Conference PaperPDF Available

Value Co-Creation in Data-Driven Services: Towards a Deeper Understanding of the Joint Sphere

Authors:

Abstract and Figures

While the co-creation of value between provider and customer(s) is a common theme in service research, continued theoretical conceptualization is required to guide more effective design of services. In order to advance understanding of value co-creation, we have conducted a pre-study consisting of 16 interviews in the application domain of data-driven services-services that support the decision-making process of customers through the provision of data and analytics. Adopting the theoretical lens of joint spheres, we present empirical evidence that the proportions of the joint sphere can vary with differences in interaction, access to customer processes or behaviors and decision power. As a next step, we propose a final case study phase to further explore factors that influence the proportion of joint sphere and, thus, the provider's real value creation. Understanding how real value creation is impacted by the resource integration of provider and customer(s) will ultimately enable the purposeful design of data-driven services for effective value co-creation.
Content may be subject to copyright.
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 1
Value Co-Creation in Data-Driven Services:
Towards a Deeper Understanding of the
Joint Sphere
Short Paper
Ronny Schüritz
Karlsruhe Institute of Technology
Kaiserstraße 89, 76133 Karlsruhe,
Germany
ronny.schueritz@kit.edu
Killian Farrell
MIT Sloan School of Management
100 Main St, Cambridge, MA 02141
USA
killianf@mit.edu
Barbara Wixom
MIT Sloan Center for Information
Systems Research
100 Main St, Cambridge, MA 02141
USA
bwixom@mit.edu
Gerhard Satzger
Karlsruhe Institute of Technology
Kaiserstraße 89, 76133 Karlsruhe,
Germany
gerhard.satzger@kit.edu
Abstract
While the co-creation of value between provider and customer(s) is a common theme in
service research, continued theoretical conceptualization is required to guide more
effective design of services.
In order to advance understanding of value co-creation, we have conducted a pre-study
consisting of 16 interviews in the application domain of data-driven servicesservices
that support the decision-making process of customers through the provision of data and
analytics. Adopting the theoretical lens of joint spheres, we present empirical evidence
that the proportions of the joint sphere can vary with differences in interaction, access to
customer processes or behaviors and decision power.
As a next step, we propose a final case study phase to further explore factors that influence
the proportion of joint sphere and, thus, the provider’s real value creation. Understanding
how real value creation is impacted by the resource integration of provider and
customer(s) will ultimately enable the purposeful design of data-driven services for
effective value co-creation.
Keywords: value co-creation, joint sphere, smart services, data-driven services
Introduction
In recent years, new ways to collect and transmit large volumes of data have emerged and advancements in
computational power have created the ability to analyze them (Chen et al. 2012; Turner et al. 2014). This
technology evolution enables a liquefaction of resources in which knowledge and its transfer is decoupled
from physical objects and their movement (Lusch and Nambisan 2015). This creates resources (such as
knowledge and information) that flow instantly and seamlessly among actors. As a result, these actorsfor
instance, a provider and its customercan interact and engage with each other digitally (Lusch and
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 2
Nambisan 2015). Our understanding of such digital interaction and engagement is still unfolding and
requires refinement of the very nature of value co-creation (Nenonen and Storbacka 2018).
The need for a more nuanced conceptualization of value co-creation becomes evident in the context of data-
driven services, defined as the use of data and analytics by a provider to support a customer’s decision-
making process with the intent to create value for the customer; the data and analytics can stand alone as
an offering or be bundled with an existing product or service offering (Beverungen et al. 2019; Davenport
2013; Schüritz and Satzger 2016; Wixom and Ross 2017). This concept is not completely new: FedEx
introduced data-driven services in the late 80’s when the company added package delivery tracking to their
transportation services. FedEx customers used this tracking informationsuch as current shipment status
and predicted delivery timeto better manage their shipments (Baldwin 2013). Since that time, myriad
companies across a diverse range of industries have launched data-driven services that analyze data, guide
users through insights, and/or even automatically act on behalf of the user. In financial services, banks
developed financial spending dashboards and alerts to help customers manage their budgets and cash flow
(e.g., Alfaro et al. 2018). Heavy equipment manufacturers developed algorithms that predict parts failure
to help customers proactively maintain equipment and reduce operational downtime (e.g., Porter and
Heppelmann 2015). In these data-driven services contexts, our existing understanding of value co-creation
through actor-to-actor interaction (Grönroos and Voima 2013) is challenged because actors can be deeply
integrated in other actor’s processes and because interactions can take place through automated algorithms
(Storbacka et al. 2016).
Inspired by the proliferation of data-driven services and by the interesting new provider-customer
relationships and interactions that they represent, we sought to explore the co-creation of value in data-
driven services contexts. This research aims to contribute towards a more nuanced conceptualization of
value co-creation and resource integration. Specifically, we investigate factors that influence the space in
which providers and customers interact to co-create value, called the joint sphere (Grönroos and Voima
2013).
To do this, we conducted an initial research phase to examine the level of provider-customer integration
across a diverse set of 36 data-driven services examples. We analyzed the examples and interviewed a subset
of 16 executives of service providers associated with eight of those examples to refine our analysis. We
learned that in data-driven services contexts, the proportions of the joint sphere are determined by
interaction, access to customer processes and behaviors, and decision power. Only in the joint sphere can
the provider purposefully integrate resources to affect real value creationthat is, the value a customer
realizes in the use (value-in-use) of a service (Grönroos and Voima 2013). The final phase of our research
will explore the co-creation of value more comprehensively by collecting data from all participating actors
of a specific service system.
We first present a brief literature review of value co-creation and data-driven services. Next, we explain the
study methodology and analysis to date. We illustrate our current thinking by conceptualizing the
influencing factors of the joint sphere and real value creationsupported by examples from our application
domain. The paper closes with a discussion of preliminary results and a description of our next steps.
