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Digital Transformation of Primarily Physical Industries – Exploring the Impact of Digital Trends on Business Models of Automobile Manufacturers



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12th International Conference on Wirtschaftsinformatik,
March 4-6 2015, Osnabrück, Germany
Digital Transformation of Primarily Physical Industries
Exploring the Impact of Digital Trends on Business
Models of Automobile Manufacturers
Andre Hanelt1, Everlin Piccinini1, Robert W. Gregory2, Björn Hildebrandt1, Lutz M.
1 Georg-August-University Göttingen, Information Management, Göttingen, Germany
{andre.hanelt,everlin.piccinini, bjoern.hildebrandt,
2 IESE Business School, Information Systems, Barcelona, Spain
Abstract. The phenomenon of digital transformation received some attention in
previous literature concerning industries such as media, entertainment and pub-
lishing. However, there is a lack of understanding about digital transformation
of primarily physical industries, whose products cannot be completely digitized,
e.g., automotive industry. We conducted a rigorous content analysis of substan-
tial secondary data from industry magazines aiming to generate insights to this
phenomenon in the automotive industry. We examined the impact of major dig-
ital trends on dominant business models. Our findings indicate that trends relat-
ed to social media, mobile, big data and cloud computing are driving automo-
bile manufactures to extend, revise, terminate, and create business models. By
doing so, they contribute to the constitution of a digital layer upon the physical
mobility infrastructure. Despite its strong foundation in the physical world, the
industry is undergoing important structural changes due to the ongoing digitali-
zation of consumer lives and business.
Keywords: Business Model, Digital Transformation, Innovation, Automotive.
1 Introduction
Research on digital technologies has predominantly focused on either describing the
transformation of industries, whose products could be completely digitized, e.g., mu-
sic, movies, photos, newspapers [1-4], or on specific effects in various industries, e.g.,
the resulting adaptations in the customer-supplier-relationship [3]. Nevertheless, as
Yoo et al. [5] point out: “Digital technology’s transformative impact on industrial-age
products has remained surprisingly unnoticed in the IS literature”.
What is missing to date is an understanding of how digital transformation mani-
fests itself in industries in which the core products are primarily physical, and to
which extent this transformation impacts dominant business models of incumbent
industry players. The key difference between industries that can completely digitize
Hanelt, A.; Piccinini, E.; Gregory, R. W.; Hildebrandt, B.; Kolbe, L. M. (2015): Digital Transformation of
Primarily Physical Industries - Exploring the Impact of Digital Trends on Business Models of Automobile
Manufacturers, in: Thomas. O.; Teuteberg, F. (Hrsg.): Proceedings der 12. Internationalen Tagung
Wirtschaftsinformatik (WI 2015), Osnabrück, S. 1313-1327
their products and those that need to rely on physical elements as a core element, is
the inevitable need to deal with the tensions that result from interweaving physical
and digital layers into business models that originate from a pure physical world [3],
[6-7]. The automotive industry is an instance of this latter type of industries. In the
last years, the amount of digitalization in and around the motor vehicle has increased,
creating substantial dynamics in markets and changes in business models, as automo-
bile manufacturers strive to find a balance between the digital trends and their estab-
lished competences and assets that relate to the physical world [8]. New technologies
are enabling this industry to become part of the consumer electronics world, where in-
vehicle information technology (IT) and consumer IT will be essential elements [6].
The topic of business model change has gained a lot of attention in recent years,
especially within the information systems (IS) community [9-10]. However, there is
still a very limited understanding about how external factors, also known as adapta-
tion factors (e.g., new technologies) force companies to change their business models
[9]. For example, in the context of the automotive industry, as mobile digital technol-
ogies enter into the traditional context of physical mobility on the ground, they bring
along challenges related to consumer demands, such as the need for information, en-
vironmentally responsible mobility solutions and safety [11], [12]. This challenges
automotive industry players to rethink and change their business models in order to
find the right mix between components focused on the physical world of mobility,
and transport and components focused on the digital world of mobile technology-
related mobility. Industry players may need to integrate new offerings, since digital
solutions may be needed within and outside the vehicle [6].
This paper focuses on examining the digital transformation of the automotive in-
dustry and aims at providing insights on the following research question: What is the
impact of key digital trends on dominant business models of automobile manufactur-
ers? We therefore employed a substantial secondary data collection from industry
magazines, which led to a data set of over 1,100 articles about digital technology-
related initiatives in the automotive industry. By employing rigorous selection criteria
in the analysis phase, this material was reduced to a set of 180 relevant articles that
were coded according to guidelines provided by Krippendorff [13]. With our research,
we mark an important first step toward opening the black box of what digital trans-
formation means for primarily physical industries and through which mechanisms
established business models are changed as a result of digital trends.
2 Theoretical Background
2.1 Digital Technologies and Digital Transformation
IT can be defined as including “all modes of information collection, processing, stor-
age, and dissemination” [14] and has been influencing business operations ever since.
Through its increased diffusion in many aspects of our lives [15], new kinds of appli-
cations become possible by combining and integrating multiple technologies that are
accessible anytime and anywhere [16]. These digital technologies are therefore de-
fined as “combinations of information, computing, communication, and connectivity
technologies” [17]. Among the biggest emergent digital technology trends that are
driving dramatic changes in the infrastructure of many organization spanning differ-
ent industries and sectors are cloud computing (e.g., remote access to centrally pro-
vided information), social media (e.g. online interactions in social networks), mobile
technology (e.g. smartphones and tablet PCs) and big data (e.g., use of huge amounts
of data for predictive analytics) [3], [17-19]. Digital technologies have enabled rapid
pace of product and service innovations, shorter product life cycles, and cross-
boundary industry disruptions, which requires new forms of business strategies [18].
For example, in the financial industry, internet and mobile technologies have funda-
mentally altered the traditional way of investing in stocks. The user experience moved
from making phone calls to a full service broker to placing electronic orders. This
enabled new organizations such as electronic stock exchanges and e-Trade to emerge
and forced major stock exchanges into mergers that totally change the traditional
structure of the industry [3]. Similarly, the media, publishing and telecommunication
industries were also affected by the transformative power of digital technologies
(ibid). Increasingly, even in industries in which products are primarily physical (e.g.,
automotive), business models are being reshaped by the implementation of digital
initiatives and solutions [6].
