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This paper reports on an exploratory, multisite case study of four organizations from the insurance, banking, telecommunications, and e-commerce industries that implemented big data analytics (BDA) technologies to provide individualized service to their customers. Grounded in our analysis of these four cases, a theoretical model is developed that explains how the flexible and reprogrammable nature of BDA technologies provides features of sourcing, storage, event recognition and prediction, behavior recognition and prediction, rule-based actions, and visualization that afford (1) service automation and (2) BDA-enabled human-material service practices. The model highlights how material agency (in the case of service automation) and the interplay of human and material agencies (in the case of human-material service practices) enable service individualization, as organizations draw on a service-dominant logic. The paper contributes to the literature on digitally enabled service innovation by highlighting how BDA technologies are generative digital technologies that provide a key organizational resource for service innovation. We discuss implications for research and practice.
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Journal of Management Information Systems
ISSN: 0742-1222 (Print) 1557-928X (Online) Journal homepage: http://www.tandfonline.com/loi/mmis20
How Big Data Analytics Enables Service Innovation:
Materiality, Affordance, and the Individualization
of Service
Christiane Lehrer, Alexander Wieneke, Jan vom Brocke, Reinhard Jung &
Stefan Seidel
To cite this article: Christiane Lehrer, Alexander Wieneke, Jan vom Brocke, Reinhard Jung &
Stefan Seidel (2018) How Big Data Analytics Enables Service Innovation: Materiality, Affordance,
and the Individualization of Service, Journal of Management Information Systems, 35:2, 424-460,
DOI: 10.1080/07421222.2018.1451953
To link to this article: https://doi.org/10.1080/07421222.2018.1451953
Published with license by Taylor and
Francis.Copyright © Christiane Lehrer,
Alexander Wieneke, Jan Vom Brocke,
Reinhard Jung, and Stefan Seidel.
Published online: 15 May 2018.
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How Big Data Analytics Enables Service
Innovation: Materiality, Affordance, and
the Individualization of Service
CHRISTIANE LEHRER, ALEXANDER WIENEKE, JAN VOM
BROCKE, REINHARD JUNG, AND STEFAN SEIDEL
CHRISTIANE LEHRER (christiane.lehrer@unisg.ch; corresponding author) is an assistant
professor of information systems at the University of St. Gallen, Switzerland. She
received her doctoral degree from the LMU Munich, Germany. Her main research
interests include IT-enabled organizational innovation, information privacy, and
human-centered design of information systems. Her work has appeared, among
others, in European Journal of Information Systems, Electronic Markets,and
Business & Information Systems Engineering.
ALEXANDER WIENEKE (alexander.wieneke@unisg.ch) is a Ph.D. candidate at the
Institute of Information Management at the University of St. Gallen, Switzerland.
He received a masters degree in business administration from the University of
Bayreuth, Germany. His research interests comprise big data analytics and informa-
tion privacy. His work has been published in Electronic Markets.
JAN VOM BROCKE (jan.vom.brocke@uni.li) is Professor of Information Systems, the
Hilti Chair of Business Process Management, Director of the Institute of Information
Systems, and Vice President Research and Innovation at the University of
Liechtenstein. His research focuses on business process management and related
aspects of digital innovation and transformation. He has published, among others, in
MIS Quarterly, Journal of Management Information Systems, Journal of
Information Technology, European Journal of Information Systems, Information
Systems Journal, Communications of the ACM, and MIT Sloan Management
Review. He has held various editorial roles and leadership positions in Information
Systems research and education.
REINHARD JUNG (reinhard.jung@unisg.ch) is a professor of business engineering at
the University of St. Gallen, Switzerland, Director of the Institute of Information
Management, and Academic Director of the Executive MBA HSG in Business
Engineering. His research interests focus on business engineering, digital transfor-
mation, and customer relationship management. He has published in such journals as
This is an Open Access article distributed under the terms of the Creative Commons Attribution-
NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided
the original work is properly cited, and is not altered, transformed, or built upon in any way.
Journal of Management Information Systems / 2018, Vol. 35, No. 2, pp. 424460.
Copyright © Christiane Lehrer, Alexander Wieneke, Jan Vom Brocke, Reinhard Jung, and Stefan Seidel. Published with
license by Taylor and Francis.
ISSN 07421222 (print) / ISSN 1557928X (online)
DOI: https://doi.org/10.1080/07421222.2018.1451953
Electronic Markets, Business & Information Systems Engineering, and Information
Systems Frontiers.
STEFAN SEIDEL (stefan.seidel@uni.li) is Professor and Chair of Information Systems
and Innovation at the Institute of Information Systems at the University of
Liechtenstein. His research explores the role of digital technologies in creating
organizational, societal, and environmental innovation and change. Moreover, he is
interested in philosophical and methodological questions about building theory and
conducting impactful research. Stefan's work has been published or is forthcoming
in prestigious journals, including MIS Quarterly, Information Systems Research,
European Journal of Information Systems, Journal of Information Technology,
Journal of the Association for Information Systems, and several others. He is an
Associate Editor to Information Systems Journal, Past Chair of the AIS Special
Interest Group on Green Information Systems (SIGGreen), and Vice President for
research of the Liechtenstein Chapter of the AIS.
ABSTRACT: The article reports on an exploratory, multisite case study of four orga-
nizations from the insurance, banking, telecommunications, and e-commerce indus-
tries that implemented big data analytics (BDA) technologies to provide
individualized service to their customers. Grounded in our analysis of these four
cases, a theoretical model is developed that explains how the flexible and repro-
grammable nature of BDA technologies provides features of sourcing, storage, event
recognition and prediction, behavior recognition and prediction, rule-based actions,
and visualization that afford (1) service automation and (2) BDA-enabled human-
material service practices. The model highlights how material agency (in the case of
service automation) and the interplay of human and material agencies (in the case of
human-material service practices) enable service individualization, as organizations
draw on a service-dominant logic. The article contributes to the literature on digitally
enabled service innovation by highlighting how BDA technologies are generative
digital technologies that provide a key organizational resource for service innova-
tion. We discuss implications for research and practice.
KEY WORDS AND PHRASES: affordances, agency, big data analytics, digital innovation,
materiality, service-dominant logic, service innovation, services.
The increasing commoditization of products and the rising customer demand for
individualized experiences and interactions is causing chief executives to shift their
focus from product innovation to service innovation [1]. Service innovation offers
customers new and unique value propositions that allow companies to differentiate
themselves from their competitors and to create strategic value [45]. Companies are
thus seeking opportunities to capitalize on the flexible and malleable nature of digital
technologies to innovate their service (e.g., [1,24,57]),
1
and service innovation is
now an important area in the broader field of digital innovation [57].
The ever increasing abundance of digital trace data, coupled with advances in big data
analytics (BDA), in particular, offer new possibilities for service innovation [1,58]. BDA
provides powerful methods and tools for gathering, processing, and analyzing large
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 425
amounts of trace data, enabling organizations to generate valuable insights by compiling
their customers’“digital footprints into a comprehensive picture of an individualsdaily
life[60, p. 21]. These insights have the potential to create competitive advantage [6,14,
32], and BDA is expected to support customer-oriented service innovation in a number of
ways [34,58].
Analyzing data gathered from sensors in cars, for instance, allows insurance firms to
create offerings that are sensitive to their customersdriving behavior [32]. Whirlpool, the
home-appliances manufacturer, uses sensors in their products to track how customers use
their products, combine these data with user-generated content from social media plat-
forms, and generate insights into their customerspreferences and behaviors [54]. While
these examples suggest that BDA provides ample opportunity for service innovation
across industries, we lack an empirically grounded theoretical understanding that attends
to the materiality of BDA and how this materiality enables service innovation. Such a
theoretical account will have to consider the role of both human and material agencies, as
service, which has traditionally been a human enterprise, is now increasingly shaped by
the use of digital technologies. Material agency describes a technologys capacity to act on
its own, apart from human intervention, while human agency refers to humanscapacity to
form and realize their goals [21]. Developing such an empirically grounded theoretical
model will complement and contribute to previous scholarly work on digitally enabled
service innovation, which has highlighted how the generativenatureofdigitaltechnolo-
gies enables service innovation [24,58]. In addition, organizations that seek to innovate
their service can benefit from such a model in their efforts to identify and implement
appropriate BDA technologies. Moreover, developing theory on the impact of BDA on
service innovation can contribute to the development of more general theories of digitally
enabled service innovation. Therefore, our research question is:
2
How do the material features of big data analytics technologies enable
service innovation?
Our study has three primary objectives: (1) to develop an empirical description of
how BDA has been used to develop service innovation, (2) to identify the pertinent
material features of BDA that facilitate the implementation of service innovation,
and (3) to integrate these findings into an empirically grounded theoretical model of
BDA-enabled service innovation. To this end, we conducted four exploratory,
theory-building case studies [9,35] with private-sector business-to-customer (B2C)
firms, which allowed us to gain an in-depth understanding of how BDA permitted
these organizations to identify opportunities for new BDA-enabled service processes
and their implementation. To develop an understanding of the more specific role of
BDA, we draw on recent research on the role of materiality in IT-enabled change and
innovation (e.g., [21,22,47]), as we are interested in what matters about BDA
technologies in developing service innovation. Specifically, we use the concepts of
materiality and affordances as analytical devices because they are predominant
426 LEHRER ET AL.
lenses through which to theorize about how digital technologies are involved in
organizational change and innovation (e.g., [11,12,19,21,25,26,42]).
