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Summary mandatory literature MDI ()
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These are the summaries of all the mandatory literature/articles for the course Management of Digital Innovation (MDI) for
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Summaries mandatory literature MDI (2019-2020)NATURE OF DIGITAL TECHNOLOGIES
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital Innovation Management:
Reinventing Innovation Management Research in a Digital World. MIS Quarterly, 41(1), 223-
238.
Yoo, Y., Boland Jr, R.J., Lyytinen, K. and Majchrzak, A., 2012. Organizing for innovation in the
digitized world. Organization Science, 23(5), 1398-1408.
DIGITAL PLATFORMS
Edelman, B. (2015). How to launch your digital platform. Harvard Business Review, 93(4), 90-
97.
Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an
integrative framework. Research Policy, 43(7), 1239–1249.
Hilbolling, S., Berends, H., Deken, F., & Tuertscher, P. (2019). Complementors as connectors:
managing open innovation around digital product platforms. R&D Management.
https://doi.org/10.1111/radm.12371
DISRUPTIVE INNOVATION
Ansari, S. S., Garud, R., & Kumaraswamy, A. (2016). The disruptor's dilemma: TiVo and the US
television ecosystem. Strategic Management Journal, 37(9), 1829-1853.
Christensen, C. M., McDonald, R., Altman, E. J., & Palmer, J. E. (2018). Disruptive innovation:
An intellectual history and directions for future research. Journal of Management Studies.
https://doi.org/10.1111/joms.12349
Klenner, P., Hüsig, S., & Dowling, M. (2013). Ex-ante evaluation of disruptive susceptibility in
established value networks—When are markets ready for disruptive innovations? Research
Policy, 42(4), 914-927.
INDUSTRY LEVEL DYNAMICS
Adner, R., & Kapoor, R. (2016). Innovation ecosystems and the pace of substitution: Re-
examining technology S-curves. Strategic Management Journal, 37(4), 625–648).
Ansari, S., & Garud, R. (2009). Inter-generational transitions in socio-technical systems: The
case of mobile communications. Research Policy, 38 (2), 382-392.
Lee, J., & Berente, N. (2013). The era of incremental change in the technology innovation life
cycle: An analysis of the automotive emission control industry. Research Policy, 42(8), 1469-
1481.
DIGITAL INNOVATION ECOSYSTEMS
Boudreau, K., & Lakhani, K. (2009). How to manage outside innovation. MIT Sloan
management review, 50(4), 69.
Kyriakou, H., Nickerson, J., and Sabnis, G. (2017). Knowledge Reuse for Customization:
Metamodels in an Open Design Community for 3D Printing. MIS Quarterly, 41(1), 315-332.
Lifshitz-Assaf, H. (2017). Dismantling Knowledge Boundaries at NASA: The Critical Role of
Professional Identity in Open Innovation. Administrative Science Quarterly,
0001839217747876.
,INNOVATION STRATEGY
Wiltbank, R., Dew, N., Read, S., & Sarasvathy, S. D. (2006). What to do next? The case for non-
predictive strategy. Strategic Management Journal, 27 (10), 981–998.
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.
Reymen, I. M. M. J., Andries, P., Berends, H., Mauer, R., Stephan, U., & Burg, E. (2015).
Understanding Dynamics of Strategic Decision Making in Venture Creation: A Process Study of
Effectuation and Causation. Strategic Entrepreneurship Journal, 9(4), 351-379.
INNOVATION PROCESSES
McGrath, R. G., & MacMillan, I. C. (1995). Discovery-driven planning. Harvard Business Review,
73(4), 44-52.
Cooper, R. G., & Sommer, A. F. (2016). The Agile-Stage-Gate Hybrid Model: A Promising New
Approach and a New Research Opportunity. Journal of Product Innovation Management,
33(5), 513–526.
AGILE & DESIGN APPROACHES
Blank, S. (2013). Why the lean start-up changes everything. Harvard Business Review, 91(5),
63-72.
Liedtka, J. (2014). Perspective: Linking Design Thinking with Innovation Outcomes through
Cognitive Bias Reduction. Journal of Product Innovation Management, 32(6), 925–938.
DIFFUSION OF DIGITAL INNOVATION
Hsu, C.-L., & Lin, J. C.-C. (2016). An empirical examination of consumer adoption of Internet of
Things services: Network externalities and concern for information privacy perspectives.