Applied management literature describes the phenomenon of digital transformation
as the employment of new digital technologies to foster major business improvements
in organizations [19]. This phenomenon is, to the best of our knowledge, still under-
explored in IS and in management research and an elaborate definition of digital
transformation has not been provided. Nevertheless, we identified discussions around
the transformational impacts of new digital technologies not only relevant for organi-
zations but also for society at large [3], [20-21]. Scholars have argued that digital
technologies are shaping the way people live, communicate, consume and work,
breaking barriers of time and space [21]. Yoo [15] states that the way people perceive
technology has also changed because it is embedded in everyday activities such as
running, driving and communicating. In relation to mobility, consumers’ digital life-
style impacts their expectations for new functionalities and features in the car that can
provide them with more information, mobility services (e.g., carsharing), and safety
[12], [22].
2.2 Business Model Change
A business model can be described as “a template of how a firm conducts business,
how it delivers value to stakeholders (e.g., the focal firms, customers, partners, etc.),
and how it links factor and product markets” [23]. There has been a lot of work on the
foundations of the concept, including definitions, its relation to other concepts in the
management literature (e.g., business strategy [23]), and the description of interlock-
ing components [24]. Osterwalder et al. [25] describe the business model as consist-
ing of four business model pillars (comprising nine building blocks) that specify, how
a product/service creates value (value proposition/product - BM1), which customer
segment is targeted as well as the way it is reached and linked to the company (cus-
tomer interface - BM2), which internal and external competences, resources and part-
ners a company needs (infrastructure management - BM3), and how products/services
produce costs and generate revenues (finance - BM4) [25].
With reference to the impact of innovations such as digital technologies, the topic
of business model change is of major importance. Concerning the degree of business
model change, Cavalcante et al. [26] offer the following view. A business model crea-
tion refers to the initial business model design based on a business idea. Business
model extension is described as adding further activities to an existing business model
without fundamentally altering the existing core logic. Business model revision refers
to a profound redesign of the existing business model and thus can be related to radi-
cal or disruptive change. Finally, business model termination is described as the elim-
ination of business activities [26]. According to Burkhart al. [9], the effect of external
forces (e.g., new technologies) forcing incumbents to adapt their business models is
not well understood yet. This also applies to the digital transformation of business
According to Veit et al., a business model is digital if changes in digital technolo-
gies trigger fundamental changes in the way business is carried out and revenues are
generated” [10]. El Sawy and F. Pereira describe that the emergence of digital tech-
nologies enables interconnected digital eco-systems that comprise new actors, struc-
tures and rules, eventually resulting in digital business models that entail such attrib-
utes as “time compression, turbulence, and new architectures” [27]. This phenomenon
affects more and more industries and is not yet captured by most existing business
model conceptualizations [27]. Research on the impact of digital technologies on
established business models mostly observes transformations in industries that are
more or less related to the digital world such as software, internet, and media indus-
tries [3-4], [28]. Thus, the insights on the transformation of business models by digital
technologies in traditional industries are difficult to transfer [5]. This especially holds
true for the automotive industry. As long as goods and persons need to be transported
physically from point A to point B, a substantial core of an automotive player’s busi-
ness model will be grounded in the physical world. Even though there are specific
differences among automobile manufacturers, the current dominant business model in
the automotive sector can be described as follows: Development, production and dis-
tribution of sophisticated, conventionally fuelled cars, mostly against a non-recurring
purchase price, as well as some additionally after sales services like maintenance,
leasing or insurance [29], [30]. Concerning the target customer, it can be broadly
differentiated between mass-market and luxury segments. The former is associated
with a high-scale and low margin business, the latter with low scale and high margins
for luxury cars [30]. Automobile manufacturers mostly employ a dealer network or
cooperate with sales agents. The customer relationship is mainly built around the
buying processes and certain maintenance appointments. In order to produce the vehi-
cles, automobile manufacturers mostly rely on competencies in engineering, design,
and electronics. They integrate a large network of suppliers, which delivers parts and
services [31]. Business models in the automotive industry have been argued to start
shifting toward more differentiated, sustainable, service-oriented, and integrated mo-
bility solutions [8]. For automobile manufacturers this represents a change, away from
their established business models, which they have been learned for decades.
3 Research Methodology, Sample and Data Analysis
In this study, we conducted a content analysis of secondary data as a starting point to
collect evidence on the phenomenon of digital transformation without being biased by
firm specific or individual characteristics that might be the case for case studies or
surveys. In deciding to analyze practitioner literature, we followed Bohnsack et al.
[32] and conducted a content analysis of two automotive industry trade magazines,
i.e., Automotive News and Ward’s Auto World, as well as a car magazine, i.e., Au-
toWeek. These magazines were chosen because they provide insights into the auto-
motive industry and organizational perceptions in relation to technologies and associ-
ated business models [32]. Krippendorff [13] defines content analysis as “a research
technique for making replicable and valid inferences from texts (or other meaningful
matter) to the contexts of their use”. With a content analysis of secondary data we aim
to understand major trends associated with digital technologies and their impacts on
dominant business models of automotive manufacturers in the context of this indus-
To identify relevant articles for our study, a keyword search was performed using
search terms referring to the biggest emergent digital technology trends mentioned
above (i.e., cloud computing, social media, mobile technology, and big data). To as-
sure a comprehensive coverage of all important articles, we used additional keywords
related to these technologies that could lead us to further significant digital technolo-
gies in the automotive industry, as well as names of each of these technologies indus-
try leaders. Thus, we applied the following search keywords in our study: “digital
technolog*,” “cloud computing,” “Amazon,” “Microsoft,” “social media,” “Face-
book,” “Twitter,” “blog*,” “mobile technolog*,” “mobile device*,” “smart phone*,”
“tablet*,” “Apple,” “Samsung,” “big data,” “connectivity,” “connected car*,” “analyt-
ics,” “Google,” and “IBM.” These keywords were applied in the title, abstract, and
main text of the magazines’ articles. Our search resulted in a total of 1107 articles.