Our analysis suggests two main types of BDA-enabled service innovation. First,
organizations use key material features of BDA technologies to automate service
processes in order to provide (a) trigger-based service actions and (b) preference-
based service actions to customers. Second, organizations identify new ways for
IT-enabled service processes where human service actors interact with BDA
technologies (i.e., human-material service practices) to engage in trigger-based
interactions and preference-based interactions with customers. In both cases, ser-
vice innovation is based on the generativity and reprogrammability of BDA
technologies as digital technologies [58].
The present research makes three primary contributions to theory and practice.
First, it contributes to the literature on service innovation by providing an empiri-
cally grounded theoretical model of how the material features of BDA technologies
enable service innovation, as organizations interpret BDA technologies as general-
purpose technologies in light of new action goals associated with a service-dominant
logic [24]. Second, we contribute to the literature on digital innovation [30,57,58]
in more general terms by highlighting how digital technologies afford two funda-
mentally different types of digital innovation: automation, which relies on material
agency, and human-material practices, which relies on the interaction between
human and material agencies. Third, our research yields practical insights for the
design of BDA infrastructures that support service innovation. The proposed con-
ceptualization provides the guidance for assessing current infrastructures and for
making decisions about the implementation of new technologies.
Theoretical Background
Service innovation
Service innovation provides businesses with opportunities to create customer value and
generate competitive advantage. The view of service innovation has shifted from a focus
on firmsoutput (i.e., in terms of new or improved products and services) to a focus on
new ways of creating customer value through service processes, so the shift has been from
a goods-dominant (G-D) logic to a service-dominant (S-D) logic. From the G-D perspec-
tive, service innovation is the production of outputs in the form of innovative service
products with new features and attributes [2], so service products are comparable to
tangible products [1]. The S-D logic, in contrast, focuses on the processes of serving,
rather than on output in the form of a product offering [24]. Here, the value of an
innovation is not delivered to the customer as a product but can offer a promise of
value creationthat is, value propositions. Customers approve these propositions by
engaging with the firms service process, thereby cocreating value with the firm [45].
Service innovation, then, is the creation of value propositions, which are generated when
firms deliver resources (e.g., information, knowledge, skills) to improve the customers
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 427
own value creation. Organizations therefore renew their service-delivery processes to
provide new value propositions to their customers [45], and this renewal becomes the
essential source of service innovation.
Service innovation can range from incremental to radical [33,45] and can be
described along dimensions of innovation (i.e., provision of new service), changes in
the client interface (e.g., intuitive design of web pages), the service delivery system (e.g.,
processes of service workers), and technology (e.g., new digital platforms for innova-
tion) [7]. Changes along these dimensions involve information technologies, and study-
ing service innovation in contemporary organizations requires that we attend to the
specific role of materiality. The relationship between materiality and humans is increas-
ingly dynamic, requiring an emphasis on relationality, materiality, and performativity
[34]. The use of information technology reconfigures how humans enact practices, and
entirely new practices emerge: Material-discursive practices redraw boundaries, chan-
ging inclusions/exclusions, and making a difference in who participates, how, and with
what consequences[34, p. 214]. Practices are clusters of recurrent human activities that
are informed by social and contextual relations [39,40]. In addition to new human-
discursive practices, digital technologies can allow for service automation, for instance,
through recommender systems [55]. Notably, IT-enabled service innovations are
grounded in the flexible, reprogrammable nature of digital technologies [58].
Against this background, we seek to examine how BDA technologies enable
service processes that create value propositions for customers. In doing so, we
focus on the roles of materiality as well as human actors. Next, we turn to the
class of digital technologies on which we focus: BDA.
Big data and big data analytics
Technological advancements in the tools and methods of business analytics provide
unprecedented access to vast amounts of data beyond the firmsbusiness transactions
big data [4,28]. Big datadescribes data that are generated from an increasing plurality
of sources, including Internet clicks, mobile transactions, user-generated content, and
social media as well as purposefully generated content through sensor networks or
business transactions such as sales queries and purchase transactions[14,p.321].
Scalable techniques (e.g., text analytics, web analytics) enable firms to process and
analyze such trace datadigital records of activities and events that involve information
technologies [18]from, for instance, websites and social media, including usersonline
activities (e.g., browsing and purchasing patterns) and online conversations (e.g., opi-
nions, feedback, and sentiment regarding a product or firm). Firms also use data trails from
digitized objects like sensor-equipped mobile phones and other devices. Web-based and
sensor data are generated in high volumes (large-scale data), at high velocity (high-speed
data), in wide variety (e.g., text-based data and numerical data), and with a high level of
veracity [28]. Tab l e 1 provides an overview of key BDA technologies.
The huge amount of information about customers from sources that reside inside and
outside the firm provides a critical source for innovation in general and a variety of
428 LEHRER ET AL.
opportunities for service innovation in particular [38,49]. Insurance firms, for instance,
offer customers electronic data recorders (EDR) for use in their cars to collect detailed
information on how they operate their vehicles (e.g., average speed, use of brakes) and
to provide the lowest rates to the safest drivers [31]. To capitalize on these opportunities
for innovation, executives must understand BDA technologies and their transforma-
tional impact in order to choose the appropriate applications and analytical models that
address their specific business needs. But what is the potential of BDA technologies in
specific contexts of use with specific objectives, such as service innovation with the aim
to create customer value? Next, we discuss the concepts of materiality and affordances,
which provide a lexicon with which to theorize about how the material features of digital
technologies are complicit in accomplishing change [21].
Materiality and affordances
Materiality refers to those properties and features of information technology artifacts
(e.g., IT infrastructures, software systems, specific algorithms) that have some
stability across contexts and across time [22], and that are also described as con-
tinuants[10]. Therefore, we identify the materiality of BDA technologies in terms
Table 1. Key BDA technologies
BDA
technologies Description
API Provides access to data sources like sensor data, clickstream data,
and social media data [3]
Data lake Stores data in its native format until it is needed; used in combination
with, for example, a Hadoop framework, this technology allows firms
to analyze large and/or unstructured data much faster than relational
data warehouse systems do [37]
Stream analytics Analyzes streaming data in real time in order to identify patterns and
trends and/or to detect current and/or future deviations from
normality [53]
Web analytics Analyzes clickstream data logs to provide insights on customersonline
activities and reveal their browsing and purchasing patterns [3]
Mobile analytics Analyzes clickstream data logs and sensor data (e.g., location data)
generated by mobile devices to provide insights on customers
mobile activities and movement patterns [3]
Social media
analytics
Analyzes social media data (e.g., user posts) to provide insights on
customersactivities, sentiment, opinions, and preferences [3]
Predictive
analytics
Uses statistical techniques to analyze current and historical facts to
make predictions about future events and/or behaviors [3]
Rule-based
system
Applies predefined sets of rules to initiate actions based on the
interaction between input and the rules [53]
Visualization
application
Transforms the results of data analytics into visually comprehensible
and customizable dashboards [3]
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 429
of hardware, that is, physical materiality, and software, that is, digital materiality [24,
58]. Examples include in-memory technologies, data lakes, and software packages
like Python and R that allow for predictive analytics.
Just how do the material features of digital technologies allow for innovation? The
concept of affordance has become the predominant way to theorize about the action
possibilities provided by the material features of information technology (e.g., [12,
25,26,21,42,59]). Affordances are potentials for actions that arise from the
relationship between technical objects (e.g., the materiality of BDA technologies)
and goal-oriented users or groups of users [26], such as organizations that seek to
innovate their service. Users and user groups interpret technical objects in light of
their objectives, which are influenced by the organizational context, including
strategies, customers, competitive environment, values, and regulations [23,42].
As affordances describe the potentials for action, they must be enacted or actualized
to result in observable outcomes like service innovations [12,21,47].
The concept of affordances has been used in a variety of individual and
organizational contexts. The material features of business process management
tools and dashboards, for instance, afford the visualization of entire work pro-
cesses [59]; features of knowledge sharing, acquisition, maintenance, and retrie-
val afford virtual collaboration [59]; interaction and information access features
afford organizational sensemaking [42],andstructureddata-entryformsand
common databases afford the capture and archiving of digital data about patients
in health care [47].
In this study, we ask what BDA technologies afford if they are interpreted by
organizations that seek to generate new value propositions for their customers as
they draw on an S-D logic. It is against this background that we conducted our
qualitative case studies, where the concepts of materiality and affordances served as
analytical lenses through which to investigate how the material features of BDA
technologies afforded service innovation in the case organizations as they interpreted
BDA in light of an S-D logic.
