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Exploring entrepreneurial pivoting and the factors that trigger pivots by tech startups



Technology entrepreneurship is an emerging domain in the field of entrepreneurship and the practice-oriented method called the Lean Startup approach (LSA) has made a big impact in this area. However, many technology startups continue to have survivability issues. This study focuses on understanding the theory of entrepreneurial pivoting and its associated factors. In this study, we have collected secondary data comprising 80 tech startups to validate the different types of pivots they pursued by the companies and the factors that triggered the pivoting. The most common pivots among these were found to be customer segment pivot and customer need pivot.
Exploring entrepreneurial pivoting and the factors that
trigger pivots by tech startups
Pavan Kumar Sala1, Simon P. Philbin1, Safia Barikzai1,2
1Nathu Puri Institute for Engineering and Enterprise, School of Engineering
2Computer Science and Informatics, School of Engineering
London South Bank Univeristy
London, United Kingdom
Abstract—Technology entrepreneurship is an emerging
domain in the field of entrepreneurship and the practice-oriented
method called the Lean Startup approach (LSA) has made a big
impact in this area. However, many technology startups continue
to have survivability issues. This study focuses on understanding
the theory of entrepreneurial pivoting and its associated factors.
In this study, we have collected secondary data comprising 80 tech
startups to validate the different types of pivots they pursued by
the companies and the factors that triggered the pivoting. The
most common pivots among these were found to be customer
segment pivot and customer need pivot.
Keywords—Lean Startup approach; pivots and factors;
technology entrepreneurship.
The Lean Startup approach (LSA) is considered as one of
the most popular practitioner-oriented approaches in the
entrepreneurship literature. This approach describes various
aspects including validated learning or customer development,
minimum viable product (MVP), market opportunity
navigation, perseverance and pivoting. Blank (2013), Ries
(2011), and Osterwalder & Pigneur (2010) are considered as
critical contributors to the LSA concept as their books on the
subject have been sold in the millions and the practices adopted
by countless entrepreneurs. However, in the field of
entrepreneurship there remains a gap between academic
research and practitioners. On the one hand, practitioners do not
necessarily pay much attention to research studies and on the
other hand, there is a need for research to fully characterise the
entrepreneurial process [1].
Technology advancement is considered a critical aspect of
economic growth as industries develop through innovating,
exploiting and commercialising emerging technologies.
Entrepreneurship has many domains, and one such domain is
technology entrepreneurship (TE), which is defined as
assembling resources, technical systems and the strategies by
an entrepreneur to pursue opportunities [2]. TE has also been
described in terms of digital entrepreneurship [3].
This exploratory research study focuses on understanding
the concept of technology entrepreneurship in the context of the
Lean Startup approach and entrepreneurial pivoting by startups.
The study seeks to validate the type of pivots pursued by tech
startups and the factors that trigger pivots.
A. Technology entrepreneurship
The universal definition of entrepreneurship is
the “creation of new enterprise” [4]. It can also be explained as
the process of extracting revenue from new and distinctive
amalgamations of resources in an uncertain environment.
Entrepreneurship is considered as a positive force that
contributes to the growth of both developed and developing
economies. Furthermore, entrepreneurship has played a
significant role in exploiting technological innovations [3]. It
can be observed that researchers in the field of entrepreneurship
seek to address questions such as the following: How do
entrepreneurs create value propositions? Why do some startups
fail to become sustainable in the long run? How does an
entrepreneur decide whether or not to persevere or pivot? [5],
[6]. Entrepreneurship has sub-branches and arguably
technology entrepreneurship is one of the essential fields.
Indeed, Spiegel and Marxt [2] defined “Formation” and
“Exploitation” as crucial phases of TE. The first phase is
formation, which involves the recognition of opportunities.
Exploitation is the second phase where strategies are developed
to exploit the recognised opportunities. TE has a third phase
known as “Renewal”. Moreover, Eliakis et al. [3] defined
technology entrepreneurship as developing innovative digital
technologies or using such technologies by forming new
startups and transforming existing businesses.
Technology entrepreneurship does not necessarily mean
just adopting digital technologies by technology startups; it acts
as an interface between innovation and entrepreneurship.