We carefully screened the articles’ abstracts and texts and selected those that were
related to the focus of our study, i.e., articles related to digital technologies related
trends and their impact on business models [33]. To check the validity of the content
reported in the articles, we searched for press releases or website reports of the re-
spective firms confirming the mentioned aspects. If we could not find this supporting
evidence, we dropped the article out of the sample. Our final sample consisted of 180
articles covering a timeframe ranging from 1996 until 2014 and can be divided as
follows concerning the four technology trends: cloud computing (13), big data (20),
mobile technology (64) and social media (83). We adopted the framework of Krip-
pendorff [13] to content-analyze the identified articles, since his approach is recog-
nized in IS and social science qualitative research [34-35]. Our framework is depicted
in Figure 1.
Fig. 1. Content Analysis Framework based on Krippendorff (2004)
Following Krippendorff [13], in the first step we unitized the texts’ segments that
were relevant for our analysis by reading every article and highlighting excerpts in the
text every time a digital technology was mentioned. Subsequently, we limited our
observations to the segments of text that related the digital technology to a trend in the
automotive industry as well as to business model components and the different types
of business model changes of automotive manufactures. We first coded emergent data
about trends associated with digital technologies in the automotive industry. This
coding procedure was thus of explorative nature. Then, we further developed our
coding instructions based on the previously described theories of Osterwarlder et al.
[25] and Cavalcante et al. [26]. Thereafter, we created manageable representations of
data by cross-tabulating the coding intersections between an excerpt referring to a
digital technology trend and an excerpt of the coding category business model com-
ponents (see Table 1). The same procedure was done concerning intersections be-
tween a digital technology trend and an excerpt of the category business model
change. Finally, the steps inferring and narrating, which represent respectively our
data analysis and our interpretation of findings, are illustrated and discussed in the
following sections. The articles were analyzed and coded using the qualitative data
analysis software NVivo 10. In order to accomplish reliability of our coding, one of
the two first authors coded an article and the other one controlled and refined that
coding, resulting in intensive discussions over the interpretation of the data.
Table 1. Overview of Coding Instructions
Coding category
Coding sub-category
Illustrative example
Digital trends
Social media; big data;
cloud computing;
mobile technology
Social media: "The goal is to turn Ford into a
"social business" that rewards its fans and gives
them a chance to influence future product.”
Business model
Product; customer
interface; infrastruc-
ture; financial aspects
Product: “Automakers keep pushing the bounda-
ries of what's possible. General Motors, for i
stance, is testing a
n application that will allow
drivers to dictate status updates to Facebook.”
Business model
Creation; revision;
extension; termination
Business model extension: “Audi also said that it
is ready for LTE, the true 4G network, as soon as
it becomes widely available in about two years,
which will allow infotainment to flow into your
car at unprecedented speeds.”
4 Findings
In the next sections, we describe how specific digital technology trends affect busi-
ness model pillars [25] in the automotive industry leading to the different types of
business model change [26]. Because these digital technology trends sometimes over-
lap and have multiple effects, we present them according to the specific phenomenon
they enable (i.e., interaction, connectivity, self-driving, mobility services, new driver
services, new data services and virtualization).
4.1 Business Model Extension Digital Enrichment of Established Business
Based on the characteristics of business model extension (see chapter 2.2), in this type
of change we categorized the digital technology trends that were used to extend the
offering for specific customer segments with minimal changes to the core logic of the
dominant business model of automotive manufactures [26].
Interaction. Concerning the trend social media, we observed that it mainly con-
cerns changes in customer interfaces (BM2). Social media allows automobile manu-
factures to respond to the general societal trends of customers wanting to be more
informed, participating and thus becoming more empowered [3], [20]. Moreover, the
target customer segments are increasingly including an emergent customer group:
young and tech-savvy people. This customer segment expects more information, dia-
logue, and dynamics [3], also when buying a car. This points to changing customer
relationships as well as the competencies required to handle them (BM3). Generally,
the group of digital natives places more emphasis on the digital aspect of life and is
used to having constant access to information as well as receiving immediate respons-
es to their requests.
‘They engage digital media, they consume it on their own terms and timetables, and it's non-
linear: They jump from watching a video to locating a vehicle to building and pricing a car to
e-mailing their dealer”. (Automotive News, 1/20/2014)
Automobile manufacturers extend their business models by adding digital interac-
tion with their customers, e.g., in the processes of product design. They need new
competencies in order to engage the customer in co-creation processes (BM3). Stud-
ies about product co-creation have indicated that by interacting with customers virtu-
ally, organizations are able to rapidly sense and respond to changing customer needs,
which has become essential for the survival of organizations in the digital age [20].
This represents a specific change for automobile manufacturers, as they were used to
leveraging their engineering competences to create a sophisticated physical product,
while the customer’s role was limited to buying this offering.
Connectivity. Concerning the trends of mobile technology and cloud computing,
many automobile manufacturers undertake high efforts to ensure the compatibility of
mobile devicesmost notably smartphoneswith the car. Automobile manufacturers
can thus offer customers the possibility to access their personal data.
“Consumers want to get their information and entertainment the same way whether they are
in their home, office, or car”. (Wards’ Auto World, 02/2008)
This compatibility also enables the customers to stay connected while they are on
the road. Thus, extending the value proposition of the vehicle to a certain extent
(BM1). Moreover, automobile manufacturers are developing mobile applications that
provide further functionalities to customers, such as vehicle status.
“To assess more distant road conditions - say, a mile or more away - the vehicle's onboard
computer would get updates from the cloud through a cell phone link”. (Automotive News,
Through these mobile applications in combination with car-connectivity, other
fields, aside from the mobility domain, are being explored by automotive manufac-
tures (e.g., service offerings to locate restaurants and make reservations on the road).
