Content uploaded by Christian Dremel
Author content
All content in this area was uploaded by Christian Dremel on Nov 21, 2018
Content may be subject to copyright.
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 1
Barriers to the Adoption of Big Data
Analytics in the Automotive Sector
Full Paper
Christian Dremel
University of St.Gallen
christian.dremel@unisg.ch
Abstract
Big data analytics as source of competitive advantages is pivoting the automotive sector. Today’s original
equipment manufacturers, however, are confronted by several barriers to the successful adoption of big
data analytics. First, the cross-disciplinary nature of big data analytics requires (1) sufficient and skilled
resources, (2) the collaboration of different business departments, supported by (3) appropriate
organizational structures, (4) a data-driven culture, and (5) a defined business value, and (6) access to
relevant data pools to achieve commitment and relevance within the organization. Drawing on the results
of a revelatory case study as well as on the socio-technical systems theory I have identified six barriers to
the adoption of big data analytics. From an academic perspective, these barriers contribute to the current
body of knowledge of the adoption of big data analytics. For practice, they provide guidance for firms in
the automotive sector as well as other traditionally goods-dominant industries which barriers need to be
tackled to leverage the business value of big data analytics.
Keywords
Adoption, big data analytics, case study, barriers, automotive.
Introduction
Digitization strongly affects the car manufacturing market and leads to disruptive product innovations
(e.g., autonomous driving), new business models (e.g., mobility-as-a-service) and digital services (e.g.,
predictive maintenance)(Luckow et al. 2015). Today’s drivers increasingly value personalized digital
services (e.g., product recommendations). Such digital services more and more complement the
traditional core product, i.e. the physical automotive technology. Against this backdrop big data analytics
is pivoting the automotive sector as it enables those services (Luckow et al. 2015; Opresnik and Taisch
2015; SAS Institute Inc. 2015). In spite of a growing body of research in practice (e.g., Bughin et al. 2010;
Henke et al. 2016.; Manyika et al. 2011) and in science (e.g., Baesens et al. 2016; Chen et al. 2012) on the
socio-technical phenomenon big data analytics, empirical research on how big data analytics can be used
to the advantage of companies and what is required to do so is largely missing (Baesens et al. 2016; Saltz
2015; Tambe 2014). The successful adoption of big data analytics, however, poses several barriers for
today’s original equipment manufacturers (OEMs). Particularly, OEMs acknowledge that data is a
potentially new source of revenue. Yet, they hazel to adopt the emerging technology big data analytics
because of the fundamental changes that are required to successfully adopt big data analytics. OEMs in
the automotive sectors in contrast to other more agile business sectors (e.g., entertainment with Spotify,
Netflix, YouTube) are traditionally focused on cost-efficient production of high-quality products which
require long development cycles of up to 6 years. This focus is also reflected by their work processes,
departmental structure, and foremost, their culture focusing on precision in engineering. In order to
innovate their product and their business model, however, the automotive sector has to face the most
dramatic shift in years through technology innovations such as big data analytics, autonomous driving,
and above all digitization (Gissler et al. 2016). Accordingly, I pose the following research question
RQ: “Which barriers need be overcome for the successful adoption of big data analytics in the
automotive sector?”
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 2
To do so I use the socio-technical system (STS) theory as conceptual lens for my revelatory case study for
three reasons. First, it allows me to broaden my view towards a holistic social-technical perspective on a
phenomenon that is rather technologically discussed (Abbasi et al. 2016; Yoo 2015). Second, relating to
the interrelationship of technical system and social system I can identify barriers which require
organizational changes. Third, STS theory is in IS research a well-established theory for considering not
only the technology aspect of technology-driven innovation such as big data analytics but also the social
system and the interrelationship of technical and social system. Drawing on the results of a revelatory
case study as well as on the socio-technical systems theory I present six barriers to the adoption of big
data analytics.
The remainder of this article is structured as follows. Next, I describe the theoretical background
providing a conceptualization of big data analytics. In section 3, I present my research approach.
Thereafter, I discuss my results supported by interview quotations. I conclude by critically reflecting my
contribution to academia and practice as well as my limitations.
Theoretical Background
Socio-Technical Systems Theory
The socio-technical perspective of Bostrom and Heinen (1977) draws on the sociotechnical model of
Leavitt (2013) to elaborate the best way to design information system in line with the organizational work
system (Bostrom and Heinen 1977). The work system as sociotechnical system consists of the (1) technical
system and (2) the social system.
