21-PM3-03: G3 - Intrapreneurship and Entrepreneurship
How Useful is Accelerator to Entreprenurs and Embrace Innovation
1Nottingham University Business School China; 2Institute of management of technology NCTU; 3Institute of management of technology NCTU;
AppWorks is Taiwan Top 1 incubators program and successfully assisted hundreds of digital technology entrepreneurs to commercial services/product through innovation. This research work is to analyze the gap between entrepreneurs and the accelerator program's expectation. In order to ensure that start-ups develop a comprehensive resource base.
Since most incubation programs are launched within the framework of “Innovation center” are advanced and innovative technologies, we need to explain how would different segment of technology requires different Business Incubators. The terms, such as science park, research park, technology park, business park, and innovation center, etc. have been used interchangeably in past studies (Currie 1985; Eul 1985; Monck et al., 1988). Yet, despite these success examples and the rapid proliferation of accelerators across Taiwan region, empirical and theoretical knowledge about the distinct characteristics and drivers of this new generation incubation model is scant (Birdsall et al., 2013). In addition, the business incubator literature lacks a theoretical lens to analyze and explain the heterogeneity from entrepreneurs’ perspective, with the majority of published studies being descriptive in nature deals with the differences in performance between incubator firms and type of incubators [1. Mian et al., TBI 2016].
Literature is inconclusive as the incubation performance differs should be compared According to both demand and supply of both incubation service and entrepreneur expectation,[7. Barbero et al.]. Yet the start-ups do not take full advantage of the incubator’s resources, which could partly explain these disappointing results (8. PattonandMarlow,2011; 9. Patton,2014).
Building on [8.] and [9.], Entrepreneur with different types of industry, entrepreneur competencies, characteristic entailing personality traits, skills and knowledge level and age of the founders requires a different design of accelerator methodology, How entrepreneur and accelerator programme align with the same expectation from both demand and supply side.
To better comprehend the early process of entrepreneur competency and expectation, we focus through the lens of a qualitative survey on the emergence of ventures, which join the APPworks in Taiwan for such an investigation. And analyze the results to show the gap between the supply and demand side. This presents a promising context in which to study the impact of entrepreneur expectation to the accelerator program and to generate fair and reachable incubation process.
This study is based on the team of the AppWorks Incubation Accelerator. At present, the accelerator has been established for more than four years and has been coached until the 8th year, cultivating more than 150 new creative teams and 350 entrepreneurs. In this paper, we collect data of six technology segments of tenant start-up firms from APPWorks accelerator program and describe these start-ups’ perception and expectation from the program. And analyzes how different segment of technologies of entrepreneurs such as M-commerce, gaming and entertainment, advertisement and media, application developer and, software and hardware integration segments affect these entrepreneurs' need for advice and support from incubators. Interpretations are also made in the six business intelligent aspects based on the criteria we develop in the satisfactory model. In summary, given from the satisfactory level between entrepreneurs’ expectation and accelerators’ resource and support, effectiveness is still unclear, this paper is an attempt to develop a satisfactory framework based on the related literature and, from the development perspective of technology start-ups, we apply the framework to analyze six cases in APPWorks program tenant firms.
Interpretations are made in the six business intelligent aspects based on the criteria we develop in the satisfactory model. In brief, an important finding is that a positive effect of good public image and reputation of accelerator on technology tenants is important. All tenants firm would join accelerator program and competition is to gain public awareness and to create network with ecosystem stakeholders.
In summary, given from the satisfactory level between entrepreneurs’ expectation and accelerators’ resource and support, effectiveness is still unclear, this paper is an attempt to develop a satisfactory framework based on the related literature and, from the development perspective of technology start-ups, we apply the framework to analyze six cases in APPWorks program tenant firms. It is believed that the gap offers an alternative perspective in the analysis of technology incubators. Furthermore, in the process of applying for the satisfactory assessment program, a number of issues concerning the effectiveness and efficiency aspects of incubators are found and presented in the conclusion section. They are worthy of further attention and revisit in the future when the incubator programme needs to be designed and improved.
