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“Digital transformation” is becoming the newest mantra of business leaders. It is clear that there are tremendous business opportunities resulting from this revolution, but there is also a price to be paid. Most management literature focuses on the benefits of digitalization, reflecting the desire to increase performance and efficiency in selected business activities. However, digital transformations may lead to the disruption of established ways of doing the work of the firm, stakeholder power may be fundamentally changed, and there is the potential for redefining the nature of the firm itself. Consequently, the decision to “go digital” requires managers to develop perspectives that have the requisite variety to cope with these challenges. Feedback systems thinking is a powerful means for managers to develop and communicate business models that include those aspects of digitalization that affects their firm’s theory of success. The Uber case illustrates the principles of applying feedback systems thinking to the radical changes that it has presented the public transportation sector. This paper analyzes Uber’s platform business by presenting an endogenous explanation of the drivers and eventual constraints to growth of the theory of success upon which the firm is based. This type of analysis has implications for all firms considering implementing a significant digital transformation process.
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Education for Managing Digital Transformation:
A Feedback Systems Approach
Michael VON KUTZSCHENBACH
Institute for Information Systems, School of Business
University of Applied Sciences and Arts Northwestern Switzerland
CH-4002 Basel, Switzerland
and
Carl BRØNN
School of Economics and Business
The Norwegian University of Life Sciences
N-1432 Ås, Norway
ABSTRACT
Digital transformation” is becoming the newest mantra of
business leaders. It is clear that there are tremendous business
opportunities resulting from this revolution, but there is also a
price to be paid. Most management literature focuses on the
benefits of digitalization, reflecting the desire to increase
performance and efficiency in selected business activities.
However, digital transformations may lead to the disruption of
established ways of doing the work of the firm, stakeholder
power may be fundamentally changed, and there is the potential
for redefining the nature of the firm itself. Consequently, the
decision to “go digital” requires managers to develop
perspectives that have the requisite variety to cope with these
challenges. Feedback systems thinking is a powerful means for
managers to develop and communicate business models that
include those aspects of digitalization that affects their firm’s
theory of success. The Uber case illustrates the principles of
applying feedback systems thinking to the radical changes that it
has presented the public transportation sector. This paper
analyzes Uber’s platform business by presenting an endogenous
explanation of the drivers and eventual constraints to growth of
the theory of success upon which the firm is based. This type of
analysis has implications for all firms considering implementing
a significant digital transformation process.
Keywords: Digital Transformation, System Dynamics, Business
Model, Theory of Success, Management Flight Simulators,
Platform Business, Growth Strategy.
1. INTRODUCTION
Managing digital transformations and, more fundamentally, the
consequences of digital business transformations are becoming
increasingly difficult in today's business environment where
neither competition nor technology is static. Disruptive
information technologies impose significant challenges both on
business organizations’ markets and on their internal processes.
Creative use of information and other technologies facilitates
development of innovative network-based businesses in a
synthesis of firms and markets. This type of business (often
called "platform business") deploys business models that are
fundamentally defined by information technology and
consequently redefine the boundaries of the established business
environment. In offering new ways of thinking, these
technologies generate a different set of strategic choices [1] about
how to understand the basic value creation process and how to
manage the potential flood of new data that becomes available.
Relationships with traditional and new stakeholders may also be
redefined (see, for example, [2] for an extreme perspective).
Companies initiate digital transformation programs in order to
optimize their existing business model, but often do not follow
through, leaving the innovative potential of information
technologies untouched. A recent survey [3] found that fully two-
thirds of the respondents strongly agreed with the statement
"[d]igital technologies have the potential to fundamentally
transform the way people in their organization work.” However,
the same survey indicated that the greatest barriers to leveraging
the potential of digital technologies came from not "[k]nowing
the business and being able to conceptualize how digital
technologies can impact current business processes/models"
(44%) and low "[w]illingness to experiment and take risks"
(44%).
The essence of the digital transformation challenge was well
captured by Schön [4], “In the swampy lowland, messy, confusing
problems defy technical solution. The irony of this situation is
that the problems of the high ground tend to be relatively
unimportant to individuals or society at large, however great
their technical interest may be, while in the swamp lie the
problems of greatest human concern.”