Related Work
Value Co-Creation
The goods-dominant logic views products and services as outputs of a production process that are valuable
as objects in existence. This value is consumed and destroyed by the user or customer. By contrast, service-
dominant logic (S-D logic) considers the value of an offering as co-created through the application of the
operand and operant resources of both provider and customer (Vargo and Lusch 2004). Ultimately, this
means that value is not determined by the features of an offering but by the value perceived by the user
(Vargo and Lusch 2004). The service-dominant view and the co-creation lens have found broad adoption,
and authors have focused on a wide range of aspects of this view. This has included modifying the initial
conceptualization (e.g., Chandler and Vargo 2011), adding new perspectives (e.g., Payne et al. 2008),
looking into applications (e.g., Yan et al. 2010), and clarifying issues around the perception of co-creation
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 3
as such (e.g., Vargo and Lusch 2016). Moreover, S-D logic serves as the foundation for service science
(Spohrer and Maglio 2008; Vargo and Lusch 2016).
Many scholars agree that the initial S-D logic view on co-creation was too vague to sufficiently understand
value creation between a service provider and a customer (e.g., Ballantyne et al. 2011; Grönroos and Voima
2013). Thus, Grönroos and Voima (2013) proposed that co-creation results from the interaction between
providers and customers, and introduced the concept of provider, customer, and joint spheres (cf. Figure
1). This view specifies co-creation to be a joint process in which firms (or other actors) and customers
together create value in interaction (Grönroos and Voima 2013). Before this joint creation of value, the
provider acts independently and without customer involvement in the provider sphere. In the provider
sphere, the provider facilitates value creation, that is, the provider creates potential value for a customer.
The customer then (in the customer sphere) independently transforms potential value from the provider
into value-in-use or real value (Gummesson 2007). Therefore, the only way the provider can directly
influence real value is by interacting with the customer and co-creating value in the joint sphere. The joint
sphere is defined as the locus where value co-creation in interaction takes place (Grönroos and Voima
2013).
Figure 1. Value creation spheres, adapted from Grönroos and Voima (2013)
The concept of value creation spheres was criticized by Vargo and Lusch (2016) as being incomprehensible
and without valuable contribution for scholars and practitioners (Vargo and Lusch 2016). Specifically, they
criticized the poor distinction between “facilitate” and “co-create (Vargo and Lusch 2016). Both
perspectives contribute intriguing and useful insights to the conversation and help us to understand how
value is co-created between actors. While Vargo and Lusch (Lusch and Vargo 2014; Vargo and Lusch 2016)
see the beneficiary of a service always as a co-creator of value, Grönroos and Voima (2013) add a more
differentiated view by determining that co-creation only occurs if provider and beneficiary interact in the
joint sphere.
Data-Driven Services
Information systems that gather, store, access, and analyze data to help business users make better
decisions, commonly referred to as decision support systems, have been widely discussed in the IS literature
(Chen et al. 2012; McAfee and Brynjolfsson 2012; Watson 2009). When building decision support systems
to generate efficiencies, the literature usually distinguishes among three steps: data, insight, and action
sometimes referred to as the data value chain (Watson 2009; Wixom et al. 2013).
Beyond using data to achieve operational efficiencies for themselves, companies can also use their data to
innovate service offerings and, thus, create meaningful value for customers. Practitioners and scholars alike
have turned to integrating this notion into their frameworks, beliefs, and systems (Beverungen et al. 2019;
Davenport 2013; Gottlieb and Rifai 2017). Because this research is still developing, many conceptualizations
and terms are being proposed. Some academics (Allmendinger and Lombreglia 2005; Beverungen et al.
2019; Spohrer et al. 2018) use the term smart services” to describe service offerings based on smart
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 4
products; smart products have connections, sensors, actuators, and interfaces that enable the products to
communicate and act. Schüritz et al. (2017) and Hunke et al. (2019) see the opportunity to utilize data and
analytics in services as part of the approach to create a new value proposition for the customer. Regardless
of different conceptualizations or terms, current research in this space consistently describes the use of data
and analytics to support the decision-making process of the customer via data and analytics-based features
and experiences in form of a stand-alone offering or bundled with an existing product or service; we refer
to this phenomenon as data-driven services.
Methodology
To explore how companies use data and analytics to co-create value with customers, we adopted an
exploratory qualitative approach that would help us learn about a new phenomenon, develop constructs
and possible relationships, and generate explanations for our observations (Myers 1997).
First, we assembled a database of diverse data-driven services examples. We involved 86 executives who
were responsible for the analytics strategy in large, multinational companies (e.g., Chief Data Officers, Chief
Analytics Officers). Within the context of a closed online communitymoderated by two members of the
research team—we asked the executives to provide their “best example in which [their] organization
provides customers with some form of data and/or analytics to increase the value of a product or service”.
The community provided us with 36 examples of which 27 were data-driven services. The others
represented traditional decision support systems applications that did not provide direct value to the
customer. For the data-driven services examples, we identified differences in maturity and in the level of
integration between provider and customer. We therefore choose to conduct interviews for select examples
to better understand meaningful distinctions.
In order to purposefully select a subset of the 27 service examples, we established a set of selection criteria
that would generate diverse perspectives (Patton 2002): we identified a set of data-driven services that were
launched and in use by customers, represented a variety of industries, and represented both B2B- and B2C-
relationships. Based on these criteria we selected eight examples, and conducted 16 semi-structured
interviews of 30 to 60 minutes with the product manager of each service as well as their analytics
counterpart—both interviewed about the same data-driven services example (cf. Table 1). The paired
interviews provide complementary information as to the nature of the service, the service design and
development, the evolution of the service, and the value creation for the customer. In two examples, the
product manager was analytics-savvy enough to answer our questions independently, and in two examples,
both a data and an analytics manager answered our set of technical questions.
