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We develop theory about how and when digital technologies enable new venture creation processes. We identify two fundamental properties of digital technologies—specificity and relationality—and develop propositions that link these properties to six enabling mechanisms: compression, conservation, expansion, substitution, combination, and generation. We use the linked properties and mechanisms to determine how and when in the venture creation process— from prospecting to developing to exploiting—digital technologies have enabled start-ups in the IT hardware sector and develop stage-dependent propositions about their sector-level effects. We conclude our theorizing by discussing its implications beyond digital technologies and the IT hardware sector.
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Article
Digital Technologies as
External Enablers of New
Venture Creation in the
IT Hardware Sector
Frederik von Briel
1
, Per Davidsson
2,3
, and Jan Recker
1
Abstract
We develop theory about how and when digital technologies enable new venture creation pro-
cesses. We identify two fundamental properties of digital technologies—specificity and relationa-
lity—and develop propositions that link these properties to six enabling mechanisms: compression,
conservation, expansion, substitution, combination, and generation. We use the linked properties
and mechanisms to determine how and when in the venture creation process—from prospecting
to developing to exploiting—digital technologies have enabled start-ups in the IT hardware sector
and develop stage-dependent propositions about their sector-level effects. We conclude our
theorizing by discussing its implications beyond digital technologies and the IT hardware sector.
Keywords
digital technologies, external enablers, hardware start-ups, entrepreneurship as a process, IT
hardware sector
Researchers increasingly call for research on the objective, actor-independent factors that
enable entrepreneurial activity (Davidsson, 2015; Nambisan, 2016; Ramoglou & Tsang,
2016; Shane, 2012). Nambisan (2016) highlighted digital technologies as one such objective
factor that has profound effects on entrepreneurial processes. Nambisan noted that existing
entrepreneurship research has largely neglected digital technologies’ role and challenged
the field to start ‘‘theorizing the role of specific aspects of digital technologies in shaping
entrepreneurial opportunities, decisions, actions, and outcomes’’ (p. 2).
In this conceptual paper we take on this challenge. Using the IT hardware sector and its
recent surge of start-up activity (Billings, 2015; Diresta, 2015) as our context, our central
research question is ‘‘Are there effective ways of conceptualizing digital technologies’ influence
Entrepreneurship Theory and Practice
1–23
!The Author(s) 2017
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DOI: 10.1177/1042258717732779
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1
School of Management, QUT Business School, QUT, Brisbane, Australia
2
Australian Centre for Entrepreneurship Research, QUT Business School, QUT, Brisbane, Australia
3
Jo
¨nko
¨ping International Business School, Jo
¨nko
¨ping, Sweden
Corresponding Author:
Jan Recker, Professor, School of Management, QUT Business School, QUT, Gardens Point campus, 2 George Street,
Brisbane, QLD 4000, Australia.
Email: j.recker@qut.edu.au
on the venture creation process?’’ Answering this question contributes to a broader scholarly
understanding of objective, actor-independent factors in venture creation.
Our theorizing begins with the observation that despite the IT hardware sector’s reputation
for high entry barriers, such as high resource intensity, low flexibility, slow process speeds, and
high external dependencies (e.g., Heirman & Clarysse, 2007; Loderer, Stulz, & Waelchli, 2016;
Marion, Eddleston, Friar, & Deeds, 2015), there has been a recent surge in independent IT
hardware start-ups (Billings, 2015; Diresta, 2015; The Economist, 2014). These developments
encouraged us to engage in thought experiments to explain this real-world phenomenon
(Alvesson & Ka
¨rreman, 2007; Byron & Thatcher, 2016; Weick, 1989), particularly around
the role of digital technologies, which arguably play a prominent role in the IT hardware
sector’s current entrepreneurial activity (e.g., Billings, 2015). In pursuing our research ques-
tion within this context, we combine and extend five conceptual tools: Davidsson’s (2015)
notion of external enablers as a workable basis for theorizing the enabling role of digital
technologies; Nambisan’s (2016) distinction between boundaries and agency as a perspective
on digital technologies’ influence on venture creation; the analytical construct of mechanisms
to add precision to digital technologies’ enabling roles; Bakker and Shepherd’s (2017) three-
stage process model (prospecting, developing, exploiting) to situate digital technologies and the
mechanisms they provide in the venture creation process; and the literature on the attention
focus of decision-makers (Ocasio, 1997; Read, 2004) to theorize the effects of enabling certain
stages of that process. Building on these tools, we construct new theory that describes six
enabling mechanisms of digital technologies and root these mechanisms within two properties
that characterize the digital technologies themselves: specificity and relationality. We use this
theory to analyze the enablement of entrepreneurial activity in the IT hardware sector across
the stages of the venture creation process and develop sector-level propositions that are
applicable to other industry contexts.
Given our context and focus, we contribute to three major themes in contemporary entre-
preneurship research. First, we theorize the roles of digital technologies as external enablers in
entrepreneurial processes, identifying specificity and relationality as two salient properties
that can be used to describe any existing and future digital technologies and developing
propositions about how these properties influence the type of enabling mechanisms that
digital technologies afford. In so doing, we help to clarify why and how digital technologies
are key objective, actor-independent factors that influence venture creation processes.
