20-PM1-02: ST3.4 - Digital innovation
Digitization, digitalization, and digital transformation are all part of the evolving digital economy. As digital innovation may cut across all of these dimensions, it offers an interesting theoretical and empirical arena for exploring certain aspects of the innovation process. Digital innovation can be highly complex as it may incorporate both digitization and digitalization in the form of new digital products and services and parallel digitally based improvements of internal organizational processes. It may further extend beyond organizational boundaries, as suppliers and customers are affected by or become actively involved in the innovation process. Codifiable technological interfaces may indeed allow for an unusual amount of innovation efforts that cut across otherwise distinctive organizational boundaries, and they should make the innovation process relatively insensitive to geographical distances. In many cases, digital innovation calls into question attitudes to value creation and value capturing, as reflected in traditional and established business models.
Both the amount and direction of digital innovation may be dictated by institutional contexts and degrees of digital transformation, as it requires both acceptance and sustained efforts to embrace digitalization at the societal level. All in all, there are reasons to expect that digital innovation can expose salient and even unique elements of the innovation process, as compared to innovation processes that prevail in more traditional settings and industries. Specific issues and topics of the digital innovation track include but are not limited to:
- The nature of digital innovation
- Digital innovation across internal and external organizational boundaries
- The geographical boundaries of digital innovation
- Managing complex digital innovation
- Value creation and value capturing in digital innovation
- The interplay between digital transformation and digital innovation
- The sources, drivers and consequences of digital innovation
Managing Digital Innovation Processes: The Case of Integrated Product-Service Systems
1University of Agder, Norway; 2OsloMet – Oslo Metropolitan University; 3NORCE - Norwegian Research Centre
Digital technology is often an important enabler when manufacturing firms create new revenue streams “by adding services to products” (Baines et al., 2009, p. 547), often referred to as the ‘digital servitization’ of manufacturing (Bustinza et al., 2018). This paper focus on the characteristics of innovation processes in servitized firms.
Research on the management of innovation processes has typically focused on either new product development (NPD) (Cooper, 2008) or new service development (NSD) (Hipp and Grupp, 2005), while the development of new integrated digital product-service systems (PSS) has received limited attention (Zhang and Banerji, 2017). Empirical studies have found that NSD processes are typically more incremental, iterative and ad-hoc than NPD processes (e.g., Hipp and Grupp, 2005), and as a consequence scholars have argued that firms need to implement different processes for NSD and NPD (Droege et al., 2009). This recommendation is reasonable in cases when services and products may be separated, but more problematic in servitized firms that offer integrated digital product-service systems (Zhang and Banerji, 2017). Thus, more research is needed on new digital product-service system development processes (Zhang and Banerji, 2017).
Since digital servitized firms offer integrated product-service systems they need innovation processes for both products and digital services (Zhang and Banerji, 2017). Research on of new digital product-service system development processes and how they are managed has, however, until now remained scarce.
In this paper we, therefore, aim to empirically explore how digital product-service system innovation processes are implemented in servitized firms. The following research questions are raised: 1) What are the characteristics of new digital product-service system development processes? 2) How are new digital product-service system development processes managed?
Since qualitative research arguably has advantages when the phenomenon to be studied is not well understood and where the variables are still unknown, we used a qualitative multiple case study approach (e.g., Yin, 2008) to answer the research questions raised in this study.
Based on a dialogue with the management of a business cluster of leading firms within the Norwegian energy and maritime sector dialogue five servitized firms were selected as case organizations. The degree of service orientation in the firms varied, but all five firms offered advanced equipment (such as drilling equipment and heavy lifting equipment) in combination with digital services. All firms also had a strategic focus on innovation and had several ongoing new digital product-service system development initiatives in their innovation portfolios. Data related to how new digital product-service development processes were implemented in the case organizations was collected through semi structured in-depth interviews with in total 43 key-employees. The data was coded and analysed in an inductive manner by performing both within-case and cross-case analysis.