The quotation highlights a fundamental feature of organizations.
They are comprised of tightly inter-related systems that must
operate harmoniously for proper performance. In this system,
making changes to one subsystem (the technical) will also affect
the other (the social). Digital transformations have significant
implications for both subsystems with the consequences of
“going digital” becoming apparent only after some time delay
and in unexpected areas of the firm. Three ways the organization
can react includes generating unintended consequences,
demonstrating counterintuitive behaviors, and pushback, or
policy resistance, from key stakeholders [5]. The primary reason
for these dysfunctions lies in employing a linear, event-oriented
perspective on managing digital transformation that relies on
many unrealistic assumptions about how an organization
functions. The net effect of these systemic reactions often
diminishes the benefits from the transformation process.
14 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 15 - NUMBER 2 - YEAR 2017 ISSN: 1690-4524
One potential explanation for these dysfunctions can be found in
the perspectives that the people in charge have on the system and
their understanding of how it functions. The cognitive organizing
structures that decision makers rely upon, called mental models,
are the collection of assumptions, routines, and networks of
causal relations that describes how a system operates.
Consequently, the quality of planning and decision making
activities depends on the adequacy of the mental models in the
problem context. While there is no foolproof method for avoiding
the undesirable reactions to change, one effective antidote to
linear thinking is to adopt a feedback systems [6] view of the
firm. This is an effective alternative perspective that enables
managers to recognize the importance of relationships between
and among organizational stakeholders and to identify the
interaction dynamics of actions, results, and reactions in a closed
loop system.
Systems are pervasive; humans live and work within both social
and technical systems. IT ‘solutions’ create complex technical
systems, but often ignore the effects of technology on the social
aspects of work. The effect digital transformation has on a
business can be understood along two dimensions: efficiency and
effectiveness. Efficiency relates to performing essentially the
same business functions, but with less resource usage. The
question is how to perform better. This is the common technical
interpretation - doing more for less cost. Effectiveness has deeper
implications and addresses the issue of what the firm should do
and how the business model needs to be adapted to accomplish
it. In either perspective, digital transformations have significant
impacts on the firm and on the market environment, but many
applications deal primarily with the efficiency aspect.
2. THE SYSTEMS PERSPECTIVE ON DIGITAL
TRANSFORMATION
Systems thinking is both a philosophy and a methodology for
understanding behavior of complex dynamic systems, of which
business organizations are an important exemplar [7]. The
feedback systems thinking approach (see [8], [6], [9]) is a rich
and evolving discipline that adopts a holistic perspective on
complex organizational systems. The non-linear feedback
interactions of system elements invalidate the notion that
optimizing individual system components will optimize the
whole system. Performance improvement is neither reductive not
additive. It follows from the systems principle that individual
performance improvements do not necessarily improve the
performance of the entire system.
Complex and adapting systems make learning about them
difficult and consequently ordinary policy designs become
fraught with problems. Policy designers usually do not have the
time to wait and see if their interventions are going to work well,
and then readjust accordingly. Systems thinking offers a set of
tools that support conversations and dialogue and processes for
learning and designing actions within these complex systems.
The disciplinary roots of feedback systems thinking are
information and control theory, behavioral decision theory, and
descriptive knowledge of the system under study. A systems-
based analysis ‘steps back’ from the level of specific events and
attempts to develop structural explanations of system behavior, a
“theory of success”. A unique key characteristic of systems
thinking is its focus on endogenous explanations of behavior.
Selecting the boundary of the system is thus a critical part of the
analysis. The endogenous perspective enables decision makers to
take a proactive, rather than reactive, approach to problem
solving.