Figure 1. Socio-Technical Perspective on organizational work systems (Bostrom and
Heinen 1977; Leavitt 2013)
First, the social system comprises structures and actors (see Figure 1)(Bostrom and Heinen 1977; Leavitt
2013). Actors include “people, but with [their] qualification” (Leavitt 2013, p. 2977) and structures
comprise “systems of communication, systems of authority (or other roles), and systems of work flow”
(Leavitt 2013, p. 2978). Second, the technical system comprises technology and tasks (see Figure 1)
(Bostrom and Heinen 1977; Leavitt 2013). Tasks relate “raison d’être [of the firm]: the production of
goods and services, including the large [...] number of meaningful subtasks that may exist in complex
organizations” (Leavitt 2013, p. 2977) and technology relates to “refers to direct problem-solving
inventions like work-measurement techniques or computers or drill presses” including programs and
machines (Leavitt 2013, p. 2977). In this context, actors include e.g., organizational culture, capabilities,
and knowledge whereas structures encompass e.g., organizational structures, ways of communication,
and project organizations. At last, technology constitutes e.g., tools and technological platform and task
e.g., the required organizational processes to fulfil work (Bostrom et al. 2009; Lyytinen and Newman
2008).
In particular, the STS theory proved as valuable lens of analysis to elaborate IS induced changes in the
organizational context and its effects on the social and technical system as well as their interrelationship
(Lyytinen and Newman 2008). The successful adoption, diffusion, and use of IS systems requires to
consider this interrelationship and dependency to successfully achieve the desired system performance
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 3
(Bostrom and Heinen 1977). Thus, knowledge about the alignment of socio-technical components is
crucial (Lyytinen and Newman 2008). This view has general implications for the adoption of big data
analytics: Before successfully exploiting a technology innovation such as big data analytics, a multitude of
barriers need to be overcome (e.g., lack of capabilities and knowledge) to successfully adopt and
assimilate big data analytics.
Big Data as Socio Technical Phenomenon
Accordingly, a conceptualization of big data analytics from a STS-perspective requires the differentiation
of (1) the technical system and (2) the social system. First, the successful use of big data analytics requires
the implementation of technology, which is able to process, store and collect a vast amount of data with
respect to data variety, variability, velocity, and value (van den Broek and van Veenstra 2015; Constantiou
and Kallinikos 2015; Goes 2014). Big data analytics technologies must enable a flexible handling of
incomplete, inconsistent, ambiguous, heterogeneous, and agnostic data (Constantiou and Kallinikos 2015)
dissolving predefined data schemas, as used before (Marton et al. 2013; Tan et al. 2015). It “forces us to
look beyond the tried-and-true methods that are prevalent” (Jacobs 2009, p. 44). These technologies
enable the task to uncover previously unknown patterns, correlations and information from diverse and
unexploited data sources (e.g., social media, product usage data, RFID) (Duan and Cao 2015) to enhance
competitiveness (Olbrich 2014; Tiefenbacher and Olbrich 2015) in terms of profits and efficiency
(Ghoshal et al. 2014), speed and service (van der Aalst 2013), products (Constantiou and Kallinikos 2015;
Duan and Cao 2015) through timely (Trieu 2013) and more wise decisions (Woerner and Wixom 2015).
Second, in regard to the social system, supposedly, the “belief that large data sets offer a higher form of
intelligence and knowledge that can generate insights that were previously impossible, with the aura of
truth, objectivity, and accuracy” (Boyd and Crawford 2012, p. 663) is crucial to successfully leverage big
data analytics. Companies using big data analytics not only need to change regarding their actors but also
their structures. The actors need to develop a supportive culture, appropriate capabilities as well as
knowledge, whereas the structures (i.e., collaboration and organizational department structures need to
support big data analytics initiatives (Chatfield et al. 2015; Davenport 2014; Porter and Heppelmann
2015). Particularly, the collaboration of IT-related and business departments plays an important role for
big data analytics as organizations ought to combine technology and domain knowledge (Miranda et al.
2015).
In summary, big data analytics can be understood as the ability to gain analytical insights from big data,
which has a specific business value and cannot be analyzed by traditional approaches such as data
warehousing (Dremel et al. 2017). Following my conceptualization above big data analytics equally affects
the social and technical system.
