Conference Agenda

21-PM3-07: G7-Agility, Ambidexterity and Innovation
Friday, 21/Jun/2019:
4:45pm - 6:15pm

Session Chair: Marine Hadengue, SKEMA
Location: Room 2.3.12 (ENSTA)


Determining degrees of complexity of different development situations within business processes in engineering sector

Jonas Heimicke, Gabriel Freire, Jan Breitschuh, Albert Albers

Karlsruhe Institute of Technology, Germany


Agile approaches increasingly find their way from software development to mechatronic system development. However, due to the nature of the product, a different constellation of requirements exists here, which leads to challenges. Assessing the complexity of the project and a suitable process adaption can support the applicability of agile approaches.


Agile processes support adapting the further course of action. By holding on to a stable goal, the path to reaching that goal may change due to e. g. new learnings or unexpected uncertainties (Bleek and Wolf, 2008). Accordingly, the context of product development is subject to a multitude of influencing factors which determine the degree of uncertainty and complexity in the project (Gericke et al., 2013). Purely agile approaches have already proven in literature and practice that they are suitable for coping with complex problems (Snowden and Boone 2007). However, in mechatronic system development projects there are still complicated and simple problems. The solution of these problems by means of purely agile approaches would be inefficient. (Albers et al. 2019) Due to that, it is important to adapt agile processes in order to provide releasable results despite dynamic changing requirements (Breitschuh et al., 2018).

Literature Gap

Complexity by its nature can only be determined by reflecting an already done task. It is unknown how to predict complexity. In order to adapt the project’s path prescriptively it is therefore of major interest to estimate the complexity of a task as accurately as possible in foresight.

Research Questions

In which ways do development teams estimate the complexity of the several situations during projects?

How can relevant key factors from the context of product development be linked to determine the degree of complexity within a development situation?


Based on a multi-dimensional construct of complexity, a quantitative survey instrument for determining more detailed characteristics of complex situations was developed. This instrument was used to gather empirical data on the characteristics of complexity in mechatronic product development. By mapping the high-level dimensions with the more detailed description of characteristics of complex situations, divergent and convergent validity of the study were investigated. Additional weighing factors to the characteristics were introduced to improve discriminatory power. The assumptions and predictions made this way regarding the degree of complexity were continuously evaluated in a real development environment of the automotive supplier industry.

Empirical Material

The assessment of requirements is carried out in the form of a quantitative questionnaire using Microsoft Forms (N = 21). The participants originate from the pre-development of the automotive service provider industry and belong to different departments. The questionnaire contains 13 questions with the aim of ascertaining the current status of the handling of complexity by development teams at different process levels during phase planning. In addition, it should be analyzed to what extent the aspect of complexity within various problems is taken into account in the process design by development teams. At the end of the questionnaire there is a qualitative question regarding problems in process design regarding the handling of complexity. This is used to gain further impetus for method development.


The quantitative study was able to prove that the majority of developers does not invest any effort in estimating the degree of complexity of problems in development. The process design is therefore based on specifications from superiors or experience and is not necessarily perceived as ideal. In order to satisfy this need identified in the research project, a tool was implemented by means of which project participants can estimate different characteristics of relevant project factors. Based on this assessment, a recommendation is made regarding the assessment of the respective degree of complexity within which the considered situation is located as well as a recommendation regarding the expected intensity by means of which activities in the project should be carried out in order to solve the respective problem. Furthermore, based on an analyze if the present team is well prepared to overcome the situation, a recommendation for a suitable competence profile of possible employees to support during the problem solving is made.

Contribution to Scholarship

The present contribution has broadened the state of research in the field of dealing with complexity in development. This construct, which has been predominantly theoretical and difficult to use in previous work, has been made manageable in this paper by assigning quantitative and qualitative factors. This contribution also serves to gain knowledge on the situation- and demand-oriented implementation of a suitable degree of flexibility and structure in agile processes of mechatronic system development. This lays the foundation for the development of a company- and project-specific systematic for the implementation of agile development processes. The findings of this work serve as an input variable here. In addition, the correlation between different degrees of complexity of different problems within the same development project and the resulting ideal process design can be described quantitatively. These correlations can be empirically extended in future work.

