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Summary DBIS articles
Digital Business and Information Systems (Vrije Universiteit Amsterdam)
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Inhoudsopgave
Week 1...................................................................................................................................................2
Carr, N. G. - IT doesn't matter.........................................................................................................2
Bharadwaj & Venkatraman - Digital business strategy: toward a next generation of insights.......4
Lecture Note: R. Fichman - Distinctive IT Characteristics: Implications for Digital Innovation and
Value Creation................................................................................................................................5
Bughin, J., Chui, M., & Manyika, J. - Ten IT-enabled business trends for the decade ahead..........6
Nylén, D., & Holmström, J. - Digital innovation strategy: A framework for diagnosing and
improving digital product and service innovation..........................................................................6
Hopkins, M. S. - Value Creation, Experiments and Why IT Does Matter.........................................8
Ross, J. W., Beath, C. M., & Sebastian, I. M - How to Develop a Great Digital Strategy..................9
Week 2.................................................................................................................................................10
Core paper........................................................................................................................................10
Dhar, V., & Sundararajan, A. - Information technologies in business: A blueprint for education
and research.................................................................................................................................10
Enterprise systems...........................................................................................................................13
Davenport, T. H. - Putting the enterprise into the enterprise system...........................................13
Ranganathan, C., & Brown, - ERP investments and the market value of firms: Toward an
understanding of influential ERP project variables.......................................................................14
Interorganizational Systems.............................................................................................................15
Johnston, H. R., & Vitale - Creating competitive advantage with interorganizational information
systems.........................................................................................................................................15
Jernigan, S., Kiron, D., & Ransbotham, S. - Data Sharing and Analytics are Driving Success With
IoT.................................................................................................................................................17
Platforms..........................................................................................................................................17
Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. - Pipelines, platforms, and the new rules of
strategy.........................................................................................................................................17
McIntyre, D. P., & Srinivasan, A. - Networks, platforms, and strategy: Emerging views and next
steps. P141-144............................................................................................................................18
Practice question discussed in class.............................................................................................19
Week 3.................................................................................................................................................20
Core Paper........................................................................................................................................20
Hatch, M. J. - Organization Theory (pp. 269–281)........................................................................20
Cloud Computing..............................................................................................................................21
Armbrust, M., et al. - A view of cloud computing.........................................................................21
Big Data............................................................................................................................................22
McAfee, A., & Brynjolfsson, E - Big data: the management revolution........................................22
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Provost, F., & Fawcett, T. - Data Science and its Relationship to Big Data and Data-Driven
Decision Making...........................................................................................................................22
Algorithms........................................................................................................................................24
Castelvecchi, D. (2016) - Can we open the black box of AI...........................................................24
Luca, M., Kleinberg, J., & Mullainathan, S. - Algorithms need managers, too..............................24
Week 4.................................................................................................................................................25
Core Paper........................................................................................................................................25
Woerner, S. L., & Wixom, B. - Big data: extending the business strategy toolbox.......................25
Mastering Data Projects...................................................................................................................26
Wixom, B. H., & Watson, H. J. - An empirical investigation of the factors affecting data
warehousing success....................................................................................................................26
Bell, P. C. - Sustaining an analytics advantage..............................................................................27
Data-Driven Organizations...............................................................................................................27
Kiron, D. - Lessons from Becoming a Data-Driven Organization...................................................27
Constantiou, I. D., & Kallinikos, J. - New games, new rules: big data and the changing context of
strategy........................................................................................................................................28
Data-Driven Business Model Innovation..........................................................................................30
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A - Big data for big business? A taxonomy of
data-driven business models used by start-up firms....................................................................30
Week 5.................................................................................................................................................32
Core Paper........................................................................................................................................32
Khodyakov, D. - Trust as a process: A three-dimensional approach.................................................32
Gig Economy.....................................................................................................................................34
Sundararajan, A. - The Sharing Economy......................................................................................34
Blockchain....................................................................................................................................34
Evans, P. - Thinking Outside The Blocks: A Strategic Perspective on Blockchain and Digital Tokens
.....................................................................................................................................................34
Digital Trust..................................................................................................................................35
Mazzella, F., Sundararajan, A., Butt d’Espous, V., & Möhlmann, M. - How Digital Trust Powers
the Sharing Economy....................................................................................................................35
The great chain of being sure about things - The Economist (2015, October 31).........................36
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Week 1
Literature lecture 1
Carr, N. G. (2003). IT doesn't matter. Harvard Business Review, 81(5), 41-49.
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. V. (2013). Digital
business strategy: toward a next generation of insights. MIS Quarterly 37(2).
Lecture Note: R. Fichman (2012). Distinctive IT Characteristics: Implications for Digital
Innovation and Value Creation, Boston College.
Bughin, J., Chui, M., & Manyika, J. (2013). Ten IT-enabled business trends for the
decade ahead. McKinsey Quarterly, 13(May).
Nylén, D., & Holmström, J. (2015). Digital innovation strategy: A framework for
diagnosing and improving digital product and service innovation. Business Horizons,
58(1), 57-67.
Hopkins, M. S. (2010). Value Creation, Experiments and Why IT Does Matter. MIT Sloan
Management Review, 51(3), 57.
Ross, J. W., Beath, C. M., & Sebastian, I. M. (2017). How to Develop a Great Digital
Strategy. MIT Sloan Management Review, 58(2), 7.
Carr, N. G. - IT doesn't matter.
Main message of Carr’s article: Instead of seeking advantage through technology, companies should
manage IT defensively by watching costs and avoiding risks.
Chief executives now routinely talk about the strategic value of information technology,
about how they can use IT to gain a competitive edge, about the “digitization” of their
business models. Technologies opened opportunities for forward looking companies to gain
real advantages. But as their availability increased and their cost decreased – as they
become ubiquitous – they became commodity inputs. From a strategic standpoint; they
became invisible; they no longer mattered. This is exactly what is happening to information
technology today, and the implications for corporate IT management are profound.
A distinction needs to be made between proprietary technologies and what might be called
infrastructural technologies.
Proprietary technologies (like a patent) can be owned, actually or effectively by a single
company. As long as proprietary technologies are protected, they can provide a sustaining
advantage.
Infrastructural technologies offer far more value when shared than when used in isolation
(railroads for example).
In the earliest phases of its buildout, however, an infrastructural technology can take the
form of a proprietary technology. As long as access to the technology is restricted – through
physical limitations, intellectual property rights, high costs, or a lack of standards. In addition
to enabling new, more efficient operating methods, infrastructural technologies often lead
to broader market changes.
The trap that executives often fall into, is assuming that opportunities for advantage from
infrastructural technology will always be available (P3). But the reality is that gaining
advantage from IT, is temporarily. When the commercial potential is clear, investments will
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be made and the technology proceeds with extreme speed. Because of this the advantage
gets lost.
The only meaningful advantage most companies hope to gain from an infrastructural
technology after its buildout is a cost advantage – and even that tends to be very hard to
sustain.
IT has all the hallmarks of an infrastructural technology. IT is, first of all, a transport
mechanism, it is far more valuable when shared than when used in isolation. IT is highly
replicable and even the most cutting-edge IT capabilities quickly become available to all (the
opportunities for gaining IT-based advantages are already dwindling. Best practices are now
quickly built into software or otherwise replicated).
The greatest IT risk facing most companies is simply overspending, IT has become a
commodity.
The new rules for IT management become:
- Spend less
- Follow, don’t lead
- Focus on vulnerabilities, not opportunities.
The key to success, for the vast majority of companies, is no longer to seek advantage
aggressively, but to manage costs and risks meticulously.
In his 2003 article in Harvard Business Review, Nicholas Carr claims that IT investments may not add
competitive advantage at the corporate level, although their influence is still felt at the
macroeconomic level. In an upcoming debate in HBR several scholars and executives have debated
this idea. Bring three arguments for and three arguments against Carr’s idea. What is your own take
on the debate? Do you think this debate is dated or still applies today? Why? Do you think ERP
systems still matter for organizations?
Three arguments for Cars statement are:
1. Hardware and software components can be seen as infrastructural technology for data,
storage, communication and processing. Therefor its mix of characteristics guarantees
particularly rapid commoditization (commoditization is defined as the process by which
goods that have economic value and are distinguishable in terms of attributes, uniqueness or
brand, end up becoming simple commodities in the eyes of the market or consumers).
2. IT is essential for competition, because you need commodities (in this case is IT the
commodity) to compete. To stay in business, companies need IT to work together with other
businesses. So, the new task of IT is not to provide an competative advantage, but to create
relationships with networks. Therefor IT is not an competative advantage anymore, but a
necessity. IT influences competition at a macroeconomic level.
3. IT is no competative advantage because it gets its value from being used by multiple people
and shared by networks. The costs are also rapidly declining, making IT accessible to
everyone. The decline in price destroys one of the most important barriers to competition.
Also, more companies replace customized applications for generic ones, making the
environment more homogenous.
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Bharadwaj & Venkatraman - Digital business strategy: toward a next generation of insights.
They propose a new type of strategy: The digital business strategy, is simply that of organizational
strategy formatted and executed by leveraging digital resources to create differential value. They
have 4 key themes of digital business strategy.