Related Work
Research in the area of big data analytics in the automotive sector discusses use case scenarios of big data
analytics (e.g., traffic prediction and autonomous driving) and their required technological (e.g., data lake
architecture with Hadoop as foundational technology) and algorithmic foundation (e.g., deep learning and
text mining) (Luckow et al. 2015). Others, discuss the possible improvement of product innovation
through automotive telematics data in combination with a detailed description of the required
technological concepts and technologies (Johanson et al. 2014), whereas a case study approach is used to
either indicate the implementation of big data services and their lessons learned (Woźniak et al. 2015) or
illustrate how incumbent firms can embrace digital innovation (Svahn et al. 2017). Initial research on the
adoption of big data analytics resulted in a research framework of the security determinants for “big data
solutions” adoption (Salleh et al. 2015), and a model on the optimal decision on big data analytics
adoption in the healthcare industry (Li et al. 2015). Additionally, preliminary big data adoption process
and generic factors affecting this process (Chen et al. 2015) and determinants of big data analytics
adoption in Asian emerging economies (Agrawal 2015) are identified. At last, literature on emerging
technologies identifies a set of issues that affect the decision to adopt an emerging IT into corporate IT
strategy (Cegielski et al. 2013). I contribute to this existing literature by identifying critical barriers to the
adoption of big data analytics from a STS perspective to particularly regard the barriers in the social
system as well as the technical system.
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 4
Research Method
Methodological Foundations
The goal of this research is to explore which barriers hinder the adoption of big data analytics in the
automotive sector. To elaborate the barriers in detail, I draw on an interpretive research design and apply
a qualitative single case study approach because of three major reasons (Benbasat et al. 1987; Eisenhardt
1989; Myers 1997). First, big data analytics represents a phenomenon characterized by novelty and
undefined boundaries (Yin 2008). In particular, I conduct a revelatory case study in the automotive sector
as harnessing big data analytics is pivoting this industry (SAS Institute Inc. 2015). Moreover, the
automotive sector provides me with a natural context for my research endeavor, in which the effective
adoption of big data analytics has an impact on the competitive edge of automotive organizations. Second,
qualitative research helps to elaborate the barriers in detail within their social or organizational
embedded contexts (Orlikowski and Iacono 2001) using a typical single case study to achieve
generalizability (Yin 2009). A case study represents an “empirical inquiry that investigates a
contemporary phenomenon within its real-life context, especially when the boundaries between
phenomenon and context are not clearly evident” (Yin 2009, p. 13). Thus, I pursue an inductive
qualitative research design, because of (1) the novelty and (2) the lack of prior research on big data
analytics (Eisenhardt 1989; Klein and Myers 1999; Sarker 2013; Yin 2009).
As case study research strongly relies on the case context (Eisenhardt 1989; Yin 2009), characteristics of
PremiumCar are briefly outlined: Ranked among the top in its market segment, PremiumCar aims not
only to differentiate products by innovation from competitors but also to stay competitive investing in
new technologies. To do so PremiumCar heavily invests in big data analytics to overcome the barriers to
its adoption on an organizational level. In 2015, PremiumCar shipped more than 2 million luxury cars to
the customers worldwide. However, PremiumCar faces a multitude of new competitors entering their
market.
I consider this case as typical because of three reasons: First, the investigated case organization exhibits a
strong tradition car manufacturing, continuously staying on the competitive edge through technology
innovations. Second, to sustain the competitive edge they adopt and diffuse big data analytics on an
organizational level. Third, big data analytics is currently pivoting the automotive industry (SAS Institute
Inc. 2015). Hence, my revelatory case provides me with unique insights to elaborate the barriers to the
adoption of big data analytics. Overall, my case is representative to other car manufacturers that pursue
the successful adoption of big data analytics.
Data Collection
To obtain in-depth qualitative data, explorative interviews with managers in the field of big data analytics
were conducted as primary source for data collection. Prior to conducting the interviews, an interview
guideline was developed following the guidelines by Schultze and Avital (2011). Based on the respective
knowledge of the interviewees and the interview context, additional questions are asked. Table 1 provides
a detailed overview on the interviewees. The interviews lasted between 45 and 80 minutes. They were
transcribed based on the audio recordings resulting in 160 pages of text. In addition to interviews,
internal documents as well as publicly available data are used to corroborate findings.
No.