Some automotive manufacturers are therefore including programming skills to their
core competencies, by either developing them inside of their organizations or extend-
ing their partner networks (BM3). Consequently, the business model of automobile
manufacturers is adapted to the customer’s desire for increased connectivity. This
adds a digital dimension to the physical driving experience. Furthermore, connectivity
allows consumers to reach (and be reached by) other consumers and companies al-
most anywhere at any time [36]. This presents great opportunities for automobile
manufacturers, since they can reach consumers, communicate with them, and better
understand or analyze their behavior to develop more individualized offerings [36].
4.2 Business Model Revision Digitalization of Established Business Models
Following the description of business model revision (see 2.2), in this type of change
we attempted to categorized digital technology trends that might substantially alter the
dominant business model of automotive manufacturers [26]. Although most of these
technologies are currently still in development, we learned that they have the potential
to completely substitute existing business models of automotive manufacturers.
Self-driving. The self-driving car is related to digital automation and concerns the
processing of a large amount of data, having the road as a data set to be mined [37].
Enabled by on-board Internet, sensor and GPS-technology, self-driving cars represent
a changed driving experience, being especially attractive for digital native customers.
In a world where Nevada and Florida have already passed laws allowing the licensing of
self-driving cars, the rush is on to make the job easier for drivers. For many, the ultimate goal
is to take the steering wheel totally out of consumers' hands and eliminate accidents altogeth-
er. (Automotive News 08/18/2012)
Here, the value proposition is substantially changed, as the customer no longer
needs to drive the vehicle (BM1). This gives the customer more freedom to use his
smartphone, check his emails or use the vehicle infotainment.
Eventually, the prospect of sleeping, snacking, entertaining themselves and even working
on the way to work could convert many a dedicated driver to self-driving vehicle. (Ward’s
Auto World, 02/2014)
Nevertheless, the change in the value architecture of most automobile manufactur-
ers is rather moderate (BM3), as some of the required technologies for self-driving are
already embedded in modern cars (e.g., lane assistance, parking pilots). Self-driving
establishes a new way of thinking about the car. In former times, the customer experi-
ence was almost solely connected with driving the car. Now, the car itself as a physi-
cal entity will become less important. The balance between digital and physical will
be truly shifting, when drivers become passengers. As a result, some engineering
competencies, formerly competitive advantages, will decline in importance.
“Vehicles then become simple mechanical devices that can be built by just about anyone.
The only important parts are the electronics and sensors made by Google and its suppliers”.
(Ward's Auto World, 12/2012)
The trend of self-driving reminds us of IT’s role of automation [14]. Although au-
tomation has been recognized in the auto industry for decades, the application fields
are now changing. Formerly, automation in the automotive industry was associated
with manufacturing processes in production lines [38]. With self-driving, digital-
enabled automation has moved forward in the value chain to actual product use, re-
sulting in increase in performance, convenience and information, enabling organiza-
tions to innovate revenue models (BM4), e.g., congestion pricing for parking [21].
Mobility Services. Automobile manufacturers have started to offer “car-
independent mobility solutions” [38], such as integrated service-based mobility offer-
ings involving several means of transportation. Through mobile devices and GPS
technology, multi-modal mobility solutions become more integrated. By such offer-
ings, automobile manufacturers revise their value proposition (BM1), which changes
from delivering only a product (the vehicle), towards delivering also a service (mobil-
ity). Moreover, the revenue model (BM4) is substantially altered as these services
comprise pay-per-use pricing schemes. By offering multi-modal solutions, automobile
manufacturers need further to extend their partner networks towards public transport
providers (BM3). The relationship to the customer is altered as there is no longer a
1:1 relationship between vehicle and customer (BM2). In service business models, the
concept of ownership is revised, leading to an n:n type of relationship. Because of the
societal mega-trend of urbanization and related phenomena (e.g., congestion, limited
parking space) owning a vehicle in cities is becoming increasingly associated with
inconvenience and limited personal freedom, and less associated with a status symbol.
In this regard, some automotive manufactures are also offering carsharing as a mobili-
ty service.
“German archrivals Daimler AG and BMW AG have launched sharing programs. The au-
tomakers say the move was prompted by changing attitudes about car ownership, especially
among young buyers, and increased urban congestion. … Vehicles can be reserved on the
Internet or by using an iPhone with an application that shows where available cars are
parked”. (Automotive News, 12/20/2010)
The mobile device is thus becoming key for the customer’s physical mobility expe-
rience, for example by directing him to the nearest transportation possibility or, mak-
ing reservations and payments. For automobile manufacturers, this represents a dras-
tic shift from a transactional product-oriented system towards pay-per use business
models. This could be a shift in what is considered fundamental to being competitive
within the automotive industry [39]. As in integrated mobility services they need to
rely more on external service providers from other mobility sectors or IT-firms that
offer apps for mobility planning, the strategy of the firms changes towards an building
open eco-systems and multi-sided business models with network partners [7].
4.3 Business Model Creation New Digital Business Models
Based on the characteristics of business model creation (see chapter 2.2), in this type
of change we classified digital technologies trends that were used for creating new
automotive business models [26]. In this case, digital technology trends are not used
for extending or substituting dominant business models, but instead they support cre-
ating separate, stand-alone models.
New Driver-Services. The connectivity of the vehicle with smartphones and the
Internet has given rise to product-related service [40] innovations.
“OnStar's model is to continue offering hands- free technologies that promote safe driving,
such as real-time navigation services that take into account traffic patterns, based on the 5,5
million subscribers using OnStar.” (Automotive News, 12/20/2010)
Product-related service offerings are primarily subscription based, thus they create
a new forms of revenue (BM4). There is a wide array of services offered, such as pre-
conditioning of the vehicle, vehicle diagnostics or automatic emergency calls (BM1).