Contribution to Scholarship
In order to capture the negative and positive effects of incubation programme on the development process of technology firms so as to form the basis of developing the assessment model, we briefly review different types of business incubation programme which is of merit (or demerit ) to young technology based firms. Types of Business Incubators (BIs) both practice and as a research field a consensual definition for BIs is yet to be found (Table 1). In their comprehensive BI research overview, Hackett and Dilts (2004) state that a business incubator is a shared office space facility that seeks to provide its incubates (…) with a strategic, value-adding intervention system of monitoring and business assistance’’ (p.57). This echoes the commonalities found between other definitions advanced by industry associations (NBIA, 2007; UKBI, 2007), large scale studies(EC, 2002; OECD, 1997) and in academic work (Aernoudt, 2004; Sherman and Chappell, 1998) (Table 1).
Contribution to Practice
In the process of applying for the satisfactory assessment program, a number of issues concerning the effectiveness and efficiency aspects of incubators are found and presented in the conclusion section. They are worthy of further attention and revisit in the future when the incubator programme needs to be designed and improved.
Recent years have seen the emergence of a new institutional form in the entrepreneurial ecosystem: the seed accelerator. In practice, accelerator programs are a combination of previously distinct services or functions that were each individually costly for an entrepreneur to find and obtain.
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Open Innovation Profiles
Ecole polytechnique, France
Open Innovation at an organisational level has been largely studied in the litterature, but Open Innovation actors are the absentees of scientific litterature. Through this study, we would like to understand (1) if there are some differences between different actors of Open Innovation (2) if there are different profiles.
Traditionnaly, Open Innovation is defined by Chesbrough (2003) as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively. Open Innovation is a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology” (Chesbrough, 2003, p.1). In this study, by Open Innovation, we mean the use of both – external (external Open Innovation or traditional Open Innovation definition) and internal (internal Open Innovation or Employee Driven Innovation theory) - sources for the large organization to innovate.
Open Innovation at an organisational-level has been largely studied in the litterature, but Open Innovation at an individual-level has been poorly studied.
What are the intrinsic characteristics and profiles of Open Innovation actors ?
Through this study, we observe two sources of innovation for the firm (1) internal sources of innovation (i.e. employees) and (2) external sources of innovation (i.e. startups), involved into two types of flows directions (a) inbound and (b) outbound.
Concerning methodology, we use two complementary methodologies. (1) A quantitative methodology by administration of a personality questionnaire to individuals, based on the Big Five model. (2) the second methodology used is case studies methodology. Indeed, we propose a detailed analysis of about ten experimentation projects carried out with internal and external sources of innovation.
Concerning methodology, we use two complementary methodologies. (1) A quantitative methodology by administration of a personality questionnaire to individuals, based on the Big Five model. The Big Five model is recognized - in the field of psychology - as the major model of personality field. The five factors of personality - extraversion, agreeableness, consciousness, neuroticism, openness to experience - are measured by the Big Five Inventory questionnaire (John et al., 1991). The questionnaire we used is a French adaptation of the Big Five Inventory (Plaisant et al., 2010), validated psychometrically. This questionnaire is composed of 45 items. Individuals should indicate on a 5-point Likert scale their degree of agreement or disagreement with the proposals.
(2) the second methodology used is case studies methodology. Indeed, we propose a detailed analysis of about ten experimentation projects carried out with internal and external sources of innovation. These case studies allow us a better understanding which profiles are sought, how are they selected, from which criteria.
Preliminary results (the study is in progress) show differences between the different actors of Open Innovation and in coherence with the results of Bager et al. (2010) 's study which highlight the intrinsic differences between the intrapreneurs and the entrepreneurs characteristics.
Contribution to Scholarship
Concerning academic perspective, this study allows a better understanding of Open Innovation actors and their intrinsic characteristics.
Contribution to Practice
Concerning management perspective, this study allows managers of Open Innovation a better understanding of these profiles and to adapt their management to these profiles.
Study intrinsic characteristics of Open Innovation actors is a new approach of Open Innovation. Furtehermore, understand intrinsic characteristics of these actors is a key driver to succeed the challenge of innovation.
Bager, T., Ottosson, H., & Schott, T. (2010). Intrapreneurs, entrepreneurs and spin-off entrepreneurs: similarities and differences. International Journal of Entrepreneurship and Small Business, 10(3), 339–358.
Bogers, M., Zobel, A.-K., Afuah, A., Almirall, E., Brunswicker, S., Dahlander, L., … Haefliger, S. (2017). The open innovation research landscape: Established perspectives and emerging themes across different levels of analysis. Industry and Innovation, 24(1), 8–40.
Chesbrough, H., Vanhaverbeke, W., & West, J. (2006). Open innovation: Researching a new paradigm. Oxford University Press on Demand.