Scope: IT strategy is cross functional and transcends traditional functional areas. By understanding
the scope, helps to conceive its relationship to firms, industries, environment and how DB strategy
can be more affective in a variety of settings.
Scale: Scape up or down, scale by network effects, scale by learning how to cope with big data,
scaling through alliances.
Speed: Speed consists of decision making, supply chain orchestration (more efficient), network
development/management and the speed of product launches.
Source: Source of value creation consist can go through coordinated business models, through
control of digital industry architecture, by using multisided business models and by the increased
value of information.
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Example: “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.”
Nike is doing just that, turning their once non-digital company into a deeply digitally involved
superstore that allows people from all across the world to purchase online products. Nike’s business
strategy has completely changed now that our world revolves around the Internet; an example of
this is Nike’s direct-to-consumer business strategy. Nike is taking advantage of Instagram in that they
are giving people links to personnel wearing Nike products that lead the user directly to Nike.com.
This is known as a digital business strategy as the authors point out: “The formulation of digital
business strategy includes the design of products and services and their interoperability with other
complementary platforms (think of Nike+), and their deployment as products and services by taking
advantage of digital resources.”
Lecture Note: R. Fichman - Distinctive IT Characteristics: Implications for Digital Innovation and
Value Creation
This paper only explains what network effects, Moore’s law, digitalization and the long tail are.
Digital convergence  no longer need another device to play another type of content.
Implications of network effects:
- The more users, the more valuable it gets (you will get self-reinforcing cycles of adoption)
- Additional diffusion pattern: 1. Standard wars
- Additional diffusion pattern: 2. Path dependent technology
- Additional diffusion pattern: 3. Technology lock-in
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Bughin, J., Chui, M., & Manyika, J. - Ten IT-enabled business trends for the decade ahead.
1. The social matrix
2. The Internet of All Things
3. Big data, advanced analytics
4. Realizing anything as a service
5. Automation of knowledge work
6. Integrated digital/physical experiences
7. Me + free + ease
8. The e-volution of commerce
9. The next three billion digital citizens
10. Transformation of government, health care, and education
Nylén, D., & Holmström, J. - Digital innovation strategy: A framework for diagnosing and improving
digital product and service innovation.
This article proposes a framework for diagnosing and improving digital innovation, that consists of
five key areas.
Given the increasing presence of digital technology in businesses, managers need to learn and apply
new tools to support the firms in managing the digital innovation processes. In this paper, the
authors built one of those tools to assess the state of digital innovation management and to support
ongoing improvements.
A Managerial Framework for Digital Innovation Strategy
In the last few years, IT role went from a supporter of internal operations to an enabler that
penetrates all aspects of the business. While IT is now omnipresent across internal operations and
the business’s services and products offerings, it is not always easy to evaluate the state and to track
the evolution of digital technology in the organisation. To this end, the authors created a holistic
framework centred around five keys areas:
1. User Experience: Digital products and services must be easy to use and provide a user
experience that will help engage its users.
2. Value Proposition: For managers to assess the value and for the organisation to be aligned
with its customers, digital products and services need to be articulated around a clear value
proposition. This involves costumer segmentation, that enables firms to reflect on pricing
and positioning of their digital products and services. Also think of the recombination
options. Firms should seek to harness complexity through digital evolution scanning.
3. Digital Evolution Scanning: Firms need to scan the digital environment and gather
information on new digital devices, channels, and associated user behaviours if they want to
identify opportunities and evolve their offerings.
4. Skills: To maximise returns on investment, firms need to acquire new skills both internally
and externally. To do so, firms can create new roles and get the help from consultants who
will put them on the right track. Skills obtained from developing products can also be
leveraged.
5. Improvisation: Managers need to promote a creative environment by providing the right
balance between structure and flexibility. Coordination is key.
With the framework, companies can make informed decisions around three dimensions: The firms
products, digital environment and organization and properties.
When the framework is successfully implemented, the framework enables firms to continuously
adjust operations in order to optimize digital innovation efforts.
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How to Use the Framework for Digital Innovation Strategy
While the framework is quite easy to use, a good
amount of planning and preparation is needed
to make the exercise successful. We recommend
proceeding as follow:
Identify who should be the leader of the
initiative. It should be somebody involved at the
intersection of business and technology as they
have a good grasp on what’s happening on both
sides. It can be a consultant or someone
internal.
We recommend to do a workshop, because the
facilitator can drive the conversation and collect
insights from the room.
Once the forms are completed and the average
scores calculated, firms should focus on the
lowest scores, because they are the areas where
the firms under performs and where they can
probably maximise the ROI of future
investments. Firms can either discuss the results
during the same workshop or do different
sessions to deep dive into a particular subject.
At the end of the workshops, firms should have a
list of action items that they can execute to
increase the score of elements that are
underperforming. Firms should establish a
monthly or quarterly follow-up to make sure
that they are on the right track and that they still
have their priorities in order.
Can digital innovation be managed?
Often, competencies of firms stand in the way of innovating. But there are macro-level strategic
models that help firms to overcome this dilemma. Also, extant research on digital technology suffers
from two limitations: It doesn’t fully open the black box and it often uses high level (macro)
descriptions.
Digital innovation processes are hard to control and predict because of the generativity of digital
technology.
Generativity  a technology’s overall capacity to produce unprompted change, driven by large,
varied and uncoordinated audiences.
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The managerial framework
In the three dimensions uncertainty occurs.
Final Words
In this paper, we explored a framework that allows managers to explore the unique characteristics of
digital innovation and allows them to continuously adjust their efforts. When applying this
framework, managers must consider political policies, regulations and the impact of digital
innovation on the internal processes of their organisation. In conclusion, each firm also need to tailor
the framework to their own needs and capabilities and can use a variety of tools to support the
process.
Limitations
Does not cover internal process innovation enabled by digital technology. No focus on political
policies and regulation.
Hopkins, M. S. - Value Creation, Experiments and Why IT Does Matter
Answers to Carr’s article.
Michael Schrage says IT is not a commodity. IT has the biggest impact on relationship, systems and
people management. IT has to work to create value, to get a certain effect, not to better manage
certain resources. IT is important to get more value for less money from the technology of use.
Managers must reverse engineer from the impact they want to have, rather than extend the
processes they are currently using. Doing experiments with IT has become much cheaper. He
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searches where the value gets created, or destroyed by segmentation of costumers. The focus has
shifted towards costumer use.
Based on this article, a good counter argument against Carr’s ‘IT doesn’t matter’ can be:
IT is not a commodity as Carr claims. IT creates value for organizations, but only when it is used
effectively.
Ross, J. W., Beath, C. M., & Sebastian, I. M - How to Develop a Great Digital Strategy.
This article is about developing a digital strategy. First of all, firms must choose one strategy or the
other. This article describes two strategies.
1. Customer Engagement Strategies
The focus of a customer engagement strategy is the development of customer loyalty and trust —
and, in the best cases, passion. Companies choosing this approach offer seamless, omnichannel
customer experiences, rapid responses to new customer demands, and personalized relationships
built upon deep customer insights. Recognizing the always-rising bar of customer expectations,
companies with a great customer engagement strategy are constantly identifying new opportunities
to connect with their customers.
Kaiser Permanente capitalizes on digital technologies by:
1. Offering digital channels that bolster patient interaction with care delivery teams. Kaiser
Permanente’s channels provide access to personal health records, secure messaging between
patients and providers, and remote care.
2. Applying data analytics to identify the need for — and the most effective approach to —
personalized medical outreach. Kaiser Permanente uses analytics to track and improve patient
compliance with medication and treatment regimens, and to identify the most effective forms of
outreach for generating healthy behaviors.
3. Leveraging social media to develop communities of patients with similar interests and needs, and
to create “care circles,” where patients and their families can engage with care providers. Kaiser
Permanente is using a carefully crafted permission system to allow approved family members and
other caregivers to help support patients, communicate with their physicians, and monitor their
treatment.
2. Digital Solutions Strategy
A digitized solutions strategy transforms what a company is selling. It seeks to integrate diversified
products and services into solutions, to enhance products and services with information and
expertise that help solve customer problems, and to add value throughout the life cycle of products
and services. Over time, digitized solutions can transform a company’s business model by shifting the
basis of its revenue stream from transactional sales to sophisticated, value-laden offerings that
produce recurring revenue.
Schindler Group, a global provider of elevators, escalators, and related services based in Ebikon,
Switzerland, is pursuing a digitized solutions strategy. The company leverages the internet of things
— collecting real-time data from its installed base of elevators and escalators and using that data to
improve the quality of its products and services. Initially, Schindler focused on operational excellence
in its digital strategy — using analytics to reduce the costs it incurred in servicing its products. But all
four global competitors in the industry are improving their operations in this way. So, Schindler
shifted its focus to digitized solutions and began using data from the internet of things to help
prevent equipment failure, optimize elevator routes, and identify potentially valuable innovations.
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Schindler has developed a new urban mobility solutions strategy that takes advantage of its ability to
manage the efficient movement of thousands of people within a building. For example, the company
has developed a digitized solution that allows visitors to bypass lobby security stations by swiping
their mobile phones at the point of entry.