As most of these product-related services are accessed by mobile devices, automobile
manufacturers need further competencies in programming apps (BM3). Moreover, as
these services produce data that needs to be stored in the cloud and that could be ana-
lyzed for other business purposes (such as optimization of vehicle diagnosis process-
es), automobile manufacturers must expand their IT-resources and infrastructures
(BM3). Through these new business models, automobile manufacturers are enabled to
find new revenue sources from the increased connectivity of the vehicles they sell
[41]. They thus enter the digital world by offering product-related digital services
New Data-Services. Through the increased penetration of digital technologies in
the vehicle [13] substantial amounts of data are generated (e.g., about mobility behav-
iors or vehicle usage). This data is of explicit value for automobile manufacturers, as
it helps them optimizing their products or production processes. Moreover, this data
could be source of new types of data services that automotive manufacturers could
provide to different types of customers (BM2). For example, data about mobility be-
haviors could be offered to local governments interested in optimizing traffic plan-
Vehicle data will generate $700 to $800 per vehicle in savings for automakers, vehicle
owners, service providers and local governments”. (Ward's Auto World, 06/2010)
New data business models are being created increasingly, representing completely
new value architecture and value capture concepts. Here, the physical mobility pro-
cess is used as an input (BM3), while the data that it produces is the output (BM1).
This shifts perception of the physical vehicle from being the focal aspect to being a
device to create value in the digital world.
4.4 Business Model Termination Eliminated Physical (Parts of) Business
Digital technologies in certain areas increasingly drive the substitution of prior physi-
cal activities. Following the description of business model termination (see chapter
2.2), in this type of change we categorized digital technology trends that eliminated
prior physical processes to some extent [26].
Virtualization. Processes before and after the vehicle production are being in-
creasingly changed by digital technologies (BM3). In the design phase, both proto-
types of new car models and production lines can be built virtually by drawing on the
increased computing possibilities, thus decreasing planning times and costs.
“DaimlerChrysler AG said digital technology shaved six months off the construction time of
its Jeep plant in Toledo, Ohio. The automaker said that by 2005 every DaimlerChrysler pro-
duction plant will be planned, built, launched and operated first using full simulation”. (Auto-
motive News, 10/11/2004)
After production, virtualization is also important in the sales process (BM2). Au-
tomobile manufacturers provide virtual showrooms or even allow potential buyers to
test the vehicles virtually in video games.
“Ford is giving Sony PlayStation3 users a look at its Edge and Fiesta in an online virtual
showroom on the PlayStation Network”. (Automotive News, 6/27/2011)
It is not the value proposition, but rather the infrastructure management (BM3) and
the customer interface that change substantially (BM2). When virtual realities are
used, competencies and cost structures are also altered. For automobile manufactur-
ers, vehicle design and production were core processes associated with their engineer-
ing competencies for decades. Now, digital competencies have started replacing them.
Summing up, our findings have shown which specific business model components
are affected by new digital technologies and how these components change over time.
Furthermore, when analyzing our longitudinal data set, we found that digital technol-
ogies drive all four of the types of business model change named by Cavalcante et al.
[26]. The following figure conceptually depicts this evolvement towards digitalization
of automotive manufacturers’ business models.
Fig. 2. Development Path of Business Model Changes
5 Discussion
Through the different types of business model changes described in the previous
chapter, automobile manufacturers react to the increasing diffusion of digital technol-
ogies, and, thus, contribute with varying intensity to building a digital layer upon the
physical mobility infrastructure. By interacting with customers through social media
and ensuring their connectivity while they are in the vehicle, automobile manufactur-
ers take account of the increasing desire of customers to be ‘always on’ as well as
their changing demands towards increased informedness and experience [3], [42].
However, with these digital initiatives automobile manufacturers do not fundamental-
ly change their established business model but expand it by adding specific digital
aspects and making it compatible to the digital world. In a greater level of change,
automobile manufacturers use digital technologies to respond to the increasing change
in customers’ digital lifestyle. Through our findings, we could observe that the vehi-
cle itself may change its role from a status symbol to a device for digital experiences
[39], [43]. Drawing on the digital trends of big data, cloud services and mobile tech-
nology, automobile manufacturers are enabled with new possibilities to offer mobility
solutions that do not require a vehicle at all. These revised business models comprise
mobility offerings that can be accessed over the digital layer or, as in the case of self-
driving, are controlled by such layer. Digital technologies open up the potential of
new types of business models based on mobility data, either by targeting at the driver
or at new possible customers, such as local governments [39]. Here, the physical pro-
cess of driving is only used to generate the core (data) offering, which is completely
digital [44]. We understand the transformation process in automotive industry as pre-
senting a technical and a societal side. On the former, it concerns advances in the
digital technologies themselves, for example increasing computation power, miniatur-
ization, and ubiquitous broadband internet access [45]. This emphasizes the im-
portance of digital in all aspects of life, including mobility [15]. Intertwined with the
technological possibilities is society’s changing expectation both about what mobility
is as well as regarding the role of digital technologies in their lives. We found that
especially the digital natives consider certain factors more relevant than other genera-
tions do when evaluating a car [42]. It appears that preferences are shifting from the
sheer feeling of driving or technical performance measures towards aspects such as
connectivity, information or entertainment. Together with these societal trends, our
findings have shown that digital technologies drive the industries’ transformation
from goods-dominant- towards a service-dominant logic [46]. Along with this trans-
formation comes a changed role of the customer towards a co-creator of value, which
cannot be captured in established business model designs [47]. The customer relation-
ship is thus getting more demanding for automobile manufacturers as customers want
to be more than just buyers. Eventually, our study shows an enormous shift in the role
of IS in the automotive sector. While they have been an engine for industry transfor-
mation ever since [39], prior applications were more or less focusing the back-end of
automobile production. In contrast to this, with the rise of digital technologies, as we
have shown, all business model pillars including also the relationships with customers
and the value propositions of business models are penetrated with IS.
