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While research on customer engagement and service innovation is rapidly emerging, limited insight exists into the interface of these topic areas. However, given the shared notion of (e.g. customer/firm) interactivity across these concepts, the purpose of this paper is to explore their theoretical interface that remains nebulous to date.

Building on a literature synthesis, the authors develop an S-D logic-informed “hamburger” model of service innovation that depicts the service innovation process, and its ensuing outcomes for particular actor groups, including the firm, its customers, etc. They conclude by proposing frontiers for future research that arise from the model.

The authors explore the theoretical foundations of customer engagement and service innovation, and integrate these in their S-D logic-informed “hamburger” model of service innovation. In the model, they acknowledge the key role of organizational resources in enabling service innovation, which will interact with specific service innovation actors (e.g. customers, employees) to create successful service innovations. The model next proposes service innovation development and implementation, from which focal service innovation actors will seek, and derive, particular types of value (e.g. profit for the actor of the firm), as shown at the top of the model. They conclude by offering a set of future research directions that arise from the model.

The S-D logic-informed “hamburger” model of service innovation can be used to guide future research into service innovation, including studies investigating service innovation’s role in driving customer engagement and value.

The attained insight will be useful to managers seeking to enhance their service innovation-based returns (e.g. by suggesting ways in which service innovation can enhance customer engagement).

The authors propose a novel, S-D logic-informed “hamburger” model of service innovation and its key antecedents (e.g. firm-based resources) and consequences (e.g. customer engagement and value).

While (customer) engagement has been proposed as a volitional concept, our structuration theory/S-D logic-informed analyses of actors’ (e.g. employees’) engagement in service innovation reveal engagement as a boundedly volitional theoretical entity, which arises from actors’ structural and agency-based characteristics and constraints. In line with this observation, the purpose of this paper is to develop a conceptual model of actor (i.e. customer, firm, employee) engagement with service innovation.

Based on the observed gap, the authors propose an integrative S-D logic/structuration theoretical model that outlines three particular service innovation actors’ (i.e. customers’, the firm’s and employees’) engagement, which comprises institution-driven (i.e. fixed) and agency-driven (i.e. variable) engagement facets. In addition, the authors integrate the key expected characteristics of positively (vs negatively) valenced service innovation engagement for each of these actor groups in the analyses.

The authors develop a 12-cell matrix (conceptual model) that outlines particular service innovation actors’ institution-driven and agency-driven engagement facets and outline their expected impact on actors’ ensuing positively and negatively valenced engagement.

The authors discuss key theoretical implications arising from the analyses.

Outlining service innovation actors’ structure- and agency-driven engagement facets, the authors’ model can be used to explain or predict customers’, the firm’s or employees’ service innovation engagement-based activities.

The purpose of this paper is to propose a typology of institutions enabling or constraining customer centricity and value co-creation in service ecosystems; illustrate the various types of institutions with examples from healthcare; and provide case study evidence on how pharmaceutical companies react to and induce institutional change.

First, a typology of institutions enabling or constraining customer centricity and value co-creation is proposed and illustrated with examples from healthcare. Next, to clarify how companies deal with these institutions by reacting to or inducing institutional change, two case companies from the pharmaceutical industry are described.

The research identifies and illustrates nine types of institutions (culture, structure, processes, metrics, language, practices, IP, legislation and general beliefs) grouped by three levels of analysis (micro, meso and macro). Furthermore, the findings of the two case studies indicate that companies react to, but also proactively induce, institutional change.

The investigation is limited to two case studies.

Organizations need to understand the micro-, meso- and macro-level institutions of their service ecosystem; react to institutional changes imposed by other actors; and proactively change institutions by breaking, making or maintaining them.

Pharmaceutical companies can improve patient well-being by inducing institutional change.

This research develops a mid-range theory of service ecosystem institutions by developing a typology. This typology is empirically examined in a healthcare context.

Organizations (data gatherers in the context) drown in data while at the same time seeking managerially relevant insights. Academics (data hunters) have to deal with decreasing respondent participation and escalating costs of data collection while at the same time seeking to increase the managerial relevance of their research. The purpose of this paper is to provide a framework on how, managers and academics can collaborate better to leverage each other’s resources.

This research synthesizes the academic and the managerial literature on the realities and priorities of practitioners and academics with regard to data. Based on the literature, reflections from the world’s leading service research centers, and the authors’ own experiences, the authors develop recommendations on how to collaborate in research.