Computers in Human Behavior, 62, 516–527.
Steiner, M., Wiegand, N., Eggert, A., & Backhaus, K. (2016). Platform adoption in system
markets: The roles of preference heterogeneity and consumer expectations. International
Journal of Research in Marketing, 33(2), 276–296.
, Digital Innovation Management
Nambisan et al. (2017)
Digital innovation: the use of digital technology during the process of innovating. Digital innovation is
the creation of (and consequent change in) market offerings, business processes or models that results
from the use of digital technologies.
Digital innovation management: the practices, processes and principles that underlie the effective
coordination of digital innovation.
The definition of digital innovation captures three important phenomena:
A range of innovation outcomes (new products, platforms, services, customers experiences);
Digital tools and infrastructure for making innovation possible (3D printing, data analytics,
mobile computing);
The possibility that the outcomes may be diffused, assimilated or adapted to specific use
contexts.
Challenging Key Assumptions of Innovation Management Theories
The digitalization of innovation challenges three key assumptions about innovations:
Innovation is a well-bounded phenomenon focussed on fixed problems;
o Outcomes: the scope, features and value of digital offerings can continue to evolve
after the innovation has been implemented. There is unpredictability with regard to
the boundaries on what is or is not an innovation outcome of the digital innovation;
o Processes: the digitization of innovation processes helps to break down the boundaries
between different innovation phases and brings a greater level of unpredictability and
overlap in their time horizons. It is difficult to say when a particular innovation process
starts and/or ends.
The nature of the innovation agency is centralized;
o With digital innovation, an innovation context appeared in which multiple actors with
diverse goals and motives engage in the innovation process (distributed innovation).
The actors involved in the innovation process can change throughout the process.
Innovation processes and outcomes are distinctly different phenomenon.
o With digitalization, dependencies between innovation processes and innovation
outcomes are complex and dynamic. Processes and outcomes of digital innovation are
influenced by each other. Changes in the innovation processes have implications for
innovation outcomes.
New logics of theorizing about digitization of innovation: a research agenda
In challenging the above mentioned assumptions, there is an opportunity for new theory building. To
start on this new theorizing, we offer four theoretical logics or conceptual elements:
Dynamic problem-solution design pairing: since digital innovation is unbounded, there should
be a focus on dynamic problem-solution design pairing. Digital innovation management should
be analysed as a parallel and heterogeneous generation, merging, termination and refinement
of problem-solution design pairs. Digital innovation involves the continuous matching of the
potential of new recombined digital technologies with original market offerings;
Socio-cognitive sensemaking: sensemaking of technology in (1) an individual innovator’s
cognition and (2) the innovator’s social system of organizations and individuals simultaneously.
Narratives are vehicles for such socio-cognitive sensemaking;
Technology affordances and constraints: digital technology use should be considered as sets
of affordances and constraints for innovating actors and should help explain how and why the
“same” technology can be repurposed by different actors or has different innovation
, outcomes in different contexts. An affordance is an action potential offered by the digital
technology with certain features and a users’ intent to which this technology is to be used
(how the actors’ goals can be related to the potential offered by the features);
Orchestration: orchestration means that one or more firms have the responsibility for
coordinating value cocreation and value appropriation. The role of digital technologies in
enabling and supporting such orchestrations should be increased so that problems and needs
can be better matched with potential solutions.
Innovate in methods to study innovation
Three methodologies that could potentially offer novel insights to the study of digital innovation:
Computational social sciences: a set of methodologies for exploring human behaviour
computationally (by computers). This would help to scale local analysis of the use of digital
technologies and innovation around them to broader contexts. Could be useful to study
solution-problem pairing and orchestration. Three forms of computational social science:
o Organizational genetics: the digital innovation is decomposed into actors, activities,
artefacts and affordances to identify routines and to determine how digital
technologies enable the creation, transformation and use of new technologies;
o Computational case study research: tries to explain how innovation in digital tools
changes the way in which engineers do their work;
o Process mining: this could be used to compare the workflows within innovation
projects to find differences that may explain who on the project is more successful
than others.
Configuration analysis: qualitative comparative analysis (QCA) compares combinations of
antecedents and outcome conditions to identify those that produce an outcome. The need for
this is created by identifying problem-solution pairs and technology affordance;
Complexity theory methods: central role of bottom-up emergence of self-organization.
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