Innovation can be defined as a degree of newness by generating
ideas, processes, products, or services that enable a technology
breakthrough. Furthermore, entrepreneurship is inferred as
exploring and exploiting business opportunities to create a
value proposition. The domain of TE can be characterised
through the following: a) science and technology policies; b)
exploration and fostering of new technologies across various
industries; c) government support to science and technology to
stimulate new technologies; and d) market regulations that
govern the entrepreneurial initiatives [7]. Technology
entrepreneurship has gained both academic and policymakers’
interest over the last two decades. Leading entrepreneurs such
as those that founded major tech companies in the Silicon
Valley area in California (USA) were able to succeed due to
identifying and evaluating opportunities to create value
propositions enabled through emerging technologies. However,
researchers like Sobel and Clark [8] argue that even though TE
is widely recognised, it still lacks a unified framework [9].
Furthermore, TE has evolved further and broadened through
assimilation and evolution of new topics in the literature on
entrepreneurship. One such new topic is product or service
development based on customer feedback, which has been
considered as the need to "probe and learn the process" [10].
B. Entrepreneurial pivoting
During the inception and launch phases, a tech startup
undergoes frequent changes in the business model and the
value-capturing technique due to resource scarcity and external
market conditions. In this regard, startup companies can adopt
the Lean Startup approach. The global manufacturing industry
adopted the principles of lean philosophy over the last several
decades. The five principles of lean are as follows: a) creating
value to the customers; b) identifying the value stream; c)
creating a process flow to prevent breakdowns, re-entrant loops,
low-quality products or services; d) producing high-quality
products that are efficient and valuable for the customers; e)
identifying and eliminating excess engineering hours in order
to be cost-effective [11].
Startups are defined as a group of talented people trying to
seize an opportunity by turning an idea into a product [12]. An
entrepreneur may encounter a question, i.e., whether to
persevere or pivot while commercializing their idea to develop
a competitive product or service. Eric Ries author of The Lean
Startup and an entrepreneur defined the pivot as a "structural
course of correction designed to test a new fundamental
hypothesis" [13]. He says entrepreneurs should pivot from time
to time to learn the customers' needs, problems, and preferences
to develop a market-fit product or service [14]. The LSA model
emphasises experimental entrepreneurship to explore
opportunities, which can be viewed as an active search and
iterative design solution. In such a case, companies such as GE,
Motorola, Searle and Corning are some of the examples that
have followed the probe and learn process [15]. For any
organization, continuous innovation will help develop new
ideas, products, and organizational structure to support long-
term goals. Those strategies help to further build the success of
companies. It has been reported that Facebook and Twitter are
two classic examples of pivoting [13].
A total of fourteen pivots have been identified from the
literature [12, 13, 16] that are further categorized into four
levels, which are as follows: Product level pivots are those that
lead to a change in the products/services offered by the
company (1). Market level pivots focus on customers and the
market in which a company operates (2). Strategy level pivots
focus on the way to generate more value for the company (3).
People level pivots are associated with resources in a startup
(4). Table I illustrates all the pivots categorized under the above
four levels.
S. No
Name of pivot
level pivot
Ries [1
out pivot
Technology pivot
Platform pivot
Market level
Customer segment pivot
Ries [1
Customer need pivot
Channel pivot
Market segment pivot
Bajwa [1
level pivot
Value capture pivot
Ries [1
Business architecture
Engine of growth pivot
Complete pivot
Bajwa [1
Side project pivot
Bajwa [1
People level
Social pivot
Hirvikoski [1
a. Name changed from zoom-in pivot to market segment pivot
The market segment pivot (name changed from zoom-in
pivot to avoid confusion), complete pivot and side-project pivot
were identified by Bajwa [16]. The social pivot was identified
by Hirvikoski [12], and the remaining ten pivots were identified
by Ries [13]. Furthermore, Bajwa [16] identified eleven factors
may trigger a startup to change its direction (pivot). Table 2
illustrates all of these factors.
S. No Factors
I Customer feedback (positive and negative feedback)
II Technology challenges
III Competition
IV Unscalable business
V Wrong timing
VI Market conditions
VII Influence of investor, partner or founder
VIII Legal issues
S. No Factors
IX Flawed business model
X Side project success
XI Business financials
The objective for this research study is to validate the type
of pivots pursued by startup companies and the factors that
trigger such pivots. In order to address this objective, we
collected secondary data from various academic journals and
company websites. Secondary data is a dataset that a researcher
does not collect by him/herself but instead analyses existing
data [17]. The benefits of using secondary data are eliminating
financial and logistical obstacles while collecting primary data
[18]. However, certain precautions must be taken. A researcher
should elucidate the limitations using secondary data and
explain the actions taken to use the original data set for the new
research gap. The secondary data should be conscientiously
interpreted to generate a hypothesis and not to prove hypothesis
[17], [18].
