Spanning Two Worlds? Corporate Accelerators and Corporate ......of “innovation theater”, and a recent report estimates that 60 percent of corporate accelerators that existed in

Spanning Two Worlds?

Corporate Accelerators and Corporate Venture Capital in Innovation Portfolios

DRAFT

Submitted to Wharton Technology & Innovation Conference 2020

January 2020

Sheryl Winston Smith

BI Norwegian Business School

[email protected]

ABSTRACT

This paper tackles a crucial problem in the theory and practice of global innovation strategy: In the face of technological and business model disruptions, often originating in innovative startups, how do companies respond with entrepreneurial ideas? Specifically, how do established companies use entrepreneurial approaches to facilitate access to startup innovation? This research examines the roles of corporate accelerators and corporate venture capital investing. Using a sample of companies with both corporate accelerators and corporate venture capital arms, this study provides preliminary descriptive evidence of the role of both types of external knowledge partnerships with startups. Specifically, the paper points to greater experimentation in CVC portfolios in terms of startup focus and industry distribution relative to corporate accelerator portfolios. At the same time corporate accelerators may provide access to greater geographic distribution in their portfolio companies. A novel algorithmic approach based on natural language processing and machine learning is used to characterize the distributions of portfolio companies. The paper raises questions for further study addressing the potential for complementarity and substitution amongst these two forms of corporate innovation in partnership with startups. Keywords: Corporate venture capital; accelerators; innovation; startups; natural language

processing

2

INTRODUCTION

This paper tackles a crucial problem in global innovation: In the face of technological and

business model disruptions, often originating in innovative startups, how do companies respond

with innovative ideas? In particular, to what extend do established, global companies able

attempt to integrate internal corporate innovation efforts with external, internationally distributed

sources of entrepreneurial ideas? The relationship between established companies and startups

takes on growing importance as the locus of innovation increasingly comprises a global

ecosystem of opportunity seekers (Reuber et al., 2018) in which innovation efforts are dependent

upon a web of relationships with external parties (Papanastassiou et al., 2019).

Companies have long incorporated external partnerships to stimulate and extend internal

innovation efforts. Staying competitive in the face of disruptive technological and business

model innovation requires significant attention to novel ideas that originate in startups.

Traditionally, companies rely on corporate venture capital funds as a key component of their

strategic approach to accessing these external, early stage ideas (Dushnitsky et al., 2005; Maula

et al., 2012; Winston Smith et al., 2013). These funds allow companies to tap into ideas

emerging from startup companies through structured equity relationships. More recently,

however, established companies are using another tool to provide them with access to

entrepreneurial ideas and knowledge: partnerships with accelerators (Deloitte, 2015; Ream et al.,

2016). Corporate accelerators are increasingly deployed as an intersection between corporate

innovation and the startup ecosystem. The number of corporate accelerators has been growing

substantially every year since 2010, with 105 new corporate accelerators launched between 2013

and 2015 alone (Ream et al., 2016). A 2016 survey of the Forbes Global 500 found that 68% of

the top 100 companies engage with startups using a variety of mechanisms (Bonzom et al.,

3

2016). The survey identified a two-way flow of benefits of such collaboration, including faster

internal execution, changes in internal culture, enhanced brand image, and the ability to identify

new trends ahead of competitors (Bonzom et al., 2016). In practice, corporate accelerators face a

balance between the goals of venture launch and strategic fit with corporate innovation (Shankar

et al., 2018).

The literature provides deep insights into corporate responses to current and anticipated

disruptive changes that may impact their technological success or interfere with their business

model. The strategy literature includes a rich understanding of corporate partnerships that take

the form of strategic alliances (Gulati et al., 2009; Leiblein et al., 2002; Rothaermel et al., 2004)

and a variety of institutional arrangements companies use to be more entrepreneurial (Keil et al.,

2008b; Ott et al., 2017). The literature also points to the importance of a firm’s own experience

in shaping the ability to adapt to disruptive changes (Chen et al., 2012). Corporate venture

capital activity is perhaps most clearly related to the distinct relationship between the innovation

activities of large companies and access to startup knowledge (Wadhwa et al., 2006; Wadhwa et

al., 2016; Winston Smith et al., 2013) and of its limitations in that regard (Benson et al., 2010;

Dushnitsky et al., 2009). The literature further provides insight into challenges startups face in

the relationship with a corporate investor (Katila et al., 2008).

This paper brings together insights from the literature on corporate innovation

mechanisms with the growing literature on accelerators. Accelerators are characterized by

several distinct features that make them a novel organizational form, including formal

application and selection mechanisms for entry; pre-determined cohorts with a fixed length of

time (typically 3-4 months); and a formal ending point typically marked by a “Demo Day” event

in which startups in a given cohort pitch to investors (Clarysse et al., 2015; Cohen et al., 2014).