To exploit numerous opportunities for delivering on either type of strategy, a company needs an
integrated platform of distinctive capabilities (operational backbone) that ensures efficient, reliable
transactions and consumer interactions.
In assessing their company’s ability to execute one of the two digital strategies, business executives
must be mindful of the gaps in their capabilities — and then, as quickly as possible, wire them into
the organization’s operational backbone. To succeed in the digital economy, companies must offer a
unique value proposition that is difficult for both established competitors and startups to replicate.
Such a value proposition stems from a digital strategy that is focused on either a set of digitized,
integrated offerings or a relationship that engages customers in ways that competitors can’t match.
Without that, you might create a flurry of innovations, but you won’t deliver value-added
applications of AI, biometrics, drones — or the next important digital technology.
Week 2
Literature lecture 2: Technology-Centric View of IT
Core Paper
Dhar, V., & Sundararajan, A. (2007). Information technologies in business: A blueprint
for education and research. Information Systems Research, 18(2), 125-141. Paragraph 3
only. IT in Business: Conceptual Foundations (pages 127-132).
Discussion Papers
Enterprise Systems
Davenport, T. H. (1998). Putting the enterprise into the enterprise system. Harvard
business review, 76(4).
Presentation Paper: Ranganathan, C., & Brown, C. V. (2006). ERP investments and the
market value of firms: Toward an understanding of influential ERP project variables.
Information Systems Research, 17(2), 145-161.
Interorganizational Systems
Presentation Paper: Johnston, H. R., & Vitale, M. R. (1988). Creating competitive
advantage with interorganizational information systems. MIS quarterly, 153-165.
Jernigan, S., Kiron, D., & Ransbotham, S. (2016). Data Sharing and Analytics are Driving
Success With IoT. MIT Sloan Management Review, 58(1).
Platforms
Presentation Paper: Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016).
Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94(4), 5462.
McIntyre, D. P., & Srinivasan, A. (2017). Networks, platforms, and strategy: Emerging
views and next steps. Strategic Management Journal, 38(1), 141-160. Pages 141-144
until paragraph “Market dynamics: the IO economics view”.
Core paper
Dhar, V., & Sundararajan, A. - Information technologies in business: A blueprint for education and
research
The authors try to answer the following question with their research:
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o
o
How should business schools be training students to assess the threats to business models,
and capitalize on opportunities enabled by emerging information technologies?
Are there some general principles that can be applied to answer these questions?
The business centric and technology centric framework helps answer questions about IT.
Business centric framework  articulates three compelling reasons why information technology
matters in business.
1. IT continuously transforms industry and society
2. Executive decisions about IT investments, governance, and strategy are critical to
organizational success
3. Deriving value from increasingly available data trails defines effective decision making in the
digital economy
This can be used as a counterargument for Carr?
Technology centric framework 
They have three invariants to make reasonable predictions about the future.
1. Digitally represented information
2. Exponential growth of computing power
3. Sustained increase in programmability in a modular way
These invariants help to identify future consequences.
1. Digital representation in conjunction with the growth in processing and communications
power facilitates the separation of information from a growing number of artifacts.
Digitization makes this separation feasible, the exponential growth in the power of hardware
and network bandwidth makes it practical, and modularity makes the associated rendering of
the information possible via software running on a general-purpose device.
The separation of the digital information contained in a song from this artefact became
useful only when there was sufficient bandwidth. You can make a connection to platforms.
2. IT infrastructures become more large, powerful and accessible. The emergence of these
infrastructures can cause the capability to develop and manage generic complex IT
infrastructures of commercial value to diminish (verminderen) in importance over time (Carr
2003).
3. The third major consequence of the three invariants is a growth in society of the importance
and variety of “spaces of interaction” that are mediated by IT. Spaces of interaction are
shaped continuously and fluidly by the participants who occupy them. More powerful
infrastructure allows participants to support the complex interfaces of these spaces.
Software modularity enables these spaces to evolve and lets participants build new ones
with little effort.
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They propose a list of questions which they claim to be important and relevant for IT in business.
Business-centric Questions:
The first set of questions flows from the business-centric framework of IT in business.
o
How Does IT Transform Industries and Change the Boundaries Between Them?
Seperation of information from its artifacts and the emergence of powerful shared
infrastructures, makes physical network ownership or network access less of a basis for
sustainable competative advantage. IT can also replicate/improve vastly in an automated and
scalable way the matching that forms the basis of many industries.
o
How Do Platforms Alter Existing Business Models and Create New Ones?
o
What Determines Success with a Firm’s IT Investments?
o
How Do Firms Effectively Get Value from and Govern Data?
o
Why Do Incumbent Companies Frequently Miss Large, New IT-Based Opportunities?
Technology-Centric Questions:
The second set of questions flow from the consequences of the technology-centric framework of IT
in business. These draw the boundaries around important areas of conceptual inquiry that may help
answer current business questions, or those that are yet to emerge.
o
How Do the Unique Characteristics of Digital Goods Impact Business Models?
o
Why Do Network Effects Pervade IT-Based Businesses and How Do They Alter Strategy?
o
How Does Human Behavior/Interaction Differ in Spaces Mediated by Information
Technology?
Answers to all these questions are highlighted in the printed text. The most important conclusions
that the authors make is that the questions are still relevant.
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Enterprise systems
Davenport, T. H. - Putting the enterprise into the enterprise system.
As discussed in class, this is more about what happens
after the investment. It focusses on the implementation of
Information technology.
According to Davenport the problem that needs to be
solved is the fragmentation of information by business
organizations.
An enterprise system is a generic solution. The
organization must be modified to fit the system. Some
degree of ES customization is possible because systems
are modular. However, the systems complexity can make
major modifications impracticable. After implementation
of ES companies become less flexible, making the core
source of advantage be of risk. It is common for a single ES
system to be used by every company in the industry.
Danger is that companies undermine their own sources of
differentiation in the market.
ES raise strategic issues for companies that compete on
cost.
On the one hand, by providing universal, real-time access to operating and financial data, the
systems allow companies to streamline their management structures, creating flatter, more flexible,
and more democratic organizations.
On the other hand, they also involve the centralization of control over information and the
standardization of processes, which are qualities more consistent with hierarchical, command-andcontrol organizations with uniform cultures.
Impact on the organization
Some executives, particularly those in fast-growing high-tech companies, have used enterprise
systems to inject more discipline into their organizations. They see the systems as a lever for exerting
more management control and imposing more-uniform processes on freewheeling, highly
entrepreneurial cultures. Some will use it for the opposite, as you can read on page 6.
Companies deriving the greatest benefits from their ERP-systems are those that, from the start,
viewed them primarily in strategic and organizational terms. They stressed the enterprise, not the
system.
The role of management  companies that have the biggest problems, are those that install an ES
without thinking through its full business implications.
A number of questions should be answered before any decisions are made.
1. How might an ES strengthen our competitive advantages?
2. How might it erode them?
3. What will be the system’s effect on our organization and culture?
4. Do we need to extend the system across all our functions, or should we implement only
certain modules?
5. Would it be better to roll the system out globally or to restrict it to certain regional units?
6. Are there other alternatives for information management that might actually suit us better
than an ES?
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Ranganathan, C., & Brown, - ERP investments and the market value of firms: Toward an
understanding of influential ERP project variables.
Aim of the paper:
To build on prior theory as well as prior empirical IS research to hypothesize differences in
stock market reactions to ERP investment announcements. Develop a theory that can help in
understanding influential ERP project variables.
Main Theory:
Authors propose that ERP systems can create value through:
Organizational Integration (of ERP systems):
- Technical Integration(IT-infrastructure)
- Business Integration (to support cross-functional processes)
Based on these two, you need to understand that another the article on ERP investments can also be
applied to datawarehousing because they have a common denominator.
Option Value:
- Some investments create value as digital options.
- Platform for value generation (you can gain option value from platform investments)
Option value increases as the variance in the potential business returns or the managerial flexibility
by implementing the platform (or both) increases.
Four determinants (of success of value creation on an IT platform):
1) Process improvements (strategic factor)
2) Organizational Learning (exploitable absorptive capacity  learning factors for implementation
and the long term strategic knowledge that is gained)
3) Technology Bandwagon (others have done it)
4) Adaptation factors (continuous improvement)
Authors recognize the following ERP project variables:
Functional Scope (the types of ERP modules a firm chooses to purchase).
- Can be enterprise support model (HR, accounting, finance) or value-chain model (operations, sales,
distribution)
- Sequentially interdependent processes (value chain) are complex to integrate – higher value
- More radical improvements in processes
- Future supply chain integration
Physical Scope (the number of sites a project encloses):
- Multiple locations–more benefit because of the increased future options
- More learning from implementing process, will increase option value
- Future interconnections
Vendor Status (The only hypothesis that was not supported)
- Best practices are mature
- Bandwagon effects (others have the same) (P151)
- Based on prior findings in literature
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Conclusions:
Authors found support for the project variables: Functional Scope and Physical Scope H1, H2 and H3
were supported. Vendor status NOT (H4 not supported).
(Important) Assumptions:
Premise: the market value of a firm provides a more accurate guide to a firm’s change in
business value than its balance sheet (p.146) - the market values expected benefits.