All interviews were conducted via an online video conferencing tool, recorded (with consent), and
transcribed. Researchers analyzed the data independently in several rounds of coding, using memoing and
an open coding approach. We later applied axial coding to group the codes into categories (e.g., type of
Table 1. Overview of interviews
Service
Industry
Revenue
Product
manager
Alpha
Professional Services
>1B USD
ü
Beta
Financial Services
>15B USD
ü
Gamma
Telecommunication Provider
>20B USD
ü
Delta
Financial Services
>15B USD
ü
Epsilon
Technology Provider
>1B USD
ü
Zeta
Governmental Agency
N/A
ü
Eta
Financial Services
>10B USD
ü
Theta
Information Services
>5B USD
ü
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 5
interaction between provider and customer, resulting customer benefits, etc.), which we then further
developed into common themes and concepts. Through continuous abstraction in small workshops among
the researchers, we identified different manifestations of the joint spheres and extracted factors that we
found to impact the joint sphere during value co-creation.
Observations
In our analysis of data-driven services examples, the joint sphere (Grönroos and Voima 2013) helped to
distinguish the provider alone creating potential value and the provider creating real value with the
customer. By distinguishing the creation of potential value from the creation of real value, we could identify
varying proportions of joint spheres in the data-driven services examples. We termed three primary varying
proportions as no or limited joint sphere, developed joint sphere, and extensive joint sphere. We also
identified properties of services that seemed associated with different proportions.
Co-Creation in Data-Driven Services
Data-driven services without any or with only a limited joint sphere provide data in raw or aggregated
form to customers. The provider facilitates value creation by collecting as well as preprocessing data and by
"delivering” the data via reports, dashboards, or APIs; the provider does not have direct understanding of
how clients use the data, what insights they derive, or how the provided data influences their actions.
Customers derive insights and take actions themselvesas our example from Beta illustrates: A bank
provides its institutional customers with an API to allow them to access their financial transactions and to
download the transactions into internal processes if and how the customers choose. A limited join sphere
may exist if customers give the provider adoption requirements, such as technical file requirements or fields
to include; this happened in the Beta example as an API was customized for top customers.
We identified another category of data-driven services through which providers deliver insights to
customers. The insights manifest as the provision of alerts, identification of aberrational activities,
comparisons against benchmarks, or proposals of next steps. In these cases, the joint sphere includes more
provider-customer activity; for example, the customer supplies the provider with data and information to
incorporate into services; customers also provide the provider with requirements regarding their intended
independent value creation. The services, however, still depend on the customer action to turn insights into
real value: for instance, in our data-driven services examples, the professional service firm Alpha offers
financial auditing for its business customers and uses investigative and trend analytics to make the process
more efficient, and customers provide access to data as part of the audit process. Using the engagement
data and engagement insights, Alpha developed data-driven services in the form of scores that reflect
fraudulent or corrupt practices and transactions. Although the scores could impact real value creation if
acted upon; the customer creates real value independently only if corrective actions are put in place post-
engagement.
Finally, we observed an extensive joint sphere in some of our examples. In these cases, providers directly
acted with or on behalf of the customer in ways that generated real value, e.g., in performing predictive
maintenance to avoid unplanned machine downtime. The action manifested as manual interventions or
automated processes. For example, in our example at Epsilon, hearing aid users grant access to behavioral
data and give the provider permission to control device settings. The provider then automatically adjusts
the hearing aid settings using algorithms that optimize hearing based on a noise context while the customer
is wearing the device. In these cases, the provider and customer processes and practices are deeply
interwoven, and there is a large joint sphere in which the provider participates substantially and regularly
in real value creation and can ensure that real value is created.
Table 2 illustrates a continuous spectrum (rather than distinct categories) of possible joint sphere
constellations. The table provides actions in the provider sphere for value facilitation, actions in the joint
sphere for real value creation, and actions in the customer sphere for independent real value creation. We
include supporting quotes regarding joint sphere activities for each spectrum.
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 6
Towards Re-Conceptualizing the Joint Sphere for Value Co-Creation
Our analysis of data-driven services leads us to a re-conceptualization of the joint sphere. We consider the
joint sphere to include the range in which the provider directly affects real value creation by integrating
resources with the customer. Our analysis identified three factors that may impact the proportion of the
joint sphere: interaction, access to customer processes and behaviors, and decision power (cf. Figure 2):
1. Interaction is defined by “physical, virtual, or mental contact, such that the provider creates
opportunities to engage with its customers’ experiences and practices and thereby influences their flow
and outcomes” (Grönroos and Voima 2013, p. 140). In data-driven services, interaction includes
contacts like data exchange, requirements elicitation, automated actions, etc. More interactions and
two-way interactions - provide opportunities for more and deeper understanding of customer needs
and meaningful services.
2. Access to customer processes or behaviors represents how and when a provider can integrate into the
daily activities of the customer. Increased access to customer processes and/or behaviors increases a
provider’s context knowledge of customer motivations, problems and latent needs. In addition, access
can better position a provider to influence or perform customer processes and behaviors in a beneficial
way, thus ensuring real value creation.
3. In this services context, we define decision power as the extent to which one actor can make decisions
regarding the decision-making process of another actor. Traditionally, the customer controls real value
creation and retains full decision power regarding services. Our analysis revealed examples in which
the provider has some decision power and thus can directly affect real value creation through action-
based data-driven services.
Table 2. Exemplary activity in spheres
Joint Sphere
Spectrum
Provider Sphere
Joint Sphere
Customer
Sphere
Quote
No or
Limited
Joint
Sphere
§ Collecting and
preprocessing
data
§ Optional: customer
communicates
requirements
§ Deriving
insights and
acting
[…] [W]e don’t know what is
the intended user of that
information once it’s delivered
to the customer.”