4

Most accelerators also take a small equity stake in the startup. The emerging literature on

accelerators suggests that accelerators facilitate experimentation by entrepreneurs, helping them

learn quickly which ideas have promise and which are likely to fail; this translates into quicker

exits both through acquisition and through quitting (Winston Smith et al., 2014). For

entrepreneurs, the impact of accelerators may be delivered through the intensive mentoring

experience (Cohen et al., 2018). Accelerators also foster intensive peer learning through the

cohorts (Winston Smith et al., 2015). The literature provides mixed evidence on whether

accelerators increase the speed of funding. In one study, entrepreneurs that participate in an

intensified learning portion of the government sponsored Startup Chile program are more likely

to raise subsequent funding (Gonzalez-Uribe et al., 2017). However, using a broader sample Yu

(2015) finds that accelerator-backed startups take longer on average to receive funding.

THEORETICAL FRAMEWORK

Companies regularly engage in external search for innovation, drawing on wide variety

of mechanisms to do so (Foss et al., 2013). Corporate venture capital (CVC) is one of the most

prominent and well-established approaches (Benson et al., 2009; Dushnitsky et al., 2005;

Wadhwa et al., 2006; Wadhwa et al., 2016). In contrast, corporate accelerators are a relatively

newer --and in some ways more controversial -addition to the corporate innovation toolbox

(CBInsights, 2019).

Several recent reports point to the growing use of corporate accelerators as an

increasingly practical intersection between corporate innovation and the startup ecosystem.

Similar to corporate venture capital, corporate accelerators are “outside-in” approaches to

innovation (Weiblen et al., 2015). In the general press, assessment of the value of corporate

accelerators is somewhat mixed. For example, one view is that corporate accelerators are a form

5

of “innovation theater”, and a recent report estimates that 60 percent of corporate accelerators

that existed in 2016 have since shut down (CBInsights, 2019). In practice, corporate accelerators

face a balance between the goals of venture launch and strategic fit with corporate innovation

(Shankar et al., 2018). Moreover, corporate accelerators also exist in relation to existing,

established forms of external knowledge seeking from startups, such as CVC.

As a first step in understanding the impact of corporate accelerators this research seeks to

first characterize the nature and extent of corporate accelerators in relation to existing CVC

programs. Similar to CVC, corporate accelerators provide the opportunity to assess the early

validity and relevance of nascent startups without a large commitment of resources.

Given the nascent status of this literature, this research takes a question-driven approach

to understanding the relationship between corporate accelerators and existing CVC investments

(Tidhar et al., 2019).

Experimentation and Corporate Innovation

There is a growing literature on the importance of experimentation and tolerance for

failure in corporate innovation (Keil et al., 2008a; Li et al.; Patel et al., 2015; Tian et al., 2011).

On the face of it, both corporate accelerators and CVC investing can be seen as approaches that

increase exposure to new, potentially different ideas, and thus serve as a funnel for greater

experimentation. Corporate accelerators and CVC investments might plausibly overlap or they

might plausibly diverge from one another, and in relation to larger to corporate innovation goals

and priorities.

Corporate accelerators and CVC are similar in that both represent portfolio approaches to

external innovation. In this regard, both approaches draw upon the logic of investing in a

relatively broad set of earlier stage ideas than would typically be developed using in-house

6

innovation (Fulghieri et al., 2009; Lin et al., 2011; Wadhwa et al., 2016). Traditionally, CVC

provides exposure to a larger set of ideas for a relatively small investment. As in independent

VC investing, CVC investors recognize that the outcomes of these investments will be skewed,

with many failures and some successes (Gompers et al., 2010). However, distinct from

traditional VC investing, which relies solely on financial return as a metric of success, CVC has

distinct performance goals of innovation outcomes as well as financial measures (Dushnitsky et

al., 2010). As in CVC portfolios, corporate accelerators are also tasked with generating

innovation returns while tracking financial performance metrics, such as VC investment, as well.

In keeping with the portfolio logic, both corporate accelerators and CVC units are

intended to increase exposure to a variety of ideas that are distinct from--yet complementary to—

in house innovation efforts. In this regard the goals of both types of programs are similar.

However, several fundamental questions pertain to the range of exposure to new ideas in

corporate accelerators relative to CVC investments. To the extent that corporate accelerators are

being established with goals of targeting innovation solutions, at the portfolio level, corporate

accelerators might plausibly be expected to provide relatively more targeted ideas compared to

CVC investments. The follow propositions apply at the portfolio level:

Proposition 1: Experimentation in range of ideas

Corporate accelerators provide a portfolio of more tightly aligned ideas

relative to the CVC investment portfolio.