As part of the secondary data collection, we reviewed
academic articles [16, 19, 20] that discuss different types of
pivots and possible factors behind pursuing those pivots. At the
same time, we have searched the internet to identify technology
startups/companies that have pivoted. Examples of the search
keywords used in the internet search are as follows: “business
pivots”, “pivots by startups”, “famous business pivots” and
“pivots”. While looking for data, we focussed only on tech
startup companies to understand what type of pivots they have
pursued. The intention behind collecting the secondary data is
to understand what type of pivots have been pursued by
technology startup companies. The secondary data was
collected from multiple sources (academic articles and different
websites), out of which 60% was collected from Bajwa et al.,
[14], 30% was from the internet (different websites searched in
google) and the remaining 10% was from Comberg et al. [19],
Terho et al. [20] and Hirvikoski [12]. We collected data of
startup companies from across the globe and this includes
startups from Canada, Finland, France, Germany, India,
Ireland, Israel, Japan, Mexico, Republic of Tunisia, Spain, UK
and USA.
The secondary data study shows what type of pivots pursued
by tech startups/companies and how successful they are after
pivoting. Tech companies such as Twitter and Facebook are
international examples that pivoted at a very early stage of their
journey [12]. During data collection, we came across websites
that illustrated the startup companies that pivoted to face
pandemic situation caused due by COVID-19. We studied the
types of pivots those tech startups pursed and labelled their
factor as market condition (due to pandemic).
Table III provides the details of companies that pursued
pivots and the factors that trigger pivots. We have anonymized
the company names.
Company Factor (s) Pivot (s)
Company-1 Influence of investor,
partner or founder
Platform pivot
Company-2 Customer feedback
Platform pivot
Company-3 Market conditions
(due to pandemic)
Platform pivot
Company-4 Market conditions
(due to pandemic)
Value capture pivot
Company-5 Customer feedback
Zoom-in pivot
Company-6 Unscalable business Customer need pivot
Company-7 Flawed business model Zoom-out pivot
Influence of investor,
partner or founder, flawed
business model, market
conditions, business
financials and technology
Customer segment
pivot, business
architecture pivot,
value capture pivot
and engine of growth
Company-9 Customer feedback
Customer need pivot
Company-10 Unscalable business Customer need pivot
Company-11 Legal issue Complete pivot
Company-12 Wrong timing and
unscalable business
Customer need pivot
Company-13 Flawed business model Customer segment
Influence of investor,
partner or founder, flawed
business model, market
conditions, business
financials and technology
Customer segment
pivot, channel pivot,
engine of growth pivot
and value capture
Company-15 Unscalable business Zoom-out pivot
Company-16 Market conditions
(due to pandemic)
Value capture pivot
Company-17 Market conditions
(due to pandemic)
Complete pivot
Customer feedback
(negative) and flawed
business model
Complete pivot
Company-19 Customer feedback
Customer segment
Company-20 Market conditions
(due to pandemic)
Customer segment
Company-21 Customer feedback
Zoom-in pivot
Company-22 Market conditions
(due to pandemic)
Complete pivot and
business architecture
Company-23 Competition Zoom-in pivot
Company-24 Side project success Side project pivot
Company-25 Market conditions
(due to pandemic)
Platform pivot
Customer feedback
(negative) and flawed
business model
Customer need pivot
and customer segment
Company-27 Side project success Side project pivot
Company-28 Market conditions
(due to pandemic)
Market segment pivot
Company-29 Unscalable business Customer need pivot
Company-30 Market conditions
(due to pandemic)
Business architecture
Flawed business model,
technology challenges and
unscalable business
Zoom-out pivot,
platform pivot,
customer segment
pivot, channel pivot
and business
architecture pivot
Company Factor (s) Pivot (s)
Company-32 Customer feedback
Market segment pivot
Company-33 Market conditions
(due to pandemic)
Customer need pivot
Company-34 Customer feedback
Zoom-in pivot
Company-35 Customer feedback
Customer need pivot
Company-36 Market conditions
(due to pandemic)
Market segment pivot
Company-37 Customer feedback
Customer segment
Company-38 Market conditions
(due to pandemic)
Customer need pivot
Company-39 Market conditions
(due to pandemic)
Side project pivot