Second, we extend Davidsson’s (2015) theorizing about external enablers by identifying
their enabling mechanisms. In doing so, we observe the process nature of venture
creation (McMullen & Dimov, 2013; Shane & Venkataraman, 2000; Zahra & Wright,
2011) and develop propositions about how the effects of enablers on start-up activity are
contingent on which process stages are being enabled. This contribution outlines an approach
to theorizing the influence of external, actor-independent factors without getting entangled
in the problematic notion of ‘‘opportunities’’ (cf. Davidsson, 2015; Dimov, 2011; Kitching &
Rouse, 2017).
Third, we recognize the importance of context in management and entrepreneurship (e.g.,
Welter, 2011; Zahra & Wright, 2011; Zahra, Wright, & Abdelgawad, 2014). Focusing on the
IT hardware sector allows us to determine how a set of digital technologies has provided
different enabling mechanisms at different stages of IT hardware start-ups’ development.
The result is a clearer explanation for recent developments in this globally significant
sector than a supposedly ‘‘context-free’’ alternative could have offered. Moreover, we assess
the generalizability of central aspects of our theorizing, especially their applicability to
other sectors, thereby demonstrating how a narrow context can facilitate theorizing about
industry-specific entrepreneurship that is of value to the focal context and beyond it.
2Entrepreneurship Theory and Practice 00(0)
We proceed as follows: first, we conceptualize two salient properties of digital technologies
as external enablers. Then we identify six enabling mechanisms that digital technologies pro-
vide and develop propositions about how digital technologies’ properties influence which
enabling mechanisms they afford. Next, we illustrate how enabling technologies and their
mechanisms map onto Bakker and Shepherd’s (2017) start-up development stages in the IT
hardware sector and develop propositions about how the effect of enabling mechanisms on a
sector’s start-up activity depends on which stage(s) are enabled. We conclude with a discus-
sion of our theory development’s contributions and implications.
Theory Development: External Enablers, Mechanisms, and the
Venture creation Process
Digital Technologies as External Enablers of Entrepreneurial Activity
Davidsson (2015) introduced external enablers as a more workable way to theorize about
what had previously been portrayed as objective, preexisting, and actor-independent ‘‘oppor-
tunities’’ (Eckhardt & Shane, 2003, cf. 2010; Shane, 2012). External enablers are distinct,
external circumstances like political and regulatory changes, demographic and social shifts,
and new technologies that can play essential roles in engendering and/or enabling start-ups
(Davidsson, 2015). Thus, an external enabler is an aggregate-level construct, where the
enabling nature follows from the assumption that any disequilibrating force will create
room for some new economic activities to establish themselves, although exactly which activ-
ities is unknowable a priori (Davidsson, 2015; Ramoglou & Tsang, 2016; Shane, 2012).
We focus on digital technologies as one important type of external enabler. Digital tech-
nologies have a potentially paradigm-shifting role in entrepreneurship because they make
boundaries more fluid and agency more dispersed in venture creation processes (Nambisan,
2016). Our theorizing focuses on how these effects occur.
Digital technologies are ‘‘products or services that are either embodied in information and
communication technologies or enabled by them’’ (Lyytinen, Yoo, & Boland, 2016, p. 49).
They exist as digital tools and infrastructure (e.g., Aldrich, 2014), digital platforms (e.g.,
Tiwana, Konsynski, & Bush, 2010), or artifacts with digitized components, applications, or
media content (e.g., Ekbia, 2009). Common to all types of digital technology is the decoupling
of digital information from the physical form of the material device and the separation of
semiotic functional logic from the physical embodiment that executes it (Yoo, Henfridsson, &
Lyytinen, 2010).
Digital technologies have been characterized in several ways (e.g., Ekbia, 2009; Kallinikos,
Aaltonen, & Marton, 2013; Nambisan, 2016), but common to these portrayals is a focus
on their ambivalent ontology (Garud, Jain, & Tuertscher, 2008; Kallinikos et al., 2013).
Because they embody digital capabilities, digital technologies can become malleable, editable,
self-referential, and interactive (Garud et al., 2008; Kallinikos et al., 2013; Nambisan, Lyytinen,
Majchrzak, & Song, 2017), traits that allow them to evolve continuously even after implemen-
tation and use and to generate new forms of agency, both within and across processes.
To describe digital technologies and explain how they act as external enablers of entrepre-
neurial activity, we build on Nambisan’s (2016) distinction between agency and boundaries as
two important attributes of entrepreneurial processes and outcomes. Thus, we acknowledge
digital technologies’ ambivalent ontology and conform to the assumptions of the external
enabler construct.
As a perspective on their agency, we focus on the specificity of digital technologies by
describing their control over actions and interactions.
1
Digital technologies unfold their
Briel et al. 3
value by enabling some actor’s actions, thereby altering the nature of the actor’s work.
As such, digital technologies perform a mediating role that gives them control over inputs,
outputs, and their transformations. In other words, digital technologies can determine what
kind of resources actors can provide as inputs and how these resources are transformed into
and provided as outputs. Hence, specificity relates to what DeSanctis and Poole (1994)
referred to as restrictiveness (the set of possible actions that can be performed) vis-a-vis
comprehensiveness (the variety of features a technology offers). However, specificity also
includes a focus on adaptivity
2
(a technology’s degree of task optimization) that is central
to digital technologies. The more specific a technology is, the more bounded is the set of
controlled actions and interactions that it enables.