Proposition 2:Industry diversity of portfolio

Corporate accelerators provide a portfolio of more tightly aligned industry

segments relative to the CVC investment portfolio.

On the other hand, the accelerators are able to draw on startups from a greater geographic

7

distribution relative to VC investing. Similarly, to the extent that corporate accelerators follow a

congruent, cohort-based and location-based approach as traditional accelerators, these corporate

accelerators might be able to bring in a portfolio of companies from a greater range of

geographic regions relative to CVC investments. Moreover, by having startups in house for a

concentrated period of time, corporate accelerators may be able to reduce the monitoring needs

that lead to geographic constraints in CVC investing. Proposition 3 follows at the portfolio level:

Proposition 3: Geographic diversity of portfolio

Corporate accelerators will invest in ventures from greater variety of

geographical locations relative to the CVC investment portfolio.

RESEARCH DESIGN, DATA, AND METHODOLOGY

This research draws on a sample of companies that have established both CVC

investment arms and corporate accelerators and analyzes these portfolios along multiple

dimensions. For the corporate investor, we seek to capture the value of experimentation through

exposure to the variety of new ideas from startups from two distinct sources, CVC investments

and corporate accelerators. We do this through several measures including deal size, industry,

and geographic distribution.

Further, we develop a novel, machine-learning natural language processing approach to

compare the relative similarity amongst the portfolio of startups in either the CVC portfolio or

the corporate accelerator.

Data and Sample

The data is derived at the deal level from corporate accelerator and corporate venture

capital deals in the CB Insights database, supplemented with information from other websites.

8

For the corporate partner, we seek to capture the value of experimentation through exposure to

the variety of new ideas from these startups. We do this through several measures including

industry, geographic, and business level diversity of investments into the startups that receive

investment.

The sample includes deals spanning the period 2009-2019 in order to include the time

period before and during which corporate accelerators become pervasive. The sample focuses on

several industries in which both CVC and corporate accelerators are prevalent, and where data

on both forms of investment are available. The final sample consists of corporate investors and

corporate accelerators in the following industries: financial services and internet

software/services.

The key criteria for inclusion are that the given corporate investor have both a CVC

investment program as well as an active corporate accelerator. Thus, while many companies

have CVC arms, they do not have corporate accelerators. Conversely, other companies have

taken the step to establish accelerators but do not make CVC investments. Finally, the sample

focuses exclusively on corporate accelerators and thus excludes stand-alone, traditional

accelerators. The final sample includes two companies within each of these two industries. The

final sample consists of Barclays and Wells Fargo in the financial services industry and

Microsoft and Amazon in internet software/services. These companies were selected for

preliminary analysis on the basis of having both CVC investment and corporate accelerators.

The final sample consists of 295 CVC investments and 678 CA investments by these four

companies.

Natural Language Processing (NLP) Analysis of Portfolio Companies

Natural language processing (NLP) is an algorithmic, machine-learning based approach

9

to computing similarity between text documents (Gentzkow et al., 2017; Hoberg et al., 2014;

Winston Smith, 2019; Winston Smith et al., 2013). CB Insights data provides uniform text

descriptions of each portfolio company. These text descriptions are then processed using Python

and the spaCy natural language processing module. Briefly, this approach involves

preprocessing steps of tokenizing and removing stop words, then mapping the text onto the large

English core corpus available in spaCy, producing a vector of words for each description. The

vectors corresponding to the texts of each of the portfolio companies are then compared

dyadically using a cosine similarity transformation trained using the GloVe w2v model

(https://nlp.stanford.edu/projects/glove/). This yields a cosine similarity score for each

combination, which is then the basis for analysis. Details are provided in the appendix.

PRELIMINARY FINDINGS

Deal Composition

The number of deals and total financing by source and company are summarized in

Table 1. In looking at the data several trends emerge. First, both the amount invested total and

amount per deal are substantially larger, in some cases by orders of magnitude, for CVC

investments relative to the corporate accelerators. This is true for all four firms and in both

industries. Second, true exits (through an IPO or an acquisition of the portfolio company) are

low overall, but particularly so for the corporate accelerator companies. Indeed, no exits are

recorded for the corporate accelerator companies.

-------------------------------------------------

Insert Table 1 about here

-----------------------------------------------------

10

Portfolio Composition

Experimentation in range of ideas.

The results from the natural language processing analysis are presented in Figures 1-8.

Summary statistics for each distribution are shown in Table 2.

Overall, the sieve of innovation ideas appears to be broad in both corporate accelerators

and CVC investments. Figures 1-8 allow us to compare the distribution of the similarity of the

startup descriptions between the corporate accelerator portfolio and the CVC portfolios within

each company. Recall that the NLP approach provides a measure of the similarity of the

descriptive texts within each portfolio. As evidenced in the histograms, in general the portfolios

of both the accelerator companies and the CVC investments reflect a distribution of ideas, seen

in the dispersion of cosine similarity scores. This suggests that both the corporate accelerators

and the CVC investments are tapping into startups pursuing a range of ideas.

