In addition, cross-quadrant characteristics and management implications could be derived.