Research Method
Because empirical evidence on the impact of BDA on service innovation is scarce,
we employed an exploratory, multisite case study approach to develop a model that
is firmly grounded in the analysis of data. The phenomenon of interest is an
emergent phenomenon that has previously not been subject to in-depth empirical
investigation, so we sought revelatory cases [8]. Despite the growing literature on
BDA, there is currently no empirically based theoretical model that explains how
BDA enables service innovation. In conducting our multiple case studies, we
followed established guidelines for case study research (e.g., [9]). While our research
process was exploratory, we were sensitized by the concepts of materiality and
affordances to analyze what material features of BDA afforded the case organiza-
tionsservice innovations. While we used this abstract framework, we remained
430 LEHRER ET AL.
open to the emergence of other concepts and relationships. For example, through this
process we found that BDA technologies afforded service innovation in two ways:
service automation and IT-supported service delivery by human service actors (i.e.,
human-material service practices); that is, the technology afforded human service
actors new actions that led to fundamentally revamped practices.
We took several measures to corroborate our findings and ensure credibility,
transferability, dependability, and confirmability, which are important measures of
the trustworthiness of findings from qualitative research [52]. First, in order to
ensure credibility, we triangulated across sources, methods, and researchers, and
we debriefed with peers and participants. Second, to ensure transferability (i.e., the
extent to which the interpretation can also be employed in other contexts), we
triangulated across sites through purposive sampling, looking for the occurrence of
phenomena across case sites, as well as for differences. Third, to ensure depend-
ability (i.e., the consistency of the interpretation over time), we met with respondents
over time, and we aimed to explain change. Finally, to ensure confirmability (i.e., the
researchersobjectivity in interpreting findings), we triangulated across researchers
by involving two researchers in conducting interviews with key respondents to avoid
subjectivity and preconceptions. Moreover, data were analyzed by the first and
second author independently. The results from this analysis and the coding decisions
were discussed with coauthors, who contributed to the conceptualization of findings
in terms of a coherent, integrated theoretical scheme. This approach led us to go
back and forth between data analysis and theory development, and thus firmly
ground our theory development in empirical data.
Site selection
We applied literal replication logic to purposefully select case organizations that
we expected to yield similar results [35]. The cases have a number of common
characteristics with regard to ownership, relevance of BDA to service innovation,
and cultural proximity. They are all B2C firms that operate in industries with
considerable experience and expertise in the collection and analysis of large
amounts of customer data: insurance, banking, telecommunications, and e-com-
merce. All of the case organizations consider service innovation to be strategi-
cally important, as competitive pressure and changing customer behavior have
led them to recognize the need to improve how they serve their customers
through new value-creation opportunities and competitive differentiation. The
case organizations see significant potential in BDA for service innovation and
have performed concrete projects. To limit cultural differences, we sampled cases
from Austria, Germany, and Switzerland, countries that have significant cultural
commonalities.
Aside from these commonalities, we sought to obtain a sample of firms that are
diverse in terms of industry, size, and BDA maturity. The use of BDA technologies
in these firms ranged from full-blown BDA solutions that use, for instance, data
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 431
Table 2. Overview of case organizations
Company A Company B Company C Company D
Country Switzerland Switzerland Austria Germany
Industry sector Insurance Banking Telecommunications E-commerce
Number of
employees
4,000 20,000 8,600 1,700
Turnover in
2015
US$11.0 billion US$28.0 billion US$2.8 billion US$1.0 billion
Big data vision Increase customer centricity
and the firms position as a
digital leader
Provide efficient but high-quality
personal advice
Enhance core business by offering
excellent individual service at all
contact points
Tailor customer interactions
and extend the service
value chain
BDA
technologies
Full-blown BDA infrastructure
with data lake and in-memory
technology
Full-blown BDA infrastructure
with data lake and in-memory
technology
High-performance data warehouse;
predictive analytics
High-performance data
warehouse; web and
mobile analytics
432 LEHRER ET AL.
lakes and in-memory technology (in the cases of the insurance and the bank firms),
to limited solutions that use, for instance, web and predictive analytics (in the cases
of the telecommunications and e-commerce firms). Choosing cases from four indus-
tries allowed us to compare the cases for commonalities and differences, and to
identify BDA-enabled service innovations that are not industry- or firm-specific.
This approach enhances the analytical generalizability of our findings [35]. Table 2
presents an overview of the four cases.
Data collection
We used semistructured interviews as our primary data sourcean approach that is
appropriate for gathering rich, empirical data, particularly when the phenomenon
under examination is episodic and infrequent. From each case firm, we sampled six
to eight participants, whom we selected through purposeful sampling; that is, we
chose respondents whom we expected would provide information that was relevant
to our theory development [35].
For each case site, we first established a relationship with a C-level manager in the
firm as the main point of contact. We briefed this person about the research project
through a written project summary and a telephone call. Suitable respondents in each
firm were then selected jointly by the manager and the first and second authors of
this study. The principal criterion for selecting respondents was their knowledge
about BDA use at the case firm and its application in the firmsservice innovation.
We chose experts from several functional areas, as the use of BDA for service
innovation involves multiple business units. We conducted 30 interviews with both
market (e.g., marketing, sales) and technical experts from a variety of functional
areas and hierarchical levels to learn about the relationship between the material
features of BDA and what they allowed for in terms of service innovation (Table 3).
The interviews were based on a set of open-ended questions that allowed us to
follow up on interesting and unexpected responses and that left the participants free
to elaborate on their perceptions, experiences, and reflections [35]. Prior to asking
the questions, we introduced the goals of our study and the goals of the interview.
The questions were guided primarily by three key issues: (1) the participants
understanding of big data and BDA in order to ensure a common understanding
of the concepts under discussion, (2) the relevance of customer orientation and
service innovation at the case organization, and (3) how BDA contributes to service
innovation and improvement in the firm. Participants with technical backgrounds
were asked additional detailed questions about current IT infrastructures and the role
of BDA technologies in their organizations. The interview protocol is shown in the
Appendix. The interviews lasted between 45 and 90 minutes and were recorded and
transcribed verbatim so we could analyze the resulting data in a rigorous and
transparent manner. Interviews and transcriptions were done in German, the partici-
pantsnative language, and native German speakers conducted all data analyses. The
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 433
resulting coding and quotations were translated into English for presentation
purposes.
Additional data in the form of publicly available company information (e.g.,
annual reports, press releases) and internal presentations provided background
information on the BDA infrastructures, data strategies, and current practices that
were related to service innovation. These documents helped us further clarify the
information gathered during the interviews and provided valuable ancillary informa-
tion about the organizational contextthat is, the firmsstrategic objectives, custo-
mers, competitive environments, and regulations.
Data analysis and theory building
The data analysis process broadly followed the recommendations of Eisenhardt [9],
Paré [35], and Yin [56] for within- and cross-case analyses. First, we analyzed each
case as a separate study so we could focus on the collected case data and understand
Table 3. Interview partners
Firm
Interview
number Participants position
Interview
number Participants position
A 1 Head of Digital Business 5 Head of Data Analytics
2 Head of Sales Applications 6 Big Data Architect
3 Head of Community
Management
7 Project Manager
4 Head of Digital Innovation 8 Transformation
Manager
B 1 Head of Strategic
Marketing
5 Solution Architect
2 Head of Digital Innovation 6 Senior Manager Big
Data
3 Head of Customer
Management
7 Head of IT
Architecture
4 Head of Online
Communication
8 Head of Big Data
C 1 Chairman of the Board 4 Head of B2B Service
2 CEO 5 CTO
3 Head of Sales and
Marketing
6 Head of Analytics
D 1 Senior Manager Business
Strategy
5 Senior Manager of
CRM
2 Manager Business
Strategy
6 Head of Business
Intelligence
3 Head of Business
Operations
7 IT Project Manager
4 Head of CRM 8 CTO
434 LEHRER ET AL.
each cases unique patterns. In a second step, we aggregated the findings of the
within-case analyses in order to determine whether they made sense beyond each
individual case [9,35]. The within-case analysis and cross-case analysis, as well as
the coding and analysis we applied, were conducted in a manner that allowed us to
move back and forth between the analysis of empirical data and theorizing, so the
steps we describe here are not strictly sequential phases.
Within-case analysis
In the initial step of the data analysis process, we read the interview transcripts and
noted our first impressions in interview and case reflection memos [29]. Memo
writing continued throughout the entire data analysis process so we could keep track
of our reflections, comments, questions, and ideas as they occurred and store them
for further investigation and refinement. We then coded the case data (i.e., interview
transcripts, and documents) using the qualitative data analysis tool ATLAS.ti, which
enabled us to store all our data in a central location, analyze it, and maintain
traceability of the coding. Each case was treated as a separate study, and each step
was conducted independently by the first and second author, with regular discus-
sions to avoid subjective interpretations and enhance validity.
In coding the case data, we first used open coding [46] in order to identify
concepts that were related to the use of BDA for service innovation that were salient
in the data,
3
while remaining as open and unconstrained by prior theory as possible.