Comparing within each corporate partner, between the corporate accelerator portfolios

and the CVC portfolios, it appears that the corporate accelerators are drawing on startups with

relatively more tightly clustered ideas compared to each CVC counterpart. Further, comparing

across the corporate partners, the pattern appears to be similar, with the distribution of ideas in

the corporate accelerator portfolio being more tightly clustered than in the CVC portfolio in each

pair.

As well, this relationship holds for companies in financial services (Barclays and Wells

Fargo) and in internet (Microsoft and Amazon). For example, this is particularly evident in

Barclays (Figure 1 and Figure 2), but the similar pattern exists for Wells Fargo (Figure 3 and

Figure 4) and for Microsoft (Figure 5 and Figure 6) and for Amazon (Figure 7 and Figure 8).

11

-------------------------------------------------

Insert Table 2 about here

-----------------------------------------------------

-------------------------------------------------

Insert Figures 1-8 about here

-----------------------------------------------------

Industry diversity.

Figure 9 through Figure 16 show the distributions of industry segments for portfolio

companies. Within each corporate pair, the range of industry segments appears to be greater in

the CVC investment portfolios relative to the corporate accelerator portfolios. This pattern is

more pronounced in some pairs, but the general pattern appears to hold for each of the four

corporate partners.

-------------------------------------------------

Insert Figures 9-16 about here

-----------------------------------------------------

Geographic diversity.

Table 3 presents the geographic distribution of startups in the corporate accelerator and

CVC portfolios for each corporate partner. The breakdown of investments among the United

States, Europe, Asia, and other regions is shown for each.

12

The results in this table provide some evidence of greater geographic diversity in the

corporate accelerator portfolio relative to the CVC portfolio. All four of the CVC programs have

a greater concentration of startups from the United States relative to their counterpart corporate

accelerators. However, the evidence is mixed and further analysis is needed to characterize the

relationship.

-------------------------------------------------

Insert Table 3 about here

-----------------------------------------------------

DISCUSSION

The preliminary exploration in this study point to several insights about the nature and

role of corporate accelerators in relation to existing CVC investment. This study provides an

early look inside the composition of corporate accelerator portfolios and compares them to CVC

portfolios along multiple dimensions of interest: targeted concentration of ideas, industry

diversity, and geographic distribution.

On balance, the results point to the role of both corporate accelerators and CVC

investments in furthering exposure to a variety of ideas from a diverse set of sources. Building

on the growing literature on experimentation in corporate innovation, this study focuses on the

role of multiple forms of external knowledge search in expanding the set of ideas to which a

company is exposed. At the same time, the results of this study suggest that several approaches

may be utilized in tandem, and that these different approaches might yield distinct results.

Taken together, the findings in this study suggest that corporate accelerators in tandem

13

with CVC are a broad sieve for innovative ideas that may or may not ultimately be incorporated

with internal innovation efforts. In keeping with the logic of experimentation, this preliminary

analysis suggests that corporate accelerators might be more tightly clustered on ideas and

industry segments relative to CVC, but that these accelerators may also provide access to more

geographically dispersed startups. The ultimate innovation performance implications are left to

future study (in progress).

Areas for Further Study: Complements or Substitutes in Corporate Innovation

A fundamental theoretical question is understanding the tension between whether these

forms of external innovation serve as complements or substitutes to one another? On one hand,

CVC and corporate accelerators might be expected to be complements, i.e. with corporate

accelerators set up to support CVC through access to earlier stage companies, exposure to new

areas (industry, geography), and expand potential deal flow. These specific roles point to

corporate accelerators as complements to existing CVC units.

On the other hand, corporate accelerators might plausibly be substitutes for CVC under

certain conditions, i.e. corporate accelerators might be established to provide similar functions as

CVC units and thus be used in place of CVC investment. For example, if CVC units are less

established (younger) or smaller (assets) we might expect to see the establishment of CA units

instead or as replacement.

At the deal level, in keeping with the logic of most corporate accelerators, several

propositions follow about the nature of these corporate accelerator investments in relation to

CVC. First, similar to traditional accelerators, corporate accelerators are intended to access

earlier stage companies relative to venture capital. Propositions 4a and 4b follow at the deal

level:

14

Proposition 4a: Early stage: Corporate accelerators invest in earlier stage

deals than CVC units.

Proposition 4b: Deal flow: Within a given company, CVC units will invest

subsequent to corporate accelerators in a given portfolio company.