Contribution to Practice

The demands identified in the area of dealing with complexity in process design are independent of the industry and company considered. Accordingly, the results can be used across industries. The tool contributes to the efficient process design in development projects. In planning activities, teams of developers are supported in assessing the degree of complexity of the problems at hand. Based on this, they can plan the appropriate process flow (iterative, hybrid or sequential). Inefficiencies and dead power in the development process are thus reduced and development risks are minimized by carrying out development activities in a problem- and complexity-appropriate manner.


The present paper provides insights on the conference track 6.3. It presents a method that provokes the discussion of uncertainties and complexity by development teams during planning activities. The understanding developed in the contribution is suitable for further development in combination with further research work from the conference track.


Albers, A., Heimicke, J., Spadinger, M., Reiss, N., Breitschuh, J., Richter, T., Bursac, N. & Marthaler, F. (2019). Eine Systematik zur situationsadäquaten Mechatroniksystementwicklung durch ASD – Agile Systems Design. Proceedings of Stuttgarter Symposium Für Produktentwicklung SSP2019.

Bleek, W.-G., & Wolf, H. (2008). Agile Softwareentwicklung: Werte, Konzepte und Methoden (1. Aufl.). it-agile. Heidelberg: dpunkt.Verl.

Breitschuh, J., Albers, A., Seyb, P., Hohler, S., Benz, J., Reiß, N. & Bursac, N.. (2018). Scaling agile practices on different time scopes for complex problem-solving: Enthropie Compass.

Gericke, K., Meißner, M., & Paetzold, K. (2013). Understanding the context of product development. DS 75-3: Proceedings of the 19th International Conference on Engineering Design (ICED13) Design for Harmonies, 75(3).

Snowden, D. J., & Boone, M. E. (2007). A Leader’s Framework for Decision Making. Harvard Business Review, 85(11), 68–77.

Antecedents and Consequences of Agile R&D Units’ Organization – A Conceptual Framework

Andre Klaus Meier, Alexander Kock

TU Darmstadt, Germany


Today’s business environment is characterized by uncertainty and change. Therefore, the paradigm of agility has emerged and was adapted by numerous enterprises. This also refers to the context of R&D and the respective R&D units, where mastering the front end of innovation is vital and therefore subject of this study.


Organizing for uncertainty and change has been investigated exhaustively in existing literature (e.g. O'Reilly & Tushman, 2004; Rafferty, 2013; Volberda, 1996). The same applies to the concept of agility. Scientific contributions include the corresponding methods (Wang et al., 2012), its enablers as well as benefits (e.g. Lu & Ramamurthy, 2011; Tallon et al., 2018; Gligor et al., 2015).

However, existing research on agility mostly stems from the fields of information systems and operations management. R&D and innovation management literature has surprisingly neglected this topic. Previous studies considered agility mostly in a mediating/moderating role, primarily examining the influence of other constructs on key innovation outcomes (e.g. Shuradze et al., 2018). Other research has investigated possible applications (Cooper & Sommer, 2016) as well as antecedents of agility but more from a knowledge management (Cai et al., 2017) or innovation portfolio management perspective (e.g. Kock & Gemünden, 2016 ).

Literature Gap

To the best of our knowledge, so far, no studies have been conducted to examine the antecedents and consequences of agility in the front end of innovation, more specifically the organization of agile R&D units in large industrial enterprises

Research Questions

What are the characteristics of agile R&D units’ organization?

What are drivers and barriers that enable or hinder, respectively, R&D units’ agile organization?

What are the consequences of R&D units’ agile organization for the front end of innovation?

How do these consequences vary under different degrees of environmental turbulences?


First, we carried out an extensive analysis of literature regarding agile organization and organizational agility. This enabled us to provide a comprehensive overview of characteristics, enablers and outcomes of agility in other research fields.

Furthermore, we conducted an explorative-qualitative study in a multinational electronics and engineering company. The obtained data was analyzed using the approach of Gioia et al. (2013).

Finally, we integrated our empirical findings with the characteristics, enablers, and outcomes of agile organizations as found in extant literature. This resulted in a research framework on the antecedents and consequences of agile R&D unit’s organization.