Systems are comprised of interlocking feedback loops whose
interactions over time give rise to systemic behaviors. There are
two types of feedback loops: reinforcing (or positive) loops and
balancing (also called negative) loops. Systems analysts employ
two types of tools to capture complexity: causal loop diagrams
(CLD) and stock-and-flow diagrams (SFD). The CLD is a
flexible and useful tool to illustrate the basic feedback structure
within a system in a problem domain while the SFD is a more
formal representation of the variables that sets the stage for
computer simulation of the system. The CLD is simply a map
that identifies the variables of interest and the causal links
between them. Arrows show the direction of causality and the ‘+’
and ‘-’ signs indicate the polarity of the relationship between
pairs of variables. A ‘+’ sign shows that the variables move in the
same direction - increasing the cause results in an increase in the
effect, and vice versa. The ‘-’ sign means that increasing the
cause will decrease the effect, and vice versa.
The overall behavior of any loop is simply determined by
counting the number of negative polarities that the loop contains.
If the number is positive, the resulting loop behavior will be
reinforcing. Reinforcing feedback loops are self-enhancing and
result in exponential growth if the variables are increasing or to
run-away collapse if they are decreasing. Respectively, these
behaviors are often described as “virtuous cycles” and “vicious
cycles.” Reinforcing loops are generally indicated by labeling the
loop with “R” inside it. An odd number of negative polarities
within the loop results in a balancing feedback loop, which is
indicated by “B.” Balancing feedback loops are equilibrating or
goal-seeking structures in systems and are both sources of
stability and sources of resistance to change.
Fig. 1a: Linear, event-oriented representation without feedback
Fig. 1b: Dynamic feedback representation including time delay
between cause and effect
From the loop relationship of Figure 1b, one would expect that
the long-term relationship of congestion and construction would
find an equilibrium state, consistent with the behavior of a
balancing feedback loop. However, most experience in the real
world of traffic shows that this is not generally the case;
construction is ongoing. Compared with the event-driven
perspective, the feedback view enables more information to be
brought into the analysis. This deepens the understanding of what
is actually driving the system. Figure 2 expands the basic
feedback model of Figure 1b to include a new variable called
“number of cars on the road.” This variable provides a plausible
explanation for why new road construction generally only
provides temporary relief from traffic congestion.
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Fig. 2: Expanded traffic model with multiple feedback loops
The availability of new roads activates a reinforcing feedback
loop that increases the number of cars on the road, thus negating
the benefits of construction after some time. One common result
can be oscillations of free flow and congestion, with time delays
measured in years. This simple example illustrates how CLDs
can be used to explore and expand understandings of a well-
known system. Visually representing the system in terms of key
variables and their relationships enables decision makers to share
their perspectives and to surface and test assumptions about the
issue under study.
Barnett [10] states that “[d]isruption is not just about changing
technology; it is about changing the logic of a business.” Most
transformation initiatives fail due to their fragmented view and
outdated theories of change that ignore the dynamic relationship
aspects of organizations. In order to implement and realize the
benefits of digital transformations, we must both understand the
intended consequences of the socio-technical change and be able
to identify the potential unintended consequences of the digital
transformation. The common ways of (linear, event-oriented)
thinking hampers our ability to generate effective mental models,
thus limits our view on the planned change. This often leads to
inappropriate theories of managing digital transformations and
more fundamentally does not address unexpected (side-) effects
of these initiatives. Systems thinking provides a powerful
language for representing and operationalizing the mental
models that strategic decision makers bring to the table.
Systems thinking interprets structure in the broadest sense of
encompassing material flows as well as information flows.
Specific combinations of reinforcing and balancing feedback
loops give rise to characteristic system behaviors that are
described as system archetypes [11]. They are a basic tool of
feedback systems thinking that contribute to diagnosing the
causes of organizational behaviors. The archetypes describe
commonly observed behavioral patterns and correlate them with
potential feedback structures that can generate these behaviors.
A shift of mind (from event-oriented thinking to feedback
systems thinking) in digital strategy management is not easy to
achieve. An effective way to make progress is through examples
of feedback systems approach applied to real-world situations.
Using the case of Uber [12], we show how a feedback systems
approach can illustrate how digital transformation affects both
the business model and the established business environment.