All case organizations provided a high number of examples of new product-service systems that had recently been successfully implemented or launched in the market. The examples varied both with respect to the types of services (digital versus non-digital services) offered and with respect to the business models used (product-oriented versus result-oriented business models). Our findings suggested that the characteristics and the management of the new product-service system development processes were contingent upon both the type of service and the business model. Four different development processes were identified: (1) Result-oriented business models and digital services: The innovation processes were characterized by the use of cross-functional teams and formal idea-to-launch systems. (2) Product-oriented business models and digital services: The innovation processes were characterized by the use of technology experts and formal idea-to-launch systems. (3) Product-oriented business models and non-digital services: The innovation processes were characterized by the use of two separate teams for NPD and NSD where the NPD teams used formal idea-to-launch systems and the NSD teams used informal idea-to-launch systems. (4) Result-oriented business models and non-digital services: The innovation processes were characterized by the use of cross-functional teams and informal idea-to-launch systems.
Contribution to Scholarship
By using comprehensive qualitative case study data from five servitized firms, the paper contributes to the ongoing debate related to new digital product-service system development processes (Zhang and Banerji, 2017). Our findings advance this debate by suggesting that the characteristics and management of these processes are contingent upon the business model and type of services. Further research is needed to verify if the same contingencies are found in other types of organizations.
Contribution to Practice
The practical experiences reported in the paper provide considerable assistance and guidance to managers searching for better ways to manage the processes of developing new product-service systems. The findings demonstrate that there is not one specific process that should be implemented and used in all new product-service system development initiatives. Instead managers need to select a process that fit the services and business model of the product-service system under development.
This research is in particular relevant to Theme 3 (Digital Technology) and Track 3.4 (Digital Innovation).
Baines, T. S., Lightfoot, H. W., Benedettini, O., & Kay, J. M. (2009). The servitization of manufacturing: A review of literature and reflection on future challenges. Journal of manufacturing technology management, 20(5), 547-567.
Bustinza, O. F., Gomes, E., Vendrell‐Herrero, F., & Tarba, S. Y. (2018). An organizational change framework for digital servitization: Evidence from the Veneto region. Strategic Change, 27(2), 111-119.
Cooper, R. G. (2008). Perspective: The stage gate® idea to launch process—update, what's new, and nexgen systems. Journal of Product Innovation Management, 25(3), 213-232.
Hipp, C., & Grupp, H. (2005). Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies. Research Policy, 34(4), 517-535.
Zhang, W., & Banerji, S. (2017). Challenges of servitization: A systematic literature review. Industrial Marketing Management, 65, 217-227.
Yin, R.K. (2008). Case Study Research: Design and Methods, vol. 5. SAGE, USA.
What does our contemporary innovation management community consider to be rigorous case study research?
1Centre for Science, Technology & Innovation Policy, University of Cambridge, United Kingdom; 2Graduate School of Management of Technology, Sogang University, South Korea; 3Centre for Technology Management, University of Cambridge, United Kingdom
Case study is a well recognised research method in the field of innovation management (Conn and Ritala 2018). However, guidance for this method tailored to innovation studies is not yet consolidated despite it being available for other research domains (e.g. Gibbert & Ruigrok, 2010; Ketokivi & Choi, 2014).
Three types of work pertaining case studies can be found in the literature. First, some authors provide classifications of case study approaches and ‘comprehensive overview of the case study process’ (e.g. Eckstein, 1975; Meyer, 2001; Thomas, 2011). The second stream of research attempts to capture methodological trends and pluralism in a given journal in a certain period (e.g. Aguinis, Pierce, Bosco, & Muslin, 2009; Ketchen, Boyd, & Bergh, 2008; Scandura & Williams, 2000). Finally, others suggest a variety of prescriptive steps for carrying out, writing up and reviewing case study research (e.g. Eisenhardt & Graebner, 2007; Gibbert, Ruigrok, & Wicki, 2008; Pratt, 2008, 2009).
Notwithstanding their abundance, extant methodological articles are likely to fall short in dealing with the idiosyncrasies underpinning our field.
What does our contemporary innovation management community consider to be rigorous case study research?
To address this research question, we conduct interviews and content analysis.
In particular, we interview the editors and editorial members of major innovation management journals, such as Research Policy, R&D Management, Technovation and JPIM. We then look into 372 articles published in the aforementioned journals in the 2009-18 period and perform a content analysis to identify the successful approaches taken by our colleagues.
Our emerging results to date include the following:
1) recurring citations of conflicting methodologists in the same manuscript. One interpretation, possibly explaining the co-existance of different and sometimes contrasting methodological approaches, is the authors’ attempt to second the preferences of different reviewers. However, the editors and assistant editors interviewed agree that this practice results in less rigorous and publishable work;
2) there is a consensus in the editorial teams that merely descriptive case research is likely to receive desk-rejection. case study work has to make theoretical contributions, whether theory-building, testing or elaboration;
3) while generalisation is hardly achieved in case study research, our informants submit that it is the authors’ responsibility to discuss the boundaries of generalisability through, for example, ‘some simple mental tests of the generalizability of core propositions’ (Whetten, 1989: 492).