The degree of digital technologies’ specificity is important because it indicates their adapt-
ability and malleability (Ekbia, 2009; Kallinikos et al., 2013; Zittrain, 2006). Digital technol-
ogies are, in principle, adaptable and malleable because their logic is separated from their
embodiment and their information is separated from their function (Yoo et al., 2010): they
can be updated. However, highly specific digital technologies are typically comparatively rigid
because their specialization and restrictiveness limits their ability to be reprogammed to
different functions. In contrast, digital technologies with low specificity are adaptable and
malleable because they are less restrictive: they can be appropriated and modified to facilitate
new functions.
A digital technology’s inherent capacity for specificity can vary. At one extreme are digital
technologies with a high degree of specificity that deterministically transform a predefined set
of specific inputs into specific outputs. At the other extreme are digital technologies with a low
degree of specificity that accept a multitude of ill-defined or indeterminate inputs and let other
actors decide how inputs are transformed into and provided as outputs. For instance, 3D
printers are optimized to enable the creation of physical objects from scratch, which is a
comparatively specific and restrictive task, as both input and output formats are tightly
controlled. In contrast, social media can enable a variety of tasks, such as creating, managing,
and distributing various types of content, establishing conversations and relationships
between content providers, and providing content providers opportunities for self-promotion.
These digital technologies’ control over input and output is inherently low.
As a perspective on process boundaries, we focus on the relationality of digital technolo-
gies, which describes their structural connections. Relationality is based on the assumption
that digital technologies are to some degree distinct from and responsive to other actors
(Orton & Weick, 1990), which makes them interactive (Kallinikos et al., 2013). Digital tech-
nologies are fundamentally interdependent, and they are reliant on at least one interaction
with other actors to enact their agency. Moreover, because of their capacity to become self-
referential (Yoo et al., 2010), digital technologies can entertain relationships and interact with
social and other technological actors.
The relationships between digital technologies and other actors form channels through
which resources flow (Podolny, 2001) such that more relationships mean potentially more
access to the resources that are inherent in these relationships. Digital technologies’ central
position in their webs of relationships allows them to channel resource flows and to accumu-
late resources that flow through them. Thus, relationality refers to the set of relationships with
other actors that digital technologies can leverage to facilitate their functionality (Kallinikos
et al., 2013). By influencing which and how many actors can participate in the processes
that digital technologies enable, relationality influences the boundaries of venture creation
processes.
Much like specificity, a digital technology’s inherent capacity for relationality can vary.
At one extreme are digital technologies with a low degree of relationality, which entertain a
4Entrepreneurship Theory and Practice 00(0)
single connection with a single type of actor at a time. For example, a typical 3D printer has a
low degree of relationality, as it usually interacts with only one actor via one operating device
at a time to perform a print job. At the other extreme are digital technologies with a high
degree of relationality, which connect with large numbers of potentially diverse actors.
For example, social media has a high degree of relationality, as it can entertain diverse
connections with large numbers of content-creating users simultaneously.
Focusing on variations of the specificity and relationality of digital technologies allows us
to evaluate the enabling potential of any digital technology, regardless of whether it already
exists or might emerge in the future. Any type of digital technology can manifest in multiple
variations in terms of its features, functionalities, and so forth, so variants of one type
of digital technology can exhibit varying degrees of specificity and relationality and, thus,
influence venture creation processes differently.
The Enabling Mechanisms Digital Technologies Provide
To describe the role that digital technologies play as external enablers of venture creation, we
draw on the analytical construct of mechanisms. Since mechanisms describe the processes that
underlie relationships between causes and effects (Gross, 2009), mechanism-based theorizing
is particularly appropriate for process-oriented, phenomenon-driven, field-level research in
the context of economic change that involves technology (e.g., Davis & Marquis, 2005;
Henfridsson & Bygstad, 2013; Henfridsson & Yoo, 2014), which applies to our setting.
We make two assumptions in our use of mechanisms (Hedstro
¨m & Swedberg, 1998;
Stinchcombe, 1998; Tilly, 2001).
3
First, we assume that mechanisms are not necessarily dir-
ectly observable and thus, we describe them through their effects. Therefore, our description
of and explanations about mechanisms selectively isolate the central elements that produce
essential aspects of the effects under investigation while excluding nonessential details (Gross,
2009; Hedstro
¨m & Ylikoski, 2010). Second, we assume that mechanisms are hierarchical and
are compositions of lower-level mechanisms—that is, that mechanisms coexist with other
potentially interrelated mechanisms that together form higher-level mechanisms (Hedstro
¨m
& Ylikoski, 2010). Consequently, while we identify mechanisms in terms of the specific pri-
mary effects they produce, mechanisms can also produce secondary effects through interaction
with other mechanisms. For example, time savings are often but not always associated with
cost savings.
Our theorizing focuses on the particular mechanisms that underlie the higher-level rela-
tionship between the emergence of new digital technologies as external enablers (i.e., the
cause) and venture creation activity in a sector (i.e., the effect). Following Malone et al.’s
(1999) seminal work on processes, we use three generic effects as starting points for our
theorizing: preservation (the protection of substance), modification (the change of existing
substance), and creation (the development of novel substance). In what follows we describe six
enabling mechanisms of digital technology for venture creation within these three categories:
compression, conservation, expansion, substitution, combination, and generation. Each of the
six mechanisms is identified by the primary effect (preservation, modification, or creation) it
produces (Table 1).