3. BUSINESS MODELS OF “THEORIES OF SUCCESS”
IN COMPLEX, DYNAMIC ORGANIZATIONS
Business models are the blueprint of how a firm does business. It
translates strategic issues into goals and actions and specifies
how the conceptual model is converted into a viable operational
form [13]. Implementing business models based on systems
thinking principles and methods have two important advantages
over traditional implementations.
The first benefit is that the business model explicitly incorporates
the dynamic relationships among the primary value creating
components. The causal loop methodology captures the overall
feedback relationships and identifies the nature of the growth
engine (see [7], chapter 10 for a comprehensive discussion of
various growth engines). Growth is generated by a reinforcing
feedback loop. Balancing feedback loops define constraints on
the system that may limit the growth potential, and identifies
opportunities to overcome them.
At the level of causal loop diagram modeling, decision makers
have an environment that makes mental models behind the theory
of success explicit and contributes to dialogue by encouraging
reflection and inquiry about the basis of the firm’s operations.
Furthermore, it provides the decision makers an environment,
where they can test out their planned change without harming the
business.
The second benefit can be realized by converting the causal loop
model into an operational model using the stock and flow
language. This enables the construction of a computer
simulation-based virtual world model of the organization ([7],
[11] and others). The virtual world model is also called a
management flight simulator. Similar to actual aircraft
simulators, the management flight simulator allows decision-
makers to experiment with the consequences of proposed
strategic decisions. In the computer, the model simulates the
firm’s feedback performance for a specific time that is long
enough, usually years for strategic analyses, to allow delayed
effects to be manifested. This allows a more systematic analysis
and comparison of different strategic initiatives, which leads to
richer discussion of the path to select.
The business model is central to how organizations successfully
navigate in these dynamic and complex environments. Business
models represent the specification of how a firm conducts its
transactions with the external and internal environments. They
represent the organization's managerial understanding of how
things are done, essentially their theory of success on how to
manage in a digital environment. Digital technology-driven
transformation represents a challenge with enormous potential
for organizational growth and development. At the same time, it
presents managers with significant organizational risks.
Externally, it affects the organization’s strategic position in the
industry; internally, it influences the nature of the relationships
between both individuals as well as organizational units.
By their nature, disruptive transformations cannot be foreseen
and accounted for in a traditional business model. For example,
mapping the business model, the theory of success, can provide
managers with deeper understanding about the true nature of the
transformation or disruption. In the case of digital technology,
the technology itself may be revolutionary but that in itself is not
sufficient for it to be disruptive in the market (see the video
lecture [14]). Rather the effect of the technology on the users
defines whether it becomes disruptive. The interaction between
advanced technology and a market that is primed to accept it is
the basis for disruption. The follow-up question is whether the
firm with the technology is able to sustain and grow from the
initial advantage it has gained. This reflects the importance of
being able to balance external demands with internal capabilities
to meet them. As we discuss in the following section, Uber must
confront and manage this challenge. We have to acknowledge to
our theories of success are incomplete or outdated. Consequently,
16 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 15 - NUMBER 2 - YEAR 2017 ISSN: 1690-4524
business model development needs to be less of a detailed
roadmap to success and more of a flexible tool to support
managerial inquiry.
4. THE EXAMPLE UBER - AN ON-DEMAND
TRANSPORTATION SERVICE
Uber is a child of the extreme forms of new organizations that
digital technologies can enable. Started in 2009 as a response to
the difficulty the founders experienced in a Parisian snowstorm,
Uber has become a contentious thorn-in-the-side of a traditional
taxi industry in cities around the world. Enabled by smartphone
technology, Uber’s radically different business model has
dramatically increased consumer efficiency improvements and
company revenues through effectivity improvements. The result
is today known as one of largest point-to-point transportation
network. Uber has become known as a “sharing economy
business.” However, the basic model is not unambiguously seen
as a good thing. The consequences of Uber’s break with
traditional taxi traditions continue to emerge as market
competitors and institutional regulators begin to respond to the
challenges.