However the analysis is still underway with the aim to realise: (a) what the editors of major innovation journals regard as rigorous, persuasive and publishable case study methods, and (b) how case study authors actually perform case study research and, in doing so, collectively define the socially constructed, de facto standard set for the case study.
Contribution to Scholarship
This work contributes to innovation management studies by suggesting a set of prescriptions for those using/reviewing case study methods.
Contribution to Practice
We believe that this effort helps researchers to conduct rigorous case studies that are, intrinsically, designed to deliver rich insights to audiences in industry and society (Pratt & Bonaccio, 2016).
Case study methods encourage researchers to travel back and forth between theory and practice. Findings emerged from these processes readily bridge research, industry and society, that is, the key them of the R&D Management conference. Our methodological work on case study, therefore, has a perfect fit with the conference.
Aguinis, H., Pierce, C. A., Bosco, F. A., & Muslin, I. S. 2009. First Decade of Organizational Research Methods: Trends in Design, Measurement, and Data-Analysis Topics. Organizational Research Methods, vol. 12.
Conn, S., & Ritala, P. 2018. The growing importance of research methods in innovation management research: A note from the 2018 ISPIM Innovation Conference. Technovation.
Eckstein, H. 1975. Case studies and theory in political science. In F. I. Greenstein & N. W. Polsby (Eds.), Handbook of political science, vol. 7: 79–138. Boston, MA: Addison-Wesley.
Eisenhardt, K. M., & Graebner, M. E. 2007. Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1): 25–32.
Gibbert, M., & Ruigrok, W. 2010. The ‘“What”’ and ‘“How”’ of Case Study Rigor: Three Strategies Based on Published Work. Organizational Research Methods, 13(4): 710–737.
Gibbert, M., Ruigrok, W., & Wicki, B. 2008. What passes as a rigorous case study? Strategic Management Journal, 29(13): 1465–1474.
Ketchen, D. J., Boyd, B. K., & Bergh, D. D. 2008. Research Methodology in Strategic Management: Past Accomplishments and Future Challenges. Organizational Research Methods, 11(4): 643–658.
Ketokivi, M., & Choi, T. 2014. Renaissance of case research as a scientific method. Journal of Operations Management, 32(5): 232–240.
Meyer, C. B. 2001. A Case in Case Study Methodology. Field Methods, 13(4): 329–352.
Pratt, M. G. 2008. Fitting Oval Pegs Into Round Holes: Tensions in Evaluating and Publishing Qualitative Research in Top-Tier North American Journals. Organizational Research Methods, 11(3): 481–509.
Pratt, M. G. 2009. From the Editors: For the Lack of a Boilerplate: Tips on Writing Up (and Reviewing) Qualitative Research. Academy of Management Journal, 52(5): 856–862.
Pratt, M. G., & Bonaccio, S. 2016. Qualitative research in I-O psychology: Maps, myths, and moving forward. Industrial and Organizational Psychology, 9(4): 93–715.
Scandura, T. A., & Williams, E. A. 2000. Research Methodology in Management: Current Practices, Trends, and Implications for Future Research. Academy of Management Journal, 43(6): 1248–1264.
Thomas, G. 2011. A typology for the case study in social science following a review of definition, discourse, and structure. Qualitative Inquiry, 17(6): 511–521.
Whetten, D. A. 1989. What Constitutes a Theoretical Contribution? Academy of Management Review, 14(4): 490–495.
High–tech Start–ups and Alternative Mechanisms of IP Protection: Evidence from Corporate Venture Capital Investments in the Automotive Ecosystem
1University Paris-Dauphine (PSL); 2Governance and Regulation Chair
Corporate Venture Capital (CVC) offers young innovative firms access to capital and to non-pecuniary resources. However, they suffer the risk for intellectual property misappropriation. This is the case of the emerging automobile ecosystem, incumbents have been challenged by new firms and partner with start-ups to gain insights on new technologies.