6 Limitations and Future Research
As we rely on secondary data from practitioner literature, the validity of the data can
be questioned to a certain degree as they also comprise opinions, assumptions or
statements that may be biased for various reasons. Nevertheless, as the topic of our
study is a relatively new phenomenon, related scientific literature in high quality out-
lets is scarce. However, we have tried to counteract the validity aspects by drawing on
three internationally accepted industry magazines and a long time scale. The selected
sources can be attributed as being more responsive to recent developments and thus
provide important insights on emergent phenomena as a starting point for further
research [32]. By drawing on the concept of digital transformation, business models
and business model change, we rely on theoretical constructs that are yet in a prema-
ture stage and surely need to be further testing and elaboration. Nevertheless, with our
exploratory approach, we hope to contribute to this very fact, provide a first step and
trigger further research. Therefore, basing the examination proposed in our study
upon in-depth cases and expert’s experiences would be recommendable.
7 Conclusion
In this study, using a substantial secondary data analysis, we shed light on the nature
of digital transformation in the automotive industry, an industry that has a strong
grounding in the physical world. To the best of our knowledge our study entails nov-
elty in that it explores how digital transformation plays out and manifests itself in a
primarily physical industry. We employed the business model concept, including its
components and change over time, as theoretical lens to examine the digital transfor-
mation of the automotive industry at the strategic business level. Our findings point to
the ongoing transition toward a digitalized world, which impacts primarily physical
industries too. With our analysis and discussion, we contribute to business model and
IS research by examining the impacts of digital technology trends on business models,
as well as by proposing a development path to explain how such changes occur. Thus,
we seek to offer a foundation and important first step for developing a comprehensive
understanding of the nature of digital transformation in the automotive industry.
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... Nesse sentido, as tecnologias digitais são empregadas com o objetivo de promover a evolução dos negócios, seja pela criação de novos modelos de negócios, pela melhoria da experiência do cliente, ou ainda pela otimização da operação (Fitzgerald et al., 2013;Hanelt et al., 2015). As tecnologias digitais constituem-se em tecnologias móveis, mídias sociais, cloud computing, análise de big data, internet das coisas e inteligência artificial (Sanchez;Zuntini, 2018), entre as quais se destacam as tecnologias móveis para atendimento às necessidades dos clientes em grande escala (Schallmo;Williamns;Boardman, 2017), assim como para a geração de eficiência (Ardizzi;Crudu;Petraglia, 2018). ...
... Essas evidências reforçam o entendimento de Matt, Hess e Belian (2015) de que as empresas, neste caso os bancos, têm realizado nos últimos anos uma série de iniciativas para incrementar a utilização de novas tecnologias digitais e, consequentemente, para usufruírem de seus benefícios, assim como para ofertarem aos seus clientes. Além do mais, pode-se notar tecnologias digitais sendo empregadas pelos bancos da amostra com o objetivo de promover a evolução dos negócios, seja pela criação de novos modelos de negócios, ou pela melhoria da experiência do cliente ou ainda pela otimização da operação (Fitzgerald et al., 2013;Hanelt et al., 2015). Por consequência, entende-se haver uso das tecnologias digitais oportunizando que os bancos acelerem seu crescimento e sua produtividade (World Economic Forum, 2018). ...
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O estudo analisa o impacto da transformação digital no desempenho de bancos brasileiros, no período de 2013 a 2017. Utilizou-se a Análise Envoltória de Dados (DEA) para calcular os escores de eficiência dos bancos com maior representatividade em ativos totais e que, concomitantemente, divulgaram iniciativas voltadas à transformação digital em seus relatórios, sendo eles: Banco do Brasil, Bradesco, Itaú e Caixa Econômica Federal. Identificaram-se ações voltadas ao fortalecimento dos canais digitais e que os bancos mais eficientes foram os com menor utilização dos canais convencionais para a realização de transações com seus clientes. Os bancos Bradesco e Itaú apresentaram eficiência máxima em 2015 e 2016, e o Banco do Brasil em 2017. Já a Caixa Econômica Federal não foi eficiente nos anos analisados. Essa análise de desempenho auxilia a melhoria da gestão dos bancos e, indiretamente, a sociedade, visto que são os principais usuários dessas organizações.
... If they adeptly interweave existing physical and new digital assets within the company, barriers can become facilitators; conversely, the inability to establish a connection between the two renders long-term gains unattainable, leading to falling behind even more. (Hadjimanolis, 2003;Hanelt et al., 2015). ...
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With the proliferation of contemporary digital technologies, Digital Transformation (DT) has become a significant theme for companies across almost all industries. DT encompasses the digitalization of internal processes, the provision of digital services and products, and the enhancement of the customer experience. Previous research has delved into different barriers that impede successful DT. In our study, we investigate further how these barriers are perceived by employees at small and medium-sized enterprises (SMEs) in contrast to larger enterprises (LEs). We employ a mixed-methods approach by performing a quantitative analysis using the Means, Mann-Whitney U test with effect size and integrating it with qualitative results converted into frequencies. Our empirical data consist of two samples consisting of participants from 189 SMEs and 221 LEs for quantitative analysis and participants from 238 SMEs and 281 LEs for qualitative analysis. Overall, the results suggest a relatively similar perception of DT processes, indicating culture and structure as major barriers. However, the establishment of resources dedicated to managing DT emerges as a vaster barrier for SMEs than for LEs. At the same time, SMEs face fewer barriers regarding general personnel resources.
... Currently, lithium (li)-ion batteries (LIBs) are considered a viable solution for electric vehicles, including battery, hybrid, plug-in hybrid, and fuel cell electric vehicles [1,2], thanks In this context, the advancements produced by Industry 4.0 concepts, including significant advancements in information technologies, have facilitated a critical role for digital twins (DTs), particularly in the transportation industry, enabled by sophisticated data analytics and connectivity through the Internet-of-Things (IoT) [10,11]. Also, the literature has exhibited how DTs can help tackle the current challenges in the automotive industry, particularly in areas such as vehicle product design, manufacturing, sales, and service [11][12][13][14][15]. In this regard, BMSs can be revolutionized by integrating cloud computing and IoT technologies. ...
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Citation: Issa, R.; Badr, M.M.; Shalash, O.; Othman, A.A.; Hamdan, E.; Hamad, M.S.; Abdel-Khalik, A.S.; Ahmed, S.; Imam, S.M. A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces. Batteries 2023, 9, 521. Abstract: Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs' SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study's results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.