Beta, product manager
Developed
Joint
Sphere
§ Collecting and
preprocessing
data
§ Applying
resources to
derive insights
from data
§ Customer shares
information and gives
access
§ Provider gains
knowledge about the
customer to derive
insights from data
§ Acting based
on provided
insight
“[The data-driven service]
allows them to see insights on
their business, and also
anonymized insights on their
peers, and maybe the area
they’re in […].”
Delta, product manager
Extensive
Joint
Sphere
§ Applying
resources to
derive insights
from data
§ Customer opens
processes and grants
decision rights
§ Provider empowered
to make decisions and
changes in customer
processes and
practices
§ No
independent
action
[…] [I]t’s really the processor
itself […] monitoring or
classifying the environment on
an ongoing basis. […] [T]he
software automatically adjusts
its setting to the appropriate
settings for that environment.”
Epsilon, product manager
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 7
We believe the joint sphere can be enlarged through some combination of interaction, access and/or
decision power. To maximize the joint sphere, we believe that all three factors need to be fully developed.
Figure 2 gives an overview of the relationship between the joint sphere, the factors influencing its
proportion, and potential and real value creation (via resource integration).
Discussion
Using data and analytics to inform decision making has long been an area of interest for academia and
industry alike. Enriching product and service portfolios by helping customers make decisions has recently
become an interesting strategy by companies and is now being actively explored by academics. When actors
co-create value using data-driven services, the data value chain is shared between a provider and a
beneficiary. The design of the service defines what activities are conducted by which actor, such as collection
of data, preprocessing of data, the application of analytics on this data to derive insights, and even decision
making and resultant action. Depending on the nature of the interaction, the access to customer processes
or behaviors, and the decision power, the data value chain may be shared in a way that creates a larger joint
sphere. Ultimately, the way the data value chain is shared has a tremendous impact on value creation. If the
nature of sharing increases the joint sphere, then the provider can participate in more real value creation.
For some time, there has been a call for a refinement of how actors contribute resources to create value
(Lusch and Nambisan 2015; Nenonen and Storbacka 2018). We see value in a more nuanced view of co-
creation of value by taking the perspective that the provider facilitates value in the provider sphere and only
creates real value in the joint sphere. In our data, this distinction was important, and we argue that the joint
sphere determines whether resources that are integrated by the provider are used purposefully to ensure
that potential value is realized and transformed into real value. Interaction initially defined the size of the
joint sphere (Grönroos and Voima 2013). This conceptualization, however, does not include the impact of
access to customer process and behaviors and the impact of decision power. We believe a deeper
understanding of the joint sphere includes three factors that influence the joint sphere proportion.
Future Research
Our research at this point has a strong dyadic customer and service provider perspective because it does
not consider other actors. Further, our research to date only included data collected from providers.
To further advance this research on value co-creation using data-driven services, we want to shift to a more
comprehensive perspective and collect data from all participating actors in data-driven services setting,
including customers. We intend to perform a series of case studies of companies that have data-driven
services with varying proportions of joint spheres. We intend to collect data from 2-3 different companies
in two different industries, and we would like one industry to reflect a B2B business types and the other to
reflect B2C.
Figure 2. Re-conceptualized value creation spheres
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 8
Within each case study we will review artifacts that that help us understand the service design and value
creation process (e.g. websites and presentation material). We will interview representatives of the provider
including product managers, service managers and executives, and technical experts. In addition, we will
interview multiple representatives of the customer. In a B2B context this will include the department that
is benefiting from the service as well as departments that are concerned with service integration. In a B2C
context this will include end users. For a comprehensive treatment of the context, we also plan to include
other actors that participate in the value creation process, such as IT service providers, partners and
platforms (Yin 2013). Data will be analyzed using qualitative methods.
The case study phase results will help us refine the concepts presented earlier in Table 2. Prior to ICIS 2019,
we will identify case sites and set up researcher visits, draft the case study interview questions, and collect
and review publicly-available artifacts in preparation for the visits. We intend to begin interview visits after
we receive and incorporate feedback during the ICIS conference.
Conclusion
Through the analysis of examples and interviews in the domain of data-driven services, we identified three
factors influencing the provider and customer joint sphere of value co-creation: interaction, access to
customer processes or behaviors, and decision power. We suggest that the proportion of the joint sphere
determines the purposeful integration of resources, which enables the provider to influence real value
creation with the customer using data-driven services. While our data collection limits our findings to the
domain of data-driven services, we believe that they are applicable for value co-creation in general.
For academics, we believe that this more nuanced conceptualization of value creation and the joint sphere
sheds new light on our understanding of value co-creation and the service logic. Adopting and manifesting
this more nuanced conceptualization of value co-creation enables academics to investigate value co-
creation in a digital era in which interaction take place via digital interfaces or devices and decisions are
enacted using algorithms.
For practitioners, our findings inform service design by proposing factors beyond interaction that can
directly affect real value creation in data-driven services. By further developing our findings, we hope to
equip managers with a better understanding of how to influence real value creation for customers in more
and different ways.
References
Alfaro, E., Bressan, M., Girardin, F., Murillo, J., Someh, I., and Wixom, B. 2018. “BBVA’s Data Monetization
Journey,” MIS Quarterly Executive (18:2).
Allmendinger, G., and Lombreglia, R. 2005. “Four Strategies for the Age of Smart Services,” Harvard
Business Review (83:10), pp. 131145.
Baldwin, R. 2013. “Shipshape: Tracking 40 Years of FedEx Tech,” Wired.
Ballantyne, D., Frow, P., Varey, R. J., and Payne, A. 2011. “Value Propositions as Communication Practice:
Taking a Wider View,” Industrial Marketing Management (40:2), pp. 202210.