The literature in economics and management has long recognized the preponderant role of young firms for innovation, challenging the market by introducing disruptive technologies (Veugelers and Schneider, 2018). This attract incumbents willing to appropriate the gains of the innovation. Indeed, investment in new firms increases the patenting activity of the CVC. The results are more pronounced in industries with “weak” appropriability regimes, as it is the case for software/internet (Dushnitsky and Lenox, 2005).
To protect their IP, entrepreneurial firms resort not only to formal (patents), but also to informal (secrecy) IPP mechanisms. Literature posits that the more developed the technology, the more difficult to imitate an innovation (Katila et al., 2008). Similarly, connections to influential third parties might facilitate trust and protect from misbehavior of the incumbents (Hallen et al., 2014). Other resources, such as trademarks provide brand and reputation, protecting them as well from misappropriation (Huang et al.,2013).
To our knowledge, no study condensate and evaluate all the informal mechanisms that firms could use to protect their IP (timing, social and resource defenses). In addition, they disregard the dimension of potential for misappropriation, measured by the industry overlap (firms in the same industry have higher potential for misappropriation).
What are the strategies that entrepreneurs adopt to protect their intellectual property and avoid bypassing from the resources offered by Corporate Venture Capitalists?
Is the use of these strategies more relevant when the venture firm and CVC investor operate in overlapping industries?
We use a logistic regression to measure the likelihood of any entrepreneurial firm form an investment tie with any CVC. The dependent variable is a dummy equal to 1 if an investment between the CVC-firm pair is realized. The independent variables are:
- Industry overlap: dummy equal to 1 if the firm and the CVC share the same 4-digit industry code.
- Legal Defense: patent stock of the firm at the time of the round.
- Social Defense: whether startups are backed by well-connected VCs (measured by the eigenvector centrality).
- Downstream Capability: cumulative number of “live” trademarks at the time of the round.
The main source of information for our dataset is Crunchbase, a crowdsourced database with information on innovative companies and investors in several countries.
We identify startups in the automotive ecosystem by using Crunchbase categorization (“automotive”, “autonomous vehicles”, “electric vehicles”). We restrict our sample to firms created from 2000 on and incorporated in the United States.
From Crunchbase, we collect information on the new ventures: founded date, headquarters’ location, and the experience of the founding team. We also retrieve the number of funding rounds, type of round (e.g., Seed, Series A), and the date of announcement.
We identify the investors participating on each round of all the ventures and consider them as CVCs based on Crunchbase’s categorization, or the organization’s (or its parent) main activity is not on venture capital.
To categorize firms into industries, we use the Global Industry Categorization Standard (GICS), developed by MSCI and Standard & Poor’s Dow Jones.
We collected data on patent registration and copyright on Patentscope and the U.S. Copyright database, respectively. Information on trademarks comes from the Trademark Electronic Search System.
The database contains 36 dyads having formed ties in the first round and 21,276 dyads not having formed ties, consistent with prior work.
Firstly, we expect that young innovative firms will be more likely to tie with a CVC investor in the same industry when they possess strong downstream capabilities.
Secondly, we expect that young innovative firms will be more likely to engage in a tie with a CVC investor in the same industry when they are connected with influential third parties.
Thirdly, we expect that young innovative firms will be more likely to engage in a tie with a CVC investor in the same industry when they have more developed innovation.
Finally, we claim that opportunistic behavior might be more likely when both, the CVC (or its parent firm) and the entrepreneur are in the same industry, as the CVC possesses greater ability and inclination to imitate. Therefore, legal and alternative protection will be more important when the venture firm and CVC investor operate in overlapping industries.
Contribution to Scholarship
Our work is closely related to the literature investigating “the paradox of CVC”, as it involves a young innovative firm facing a challenging situation in which a potential partner can be, at the same time, attractive and dangerous.
First, we bring together the insights from the pertinent literature about of how young innovative firms can protect their intellectual property. More specifically, we consider timing defense (Katila et al., 2008) social defense (Hallen et al., 2014), and downstream capabilities (Huang et al., 2013). These strategies help balance the tension between cooperation and competition that emerge in a relationship between entrepreneurial firms and incumbents.
To this, we add the dimension of potential for misappropriation, including the industry overlap (Dushnitsky and Shaver, 2009). Third, we study firms participating in the same ecosystem, therefore with a high potential of tie formation.