... Currently, lithium (li)-ion batteries (LIBs) are considered a viable solution for electric vehicles, including battery, hybrid, plug-in hybrid, and fuel cell electric vehicles [1,2], thanks In this context, the advancements produced by Industry 4.0 concepts, including significant advancements in information technologies, have facilitated a critical role for digital twins (DTs), particularly in the transportation industry, enabled by sophisticated data analytics and connectivity through the Internet-of-Things (IoT) [10,11]. Also, the literature has exhibited how DTs can help tackle the current challenges in the automotive industry, particularly in areas such as vehicle product design, manufacturing, sales, and service [11][12][13][14][15]. In this regard, BMSs can be revolutionized by integrating cloud computing and IoT technologies. ...
Full-text available
Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.
... А. Ганельт, Е. Пічініні, Р.-В. Грегорі, Б. Гільдебрандт, Л. Кольбе [15] зауважують, що ступінь впливу платформної економіки на ринки праці залежить від ступеня оцифрування ринків. К. Берефут, Д. Кертіс, В. Джолліфф, Дж. ...
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The purpose of the article is to identify the triggers and consequences of the digital transformation of the labor market with an emphasis on the platform economy and the threats of inequality produced by technoglobalism. The article examines the impact of the pandemic on the development of the platform economy. It is argued that the platform economy, in fact, chaotizes the labor market, thereby influencing both highly skilled and low-skilled workers. The platform economy forms a new structure of the labor market both at the local and global levels, complementing local and global online labor platforms, and also modifies its functions — social, economic, stimulating, regulatory. The labor market, which is a complex dynamic system of social, economic and legal relations regarding the demand and supply of labor and the forms and methods of coordinating the interests of its market subjects, is experiencing the consequences of the digital transformation of economic activity in the processes of its global structuring, which is manifested in an increase in outsourcing on online platforms labor. The platform-based remote work model may be adopted in most segments of the traditional economy in the near future, due to a whole range of interrelated reasons. The new business model allows employers to use platforms to organize work without the need to invest in capital assets or hire employees. Instead, they act as intermediaries between performers and clients, and also manage the workflow using algorithms. A situation is being created in which the digital platform creates quasi-workers — performers who are provided with employment and income, but are not provided with the social guarantees that accompany traditional labor relations. Platforms perform an infrastructural function: by bringing labor supply and demand closer together, they increase market efficiency. It has been established that digitalization, affecting the labor market and employment structure, produces new forms of inequality and the digital divide. It should be noted that the platform economy, access economy, crowd economy, gig economy,freelance economy, on-demand economy are not necessarily characterized by mass technological unemployment, and this fact is consistent with the conclusions of economic theory, which assumes the presence of compensation mechanisms and the action labor saving effect from the implementation of new technologies. The problem of the digital divide between workers with different skills is becoming increasingly clear. The uneven impact of workplace digitalization on workers with different skill levels is confirmed by empirical data. The development of the platform economy has mixed effects on inequality. On the one hand, education and qualifications, the ability to learn quickly, access to digital technologies, quality of Internet connection, digital infrastructure available to the employee already predetermine his position in the freelance market. On the other hand, labor platforms are able to provide jobs to a large audience, not limited by the borders of the state, and, therefore, growing competition in both the group of qualified and unskilled workers leads to a deterioration in working conditions and lower wages. Platform employment is an effective tool for generating income both on an occasional and regular basis. From the standpoint of the sociological approach, freelancers are classic representatives of the precariat class, which is understood as workers who do not have full guaranteed employment. Globally, workers have resorted to the practice of "race to the bottom", which implies that they are willing to accept lower wages for various reasons, such as: (1) a cost of living crisis (a phenomenon describing the consequences of simultaneous recession and inflation in developed countries); (2) average wages in the country; (3) the unemployment rate in the country; (4)market conditions; (5) level of specialization; (6) the degree of difficulty and hopelessness in which the person found himself.
Digital transformation (DT) is a trending topic in nearly all industries. Enterprises digitalize processes, offer digital services and products, and enhance the customer experience. However, complex barriers hinder the ability of entrepreneurial-oriented small and medium-sized enterprises (SMEs) to advance in digital transformation. Using a questionnaire, we collected and analyzed data on the barriers to digital transformation in SMEs. The data revealed a heterogonous picture of SMEs regarding digital transformation and barriers. Based on 195 completed questionnaires, we demonstrated a significant negative influence of organizational and technical barriers and missing skills on digital transformation success. The identified predictors can explain 58% of the variance in the transformation process based on 195 respondents. We could not demonstrate a significant relationship to digital transformation success for the three additional barrier dimensions individual, lack of standards, and lack of laws. Nevertheless, respondents perceive the existence of a lack of standards and laws as well as individual barriers. Our results indicate that addressing organizational barriers is an excellent way to start a DT journey.
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This article presents an automotive control approach for information-rich future mobility. It integrates in-vehicle networked controls with cloud computing accessible through a wireless network to elevate current on-board controls to a new level for additional benefits and performance. Outsourcing computation-intensive tasks to a cloud-computing server is an extension of the current server-based concierge/infotainment type features. While in-vehicle controls remain essential for safety critical and real-time functionality, the cloud-computing paradigm offers another degree of freedom for control system design. In future vehicle controls, the cloud can be used for very demanding computations that otherwise cannot be accomplished by on-board electronic control units (ECUs), especially for information-intensive tasks. The so-called local-simple-remote-complex vehicle control strategies are likely to unlock the potential of implementing methods and tools that are presently used only in an off-line setting. The cloud can also be used as a storage place to record current and historic vehicle data that can be used for predictive diagnosis and prognostics of the vehicle health.
