Conference Agenda

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Session Overview
21-AM-10: ST4.1 - the Design of New Industrial Ecosystems
Friday, 21/Jun/2019:
8:30am - 10:00am

Session Chair: Pascal Le Masson, MINES ParisTech - PSL
Session Chair: Gordon MULLER-SEITZ, Technische Universität Kaiserslautern
Session Chair: Susanne Ollila, Chalmers University of Technology
Location: Room PC 18

Session Abstract

Recent works on business model innovation (Schneckenberg and Velamuri 2018) (Spieth et al. 2014; Demil and Lecocq 2010) and ecosystems (Jacobides et al. 2018) have underlined the variety of configurations, roles and strategic positioning in the contemporary industry. They raised the critical issue of designing these ecosystems: is it possible to design a technological core to become a platform leader? who are the actors capable of designing an ecosystem? And also: what are the required managerial competencies? What kind of leaders? What kind of work division and value division? What kind of collaborations?

On the other hand, the study of design regimes and innovation dynamics has shed lights on new actors and new forms of interactions supporting intensive innovation dynamics at ecosystem level (Lange et al. 2013; Le Masson et al. 2012; Ollila and Yström 2016). These works underline that new ecosystem dynamics call for new forms of relationships between economic actors, based on the capacity to collectively explore the unknown. In particular it can lead to new forms of relationships between industrial ecosystem, society and scientific research. These works also relied on recent advances in design methods, design theory and even design cognition (Agogué et al. 2012) to improve the analytical framework and experiment with new methods and organizational forms.

This track will study these actors in charge of new ecosystem dynamics. Papers can be based on the empirical study of the actors; the track also welcomes theoretical papers that could help rediscuss the nature of the relationship in this process of ecosystem design, a relationship that probably goes far beyond the usual economics transaction. The track also welcomes methodological papers that propose new instruments and new techniques to help study ecosystems design.


- research / industry relationship for double impact;

- managing collective innovation for industry 4.0

- platform emergence and platform overthrown

- design regimes

- prescription and ecosystem dynamics

- cognitive approach of ecosystem dynamics

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The Influence of Technological Disruptions in Business Ecosystems on Elements of Companies’ Business Models

Michael Rachinger, Ines Korajman, Stefan Vorbach, Thomas Guggenberger

Graz University of Technology, Austria


Automotive companies increasingly operate in a complex business ecosystem (BE). It is characterized by new technologies, intensified and fast-paced innovation, increased competition, changing customer needs, and volatile government policies. As a result, companies operating in the automotive BE are currently facing multiple potential disruptive technologies (DT). (Utterback, 2005; McKinsey, 2017)


When DTs influence an industry, companies have incentives to open up their business model (BM) and cooperate with external partners in their BE to access relevant new resources (Lavie and Singh, 2012). The potential impact of BEs on BMs becomes clear looking at Zott and Amit (2010, p. 216), who define a BM as “a system of interdependent activities that transcends the focal firm and spans its boundaries”. Creating open BMs enables organizations to increase the effectiveness of their value creation and value capture activities (Chesbrough, 2007). Subsequently, changes in collaborations and partner-structure enable business model innovation (BMI) in companies operating in a BE. In a similar vein, Spieth et al. (2017) find that incumbent companies increasingly rely on partners for additional resources resulting in BMI in alliances. However, an ecosystem-based BM approach can be delicate, since it requires an understanding of markets, organizations, stakeholders and their network (Moore, 1996).

Literature Gap

Surprisingly, the literature investigating technology-driven BMI on a BE-level is still scarce. In order to address this issue, this paper investigates the business ecosystem-based BMI in the automotive industry, with a focus on energy storage technologies for electric vehicles.

Research Questions

This paper investigates technology driven BMI in BEs addressing the following research questions:

How does the technological development of energy storage solutions enable BMI in the automotive BE?

What archetypes of companies can be identified with regard to the degree of technological innovation and BMI in the automotive BE?


A preliminary qualitative study was conducted to improve the understanding of the topic and narrow down the focus of investigation. The subsequent main study uses the example of energy storage technologies to investigate their influence on BMI in the context of the automotive BE. Multiple case studies were conducted (Eisenhardt, 1989; Yin 2009). Gathered data were analyzed combining qualitative content analysis according to Mayring (2010) with a clustering logic proposed by Gioia (2012). Intracoder- and Intercoder-reliability were ensured through multiple coding iterations performed by three researchers. In addition, obtained results were contrasted using conceptual considerations.