Contribution to Practice
Our main contribution to practice is to entrepreneurs seeking to attract investment from CVCs, while protecting their intellectual property. Specially in weak IP regimes, as it is the case of software, they could use alternative defense mechanisms. First, they could establish partnerships with well-connected venture capitalists. Second, they could appeal to timing defenses, by receiving CVC funding at a more mature stage of the innovation. Finally, they could register trademarks to create a brand a reputation.
We also highlight the role of entrepreneurial firms as active decision-makers in a decision to form a corporate investment relationship.
The digital innovation is a complex process involving multiple actors, such as startups, who frequently introduce disruptive technologies, and incumbents, willing to capture the innovation. However, knowledge misappropriation detriments the incentives for innovation. Hence, it is important to understand the mechanisms that could be used to protect digital innovation.
Dushnitsky, G. and Lenox, M. J.: 2005, When do incumbents learn from entrepreneurial ventures?: Corporate venture capital and investing firm innovation rates, Research Policy 34(5), 615–639.
Dushnitsky, G. and Shaver, J. M.: 2009, Limitations to inter-organizational knowledge transfer: The paradox of corporate venture capital, Strategic Management Journal 30(10), 1045–1064.
Veugelers, R. and Schneider, C.: 2018, Which IP strategies do young highly innovative firms choose?, Small Business Economics 50(1), 113–129.
Hallen, B. L., Katila, R. and Rosenberger, J. D.: 2014, Unpacking Social Defenses: A Resource-Dependence Lens on Technology Ventures, Venture Capital, and Corporate Relationships, Academy of management Journal (December 2013), 1–51.
Huang, P., Ceccagnoli, M., Forman, C. and Wu, D. J.: 2013, Appropriability Mechanisms and the Platform Partnership Decision: Evidence from Enterprise Software, Management Science 59(1), 102–121. URL: http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1120.1618
Katila, R., Rosenberger, J. D. and Eisenhardt, K. M.: 2008, Swimming with Sharks : Technology Ventures, Defense Mechanisms and Corporate Relationships, Administrative Science Quarterly 53(2), 295–332.
Digital transformation of business model innovation: A structured literature review
Ca' Foscari University of Venice, Italy
In recent years, the phenomenon of digital transformation (DT) has become very popular (Fitzgerald et al. 2013; Kane et al., 2015). The effect of new digital technologies such as social, mobile, analytics, cloud and Internet of Things (SMACIT) arises digital transformation phenomenon (Sebastian et al., 2017).
Following Westerman’s et al. (2014) categorization on digital transformation effects, the role of digital technologies is central to the creation of new dynamics for business operations, which in turn forces changes to existing business models. One reason for this is that digital transformation includes the integration of digital products and services within the core business model to improve or introduce new customer experiences or value pathways (Nambisan et al. 2017). The other reason is that digital infrastructure offers to companies the capabilities to develop new business models (Rayna & Striukova, 2016; Berman et al., 2012), as they can re-appropriate existing resources and experiment with new forms of value creation mechanisms, while also providing greater value for all stakeholders (Tilson et al., 2010). Thus, new business models based on digital technologies offer competitive advantage to firms (Berman et al., 2012).
The latest call of Visnjic et al. (2016) shows that the understanding of digital transformation of business models remains poor. Moreover, understanding how digital transformation enables innovation of BMs is essential requirement for their adaptions they represent the new logic for companies how to create and capture value (Afuah, 2004).
RQ1. How has the field of digital transformation developed over time?
RQ2. What is the focus of literature in digital transformation of BMI?
RQ3. How has digital transformation facilitated Business Model Innovation?
This paper adopts a structured literature review. According to Massaro et al. (2016), a structured literature review is “a method for studying a corpus of scholarly literature, to develop insights, critical reflections, future research paths and research questions”. The reason for adopting a structured literature review is because “it is based on a positivist, quantitative, form-oriented content analysis for reviewing literature” (Massaro et al., 2016). This method follows a ten-steps process that ensures the researcher to “potentially develop more informed and relevant research paths and questions” (Massaro et al., 2016).