Company-40 Side project success Side project pivot
Influence of investor,
partner or founder, flawed
business model, market
conditions and technology
Technology pivot,
channel pivot, engine
of growth pivot and
value capture pivot
Company-42 Market conditions
(due to pandemic)
Business architecture
Company-43 Unscalable business and
technology challenges
Zoom-in pivot, Zoom-
out pivot and
technology pivot
Unscalable business and
customer feedback
Complete pivot and
customer need pivot
Company-45 Technology challenges Technology pivot
Company-46 Flawed business model Customer need pivot
Company-47 Unscalable business Complete pivot
Company-48 Influence of investor,
partner or founder
Complete pivot
Influence of investor,
partner or founder and side
project success
Side project pivot
Company-50 Influence of investor,
partner or founder
Customer need pivot
Company-51 Market conditions
(due to pandemic)
Platform pivot
Customer feedback
(positive) and wrong
Zoom-in pivot
Customer feedback
(negative), unscalable
business and competition
Complete pivot
Company-54 Customer feedback
Complete pivot
Company-55 Customer feedback
Zoom-in pivot
Company-56 Competition Customer need pivot
Customer feedback
(negative) and influence of
investor, partner or founder
Channel pivot
Company-58 Customer feedback
Zoom-in pivot
Company-59 Customer feedback
Customer segment
Influence of investor,
partner or founder, flawed
business model and
business financials
Customer segment
pivot, value capture
pivot and engine of
growth pivot
Company-61 Influence of investor,
partner or founder
Complete pivot
Company-62 Unscalable business Platform pivot
Company-63 Technology challenges Platform pivot
Company Factor (s) Pivot (s)
Flawed business model,
business financials and
marker conditions
Zoom-in pivot, Zoom-
out pivot, customer
segment pivot and
business architecture
Unscalable business and
customer feedback
Customer need pivot
Company-66 Influence of investors,
partners or founders
Side project pivot
Company-67 Unscalable business Complete pivot
Company-68 Competition Complete pivot
Company-69 Customer feedback Customer need pivot
Company-70 Customer feedback
Zoom-in pivot
Company-71 Market conditions
(due to pandemic)
Side project pivot
Company-72 Market conditions Value capture pivot
Company-73 Technology challenges Technology pivot and
customer need pivot
Company-74 Customer feedback
Complete pivot
Company-75 Technology challenges Technology pivot and
customer need pivot
Company-76 Customer feedback
Customer need pivot
Company-77 Market conditions
(due to pandemic)
Value capture pivot
Company-78 Market conditions
(due to pandemic)
Platform pivot
Customer feedback
(negative) and wrong
Customer need pivot
Company-80 Side project success
Side project pivot and
customer segment
The objective behind studying the 80 technology startup
companies through collecting secondary data was to secure a
more in-depth understanding of pivoting and its associated
factors. While analysing the secondary data, we calculated
across the 80 companies the number of times (and percentage)
a pivot was pursued and number of times a factor triggered
pivoting, which is provided in Table IV and V respectively. The
bar charts (Fig. 1 and 2) are graphical representations of these
tables. For example, the factor customer feedback was a driver
for selecting a pivot by twenty-seven different startups. Of these
twenty-seven startups, seven of them used zoom-in pivot; four
startups pursued customer segment pivot; eight startups
pursued customer need pivot; five startups pursued complete
pivot while three startups pursued platform pivot, channel pivot
or a market segment pivot.
Type of pivots No. of times pursued Percentage
Social pivot 0 0%
Market segment pivot 2 1%
Complete pivot 8 4%
Technology pivot 9 5%
Type of pivots No. of times pursued Percentage
Zoom-out pivot 10 5%
Platform pivot 11 6%
Zoom-in pivot 13 7%
Channel pivot 14 7%
Business architecture pivot 14 7%
Engine of growth pivot 17 9%
Side project pivot 17 9%
Value capture pivot 21 11%
Customer need pivot 23 12%
Customer segment pivot 28 15%
Grand Total 187 100%
The bar chart in Fig. 1 represents the frequency of each
pivot pursued by a tech startup. For instance, customer segment
pivot was the highest pursued pivot (N=28, 15%). Customer
need pivot is the second most pursued pivot among the 80
startup companies (N=23, 12%) followed by value capture
pivot (N=21, 11%). Market segment pivot is the least pursued
pivot (N=2, 1%). We could not identify a single tech startup
that pursued social pivots in order to validate.