Empirical Material

This research follows a two-step approach. A pre-study consisting of nine interviews was conducted. The interviews were conducted with companies in the automotive BE between 2015 and 2017. Informants were selected based on their awareness of technological topics and BMI. The main study focuses on original equipment manufacturers, engineering service providers, as well as suppliers in the automotive BE. In the main study, a total of five companies were analyzed in detail. The used interview guide consisted of questions addressing the company’s BM-elements as well as interferences from the BE with regard to technologies. In addition, public documents were investigated to enrich the database and triangulate the interview data.


This paper sheds light on the complex relationship between technological disruptions and BMI in the context of BEs. Empirical findings reveal the impact of the overarching value creation structure of BEs as well as the role of communication between BE-actors involved in value creation regarding the development, adoption, and diffusion of technologies. Furthermore, the necessity to utilize BE-resources due to a steadily increasing technological complexity is identified as a potential driver for BMI in BE. In addition, the interdependencies of the technological trajectory of mobile energy storage devices in the automotive BE with value creation and value capture activities of BE-actors is analyzed. Potential influences on the given example of energy storage technologies for the automotive BE are investigated revealing the need for business model flexibility to address issues posed by technologies for future mobility.

Contribution to Scholarship

Our paper contributes to the literature on technology-driven BMI in the context of BE. Although investigations of BMI in a BE context have gained attention in the last years (e.g. Spieth et al. (2017)), the literature combining the topics of BMI in BE with technological aspects is still scarce. Therefore, our contribution is threefold: First, we cluster our qualitative findings according to a logic proposed by Gioia (2012). Thereby we are able to derive the main issues companies face when handling DTs through BMI in BEs looking at energy storage technologies in the automotive BE as an example. Second, using in-depth case studies, we are able to broaden the understanding of how actors in a BE perform BMI to handle technological disruptions. Third, based on our findings and conceptual considerations we propose a scheme to identify archetypes of companies according to their use of DT and their BMI activities.

Contribution to Practice

Our results provide insights for practitioners on how companies can use BMI in BE to handle technological disruptions. This article highlights potential downfalls the companies we investigated perceived. With this knowledge, practitioners can adjust their company-activities to avoid these downfalls. Further, practitioners can utilize our proposed scheme of company archetypes to classify their specific situation and derive appropriate actions.


Technological innovations towards future mobility promise to impact both industry and society. Currently, incumbent companies are in desperate need to access - or even develop - new resources and capabilities. It is therefore imperative to investigate ways companies can handle these developments by innovating their BM through accessing resources from their BE.


Zott, C. and R. Amit (2010): "Business Model Design: An Activity System Perspective." In: Long Range Planning 43(2-3), pp. 216–226.

Chesbrough, H. and Schwartz, K. (2007): “Innovating Business Models with Co-Development Partnerships.” In: Research-Technology Management, 50 (1), pp. 55-59.

Eisenhardt, K., M. (1989): "Building Theories from Case Study Research." In: AMR 14 (4), pp. 532–550.

Gioia, D., A., Corley, K, G. and Hamilton, A. L. (2012): “Seeking Qualitative Rigor in Inductive Research: Notes on the Gioia Methodology.” Organizational Research Methods 16 (1), pp. 15–31.

Lavie, D. and Singh, H. (2012): “The evolution of alliance portfolios: the case of Unisys.” In: Industrial and Corporate Change 21(3): 763-809.

Mayring, P. (2010): “Qualitative Inhaltsanalyse. Grundlagen und Techniken”. 1st ed. Weinheim, Basel.

McKinsey&Company (2017): “Electrifying insights: How automakers can drive electrified vehicle sales and profitability” In: Advanced Industries.

Moore, J. F. (1996): “The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems.” 1st ed., New York: HarperBusiness.

Spieth, P. and Meissner, S. (2017): “BUSINESS MODEL INNOVATION ALLIANCES: HOW TO OPEN BUSINESS MODELS FOR COOPERATION.” In: International Journal of Innovation Management 22 (4), pp. 1850042-1-1850042-23.