Interviewee Department
#1 Manager Data Analytics/Strategy Analytics department
#2 Product Owner Car Data #1 Analytics department
#3 Product Owner Car Data #2 Analytics department
#4 Product Owner Analytical Services #1 Analytics department
#5 Product Owner Analytical Services #2 Analytics department
#6 Product Owner Analytical Services #3 Analytics department
#7 Product Owner Analytical Services #4 Analytics department
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 5
#8 Product Owner Analytical Services #5 Analytics department
#9 Product Manager Data analytics-as-a-service #1 Analytics department
#10 Product Manager Data analytics-as-a-service #2
IT department
#11 Manager IT for Data Analytics IT department
#12 Manager Big Data Competence Center IT department
#13 Manager Car-Related Services R&D department
#14 Manager Strategy Development Strategy department
Table 1. Interviews in detail
Analysis
Following the exploratory character of my research, I adapted grounded theory techniques of Straus and
Corbin (1990) for data analysis which are applicable if the phenomenon of interest is novel and data-
grounded, to develop insights with relevance for both scholars and practitioners. Specifically, I pursue a
step-wise coding that consists of open, axial, and selective coding. In the open coding stage, codes
emerged based on case write-ups and summaries to condense the transcripts and obtain an initial
overview of all case data (Yin 2008). Codes are initially developed inductively due to the novelty of the
topic. Furthermore, similarities and differences are identified by conducting cross-interview analysis
(Patton 2015). In the axial coding stage, I condense data based on the dimensions of the STS theory
identifying connections and interrelationship between codes (see Figure 1). Along the dimensions of
technology and task, I identify relevant barriers of the technical system. My analysis regarding the social
system are structured along actor characteristics and structural aspects of the organization. I aggregate
emerging codes to identify reoccurring themes emerged. Selective coding finally allows me to sharpen my
focus on the relations between the identified concepts as well as the concepts itself (Corbin and Strauss
1990)
During the coding procedures, computer-assisted qualitative data analysis software ATLAS.ti was utilized
to support and to ensure transparent and efficient data analysis. In total, 31 open codes acted as empirical
evidence. For each identified barrier in the selective coding stage, the code frequency ranged from 8 to 12
codes (see Table 2).
Step in Coding
Process
Number of
codes
Mean code
frequency
Open 31 2,75
Axial 6 10
Selective 6 10
Table 2. Number of codes and code frequency
Results
In the case study, barriers to the organizational adoption of big data analytics in the automotive industry
are identified. Table 3 provides an overview of all identified barriers. I present the six identified barriers
in the following illustrated by the aid of interviewee quotations.
No.
Barrier to adoption of big data analytics STS
dimension
#1 Development of a big data analytics competence Actors
#2 Establishing a data-driven organizational culture Actors
#3 Fostering cross-disciplinary and cross departmental collaboration Structure
#4 Establishing the organizational frame to support big data analytics initiatives Structure
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 6
#5 Definition of a clear business value Task
#6 Accessible data sources and unified IT landscape Technology
Table 3. Barriers to the Adoption of Big Data Analytics along the STS theory
#1 Development of a big data analytics competence
The barrier to build up a critical number of resources that comprises the trinity of (1) analytical, (2)
technological, and (3) business capabilities and domain knowledge.
When pursuing to leverage big data analytics an organization has on the one hand to develop knowledge
and capabilities regarding (1) data analysis and visualization, (2) data processing and storing, and (3)
business understanding and domain knowledge to understand what is hidden behind the company’s data.
E.g., in my case study car data is not understandable and interpretable without a fundamental knowledge
about car infrastructure and car behavior.
“One big disadvantage from the beginning was always a lack of skilled resources […] not only in the IT
department but also regarding our analysts. Through our subsidiary […], we could at least provide
sufficient big data architects and analysts whereas our IT department still needs additional resources to
keep up with our development pace.” Product Owner Analytical Services #1, 2016
#2 Establishing a data-driven organizational culture
The barrier to develop a so-called data-driven culture. This comprises the commitment for big data
analytics on executive level as well as on operational level. A mindset that data is a valuable resource
for the organization is indispensable as well as the step to transform restraining forces against the
transparency of business actions stemming from big data analytics.
My researched OEM’s culture was characterized by the belief that producing cars with the best quality will
suffice. However, on the one side competitors who put their focus on the value proposition of the car in
combination with digital (assistance) services initiated a change in the mind-set and on the other side
employees, particularly, from the analytics department continuously discussed with stakeholder on
executive level as well as responsible in the business unit the business value stemming from big data
analytics (e.g., customer specific marketing campaigns or predictive maintenance) to transform
restraining forces towards accelerating ones.