Beverungen, D., Müller, O., Matzner, M., Mendling, J., and vom Brocke, J. 2019. “Conceptualizing Smart
Service Systems,” Electronic Markets (29:1), Electronic Markets, pp. 718.
Chandler, J. D., and Vargo, S. L. 2011. “Contextualization and Value-in-Context: How Context Frames
Exchange,” Marketing Theory (11:1), pp. 3549.
Chen, H., Chiang, R. H. L., and Storey, V. C. 2012. “Business Intelligence and Analytics: From Big Data to
Big Impact,” MIS Quarterly (36:4), pp. 11651188.
Davenport, T. H. 2013. “Analytics 3.0,” Harvard Business Review (91:12).
(https://hbr.org/2013/12/analytics-30).
Gottlieb, J., and Rifai, K. 2017. “Fueling Growth through Data Monetization.”
(https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/fueling-growth-
through-data-monetization?cid=other-eml-alt-mip-mck-oth-1712, accessed April 16, 2019).
Grönroos, C., and Voima, P. 2013. “Critical Service Logic: Making Sense of Value Creation and Co-
Creation,” Journal of the Academy of Marketing Science (41:2), pp. 133150.
Gummesson, E. 2007. “Exit Services Marketing Enter Service Marketing Keywords:,” The Journal of
Customer Behaviour (6:2), pp. 113141.
Value Co-Creation in Data-Driven Services
Fortieth International Conference on Information Systems, Munich 2019 9
Hunke, F., Engel, C., Schüritz, R., and Ebel, P. 2019. “Analytics-Based Services a Taxonomy To
Conceptualize the Use of Data And,” in Twenty-Seventh European Conference on Information Systems
(ECIS2), Stockholm-Uppsala.
Lusch, R. F., and Nambisan, S. 2015. “Service Innovation: A Service-Dominant Logic Perspective,” MIS
Quarterly (39:1), pp. 155175.
Lusch, R. F., and Vargo, S. L. 2014. Service-Dominant Logic: Premises, Perspectives, Possibilities,
Cambridge: Cambridge University Press.
McAfee, A., and Brynjolfsson, E. 2012. “Big Data. The Management Revolution,Harvard Buiness Review
(90:10), pp. 6168.
Myers, M. D. 1997. “Qualitative Research in Information Systems,” Management Information Systems
Quarterly (21:June), pp. 118.
Nenonen, S., and Storbacka, K. 2018. “Actors, Actor Engagement and Value Creation,” Journal of Creating
Value (4:2), pp. 196198.
Patton, M. 2002. Qualitative Research and Evaluation Methods, (3rd ed.), Thousand Oaks, CA: SAGE.
Payne, A. F., Storbacka, K., and Frow, P. 2008. “Managing the Co-Creation of Value,” Journal of the
Academy of Marketing Science (36:1), pp. 8396.
Porter, M. E., and Heppelmann, J. E. 2015. “How Smart, Connected Products Are Transforming
Companies,” Harvard Buiness Review (93:10), pp. 97114.
Schüritz, R., and Satzger, G. 2016. “Patterns of Data-Infused Business Model Innovation,” in Proceedings
of IEEE 18th Conference on Business Informatics (CBI), pp. 133142.
Schüritz, R., Satzger, G., Seebacher, S., and Schwarz, L. 2017. “Datatization as the Next Frontier of
Servitization Understanding the Challenges for Transforming Organizations,” in International
Conference on Information Systems (ICIS), Seoul, pp. 121.
Spohrer, J., and Maglio, P. P. 2008. “The Emergence of Service Science: Toward Systematic Service
Innovations to Accelerate Co-Creation of Value,” Production and Operations Management (17:3), pp.
238246.
Spohrer, J., Moghaddam, Y., Allen, D., Demirkan, H., Bess, C., and Rayes, A. 2018. “Innovations with Smart
Service Systems: Analytics, Big Data, Cognitive Assistance, and the Internet of Everything,”
Communications of the Association for Information Systems (37).
Storbacka, K., Brodie, R. J., Böhmann, T., Maglio, P. P., and Nenonen, S. 2016. “Actor Engagement as a
Microfoundation for Value Co-Creation,” Journal of Business Research (69:8), Elsevier Inc., pp. 3008
3017.
Turner, V., Gantz, J., Reinsel, D., and Minton, S. 2014. The Digital Universe of Opportunities: Rich Data
and the Increasing Value of the Internet of Things., IDC.
Vargo, S. L., and Lusch, R. F. 2004. “Evolving to a New Dominant Logic for Marketing,” Journal of
Marketing (68:1), pp. 117.
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 (44:1), pp. 523.
Watson, H. J. 2009. “Tutorial: Business Intelligence Past, Present, and Future,” Communications of the
Association for Information Systems (25:39), pp. 487510.
Wixom, B. H., and Ross, J. W. 2017. “How to Monetize Your Data,” MIT Sloan Management Review (58:3).
Wixom, B. H., Ross, J. W., and Beath, C. M. 2013. “ComScore, Inc.: Making Analytics Count,” MIT CISR
Working Paper (Vol. 392).
Yan, J., Ye, K., Wang, H., and Hua, Z. 2010. “Ontology of Collaborative Manufacturing: Alignment of
Service-Oriented Framework with Service-Dominant Logic,” Expert Systems with Applications (37:3),
pp. 22222231.
Yin, R. K. 2013. Case Study Research: Design and Methods, Applied Social Research Methods Series, (L.
Bickman and D. J. Rog, eds.), Sage publications.
... Data-driven business models are digital business models with a conceptual focus on value creation with data (Guggenberger et al., 2020). DDBMs rely on data as a key resource and apply data analytics techniques as key activities to discover insights from data and that are transformed into a data-based value proposition that supports customers in their decision-making process (Hartmann et al., 2016;Kühne and Böhmann, 2019;Schüritz et al., 2019). Other researchers denote such models as »data-infused business models« (Schüritz and Satzger, 2016) or »data-driven services« (Azkan et al., 2020). ...