Thus, perceived value is measured. This is considered valid because:
o
Managers take into account potential gains in business value as they make ERP
investment decisions;
o
Financial investors attempt to assess the potential for related investments [..]
following the ERP investment announcement;
EMH (Efficient Market Hypothesis)
Strengths:
Contribution to IT value assessment
Enhances understanding how ERP can create value
Recognizes value drivers (influential ERP project variables)
Weaknesses:
‘Market value’ and ‘actual value’ are not the same things.
H1 - H4 are also conclusions from an improved tradability of the company due to “unplug
and play” architecture.
ERP implementations are high impact ‘risky’ change processes. Increasing functional scope
and physical scope will increase complexity and risk…
Organizational Integration and Option Value only hypothesized but not actually measured
Interorganizational Systems
Johnston, H. R., & Vitale - Creating competitive advantage with interorganizational information
systems
Aim of the paper (1):
To present a set of frameworks that can guide the search for opportunities for realizing
competitive advantage through interorganizational information systems (IOIs).
To provide guidelines for evaluating potential IOS projects (Frameworks that can guide exploration
of):
o
choosing appropriate organizations
o
deciding what functions, the IOS will perform
o
determining how these functions will provide sustainable competitive
advantage
o
the potential impacts of the IOS to all participants
o
key management and implementation issues
Aim of the paper:
When involved in IOS project, paper can be used as point of departure to address feasibility,
organization, structure, participants, cost/benefits, impact, etc. of IOS.
Main Theory/Concepts:
Interorganizational Information System: An automated information system shared by two or
more companies (Cash & Konsynski). IOS will perform an information function.
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Assumption:
The first company to build an IOS in a given
industry achieves a longterm, sustainable
advantage. The notion that IS can be used to
achieve CA has passed from concept to cliché.
Inter Organizational System Framework
• Four dimensions:
Main Theory/Concepts:
o
Business purpose
o
Relationship between participants
Information function
o
Improvement focus
Whether IOS can be an competative advantage
relies on:
- Comparative efficiency  produce goods/services cheaper than competitors (determined by
internal and interorganizational efficiency)
- Bargaining power  Allows a firm to resolve a bargaining situation with costumers/suppliers in
its own advantage (determined by switching costs, unique product features and search-related
costs)
Looking for IOS opportunities:
Conclusion:
Authors propose…most significant outcome of an effective search and planning process should
be the recognition that the electronic link between separate organizations is a part of a major
change in the relationships between the parties.
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Jernigan, S., Kiron, D., & Ransbotham, S. - Data Sharing and Analytics are Driving Success With IoT.
Aim of the paper:
To understand the challenges and opportunities associated with internet of things.
Main theory:
Dataflows between organizations  Two-thirds (66%) of the respondents who are actively
working on IoT projects gather data from and/or send data to their customers, suppliers, or
competitors. Organizations share data faster with costumers than suppliers or competitors.
The flow of data of the IoT devices deepen interdependence and existing relationships between
organizations
IoT expands the value chain (data sharing with competitors increases with experience and the
ability to derive value from data.
Issues with taking IoT to the next level
Scaling up might be a problem because the IoT are actual things and not just software. Also
scaling up may bring additional maintenance costs
The reaction on IoT devices by the people who use the devices
Keeping IoT safe and trustworthy. Relationships require mutual trust and the devices need
updating to remain safe. But the client decides to update the software or not.
Creating business value with IoT:
Analytical skills  The report showed that businesses with analytical capabilities that are good
or excellent are three times more likely to report having no trouble getting business value from
IoT, compared to those who rated their analytical capabilities as worse than good.
Embrace complexity  organizations and devices are diverse and use diverse networks,
Competative advantage from IoT must be VRIN. Managers must strengthen analytics capabilities, to
manage more data, they need to prepare to share and they need to prepare the market.
Platforms
Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. - Pipelines, platforms, and the new rules of
strategy.
Aim of the article: How platforms are reshaping business
Platforms all have an ecosystem with the same basic structure, comprising four types of players.
1. The owners of platforms control their intellectual property and governance.
2. Providers serve as the platforms’ interface with users.
3. Producers create their offerings, and
4. Consumers use those offerings.
Pipeline businesses create value by controlling a linear series of activities—the classic value-chain
model. Inputs at one end of the chain (say, materials from suppliers) undergo a series of steps that
transform them into an output that’s worth more: the finished product.
The move from pipeline to platform involves three key shifts:
1. From resource control to resource orchestration  With platforms, the assets that are hard
to copy are the community and the resources its members own and contribute, be they
rooms or cars or ideas and information. In other words, the network of producers and
consumers is the chief asset.
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2. From internal optimization to external interaction  Platforms create value by facilitating
interactions between external producers and consumers. Because of this external
orientation, they often shed even variable costs of production.
3. From a focus on customer value to a focus on ecosystem value  Platforms seek to
maximize the total value of an expanding ecosystem in a circular, iterative, feedback driven
process.
How platforms change strategy
1. Forces within the ecosystem  The new roles that players assume can be either accretive or
depletive. For example, consumers and producers can swap roles in ways that generate value
for the platform.
2. Forces exerted by ecosystems  Managers must anticipate on competition from
unexpected platforms. Competitive threats tend to follow one of three patterns. First, they
may come from an established platform with superior network effects that uses its
relationships with customers to enter your industry. Second, a competitor may target an
overlapping customer base with a distinctive new offering that leverages network effects.
The final pattern, in which platforms that collect the same type of data that your firm does
suddenly go after your market, is still emerging.
3. Focus  For platforms, the focus shifts to interactions—exchanges of value between
producers and consumers on the platform. The unit of exchange (say, a view of a video or a
thumbs-up on a post) can be so small that little or no money changes hands. Nevertheless,
the number of interactions and the associated network effects are the ultimate source of
competitive advantage. With platforms, a critical strategic aim is strong up-front design that
will attract the desired participants, enable the right interactions (so-called core
interactions), and encourage ever-more-powerful network effects.
4. Access and governance  An open architecture allows players to access platform resources,
such as app developer tools, and create new sources of value. Open governance allows
players other than the owner to shape the rules of trade and reward sharing on the platform.
5. Metrics  Monitoring and boosting the performance of core interactions becomes critical.
Here are new metrics managers need to track:
Interaction failure
Engagement
Match quality (Poor matches between user and producer needs weaken network effects)
Negative network effects
McIntyre, D. P., & Srinivasan, A. - Networks, platforms, and strategy: Emerging views and next
steps. P141-144
Aim of the article: It reviews current perspectives on network effects and the emergence of
platforms, and offers several areas of future consideration for optimal strategies in these settings.
This article makes a distinction between direct and indirect network effects.
Direct network effects (via a large number of users with whom to interact) and indirect network
effects (via the availability and variety of complements) can foster the emergence and persistence of
dominant platforms, and thus, strong competitive positions for their sponsoring firms.
Direct network effects  when the benefit of network participation to a user depends on the
number of other network users with whom they can interact.
Indirect network effects  different “sides” of a network can mutually benefit from the size and
characteristics of the other side
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The Industrial Organization perspective (market dynamics):
- Platforms are interfaces that serve to mediate between two or more parties
- Dominant platforms will attract more complementors and users (winner takes it all)
Limitations:
- Winner takes it al and positive feedback are exogenous ( Exogeen  een actie of een schok die van buiten het
systeem komt) and constant factors in an industry
- Network effects are dichotomous (they are present or absent)
- The relationship between complementors and firms is a black box (this excludes strategic
positioning)
The strategic perspective (firm dynamics):
- Expectations of a platforms growth can influence users’ adoption choices (entry timing decisions)
- Product quality can influence success
Limitations
- The impact of firm level strategies such as entry timing or platform quality remain unresolved
- Primary focus of study was only on users
- No focus on the dynamic evolvement of platform complementor interactions over time
The integrative perspective (technology management):
- Platforms are building blocks that serve as the foundation on which others can build related goods
- Platforms attract developers not just by superior quality but also by providing good toolkits
Limitations
- Lack of empirical studies about how design decisions impact complementor choices
- There is little understanding of platform dynamics
Practice question discussed in class
Based on your readings about cloud computing and digital platforms, argue how the economics of
cloud computing have helped the rise of platform business models.
You have to recall the paper, and have you seen anything in the papers that match?
The two main ones on platforms. You have to see the common things between the papers.
If you look at the characteristics of the platforms:
Scaling, rapid scaling to match demand. You need this for platforms.
Once your ?hetreat? goes up, you want to scale up your service provider. And that is a great match
for an environment where you can rapidly grab new resources with linear costs. The scalability of the
cloud matches the rapid scaling needs of the platforms.
Of the high ability that is needed to ….. with failure???
You moving from ownership from recources in the pipeline economy to the orchestration of different
resources that are not nesseceraly inside the organisanation. Again the reason of scalability but also
the lineair costs it is usefull to run the platform on the cloud. There are also no entry barriers in the
cloud economy which means that the new start ups that want to build platforms have less… st
advantages strategically speaking together.
You have to write down which arguments match and why that is the case.
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Week 3
Literature lecture 3: The Nature of Decision Making
Core Paper
Hatch, M. J. (1997). Organization Theory (pp. 269–281). Oxford University Press.