Uber is an example of a platform organization. Technology plays
the central role in providing consumers with ‘me-here-now’
logistics services that drives the efficiency gains. Customers’
waiting times and access to some urban areas has been
significantly improved through the information supplied by the
smartphone customer app. By relying on well-paid, independent
contract drivers and equipping them with sophisticated app-
based decision support systems, the traditional business
structures must deal with a competitor that refuses to play by the
old rules. Its business model is outpacing many of the laws
regulating of the taxi industry as it addresses future customer
benefits and new customer relationships [12]. One of Uber’s core
challenges is that it must manage satisfaction on both sides of a
two-sided market (riders and drivers).
Uber originated from a simple idea: Kalanick and Camp’s notion
that they could disrupt the taxi business by replacing the outdated
centralized dispatch system with an app. Despite the market’s
initial acceptance of the business model, “Uber should feel
magical to the customer. They push the button and the car comes.
But there’s a lot going on under the hood make it happen. - CEO
Travis Kalanick” ([12], p. 3). Thus, Uber provides a unique
opportunity to illustrate the use of feedback systems view to
operationalize the theory of success of a platform business.
The founders soon realized that Uber users’ satisfaction
depended on rapid availability of cars and drivers. If an Uber user
summoned a driver and the driver appeared within minutes, user
satisfaction was extremely high. By contrast, if it took a driver a
long time to pick-up a passenger, user satisfaction decreased.
Consequently, in order to ensure high user satisfaction, Uber
always had to ensure that a large number of drivers were always
available in the city. On the drivers’ side, the amount of
information about users enabled development of applications
that would support driver decisions aiding their siting decisions.
Uber’s business model reveals that the company relies on a series
of reinforcing feedback loops that reinforce the power of the
system from one side of the market to the other, thereby creating
a growth engine (see Fig. 3). The most important component of
this growth engine is also known as ‘get-big-fast’ (GBF) strategy
[15].
Fig. 3: Reinforcing feedback loops driving Uber’s growth
(modified from [15], p. 102)
Reinforcing feedback loop (R1: Satisfaction Cycle): It became
quickly apparent that Uber user satisfaction with the Uber app
depends almost entirely on the rapid availability of a car. At the
same time, the more that people use the Uber app, the more Uber
drivers will be able to do business with Uber. The more Uber
drivers in a city, the shorter the waiting time. The shorter the wait
the greater the satisfaction of the Uber user.
Reinforcing feedback loop (R2: Attractiveness to Investors):
Rapid revenue growth drives high stock valuations during the
honeymoon period when investors are not troubled by losses.
Higher stock prices lower the firm’s cost of capital and bring in
additional resources. New capital increases spending, which
leads to better performance, greater user acquisition and a further
increase in revenue. Growth attracts investors, and in the case of
Uber, these capital providers have enabled the company to spend
heavily to further grow the business. Uber has been able to raise
an extraordinary amount of capital at a relatively low cost, and
thus can essentially operate at a loss as necessary, spending
money to win markets. It is similar to Amazon in its willingness
to lose money in order to win market share and achieve scale.
Reinforcing feedback loops (R3-5: Investment Loops):
Investment in adequacy of IT infrastructure (speed, data analysis
and security, etc.), adequacy of service delivery infrastructure
(access to necessary technical and legal expertise, fulfillment
speed, etc.), and brand equity (awareness, reputation, etc.)
improves the attractiveness of the Uber app. Furthermore,
investments in these attributes are driven by the availability of
capital, which are in turn increased by the attractiveness of Uber,
thus creating the growth engine that drive the growth of the
organization.
Finally, regardless of how compelling Uber’s service is, there are
also a number of limiting feedback loops to Uber’s GBF strategy
(see Fig. 4).
Balancing feedback loop (B1: Drivers on Hold): Insufficient
working conditions will cause a poor fulfillment experience,
eroding attractiveness and limiting organizational growth. Uber
is repeatedly facing lawsuits because its drivers are classified as
independent contractors instead of employees. Some even claim
that Uber’s entire business model is based on an improper and
even unethical exploitation of labor.