Fig. 1. Frequency of pivots pursued by the tech startups
Factors No. of times occurred Percentage
Legal issues 1 1%
Wrong timing 3 2%
Competition 4 2%
Side project success 5 3%
Business financials 15 8%
Market conditions 17 9%
MC-Pandemic 19 10%
Unscalable business 20 11%
Influence of investor,
partner or founder 22 12%
Factors No. of times occurred Percentage
Technology challenges 24 13%
Customer feedback 27 14%
Flawed business model 30 16%
Grand Total 187 100%
Fig. 2 illustrates the frequency of each factor that has
triggered a tech startup company to pivot. For example, the bar
chart shows that the flawed business model was the most
triggered factor (N=30, 16%), followed by customer feedback
(N=27, 14%) and technology challenges (N=24, 13%).
Whereas competition (N=4, 2%), wrong timing (N=3, 2%) and
legal issues (N=1, 1%) are the least triggered factors. Therefore,
the customer segment, customer need, and value capture pivots
are the most pursued pivots. Similarly, the flawed business
model, customer feedback and technology challenges are the
most triggering factors.
Fig. 2. Frequency of each factor that triggered the pivot
This exploratory research study focuses on understanding
the concept of technology entrepreneurship through examining
the lean startup approach, pivots and the factors that trigger
such pivots. Technology entrepreneurship is a critical field that
can enhance economic growth and create new technology-
driven market opportunities [3]. Therefore, the question arises:
How can a technology startup company survive in the long run?
One way to address the question is by implementing LSA. As
the Lean Startup approach (LSA) encourages startups to
interact with customers and promotes them to test new
fundamental hypotheses to improve the product/service based
on the feedback [35]. From the secondary data analysis, we
observed that the most frequently pursued pivots are customer-
oriented types of pivots i.e., customer segment pivot (15%) and
customer need pivot (12%) followed by the value capture pivot
(11%). However, we could not identify any examples for the
social pivot which was proposed by Hirvikoski [12].
Pivoting is defined as changing the course of direction by a
tech startup. Product, market, strategy and people level pivots
are the four categories out of which a startup can opt a single
pivot or multiple pivots to test their new hypothesis. However,
0 5 10 1 5 20 25 3 0
Social pivot
Market Segment Pivot
Complete pivot
Technology pivot
Zoom-out pivot
Platform pivot
Zoom-in pivot
Channel pivot
Business architecture pivot
Engine of growth pivot
Side project pivot
Value capture pivot
Customer need pivot
Customer segment pivot
No. of times pursued
0 5 10 15 20 2 5 30 3 5
Legal issues
Wrong timing
Side project success
Business financials
Market conditions
Unscalable business
Influence of investor, partner or founder
Technology challenges
Customer feedback
Flawed business model
No. of times triggered
the following aspects are yet to be explored: How many times
a tech startup has to pivot to identify a market fit product? Does
a pivot lead to another pivot (i.e., a domino effect)? Whether
there is a correlation between pivots and the factors? Therefore,
future research should focus on collecting primary data to
identify new pivots and factors, determine the domino effect,
and the influence of pivoting on the value proposition.
This research study is funded by The Puri Foundation and
their support is gratefully acknowledged.
[1] D. Shepherd and M. Gruber, “The Lean Startup Framework: Closing the
Academic–Practitioner Divide”, Entrepreneurship Theory and Practice,
p. 104225871989941, 2020.
[2] M. Spiegel and C. Marxt, “Defining Technology Entrepreneurship”, in
Proc. IEEE International Conference on Industrial Engineering and
Engineering Management, 2011.
[3] S. Eliakis, D. Kotsopoulos, A. Karagiannaki and K. Pramatari, “Survival
and Growth in Innovative Technology Entrepreneurship: A Mixed-
Methods Investigation”, Administrative Sciences, vol. 10, no. 3, p. 39,
[4] M. Low and I. MacMillan, “Entrepreneurship: Past Research and Future
Challenges”, Journal of Management, vol. 14, no. 2, pp. 139-161, 1988.
[5] D. Frederiksen and A. Brem, “How do entrepreneurs think they create
value? A scientific reflection of Eric Ries’ Lean Startup
approach”, International Entrepreneurship and Management Journal,
vol. 13, no. 1, pp. 169-189, 2017.
[6] R. Amit, L. Glosten and E. Muller, “Challenges to theory development in
entrepreneurship research*”, Journal of Management Studies, vol. 30, no.
5, pp. 815-834, 1993.
[7] D. Urbano, M. Guerrero, J. Ferreira and C. Fernandes, “New technology
entrepreneurship initiatives: Which strategic orientations and
environmental conditions matter in the new socio-economic
landscape?”, The Journal of Technology Transfer, vol. 44, no. 5, pp.