“We and our colleagues have understood that data has an extreme value for the company. Thus, it is
easier for us to get access to data pool and slowly the restraining forces against transparency stemming
from data analysis is diminishing. We slowly get to the point where everyone provides data access to
make data-based decision even possible and, thus, to get better, but our journey is still not finished.”
Product Manager Data Analytics-as-a-service, 2016
#3 Fostering cross-disciplinary and cross departmental collaboration
The barrier to successfully overcome departmental boundaries regarding to data ownership and
organizational power issues. Access to data pools needs to be as easy as possible complying with
security and privacy to enable the success of big data analytics initiatives.
As big data analytics requires the trinity of analytical, technological, and business capabilities and domain
knowledge it is crucial to overcome departmental boundaries to ensure cross-disciplinary and cross-
departmental collaboration. E.g., the collaboration of an analytics department with R&D at an OEM is
often characterized by power issues as well as prejudices. Thus, a common goal to the profit of the overall
company overall as well as the people on work level is one key to success. Moreover, it was
acknowledgeable that data ownership is sometimes used to expand the own power. This ultimately
neglects the potential value big data analytics as the access to data is crucial for all units that are
specifically concerned with the analysis of data from diverse sources (e.g., sales data, car usage data, social
media data, and customer data).
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 7
“Most important is collaboration and a common understanding of all parties. This includes the business,
IT, analytics department, and […] [our subsidiary]. You need to be able to work together in a dynamic
mode without a defined organizational structure… it must be project-based, topic-based. Close teams
than build the prototypes and develop productive services. You need as well the interest and
involvement of your customer. At […][PremiumCar] a lot of politics is going on and you have to
overcome any political issues through strategic partnerships.” Product Manager Data Analytics-as-a-
service #1, 2016
#4 Establishing the organizational frame to support big data analytics initiatives
The barrier to implement not only an organizational structure that allows the proper collaboration
needs to be established but also the respective work process which include all relevant parties of the
organization.
The researched OEM’s structures were characterized by clear hierarchies to perform the work with a top-
down approach. However, these structures conflict the explorative approach to data analysis (Debortoli et
al. 2014) required for the development of big data analytics services. To appropriately support big data
analytics initiatives a subsidiary supporting all initiatives with sufficient resources (see #1 Development of
a big data analytics competence) and the work methods to quickly provide stakeholder with actual
valuable results (see #5 Definition of a clear business value). Additionally, new agile methods (e.g.,
adapted from software engineering) need to accompany these structures.
“We work in a matrix-structure as the traditional structures do not work anymore for our topic.
However, it is not easy to master this change within our company as we traditionally are characterized
by hierarchy-driven department structures. That is something we as employees must get rid of in our
minds. […] Thus, we had to create a more agile subsidiary and use now agile development methods.”
Product Owner Analytics Services #3, 2016
#5 Definition of a clear business value
The barrier to gain the visibility and relevance of big data analytics. To do so a clear understanding of
the business value of big data analytics for the organization and its customer is a prerequisite to
achieve commitment of all stakeholder of the organization.
To achieve the commitment, the willingness to invest budget and resources (see #3 Fostering cross-
disciplinary and cross departmental collaboration and #1 Development of a big data analytics
competence), as well as the possibility to alter processes towards a data-driven approach from the
beginning a clear value of big data analytics must be defined. Though, big data analytics is often
explorative the way a traditional OEM works is business case-driven. Investments are made because of a
specific return-on-investment in mind. Hence, the responsible people pushing big data analytics forward
need a clear vision where the business value for the company lies within big data. Without a defined
business value, it is nearly impossible to achieve the needed commitment and support.
“At first, we had to build short-term solution to prove that analytics works for our company. In this
context, we had to include our customers on the way to achieve the commitment we needed to proceed.
[…] we learned very early that on our own without a supporting business unit, IT department and our
big data architects and analysts it is not possible to bring the value we are supposed to.” Manager Data
Analytics & Strategy, 2016
#6 Accessible data sources and unified IT landscape
The barrier to develop knowledge of all existing data pools within the whole IT landscape. This
includes the implementation of guidelines for data access and a critical reflection of the current IT
landscape.
To ensure the analysis of all required data pools a clear understanding and architecture of all operating IT
systems that potentially generate relevant data is crucial. However, IT departments mostly act as project
leader thus risking a substantial knowledge loss when a project with an external service provider is
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 8
finished. Through the multitude of external service providers, the IT landscape become more and more
fragmented.