Four dimensions of different data realities and priorities were identified: research problem, research resources, research process and research outcome. In total, 26 recommendations are presented that aim to equip academics to leverage the potential of corporate data for research purposes and to help managers to leverage research results for their business.

This paper argues that both practitioners and academics have a lot to gain from collaborating by exchanging corporate data for scientific approaches and insights. However, the gap between different realities and priorities needs to be bridged when doing so. The paper first identifies data realities and priorities and then develops recommendations on how to best collaborate given these differences.

This research has the potential to contribute to managerial practice by informing academics on how to better collaborate with the managerial world and thereby facilitate collaboration and the dissemination of academic research for the benefit of both parties.

Whereas the previous literature has primarily examined practitioner–academic collaboration in general, this study is the first to focus specifically on the aspects related to sharing corporate data and to elaborate on academic and corporate objectives with regard to data and insights.

The study is motivated by business' mixed response to increasing demand for customer service, leaving the question as to its impact on performance open. The study is concerned with the impact of customers' perception of customer service (bad/good) on variables that are known to drive revenue, i.e. customer satisfaction, perceived relative attractiveness, and commitment.

Data were collected through a survey among bank customers. Two groups were sampled: customers who have experienced good or bad customer service. The hypotheses were tested by applying structural equation modeling and running two group analysis using the PLS and LISREL softwares.

Customers that experience bad customer service do take into account the same variables in their evaluation as do customers that experience good customer service. They do however, put different weights on every factor in the evaluation process. Also the strength of the relationships between the variables seems to differ. Typically, analyses showed that customers experiencing bad customer service tend to consider more thoroughly all aspects of the service; the relationships between the variables were stronger and the explained variance of each construct higher, than in the group of customers experiencing good customer service. However, the paths are not different across the groups.

The paper has only tested the model and hypotheses in one industry. Future research should test the same model using different industries reflecting different customer involvement levels.

From this study, service managers can learn that investing in customer service in ongoing customer relations is “the right thing to do” as it is linked to customer equity through customers' commitment to the firm. Second, as customer service in such relationships drives perceived relative attractiveness, saving the bottom line by cutting back on the human side of the customer interaction, may harm the firm's competitive position in the marketplace.

The impact of customer service on key performance variables in ongoing relations has to the knowledge never been studied before.

The purpose of this paper is twofold. First, develop and test a conceptual model to understand customers’ intention to adopt online complaining. Second, to assess two competing perspectives regarding elaboration likelihood for the moderating impact of individual differences.

A scenario‐based survey was used to assess respondents’ beliefs, attitude, and usage intentions toward online complaining. Furthermore, individual and situational characteristics were assessed. The data were analyzed using partial least squares path modeling.

Attitude toward online complaining is a function of both process and outcome beliefs. It is also influenced by individual characteristics, but remains unaffected by situational characteristics. In contrast, usage intentions are influenced by situational characteristics, but by personal differences. For the moderating impact of affect‐based personality characteristics, the often used cognitive effort perspective to elaboration likelihood is not supported. Rather the consumption value perspective applies for these variables.

The use of a single setting, as well as the use of scenarios, may negatively impact external validity. Future research is needed to further explain the contradictory perspectives regarding information processing.

The results provide insight into determinants of customer online complaining. This opens up new possibilities to increase the number of complainants in case of service failures and for firms to take corrective action.

To the authors’ best knowledge, this is a first empirical study aimed at understanding what drives online customer complaining.

The purpose of this research is to examine different customer satisfaction and loyalty metrics and test their relationship to customer retention, recommendation and share of wallet using micro (customer) level data.

The data for this study come from a two‐year longitudinal Internet panel of over 8,000 US customers of firms in one of three industries (retail banking, mass‐merchant retail, and Internet service providers (ISPs)). Correlation analysis, CHAID, and three types of regression analyses (best‐subsets, ordinal logistic, and latent class ordinal logistic regression) were used to test the hypotheses.

Contrary to Reichheld's assertions, the results indicate that recommend intention alone will not suffice as a single predictor of customers' future loyalty behavior. Use of a multiple indicator instead of a single predictor model performs better in predicting customer recommendations and retention.

The limitation of the paper is that it uses data from only three industries.

The presumption of managers when looking at recommend intention as the primary, even sole gauge of customer loyalty appears to be erroneous. The consequence is potential misallocations of resources due to myopic focus on customers' recommend intentions.

This is the first scientific study that examines recommend intentions and its impact on retention and recommendation on the micro (customer) level.