The following competing propositions follow at the portfolio level:

Proposition 5a: Complements: CVC investments will increase in companies

coming through the corporate accelerator relative to other companies.

Proposition 5b: Substitutes: CVC investments will decrease overall following

establishment of CA.

Conclusion

This paper contributes to advancing theory in strategic management in several ways.

From the perspective of understanding corporate innovation, particularly through external

knowledge search and acquisition, this research expands our understanding of how companies

access very early stage ideas. This research also provides insight into how this newer form of

interaction with startups—through a focused and intense, but relatively short duration—

compares to and complements longer standing relationships through CVC equity stakes. Finally,

from the corporate innovation perspective, this paper informs the scholarly understanding of how

interorganizational partnerships may provide complementary assets which work in tandem.

The results of this paper also promise to advance the strategic management literature on

the development of new ventures through relationships with other entities. To the extent that

traditional CVC relationships offer both resources and challenges to startups, this research may

provide insights into ways in which a third-party intermediate can help navigate these

15

boundaries.

Finally, the results of this study also advance the practice of strategic management,

particularly in innovation driven companies by informing managers about the relationship

between existing forms of external knowledge search, such as CVC, and the potential

complementarities in establishing a corporate accelerator.

16

Table 1. Corporate investors with CA and CVC units

Primary industry

(parent) Corporate CVC

# Deals

CVC

Amount

invested CVC

Accelerator # Deals

Accelerator

Amount

invested

Accel

Financial Services Barclays Barclays Ventures 23 $508.1M, 2

exits, 4 others

Barclays

Accelerator

(Techstars)

154 $10.8M

Barclays Barclays (CVC) 11 $317.1M

Financial Services Wells

Fargo

Wells Fargo

Strategic Capital

16 $605.5M

Wells Fargo

Startup

Accelerator

26 $14.8M

Wells

Fargo

Wells Fargo &

Co.

18 $689.9M, 1

other

Internet / Internet

Software & Services /

Web Development

Microsoft Microsoft

Ventures

33 $2.53bn, 1

exit, 3 others

Microsoft

Scaleup

468 $14.5M, 2

others

Microsoft M12 104 $2.73bn $

Internet / Internet

Software & Services /

Web Development

Amazon Amazon Alexa

Fund

68 $1.18bn Alexa

Accelerator

30 $3.48M

Amazon Amazon 22 $2.32bn

17

Table 2. Summary statistics for distributions of portfolio company descriptions

Barclays

Accelerator

Barclays

CVC

Wells Fargo

Accelerator

Wells

Fargo CVC

Microsoft ScaleUp

Accelerator

Microsoft

CVC

Amazon Alexa

Accelerator

Amazon

CVC

mean 0.784 0.738 0.828 0.808 0.803 0.810 0.794 0.776

std 0.075 0.129 0.053 0.074 0.066 0.066 0.063 0.074

min 0.393 0.325 0.624 0.553 0.368 0.538 0.583 0.414

25% 0.744 0.679 0.795 0.763 0.768 0.766 0.758 0.731

50% 0.794 0.758 0.837 0.810 0.813 0.816 0.803 0.782

75% 0.837 0.836 0.864 0.863 0.850 0.858 0.843 0.828

max 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

18

Table 3. Geographic distribution of corporate accelerator and CVC portfolio companies

Barclays

Accelerator

Barclays

CVC

Wells Fargo

Accelerator

Wells

Fargo

CVC

Microsoft ScaleUp

Accelerator

Microsoft

CVC

Amazon Alexa

Accelerator

Amazon

CVC

US 39% 50% 88% 96% 20% 75% 82% 77%

avg deal

(M $)

0.170 49.400 3.690 42.500 14.000 31.500 0.560 47.300

Europe 33% 4375% 0% 0% 15% 9% 12% 8%

avg deal

(M $)

0.120 14.200 0.000 0.000 14.300 36.100 0.370 13.600

Asia 14% 6% 0% 4% 62% 10% 3% 5%

avg deal

(M $)

0.120 0.836 0.000 39.000 0.000 172.200 0.120 27.100

other 14% 0% 4% 0% 3% 6% 3% 10%

avg deal

(M $)

0.120 0.000 0.038 0.000 12.200 49.000 0.120 46.900

19

Figure 1. Distribution of portfolio company descriptions using NLP: Barclays Accelerator

Figure 2. Distribution of portfolio company descriptions using NLP: Barclays CVC

20

Figure 3. Distribution of portfolio company descriptions using NLP: Wells Fargo

Accelerator

Figure 4. Distribution of portfolio company descriptions using NLP: Wells Fargo CVC

21

Figure 5. Distribution of portfolio company descriptions using NLP: Microsoft Accelerator

Figure 6. Distribution of portfolio company descriptions using NLP: Microsoft CVC