Empirical Material

The empirical material that enabled us to achieve our above-mentioned research objectives was obtained via an explorative-qualitative study in a multinational electronics and engineering company.

At the time of our study, the company employed almost half a million associates in approximately 60 countries and generated a sales revenue of nearly 80 billion Euro. It possesses over 100 engineering locations worldwide and is among practitioners as well as scholars well known for its innovativeness and numerous patents throughout its successful history.

The study included 12 semi-structured interviews with R&D managers, R&D team members as well as agile experts with a R&D background from different business units of the corresponding company. The duration of the interviews varied between 25 and 59 minutes, resulting in approximately 160 pages of transcribed material.


The developed framework provides a comprehensive overview of antecedents and consequences of agile R&D units’ organization. It outlines various factors, in particular flat-hierarchies, cross-functionality, customer orientation and iterative work behavior that describe the organization of agile R&D groups and therefore serves to identify such organizational units in the field of R&D. Among the characterization of agile R&D units, the model contains a set of organizational antecedents, specifically top-management support, change in leadership style, failure resilience, available infrastructure and team culture that support R&D units to become agile.

Likewise, the model states barriers namely executive staff, unclear career paths, missing employee competences and legal aspects that hinder an agile organization of R&D units.

Furthermore, several positive effects of agile organized R&D units in the front end of innovation, particularly uncertainty reduction, reduced time-to-market, faster decision-making and increased front-end-success are represented.

In addition to that, the framework considers turbulences that are moderating the effect of agile R&D units’ organization on the mentioned outcomes in the front end of innovations. This moderator, like the antecedents and outcomes as well, also derived from the qualitative study and existing literature (Kock & Gemünden, 2016; Lu & Ramamurthy, 2011 ).

Contribution to Scholarship

The paper extents existing research on organizational agility to the field of R&D and innovation management, more specifically to the front end of innovation. Furthermore, our research shifts the focus from an overall corporate and software development perspective of other research streams to individual organizational R&D units within large industrial enterprises, where the often-stated agility is vital (Globocnik & Salomo, 2015; Markham, 2013).

Our characterization of agile R&D groups’ organization in the front end of innovation serves as a solid base to identify and measure such organizational units and conduct further research in this field.

Most importantly nevertheless, the developed framework facilitates future quantitative research and hence an empirical validation of the derived model. Therefore, our results are of high relevance for scholarship and provide multiple directions for further research .

Contribution to Practice

The results of our research endeavors provide valuable insights for managers to foster agile R&D units. First, our framework offers a possibility to identify agile organized R&D groups or to assess whether an organizational unit can be considered as agile or not.

Furthermore, it helps to successfully master the evolution to an agile organization of R&D units by describing boosters as well as impediments of such a transition process.

Finally, our study illustrates the valuable outcomes of agile R&D groups for the respective organization and therefore encourages R&D managers and senior management to cultivate such forms of organizational units.


This research can be seen as one possible solution to master the “Innovation Challenge”. The benefits of agility like the early detection of upcoming trends and changes, fast responsiveness and the strong customer focus are vital abilities to align the different requirements and innovation capabilities of research, industry and society.


Cai, Z., Liu, H., Huang, Q., & Liang, L (2017). Developing organizational agility in product innovation: the roles of IT capability, KM capability, and innovative climate. R&D Management. DOI: 10.1111/radm.12305

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.

Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, 16(1), 15-31.

Gligor, D. M., Esmark, C. L., & Holcomb, M. C. (2015). Performance outcomes of supply chain agility: when should you be agile?. Journal of Operations Management, 33, 71-82.

Globocnik, D., & Salomo, S. (2015). Do formal management practices impact the emergence of bootlegging behavior?. Journal of Product Innovation Management, 32(4), 505-521.

Kock, A., & Gemünden, H. G. (2016). Antecedents to decision‐making quality and agility in innovation portfolio management. Journal of Product Innovation Management, 33(6), 670-686.

Lu, Y., & Ramamurthy K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. Mis Quarterly, 931-954.

Markham, S. K. (2013). The impact of front‐end innovation activities on product performance. Journal of Product Innovation Management, 30, 77-92 .