“Our biggest obstacle was our old IT landscape. No one knew where our data is stored and what is
going on with the data except for some little examples which had a clear data logic and structure. Thus,
we had to do ground work just to be able to collect the data we needed for our analyses.” Manager Data
Analytics & Strategy, 2016
Discussion and Conclusion
Although “big data analytics” belongs to the most intensively discussed topics today, little research
explains how to effectively perform big data analytics. Building upon the holistic STS theory and a
revelatory case study at a leading OEM in the automotive sector, I present six barriers that companies
trying to adopt big data analytics need to overcome.
The results of my research have implications for academia and practice alike. From an academic
perspective, these barriers contribute to the current body of knowledge in the area of the adoption of big
data analytics (Abbasi et al. 2016; Agrawal 2015). Though some overlaps to (Agrawal 2015) exists (e.g.,
‘technological resource competency’ and ‘#1 Development of a big data analytics competence’), it
advances and expands the current body of knowledge with novel findings. Particularly, my results indicate
which barriers in detail are relevant for the social (barrier #1, #2, #3, #4) as well as for the technical
system (barrier #5, #6) of a company’s work system. For practice, they provide guidance for firms in the
automotive sector as well as other traditionally goods-dominant industries which barriers need to be
tackled to leverage the business value of big data analytics. Thus, they provide a first agenda for
companies thinking of investing in big data analytics. Additionally, I argue that the successful adoption of
big data analytics requires fundamental organizational changes before a big data analytics technology is
put in place.
Several limitations exist in the light of which my research results must be interpreted. Most notably, I
conducted my research using a revelatory single case study, thus, my results are influenced by the sample
bias. However, using a leading and traditional OEM I regard my case as typical and, in consequence, my
results are transferrable to other product-oriented manufacturers struggling to adopt big data analytics.
Though, I elaborated the barriers in a natural context of a single OEM these barriers will be
acknowledgeable at other product-oriented manufacturers as their business and thinking was as well
characterized by producing goods instead of a combination of services and goods (servitization) (Opresnik
and Taisch 2015).
To fully leverage the potential of big data analytics, a company needs to successfully address the challenge
of the (1) Development of a big data analytics competence, (2) Establishing a data-driven organizational
culture, (3) Fostering cross-disciplinary and cross departmental collaboration, (4) Establishing the
organizational frame to support big data analytics initiative, (5) Definition of a clear business value, and
(6) Accessible data sources and unified IT landscape.
REFERENCES
van der Aalst, W. 2013. “Mine your own business": using process mining to turn big data into real value,”
in Proceedings of the 21st European Conference on Information Systems, Utrecht, The
Netherlands.
Abbasi, A., Sarker, S., and Chiang, R. H. 2016. “Big Data Research in Information Systems: Toward an
Inclusive Research Agenda,” Journal of the Association for Information Systems (17:2), p. 3.
Agrawal, K. 2015. “Investigating the determinants of Big Data Analytics (BDA) adoption in Asian
emerging economies,” in Proceedings of the 21st Americas Conference on Information Systems,
Puerto Rico, USA, June 26.
Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., and Zhao, J. L. 2016. “Transformational Issues of
Big Data and Analytics in Networked Business,” MIS Quarterly (40:4), pp. 807–818.
Benbasat, I., Goldstein, D. K., and Mead, M. 1987. “The Case Research Strategy in Studies of Information
Systems,” MIS Quarterly (11:3), p. 369.
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 9
Bostrom, R. P., Gupta, S., and Thomas, D. 2009. “A Meta-Theory for Understanding Information Systems
Within Sociotechnical Systems,” Journal of Management Information Systems (26:1), pp. 17–48.
Bostrom, R. P., and Heinen, J. S. 1977. “MIS Problems and Failures: A Socio-Technical Perspective. Part I:
The Causes,” MIS Quarterly (1:3), pp. 17–32.
Boyd, D., and Crawford, K. 2012. “Critical Questions for Big Data,” Information, Communication &
Society (15:5), pp. 662–679.
van den Broek, T., and van Veenstra, A. F. 2015. “Modes of Governance in Inter-Organizational Data
Collaborations,” in Proceedings of the 24th European Conference of Information Systems,
Münster, Germany.
Bughin, J., Chui, M., and Manyika, J. 2010. “Clouds, big data, and smart assets: Ten tech-enabled
business trends to watch,” McKinsey Quarterly (56:1), pp. 75–86.