22

Figure 7. Distribution of portfolio company descriptions using NLP: Amazon Accelerator

Figure 8. Distribution of portfolio company descriptions using NLP: Amazon CVC

23

Figure 9. Distribution of portfolio company industries: Barclays Accelerator

Figure 10. Distribution of portfolio company industries: Barclays CVC

24

Figure 11. Distribution of portfolio company industries: Wells Fargo Accelerator

Figure 12. Distribution of portfolio company industries: Wells Fargo CVC

25

Figure 13. Distribution of portfolio company industries: Microsoft Accelerator

Figure 14. Distribution of portfolio company industries: Microsoft CVC

26

Figure 15. Distribution of portfolio company industries: Amazon Accelerator

Figure 16. Distribution of portfolio company industries: Amazon CVC

27

APPENDIX

The intuition and application of natural language processing techniques (NLP) and the

related field of information retrieval (IR) is developed in the computer science literature (Kwon

et al., 2003; Manning et al., 2008; Salton et al., 1986; Salton et al., 1975).2 Mathematically, IR

weights the relative similarity of knowledge from two sources of text to facilitate efficient

identification, based on the criteria of precision, i.e., the proportion of correct documents

retrieved, and recall, i.e., the proportion of relevant documents returned (Lease, 2007; Lewis et

al., 1996; Singhal, 2001).

The vector-space model underlies this process and enables direct quantification of document

similarity between a given pair of retrieved texts. This paper algorithmically executes a series of

steps to simplify text documents, assign relative weights to words, and map them into n-

dimensional vector space. A meaningful overlap between these vectors is then computed to

provide a metric for document comparison (Salton et al., 1986; Salton et al., 1975; Singh et al.,

2012). As such, this method is considered to be a ranked retrieval model (Manning et al., 2008).

These algorithms comprise the backbone of search engines (Google, 2013a, 2013b; Kwon et al.,

2003; Singhal, 2001)..1

Several papers in strategy research look to variants of NLP methodologies to quantify the

relative importance of words within a given text. Menon et al. (2018) use NLP to analyze

2 Briefly, IR as a field can be thought of as ‘… finding material (usually documents) of an unstructured nature

(usually text) that satisfies an information need from within large collections (usually stored on computers)

(Manning et al., 2008, p.1). IR covers wide ground: open-ended web search, domain-specific sea This paper focuses

on domain-specific search (Manning et al., 2008, p. 2). Search space can be conceptualized as a query seeking

answers relevant to a question at hand Technically, IR and NLP are distinct fields, where the former focuses on

probabilistic approaches and the latter on the use of “natural” (as opposed to “controlled”) queries (Lewis et al., 1996). However, although these fields were largely separate for decades, they are becoming increasingly

interchangeable as NLP adopts probabilistic approaches and IR broadens in scope (Lease, 2007; Lewis et al., 1996;

Manning et al., 1999; Singhal, 2001).

28

strategy change and differentiation from rivals. Lee and James (2007) use centering resonance

analysis (CRA), which utilizes network positioning of words in text to determine influence, in

this case to relate gender to media slant in the appointment of new chief officers. Ronda-Pupo

and Guerras-Martin (2012) use co-word analysis, in which they measure the likelihood of two-

words appearing together in a given document against a probabilistic backdrop, to study the

evolution of research concepts in the field of strategic management. In a similar fashion,

Kabanoff et al. (2008), apply machine learning techniques based on text classifying algorithms

to examine the evolution of strategic discourse by CEOs. In the context of innovation, and

closely related to the methodology in this paper, Kaplan et al. (2014) use topic modeling to trace

the evolution of breakthrough ideas in the emergent field of nanotechnology based on a form of

natural language processing known as latent Dirichlet allocation (LDA) analysis of patents. This

approach is based on probabilistic modeling of co-occurrence of words to infer meaning about

‘latent’ topics (Blei et al., 2003; Chang et al., 2009).

29

REFERENCES

Benson D, Ziedonis RH. 2009. Corporate Venture Capital as a Window on New Technologies:

Implications for the Performance of Corporate Investors When Acquiring Startups.

Organization Science 20(2): 329-351.

Benson D, Ziedonis RH. 2010. Corporate venture capital and the returns to acquiring portfolio

companies. Journal of Financial Economics 98(3): 478-499.

Blei DM, Ng AY, Jordan MI, Lafferty J. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3(4/5): 993-1022.

Bonzom A, Netessin S. 2016. 500 Corporations: How do the world’s biggest corporations deal

with the startup revolution?Startups (ed.), 500 Startups.

CBInsights. 2019. Why 60% Of Corporate Accelerators Fail After 2 Years.