Uber App
attr ac tivn ess
Uber u sers
Transaction
Revenu es Available
capital
Uber
drivers
+
+
R1
+
+
++
Inv est or
capital
+
+
R2
IT
devel o pment
Adequacy of IT
infras tructure
+
+
+
R3
Mar keti n g
Brand
equity
+
+
+
R5
Serv ice d eli very
infras tructure
Adequacy of
ser vic e deli very
infras tructure
+
+
+
R4
Satisf action
cycle
Serv ice
investment A ttractivne ss
to inves tors
IT inve stment
Brand
investment
ISSN: 1690-4524 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 15 - NUMBER 2 - YEAR 2017 17
Balancing feedback loop (B2: Disrupting Trends): Uber is
clearly a disruptive company. On the one hand Uber’s success
illustrates the latent discontent that customers have historically
experienced with existing transportation alternatives, while on
the other hand, it illustrates how dramatically changes in
behavior affects the attractiveness of an internet solution. The
behavioral change, resulting in different customer demands, is
the disruption, enabled by technology.
Balancing feedback loop (B3: Cyber Security): More users
means more access to sensitive data. This opens the opportunity
for cyber-crime, which negatively affects the adequacy of the
existing IT infrastructure. At the same time, some users may not
be willing to share their private data, thus limiting the
attractiveness of the Uber app.
Fig. 4: Main balancing feedback loops that constrain growth
(modified from [15], p. 103)
Balancing feedback loop (B4: Service Delivery Adjustment):
Uber is waging a battle on multiple fronts: against city and state
regulators, and against entrenched taxi interests. The
complicating issue is that laws regulating these industries were
written before the advent of ubiquitous mobile technologies. In
any case, adjusting the service delivery infrastructure can
consume significant resources that will affect the company’s
ability to develop its service offerings.
Balancing feedback loop (B5: IT Adjustment): Uber is
continuously improving their IT infrastructure, including
smartphone integration, GPS-tracking, wallet-less payment, and
ratings-based reputation systems. However, delays in improving
IT infrastructure to support the growing number of riders and
transactions will decrease the app attractiveness and limit the
growth of the user base.
Balancing feedback loop (B6: Marketing Adjustment): A
challenge facing Uber is the tension over surge pricing. The
public relations around Uber’s surge pricing policy was very
negative. Although surge pricing is common, what makes Uber’s
version particularly aggrieving is that it is significantly more
precise than other dynamic pricing models. Pricing is
experienced by consumers in deeply emotional ways, and
companies whose pricing is perceived to be randomly variable
are often the subject of brutal consumer complaints.
5. DISCUSSION AND CONCLUSIONS
The digital economy is real - and it is here to stay.” Advanced
information technology is significantly affecting the
development of organizations in all businesses. Managers have
found that to survive and prosper in the 21st century they need to
understand the opportunities and forces that digital
transformation imposes on organizations. When answering this
question, business models become of vital importance.
According to our understanding, business models represent the
managers’ operationalized theory of success concerning
successful management of the consequences of digital
transformation.
Applying a feedback systems approach decision makers learn
how to map and interpret the underlying causal structure of
different business models. This is important in order to manage
organizations and to understand and cope with the consequences
of socio-technical changes caused by digital transformation. In
doing so, decision makers need to answer how efficiently and
effectively available technologies and infrastructure is used to
satisfy stakeholders and to achieve organizational goals. Beyond
efficiency considerations, managers can utilize these models to
identify and exploit new opportunities for other types of
customers.
Recent literature confirms [3] that companies often initiate digital
transformation programs in order to optimize their existing
business model. Reasons for doing so include risk avoidance in
experimenting with new ideas and an addiction to solving
business problems that worked well in the past. Thus, digital
transformation initiatives lead to digitization, changing from
analogue to digital, in order to increase the efficiency of existing
business.
In the taxi industry, taxi companies heavily invest in new cars
and dispatching equipment but still do business the traditional
way. Historically, it has been easier to summon transportation
from a centrally organized firm like a taxi organization than it has
been to scour the streets yourself for a driver. However, in recent
years, technology has turned this logic on its head. Now that most
people carry smart devices (in form of cellphones) in their
pocket, it has become easier for companies to develop systems in
which potential taxi riders are matched with potential drivers on
a real-time basis via a platform company.