Compression and Conservation Mechanisms
Compression mechanisms reduce the amount of time that is required to perform an action,
whereas conservation mechanisms reduce the resources that are required to perform an
action. Digital technologies’ degree of specificity largely influences their potential to enable
Briel et al. 5
compression and/or conservation mechanisms during venture creation. The more specific a set
of actions is the more a digital technology can control and optimize those actions’ execution
because the technology is dealing with a more predictable and narrow set of inputs, trans-
formations, and outputs. A more efficient execution of actions goes hand in hand with a
reduced amount of time and/or resources. Hence, high specificity allows a digital technology
to automate the execution of specific actions and improve their efficiency, freeing actors and
resources that would normally be required to perform these actions to do other things
(Leonardi, 2011). For example, a stable set of inputs and outputs reduces coordination efforts,
transaction costs (Baldwin & Woodard, 2009), and variations in quality, allowing digital
technologies to channel resources more efficiently (Faraj, Jarvenpaa, & Majchrzak, 2011).
As specificity decreases, the potential fragmentation of inputs and outputs increases, prevent-
ing automated transformations and reducing the overall efficiency of a digital technology’s
transformations. For example, increasing input variety can negatively affect input and output
quality (Wareham, Fox, & Giner, 2014), which increases transaction costs and coordination
needs (Wolter & Portuguesa, 2008) and undermines a digital technology’s ability to provide
compression and/or conservation mechanisms.
Consider cloud computing, which exists in Infrastructure-as-a-Service (IaaS) and Software-
as-a-Service (SaaS) formats. IaaS, which is less specific than SaaS, gives users access to virtual
computing resources for a variety of tasks, whereas SaaS provides users access only to certain
software applications. IaaS has less potential to enable compression and conservation mech-
anisms than SaaS because IaaS can improve only the efficiency of physical infrastructure
management, whereas SaaS can also automate installation and maintenance of applications
because of the more bounded set of actions it enables.
Proposition 1: As the specificity of digital technologies increases, their potential for enabling
compression and conservation mechanisms increases.
Relationality moderates the capacity of digital technologies to enable compression and/or
conservation mechanisms. Highly specific digital technologies can enable compression and
conservation mechanisms independent of their relationality, but increasing relationality can
amplify their ability to do so. Digital technologies with low relationality exchange resources
Table 1. Mechanisms and Their Definitions
Mechanism Definition
Effect
category
Compression Reduces the amount of time that is required to perform an
action
Preserve
Conservation Reduces the resources that are required to perform an
action
Preserve
Expansion Increases the availability of a resource Modify
Substitution Replaces one resource with another Modify
Combination Bundles different resources to create new artifacts, such as
devices, functionalities, and business models
Create
Generation Creates new artifacts, such as devices, functionalities, and
business models, by changing existing ones
Create
Note. ‘‘Effect category’’ refers to Malone et al.’s (1999) generic types of effects.
6Entrepreneurship Theory and Practice 00(0)
with a small number of homogeneous actors, which allows the technologies to adapt to the
idiosyncratic interaction requirements of these actors and, thus, to improve coordination
efficiency and the speed of resource flows (Roberts & Grover, 2012). As digital technologies’
relationality increases, the number and diversity of actors with which they can interact
increase. While more interactions with more diverse actors increases digital technologies’
potential to access and channel resources that flow through them, the potentially increasing
homogeneity of inputs and outputs also risks increasing coordination efforts and can nega-
tively affect the speed of resource flows. High specificity can ensure the consistency of inputs
and outputs even with an increasingly large and heterogeneous set of actors, so increasing
relationality can positively influence the volume of resources that a highly specific digital
technology can channel efficiently. For compression mechanisms, relationality can have this
amplifying effect if a digital technology connects with more actors on the supply side because
relationality allows the technology to pool the resources that are required to perform the set of
actions. For conservation mechanisms, relationality can have an amplifying effect if a digital
technology connects with more actors on the demand side because it allows the technology to
distribute among these actors the expenditures involved in performing the set of actions.
As an example, consider 3D printers, which compress the time that is required to transform
virtual 3D models into physical objects and conserve the resources that are required to do so.
A typical desktop 3D printer is highly specific to this one task and can perform the task only
for a single user at any one time. However, if this 3D printer becomes more relational by
connecting and coordinating with other 3D printers to form a 3D printing cluster, compres-
sion mechanisms can be amplified, as the cluster can print faster than the single printer can.
Similarly, if the printer (or the cluster) becomes more relational by allowing multiple users to
use it, conservation mechanisms can be amplified, as the costs of purchasing, maintaining, and
upgrading the printer can be distributed among users.
Proposition 2: As the relationality of digital technologies that provide compression and/or conserva-
tion mechanisms increases, their overall capacity for enabling these mechanisms increases.
Expansion and Substitution Mechanisms
Expansion mechanisms increase the availability of a particular resource, whereas substitution
mechanisms replace one resource with another. Digital technologies’ potential to enable
expansion and/or substitution mechanisms during venture creation is largely contingent on
their relationality. To enable expansion mechanisms, digital technologies must accumulate
resources, which they can do if they can access and channel resources among multiple actors.