Chang J, Boyd-Graber J, Wang C, Gerrish S, Blei DM. 2009. Reading Tea Leaves: How

Humans Interpret Topic Models. Neural Information Processing Systems.

http://www.cs.princeton.edu/~blei/papers/ChangBoyd-

GraberWangGerrishBlei2009a.pdf [2009].

Chen PL, Williams C, Agarwal R. 2012. Growing pains: Pre‐entry experience and the challenge

of transition to incumbency. Strategic Management Journal 33(3): 252-276.

Clarysse B, Wright M, Van Hove J. 2015. A look inside accelerators, Nesta: United Kingdom.

Cohen S, Hochberg YV. 2014. Accelerating Startups: The Seed Accelerator Phenomenon. SSRN

eLibrary. http://ssrn.com/abstract=2418000.

Cohen SL, Bingham CB, Hallen BL. 2018. The Role of Accelerator Designs in Mitigating

Bounded Rationality in New Ventures. Administrative Science Quarterly:

0001839218782131.

Deloitte. 2015. Design principles for building a successful corporate acceleratorDigital D (ed.).

Dushnitsky G, Lenox MJ. 2005. When do firms undertake R&D by investing in new ventures?

Strategic Management Journal 26: 947-2005.

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.

Dushnitsky G, Shaver JM. 2009. Limitations to interorganizational knowledge acquisition: the

paradox of corporate venture capital. Strategic Management Journal 30(10): 1045-

1064.

Foss NJ, Lyngsie J, Zahra SA. 2013. The role of external knowledge sources and organizational

design in the process of opportunity exploitation. Strategic Management Journal 34(12): 1453-1471.

30

Fulghieri P, Sevilir M. 2009. Size and Focus of a Venture Capitalist's Portfolio. Review of Financial Studies 22(11): 4643-4680.

Gentzkow M, Kelly BT, Taddy M. 2017. Text as Data. National Bureau of Economic Research Working Paper Series No. 23276.

Gompers P, Kovner A, Lerner J, Scharfstein D. 2010. Performance persistence in

entrepreneurship. Journal of Financial Economics 96(1): 18-32.

Gonzalez-Uribe J, Leatherbee M. 2017. The Effects of Business Accelerators on Venture

Performance: Evidence from Start-Up Chile. The Review of Financial Studies:

hhx103-hhx103.

Google I. 2013a. Information Retrieval at Google.

http://research.google.com/pubs/InformationRetrievalandtheWeb.html (August 31,

2013.

Google I. 2013b. Natural language processing at Google.

http://research.google.com/pubs/NaturalLanguageProcessing.html (August 31, 2013.

Gulati R, Lavie D, Singh H. 2009. The nature of partnering experience and the gains from

alliances. Strategic Management Journal 30(11): 1213-1233.

Hoberg G, Maksimovic V. 2014. Redefining Financial Constraints: A Text-Based Analysis.

Review of Financial Studies.

Kabanoff B, Brown S. 2008. Knowledge structures of prospectors, analyzers, and defenders:

content, structure, stability, and performance. Strategic Management Journal 29(2):

149-171.

Kaplan S, Vakili K. 2014. The Double-Edged Sword Of Recombination In Breakthrough

Innovation. Strategic Management Journal: n/a-n/a.

Katila R, Rosenberger JD, Eisenhardt KM. 2008. Swimming with Sharks: Technology Ventures,

Defense Mechanisms and Corporate Relationships. Administrative Science Quarterly

53(2): 295-332.

Keil T, Autio E, George G. 2008a. Corporate Venture Capital, Disembodied Experimentation

and Capability Development. Journal of Management Studies 45(8): 1475-1505.

Keil T, Maula M, Schildt H, Zahra SA. 2008b. The effect of governance modes and relatedness

of external business development activities on innovative performance. Strategic Management Journal 29(8): 895-907.

Kwon O-W, Lee J-H. 2003. Text categorization based on k-nearest neighbor approach for Web

site classification. Information Processing & Management 39(1): 25-44.

Lease M. 2007. Natural language processing for information retrieval: the time is ripe (again).

Paper presented at the Proceedings of the ACM first Ph.D. workshop in CIKM,

Lisbon, Portugal.

Lee PM, James EH. 2007. She-e-os: gender effects and investor reactions to the announcements

of top executive appointments. Strategic Management Journal 28(3): 227-241.

31

Leiblein MJ, Reuer JJ, Dalsace F. 2002. Do make or buy decisions matter? The influence of

organizational governance on technological performance. Strategic Management Journal 23(9): 817-833.

Lewis DD, Jones KS. 1996. Natural language processing for information retrieval. Commun. ACM 39(1): 92-101.

Li K, Qiu J, Wang J. Technology Conglomeration, Strategic Alliances, and Corporate

Innovation. Management Science 0(0): null.