Cegielski, C. G., Bourrie, D. M., and Hazen, B. T. 2013. “Evaluating Adoption of Emerging IT for
Corporate IT Strategy: Developing a Model Using a Qualitative Method,” Information Systems
Management (30:3), pp. 235–249.
Chatfield, A., Reddick, C., and Al-Zubaidi, W. 2015. “Capability Challenges in Transforming Government
through Open and Big Data: Tales of Two Cities,” in Proceedings of the 36th International
Conference on Information Systems, Fort Worth, USA.
Chen, H., Chiang, R. H. L., and Storey, V. C. 2012. “Business Intelligence and Analytics: From Big Data to
Big Impact,” MIS Quarterly (36:4), pp. 1165–1188.
Chen, H.-M., Kazman, R., and Matthes, F. 2015. “Demystifying Big Data Adoption: Beyond IT Fashion
and Relative Advantage,” in DIGIT 2015 Proceedings, , December 13.
Constantiou, I. D., and Kallinikos, J. 2015. “New games, new rules: big data and the changing context of
strategy,” Journal of Information Technology (30:1), pp. 44–57.
Corbin, J., and Strauss, A. 1990. “Grounded Theory Research: Procedures, Canons, and Evaluative
Criteria,” Qualitative Sociology (13:1), pp. 3–21.
Davenport, T. 2014. Big data @ work: dispelling the myths, uncovering the opportunities, Harvard
Business Review Press.
Debortoli, S., Müller, O., and Brocke, J. vom. 2014. “Comparing Business Intelligence and Big Data
Skills,” Business & Information Systems Engineering (6:5), pp. 289–300.
Dremel, C., Overhage, S., Schlauderer, S., and Wulf, J. 2017. “Towards a Capability Model for Big Data
Analytics,” in Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), St.
Gallen, Switzerland.
Duan, Y., and Cao, G. 2015. “Understanding the Impact of Business Analytics on Innovation,” in
Proceedings of the 23rd European Conference on Information Systems, Münster, Germany.
Eisenhardt, K. M. 1989. “Building Theories from Case Study Research,” Academy of Management Review
(14:4), pp. 532–550.
Ghoshal, A., Larson, E., Subramanyam, R., and Shaw, M. 2014. “The Impact of Business Analytics
Strategy on Social, Mobile, and Cloud Computing Adoption,” in Proceedings of the 35th
International Conference on Information Systems, Auckland, USA.
Gissler, A., Clemens, O., Knackfuß, C., and Kupferschmidt, F. 2016. “Are you ready for pole position?
Driving digitization in the auto industry.,”
Goes, P. 2014. “Editor’s Comments: Big Data and IS Research,” Management Information Systems
Quarterly (38:3), pp. iii–viii.
Henke, N., Bughin, J., Chui, M., Manyika, J., Saleh, T., Wiseman, B., and Sethupathy, G. 2016. “The age of
analytics: Competing in a data-driven world,” McKinsey Global Insitute.
Jacobs, A. 2009. “The pathologies of big data,” Communications of the ACM (52:8), pp. 36–44.
Johanson, M., Belenki, S., Jalminger, J., Fant, M., and Gjertz, M. 2014. “Big Automotive Data: Leveraging
large volumes of data for knowledge-driven product development,” in 2014 IEEE International
Conference on Big Data (Big Data), , October, pp. 736–741.
Klein, H. K., and Myers, M. D. 1999. “A Set of Principles for Conducting and Evaluating Interpretive Field
Studies in Information Systems,” MIS Quarterly (23:1), pp. 67–93.
Leavitt, H. J. 2013. “Applied organizational change in industry: Structural, technological and humanistic
approaches,” in Handbook of organizations, London: Routledge, pp. 2976–3045.
Li, H., Wu, J., Liu, L., and Li, Q. 2015. “Adoption of Big Data Analytics in Healthcare: The Efficiency and
Privacy,” in Proceedings of the 19th Pacific Asia Conference on Information Systems, Singapore,
January 1.
Barriers to the Adoption of Big Data Analytics
Twenty-third Americas Conference on Information Systems, Boston, 2017 10
Luckow, A., Kennedy, K., Manhardt, F., Djerekarov, E., Vorster, B., and Apon, A. 2015. “Automotive big
data: Applications, workloads and infrastructures,” in 2015 IEEE International Conference on
Big Data, , October, pp. 1201–1210.