Utterback, J., M. and Acee, H. J. (2005): “DISRUPTIVE TECHNOLOGIES: AN EXPANDED VIEW” In: International Journal of Innovation Management 9 (1) pp. 1-17.

Yin, R. K. (2009): "Case study research. Design and methods." 4. ed. Los Angeles: SAGE Applied social research methods series (5).

Dynamic capabilities in innovation ecosystems: a comparative case study

Jaan Kets

Vrije Universiteit Amsterdam, Netherlands, The


Innovative companies are known for being more international and for having a greater competitive advantage. However it is hardly possible to look at a company's competitive position separately, but more in connection with what other companies are achieving. We look at this in the context of life science.


Ecosystems are needed to foster innovation, which is about change and as claimed by Nelson and Winter (1982) and Di Stefano et al., (2012). There is a growing consensus that business ecosystems provide entrepreneurial firms with resources and information to navigate in a constantly changing competitive environment (Zahra and Nambisan, 2012). Through collaboration in a value network, firms exploit their interdependencies and have a competitive advantage over isolated companies, which internalize all components of a value chain (Iansiti and Levien, 2004). The ability to respond to consumer preferences and reconfigure resources dynamically requires firm to work together. Innovation ecosystems allow firms to create value which no single firm could create by itself (Adner, 2017). An innovation ecosystem helps to develop innovation. The cooperation with external actors to the firm is crucial in the process to build the technological and market innovation (reason).

Literature Gap

Dynamic capabilities are mostly studied at the firm level and very little is known about dynamic capabilities at the innovation ecosystem level and in between innovation ecosystems.

Research Questions

How do dynamic capabilities develop in different innovation ecosystems?


This study is carried out in two-stages: the first stage was a process study to describe the critical events that had taken place in the development of the life science ecosystems in Groningen and Leiden/Amsterdam. The second stage was a qualitative case study to compare the dynamic capability development in both ecosystems.

Empirical Material

Multiple methods of data collection, i.e.: document studies, newspaper article studies, actor websites and interviews, are selected. The documents study covers documents and newspapers articles. It aims at developing a general overview of the life science & technology in the Netherlands, notably around the city of Groningen and in the Leiden/Amsterdam region. The portfolio of gathered and analysed documents and newspapers (database LexusNexus Academic) consists of 200 documents

The actor study consists of 40 interviews and information gathered on websites and in the database Reach of the various actors (venture capitalist, government, government related institutes, knowledge institutions, life science & technology companies).The interviewees were asked to explain their role in the life science & technology innovation ecosystem and to elaborate on their sensing, seizing and reconfiguring capabilities.


We find that company size plays an important role in the development of dynamic capabilities, but also for the development of the life science eco-system as a whole. The Netherlands lacks a big sized firm to create more economic development in the country. The basic infrastructure (labs, airports) and path dependency also plays an important role in the development of dynamic capabilities. The innovation eco-system has strongly developed sensing capabilities for technological innovations. It has also reconfigured the innovation eco-system structure by developing legal entities such as cooperatives and a knowledge board. The life science eco-system in both regions does not have very strongly developed seizing capabilities for internationalization and technological innovation. We further find that distance plays a role in the development of dynamic capabilities. The closer the actors, the better the development of dynamic capabilities, notably in the city of Groningen.

Contribution to Scholarship

Distance and infrastructure are antecedents for dynamic capability development at the innovation eco-system level. The development of dynamic capabilities like sensing, seizing and reconfiguring is not a linear process and is influenced by co-ideation, co-innovation and co-institutionalization (i.e role of government)

Contribution to Practice

Policy makers need to carefully plan and organize the development of an eco-system by providing funding, infrastructure, a variety of firms in terms of size and proximity. This will increase the economic value an industry may have on an eco-system. Managers need to realize that cooperation with government, knowledge institutes and other firms helps to develop dynamic capabilities and will speed up the technological innovation process.


This paper is about innovation eco-systems and how these innovation ecosystems have taken shape over the years.


Adner, Ron. 2017. "Ecosystem as Structure: An Actionable Construct for Strategy." Journal of Management 43 (1): 39-58.