(Chapter 9 – Organizational Decision Making)
Discussion Papers
Cloud Computing
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., … Zaharia, M.
(2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
Big Data
McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard
Business Review, 90(10), 60–6, 68, 128.
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and DataDriven Decision Making. Big Data, 1(1), 51–59.
Algorithms
Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538, 20–23.
Luca, M., Kleinberg, J., & Mullainathan, S. (2016). Algorithms need managers, too.
Harvard Business Review, 94(1), 96–101.
Core Paper
Hatch, M. J. - Organization Theory (pp. 269–281)
Aim of the article: To define decision making at the organizational level and consider challenges to
the rational model. Paper also describes 3 alternative decision-making models.
Decisions of all types and magnitude form and shape organizations. The rational decision-making
model is bounded. The decision making in the functional, hierarchical or divisional organization are
no longer applicable to networks, joint ventures or strategic alliances.
Shortcomings of the rational model:
1. Incomplete and imperfect information of alternatives (organization theory perspective)
2. The complexity of problems (technical core problem)
3. Human information processing capacity (organization theory perspective)
4. Time available for decision making processes
5. Conflicting preferences of decision makers (according to this model everyone agrees about
organization goals) (Organization theory perspective)
Disagreement may involve ambiguity (what direction to take) and uncertainty (too little information)
De implicaties van bounded rationality is
1) Incomplete informatie en dus ->
uncertainty
2) understanding van conflicting goals.
There is also a dynamic view of decision
making:
(1) analyze few alternatives, and of these
it is best if only one has a good chance of
being accepted (proposing unacceptable
alternatives reinforces the decision to
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accept the preferred alternative with positive consequences for expectations of success, motivation,
and commitment);
(2) consider only the positive consequences of the favored alternative (this reduces doubt, bolsters
expectations of success, and creates enthusiasm and commitment to the alternative);
(3) avoid formulating objectives in advance; instead reformulate the predicted consequences of the
favored alternative as your decision-making criteria (this supports motivation and commitment to
the selected alternative).
Cloud Computing
Armbrust, M., et al. - A view of cloud computing
Aim of the paper: Reduce confusion by clarifying terms, providing simple figures to quantify
comparisons between of cloud and conventional computing, and identifying the top technical and
non-technical obstacles and opportunities of cloud computing.
Cloud computing refers to both the applications delivered as services over the Internet and the
hardware and systems software in the data centers that provide those services.
Cloud  The data center hardware and software
Public cloud  a cloud is made available in a pay-as-you-go manner to the general public,
Utility computing  the service being sold
Private cloud  internal data centers of a business or other organization, not made available to the
general public, when they are large enough to benefit from the advantages of cloud computing.
Vanuit het Hardware provisioning perspectief, biedt de cloud de volgende kenmerken in
tegenstelling tot classical computing:
1. Forecasting  door de appearance van ongelimiteerde capaciteit en daardoor de bijbehorende
mogelijkheid om op te schalen wanneer nodig is dwing het bedrijven om IT te gaan plannen
2. Geen vendor lock-in  geen dure contracten etc en daardoor een lager Capex risico
3. Pay-as-you-use  als een bedrijf behoefte heeft aan een VM + Storage dan kan dit on-demand
aangevraagd worden, is dit niet meer nodig dan kun je makkelijk de VM+Storage uitzetten ->
dynamische afname van IT-capaciteit = lagere kosten en duidelijke TCO
Elasticity  The key observation is that cloud computing’s ability to add or remove resources at
a fine grain (one server at a time with EC2) and with a lead time of minutes rather than weeks allows
matching resources to workload much more closely.
Utility computing vs conventional hosting
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Pay as you go  als bijvoorbeeld 1 keer per maand er intensief gebruik word gemaakt van een IS dan
kan Cloud Computing uitkomst bieden vanwege de schaalbaarheid. Zelfs als de kosten hoger van
Cloud Computing i.t.t. hosting dan kan Cloud nog steeds goedkoper zijn als je de kosten van
uitsmeert over het desbetreffende jaar.
Start-ups  als je bijvoorbeeld nog geen idee hebt wat je nodig hebt aan capaciteit dan kan cloud de
uitkomst bieden door de scalability
Properties that give cloud computing its appeal: short-term usage (which implies scaling down as well
as up when demand drops), no upfront cost, and infinite capacity on demand.
Big Data
McAfee, A., & Brynjolfsson, E - Big data: the management revolution
Aim of the paper: Measuring big data and decision making with big data.
Three key differences between analytics and big data:
1. Volume
2. Velocity (real time information)
3. Variety (Unstructured date)
New culture of decision making:
The managerial challenges using big data are:
1. Muting the Higher Paid Person Optionions (HiPPOs). Often these people make important
decisions. Now: rely on data not intuition.
2. New roles. Executives must ask the right questions like ‘where did the data come from’, etc.
Management challenges
If change is not managed effectively, companies will not get full benefits of big data.
1. Leadership  for goals, definition of success and asking the right questions
2. Talent management
3. Technology  tools to handle volume, velocity, variety
4. Decision making  maximize cross functional cooperation
5. Company culture  What do we know?
Provost, F., & Fawcett, T. - Data Science and its Relationship to Big Data and Data-Driven Decision
Making
Aim of article: They explain what data science is, explain the related concepts and identify the
fundamental principles. They present a perspective on al these things. The ultimate goal of data
science is improving decision making, as this generally is of paramount interest to business.
Data science  a set of fundamental principles that support and guide the principled extraction of
information and knowledge from data.
Data mining  the actual extraction of knowledge from data, via technologies that incorporate these
principles.
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THE ELEMENTS OF DATA-DRIVEN DECISION MAKING
▪ Cloud Computing: The economies and the scalability of computing resources that enables DDD
▪ Big Data: DDD relies on unprecedented amounts of data being generated, captured, stored,
retrieved and analysed
▪ Algorithms: DDD relies on advanced machine learning, deep learning, and more generally AI
algorithms to generate insight from the data
Data driven decision making
Data-driven decision making (DDD)  The practice of basing decisions on the analysis of data, rather
than purely on intuition. More data-driven firms are more productive.
There are 2 kind of decisions that can be made with data.
1) decisions for which “discoveries” need to be made within data
2) decisions that repeat, especially at massive scale, so decision making can benefit from even small
increases in decision-making accuracy based on data analysis.
One of the most critical aspects of data science is the support of data-analytic thinking. Investments
in analytics can be useless, even harmful, unless employees can incorporate that data into Data
Science and Big Data
Fundamental concepts of data science are described in chapter 8.
Discussed in class:
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Algorithms
Castelvecchi, D. (2016) - Can we open the black box of AI
Aim of the article: In this article is explained why machine learning is a black box. AI must explain why
certain decisions or conclusions are made. If we understand how machines make decisions, it is
possible to apply algorithms more widely
Luca, M., Kleinberg, J., & Mullainathan, S. - Algorithms need managers, too.
Aim of the article: They explain what algorithms do well to avoid missteps and how to manage them
better.
Algorithms make predictions more accurate—but they also create risks of their own, especially if we
do not understand them.
Algorithms are extremely literal  It does exactly what it’s told—and ignores every other
consideration. We get into trouble when we don’t manage algorithms carefully.
Algorithms are black boxes  algorithms often can predict the future with great accuracy but tell you
neither what will cause an event nor why.
Managing them better:
1. Be Explicit About All Your Goals  Algorithms will pursue a specified objective single
mindedly. The best way to mitigate this is to be crystal clear about everything you want to
achieve.
2. Minimize Myopia (bijziendheid)  They focus on the data at hand—and that data often
pertains to short-term outcomes. There can be a tension between short-term success and
long-term profits and broader corporate goals. Humans implicitly understand this; algorithms
don’t unless you tell them to.
3. Choose the right data inputs  Wider is better, diversity matters
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Week 4
Literature lecture 4: Data-Driven Business Model Innovation
Core Paper
Woerner, S. L., & Wixom, B. H. (2015). Big data: extending the business strategy
toolbox. Journal of Information Technology, 30(1), 60-62.
Discussion Papers
Mastering Data Projects
Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting
data warehousing success. MIS quarterly, 17-41.
Bell, P. C. (2015). Sustaining an analytics advantage. MIT Sloan Management Review,
56(3), 21.
Data-Driven Organizations
Kiron, D. (2017). Lessons from Becoming a Data-Driven Organization. MIT Sloan
Management Review, 58(2).
Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the
changing context of strategy. Journal of Information Technology, 30(1), 44-57.
Data-Driven Business Model Innovation
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2014). Big data for big business?
A taxonomy of data-driven business models used by start-up firms. A Taxonomy of
Data-Driven Business Models Used by Start-Up Firms.
Core Paper
Woerner, S. L., & Wixom, B. - Big data: extending the business strategy toolbox.
Aim of the paper: How is big data used to craft a strategy and business model.
Key takes:
Big data to generate revenue:
1. By monetizing data
2. By digital transformation
This is a counter opinion to Constantinou that big data offers exiting opportunities to leverage a
company’s toolbox. Constantinou believes that big data is an impediment (belemmering) to strategy.