With increasing relationality, the number of actors and the volume of resources that digital
technologies can access increases, thereby providing expansion mechanisms. Digital technol-
ogies’ potential to enable substitution mechanisms also increases with their relationality.
More interactions with diverse actors mean more access to resources that are provided by
these relationships. The higher the number of complementary actors that can provide close
substitutes for resources, the greater the technologies’ potential to replace these resources and
the actors that provide them.
As an example, consider online repositories like GrabCAD, NIH 3D Print Exchange, and
Thingiverse. These social media platforms enable expansion mechanisms because their high
level of relationality allows them to expand 3D design knowledge by attracting and connect-
ing large numbers of users who contribute and comment on 3D design files. The high levels of
relationality in crowdfunding platforms like Indiegogo, Kickstarter, and Selfstarter also gives
Briel et al. 7
them the capacity to substitute traditional sources of funding and market research through the
number of users who back campaigns and comment on them.
Proposition 3: As the relationality of digital technologies increases, their potential for enabling
expansion and substitution mechanisms increases.
Specificity moderates digital technologies’ ability to enable expansion and/or substitution
mechanisms. Digital technologies that have high levels of relationality have high potential for
enabling expansion and substitution mechanisms independent of their specificity, but increas-
ing specificity can amplify their ability to do so. This is because specificity reflects the tension
between controlled and autonomous actions. High specificity and its control over actions leads
to more predictable inputs, which allow a digital technology to reduce the variance in outputs.
Hence, digital technologies with high specificity can focus on efficiently accumulating resources
for one or a few actions and ensure that they provide a consistent output. On the other hand,
low specificity and its autonomy of actions foster input and output variance, stimulating the
breadth of output. Hence, digital technologies with low specificity can accumulate a more
diverse set of resources to enable multiple types of actions. In other words, specificity influences
the potential efficiency and range of expansion and/or substitution, rather than the inherent
capacity to enable expansion and/or substitution mechanisms per se.
For example, crowdfunding platforms provide substitution mechanisms through their cap-
acity to replace money and feedback from traditional investors and market research, respect-
ively, with money and feedback from crowds of potentially unknown and geographically
dispersed actors. If the platforms would be less specific, they would give actors the possibility
to provide a broader set of resources, not just money and feedback. For example, online
charity platforms allow people to donate a variety of resources, such as furniture, clothes,
other goods, and money. The less specific the online charity platform, the more diverse the
resources they can collect and the actors they can replace, but they will be less efficient in
replacing specific resources.
Proposition 4: As the specificity of digital technologies that provide expansion and/or substitution
mechanisms increases, their overall capacity for enabling these mechanisms increases.
Combination and Generation Mechanisms
Combination mechanisms create new artifacts like devices and functionalities by bundling
resources, whereas generation mechanisms create new artifacts by changing existing ones.
Digital technologies’ potential to enable combination and/or generation mechanisms during
venture creation is contingent on both the technologies’ specificity and their relationality.
Digital technologies’ specificity is inversely related to their potential to enable combination
and generation mechanisms because increasing specificity means that digital technologies
tighten their control over the set of actions and interactions that can be performed with
them, reducing their potential to be appropriated in new, perhaps unanticipated ways to
create new functionality. Hence, whereas increasing specificity allows digital technologies to
increase their efficiency by reducing the variance in inputs and outputs, the same effect con-
strains technologies’ potential to enable combination and generation mechanisms. As a result,
digital technologies with high specificity can only enable third parties (e.g., their users) to
appropriate existing functionality and create new functionality if these technologies loosen
their control over actions (Boudreau, 2010). In doing so, a digital technology can shift the
8Entrepreneurship Theory and Practice 00(0)
locus of value creation to third parties (Parker, Alstyne, & Jiang, 2017), who can then adapt
the digital technology to a range of new actions. By loosening control over actions, digital
technologies also distribute control over interactions among actors, as less control over inter-
actions enables the actors that are connected via a digital technology to interact autono-
mously and nondeterministically. In other words, relaxing control over actions and
interactions fosters digital technologies’ capacity to enable unprompted and uncoordinated
change (Zittrain, 2006): the less specific a digital technology is and the more it can be appro-
priated for different actions and interactions, the higher its potential to enable combination
and generation mechanisms.
Proposition 5: As the specificity of digital technologies increases, their potential for enabling com-
bination and generation mechanisms decreases.
To enable combination mechanisms, digital technologies must connect with at least one
actor that provides access to complementary resources, which the technology can then bundle
with its own resources to create new artifacts, such as new functionalities. As the number and
diversity of complementary actors with which digital technologies can connect increases, the
technologies’ potential to enable the creation of new resource combinations increases, as does
their potential to stimulate dynamic and collective resource modification through these actors
and, thus, their potential to enable generation mechanisms (Zittrain, 2006).
Proposition 6: As the relationality of digital technologies increases, their potential for enabling
combination and generation mechanisms increases.