Zahra & Nambisan, 2012, "Entrepreneurship and strategic thinking in business ecosystems, Business Horizon 55 (3): 219-229

Iansiti & Levien, 2004, "Creating value in your business ecosystem", Harvard Business Review.

Nelson & Winter, 1982, "An evolutionary theory of economic change, Harvard University Press.

Di Stefano et al (2012), "Technology push and demand pull perspectives in innovation studies: current findings and future research directions", Research Policy 41 (8): 1283-1295

The Ambidexterity in Outbound Open Innovation Processes of Apache Hadoop Distributors

André Gomes de Sozua

University of Manchester, United Kingdom


While community version is “freely” available there is a high price to pay for the enterprise version. Consequently, firms are often confronted with the questions of what distribution to use when trying to innovate by aligning big data tools with their innovation streams.


The Apache Hadoop first appeared in a research paper in “MapReduce: Simplified Data Processing on large Clusters” (Dean and Ghemawat, 2004). The Apache Hadoop is an open source software library of codes for distributed computer processing. This study claims that outbound open innovation is fundamental in the commercialisation of free and open source software technologies. Outbound OI considers the usage of peripheral routes to drive development and the commercialisation of an innovation (Chesbrough and Crowther, 2006). It is also discussed as outward technology transfer (van de Vander et al., 2009) and/or desorptive capacity (Ziegler et al., 2013). the lens of innovation streams offers three parameter for the understanding of technological circles within the Apache Hadoop framework. This presents a clear advantage to understanding a platform that consists of many different modules, architectures and markets. Another important aspect of innovation streams is the concept of ambidexterity (O'ReillyIII and Tushman, 2008).

Literature Gap

Scholars have highlighted the lack of research in outbound open innovation and expressed the need for complementary studies (Mortara and Minshall, 2011, Ziegler et al., 2013; Chesbrough and Bogers, 2014; Bogers et al., 2018). According to Hu et al. (2015), “outbound open innovation [...] remains a challenge for most firms”.

Research Questions

How can Apache Hadoop distributors generate new innovation circles?


Based on qualitative interpretive case studies, we propose a conceptual model for the deeper understanding of how Apache Hadoop frameworks matures from explorative to exploitative and, later, develop into new business opportunities for firms. Detailed methodology will be presented in the full paper.

Empirical Material

We report empirical research findings based on insights obtained from 28 semi-structured interviews undertaken in Brazil in 2016. The interviewees are from 24 different firms in four Brazilian cities – 14 in greater São Paulo area, three businesses in Rio de Janeiro, and the remaining participants were from Brasília (2) and Porto Alegre (1). Detailed empirical data will be presented in the full paper.


Different types of distributions of the Apache Hadoop frameworks offer a more suitable environment for a certain streams of innovation. As summarised in the graph in the document attached.

Contribution to Scholarship

this model contributes to theory by offering further understanding of how firms can generate alternative path ways to commercialise and/or outbound their technologies.

Contribution to Practice

This research findings are important for practitioners as it offers a conceptual model for better understand what distribution to use to achieve what type of innovation streams.


This document explore the Apache Hadoop framework the industry standard ecosystem for big data analysis and is used by most firms today across industries. It is very important to understand who these firms innovate.


Bogers, M., Chesbrough, H. and Moedas, C. (2018). Open Innovation: Research, Practices and Police. California Management Review, 60 (2), 5-16.

Chesbrough, H. (2006). Open innovation: Research a new paradigm for the understanding industrial innovation. In Chesbrough, H., Vanhaverbeke, W. and West, J. (Ed.), Open innovation: Research a new paradigm. . Oxford, United Kingdom: Oxford University Press.

Chesbrough, H. and Bogers, (2014). Explicating Open Innovation: Clarifying an Emerging Paradigm for Understanding Innovation. In Chesbrough, H., Vanhaverbeke, W. and West, J. (Ed.), New Frontiers in Open Innovation. Oxford, United Kingdom: Oxford University Press.

Chesbrough, H. and Crowther, A. K. (2006). Beyond high tech: early adopters of open innovation in other industries. R&D Management, 36(3), 229–236.

Cohen, W. and Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35, 128–152.

Dean, J. and Ghemawat, S. (2004). MapReduce: Simplified data processing on large clusters. In Operating Systems Design and Implementation (OSDI) (pp. 137–150). San Francisco, CA.