...  A service provider is an actor who utilizes data as a resource to create or co-create value for other actors (Immonen et al., 2014;Kaiser et al., 2019;Schüritz et al., 2019), for instance, by adding data-driven services to a physical product (Terrenghi et al., 2018).  A customer is a recipient of the offering who also has a need. ...
... Such values are denoted in general as services (e.g., Immonen et al., 2014;Täuscher and Laudien, 2017) and digital offerings (Sklyar et al., 2019;Täuscher and Laudien, 2017). On a more granular level, flows can be divided into data (e.g., Engelbrecht et al., 2016;Terrenghi et al., 2018), information (e.g., Curry, 2016;Schüritz et al., 2019), knowledge (e.g., Brownlow et al., 2015;Schüritz et al., 2019), and models or configuration of models (Hirt and Kühl, 2018). ...
Conference Paper
The increasing volume of available data and the advances in analytics and artificial intelligence hold the potential for new business models also in offline-established organizations. To successfully implement a data-driven business model, it is crucial to understand the environment and the roles that need to be fulfilled by actors in the business model. This partner perspective is overlooked by current research on datadriven business models. In this paper, we present a structured literature review in which we identified 33 relevant publications. Based on this literature, we developed a framework consisting of eight roles and two attributes that can be assigned to actors as well as three classes of exchanged values between actors. Finally, we evaluated our framework through three cases from one automotive company collected via interviews in which we applied the framework to analyze data-driven business models for which our interviewees are responsible.
... With empowering technologies, such as sensor technology, IoT, and cloud computing, product-centric companies can analyze data from and about their customers, creating new, more timely, and accurate services, which are more appealing than purely nondigital services [38], [42]. Data and analytics support the customer's decision-making process giving sound data-based insights and creating new customer value [39], [43]. These novel value propositions use data as the key resource and are called data-driven services [10], [13]. ...
... SDL focuses on "value-in-use" rather than "value-in-exchange" [5], [55]. That implies that value is not determined by the features of an offering but by the value perceived by the user [43], [52]. A critical factor in SDL is value cocreation that describes collaborative and reciprocal value creation between actors and entities through mutually beneficial integration of resources [43], [56]. ...
... That implies that value is not determined by the features of an offering but by the value perceived by the user [43], [52]. A critical factor in SDL is value cocreation that describes collaborative and reciprocal value creation between actors and entities through mutually beneficial integration of resources [43], [56]. In the context of data-driven services, customers are mainly involved in value creation since data are the central resource, primarily coming from sensors and IoT devices generated in customer processes [5], [53], [57]. ...
Article
Full-text available
The continuously growing availability and volume of data pressure companies to leverage them economically. Subsequently, companies must find strategies to incorporate data sensibly for internal optimization and find new business opportunities in data-driven business models. In this article, we focus on using data and data analytics in product-oriented industrial companies. Although data-driven services are becoming increasingly important, little is known about their systematic design and development in research. Surprisingly, many companies face significant challenges and fail to create these services successfully. Against this background, this article presents findings from a multicase based on qualitative interviews and workshops with experts from different industrial sectors. We propose ten design principles and corresponding design features to successfully design industrial data-driven services in this context. These design principles help practitioners and researchers to understand the peculiarities of creating data-driven services more in-depth on a conceptual, technical, and organizational level.
... Nevertheless, Nenonen and Storbacka [24] argued that the comprehension of such digital interaction and engagement is still unfolding and requires refinement of the very nature of value co-creation. Schüritz et al. [25] raised the need for a more nuanced conceptualization of value co-creation in the context of data-driven services, addressing the use of data and analytics by a provider to support a customer's decisionmaking process, with the intent to create value for the customer. In detail, when actors cocreate value using data-driven services, this context is shared between a provider and a beneficiary and the design of the service defines what activities are conducted by which actor, such as collection of data, preprocessing of data, the application of analytics on this data to derive insights, and even decision making and resultant action [25]. ...
... Schüritz et al. [25] raised the need for a more nuanced conceptualization of value co-creation in the context of data-driven services, addressing the use of data and analytics by a provider to support a customer's decisionmaking process, with the intent to create value for the customer. In detail, when actors cocreate value using data-driven services, this context is shared between a provider and a beneficiary and the design of the service defines what activities are conducted by which actor, such as collection of data, preprocessing of data, the application of analytics on this data to derive insights, and even decision making and resultant action [25]. More recently, the topic has been approached more specifically, whereas when humans can flexibly cope with unexpected inputs from the customer and still co-create value, while IT technology is inherently limited to dealing within the range of inputs and requests it was designed to do. ...
Article
Full-text available
Artificial Intelligence-based Decision Support Systems (AI-based DSS) are becoming increasingly important in many contexts. This work aims to define a type of human-machine interactions for new value co-creation processes' ranks, to help identify factors that can stimulate value co-creation in human-machine interactions. To understand if the outcome of a man-machine interaction can contribute to the co-creation of value, and in what way, the work carried out is epistemological and typological, also based on System Thinking. A matrix of novel gradients of the relationships between humans and non-humans has been created, and the typology of human-machine interactions has been identified for the new degrees of value co-creation processes, as well as the new specific scale of skills, in terms of language, learning, know-how, level of trust and endowment of knowledge, as a whole. The main implications concern the need to customize Decision Support Systems (DSS), to enhance different levels of intensity of relationships, and to identify insights for Decision Making AI - based users.
... This extensive digital networking is made possible by the Internet of Things which ensures the creation of huge amounts of data, which in turn enable companies to offer innovative services based on this information [6]. Such are known as data-driven service [7,8], smart service [9,10], or advanced service [11,12]. Further we will continue to use the term smart service in this study. ...