As an example, consider traditional mobile phones compared to smartphones. Both have
relatively high relationality vested in their structural features (e.g., 4G, Bluetooth, and Wi-Fi
connectivity) that, in theory, allows them to connect with many complementary digital
devices, such as activity trackers, speakers, and smart door locks. However, traditional
mobile phones’ operating systems typically have relatively high specificity that constrains
the set of possible actions and interactions that can be performed with them alone or in
connection with other devices. Hence, traditional phones’ structural features can lead only
to limited new functionality. In contrast, smartphones typically have relatively low specificity,
which manifests, for example, as shared, complementary digital platforms and generic appli-
cation programming interfaces (APIs) that allow third parties to amend the set of actions and
interactions they can perform (Tiwana, Konsynski, & Bush, 2010). Hence, third parties can
leverage smartphones’ structural features to create new functionalities.
Digital Technologies as Enablers of Venture Creation in the IT Hardware Sector
The IT hardware sector is a particularly suitable context in which to explain how our theoriz-
ing about digital technologies and mechanisms can be used to analyze entrepreneurial activity
on the sector level. The IT hardware sector is a high-tech manufacturing sector in which firms
use similar inputs and technologies to produce various digital devices (i.e., physical devices
that are either embodied in or enabled by digital technologies). Examples of such digital
devices are drones, home automation devices, robots, smart kitchen appliances, and wear-
ables. The IT hardware sector has traditionally been characterized by a number of entry
barriers, including a high resource intensity, low flexibility, slow process speeds, and high
external dependencies (e.g., Heirman & Clarysse, 2007; Loderer et al., 2016; Marion et al.,
2015). Despite these entry barriers, there has been a recent surge in independent IT hardware
Briel et al. 9
start-ups (Billings, 2015; Diresta, 2015; The Economist, 2014) in which advances in digital
technology have arguably played a prominent role (e.g., Billings, 2015). Therefore, the IT
hardware sector is a prime candidate for our theorizing to provide insights into what would be
required to create similar surges in other sectors.
In such theorizing, we must consider not only how but also when in the process digital
technologies enable entrepreneurial activity in the IT hardware sector. Prior research on the
influence of external enablers on new venture creation (Davidsson, 2015) has paid little atten-
tion to what stage of the venture creation process (McMullen & Dimov, 2013; Zahra &
Wright, 2011) is being enabled. We apply Bakker and Shepherd’s (2017) process model,
which was developed in the similarly resource-intensive and slow-process-speed context of
mining and which has a granularity of process that is suitable for our purposes. Their model
revolves around three distinct stages: prospecting, developing, and exploiting. However, in line
with Nambisan’s (2016) argument that digital technologies make entrepreneurial processes
more fluid and open-ended, we re-interpret these stages as sometimes referring to individual
activities, rather than necessarily pertaining to the entire venture. This adapted view regards
venture creation as consisting of multiple elements that are gradually brought together over
time (cf. Dimov, 2007).
Table 2 summarizes our analysis by describing the three process stages and associated
success factors. Emerging IT hardware start-ups start with prospecting, which has tradition-
ally been a resource-intensive, slow process in this sector because the identification, explor-
ation, and adaptation of promising ideas rely on experimental development of physical
‘‘works-like’’ (i.e., technical functionality) and ‘‘looks-like’’ (i.e., physical design) prototypes.
Next, IT hardware start-ups enter the developing stage, which focuses on progressing ideas
toward scalable mass production. The developing stage is when IT hardware start-ups are
traditionally hit by the ‘‘valley of death’’ (Barr, Baker, Markham, & Kingon, 2009, p. 371)
because progressing becomes increasingly costly. For example, the tooling for the high-pres-
sure injection molding that is typically used in mass production to reduce production time and
improve durability of casings traditionally costs tens of thousands of dollars (Mitchell, 1996).
Finally, when IT hardware start-ups enter the exploiting stage, they have to establish efficient
and scalable systems and routines to produce, market, and distribute the offering they have
been developing efficiently. Entering the exploiting stage is one of the most important events
in an IT hardware start-up’s journey, as it shifts attention from development to manufactur-
ing processes (Wu, Wang, Chen, & Pan, 2008). Once manufacturing commences, start-ups
lock in to a physical product design and face risks related to the decrease in flexibility that
stems from physical rigidity. Table 2 shows how several digital technologies provide IT hard-
ware start-ups with mechanisms that ease particular entry barriers to the three stages of
development.
The observation that the enablement particular digital technologies provides pertains to
particular stages of venture development gives reason to probe more deeply into how the
effects of these technologies on the start-up activity in a sector are contingent on what stage or
stages are being enabled. We use as our theoretical basis Ocasio’s (1997) attention-based view
of the firm (ABV), which builds on Simon’s (1996) insights on bounded rationality. In
developing ABV, Ocasio elaborated on three principles that govern decision-makers’ atten-
tion: a focus of attention on a limited set of items at the expense of others; a situated attention,
such that the focus of attention is geared toward the demands of the situation in which
decision-makers find themselves; and a structural distribution of attention according to the
rules, controls, and identity of the decision-makers’ organizations. While other studies have
paid at least tangential attention to temporal issues (Barnett, 2008), Ocasio did not, so we
draw on the literature on intertemporal choice and decision-making (Read, 2004; Weber &
10 Entrepreneurship Theory and Practice 00(0)
Table 2. The Role of Digital Technologies and Enabling Mechanisms Across the Stages of the Venture Creation Process in IT Hardware
Stages
Prospecting Developing Exploiting
Description Broad, low-cost exploration of markets
and technologies to detect potential
demand, and development of ideas
about serving such demand
Deeper and typically costlier explor-
ation of a narrower set of possible
routes forward, increasingly com-
mitting to a particular business
model, product, and target market
Establishment of efficient and scalable sys-
tems and routines to produce, market,
and distribute the offering developed
during the preceding stages
Critical success
factors
Access to diverse information sources and
the ability to probe customer needs and
technological feasibility (Verworn, 2009)
Acquisition and accumulation of neces-
sary resources; minimization of
development costs and time-to-
market (Pavlou & El Sawy, 2006)
Minimization of production and distribu-
tion costs, and maximization of value
delivery to customers (Kopczak &
Johnson, 2003)
Examples of
digital tech-
nology
enablers,
mechanisms,
and the bar-
riers they
typically
address
Rapid prototyping technologies like 3D
printers and mini-mills provide com-
pression and conservation mechanisms,
reducing the traditional barriers of
process duration and resource intensity.