Hu, Y., McNamara, P. and Mcloughlin, D. (2015). Outbound open innovation in bio-pharmaceutical out-licensing. Technovation, 35, 46–58.

Lee, S.-Y., Kim, H.-W. and Gupta, S. (2009). Measuring open source software success. Omega, 37(2), 426–438.

Mortara, L. and Minshall, T. (2011). How do large multinational companies implement open innovation? Technovation, 31, 586–597.

Munir, H., Linåker, J., Wnuk, K., Runeson, P., & Regnell, B. (2018). Open innovation using open source tools: a case study at Sony Mobile. Empir Software Eng, 23, 186–223.

Munir, H., Wnuk, K. and Runeson, P. (2016). Open innovation in software engineering: A systematic mapping study. Empir Softw Eng, 21(2), 684–723.

Smith, W. K. and Tushman, M. (2005). Managing Strategic Contradictions: A Top Management Model for Managing Innovation Streams. Organization Science, 16(5), 522–536.

Trott, P., Cordey-Hayes, M. and Seaton, R. A. F. (1995). Inward Technology Transfer as an Interactive Process. Technovation, 15(1).

Tushman, M. and O’Reilly, C. (1997). Winning through innovation. Strategy and Leadership, 25, 14–19.

Tushman, M., Smith, W. K., Wood, R. C. and Westerman, G. (2010). Organizational designs and innovation streams . Industrial and Corporate Change, 19(5), 1331–1366.

van de Vrande, V., de Jong, J. P. J., Vanhaverbeke, W. and de Rochemont, M. (2009). Open innovation in SMEs: Trends, motives and management challenges. Technovation, 29, 423–437.

Ziegler, N., Ruether, F., Bader, M. A. and Gassmann, O. (2013). Creating value through external intellectual property commercialization: A desorptive capacity view. Journal of Technology Transfer, 38(6), 930–949.

The Dynamics of Innovations of Complex Product Systems: Evidence from the Analysis of The Patent Data of Emerging Mobility

Yujie Jiang1, Shangke Wang2, Masanori Yasumoto2

1Chongqing University, China, People's Republic of; 2Yokohama National University, Japan


The emerging mobility system of CASE covers a wide scope of technologies beyond the traditional automobile industry. Developing a product system in such a sector requires firms with different technologies to cooperate with each other in order to cope with the complexity of systems and rapid technological changes.


A empirical study by Takeishi(2001) revealed that automotive manufacturers need to mutually share technologies with their suppliers in order to promote innovations and preserve their capabilities of system innovations. As there are various technology development activities associating with each other in the development of automobiles, the ability to absorb a wide scope of technologies will be an important factor in innovations.

By accumulating component knowledge, automotive manufacturers can effectively integrate components and technologies developed by other firms with responding to technical changes (Prencipe, 1997; 2000). In such a process, acquiring the knowledge of peripheral technologies helps systemic innovations (Granstrand, et. al., 1997). As the way to acquire technological knowledge, strategic technology alliance, M&A, and exploration-intra-innovation are identified in previous studies (Bekkers, 2002; Haspeslagh and Jemison,1991).

Literature Gap

These studies, however, have not sufficiently explained the technological trajectories of the development of complex product systems in the emerging mobility sector. Accordingly, we still do not know clearly about the effective way of technology acquisition by firms.

Research Questions

This paper attempts to: (i) reveal how firms shape the technological trajectories of the development of complex product systems of the emerging mobility sector and (ii) figure out the effective way to acquire various technologies necessary for developing such systems.


This study used quantitative methods in order to reveal the trajectories of technology developments and the effectiveness way of firms’ technology acquisition in the mobility sector. First, we examined the development of related technologies by conducting a network analysis on the downloaded patents by technology classes (IPCs). Next, we elucidated firms’ technological positions by using IPCs of their patents. At the same time, we selected leading firms positioned well and revealed the networks of technologies acquired by these firms. Finally, we chronologically examined these analysis results to reveal the transition of the networks of technologies by leading firms.

Empirical Material

Based on a series of interviews with leading automotive and ICT firms, we downloaded the data of 1,655 patents of V2X (including V2V, V2I and V2P) filed to the USPTO until Nov. 30, 2016. By drawing network graphs with using the data, this study attempts to elucidate the change of related technologies and firms’ technological knowledge. While the network nodes indicated the requirements and functionality (i.e., technology classes) of the sub-systems, the network ties stood for certain technologies (i.e., patents) that were implemented for such requirements and functionalities.