O'Reilly III, C. A., & Tushman, M. L. (2004). The ambidextrous organization. Harvard Business Review, 82(4), 74-81.

Rafferty, A. E., Jimmieson, N. L., & Armenakis, A. A. (2013). Change readiness: A multilevel review. Journal of Management, 39(1), 110-135.

Shuradze, G., Bogodistov, Y., & Wagner, H. T. (2018). The role of marketing-enabled data analytics capability and organisational agility for innovation: empirical evidence from german firms. International Journal of Innovation Management, 22(04), 1850037.

Tallon, P. P., Queiroz, M., Coltman, T., & Sharma, R. (2018). Information technology and the search for organizational agility: A systematic review with future research possibilities. The Journal of Strategic Information Systems. DOI: 10.1016/j.jsis.2018.12.002

Volberda, H. W. (1996). Toward the flexible form: How to remain vital in hypercompetitive environments. Organization science, 7(4), 359-374.

Wang, X., Conboy, K., & Pikkarainen, M. (2012). Assimilation of agile practices in use. Information Systems Journal, 22(6), 435-455.

Agile Practices in Open Innovation

Mehmet Kerem Kızıltunç1, Mehmet Gençer2

1İstanbul Bilgi University, Turkey; 2İzmir Economy University, Turkey


Our research focuses on the dynamics of agile project governance in open innovation. Agility, is being adopted especially in fast moving industries such as software but there is limited theoretical research on its dynamics. We analyze agility in an open innovation context from a dynamic capabilities perspective.


Open innovation (Chesbrough, 2006) studies cross-boundary innovation scenarios as transactional processes. Knowledge or product assets move from one side of the collaboration boundary to the other through licensing, venturing or spinning. Gassman and Enkel (Gassmann & Enkel, 2004)'s coupled process then emphasizes the joint innovation processes in alliances with complementary partners in which give and take is crucial for success.

“Global competition, and diversification in the sources of new knowledge compels firms to make decisions faster, and to reduce time to market in order to capture value from technological innovation (Teece, 1996)”. Teece thus calls agility a dynamic capability that can be instrumental in either of the sensing, seizing or transforming phases . (Teece, Peteraf, & Leih, 2016). For inter-firm scenarios, the compatibility of the backgrounds of two companies in a relationship define the outcome of the desired knowledge exchange (Nooteboom et al., 2007).

Literature Gap

Open innovation approach does have an transactional focus and concerns the complementarity of collaborating firms’ resources. This resource centric view have less to say about the actual process of collaboration. Following this, we suggest that the compatibility of agile practices may also impact the outcome of agile inter-firm innovation projects.

Research Questions

What are the dynamics of governance in an agile open innovation context from a dynamic capabilities perspective?


We conducted our research in a major telecommunications company, which we call Theta here. We used a qualitative approach based on ethnographic and autoethnographic methods and have conducted in-depth, semi-structured interviews with project managers and team leaders. One of this paper’s authors has been working as a manager at Theta. He was not only involved in the projects chosen in the sample directly but is in charge of projects. This enabled us to both have a deep contextual insight into the nature of the projects researched but also maintain a certain level of observant distance to the interviewees.

Empirical Material

We chose four software development projects have run in Theta in the period of 2015-2017. The projects are typically sizeable product development projects, with running durations ranging between 12 and 36 months. Cases were selected with a convenience sampling method: all projects apply agile practices to some degree and partners’ experience and maturity in agile varies with those of Theta. Some projects have started with more classical Waterfall methods and later have tried agile with or without success, while some have started with agile early on. This selection allowed us to compare different combinations and polar cases as well as exploring reasons behind project performance in terms of contingency conditions.

We conducted two rounds of interviews with 15 managers both from Theta and its four collaborating open innovation partners. The interviews last an average of 48 minutes. The transcribed texts then were coded using a qualitative data analysis tool, with most frequent codes later being used as the basis for the findings below.


We built a model on three major factors contributing to agile co-capability.

First, co-location of teams emerge as a basic, yet significant factor in agile innovation. Agile practices require teams to work in very tight collaboration, in daily and weekly rituals where communication is mostly verbal.