Lyytinen, K., and Newman, M. 2008. “Explaining information systems change: a punctuated socio-
technical change model,” European Journal of Information Systems (17:6), pp. 589–613.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., and Roxburgh, C. 2011. “Big data: The next
frontier for innovation, competition, and productivity.,”
Marton, A., Avital, M., and Jensen, T. B. 2013. “Reframing Open Big Data,” in Proceedings of the 21st
European Conference on Information Systems, Utrecht, The Netherlands.
Miranda, S., Kim, I., and Wang, D. D. 2015. “Whose Talk is Walked? IT Decentralizability, Vendor versus
Adopter Discourse, and the Diffusion of Social Media versus Big Data,” in Proceedings of the 36th
International Conference on Information Systems, Fort Worth, USA.
Myers, M. D. 1997. “Qualitative Research in Information Systems,” MIS Quarterly (21:2), pp. 241–242.
Olbrich, S. 2014. “Madness of the Crowd - How Big Data Creates Emotional Markets and What Can Be
Done to Control Behavioural Risk,” in Proceedings of the 21st European Conference on
Information Systems, Tel Aviv, Israel.
Opresnik, D., and Taisch, M. 2015. “The value of Big Data in servitization,” International Journal of
Production Economics (165), pp. 174–184.
Orlikowski, W. J., and Iacono, C. S. 2001. “Research Commentary: Desperately Seeking the ‘IT’ in IT
Research—A Call to Theorizing the IT Artifact,” Information Systems Research (12:2), pp. 121–
134.
Patton, M. Q. 2015. Qualitative research & evaluation methods: integrating theory and practice (Fourth
edition.), Thousand Oaks, California: SAGE Publications, Inc.
Porter, M. E., and Heppelmann, J. E. 2015. “How Smart, Connected Products are Tranforming
Companies,” Harvard Business Review (93:10), pp. 96–114.
Salleh, A. K., Janczewski, L., and Beltran, F. 2015. “SEC-TOE Framework: Exploring Security
Determinants in Big Data Solutions Adoption,” in Proceedings of the 19th Pacific Asia Conference
on Information Systems, Singapore, January 1.
Saltz, J. S. 2015. “The need for new processes, methodologies and tools to support big data teams and
improve big data project effectiveness,” Presented at the 2015 IEEE International Conference on
Big Data, pp. 2066–2071.
Sarker, S. X., Tanya. 2013. “Qualitative Studies in Information Systems: A Critical Review and Some
Guiding Principles,” MIS Quarterly (37:4), pp. iii–xviii.
SAS Institute Inc. 2015. “The Connected Vehicle. Big Data, Big Opportunities.,”
Schultze, U., and Avital, M. 2011. “Designing Interviews to Generate Rich Data for Information Systems
Research,” Information and Organization (21:1), pp. 1–16.
Svahn, F., Mathiassen, L., and Lindgren, R. 2017. “Embracing Digital Innovation in Incumbent Firms:
How Volvo Cars Managed Competing Concerns,” MIS Quarterly (41:1), pp. 239–254.
Tambe, P. 2014. “Big Data Investment, Skills, and Firm Value,” Management Science (60:6), pp. 1452–
1469.
Tan, C., Sun, L., and Liu, K. 2015. “Big Data Architecture for Pervasive Healthcare: A Literature Review,”
in Proceedings of the 23rd European Conference on Information Systems, Münster, Germany.
Tiefenbacher, K., and Olbrich, S. 2015. “Increasing the Level of Customer Orientation-A Big Data Case
Study from Insurance Industry,” in Proceedings of the 23rd European Conference on
Information Systems, Münster, Germany.
Trieu, T. 2013. “Extending the theory of effective use: The impact of enterprise architecture maturity
stages on the effective use of business intelligence systems,” in Proceedings of the 34th
International Conference on Information Systems, Milan, Italy.
Woerner, S., and Wixom, B. H. 2015. “Big data: extending the business strategy toolbox,” Journal of
Information Technology (30:1), pp. 60–62.
Woźniak, P., Valton, R., and Fjeld, M. 2015. “Volvo Single View of Vehicle: Building a Big Data Service
from Scratch in the Automotive Industry,” in Proceedings of the 33rd Annual ACM Conference
Extended Abstracts on Human Factors in Computing Systems, New York, USA, pp. 671–678.
Yin, R. K. 2008. Case Study Research: Design and Methods (5th Edition.), Thousand Oaks, CA: Sage
Publications.
Yin, R. K. 2009. Case study research : design and methods, Los Angeles: Sage.
Yoo, Y. 2015. “It is not about size,” Journal of Information Technology (30:1), pp. 63–65.