Plaisant, O., Courtois, R., Réveillère, C., Mendelsohn, G. A., & John, O. P. (2010). Validation par analyse factorielle du Big Five Inventory français (BFI-Fr). Analyse convergente avec le NEO-PI-R. In Annales Médico-psychologiques, revue psychiatrique (Vol. 168, p. 97–106). Elsevier.
Uncovering Practice Dynamics of Corporate Accelerators and Their Role in Open Innovation
ESCP Europe, France
This study focuses on corporate accelerators, one of the latest organizational forms used in outside-in open innovation, to help large firms to engage with the startup ecosystem (Kohler, 2016; Weiblen & Chesbrough, 2015).
Extant research on accelerators has largely focused on independent programs, launched by seed capital investors, and how those accelerators’ value proposition differentiates from that of incubators and business angels (Cohen & Hochberg, 2014; Pauwels et al., 2016). Recently, an emerging literature stream on accelerators sponsored by large firms has started to provide insights on these new organizations (Ben Mahmoud-Jouini et al., 2018; Kohler, 2016; Kupp et al., 2017; Prexl et al., 2018). Corporate Accelerators (CAs) deserve our attention as scholars given that corporate investors usually have strategic goals that go beyond pure financial returns, as literature on Corporate Venture Capital has highlighted (Dushnitsky & Lenox, 2006). Literature shows CVCs funds are subject to divergent institutional logics, compared to those sponsored by independent investors, which produces practice deviations (Dushnitsky & Shapira, 2010; Souitaris & Zerbinati, 2014). Therefore, CA practices might be diverge from documented practices of independent accelerators and impact innovation.
Despite CAs’ increased prevalence, our understanding of their organizational practices and their dynamics is limited. As expressed in recent calls “we need more research on how accelerators evolve” (Colombo, Rossi-Lamastra, & Wright, 2018). A process approach is needed to increase our understanding of the acceleration phenomenon (O’Mahony & Karp, 2017)
This study addresses those gaps by focusing on:
- How corporate accelerators, as organizations, have developed over time?
- Why have their practices changed?
- How those changes might impact the open innovation process?
Given the novelty of the accelerator phenomenon and the embryonic stage of literature on this field, I employed a qualitative approach, grounded on six corporate accelerators in Europe. The case study method is appropriate to understand complex processes occurring in their natural context and answer to how questions, like those driving this study (Yin, 2009). Data analysis followed recommendations for qualitative rigor in inductive research, taking a process approach (Gioia, Corley, & Hamilton, 2013). For the multiple case perspective, it draws from pattern recognition techniques suggested by Miles & Huberman (1994).
Data were collected from 6 corporate accelerators in Europe. Notably, it includes Alpha, a pioneering corporate accelerator launched in 2011, one of the oldest active corporate accelerators.
The longitudinal data collection involved multiple data sources gathered over four years. It includes 52 semi-structured interviews to accelerators’ management at different levels, startup founders that participated in the program, as well as partners and other ecosystem stakeholders. In addition, primary data was complemented with internal documents such as presentations and brochures, as well as an extensive review of publicly available data from accelerators’ websites, newsletters, and press releases over the life of the organizations.
These varied data sources helped me to gain a rich understanding of corporate accelerators’ ways of working and how those have changed.
Corporate accelerators started by imitating the “accelerator model” created by independent investors. In this sense, they typically looked for young entrepreneurial ventures through an open call for applications, selecting those that had the highest growth potential and investment upside. To support startups during the acceleration period, corporate accelerators provided ventures with educational workshops on entrepreneurial skills. In addition, they give them access to various internal and external mentors, and finally, they showcased them in front of potential investors in a demo day.
However, corporate accelerators goals and practices have significantly deviated from their initial financial orientation. Increasingly, corporate accelerators have focused on business development as central objective and have introduced new practices encouraging experimentation and co-creation between business units from the sponsoring firm and the startups. Taking an institutional lens, this study proposes an empirically grounded model to increase our understanding of how organizational practices in corporate accelerators have evolved. It reveals how different strategies have been used by corporate accelerators to align conflicting institutional logics from its different stakeholders (i.e., corporations, entrepreneurs and investor community), in order to facilitate the adoption of outside-in open innovation in the context of large firms collaborating with new ventures.