They only describe the challenges to big data.
Woerner & Wixom (2015) stellen dat big data strategische kansen biedt om de business strategy
toolbox uit te breiden, m.b.v.:
Nieuwe data;
Nieuwe inzichten;
Nieuwe acties.
Innoveren van het business model door middel van big data:
1. Monetization  het uitwisselen van informatie gebaseerde producten en services in ruil voor
wettelijke betaalmiddelen. Hoe: een aparte BU opgezet om de vereiste technische en
zakelijke competenties op te bouwen die past bij de scope van de monetization business,
zoals datamanagement, databeveiliging en compliance, data science. Data monetization
door data of inzichten te verkopen, ruilen, of door het versterken van klantervaringen door
nieuwe waardeproposities. Monetization kan door:
 Wrapping;
 Selling;
 Bartering.
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2. Digital transformation  Digitization: wanneer bedrijven digitalisatie gebruiken om in
compleet nieuwe industrieën te opereren of nieuwe te creëren.
Conclusie: “Wanneer beschreven wordt hoe bedrijven big data moeten gebruiken om bedrijfskeuzes
en activiteiten te verbeteren, kunnen beperkingen die samengaan met big data verholpen worden”:
Constantiou & Kallinikos gaat in op de beperkingen van big data op de strategie;
MIT Sloan Management gaat in op de vraag hoe bedrijven kunnen omgaan met big data.
Mastering Data Projects
Wixom, B. H., & Watson, H. J. - An empirical investigation of the factors affecting data
warehousing success.
Aim of the paper: describes the success factors of data warehousing implementation. They have
created a research model for data warehousing success. They say that IS implementation literature
also benefits from this research.
Warehouses differ in:
- organizational implementation success
- project implementation success
Implementation factors, such as management support and user participation, are proposed to
influence the success of the data warehouse implementation, which has been broken down into
three unique facets. These include success with organizational, project, and technical issues that
arise during the lifetime of the warehouse project. A datawarehouse support applications.
Information systems success
Supported - H1a: A high level of data quality is associated with a high level of perceived net benefits.
Supported - H1b: A high level of system quality is associated with a high level of perceived net benefits.
Implementation success
Three facets of warehousing implementation success were identified: success with organizational
issues, success with project issues, and success with technical issues.
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Not supported - H2a: A high level of organizational implementation success is associated with a high
level of data quality.
Supported - H2b: A high level of organizational implementation success i5 associated with a high level of
system quality.
Project implementation success
Success
with project issues can be measured by how well the team meets its critical time, budgetary, and
functional goals
Not supported - H3a: A high level of project implementation success is associated with a high level of
data quality.
Supported - H3b: A high level of project implementation success is associated with a high level of system
quality.
Technical implementation success
Not supported - H4a: A high level of technical implementation success is associated with a high level of
data quality.
Not supported - H4b: A high level of technical implementation success Is associated with a high level of
system quality.
Implementation factors
Seven implementation factors were included in the research model because of their potential
importance to data warehousing success: management support, champion, resources, user
participation, team skills, source systems, and development technology.
Management support
Supported - H5: A high level of management support is associated with a high level of organizational
implementation success.
Champion
Not supported - H6a: A strong champion presence is associated with a high level of organizational
implementation success.
Not supported - H6b: A strong champion presence is associated with a high level of project
implementation success.
Recourses, User participation, team skills and source systems also supports the implementation
process of some kind.
Bell, P. C. - Sustaining an analytics advantage.
Aim of the paper: Describes how to keep a sustaining advantage of analytics.
1. Keep your analytics secret
2. Implement the analytics fast and defeat the competitors
3. Apply the analytics to the right problems
4. Control of the data is sometimes more important than control of the analytics
5. Become an truly data-driven corporation
Data-Driven Organizations
Kiron, D. - Lessons from Becoming a Data-Driven Organization.
Aim of the article: Case study on how different companies gained an advantage of using/processing
or understanding their data. This article answers to the core paper.
Using digital technology to transform the organization along three crucial dimensions:
1. improving the customer experience;
2. overhauling operational processes;
3. designing and executing new, digitally powered business models.
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Also names Kaiser permentante, like Ross, as an example.
Het artikel is een antwoord op de conclusie van Woerner & Wixom (2015). Beide stellen:
Data maakt bedrijven meer productief en efficiënt en verandert de fundamentele Business Model;
Mensen in het bedrijf zijn minstens zo belangrijk als de technologie, hoe vertaal je analytics in
business actions?
Data kan bedrijven transformeren tot onderdeel van ecosystemen en verandert het traditionele
competitieve landschap.
Maar hoe dient men Big Data te gebruiken?
- Data governance 
- Maak iemand verantwoordelijk voor data governance: stel een data council aan.
Deze is onder andere verantwoordelijk voor het definiëren en verspreiden van een
algemene vocabulaire rondom data, specificeer wie toegang heeft tot de data, en
hoe en met wie de data gedeeld kan worden. Effectief informatiemanagement heeft
de interventie van mensen nodig begeleidt door regels, normen en cultuur. Het is
een lastig werkje en heeft een significante resources investering nodig.
- Data partnerships: data en analytics behoeven steeds meer samenwerking binnen en
buiten organisaties om het volle potentieel ervan te kunnen benutten. In plaats van
ieder voor zich werken bedrijven nu samen, ondersteund door de data, en dit creëert
waarde voor beide partijen. Vraag die hierbij opspeelt is wel: wie is de dataeigenaar,
wie krijgt toegang tot de data en hoe zit het met datarechten.
- Gebruik data als core asset, leiders die deze blik hebben, ondersteunen het
systematische gebuikt van data in besluitvorming en strategie. Ze gebruiken data om
resources te alloceren en nieuwe producten en BM s te creëren. Data kan
veranderen wat een bedrijf verkoopt.
- Commercieel potentie  Schaal je van binnenuit of van buiten op. Van binnenuit
vraagt om grote investeringen, het laatste vervaagt de grenzen van de organisatie,
maar deze partnerships versterken wel de performance en finetunen de strategie.
- Creëer information sharing partnerships
- Gebruik data als core asset
- Ontdekken van het commerciële potentieel van de data en innoveer het BM
- Geef formele trainingen en verander de mindsets.
Echter, blijft hier onbeantwoord hoe men de beperkingen die Big Data heeft op bestaande besluit- en
strategievormingssystemen kan aanpakken. Dit artikel gaat in op de menselijke kant van de vraag
hoe?
Constantiou, I. D., & Kallinikos, J. - New games, new rules: big data and the changing context of
strategy
Aim of the article: they have theories  The internal view and positioning school.
The role of information in strategy making
1. The internal view  Ownership of a superior resource is considered a critical source of
competitive advantage (Barney, 1986). In this respect, the internal view of the firm is
relational. While predominantly focusing on the internal relations of the firm, this view still
draws on key assumptions of theories of industrial organization, highlighting the importance
of the firm owning heterogeneous resources vis-à-vis its competitors. Know-how and other
types of idiosyncratic tacit knowledge or longformed IT capabilities (Lim et al., 2011) are
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typical examples of resources that can create and sustain competitive advantage in this
context.
The internal view underlines the need to collect data on business activities and performance
indicators in order to identify reliable signals of efficient processes and mechanisms. Internal
data is combined with external data from the environment, enabling the firm to identify new
business opportunities and exploit its superior resources. Many firms have historically
created business intelligence units to collect data and calculate departmental performance.
1. The positioning school  The positioning school has had strong influence on strategy making
since it provides its philosophical outlook, logical justification and the guidelines for data
collection and use (Gavetti and Rivkin, 2007). A number of strategic tools, still used
frequently and offering more detailed analysis of the industry, have been developed.
Examples include competitor analysis.
Dynamic capabilities  cognitive skills and organizational processes, which are very difficult to
imitate. Dynamic capabilities can be described as the capacity:
(1) to sense and shape opportunities and threats
(2) to seize opportunities
(3) to maintain competitiveness through enhancing, combining, protecting, and, when necessary,
reconfiguring the business enterprise’s intangible and tangible assets’. Information flows from both
internal and external sources play an important role in dynamic capabilities and their strategic use.
Daar waar Woerner & Wixom (2015) beschrijven wat big data kan toevoegen aan strategische
beslissingen, stellen Constantiou & Kallinikos dat de aard van big data het vermogen om inzicht te
verkrijgen beïnvloedt, en daardoor strategie creatie remt.
Constantiou & Kallinikos stellen dat de volgende kenmerken van big data ervoor zorgen dat zij niet
bruikbaar zijn voor strategische beslissingen:
Standaard Strategie Context
- Relatief homogeen
- Gestructureerd
- Doelgericht
- Alfanumeriek
- Deductief, top-down
- Lage termijn horizon
- Voorspellend
Big data - digitaal ecosysteem
- Heterogeen
- Ongestructureerd
- Willekeurig
- Semiotisch (tekst, plaatjes, geluid)
- Inductief, bottom-up
- Korte termijn horizon
- Verklarend (“Nowcasting”)
Big Data is overigens wel bruikbaar voor strategische beslissingen, zij stellen echter dat de bestaande
voorspellende strategische tools en modellen niet geschikt zijn voor de verwerking van deze data. De
genoemde kenmerken zorgen dat die conservatieve oude modellen dit niet aan kunnen.