During this stage, we frequently compared the intervieweesresponses in an effort to
group answers that pertained to common codes and to analyze different perspectives
on emerging codes. The process of open coding generated an initial list of more than
300 descriptive codes (e.g., goals like identifying changes in the state of customers
house,”“identifying customers wedding) that were further grouped and integrated
in order to derive more abstract categories (e.g., the open codes identifying changes
in the state of customers houseand identifying changes in the state of customers
carwere objectives that were grouped under the more abstract category of identi-
fying changes in the state of relevant objects.)
When no new concepts emerged, we conducted a coding stage similar to axial
coding [46], where we organized the categories identified using the analytical
framework of materiality and affordances; that is, we looked for the use context in
terms of organizational goals and the service innovations that were afforded by the
material features of BDA technologies. The concepts of materiality and affordances
were appropriate theoretical lenses, as we saw that, indeed, BDA technologies
afforded service innovation as material features of BDA were interpreted in light
of new action goals related to creating improved service delivered through both
human agency and material agency. By considering material features as well as what
these features allowed for when they were interpreted under an S-D logic, we were
able to establish links between the material features and specific service innovations.
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 435
We compared the codersresults, discussed differences and commonalities, and
merged the results.
Cross-case analysis
After analyzing the data in each case, we used cross-case analysis to identify cross-
case patterns and determine whether the findings of the within-case analyses were
applicable across the cases [9,35]. We analyzed the casessimilarities and differ-
ences regarding the material features and what they afforded to identify patterns
across the cases. We discussed the differences, focusing on the reasons that these
differences occurred, and learned to what extent the cases were comparable. Then
we compared the patterns for consistency and aggregation, discussed our conclu-
sions, and refined the patterns, which helped us move toward the integrated theore-
tical scheme that was emerging from our analysis.
Next, we describe our analysis of the four cases using our lexicon of materiality,
affordance, and human and material agencies, and then present a theoretical model
of BDA-enabled service innovation.
Case Analysis
Company A: Insurance
Case organization A is the Swiss subsidiary of a multinational insurance firm that
offers private individuals and corporate customers a broad range of personal, property,
liability, and motor vehicle insurance. A decade ago the company was focused on the
core insurance business of selling insurance policies and paying bills on time, but now
it aims to increase its customer orientation and to change its role from payerto
playerby taking a more proactive stance in engaging with its customers. These
strategic objectives translated into how the firm innovated its service processes, as it
envisioned increasing its customer centricity so customers would feel they were in
good hands before and in the event of damage, and improving its customer interaction
beyond handling insurance claims. Accordingly, the firm sought to provide support to
customers by meeting their needs at the right moment.
As part of the firms overall strategy to become more customer-centric, the firm
implemented advanced BDA technologies, including in-memory technology and a
data lake with a Hadoop framework that allowed it to store data in a central location
and analyze data from a variety of sources with reduced latency. Trace data were
gathered from internal sources (e.g., the firms website, mobile apps) and external
sources (e.g., social media, price comparison portals, digitized objects like sensor-
equipped homes and cars). The company used several analytical applications,
including stream analytics for analyzing sensor-based data streams in order to
recognize insurance-related events (i.e., deviations from a normal state) and to
initiate appropriate actions in real time using rule-based systems. The firm also
436 LEHRER ET AL.
applied web and social media analytics and predictive analytics in order to recognize
and anticipate insurance-related changes in the customers life at an early stage.
As the company pursued its goal of providing customer support at the right
moment, the material features of BDA enabled the implementation of two types of
service innovation: automation of certain customer service-related processes in a
way that facilitated the individualized interaction with customers in real time, and
opportunities for employees to engage in new service-provision practices like
proactive advice. Thus, the material features of BDA in response to new strategic
action goals afforded automation and new human-material service practices.
Consider two examples.
First, stream analytics facilitated the automated, real-time recognition of insurance-
related events in, for instance, the customers home (e.g., open window, burglary) or
car (e.g., accident). These material features afforded automated trigger-based service
actions on the customers behalf. For example, the detection of a forced entry into
the customers home automatically triggered predefined actions in real time, such as
starting an alarm or calling the police, potentially preventing loss:
We are trying to expand our business and think innovatively. . . . As people are
increasingly building connected objects into their homes, we are thinking
about how to offer a service in terms of security.. . . If you build such a
connected protective shield around the house, then it must work reliably in real
time. (Respondent 2, Company A)
The automation of customer-related processes enabled the company to provide an
entirely new kind of servicedamage prevention and support. While the insurance
firm previously got involved only after the damage had occurred, this service
innovation improved customerssense of well-being and security.
In the second example, the insurance agents were afforded entirely new ways of
engaging with customers such that they proactively approached them with indivi-
dualized information, products, or service at the right time. The material features of
BDA, particularly web and social media analytics, as well as predictive analytics,
facilitated the recognition and prediction of major lifetime events (e.g., marriage,
property purchase, starting a family) that indicated changed insurance needs. For
instance, the firm gathered and analyzed clickstream data from related third-party
websites, price-comparison portals, and the social-media data of customers who
were connected to their insurance agents through social networking sites.
Visualization applications (i.e., dashboards) gave the insurance agents information
about identified or predicted triggers so they could approach their customers with
appropriate service offerings in a timely way:
We recognize a customers lifetime event and, after a while, the customer
receives information [related to that event]. What we want to achieve is [using]
an event . .. in order to react immediately to the customers current situation.
(Respondent 3, Company A)
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 437
This support was in the form of new human-material service practices. In these
cases, insurance agents still had to make their own decisions about how to address
the specific events; that is, the employeesskill sets, experiences, and customer
contact strategies interacted with the material features of BDA to create new
practices. This fundamentally revamped how service was delivered. Previously,
insurance agents had no (or only limited) access to information about customers
lifetime events (e.g., through their personal social networks), so service provision
was primarily reactive to customersrequests:
It used to be the decentralized insurance agents who went through life with
open eyes and who saw that, for instance, a woman was expecting a child. It
was the human sensor that brought the information. As the business and its
services become more digitized and the customer increasingly communicates
with us via digital channels, we have more information to find these magic
moments digitally. (Respondent 1, Company A)
The new, innovative practices served the individual customer at the critical
moment, thereby increasing the relevance of product and service offerings and
creating a sense of convenience.
To summarize, the insurance company used several BDA technologies in two
ways. First, BDA allowed for service automation, taking action on the customers
behalf based on triggers (material agency). Second, BDA technologies afford human
service actors to serve the customer based on lifetime events and adapting the
customer contact strategy accordingly (human and material agencies). This approach
changed how the firm served its customers by facilitating its ability to provide
proactive service (either automatically or via human interaction) tailored to the
individual customers needs. Table 4 provides an overview of how the companys
Table 4. BDA-enabled service innovation at Company A
Organizational goals
Material features of
BDA BDA-enabled service innovation
Provide tailored service
to customers at the
right moment
Sensor data
Data lake
Stream analytics
Rule-based systems
Automatically taking action on the
customers behalf in response to
triggers (material agency)
Clickstream and
social media data
Data lake
Web and social
media analytics, pre-
dictive analytics
Visualization
applications
Approaching the customer in response
to triggers (i.e., lifetime events)
through a human service actor who
adapts the contact strategy to the
customers individual needs (human
and material agencies)
438 LEHRER ET AL.
organizational goals translated into the provision of automated and human-material
service, afforded by the material features of BDA technologies.
Company B: Banking
Case organization B is a leading global financial services firm and one of the
largest full-service banks in Switzerland. The firm has a strong position in retail
banking for private customers and wealth management for high-net-worth indivi-
duals. One of the firms key strategic objectives was to further improve its
provision of first-class financial advice and solutions. The use of digital technol-
ogies, including BDA, was an important pillar in implementing the banks strategy
of improving service provision and offering a unique customer experience. Against
this background, the firm had implemented an omnichannel strategy in retail and
wealth management, integrating its offline (i.e., branches, personal bank advisers)
and online channels (i.e., online and mobile banking) and allowing customers to
choose their preferred interaction channels, which were customized to their needs
and habits. The online channels were not intended to substitute for the offline
channels but to support personal advice, which the firms customers expected
because of the nature of the financial products. Accordingly, the firm sought to
provide a convenient and highly individualized customer experience and a con-
sistent and seamless customer journey across all channels.
In an effort to realize these objectives, Company B made significant investments in an
advanced BDA infrastructure that included in-memory technology and a data lake,
combined with a semantic knowledge base using open-source software. This infrastruc-
ture made it possible to complement traditional data sources (e.g., transaction and
customer relationship management [CRM] data) with previously unavailable data
sources, including new internal data sources like the firms website, its online and
mobile banking portal, and unstructured data generated from business-related interac-
tions between customers and the firm (e.g., e-mail, letters). Various analytical applica-
tions facilitated the identification of patterns in the data, including web and text
analytics. Moreover, based on a data discovery workbench, data scientists developed
algorithms and statistical models for analyzing the variety of data stored in the data lake.