1577-1602, 2019.
[8] R. Sobel and J. Clark, “The use of knowledge in technology
entrepreneurship: A theoretical foundation”, The Review of Austrian
Economics, vol. 31, no. 2, pp. 195-207, 2018.
[9] J. Rakicevic, M. Levi Jaksic and M. Jovanovic, “Measuring the Potential
for Technology Entrepreneurship Development: Serbian
Case”, Management:Journal of Sustainable Business and Management
Solutions in Emerging Economies, vol. 23, no. 2, p. 13, 2018.
[10] T. Ratinho, R. Harms and S. Walsh, “Structuring the Technology
Entrepreneurship publication landscape: Making sense out of
chaos”, Technological Forecasting and Social Change, vol. 100, pp. 168-
175, 2015.
[11] A. Ghezzi and A. Cavallo, “Agile Business Model Innovation in Digital
Entrepreneurship: Lean Startup Approaches”, Journal of Business
Research, vol. 110, pp. 519-537, 2020.
[12] K. Hirvikoski, “Startups Pivoting Towards Value”, Data-and Value-
Driven Software Engineering with Deep Customer Insight, vol. 1, 2014.
[13] E. Ries, The Lean Startup: How Today's Entrepreneurs use Continuous
Innovation to Create Radically Successful Businesses. Crown Books,
[14] J. McMullen, “Are you pivoting away your passion? The hidden danger
of assuming customer sovereignty in entrepreneurial value
creation”, Business Horizons, vol. 60, no. 4, pp. 427-430, 2017.
[15] R. Harms and M. Schwery, “Lean Startup: Operationalizing Lean Startup
Capability and testing its performance implications”, Journal of Small
Business Management, vol. 58, no. 1, pp. 200-223, 2020.
[16] S. S. Bajwa, X. Wang, A. N. Duc and P. Abrahamsson, “Failures to be
celebrated: an analysis of major pivots of software startups”, Empirical
Software Engineering, vol. 22, no. 5, pp. 2373, 2017.
[17] F. S. Martins, da Cunha, Júlio Araujo Carneiro and F. A. R. Serra,
“Secondary data in research–uses and opportunities”, PODIUM Sport,
Leisure and Tourism Review, vol. 7, no. 3, 2018.
[18] Q. Trinh, “Understanding the impact and challenges of secondary data
analysis”, Urologic Oncology: Seminars and Original Investigations,
Vol. 36, no. 4, pp. 163-164, 2018.
[19] C. Comberg, S. Friedemann, A. German and V. K. Velamuri, “Pivots in
startups: Factors influencing business model innovation in startups”, in
Proc. The International Society for Professional Innovation Management,
p. 1, 2014.
[20] H. Terho, S. S Suonsyrjä, A. Karisalo and T. Mikkonen, “Ways to cross
the rubicon: Pivoting in software startups”, in Proc. International
Conference on Product-Focused Software Process Improvement, pp.
555-568, 2015.
[21] I. Woodford. “These 19 European startups have pivoted in the face of
startups/ (accessed Mar. 21, 2021).
[22] University of Oxford. “OXFO Ventures responding to COVID-19.”
covid-19 (accessed Mar. 21, 2021).
[23] A. Langevin. “22 businesses pivoting through COVID-19.” Maddyness.
through-covid-19/ (accessed Mar. 21, 2021).
[24] N. Arora. “COVID-19: 7 Brands that have pivoted.” ChannelSight.
(accessed Mar. 21, 2021).
[25] J. Nazar. “14 Famous business pivots.” Forbes.
business-pivots/?sh=6739801b5797 (accessed Mar. 21, 2021).
[26] J. Pruitt. “4 Companies and how they successfully pivoted.” Inc.
successfully-pivoted.html (accessed Mar. 21, 2021).
[27] R. Hinchliffe “Moven shuts all consumer accounts, pivots to B2B-only
service for banks.” Fintech
consumer-accounts-pivots-to-b2b-only-service-for-banks/ (accessed
Mar. 21, 2021).
[28] S. Glaveski. “The top 10 company pivots of all-time.” Collective Campus.
all-time (accessed Mar. 21, 2021).
[29] M. Gebel. “9 of the biggest pivots in tech history, from Nintendo to
Instagram.” Insider.
(accessed Mar. 21, 2021).
[30] K. O’Sullivan. “Most successful business pivots in history.” Superscript.
history/ (accessed Mar. 21, 2021).