De focus op real-time data (kenmerk big data) ondermijnt de lange termijn planning en vraagt om
een herziening van de trade-offs tussen kortetermijn en langetermijn beslissingen. (dit kan je
additioneel in de presentatie erbij zeggen)>
Als organisaties gebruik willen maken van big data, stellen Constantiou & Kallinikos, dan staan zij
voor de volgende uitdaging:
Top-down vs bottom up beslissingsproces
Organisaties moeten de details uitwerken en vaardigheden, ervaring en mindsets ontwikkelen
om big data te kunnen gebruiken.
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-
Het genereren van data vindt traditioneel top down plaats op een deductieve manier, waar
bij de uitgangspunten en omstandigheden door de organisatie zelf worden bepaald.
Big data wordt gegenereerd door users en niet door de organisaties zelf. Organisaties
moeten de details uitwerken en vaardigheden, ervaring en mindsets ontwikkelen om big data
te kunnen gebruiken.
Lange termijn vs korte termijn perioden
Het is echter lastig om op basis van deze data toekomstige trends te voorspellen en lange
termijnplanningen te maken (strategisch).
- De voortdurende actualisatie van big data is bruikbaar voor organisaties die zich constant aan
moeten passen aan trends in de markt en gewoonten van consumenten (operationeel,
tactisch). Het is echter lastig om op basis van deze data toekomstige trends te voorspellen en
lange termijnplanningen te maken (strategisch).
Veranderende basisomstandigheden
Bestaande strategiemodellen zijn niet ingericht om de trends van big data te interpreteren.
- Organisaties hebben een routine en competenties ontwikkeld om op basis van
waarneembare patronen zich aan te passen aan omgevingsfactoren. Davenport (2014) stelt
dat organisaties de omgeving beter kunnen begrijpen door big data. Bestaande
strategiemodellen zijn niet ingericht om de trends van big data te interpreteren.
- George et al. (2014) stelt dat bij de analyse van big data gekeken kan worden naar Outliers
om toekomstige trends te ontdekken. Door de omvang van big data is de omvang van deze
Outliers vaak ook groot en hierdoor een goede basis. Constantiou & Kallinikos stellen echter
dat het ongestructureerde karakter van big data er voor zorgt dat deze Outliers niet
betrouwbaar genoeg zijn om conclusies aan te verbinden.
Constantiou & Kallinikos stellen dat het ongestructureerde karakter van big data maakt dat
deze niet betrouwbaar genoeg is om voorspellingen te doen op basis van Outliers zoals
George et al. (2014) voorstellen.
Data-Driven Business Model Innovation
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A - Big data for big business? A taxonomy of
data-driven business models used by start-up firms
Aim of the paper: They have identified business models that can serve as an inspiration or blueprint
for companies that want to create a new data driven business model. Building on the motivation and
literature overview, they aim to contribute to answering the overarching research question:
What types of business model are present among companies relying on data as a resource of major
importance for their business (key resource)?
This paper contributes by providing a definition of a data-driven business model as a business model
that relies on data as a key resource. This definition has three implications:
- First, a data-driven business model is not limited to companies conducting analytics, but also
includes companies that are ‘merely’ aggregating or collecting data.
- Second, a company may sell not just data or information, but also any other product or service that
relies on data as a key resource.
- Third, it is obvious that any company uses data in some way to conduct business – even a small
restaurant relies on the contact details of its suppliers and uses a reservation book.
Based on this review, the DDBM framework consists of six dimensions common to most of
the business model frameworks, namely:
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1. Key resources  the focus of the key resource was on what kind of data is used by the
specific company.
Internal sources include data that already exists in, or is currently created through, existing IT
systems but which is not used (e.g. ERP, CRM data), and data generated for the specific
purpose, either through Web tracking or any other physical tracking device, sensor or
crowdsourcing; and data that is created through contribution by a broad, distributed set of
contributors using the Web and social collaboration techniques
External data comprises acquired data, which is commercially available and can be purchased
from data providers, social media companies, and so on; data that is provided by customers
and/or business partners and not available to the general public; and freely available data,
which is publicly available with no direct costs. Freely available data can be further
subdivided into three categories: open data, which is released, by definition, by ‘open data’,
is free, downloadable, machine readable, and structured without prior processing
2. Key activities  Otto and Aier (2013) identified several key activities performed by the
examined companies, including retrieving data, data mining and distribution thereof.
Analytics activities can be further subdivided into three main categories: descriptive,
predictive and prescriptive analytics. Descriptive analytics includes business reporting and
answers the question, ‘What happened and/or what is happening?’ Predictive analytics
concerns the use of machine learning techniques and mathematical models to predict the
future outcome given the existing data inputs. Prescriptive analytics seeks to determine the
optimal decision given a complex set of objectives, requirements and constraints with the
goal of improving business performance (Delen and Demirkan, 2013)
3. Value proposition  the offering of a company can be divided into two categories: data and
information/knowledge
4. Customer segment  B2b/B2c
5. Revenue model  asset sale, giving away the ownership rights of a good or service in
exchange for money; lending/renting/leasing, temporarily granting someone the exclusive
right to use an asset for a defined period of time; licensing, granting permission to use a
protected intellectual property
6. Cost structure
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Type A: ‘Free data collector and aggregator’ Companies of this cluster create value by collecting and
aggregating data from a vast number of different, mostly free, available data sources. Subsequently,
the other distinctive key activity is data distribution
Type B: ‘Analytics-as-a-service’ The second cluster comprises companies providing analytics as a
service. These companies are characterised by conducting analytics (100%) on data provided by their
customers (100%)
Type C: ‘Data generation and analysis’ Companies in this cluster all share the common characteristic
that they generate data themselves rather than relying on existing data. Subsequently, all companies
in this cluster share the key activity ‘data generation’. Besides generating data, most of the
companies also perform analytics on this data.
Type D: ‘Free data knowledge discovery’ The companies in this cluster are characterised by the use of
free available data and analytics performed on this data. Furthermore, as not all free data sources
are available in a machine-readable format, some such companies crawl data from the Web (data
generation 50%).
Type E: ‘Data-aggregation-as-a-service‘ Companies in this cluster create value neither by analysing
nor creating data but through aggregating data from multiple internal sources for their customers.
This cluster can therefore be labelled ‘aggregation-as-a-service’. After aggregating the data, the
companies provide the data through various interfaces (distribution: 83%) and/or visualise it (33%).
Type F: ‘Multi-source data mash-up and analysis’ Cluster F contains companies that aggregate data
provided by their customers with other external, mostly free, available data sources, and perform
analytics on this data. The offering of companies in this cluster is characterised by using other
external data sources to enrich or benchmark customer data.
Week 5
Literature lecture 5: The Trust Business
Core Paper
Khodyakov, D. (2007). Trust as a process: A three-dimensional approach. Sociology,
41(1), 115–132.
Discussion Papers
Gig Economy
Sundararajan, A. (2016). The Sharing Economy (pp. 1–19). Cambridge, MA: MIT Press.
Blockchain
Evans, P. (2016). Thinking Outside The Blocks: A Strategic Perspective on Blockchain
and Digital Tokens (BCG Perspectives). The Boston Consulting Group.
Digital Trust
Mazzella, F., Sundararajan, A., Butt d’Espous, V., & Möhlmann, M. (2016). How Digital
Trust Powers the Sharing Economy: The Digitization of Trust. IESE Insight, (30), 24–31.
The great chain of being sure about things. The Economist (2015, October 31).
Core Paper
Khodyakov, D. - Trust as a process: A three-dimensional approach.
Aim of the article:
Wat is vertrouwen? Is het een variabele of een process? Is vertrouwen in mensen hetzelfde als vertrouwen in
instituten? Dit zijn drie vragen die in dit artikel behandeld worden. Khodyakov stelt dat trust een complex en
multi-dimensioneel fenomeen is, welke bestaat uit een mix van vertrouwen in sterke en zwakke banden, en
instituties. Hij gebruikt de Sovjet Union als een voorbeeld. Hij vindt dat strenge onderscheiding in social capital
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theory tussen high- en low trust samenlevingen geen rekening houdt met de complexiteit van trust. Hij ziet
vertrouwen als een proces.
Weaknesses van de ‘one dimensional approach to trust’:
1. It views social capital as ‘features of social organization, such as trust, norms and networks that can improve
the efficiency of society by facilitating coordinated actions’
2. Institutional or public trust is seen as being superior to interpersonal or private trust and is used as a
criterion to judge the extent of a society’s modernization.
3. Fukuyama treats trust in people as being a necessary factor for the development of trust in institutions: if
there is no interpersonal trust, instritutional trust is impossible.
4. Fukuyama’s approach is not able to adequately characterize a society with high levels of interpersonal and
low levels of institutional trust. It is impossible to find a place for such societies on the low-trust – high-trust
axis.
Uit een eerdere analyse bleek dat er scheiding nodig is tussen institutional and interpersonal trust en tevens
tussen trust in strong ties en trust in weak ties. Khodyakov presenteert een drie-dimensionale kijk op trust.