As the company followed its new goals in terms of providing a convenient and
individualized customer experience, and offering a consistent and seamless customer
journey, the material features of BDA allowed it to implement service innovation in
two ways. First, BDA technologies allowed it to automate the customization of
content that was provided through digital channels. Second, BDA technologies
provided employees opportunities for engaging in new service practices in terms
of highly individualized and consistent customer support. Consider two examples.
First, the newly available material features of BDA, particularly web analytics, allowed
for the real-time recognition of business-related events on digital channels. For instance,
the detection of a certain user behavior on the e-banking portal based on clickstream data,
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 439
combined with insights from historical customer data, automatically resulted in the
display of an appropriate message that addressed the customers anticipated needs:
In e-banking, they [the campaigns] are quite specific because we measure the
customers behavior. We analyze the logs and compare them with the history.
For example, a young man suddenly uses our mortgage calculator. We see this,
of course, and then say: this is a young man who has never clicked on this
before and now he has been doing it for three, four weeks or even two months.
He is probably interested in a house. This might be a good moment to
approach him and say: Come in and talk with us. If you are interested in a
mortgage, we can advise you in this way.This is much more individualized
than the approach where the customer is 40 to 50 years old, or these are all
students; lets offer them a credit card. (Respondent 8, Company B)
The firm used BDA technologies in a way that enabled them to customize user
interfaces (e.g., provide tailored content) automatically. Compared to their previous
process, the new process did not base content on general customer segmentation
criteria but adapted the content to observed customer activities combined with
historical data. Therefore, it was highly individualized.
As for the second example, personal bank advisers and service employees were
afforded new individualized ways of interacting with customers considering their
customerspreferences. The material features of BDA allowed for collecting
customer data from multiple new and traditional data sources, storing it in the
data lake, and compiling a rich and up-to-date customer profile. Visualization
applications (i.e., dashboards) provided insights for the advisers and service
employees, allowing them to cater to their customersindividual preferences
when they interacted with them:
It is about gaining a holistic view of the customers. . . . The great driving force
and the keyword that drives us to know more about the customer is precisely
this multichannel idea: we want to know what the customer is doing outside
[the business relationship]. While once this view was never consistent and we
did not know it, now we start to learn more about the customer, including her
activities and behavior outside [the business relationship]. We also consider
how this information is delivered to the personal adviser or call center agent
and what we do with this datahow we use it to interact with the customer. It
is very important that we record and understand the data in a way that allows
us to improve how we speak with the customer. (Respondent 5, Company B)
The customer profile informed employees about a customers prior interactions
with the bank, allowing for a seamless customer journey such that, if the customer
initiated a process in one channel (e.g., on the e-banking portal), it could be taken up
by another channel (e.g., a personal consultation conversation with the adviser).
Employees were also given concrete recommendations for action based on the
customers preferences. For instance, they were instructed on topics that should be
440 LEHRER ET AL.
addressed during consultation or were informed about the customers preferred
interaction channel. In these cases, the customer profile provided additional support
for employees in their efforts to interact with the customer in a customer-centric
manner. Employees still had to make their own decisions on how they used the
information and how they adapted their behavior, so the customers profile and the
employees skill set and experience worked together.
To summarize, Company B used several BDA technologies in two ways. First,
BDA allowed for service automation, for instance, customizing user interfaces based
on triggers (material agency). Second, BDA technologies provided employees with
comprehensive information on a customers profile and history so employees could
adapt the customer interaction to the customers preferences (human and material
agencies). Company B was enabled to provide highly individualized customer
support and a consistent customer journey along all touchpoints. Table 5 provides
an overview of how the banks organizational goals translated into service
innovation.
Company C: Telecommunications
Case organization C provides private and corporate clients with telecommunication
services. The company operates its own mobile network and distributes products in
the areas of fixed net, mobile voice, Internet, and TV. As a full-service provider, it
also offers corporate clients cloud and machine-to-machine service. An infrastructure
provider, the firm sees itself as an enabler of digitalization by offering high-quality
networks in terms of availability, performance, and security. In the B2C sector, the
company operates in a mature market, so it sought new ways to increase revenues
and profits. While competition in the telecommunications sector had for a long time
Table 5. BDA-enabled service innovation at Company B
Organizational goals Material features of BDA
BDA-enabled service
innovation
Provide individualized
and consistent
service at all
touchpoints
Clickstream data, histori-
cal customer data
Data lake
Web analytics, predictive
analytics
Rule-based systems
Automatically customizing user
interfaces (e.g., providing
tailored content) in response to
triggers (material agency)
Digital trace data from
various sources compiled
in the customer profile
Data lake
Predictive analytics
Visualization applications
Interacting with the customer in
accordance with the customers
preferences, as derived from
previous interactions and
behavior (human and material
agencies)
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 441
been driven by price and product, customer experience became the key brand
differentiator. Therefore, in order to enhance their core business, the firm wanted
to offer excellent individual service at all contact points to retain and create loyal
customers. Accordingly, Company C sought to provide the product offering or
problem solution that was most relevant to each customer.
The firm operates a modern and flexible analytics infrastructure that draws on an
enterprise data warehouse that centrally stores large amounts of customer data (e.g.,
how and where subscribers use their phones) and data from network equipment and
server logs. The analysis of customer data (e.g., demographic data, use patterns,
browsing behavior from clickstream logs, and call center contacts) allowed the firm
to generate a detailed profile of each customer and her needs.
We found evidence of one type of BDA-enabled service innovation in this firm.
BDA technologies provided affordances to employees for engaging in new service
practices, particularly preference-based support. In contrast to Case A and Case B,
we found no evidence of automation. New human-material service practices
emerged in response to strategic action goals offered by the material features of
BDA. Consider the following example.
Sales and service employees (i.e., shop assistants and call center agents) were
afforded entirely new ways of engaging with customers. They could convey the
right message, make the right offer, and choose the right level of service during
every customer engagement so every customer had an individual customer experi-
ence. The material features of BDA allowed for the recognition and anticipation of
customer behavior by applying statistical models (e.g., predictive analytics) to the past
interactions, usage, and purchase behavior that were compiled in individual customer
profiles. The analyses resulted in concise, clear, and timely metrics on, for example,
calling patterns, data consumption, and customer satisfaction. Moreover, recommen-
dations for the next best actions (e.g., the most suitable offer, problem solution, or
interaction channel) were derived and provided to the frontline employees through a
uniform visualization application. Thus, employees were equipped with timely,
actionable insights about the customers history and anticipated behaviors and given
decision support at the point of customer interaction. As a result, they were able to
cater to the customers individual preferences while engaging with that customer:
Its about anticipating . . . from the datathe human-like, the empathic. This is
exactly what makes the difference to the customer, whether it is genuine or
artificial. Empathy, in the case of a firm, means that it can put itself into the
customers shoes, know what she wants next. I think that is what differentia-
tion must be all about because the rest is more of the same. (Respondent 3,
Company C)
Guided in their decisions and customer interactions, sales and service employees
applied their personal skill sets or experiences to incorporate the available informa-
tion into their service practices. The new practice fundamentally changed the firms
service delivery, as previously frontline employees had limited visibility of their
442 LEHRER ET AL.
customers and could react to customer requests only on the spot, mainly based on
the companys guidelines and their intuition. BDA technologies enabled the agents
to optimize their service practices by providing them with the contextual information
they needed to engage with their customers in a way that was sensitive to their
customerspreferences.
To summarize, BDA technologies at Company C provided employees with com-
prehensive information about a customers profile and history, enabling them to
adapt their interactions to the individual customers preferences (human and material
agencies). Table 6 provides an overview of how the telecommunications companys
organizational goals translated into human-material service practices that were
enabled by the material features of BDA technologies.
Company D: E-Commerce
Case organization D is a leading online provider in the German travel sector. It
operates several websites that cover the entire travel-booking cycle, from weather
forecasts to flight and hotel portals to rental car bookings. In line with the websites
transaction-based business model, the firm sought to increase growth through four
key levers: traffic, conversion, cross-selling and up-selling, and retention. In addi-
tion, Company D sought to increase the efficiency of its communication activities by
decreasing wasted coverage. It operates in a highly competitive market with a large
number of competitors that offer similar or even the same products with a high level
of price transparency. In order to attract customers and gain market share, the
common practice was to offer the lowest price. However, in recent years,
Company D began to pursue customer-centrism, placing a stronger focus on service
differentiation, instead of pricing only, in order to deliver a superior customer
experience and gain customer loyalty. Accordingly, it sought to improve its customer
interaction through individualization of its user interfaces and to extend the service
value chain by also serving the customer beyond the booking.