[31] H. Ringle. “Tallwave triples employees and offers digital marketing with
new acquisition.”, Phoenix Busniess Journal.
employees-and-offers-digital.html (accessed Mar. 21, 2021).
[32] B. Morgan. “10 examples of how covid-19 forced business
transformation.” Forbes.
(accessed Mar. 21, 2021).
[33] H. Zhang. “360 Pivot: How covid-19 disrupted then propelled yhangry.”
Startups Magazine.
how-covid-19-disrupted-then-propelled-yhangry (accessed Mar. 21,
[34] P. Whitney. “How 5 companies used Covid to pivot to success.”
companies-used-covid-to-pivot-to-success (accessed Mar. 21, 2021).
[35] T. Felin, A. Gambardella, S. Stern and T. Zenger, “Lean startup and the
business model: Experimentation revisited”, Long Range Planning, vol.
53, no. 4, p. 101953, 2020.
Full-text available
Many startups use Lean Startup (LS). But is it effective? While there are emerging qualitative findings, quantitative evidence does not yet exist. To address this gap, we developed an operationalization of the degree to which startups use LS (Lean Startup Capability, LSC). We then analyzed the LSC-performance relationship. We found a strong and robust relationship. A discussion contextualizes our findings. The LSC operationalization is relevant for research as future efforts can build on and extend it. The contribution to entrepreneurial practice is that we carved out the element of LSC, and showed that LS is indeed linked to performance.
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Innovative technology enterprises are recognized internationally as an important pillar in modern economic activity. This paper presents the findings from a research combining qualitative and quantitative methods, with the specific goal of identifying and verifying the characteristics that affect their survival and growth. Results from an in-depth longitudinal qualitative case study, that examines a mature and constantly growing (in its 10-year operation) technologically innovative enterprise, reveal that a number of characteristics pertaining to both the profile of the entrepreneurial team, as well as of the employees, significantly affect company survival and growth in this context. Moreover, we recognize and analyze three stages in its evolution: an initial "evolutionary" growth (infancy and youth), followed by a "revolutionary" (crisis), and a second "evolutionary" (maturity) stage. Our findings are further corroborated and enriched through a survey with N = 27 entrepreneurs in innovative technology startups. We contribute to existing literature on innovative technology entrepreneurship, by identifying characteristics that entrepreneurs and employees should bear, towards its survival and growth. Moreover, a practical application of the life cycle approach is described for technologically innovative companies. Finally, a specific prescription that can help guide future theoretical and practical endeavors in innovative technology entrepreneurship is also provided accordingly.
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The lean startup framework is one of the most popular contributions in the practitioner-oriented entrepreneurship literature. This study seeks to generate new insights into how new ventures are started by describing the five main building blocks of the lean startup framework (business model, validated learning/customer development, minimum viable product, perseverance vs. pivoting, market-opportunity navigation), enriching the framework with existing research findings, and proposing promising research opportunities in a way that reduces the academic practitioner divide. In so doing, we hope to enhance researchers’ understanding of the startup process; provide knowledge for educators; and, ultimately, improve the startup process for practitioners.
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Lean startup and the idea of a business model have become popular in the context of startup experimentation, innovation and strategy. In this paper we discuss and critique the assumptions behind lean startup, specifically how the approach conceives of hypothesis development and startup experimentation. While the scientific aspirations of the approach are certainly to be applauded, we argue that the prescriptions suggested by lean startup feature challenges and unintended consequences. We claim that the approach's heavy emphasis on readily observable feedback and immediately validated learning undersells the entrepreneurial scientist's central task of composing a novel theory and hypotheses, prompting instead a search for value and validation only where it is easy to observe it. Thus we argue that lean startup inadvertently mis-specifies the nature of hypothesis development and promotes incremental experiments that, more often than not, only generate incremental value. Furthermore, the favored hypothesis-generating tool of lean startup—the business model canvas—lacks specificity in helping startups craft unique, firm-specific hypotheses and critical experiments for testing theories. After considering these challenges, we offer the outlines of an amended and alternative approach to startup science, innovation and experimentation.
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We wrote this editorial thinking about future opportunities for research, especially in a topic that still has room for further development: the use of secondary data. Along with this maturation process, this now reinforced area needs to step up its game (pun intended) in terms of methodological approaches. This means that both the qualitative and quantitative sides of the equation must now become more focused in improving results. In strategic management, on the other hand, the use of secondary data is now common and widespread, but not as much in Iberoamerican countries. In this editorial, we will focus more specifically on the use and opportunities in employing secondary data.