Welke vertrouwen in strong (thick interpersonal trust) weak (thin-interpersonal trust) en instituties
(institutional trust) onderscheidt.
-
-
Thick interpersonal trust: vertrouwen dat mensen hebben in familieleden of bijv. hele goede
vrienden.
Thin interpersonal trust: vertrouwen tussen mensen die elkaar niet zo goed kennen, bijv. met
je projectleden. Je weet hier niet precies wat de intenties zijn van de andere persoon,
daardoor is thin interpersonal trust meer risicovol.
Trust in institutions (political trust/ system trust): very different from trust in people, because
the former may presuppose no ‘encounters (ontmoetingen) at all with the individuals or
groups who are in some way ‘responsible’ for them.
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Theoretisch laat de analyse zien dat het grote verschillen tussen interpersonal trust and institutional trust ligt in
het type maatschappelijke ruil. In low-trust societies vertrouwen mensen elkaar alleen als ze ‘similar’ zijn.
In high-trust societies vertrouwen mensen elkaar in publiekelijke sfeer.
Gig Economy
Sundararajan, A. - The Sharing Economy
Aim of the article: This article is about the sharing economy and whether this is new or not.
The gig economy is not a gift economy but it is mediated by money. The sharing economy is not new,
it is just an improved form of something familiar. The ‘new’ thing is that it is powered by technology
that extend the economic ‘community’.
Crowdbased capitalism could radically transform what it means to have a job. Our regulatory
landscape will be reshaped. Our social safety net, often funded by corporate employment, will be
challenged. The way we finance, produce, distribute, and consume goods, services, and urban
infrastructures will evolve.
New ways of organizing economic activity will redefine whom we trust, why we trust them, what
shapes access to opportunity, and how close we feel to each other.
In the introduction there are all examples of sharing companies, that struggle with kind of the same
things: Getting people to trust.
Blockchain
Evans, P. - Thinking Outside The Blocks: A Strategic Perspective on Blockchain and Digital Tokens
Aim of the essay: Outlines how the economics of transaction costs and trust can be reshaped by
tokens and blockchain and the stacked architecture on which they are built.
Bitcoin  A digital bearer instrument: Ownership and control are the same thing. The underlying
technologies are a token and blockchain. A blockchain can serve as a shared, secure, irrevocable and
trusted ledger for any kind of transaction.
Blockchain and digital tokens deliberately waste storage, which is cheap, to create something new
which is valuable. Blockchain is the disruptive technology for storage, now that the costs of storage
are at a freefall.
Digital tokens waste massive storage
to create virtual continuity.
Continuity permits property, because
a continuously identified thing can
be owned by a continuously
identified person. Therefore, it
permits transactions (transfer of
property) and trust.
The structure of bitcoin guarantees
ancestry. The coin of earlier
transaction X is the only parent of
transaction Y. The coin cannot be
spent twice.
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Bitcoin has stacked architecture. This is a set of interoperable modules arranged in a hierarchy.
Upper level functions depend on lower level functions, but not reverse.
The blockchain is divided in blocks and replicated in nodes. Nodes are racks of dedicated computer
services, operated in datacentres owned by mining pools. The protocol is bitcoins operating system
which is open source. The next layer are the tokens, minted by miners.
Trust
Brokers can trade on a large blockchain to disintermediate the custodians and reduce the transaction
costs. These ambitions are limited because of peripheral trust that is hard to achieve between small
brokers. This is the trade off between trust transaction costs and peripheral trust.
Scalability
Bitcoin has huge problems in scalability. Faster block creation could destabilize validation.
Digital Trust
Mazzella, F., Sundararajan, A., Butt d’Espous, V., & Möhlmann, M. - How Digital Trust Powers the
Sharing Economy
Dit artikel gaat over de invoeren van de zogenaamde ‘trust-age’. Het opbouwen van vertrouwen is
afhankelijk van verschillende dimensies. Wanneer er geen sprake is van face-to-face contact zijn
onderstaande drie aspecten belangrijk:
1. Vaststellen van de echtheid. Is dit een echt persoon? Is de persoon wie hij zegt dat hij is?
2. Beoordelen van de intenties. Hebben ze goede bedoelingen of zijn ze uit op je te beroven?
3. Beoordelen van expertise en/of kwaliteit. Is deze persoon een goede loodgieter?
Kortom, vertrouwen is de sprong naar geloof die zonder weinig menselijke samenwerking kan
bestaan
In dit artikel worden de oude ‘spelregels’ van trust besproken en gaat men vervolgens in op nieuwe
‘spelregels’ van trust. Met de oude spelregels bedoelen zij dat er voorheen onder andere vertrouwen
werd opgebouwd middels het opstellen van bijvoorbeeld contracten. Een contract opmaken is duur,
ook als het een smart blockchain-based contract is, maar ook het inhuren van een advocaat is duur.
Middels het nieuwe model wordt online trust opgebouwd met behulp van het D.R.E.A.M.S.
framework. Onderzoek laat zien dat met de juiste digitale tools en met behulp van dit framework,
individuen hoge levels van trust kunnen bereiken zonder een persoon daadwerkelijk te ontmoeten.
Hieronder is een voorbeeld weergegeven van de toepassing van het D.R.E.A.M.S. model op Blabla Car
waarin voor elk aspect een voorbeeld wordt gegeven.
Voor een uitgebreide toelichting op
de zes pilaren welke onderdeel van
het DREAMS framwork zijn, verwijs
ik naar pagina 27 van het volledige
artikel.
Kortom we bewegen van een ‘oneto-many’ configuratie naar een
wereld waar vertrouwde interacties
kunnen ontstaan op een ‘many-to-
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many‘ basis. Zoals inter-personal trust wordt omgezet in een overvloedige bron, worden de
individuen eigenlijk hun eigen merk.
Ze maken profielen aan en stapelen feedback op van meerdere eenmalige interacties. Wanneer zij
deze samenstellen, samenvatten, en zichtbaar maken voor andere, wordt deze ‘historie’ deel van
hun ‘trust capital’.
Op dit moment kan je je opgebouwde reputatie via eBay nog niet gebruiken of overdragen aan bijv.
Airbnb. Je zal dan weer onderaan moeten beginnen met het opbouwen van je reputatie. De
schrijvers van dit artikel hopen dat dit naar aanleiding van hun artikel zal veranderen.
As discussed, trust is built by verifying identities, intentions and capabilities. More specifically, in
semi-anonymous internet-based peer-to-peer settings, trust stems from at least six cues:
 From one’s own prior interaction;
 Through familiarity that comes from the nature of exchange being part of the “cultural dialogue”;
 By learning from the explicit experiences of others;
 Through brand certification;
 By relying on digitized social capital;
 By relying on digitized forms of real-world identity, and more generally, validation from external
institutions or entities, government and non-government, digital and otherwise.
The great chain of being sure about things - The Economist (2015, October 31).
De cryptografische technologie die ten grondslag ligt aan de bitcoin genaamd de ‘blockchain’ kent
veel meer toepassingen dan contant geld en valuta. Het biedt een manier voor mensen die elkaar
niet vertrouwen om vast te leggen wat men nog aan elkaar schuldig is. Het kan gezien worden als
een afhankelijk grootboek.
Blockchain kan als vervanging dienen van de voorheen vertrouwde derde partijen, banken.
Problemen van banken zijn niet uniek. Niet-compatibele databases en hoge transactiekosten. Dit zou
worden opgelost door blockchain. De waarde van de bitcoin is onstabiel en onvoorspelbaar, het
totale bedrag in circulatie is opzettelijk beperkt, maar het blockchain mechanisme werkt goed. De
meeste data in blockchain zijn bitcoins, maar dit hoeft niet zo te zijn. Het is een open platform, een
gedistribueerd systeem welke openstaat voor verder onderzoek en uitwerking.
Op dit moment valt het op aanbod van blockchain te splitsen in drie ‘buckets’:
1. The first takes advantage of the fact that any type of asset can be transferred using the
blockchain (dus elk type activum kan worden overgedragen met behulp van de blockchain)
2. Protecting land titles is an example of the second bucket: applications that use the
blockchain as a truth machine (hiermee wordt gebruiksrecht/eigendomsrecht bedoeld,
toepassingen die gebruikmaken van de blockchain als een machine van de waarheid. Bitcoin
transacties kunnen namelijk worden gecombineerd met fragmenten van aanvullende
informatie, die vervolgens ook ingebed worden in het grootboek.
3. It is the third bucket that contains the most ambitious applications: Smart contracts: that
execute themselves automatically under the right circumstances (smart contracten zijn
contracten die zichzelf automatisch uitvoeren onder de juiste omstandigheden).
Een mogelijk effecten van blockchain technologie op the ‘internet of things’ kan zijn dat
blockchain technologie ook toegepast gaat worden op verschillende devices (bijv. zelfsturende
auto’s of koelkasten). Denk aan het voorbeeld dat in de les is besproken over de koelkast. Er kan
worden bijgehouden of er nog melk aanwezig is, maar er kunnen naar aanleiding daarvan bijv.
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ook direct bestellingen geplaatst worden bij de supermarkt. Dit kan via blockchain gerealiseerd
worden.
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