Table 6. BDA-enabled service innovation at Company C
Organizational goals
Material features of
BDA
BDA-enabled service
innovation
Provide the product offering
or problem solution that is
most relevant to each
customer
Digital trace data from
various sources that is
compiled to a customer
profile
Data warehouse
Predictive analytics
Visualization
applications
Interacting with the customer
in accordance with the
customers preferences
derived from previous
interactions and behavior
(human and material
agencies)
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 443
To achieve these objectives, Company D applied BDA technologies to continually
analyze how customers interact with their websites and mobile apps. Web and
mobile analytics provided insights into usersonline activities enabling the company
to better understand their browsing and purchasing patterns. For example, when a
customer visited one of the firms websites, clickstream data were generated through
cookies and logged in a database for web log analysis. These activities were
supported by web analytics tools like Google Analytics. Company D ran a high-
performance data warehouse to store data from multiple sources centrally, which
allowed data scientists to conduct analyses. Based on the insights gained through
web and mobile analytics, rule-based recommender systems automatically created
targeted product- and service-related suggestions that had a high probability of
meeting customer needs. Moreover, the firm used location-aware analysis of sensor
data from smartphones to provide context-aware content through its mobile apps.
The firm emphasized measuring usersresponses (e.g., e-mail open and click-
through rates from mail campaigns and newsletter) in order to continuously learn
about customerspreferences and improve its targeting in the future:
You open the response data and see yes, it worked. We caught her.This then
goes back to the data warehouse, where the database is updated and the algorithm
is optimized. In this way, we build a small circuit. For me, this is data-driven
marketing to the extent it is currently possible. (Respondent 4, Company D)
As the company followed its new goals in terms of improving customer interaction,
and extending the service value chain, material features of BDA allowed it to implement
a strategy of improving customer service processes through automation. First, BDA
technologies allowed to automate the adjustment of user interfaces. Second, BDA
technologies supported the establishment of additional touchpoints before and during
the customers travel. Consider two examples.
First, BDA enabled the firm to adjust user interfaces automatically in terms of the
types and order of the travel options presented, their visual appearance, and recommen-
dations for complementary products and services. Instead of presenting the same user
interface to all customers, it was adjusted to the customers individual characteristics:
The other aspect is the personalized appearance of websites. At the moment, the
online business is rather one-size-fits-all. This means I see the same website you
do, although you and I are completely different target groups. I am male and live
in Munich. You are female and live in Switzerland. Why should you get the
same website as I do? This topicthe personalized delivery of UI [user inter-
face] and UX [user experience] concepts and personalized website creationis
of great importance to us. (Respondent 3, Company D)
Predictive analytics facilitated the combination of historical behavioral patterns
and current navigation behavior in order to predict the probability that the customer
would buy certain products. This approach allowed the firm to display related
products (e.g., offering museum packages to a customer with a history of traveling
444 LEHRER ET AL.
to cultural sites) when the customer processed a transaction. Providing an indivi-
dualized customer experience enhanced convenience by helping customers find
appropriate offers much faster:
I believe it has added value for the customer because he gets only the offer that
really interests him. He does not need to search for three hours because we already
know what he wants, so this makes it easier. (Respondent 2, Company D)
As for the second example, BDA enabled the firm to interact with customers in an
automated manner beyond the transaction on the website, based on events. For
example, smartphone technology allowed the firm to monitor the customers loca-
tion during the holiday and to deliver context-specific, highly personalized messages
in real time:
If we detect the customers current location, we can act like a kind of travel
guide. Then we can tell him, You are in London, so take a look at this tourist
attraction. Keep in mind it is a weekend and there is a street festival.
(Respondent 2, Company D)
Thus, the firm could interact with its customers while they were out and about, for
example, looking at tourist landmarks, thereby enriching their offline experience.
The firm could also extend the service value chain beyond the online world by
partnering with local service providers to create new value propositions jointly.
To summarize, Company D used web and mobile analytics to improve its service
provision through automation. In contrast to the other three companies, we did not find
evidence of new human-material service practices. First, BDA allowed for the auto-
mated adjustment of user interfaces (material agency). Second, location-aware analysis
based on sensors in smartphones enabled the firm to interact with customers in real time
Table 7. BDA-enabled service innovation at Company D
Organizational goals
Material features
of BDA BDA-enabled service innovation
Provide individualized service
to customers and extend the
service value chain
Clickstream data
Data warehouse
Web and mobile
analytics, predic-
tive analytics
Rule-based sys-
tems
Automatically customizing digital
user interfaces in accordance
with customer preferences
(material agency)
Sensor data
Data warehouse
Mobile analytics
(location-aware
analysis)
Rule-based
systems
Automatically customizing user
interfaces (e.g., providing tailored
content) in response to triggers
(i.e., a customers current
location) (material agency)
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 445
(material agency). Thus, Company D was afforded the ability to consider their custo-
mersindividual needs instead of treating all customers the same, thereby enhancing
convenience. Moreover, Company D could interact with customers in a personalized
manner, even long after the customers booked their travel, by offering additional service
on-site, thereby extending service provision from the online to the offline world. Tabl e 7
provides an overview of Company Ds BDA-enabled service innovation.
A Theoretical Model of BDA-enabled Service Innovation
This section presents an integrated model of IT-enabled service innovation that is
grounded in our analysis of the four cases. Our model explains how BDA technol-
ogies enable service innovation as organizations interpret them in light of new action
goals that are related primarily to individualizing service. We identified two key
types of service innovation afforded by BDA technologies: automation of customer-
sensitive service provision and new human-material customer-sensitive service
practices. Both types of innovation are enabled by the material features of BDA
technologies in terms of sourcing, storage, event recognition and prediction, beha-
vior recognition and prediction, rule-based actions, and visualization. When enacted,
they lead to service individualization. Figure 1 visualizes this model.
Automated preference-sensitive ser vice action
Human-material c ustomer-sensitive service pr actices
(human agency & material agency)
Strategic goal of service innovation: indi vidualization
affords yields
Automatically takes
action when triggered
Automatically adjusts
user interfaces
Provides
trigger information to
service actor
Proactively
approaches and
interacts with the
customer
Provid es
customer profile & action
recomm enda tion s
Adjusts c ustomer
interaction
Legend
Trigger-based c ustomer service interacti on
Preference-sens itive customer service interaction
Material feature
Material agenc y
Human agency
Service
individualization
Automation of customer-sensitive service provision
(material agency)
Outcome
Automated trigger-based service action
Trace data sourcing &
storage feat ures
Sourcing features
Storage features
Trace data analytics
featu res
Event recognition &
prediction features
Behavior recognition &
prediction features
Trace data exploitation
featu res
Rule-based features
Visualization features
Material features of BDA
Figure 1. Theoretical model of BDA-enabled service innovation
446 LEHRER ET AL.
In what follows, we provide a general overview of the model and then describe its
key components in terms of (1) the material features of BDA, (2) automation of
service provision, and (3) human-material service practices.
By differentiating how the material features of BDA afford both service automa-
tion and human-material service practices, our model highlights how both material
agency and human agency play roles in shaping organizational service processes and
in creating value propositions for customers. In the case of service automation, the
focus is on material agencythat is, the technologys capacity to act on its own and
apart from human intervention [21]. In contrast, in human-machine service practices,
human and material agencies interpenetrate in what Pickering (1995) referred to as
the mangleof practice [36], and human agency is enacted in response to the
technologys material agency [21,51]. In the case of service automation, BDA
technologies provide both necessary and sufficient conditions for service innovation,
as the technology acts without the intervention of human actors. In the case of
human-material service practices, BDA technologies provide only necessary condi-
tions, as the observable practice results from the interpenetration of human and
material agencies in practice. Table 8 compares the two types of service innovation.
Next, we provide detailed descriptions of the models components, along with
conceptual definitions.
Material features of BDA affording service innovation
The flexible nature of BDA technologies and their reprogrammability afford both
service automation and human-material customer-sensitive service practices. BDA
technologies are digital artifacts that are part of a wider ecosystem, and they derive
their utility from the functional relationships they maintain [20]. Features of sourcing
[3], storage [37], event recognition and prediction [53], behavior recognition and
prediction [3], rule-based actions [53], and visualization [3] are built on technologies
that maintain relationships and provide functions like sourcing trace data, storing
trace data in databases, analyzing these data using various approaches to supervised
Table 8. Service automation and human-material service practices
Service automation Human-material service practices
Goal Service individualization through
automated activities that are
carried out without human
intervention
Service individualization through
interaction of the customer with a
human service actor who interacts
with a digital service actor
Role of agency Focus on material agency in
delivering the service
Interaction of human and material
agencies in delivering the service
Nature of
outcome
Deterministic provision of service Nondeterministic provision of
service: technology provides
space for action
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 447
and unsupervised learning, and exploiting the generated insights. Table 9 provides
an overview of the key categories of BDAs material features affording service
individualization that emerged from our analysis.