Second, despite preaching of self-managing teams, the existence of a second leadership from the partner’s side limits the decisions the team can take. Successful implementations demonstrated that leadership teams had to keep an eye on the cross-functional team’s decision making processes.

Third, inter-firm agile practices have specific challenges as agile practices are not uniform across the board. Once the innovation practice becomes a joint co-creation effort, the proximity of the team members and the formation of the teams are not enough to ensure innovation success. Success also requires compatibility of practices, i.e. the ability to apply agile practices across firm boundaries in unison and at the same cadence.

In result, agile co-capability requires an understanding of those constraints and challenges and finding the right strategies and decisions to address those. Agile co-capability is not only required for the focal firm. The partner firms also need to align with the focal firm.

Contribution to Scholarship

Few literature has looked beyond the focal firm in understanding joint innovation. Lichtenthaler & Lichtenthaler (2009) have provided a comprehensive list of capacities a focal firm needs to have in order to achieve its open innovation targets. Noteboom et al.’s work also provided a perspective on two firms’ knowledge base in achieving absorptive capacity (Nooteboom et al., 2007). However, this is still a transactional perspective and the knowledge that is referred to here is knowledge acquired or built a priori to the partnership.

We take a dyadic perspective and argue that agile capabilities of partner firms and the compatibility of those with those of the focal firms are a major factor in achieving success in value co-creation. We extend the dynamic capabilities and open innovation literature beyond the unidirectional “knowledge flows” perspective and also focus on practices. This allows for new opportunities in understanding the dynamics of inter-firm collaboration.

Contribution to Practice

Managers should continue exploring how to manage agile practices in cross-boundary scenarios. Agile practices require building self-governing, cross-functional teams and this cross-functionality should be extended beyond the firm’s boundaries. Existing agile literature does not differentiate between intra-firm and inter-firm agile but our research demonstrates that inter-firm agile practices require different managerial approaches. In particular, managers need to seek a certain level of practice compatibility across inter-firm boundary.


Agility, a rarely researched domain, is becoming an important practitioner methodology in digital transformation and Industry 4.0 discussions but lacks theoretical substance, which we aim to address within the dynamic capabilities literature. We also contribute to open innovation literature.


Chesbrough, H. (2006). Open Innovation: A New Paradigm for Understanding Industrial Innovation. In H. Chesbrough, W. Vanheverbeke, & J. West, Open Innovation: Researching a New Paradigm (pp. 1-14). Oxford University Press.

Gassmann, O., & Enkel, E. (2004). Towards a theory of open innovation: three core process archetypes. R&D management conference, 6(0), 1-18.

Lichtenthaler, U., & Lichtenthaler, E. (2009). A Capability-Based Framework for Open Innovation: Complementing Absorptive Capacity. Journal of Management Studies, 46(8), 1315-1338. doi:10.1111/j.1467-6486.2009.00854.x

Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & Van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research policy, 36(7), 1016-1034.

Rigby, D. K., Sutherland, J., & Takeuchi, H. (2016). The secret history of agile innovation. Harvard Business Review. Retrieved 2017, from

Teece, D. J. (1996). Firm organization, industrial structure and technological innovation. Journal of Economic Behavior and Organisation, 193-224.

Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 13-35.

Establishing innovation: Relevant process steps for the automotive predevelopment process

Miriam Wilmsen1, Laura Groschopf2, Albert Albers1

1KIT - Karlsruhe Institute of Technology, Germany; 2Ernst-Abbe-Hochschule, Jena, Germany


Due to fast changing environmental conditions, especially within the automotive industry, the uncertainties within predevelopment projects increased within the last years. However, many developers lack knowledge concerning suitable processes and methods to decrease these uncertainties. Thus, this contribution provides relevant process steps for the successful execution of automotive predevelopment projects.


There are many process models in literature to describe product development [1]. These process models are either very generic or they are very specific and represent best practices. In mechatronic product development, the V-Model is one of the most known and used process meta-model [2]. The VDI 2221 addresses the systematic development of technical procudts and shows relevant process activities [3]. In addition, the meta-model iPeM – integrated Product development Model focuses activities of product engineering as well as activities of problem solving and provides relevant process steps and methods [4]. Design Thinking supports creative problem solving processes, especially in an early stage of an innovation project [5]. Furthermore, there are agile approaches, such as Lean Startup [6], user-centered design process [7] or Sprint [8]. However, all these approaches describe innovation processes, but project managers lack knowledge on the process steps that are relevant for their specific context and innovation project.