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Research Question: This paper explores the potential for technology entrepreneurship development at the country level through the creation of a new composite index. Motivation: Motivation for this paper arises from the fact there is a lack of the composite indices used exclusively for technology management as identified by Jovanovic et al. (2017). Technology indices are mostly used as important components of other composite indices used for tracking a country performance from the perspective of other global phenomena (e.g. competitiveness and innovativeness). The novelty of this paper reflects in the proposed Technology Entrepreneurship Development Potential (TED-pot) index which has multiple significances. It could serve as a help for policy makers in creating national policies; other companies and countries looking for the adequate environment to invest in technology entrepreneurship projects; academics who benefit from a new country-level view on technology entrepreneurship, especially ICT entrepreneurship. Idea: The idea of the paper was to create the TED-pot index to enable the cross-countries comparisons and examine whether the potential of Serbia lies in its entrepreneurial ICT sector. Data: Four indicators included in the created index are measured by the World Bank. The index is applied on six ex-Yugoslav countries and the EU for the period 2009-2014. All the data is collected from the World Bank database. Tools: The final index value is obtained by using the simple weighted function with equal weights. The overall TED-pot has been built upon the equal weighting of the two created pillars: ICT potential (ICT-pot) and Entrepreneurial potential (E-pot). The values for each pillar are calculated by the same procedure, through the simple mean of certain indicators. Findings: According to the calculated TED-pot values, Serbia stands out as a country with the greatest potential for technology entrepreneurship development in the region. Analysing individual pillars, ICT-pot indicates Serbia has very strong ICT sector, far ahead of other countries in the region, while the E-pot values show there is a space for administration to ease and speed up the process of starting new businesses in Serbia. This is a pilot research and the first presentation of the created index, which calls for further investigation. Contribution: This paper expands exiting research related to the country-level measurement in the field of technology management and entrepreneurship, especially focusing on ICT entrepreneurship development.
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Digital startups in the early stages of their development frequently undergo innovation to their value architecture and Business Model. A set of pragmatic methods drawing on lean and agile principles has recently been proposed to support digital entrepreneurs facing Business Model Innovation (BMI), known as Lean Startup Approaches (LSAs). However, the theoretical and practical relationship between BMI and LSAs in dynamic digital environments has seldom been investigated. To fill this gap, our study draws on an exploratory multiple-case study based on three digital multisided platform startups to craft a unified framework that can disclose the relationship between BMI, LSAs and Agile Development (AD), within the context of Strategic Agility. Our findings, which emerge from the unified framework, show that LSAs can be employed as agile methods to enable Business Model Innovation in Digital Entrepreneurship. These findings are then organized around a set of propositions, with the aim of developing a research agenda directed towards integrating BMI, LSAs and AD processes and methods.
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The transformation of ideas into new technologies depends not only on how knowledge diffuses but also on which context/time this transformation is developed. In the assumption that internal and environmental conditions directly affects the decision of exploiting technological opportunities, this paper explores how some strategic dynamic capabilities (entrepreneurial and export market) and supportive environmental conditions (regulative and normative) influence the configuration of technology entrepreneurship initiatives. A proposed conceptual model is tested with 30,648 ventures in 23 countries participating in the Global Entrepreneurship Monitor for the years 2005 (pre-financial crisis), 2008 (financial crisis), and 2011 (recession). The main findings suggest the positive role of entrepreneurial orientation and export market orientation in the development of new technology entrepreneurship initiatives. Also, environmental conditions influence on the development of initiatives of technology entrepreneurship. Particularly, the study evidences how regulative environmental conditions (property rights and government programs) enhance while other regulative conditions (support for science and technology) and normative conditions (opportunity perception and national culture) simultaneously retard the probability that a new/established venture develops new technology entrepreneurship initiatives. These effects are moderated and intensified by the influence of the economic cycles. The paper provides important insights to the field of entrepreneurship, innovation, and strategic management.
Secondary data analysis is commonly defined as the use of datasets, which were not collected for the purpose of the scientific hypothesis being tested. Examples of datasets range from private insurance claims to nationally administered health surveys. The use of secondary data confer several benefits, most notably by eliminating many of the financial and logistical obstacles related to primary data collection. The issues in using secondary data to answer important clinical and health policy questions are complex, but with appropriate and rigorous approaches there is an opportunity to produce high-effect research which can improve the care of patients with urologic malignancies.