After having identified the keywords and the framework of the study, we started the collection and selection of papers followed a multi-staged process. Firstly, we searched in the SCOPUS database with the defined keywords in the protocol. This first search revealed 193 publications. In a second step, in order to control over the quality of articles we restricted the search to peer-reviewed journals in the category of Business and Management that are ranked 3, 4 and 4* in ABS evaluation. With this additional restriction we did not take into consideration book chapters, book reviews and conference articles. Therefore, in this second search we found articles published in peer-reviewed journals over the time span from 1996 to 2018, which reduced the number of publications to 94. After the collection of all the articles, each paper was checked for the inclusion of key words in the title, abstract and keywords, in order to ensure that articles fit the research objective of this study. During the screening stage of publications we found only few articles which were published previous to 2014 to be about digital transformation and business model innovation. Thus, the final sample of considered publications included 40 research articles.
The review of the literature shows that digital transformation of BMI is a new field of research with a growth of interest from researchers starting in 2014. This implies an evolution of the maturity of the field towards pragmatic science, as researchers have addressed relevant issues with robust methodology (Anderson, 2001). However, more collaboration is needed in the future between practitioners and academics, to lower the divide between practitioners and academics (Anderson, 2001). As there is an increased interest of researchers we expect a further growing number of publications in the field.
The shift of topics over time reveals on the one hand the practitioner-led nature of research in this field. On the other hand, we observe no dominating author in the field, implying that few authors remain focused on exploring further aspects of BMI driven by digital transformation. This hinders the knowledge-building process in the field, as only few authors make use of prior findings to build cumulative knowledge. In the future authors should rely more on previous findings to build upon them. Furthermore, results show for a need of research in developing countries and other industries such as design, architecture, advertising and fashion industry (Mangematin et al., 2014).
Contribution to Scholarship
From the theoretical perspective, this study contributes to these digitally enabled types of BMI, that make the emergence of business models a promising unit of analysis for undertaking innovation strategies. Firstly, this study provides an overview of the development of the field of study of digital transformation of business model innovation, highlighting the avenues for further research. Secondly, our research showed the impacts of digital technologies on value creation, capture and delivery of BMs. Thirdly, we conclude that digital transformation is enabling companies to work for issues of sustainability by engaging them in circular and sharing economy approaches. Thus, Business Models have become an open tool to everyday changes related to technological improvements and knowledge management with regard to stakeholders and sustainability issues.
Contribution to Practice
Concerning practice, the results of this study may help practitioners to understand how digital transformation of business model innovations can be achieved.
With regard to the conference, this paper brings theoretical and practical insights on the role of digital technologies in innovation of business models.
Afuah, A. 2004. Business models: a strategic management approach. McGraw-Hill, New York
Anderson, N., Herriot, P. and Hodgkinson, G.P. (2001), “The practitioner-researcher divide in Industrial, work and organizational (IWO) psychology: where are we now, and where do we go from here?”, Journal of Occupational and Organizational Psychology, Vol. 74 No. 4, pp. 391-411.
Berman, S. J, and S. J. Berman, 2012. “Digital transformation: opportunities to create new business models.” Strategy & Leadership, 40: 16-24
Fitzgerald, Michael, Nina Kruschwitz, Didier Bonnet, and Michael Welch. 2013. “Embracing digital technology: A new strategic imperative.” MIT Sloan Management Review, 1–12.
Kane, G. C., Palmer, D. Phillips A. N. Kiron, D., and N. Buckley. 2015. “Strategy, not technology, drives digital transformation ” 571-81.
Mangematin, V., J. Sapsed, and E. Schüßler. 2014. “Disassembly and reassembly: An introduction to the Special Issue on digital technology and creative industries.” Technological Forecasting and Social Change, 83: 1–9.
Massaro, M., J. Dumay, and J. Guthrie, 2016. “On the shoulders of giants: Undertaking a structured literature review in accounting.” Accounting, Auditing and Accountability Journal 29: 767–801.
Nambisan, S., K. Lyytinen, A. Majchrzak, M. Song. 2017. “Digital innovation management: reinventing innovation management research in a digital world”. MIS Quarterly, 41: 223–38.
Rayna, Thierry, and L. Striukova. 2016. “From rapid prototyping to home fabrication: How 3D printing is changing business model innovation.” Technological Forecasting and Social Change, 102: 214–24.
Sebastian, I. M, J. W Ross, and C. Beath. 2017. “How big old companies navigate digital transformation” : 197–213.
Tilson, D. 2010. “Digital infrastructures: The missing IS research agenda”. Infomration Systems Research, 21: 748–59.
Visnjic, I., F. Wiengarten, and A. Neely. 2016. “Only the brave: product innovation, service business model innovation, and their impact on performance.” Journal of Product Innovation Management, 33: 36–52.