Platform companies like Uber provide a matching system for
riders and drivers, which is more efficient than the service
provided by a traditional taxi company. Uber addressed the
effectiveness question “Are we doing the right thing?” by taking
advantage of advanced information technology in order to
change behavioral trends. This increased the attractiveness for
on-demand transportation for a new customer segment.
Uber’s underlying strategy can be described as a ‘get big fast’
(GBF) strategy, which is well known in e-business. GBF
strategies promote a strong focus on reinforcing feedbacks that
create a large customer base and the acquisition of capital for
rapid growth.
A successful GBF strategy requires that managers be aware of
the relationship between two critical feedback loops. One loop
describes the growth process. In Uber’s case, this is represented
mostly by “R4: Service Investment. This loop generates
revenues in the form of fares paid to drivers and their commission
payments to Uber. However, in order for transport services to be
delivered, there must be sufficient capacity to deliver the service,
18 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 15 - NUMBER 2 - YEAR 2017 ISSN: 1690-4524
which is Uber drivers. Recruiting new drivers is a key element of
Uber’s theory of success. The system archetype called “Growth
and Underinvestment” [19] captures the dynamics of quality
service provision and the need for timely investment in provision
capacity to maintain service quality. Capacity adjustment takes
more time than earning revenues. An uncritical GBF strategy
may result in managers seeing only the benefits of meeting
demand for service while ignoring the need for adding more
drivers, perhaps well in advance of the demand for service. This
shortsightedness will lead to demand falling off and financial
problems for the firm.
Modeling the theory of success of Uber applying a GBF strategy
enables the decision makers to investigate the potential side
effects of digital transformation. The model captures the
interplay of the powerful reinforcing feedbacks that drive Uber’s
rapid growth and their interaction with limits to growth arising
from the behavioral changes of major stakeholders, potential
decline of the customer base resulting from limited availability
of capital and the delays in deploying the capabilities and
competencies needed to provide an attractive Uber app. Thus,
decision makers will be empowered to better understand the
interdependencies of socio-technical changes and how balancing
feedback loops can limit growth, e.g. service erosion.
However, driving digital business transformation requires a
delicate balancing act between fundamental change of business
by advanced technologies and disruptive business models on the
one hand, and developing infrastructure required to serve a
changing customer demands, keep customer attracted as well as
managing the resulting frictions with the established
environment.
This paper has not discussed the second systems thinking tool,
the stock and flow diagram (SFD). The natural next step in a
systems thinking based analysis is to convert the CLD into a
format that enables decision makers to experiment with different
change initiatives in a software environment. Using a SFD
model, decision makers can create computer-based virtual micro-
worlds, also known as management flight simulators [16], to
visualize and operationalize their mental models. These virtual
worlds have many advantages. They enable decision makers to
discuss, test, and experiment with their knowledge in a more
scientific manner. The immediate feedback of the short- and
long-term consequences of their plans encourages learning that
is more effective and supports the development of robust and
realistic theories of success. This provides digital business
transformation manager to experiment and test their strategy in a
risk-free environment.
The CLD-model shows the difficulties of succeeding in digital
transformations even when there are reinforcing feedback loops
that can lead to rapid growth. Educating decision-makers about
the opportunities and application of a feedback systems approach
enriches their strategic choices about digital technology-driven
transformations and their potential long-term consequences.
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[2] R. Levine, C. Locke, D. Searls, and D. Weinberger, The
Cluetrain Manifesto: The End of Business as Usual,
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ISSN: 1690-4524 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 15 - NUMBER 2 - YEAR 2017 19
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  • M W Van Alstyne
  • G G Parker
  • S P Choudary
M.W. van Alstyne, G.G. Parker, and S.P. Choudary, "Pipelines, Platforms, and the New Rules of Strategy", Harvard Business Review, 94, 4, 2016, pp. 54-62.
  • G Richardson
G. Richardson, Feedback Thought in Social Science and Systems Theory, Philadelphia: University of Pennsylvania Press, 1999.
  • J W Forrester
J.W. Forrester, Industrial Dynamics, Cambridge: MIT Press, 1961.