In addition, firms’ positions are mapped on the interfirm networks of technologies by using technology classes. Firms covering a wide scope of technologies were positioned in the center of the networks with high network centralities. Accordingly, these firms acquired peripheral technologies in relation with their core technologies.


Our analysis illustrates the part of the technological trajectories in the development of complex product systems of the emerging mobility sector. In 2007, ICT firms had dominant positions in relation to the development of the technologies of connected car. At that time, most firms focused on the technologies of the telecommunication and data processing. However, by 2016, the technologies of connected car are multiple and the participants became more and more, while the automobile firms have dominant position. From 2010, automobile firms like GM, Ford, and Toyota published patents about telecommunication technologies. By 2013, a lot of automotive firms had finished covering a wide scope of technologies while a few ICT firms acquired vehicle technologies. Our analysis also illustrates that compared with M&As and strategic alliances, firms from the automotive industry prefer to develop a wide scope of technologies by themselves.

Contribution to Scholarship

Our main contribution is that we elucidated the technological trajectories of the development of complex technology systems of the emerging mobility sector. And we also lead a direct insight into the effective way of technology acquisitions of firms which attempt to cope with complex and versatile technologies. The results show that while ICT firms focus on limited technologies of telecommunications and data processing, leading automotive firms attempt to accumulate a wide range of technologies of V2X technologies for themselves.

Specialization in a limited scope of technologies have been favored in the development of ICT-related complex systems with rapid technological changes. Correspondingly, firms are assumed to specialize in specific technologies in their line drawing on other firms’ technologies. Yet, this study reveals that firms to innovate complex product systems, like automotive firms, are still likely to internalize unfamiliar critical technologies in response to a wide scope of technological changes.

Contribution to Practice

Our study has a value for practitioners as well. We provided a cutting-edge view on the emerging mobility beyond the traditional automotive sector with showing the latest snapshot of the direction of technology development and the effective way of the acquisition of a wide scope of technologies beyond the traditional automotive technologies.


This study corresponds to “Track 11.1 Managing the new mobility transition” in “Theme 11 Sustainable Development.” It has potential contributions to “Open Innovation and Co-creation.”


Bekkers, R., Duysters, G. and Verspagen, B. (2002). Intellectual property rights, strategic technology agreements and market structure: The case of GSM. Research Policy, 31, 1141-1161.

Brusoni, S. and Prencipe, A. (2001). Unpacking the black box of modularity: Technologies, products and organizations. Industrial and Corporate Change, 10, 179–205.

Davies, A. (1999). Innovation and competitiveness in complex product systems: The case of mobile phone systems. In Bastos, M. I. and Mitter, S. (eds.), Europe and Developing Countries In the Globalized Information Economy: Employment and Distance Education. UNU/INTECH studies in new technology and development, Routledge.

Hobday, M. (1998). Product complexity, innovation and industrial organization. Research Policy, 26, 689– 710.

Hobday, M. (2000). The project-based organisation: an ideal form for managing complex products and systems? Research Policy, 29, 871–893.

Prencipe, A. (1997). Technological competencies and product’s evolutionary dynamics: A case from the aero-engine industry. Research Policy, 25, 1261–1276.

Prencipe, A. (2000). Breadth and depth of technological capabilities in CoPS: The case of the aircraft engine control system. Research Policy, 29, 895–911.

Mukai, Y. (2014). Innovation of Complex Products and Systems (CoPS): Technical notes on Hobday (1998)(in Japanese). Akamon Management Review, 13, 21–36.

Takeishi, A. (2001). Bridging Inter- and Intra-Firm Boundaries: Management of supplier involvement in automobile product development. Strategic Management Journal, 22, 403–433.

Granstrand, O., Patel, P., and Pavitt, K. (1997). “Multi-technology corporations: Why they have “distributed” rather than “distinctive core” competencies,” California Management Review, 39(4), 8-25.

Haspeslagh, P. C. and Jemison, D. B. (1991) Managing Acquisitions: Creating Value through Corporate Renewal, New York: Free Press.

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