Contribution to Scholarship
This article articulates how different institutional logics interact and are managed by a new organizational form, the corporate accelerator, trying to implement outside-in open innovation between startups and large corporations. In this sense, this study contributes open innovation literature, by enhancing our understanding of outside-in process and how a new organizational form represented by the accelerators facilitate this process. Moreover, using a process approach it reveals different phases necessary for the organizations to adopt these practices.
In addition, the study contributes to institutional theory by shedding light on how multiple institutional logics (three or more) interact, and it also identifies novel strategies to manage institutional complexity. This something that has been raised as an important gap in organizational studies, because existing literature has limited the analysis to few logics (Greenwood, Raynard, Kodeih, Micelotta, & Lounsbury, 2011).
Contribution to Practice
This article contributes to management practice by opening the black box of corporate accelerator practices and their evolution over time. It also reveals some of the challenges that business units and accelerator management face when trying to implement open innovation through this new tool and provides some insight on how experienced companies from the case studies have overcome them.
This research fits well with the conference because it addresses a central topic proposed in the general call for papers: Open Innovation and Co-creation. The article focuses on the corporate accelerators’ practice changes over time towards facilitating co-creation. Corporate accelerators role in outside-in open innovation is still poorly understood.
Ben Mahmoud-Jouini, S., Duvert, C., & Esquirol, M. (2018). Key Factors in Building a Corporate Accelerator Capability. Research-Technology Management, 61(4), 26–34.
Cohen, S., & Hochberg, Y. V. (2014). Accelerating Startups: The Seed Accelerator Phenomenon. SSRN Electronic Journal, 1–16. https://doi.org/10.2139/ssrn.2418000
Dushnitsky, G., & Lenox, M. J. (2006). When does corporate venture capital investment create firm value? Journal of Business Venturing, 21, 753–772. https://doi.org/10.1016/j.jbusvent.2005.04.012
Dushnitsky, G., & Shapira, Z. (2010). Entrepreneurial finance meets organizational reality: Comparing investment practices and performance of corporate and independent venture capitalists. Strategic Management Journal, 31(9), 990–1017. https://doi.org/10.1002/smj.851
Greenwood, R., Raynard, M., Kodeih, F., Micelotta, E. R., & Lounsbury, M. (2011). Institutional complexity and organizational responses. Academy of Management Annals, 5(1), 317–371. https://doi.org/10.1080/19416520.2011.590299
Hallen, B. L., Bingham, C. B., & Cohen, S. (2014). Do Accelerators Accelerate? A Study of Venture Accelerators as a Path to Success? Academy of Management Proceedings, 2014(1), 12955–12955. https://doi.org/10.5465/AMBPP.2014.185
Kohler, T. (2016). Corporate accelerators: Building bridges between corporations and startups. Business Horizons, 59(3), 347–357. https://doi.org/10.1016/j.bushor.2016.01.008
Kupp, M., Marval, M., & Borchers, P. (2017). Corporate accelerators: fostering innovation while bringing together startups and large firms. Journal of Business Strategy, 38(6), 47–53. https://doi.org/10.1108/JBS-12-2016-0145
O’Mahony, S., & Karp, R. (2017). The Promise and Perils of Field Research in Entrepreneurial Accelerators. In Princeton Kauffman Conference (Vol. July, pp. 1–12).
Pauwels, C., Clarysse, B., Wright, M., & Van Hove, J. (2016). Understanding a new generation incubation model: The accelerator. Technovation, 50–51, 13–24. https://doi.org/10.1016/j.technovation.2015.09.003
Prexl, K.-M., Hubert, M., Beck, S., Heiden, C., & Prügl, R. (2018). Identifying and analysing the drivers of heterogeneity among ecosystem builder accelerators. R&D Management, 0(0). https://doi.org/10.1111/radm.12352
Souitaris, V., & Zerbinati, S. (2014). How do Corporate Venture Capitalists do Deals? An Exploration of Corporate Investment Practices. Strategic Entrepreneurship Journal, 8(4), 321–348. https://doi.org/10.1002/sej.1178
Weiblen, T., & Chesbrough, H. W. (2015). Engaging with Startups to Enhance Corporate Innovation. California Management Review, 57(2), 66–91.
Unpacking the entrepreneurial perception of uncertainties in the nascent ecosystem: the role of heuristics
Universidade de São Paulo, Brazil
It is widely accepted that the entrepreneurial judgment and action occur under uncertainty (Klein and Foss, 2018, Parckard et al., 2017). Although the uncertainty construct occupies the center of stage in entrepreneurship and innovation literature (Townsend et al, 2018), traditionally scholars approach the uncertainty construct more as a contextual variable.