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Over the last three decades, the prevailing view of information technology strategy has been that it is a functional-level strategy that must be aligned with the firm's chosen business strategy. Even within this socalled alignment view, business strategy directed IT strategy. During the last decade, the business infrastructure has become digital with increased interconnections among products, processes, and services. Across many firms spanning different industries and sectors, digital technologies (viewed as combinations of information, computing, communication, and connectivity technologies) are fundamentally transforming business strategies, business processes, firm capabilities, products and services, and key interfirm relationships in extended business networks. Accordingly, we argue that the time is right to rethink the role of IT strategy, from that of a functional-level strategy-aligned but essentially always subordinate to business strategy-to one that reflects a fusion between IT strategy and business strategy. This fusion is herein termed digital business strategy. We identify four key themes to guide our thinking on digital business strategy and help provide a framework to define the next generation of insights. The four themes are (1) the scope of digital business strategy, (2) the scale of digital business strategy, (3) the speed of digital business strategy, and (4) the sources of business value creation and capture in digital business strategy. After elaborating on each of these four themes, we discuss the success metrics and potential performance implications from pursuing a digital business strategy. We also show how the papers in the special issue shed light on digital strategies and offer directions to advance insights and shape future research.
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Marketing inherited a model of exchange from economics, which had a dominant logic based on the exchange of “goods,” which usually are manufactured output. The dominant logic focused on tangible resources, embedded value, and transactions. Over the past several decades, new perspectives have emerged that have a revised logic focused on intangible resources, the cocreation of value, and relationships. The authors believe that the new per- spectives are converging to form a new dominant logic for marketing, one in which service provision rather than goods is fundamental to economic exchange. The authors explore this evolving logic and the corresponding shift in perspective for marketing scholars, marketing practitioners, and marketing educators.
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With the growing recognition of the customer's role in service creation and delivery, there is an increased impetus on building customer-centric organizations. Digital technologies play a key role in such organizations. Prior research studying digital business strategies has largely focused on building production-side competencies and there has been little focus on customer-side digital business strategies to leverage these technologies. We propose a theory to understand the effectiveness of a customer-side digital business strategy focused on localized dynamics--here, a firm's customer service units (CSUs). Specifically, we use a capabilities perspective to propose digital design as an antecedent to two customer service capabilities--namely, customer orientation capability and customer response capability--across a firm's CSUs. These two capabilities will help a firm to locally sense and respond to customer needs, respectively. Information quality from the digital design of the CSU is proposed as the antecedent to the two capabilities. Proposed capability-building dynamics are tested using data collected from multiple respondents across 170 branches of a large bank. Findings suggest that the impacts of information quality in capability-building are contingent on the local process characteristics. We offer implications for a firm's customer-side digital business strategy and present new areas for future examination of such strategies.
While technological disruptions are changing the competitive landscape, their full impact on business structures, processes, and innovativeness are less understood and vary significantly across companies in the same industry, and may ironically be similar for companies in different industries. A primary reason for such a seemingly “random process” is the lack of a generally accepted definition of the term “business model” within which to provide systematic analyses. In fact, multiple definitions of business models exist, which pose significant challenges for understanding essential components.
The automotive industry ranks among the most significant business phenomena of the 20th century and remains vitally important today, accounting for almost 11% of the GDP of North America, Europe and Japan and one in nine jobs. Although its products have had a fundamental impact on modern society in economic and social terms, the industry has found it hard to adjust to contemporary conditions and is thus no longer esteemed in capital markets. Riven with internal contradictions that inhibit reform, it now faces a stark choice between years of strife or radical change. Highlighting the challenges and opportunities that exist for managers, legislators, financial institutions and potential industry entrants, this book is a wake-up call for those who work in the automotive industry. Most of all, it gives us all cause to reflect on the value of mobility, today and tomorrow. Graeme Maxton is director of AutoPolis, a firm that specializes in the structures and dynamics of the world automotive industry and helps clients position themselves for profitable growth. He is responsible for its activities in Asia and since 1992, has been closely affiliated with the Economist Newspaper Group and chairs all of The Economist's automotive industry conferences throughout the world. He writes for Business China, Business Asia, and various other Group publications, as well as for numerous newspapers throughout Europe and Asia. He is also a television, radio, and press commentator on the industry. Maxton and Wormald were co-authors of Driving Over a Cliff? Business Lessons from the World's Car Industry (Addison Wesley, 1994), which was nominated for the Financial Times Best Book about Business Award. John Wormald is a director and co-founder of Autopolis. He has worked in and for the automotive industry for over 25 years. He advises vehicle manufacturers, component suppliers, distribution and service companies, and financial and government institutions, with a particular emphasis on the downstream distribution and service sectors of the industry. He regularly lectures about the industry, speaks at industry conferences, writes for automotive and general publications, and is quoted and interviewed in the media. He is a co-author of Driving Over a Cliff? © Graeme P. Maxton, John Wormald 2004 and Cambridge University Press, 2009.
Hospitals are adopting advanced messaging systems to facilitate communication among healthcare personnel with the aim of improving patient care. However, users face various challenges in employing these systems, with serious consequences of miscommunication. Nevertheless, past studies on healthcare messaging systems tend to be descriptive with a lack of theoretically grounded and empirical validated research to explain their nature of use. Motivated thus, we study the usage patterns of a web-based messaging system (WMS) in the context of a public hospital. Using media synchronicity and sensemaking theories, we explain how healthcare personnel on the move use WMS to make sense of their work for achieving a shared understanding for patient care. Through a preliminary content analysis of the WMS messages during a month, our results showed salient differences in the usage patterns of different user groups and for different kinds of sensemaking. The study's potential contributions and future plan are discussed. © (2013) by the AIS/ICIS Administrative Office. All rights reserved.
Information technology has arguably been one of the most important drivers of economic and social value in the last 50 years, enabling transformational change in virtually every aspect of society. Although the Information Systems community is engaged in significant research on IT, the reach of our findings may be limited. In this commentary, our objective is to focus the IS community's attention on the striking transformations in economic and social systems spawned by IT and to encourage more research that offers useful implications for policy. We present examples of transformations occurring in four distinct sectors of the economy and propose policy-relevant questions that need to be addressed. We urge researchers to write papers based on their findings that inform policy makers, managers, and decision makers about the issues that transformational technologies raise. Finally, we suggest a new outlet to publish these essays on the implications of transformational informational technology.