... By sharing information and providing access, the provider gains knowledge about the customer so that the services can be tailored to the client. In addition, the provider can use insights from the data for further development [7]. ...
... Customer relation (Berger et al. 2020;Coreynen et al. 2017;Culot et al. 2020;Kamalaldin et al. 2020;Paiola and Gebauer 2020;Schüritz et al. 2017;Vargo and Lusch 2004;Zhang et al. 2019) Co-creation (Bu et al. 2021;Herterich et al. 2016a;Kohtamäki et al. 2019;Rabetino et al. 2017;Schüritz et al. 2017;Vargo and Lusch 2004) Continuous improvement (Beverungen et al. 2019;Frank et al. 2019b;Kamalaldin et al. 2020;Porter and Heppelmann 2014;Schüritz et al. 2019) Process ownership (Berger et al. 2020;Raddats et al. 2019;Riasanow et al. 2017;Schüritz et al. 2017) IT-organization type (Bilgeri et al. 2019;Bilgeri et al. 2017) Customization ( Pricing (Cenamor et al. 2017;Paiola and Gebauer 2020;Rabetino et al. 2017;Schüritz et al. 2017) Value creation in networks (Bürger et al. 2019;Lusch and Nambisan 2015;Riasanow et al. 2017;Vargo and Lusch 2004) Continuous innovation (Frank et al. 2019b;Kamalaldin et al. 2020;Porter and Heppelmann 2014) Skillset (Culot et al. 2020;Kohtamäki et al. 2019;Porter and Heppelmann 2014;Rabetino et al. 2017) Interdisciplinary teams (Dremel et al. 2017;Herterich et al. 2016b) Openness & agility (Berger et al. 2020;Bueno et al. 2020;Gimpel et al. 2018;Tronvoll et al. 2020) Costs (Gebauer et al. 2005;Kohtamäki et al. 2019;Rabetino et al. 2017; Data-driven culture (Baines and Lightfoot 2014;Dremel et al. 2017;Schüritz et al. 2017) Integration and interfaces (Birch-Jensen et al. 2020;Coreynen et al. 2017;Porter and Heppelmann 2015) Strategic Partnerships (Herterich et al. 2016b;Kamalaldin et al. 2020;Rabetino et al. 2017;Sjödin et al. 2020) Simultaneously, this means that service system participants need new skills and must work in new organizational structures. On the one hand, interdisciplinarity and, on the other, the collaboration between IT and the business are emphasized (IT-organization type). ...
... Third, our study neglects the potential of multiple actors aligning their actualization actions, which might be a valuable perspective to operationalize the understanding of value co-creation in smart service systems. Hence, it might be interesting to further investigate the interaction in the joint sphere of smart service systems and how such interaction can be purposefully promoted-e.g., by building trust among actors or formalizing governance mechanisms [49,53]. If all these research issues can be resolved, the results might also contribute to general affordance theory by adapting and expanding the theory's implications from an organizational towards an (eco-)system-level where multiple actors and technologies jointly give rise to and realize affordances. ...
Conference Paper
Full-text available
Smart physical products increasingly shape a connected IoT world and serve as boundary objects for the formation of ‘smart service systems’. While these systems bear the potential to co-create value between partners in various industries, IS research still struggles to fully capture the phenomenon to support successful digital innovation in IoT settings. In our work, we analyze the phenomenon of smart service systems taking an affordance-actualization perspective. Based on a qualitative content analysis of a multi-case study, we identify elements and propositions to build mid-range theoretical knowledge for smart service systems. We suggest that providers and users of smart products not only realize their own affordances via their actions but might also affect the immediate concrete outcomes of partners. The developed theoretical framework and six distinct propositions should build the theoretical base for further research into the phenomenon in IS research.
... It builds upon the service-dominant logic (SDL), which declares that value is always co-created, i.e., that value results from the interaction and resource integration of multiple actors for mutual benefit [1]. Yet, scholars agree that the SDL view on value co-creation is still too abstract to be empirically observable [18,19,25]. With the concept of actor engagement, Storbacka et al. [18] address the need for a more nuanced perspective on value co-creation [19,26]. ...
Chapter
Full-text available
Digital platforms (e.g., Industrial Internet of Things (IIoT) platforms) are on the rise aiming to foster value co-creation in business-to-business (B2B) ecosystems. However, we often observe actors to only hesitantly engage, and activity levels that fall short of expectations. Arriving at a sound understanding of why and how actors decide to engage in co-creation practices is a crucial first step to further promote and facilitate value co-creation in B2B platform ecosystems. This work builds upon the concept of actor engagement, which offers an actor-centric microlens on the hitherto vague theoretical idea of value co-creation. By pursuing a qualitative approach to theory development based on interviews with platform complementors, we identify factors influencing the formation and extent of actor engagement. Eventually, our research aims to contribute to a refined conception of value co-creation in B2B platform ecosystems by understanding the emergence and nature of actor engagement.
... Kim et al. (2018) identify value co-creation in the case of a digital content platform through convergence, re-purposing, and co-production among actors such as broadcasting companies, entertainment agencies, and fans. Schüritz et al. (2019) illustrate that value co-creation between a data-driven service provider and a customer depends on the size of their so-called 'joint sphere'. The joint sphere can be enlarged through increased interaction (e.g., data exchange, automated actions), improved access to the processes and/or behaviors of the customer, and greater decision-making power, which is defined as the degree to which one actor can decide things for another actor. ...