Social media like Hackster.io and
GrabCAD provide expansion mechan-
isms, providing access to a broad range
of information and expertise that sig-
nificantly reduce external dependencies.
Electronics development platforms like
Arduino and Raspberry Pi provide com-
bination mechanisms, increasing flexibil-
ity and the variety of prototypes
Compression, conservation, and combin-
ation mechanisms continue to oper-
ate, but their effects weaken because
of the move to large-scale manufac-
turing, which typically involves
custom-printed circuit boards
(PCBs) and high-pressure injection
molding. Crowdfunding platforms
like Indiegogo and Kickstarter pro-
vide substitution mechanisms,redu-
cing traditionally high external
dependencies (e.g., traditional fund-
ing sources and market research)
and helping to satisfy resource needs
Combination and substitution mechanisms
continue to operate largely as they do in
the developing stage, provided, for
example, by smartphones that extend
the functionality of offerings and
crowdfunding platforms that substitute
for the traditional funding sources that
are required to sustain and grow busi-
ness operations. In addition, cloud
computing like Amazon Web Services
and platforms like IFTTT provide gener-
ation mechanisms, reducing traditional
rigidity barriers by making physical
products receptive to change even after
product launch
11
Huettel, 2008) to supplement the standard ABV arguments.Based on both normative eco-
nomic theory and psychological experiments with human and animal participants, this litera-
ture suggests that benefits that are temporally proximal weigh more heavily in decisions than
do those that occur at a later time, so ‘‘the future is less important than the present’’ (Hardisty
& Weber, 2009).
On this basis, we perform thought experiments regarding the effects of a given extent of
enablement that pertains to a single stage in Bakker and Shepherd’s (2017) model. If a digital
technology enables prospecting, those whose focused attention includes new technology and/
or the possibility of engaging in start-ups will see immediate benefits. ABV’s situated attention
principle and the temporal proximity principle both work in favor of acting on the benefit the
digital technology offers. At the same time, remaining obstacles to successful completion of
the process that pertain to later stages are temporally distant and, therefore, cognitively
undervalued. While decision-makers in incumbent firms should also be positively influenced
by the immediacy of the benefit, the situated attention and structural distribution principles
make them less likely to pay attention to and/or act on prospecting enablement in the first
place (Barnett, 2008) and more likely to weigh it against anticipated hurdles that pertain to
exploiting. Thus, enablement of prospecting is likely to trigger a particularly strong response
in the number of independent attempts at venture creation.
A digital technology that provides a corresponding extent of enablement of only developing
should trigger fewer attempts at venture creation because the temporal distance from the per-
ceived benefit discounts its perceived value. However, for start-ups that are in or approaching the
developing stage, enabling mechanisms in the developing stage are both temporally proximal and
prime candidates for situated focus. Hence, the primary effect of enablement of this stage should
be an increase in the proportion of ongoing start-ups that survive the ‘‘valley of death’’ (Barr,
Baker, Markham, & Kingon, 2009, p. 371) that is traditionally associated with this stage.
Increased start-up activity that is stimulated by enablement of the early stages of prospect-
ing and developing does not necessarily translate to a significant increase in the number of
independent ventures that are successfully established in a sector. Although more founders are
attracted to entering and/or staying longer in the process they will encounter the traditional
barriers at later process stages that are not enabled—or even worse barriers because of the
stiffer competition for resources and customers in the later stages. The heightened later-stage
barriers force many to give up or consider alternative modes of exploitation, such as licensing
or outright sale of the venture to an incumbent, when they approach the exploiting stage
(Shane & Venkataraman, 2000). The latter scenario is likely to bring the product or service to
the market and bestow the founders with financial reward, but it will not increase the inflow of
new, independent businesses in the sector.
For these reasons, enablement of the early prospecting and developing stages is unlikely to
produce a surge in successfully established new ventures of the kind currently witnessed in the
IT hardware sector, nor is a similar extent of enablement solely of the exploiting stage likely to
have such a profound effect. Some increase is likely because ongoing start-up attempts in or
near the exploiting stage have the requisite temporal proximity and situated attention to
attend to and act on an enabler whose mechanisms facilitate this stage, but such enablement
is even more temporally distant than is enablement of the developing stage for actors who
have not yet begun the start-up process. As a result, enablement of exploiting is unlikely to
trigger a strong response in the number of independent start-up attempts in a sector. In fact,
based on the structural distribution of attention principle, incumbent firms whose main inter-
est lies in the optimization of established production and distribution activities may be more
prone to let enablement of the exploiting stage trigger initiatives toward new market offerings
(cf. Naldi & Davidsson, 2014). Therefore, we conclude that a considerable increase in the
12 Entrepreneurship Theory and Practice 00(0)
number of successfully established new ventures in a sector is likely to require the simultan-
eous enablement of prospecting, developing, and exploiting.