Automation of customer-sensitive service provision
Organizations see BDA technologies as malleable technologies that afford automa-
tion of customer-sensitive service provision, consistent with new action goals related
to individualized service, such as an insurance company that automatically takes
action when security incidents occur. To implement service automation, organiza-
tions use algorithmic solutions that are based on the material features of BDA in
terms of trace data sourcing and storage, event recognition and prediction, behavior
recognition and prediction, and rule-based actions. Two types of service automation
emerged as salient from our analysis: automated trigger-based service action and
automated preference-sensitive service action. In the first case, the system indepen-
dently carries out actions like sounding an alarm or calling the police (material
agency) when triggered by an event like forced entry into a customers home
(detected by sensors), thereby, providing service at the right time. In the second
case, the system automatically adjusts user interfaces, for instance, by providing
tailored content (material agency) when a certain user behavior on an online channel
or a customers current location are detected, thereby, providing service in the right
way. Thus, trigger-based action can lead to preference-sensitive action, as indicated
in Figure 1.Table 10 provides an overview, including underlying material features
and examples.
Table 9. Key material features of BDA technologies
BDA material
features Description
Examples of underlying
BDA technologies
Sourcing features Features for collecting and integrating
digital trace data from various
sources
APIs for accessing sensor data,
clickstream data, social
media data
Storage features Features for storing digital trace data Data lake
Event recognition
and prediction
features
Features for detecting and predicting
events (i.e., deviations from a
normal state)
Stream analytics, predictive
analytics
Behavior
recognition and
prediction
feature
Features for analyzing customers
behavioral patterns and predicting
their future behavior
Web analytics, mobile
analytics, social media
analytics, predictive analytics
Rule-based
features
Features for initiating automated
actions
Rule-based systems
Visualization
features
Features for making outcomes
available to employees
Visualization applications
448 LEHRER ET AL.
Table 11. Human-material service practices
Organizational
goals Service innovation
Material features of
BDA Example
Provide
individualized
service to
customers
Trigger-based customer
service interaction
describes the interplay
of human and material
agencies in providing
individualized
interactions with
customers.
Features of sourcing,
storage, behavior
recognition and
prediction,
visualization
The system provides
trigger information to
human service actors
who then proactively
approach and
interact with
customers.
Preference-sensitive
customer service
interaction describes
the interplay of
human and material
agencies in providing
individualized
interactions with
customers based on
their preferences.
Features of sourcing,
storage, behavior
recognition and
prediction,
visualization
The system provides
recommendations for
actions based on
customer profiles,
which allow human
service actors to
adjust their customer
interactions.
Table 10. Automation of customer-sensitive service processes
Organizational
goals Service innovation
Material features of
BDA Example
Provide
individualized
service to
customers
Automated trigger-
based service action
describes activities
that are
independently
carried out by a
system to create
value for a customer.
Features of sourcing,
storage, event
recognition and
prediction, behavior
recognition and
prediction, rule-
based actions
Starting an alarm or
calling the police in
response to a forced
entry into a
customers home, as
detected by a
sensor
Automated preference-
sensitive service
action describes
activities that are
independently carried
out by a system to
adjust user interfaces
in accordance with
customer
preferences.
Features of sourcing,
storage, behavior
recognition and
prediction, rule-
based actions
Providing tailored
content in response
to certain user
behavior on an
online channel
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 449
Human-material customer-sensitive service practices
BDA technologies afford human service actors new ways of interacting with custo-
mers, leading to human-material customer-sensitive service practices that are con-
sistent with new action goals related to service individualization, such as proactively
approaching and interacting with a customer. Two types of human-material service
practices emerged as salient from our analysis: trigger-based customer service
interaction and preference-sensitive customer service interaction. In the first case,
the system provides service actors with trigger information (material agency), such
as a customers business-related lifetime event, after which the service actor proac-
tively approaches and interacts with the customer (human agency). In the second
case, the system uses customer profiles to make recommendations for actions
(material agency), allowing the service actor to adjust interaction with the customer
(human agency). Thus, trigger-based customer service interaction can lead to pre-
ference-sensitive customer service interactions, as shown in Figure 1.Table 11
provides an overview, including underlying material features and examples.
Discussion
This study presents a theoretical model of BDA-enabled service innovation that
extends prior work on IT-enabled service innovation [1,24,34] by explaining how
service automation and human-material service practices yield service individualiza-
tion, grounded in the material features of BDA technologies: sourcing, storage, event
recognition and prediction, behavior recognition and prediction, rule-based actions,
and visualization. In this section, we discuss how our model contributes to the
literature on service innovation and to the literature on digital innovation.
Contribution to service innovation scholarship
At a general level, we found that BDA allowed firms to generate customer insights
and heightened awareness about customersneeds and preferences. The material
features of BDA technologies facilitate firmsability to gather and analyze the broad
variety of data sources related to customerseveryday activities so firms can increase
their awareness of their customersbehaviors, interests, and current situations.
Complicity of automation and human-material practices in service innovation
Our study suggests the complicity of automation and human-material practices in
service innovation. Organizations follow a twofold strategy based on service auto-
mation and the implementation of new, improved human-material practices that are
afforded by the material features of BDA technologies. The two are complicit in that
they allow organizations to simultaneously provide their service in real time, while
others still require human activity. Service automation is dominated by material
450 LEHRER ET AL.
agency, while human-material service provision is characterized by the interpenetra-
tion of human and material agencies to deliver value to the customer. This view is
important, considering the prevalence of and emphasis on human-material-discursive
practices in the recent literature (e.g., [11,12,19]).
Proactive service provision
BDA facilitates proactive service provision that is based on insights into the
customer and the customers context. Service provision has typically been reactive
in nature, requiring customers to approach the firm with a service request. However,
digitized objects enable firms to gather and analyze data generated by the customer
outside the business relationship in the customers private sphere. Using such data to
initiate timely interactions enables firms to extend their service value chains and
support their customers in various life situations precisely when they need it. Being
aware of customersproblems in everyday life facilitates the firms development of
new value-added service and improves the customers experience and perception of
the value the firm offers. New customer interaction points can be developed both
inside and outside the business relationship, thereby increasing the frequency of
interactions. This nuanced view shows how organizations create new value proposi-
tions for customers under an S-D logic [1,24].
Speed of service provision
BDA increases the speed of service provisioneven real-time service provision. For
this purpose, service based on BDA is often provided through automated systems
that facilitate immediate action. Prominent examples of such offerings are in the
field of smart homes and telematics. By acting on events, firms can convey the
impression that there is no need for their customers to deal with or to worry about
such things as the safety of their homes because the firms take action on their behalf.
This approach to real-time service provision is in line with the basic tenets of BDA
analytics in terms of the velocity with which new data are generated and analyzed
[28], and it adds another nuance to how organizations create new value propositions
under an S-D logic [1,24].
Service individualization
Enabled by insights gained into the customer both inside and outside the business
relationship, service can be highly individualized and tailored to customersneeds.
Instead of mass customization, BDA enables firms to tailor service cost-effectively
to a segment of oneby using knowledge gained from analyzing the customers
behavioral patterns. Based on the customers inferred preference, a firm can auto-
matically tailor the channel used to deliver service or the user interfaces. According
to den Hertog [7]. The way the firm interacts with the customer can itself be a source
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 451
of innovation, and our analysis highlights how this way is grounded in the material
features of BDA technologies that allow for interactions between human agency and
material agency in delivering services. Our explanation of how BDA contributes to
service innovation by providing customers with added value through individualized
and convenient customer experiences contributes to the debate on personalization of
information systems (e.g., [17,48]) and to the emerging research on omnichannel
management (e.g., [15]).
Our analysis also suggests that BDA-enabled service processes might be extended
to address the customer on an emotional level, although such emotion-sensitive
service was only mentioned in the interviews and has not been fully implemented in
the case organizations. Knowledge about the customers current or future emotional
state, gained through BDA, might allow firms to adapt how they speakto the
customer on an affective level, a phenomenon that is central to the emerging
research field of emotion-sensitive technology (e.g., [16]). This field has started to
design and build information systems that are sensitive to human emotions and that
can change their behavior accordingly. This bears the potential to emphasize the
human componentin increasingly electronic and automated customer interactions
and highlights the role of human agency in service delivery. Such emotion-sensitive
service processes promise to deliver emotional or hedonic value, such as by provid-
ing customers with a positive feeling when they interact with the firm, thereby
enriching and deepening the customers experience.
Contribution to digital innovation scholarship
Digital technologies are reprogrammable [20,58], so organizations explore config-
urations of technologies that form functional relationships to identify new potentials
for action as they are confronted with new action goals. Our analysis shows that
BDA technologies are an example of such malleable, flexible digital technologies
and that, in order to innovate service, organizations should capitalize on the com-
bined effects of technologies that are related to sourcing, storing, analyzing, and
exploiting data. Technology is reprogrammed in some cases to automate service
processes and in other cases to provide actionable spaces to human actors, leading to
novel interpenetrations of human and material agencies. Our study suggests that
reprogrammable digital technologies allow for innovations that are shaped by
material agency and the interpenetration of human agency and material agency.
The concept of affordances helped us explain how this digital innovation occurred.