Heuristics can be defined how cognitive shortcuts adopted by individuals when there is a restriction of time, information and processing capacity (Simon, 1965). Although scholars traditionally address the heuristics construct as biases (Tversky e Kahneman, 1974; Baron, 2007; Sbicca, 2014), which affect negatively the decision outcomes, Gigerenzer e Goldstein, 1996; Saravasthy, 2001; Bingham e Eisenhardt, 2011; Sull e Eisenhardt, 2015 consider that the heuristics might be positive and lead to superior performance in uncertain settings. Our main goal is to refine the entrepreneurial action and uncertainty management by identifying which heuristics are employed by entrepreneurs to perceive uncertainties.
Before the decision about which uncertainty is more critical to the future performance of entrepreneurial performance (as suggested Rice et al., 2008), there is a previous phase, less investigated, the perception of uncertainty. Indeed, there is little knowledge about the mechanisms related to the entrepreneurial perception of uncertainties.
The research question that guides this scientific inquiry is: how do entrepreneurs perceive uncertainties in nascent markets? To approach our research question, we adopt the heuristics perspective. Heuristics can be defined how cognitive shortcuts adopted by individuals when there is a restriction of time, information and processing capacity (Simon, 1965).
To address our research question, we adopt the case study approach (Eisenhardt, 1989). Following Eisenhardt’s (1989) recommendations for building theory from cases, we adopt an intentional, not random sampling. We looked for entrepreneurs who: (i) created a new venture for developing and commercializing a radical innovation, (ii). who had launched the new venture and commercialized his/her first product or service. The nature of this research is qualitative.
We produced 5 startups from an initial list with 12 candidates. We built a conceptual framework, linking the uncertainty construct (Knight, 1921), entrepreneurial judgment and heuristics (Tversky e Kahneman, 1974; Gigerenzer e Goldstein, 1996; Bingham and Eisenhardt, 2011). Based on this framework, we develop our script and research protocols. We conducted 23 interviews with entrepreneurs in different moments of startup development.
We adopted a well-structured process for coding and data analysis. We started with an open coding process by developing the first order codes from our empirical data. Based on the interaction between our conceptual framework and the empirical data, some patterns of heuristics emerged. These findings refer more to categories of heuristics than specific heuristics (once entrepreneurs expressed in different terms similar heuristics). This understanding is in line with Tversky e Kahneman, 1974; Gigerenzer e Goldstein, 1996; Bingham and Eisenhardt, 2011 and other authors.
We identified five categories of heuristics related to entrepreneurial perception. The first heuristic category is the temporal heuristics, i.e., heuristics related to how entrepreneurs search for uncertainties related to time (e.g., short term, long term). We identify that some entrepreneurs looked for uncertainties related to the long term (aftermarket entry), while others focused more on uncertainties in the short term. The second category is related to the area (market, technology, resources, business model, organizational). We identified that the heuristics related to how entrepreneurs explored some themes or areas to identify uncertainties. Some entrepreneurs overemphasized a particular area (e.g., technology), ignoring others that might affect the new venture performance. The following category is scaling up heuristics. Some entrepreneurs focused on the identified uncertainties related to bottlenecks for new venture growth. We also some heuristics related to how entrepreneurs value some uncertainties as more critical or not. Finally, we identify a heuristic category related to the extent of uncertainty, i.e., which actors are affected by a particular uncertainty.
Contribution to Scholarship
Our results advance the literature in some ways. First, our findings complement the research stream in uncertainty management (e.g., Gomes et al., 2018, Rice et al., 2008). We show how entrepreneurs explore the problem space and identify critical uncertainties that shape the action course. We also advance the heuristics research stream. We enlarge the set of entrepreneurial heuristics and connecting them into the cognition literature. Our findings show how entrepreneurs allocated the attention and advance the attention orientation approaches. Based on our findings, we propose some hypotheses, which can guide further research.
Contribution to Practice
Our research will aid the process of perception of uncertainties of the entrepreneurs. With a better understanding of heuristics, entrepreneurs can improve the process of perceiving uncertainty and be more responsive in the process of choosing the best way to manage the uncertainties identified by them.
Our research contributes to the Theme 6: Radical and systemic innovation.
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