Lin S-J, Lee J-R. 2011. Configuring a corporate venturing portfolio to create growth value:

Within-portfolio diversity and strategic linkage. Journal of Business Venturing 26(4):

489.

Manning C, Raghavan P, Schutze H. 2008. Introduction to Information Retrieval. Cambridge

University Press: Cambridge.

Manning C, Schuetze H. 1999. Foundations of Statistical Natural Language Processing. MIT

Press: Cambridge.

Maula MVJ, Keil T, Zahra SA. 2012. Top Management's Attention to Discontinuous

Technological Change: Corporate Venture Capital as an Alert Mechanism.

Organization Science.

Menon A, Choi J, Tabakovic H. 2018. What You Say Your Strategy Is and Why It Matters:

Natural Language Processing of Unstructured Text. Academy of Management Proceedings 2018(1): 18319.

Ott TE, Eisenhardt KM, Bingham CB. 2017. Strategy Formation in Entrepreneurial Settings:

Past Insights and Future Directions. Strategic Entrepreneurship Journal 11(3): 306-

325.

Papanastassiou M, Pearce R, Zanfei A. 2019. Changing perspectives on the internationalization

of R&D and innovation by multinational enterprises: A review of the literature.

Journal of International Business Studies.

Patel PC, Kohtamäki M, Parida V, Wincent J. 2015. Entrepreneurial orientation‐as‐

experimentation and firm performance: The enabling role of absorptive capacity.

Strategic Management Journal 36(11): 1739-1749.

Ream J, Schatsky D. 2016. Corporate accelerators: Spurring digital innovation with a page from

the Silicon Valley playbookPress DU (ed.), Deloitte University Press.

Reuber AR, Knight GA, Liesch PW, Zhou L. 2018. International entrepreneurship: The pursuit

of entrepreneurial opportunities across national borders. Journal of International Business Studies 49(4): 395-406.

Ronda-Pupo GA, Guerras-Martin LÁ. 2012. Dynamics of the evolution of the strategy concept

1962–2008: a co-word analysis. Strategic Management Journal 33(2): 162-188.

Rothaermel FT, Deeds DL. 2004. Exploration and exploitation alliances in biotechnology: a

system of new product development. Strategic Management Journal 25(3): 201-221.

Salton G, McGill MJ. 1986. Introduction to Modern Information Retrieval. McGraw-Hill: New

York.

32

Salton G, Wong A, Yang CS. 1975. A vector space model for automatic indexing. Commun. ACM 18(11): 613-620.

Shankar RK, Shepherd DA. 2018. Accelerating strategic fit or venture emergence: Different

paths adopted by corporate accelerators. Journal of Business Venturing.

Singh JN, Dwivedi SK. Year. Analysis of Vector Space Model in Information Retrieval. In

Proceedings of the National Conference on Communication Technologies & its impact

on Next Generation Computing CTNGC 2012.

Singhal A. 2001. Modern information retrieval: A brief overview. Bulletin IEEE Computer Society Technical Committee on Data Engineering 24(4): 35–43.

Tian X, Wang TY. 2011. Tolerance for Failure and Corporate Innovation. Review of Financial Studies.

Tidhar R, Eisenhardt K. 2019. Get Rich or Die Trying: Unpacking Revenue Model Choice Using

Machine Learning and Multiple Cases. Academy of Management Proceedings

2019(1): 12218.

Wadhwa A, Kotha S. 2006. Knowledge creation through external venturing: Evidence from the

telecommunications equipment manufacturing industry. Academy of Management Journal 49(4): 1-17.

Wadhwa A, Phelps C, Kotha S. 2016. Corporate venture capital portfolios and firm innovation.

Journal of Business Venturing 31(1): 95-112.

Weiblen T, Chesbrough HW. 2015. Engaging with Startups to Enhance Corporate Innovation.

California Management Review 57(2): 66-90.

Winston Smith S. 2019. Hard to get? Tracking Knowledge Heritage From Invention to Product

Market Innovation Using NLP. Academy of Management Proceedings 2019(1):

18011.

Winston Smith S, Hannigan TJ. 2014. "Home Run, Strike Out, or Base Hit: How Do

Accelerators Impact Exit and VC Financing in New Firms?". Academy of Management Proceedings 2014(1).

Winston Smith S, Hannigan TJ, Gasiorowski L. 2015. Peering Inside: How do Peer Effects

Impact Entrepreneurial Outcomes In Accelerators? Academy of Management Proceedings 2015(1): 17072.

Winston Smith S, Shah SK. 2013. Do Innovative Users Generate More Useful Insights? An

Analysis of Corporate Venture Capital Investments in the Medical Device industry.

Strategic Entrepreneurship Journal 7(2): 151-167.

Yu S. 2015. The Impact of Accelerators on High-Technology Ventures. Working paper.