Affordances are both dispositional (i.e., associated with the technology) and rela-
tional (i.e., in relation to a specific use context) [12]. As the use context changes,
new, innovative applications of digital technologies emerge [41]. As our analysis of
four cases from different industries suggests, these innovative affordances occur
across contexts. But what explains the similarities in the occurrence of innovations
across contexts? There have been advances to theorize about how such regularities
occur, for instance, using arguments that draw on institutional theory [19,41]or
452 LEHRER ET AL.
concepts like habit [12] or performativity [5]. Our study highlights how BDA gives
rise to similar innovative affordances across our case organizations, as these orga-
nizations draw on an S-D logic, even though these similar applications are grounded
in different technologies. While one company might use, for instance, Hadoop, as
was the case in Company A, another company might use a different technological
platform. Still, we were able to identify the material features of those technologies at
an abstract level and can explain the similarities by means of the prevalence of an
S-D logic, where organizations seek to implement customer centricity and service
individualization. This explanation is consistent with the view that the identification
and enactment of technology affordances is shaped by the institutional context and
associated logics on which an organization draws [19,41].
This view suggests that the same technology might be reinterpreted in such a way
as to afford new actions in light of new action goals. The argument is that malleable
digital technologies are (1) (re-)interpreted in light of changing action goals, (2) that
this (re-)interpretation leads to certain development and implementation activities
that enable new functional relationships among the material features of digital
technology, and that (3) these new relationships afford new configurations of
material and human agencies. In this view, affordances are at the organizational
level (e.g., [42,47,59]). Therefore, our work is in line with work that has recognized
the observable regularities in the enactment of information technology across con-
texts and time [13,41]. Information technologies are used in strikingly similar ways
across organizations, which is also the case for BDA-enabled service innovation.
Implications for IS Research and Practice
Implications for research
Our study highlights how BDA technologies enable service innovations and, thus,
contribute to creating new value propositions. In so doing, the study adds an
integrated perspective on IT-enabled service innovation in organizations [34]. Our
research identifies material features of BDA technologies in terms of sourcing,
storage, event recognition and prediction, behavior recognition and prediction,
rule-based actions, and visualizationa conceptualization that accounts for both
the retrospective and the prospective (e.g., in terms of predictive and prescriptive
analytics) characteristics of BDA [44]. Moreover, instead of treating BDA as an
undifferentiated whole, our empirical results support the notion that BDA consists of
the interplay of multiple applications for gathering, storing, analyzing, and commu-
nicating big data from external and internal data sources [3], highlighting the
functional relationships among digital artifacts [20] and their combined potential
for service innovation [58]. We also highlight how this materiality translates into
both automation and the provision of human-material service practices, a perspective
that can inform future research in four primary ways.
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 453
First, our theory development suggests that future research should consider the
potential of BDA technologies in developing service automation and human-mate-
rial service practices. Automation can relate to both automating existing service
practices and implementing new automated processes that were once impossible.
Similarly, human-material service practices can be improvements of existing pro-
cesses or entirely new practices.
Second, our study identifies important context factors in terms of organizational
goals that are associated with an S-D logic. Future research efforts should focus on
the enabling and constraining factors in actualizing the service practices and how
these practices should be implemented (cf. [47]). For example, further research could
investigate certain service features to determine whether a service should be auto-
mated or provided as a human-material practice.
Third, our study supports recent work highlighting that understanding technology
affordances requires analytic approaches that simultaneously consider, for example,
aspects of materiality, humans, and context in light of organizational level goals.
Fourth, both the dynamic changes in material features of BDA and the organiza-
tional context offer opportunities for longitudinal studies that examine the develop-
ment of BDA affordances and service-provision practices.
Implications for practice
Our findings have four primary implications for practitioners who design BDA
infrastructures to support service innovation. They provide guidance for the design
and implementation of technologies that deliver the material features for service
automation and human-material service practices.
First, the development of BDA technologies is highly dynamic, and different
instantiations of a technology might provide similar material features. Practitioners
can use the categories of features identified in this study (sourcing and storage
features, analytic features in terms of event recognition and prediction and behavior
recognition and prediction, and exploitation features) to identify suitable and scal-
able technologies. At the same time, they can revisit their IT infrastructures to
determine to what extent such features are present that might be exploited to afford
service innovation or to determine whether they can be created through reprogram-
ming. Future research could identify additional material features and associated
affordances, thereby, informing BDA research about new material features that
might be beneficial or even critical to additional service innovations, such as those
in the area of security and privacy.
Second, practitioners can use the theoretical model to analyze their need for
service automation or human-material service practices. As our analysis shows,
some organizations balance automation and human-material service practices (as in
the case of Companies A and B), while others focus only on human-material
practices (as in the case of Company C) or automation (as in the case of Company
454 LEHRER ET AL.
D). The appropriate strategy depends on the type of service as well as the customers
expectation. Our description of four cases provides some examples.
Third, the empirical insights from our case studies and our theorizing based on
those cases provide fine-grained information about BDAs specific contribution to
service innovation. Thereby, our results provide guidance to firms that seek to
launch BDA-enabled service innovation. IT managers must have a holistic grasp
on how BDA technologies afford different models of service provision such that the
service provision is aligned with the organizations strategic goals. All four cases
provide evidence that the companiesinvestments in BDA technologies and their
application to service innovation was in response to specific action goals and that
these goals had in common their focus on individualized service.
Fourth, the service innovations identified in this study might inspire the develop-
ment of use cases for firmsspecific use contexts and strategic goals.
Limitations
Despite the careful design of our research approach, our findings are subject to
several limitations. First, qualitative research relies on the researchersinterpretation
in coding and analyzing the data. While we applied established techniques suggested
by Wallendorf and Belk [52] to ensure high-quality results, future research should
repeat and refine our analysis. Second, as the use of information technologies is
subject to subjective interpretations in specific contexts of use, it is unlikely that our
account of the potential for service automation and human-material service practices
is exhaustive. Future research could investigate whether additional uses emerge
based on a comparable sample. Third, as our case organizations had a number of
common characteristics with regard to ownership, business model, relevance of
BDA to service innovation, and cultural proximity, our results may not be general-
izable beyond this context. Future research might verify whether our results apply
across contingency factors like other industries and other regulatory and cultural
contexts. Fourth, although our firms have strong technological capabilities, there
may be other firms, especially in the tech industry, that are pioneers in applying
BDA. Future research could investigate whether these firms have put BDA to other
uses, and in case of differences, shed light on why they occurred.
Conclusion
Our research lends support to the argument that BDA holds potential for service
innovation [58] and identifies the factors that are pertinent to the creation of new
value propositions. It identifies two key primary roles of BDA in the context of
service innovation: (1) automation of customer-sensitive service provision, and (2)
human-material customer-sensitive service practices, and highlights how these are
grounded in material features of BDA. Together, these two types of service innova-
tion allow organizations to revamp their value propositions.
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 455
NOTES
1. The singular term serviceused here instead of the plural services,emphasizes the
focus on service processesinstead of services in terms of units of output[24,50].
2. Please note that we have adjusted our research question throughout this qualitative,
exploratory study. However, the essence of our question in terms of the impact of BDA on
service innovation remains the same as it was when we commenced the study.
3 While open coding is typically associated with grounded theory method, it is indeed
used in exploratory, qualitative research in general [27,43].
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458 LEHRER ET AL.
Appendix
Interview Questions
A. Background
What is your position within [case organization]?
What projects do you typically/currently work on?
What is your understanding of the term big data?
Are you aware of big data initiatives at [case organization]?
Do you have any tasks and responsibilities that are directly related to big data
initiatives?
What is your understanding of the term big data analytics?
To ensure a common understanding of big data analytics, we would like to introduce
the following definition: Big data analytics refers to technologies for gathering,
processing and analyzing big data, which commonly describes a vast amount of
complex data.
Based on this understanding, is [case organization] using big data analytics?
And what role does it play at [case organization]?
B. Service innovation at [case organization]
In this interview, we aim to get an in-depth understanding of the role that BDA plays
for service innovation at [case organization]. Therefore, we would like to ask a few
questions about this topic.
Do consumer-oriented services play a role in your organization? If yes, please
describe them.
What is your understanding of the term service innovation?
Does service innovation play a role in your organization? If yes, please
describe it.
What do you think is the motivation of [case organization] with regard to
service innovation?
C. The role of BDA for service innovation
What role does BDA play for service innovation?
What are the things you expect to be able to do with BDA in the context of
service innovation?
What do you think are the underlying goals of harnessing BDA for service
innovation?
Do you know about any BDA technology that is used at [case organization]
for service innovation?
HOW BIG DATA ANALYTICS ENABLES SERVICE INNOVATION 459
Let us now assume that your organization had all the necessary BDA technologies in
place.
What do you think could be the role of BDA for innovating or improving
consumer-oriented services?
Subquestions, especially for interviewees with a technical background
What does the current technological infrastructure for data collection and
analysis look like at [case organization]? Please describe it in detail.
By means of which technologies does [case organization] collect, analyze and
apply big data? Or how does it plan to do this? Please describe the technol-
ogies in detail.
D. Conclusion
Did we forget anything? Is there anything else you would like to discuss?
Could we get back to you in case we have some (minor) further questions
from our data analysis?
460 LEHRER ET AL.
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