Literature Gap

As described previously, there are many process models for innovation projects available in research and in practice. However, there is no information available, which of the process steps are in general relevant for every innovation project and which process steps are only relevant in a specific context.

Research Questions

Based on the research gap, following research questions are under investigation within this contribution.

- Which process steps are relevant for automotive predevelopment projects?

- Which impact does these process steps have on the project results?

- Which process steps are only necessary in a specific context or project situation?


Firstly, numerous conventional and agile process models from literature were analyzed. The process steps of each of these process models were documented and compared to identify process steps that are considered by each analyzed process model. Through an analysis of current predevelopment projects within the automotive industry, the most relevant process steps could be identified. Through expert interviews within the automotive predevelopment, it was possible to quantify the impact of the relevant process steps on the project results. Finally, differences of the analyzed predevelopment projects and respective processes were identified to build a correlation between the context and process steps.

Empirical Material



Through the analysis of seven different innovation process models, more than 200 different process steps were identified. The comparison of these process steps showed, that only 25 process steps occur in at least two of the analyzed process models. For example, many of the analyzed process models considered the process steps “defining project objectives”, “building prototype” and “testing with customers”. In addition to that, an analysis of currently defined predevelopment processes led to 170 different process steps for automotive predevelopment projects. However, the analysis of an actual-process of an automotive predevelopment project consists of more than 40 different process steps. Some of the process steps are identic with the previously identified process steps, such as “defining project team”, “Identifying project requirements” or “defining project objectives”. Furthermore, there are process steps, which seem to be relevant within a specific project context. An example for a context-specific process step is “designing user interface”, because this process step is only relevant for functions, which require a direct interaction with users. All these process steps are consolidated within a process model that provides project managers a framework to plan their respective predevelopment project based on the identified process steps.

Contribution to Scholarship

There are many process models available in literature and practice that provide project managers a methodological support to plan their innovation project. However, these process models were developed for specific fields of application and they are either too generic or too specific. Hence, this contribution provides an overview on the relevant process steps within the automotive predevelopment. In addition to that, context-specific process steps were identified and were linked to the respective context-factors. Based on these research results, it is possible to identify the relevant process steps based on the characterization of the project context of a predevelopment project.

Contribution to Practice

Project managers often use the known or the most common processes and methods within their projects. However, the chosen processes and methods are not always suitable for the specific predevelopment project. Hence, this contribution provides project managers an evaluated support to consider the relevant and context-specific process steps for their predevelopment project. Thus, the process uncertainties can be reduced and the effectivity of the automotive predevelopment can be increased.


This contribution is highly relevant to improve R&D management especially in a very early stage of innovation projects. Hence, the developed process model does especially support project managers of open innovation projects, because of the high involvement of users, suppliers and other external partners within the automotive predevelopment.


[1] Wynn, D. C., & Clarkson, P. J. (2018). Process models in design and development. Research in Engineering Design, 29(2), 161-202.

[2] Richtlinie, V. D. I. (2004). 2206: Entwicklungsmethodik für mechatronische Systeme. VDI-Verlag, Düsseldorf.

[3] Richtlinie, V. D. I. (1993). 2221: Methodik zum Entwickeln und Konstruieren technischer Systeme und Produkte. Düsseldorf: VDI-Verlag.

[4] Albers, A., Reiss, N., Bursac, N., & Richter, T. (2016). iPeM–integrated product engineering model in context of product generation engineering. Procedia CIRP, 50, 100-105.

[5] Plattner, H., Meinel, C. and Weinberg, U. (2009). Design thinking. Landsberg am Lech: Mi-Fachverlag.

[6] Ries, E. (2011). The lean startup: How today's entrepreneurs use continuous innovation to create radically successful businesses. Crown Books.

[7] ISO, D. 9241-210 (2010): Prozess zur Gestaltung gebrauchstauglicher interaktiver Systeme. Berlin. Endter.

[8] Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to solve big problems and test new ideas in just five days. Simon and Schuster.