Article
Full-text available
In recent years, a change in business logic from goods-dominant (G-D) to service-dominant (S-D) logic can be observed widely. For instance, in the case of the mobility sector, companies such as Daimler AG and the BMW Group are shifting from solely producing cars to also providing mobility services. One fruit of their efforts is the Reach Now app, which supports users by combining multiple mobility services. Although such an app can contribute significantly to achieving smart mobility and thereby making the use of the private car less predominant, only a relatively small number of people use it. In this article, we adopt the S-D logic perspective to analyze the link between value formation (i.e., value co-creation and co-destruction) in customer-to-business relationships and business-to-business relationships in the service ecosystem of the Reach Now app based on an analysis of customer reviews of the Reach Now app in the Android Google Play Store between 2016 and 2019. We complement this analysis with interviews with representatives from six German public transport organizations and the Moovel Group GmbH, the app provider. Based on our analysis, we develop an interactional phase-based perspective on value formations in the tripartite relationship between app users, the Moovel Group GmbH, and public transport organizations. Our work complements previous S-D logic studies that (1) do not focus on information technology-enabled value formation, (2) neglect the concept of value co-destruction, (3) analyze only single dyadic actor-to-actor relationships, and/or (4) examine an established service ecosystem.
... Currently, the internet of things (IoT) and the availability of new sources of data are at the focus of numerous articles, most of which examine their role in service systems theoretically and practically (Beverungen et al., 2018;Böhmann et al., 2014). Data application can either be bundled with existing products or services, enhancing the efficiency ("data-enriched products and services"), or create new opportunities for delivering and creating value ("data-driven services") (Schüritz et al., 2019). To describe this new phenomenon, Allmendinger and Lombreglia coined the term smart services (Allmendinger and Lombreglia, 2005), which was later conceptualised as "the application of specialised competences, through deeds, processes, and performances that are enabled by smart products" (Beverungen et al., 2019b, p. 12). ...
Conference Paper
Full-text available
IoT generates an enormous amount of data during their operation, which can be analysed and interpreted to discover useful knowledge and ultimately to support or make decisions. Smart service systems constitute networks of multiple actors that co-create value based on this potential of IoT. The intention to adopt IoT is positively related to the willingness of the actors to co-create. As smart service systems are embedded in an overarching social system, the socio-economic context significantly influences this willingness to co-create. In this article, we argue that this systemic perception of value co-creation is an alternative approach to study IT adoption by broadening the focus to all actors involved and to the macro context. We collected interview and secondary data from six firms that offer or participate in IoT applications in Mexico. We identified several factors that motivate and inhibit the company's willingness to co-create. Our study extends research on smart service systems design and provides empirical data of a specific socio-economic context.
Conference Paper
Service customization is a central issue in socio- technical service ecosystems, enabled and fueled by new data- driven approaches, and with the goal of increasing value creation for the customer, and value capture for the provider. In this paper, we address the question of how to design service customization within the provider-customer interaction. We propose a novel quantitative approach for modeling the relation between cus- tomization level at the various steps of the customer journey on the one hand, and its effect on the value created for customer and provider on the other hand. Combining this model with approaches from multi-objective optimization, optimum levels of customization from both the customer and the provider perspective can be determined. Thus, the proposed model allows the identification of service designs which are optimized in terms of their value creation and value capture.
Conference Paper
Full-text available
The abundance of data accompanied by advances in analytics technologies increasingly drive companies to introduce analytics-based services, i.e. customer-facing services in which data and analytics help customers make decisions. Despite its growing application in practice, theoretical and conceptual work on analytics-based services is still scarce. In this paper, we develop a taxonomy of analytics-based services unveiling their conceptually grounded and empirically validated characteristics. Applying an established taxonomy building method, we draw upon an analysis of 85 use cases of analytics-based services. The results of an expert evaluation indicate both the usefulness and robustness of our taxonomy. The developed taxonomy of analytics-based services contributes in two ways: First, we add to the descriptive knowledge on this new service type, establish a common language among researchers and equip them with the means to analyze analytics-based services in a structured manner-thus laying the foundation for a deeper theorizing process in the future. Second, we provide a concrete conceptualization of analytics-based services for practitioners for initial guidance in new service development.
Article
Full-text available
Recent years have seen the emergence of physical products that are digitally networked with other products and with information systems to enable complex business scenar- ios in manufacturing, mobility, or healthcare. These “smart products”, which enable the co-creation of “smart service” that is based on monitoring, optimization, remote control, and autonomous adaptation of products, profoundly transform service systems into what we call “smart service systems”. In a multi-method study that includes conceptual research and qualitative data from in-depth interviews, we conceptualize “smart service” and “smart service systems” based on using smart products as boundary objects that integrate service con- sumers’ and service providers’ resources and activities. Smart products allow both actors to retrieve and to analyze aggregat- ed field evidence and to adapt service systems based on con- textual data. We discuss the implications that the introduction of smart service systems have for foundational concepts of service science and conclude that smart service systems are characterized by technology-mediated, continuous, and rou- tinized interactions.
Conference Paper
Full-text available
Business models have been a concept widely discussed over the last 20 years. The increasing availability of data and the growing capability to exploit them with analytics has sparked a new set of discussions, though: it is claimed that data and analytics bring to bear entirely new “data-based” or “data-driven” business models. However, there is neither a common understanding of these business models nor of the ways existing business models are transformed into those. This paper aims to create a coherent framework and common understanding of the infusion of business models by data and analytics. Contrasting popular views, our conceptual analysis reveals that there are no data-driven business models per se; instead, the utilization of data and analytics opens a “continuum” of transformation options for business models. We identify five distinct patterns in which the use of data may alter the business model, illustrate them with representative case studies and evaluate the patterns analyzing a sample of 115 industry business models. This paper will contribute to the fundamental understanding of how the use of data and the application of analytics may trigger business model innovation. The identified patterns will provide guidance to practitioners on how to utilize (big) data and analytics, in particular to draw attention to those that still seem underutilized for innovation. The developed pattern concept will also open up a broad agenda for future research.