For individual start-ups, a range of factors, such as entrepreneurial experience and expert-
ise (Cook & Yamamoto, 2011), team composition (Amason, Shrader, & Tompson, 2006), a
high level of conscientiousness (Zhao, Seibert, & Lumpkin, 2010), individual differences in
time preferences (Chapman, 2005), and/or the systematic use of business planning (Chwolka
& Raith, 2012), might moderate or alter the tendencies discussed above. However, on the
sector level, such individual differences should largely cancel out, leaving the main effects
derived from ABV and the intertemporal choice literature to predominate. Therefore, we
make three propositions ceteris paribus:
Proposition 7: When digital technologies enable a single stage of the venture creation process (pro-
specting, developing, or exploiting) in a sector, the effect on the number of independent attempts at
venture creation in that sector is greater if the enablement pertains to the prospecting stage; less if it
pertains only to the developing stage; and least if it pertains only to the exploiting stage.
Proposition 8: When digital technologies enable a single stage of the venture creation process (pro-
specting, developing, or exploiting) in a sector, the relative size of the enablement effect on the
number of independent new ventures that are successfully established in that sector is indeterminate.
Proposition 9: The effect of digital technologies on the number of independent new ventures that
are successfully established in a sector depends on the extent to which digital technologies simultan-
eously enable all stages of the venture creation process in that sector within which significant entry
barriers exist.
Proposition 7 is based primarily on the temporal proximity principle, with additional
support from ABV principles that suggest differences in the focus of attention between pro-
spective founders and incumbents. Proposition 8 reflects that an increase in the volume of
start-up attempts that are triggered by enablement of prospecting may increase the entry
barriers that pertain to later stages, while enablement of later stages has limited power as
process trigger because of the temporal distance putting it outside the focus of attention of
those who are entering the process. Proposition 9 establishes an important lesson from the IT
hardware sector, where significant barriers are present in all three development stages.
Consequently, although other factors may also have contributed to the recent surge in suc-
cessfully established, independent IT hardware start-ups, the surge is unlikely to have been
possible had digital technologies not provided enabling mechanisms in each stage of the
venture creation process. In sum, our theorizing suggests that all process stages and the
combined effects of multiple enabling technologies should be considered when one assesses
their influence on sector-level start-up activity.
Discussion
Our theory development started with conceptualizing how digital technologies enable venture
creation processes. We identified two conceptual dimensions that characterize digital technol-
ogies and linked them to six mechanisms that describe how digital technologies enable venture
creation processes. Taking the IT hardware sector as a particularly suitable context, we then
looked into when in the venture creation process digital technologies’ enabling mechanisms
come into play and developed stage-specific propositions about the influence of enabling
digital technologies on sector-level start-up activity.
Briel et al. 13
This theorizing about the enabling mechanisms of digital technologies has implications for
three major themes in contemporary entrepreneurship research: the transformational effects
of digital technology on entrepreneurship; the role of objective, actor-independent factors in
venture creation processes; and the importance of context for entrepreneurship practice and
research. We discuss each in turn.
Implications for Research on the Transformational Effects of Digital
Technology in Entrepreneurship
Our theorizing heeds Nambisan’s (2016) call for theorizing the roles of digital technologies in
venture creation. We do so in three ways. First, we identify two dimensions—specificity
and relationality—along which digital technologies can be characterized. Using these two
dimensions, we can describe any digital technology—whether it already exists or might
emerge in the future—based on its potential to influence the agency and boundaries of venture
creation. Our conceptualization is situated within existing characterizations of digital tech-
nologies, objects, and artifacts (e.g., DeSanctis & Poole, 1994; Ekbia, 2009; Kallinikos et al.,
2013; Yoo, 2010) but bound to our purpose of viewing digital technologies as external
enablers of entrepreneurial processes. To the best of our knowledge, it is the first conceptu-
alization of this kind. The two-dimensional conceptualization of digital technologies provides
an important foundation for future research on entrepreneurship and the role that digital
technologies play as enablers and change agents of venture creation. However, we do not
believe our conceptualization is exhaustive; future work may expand on these two dimensions
and add others.
Second, we identify six particular enabling mechanisms of digital technologies—compres-
sion, conservation, expansion, substitution, combination, and generation—and link them to
digital technologies’ manifestations of specificity and relationality. The mechanisms may not
represent an entirely new discovery, but discussing them in depth jointly with the properties of
digital technologies that influence them has significant theoretical and practical value. Future
research can apply our resulting theory of digital technology enablement to the venture,
sector, and country levels to provide insights for research, policy-making, and regulations
that propose to stimulate start-ups. For example, our theory can serve as the foundation for
configurational approaches (Misangyi et al., 2017) that determine whether digital technologies
with certain properties make some start-ups more successful than others, how the availability
of different digital technology configurations gives rise to start-up activity in certain sectors,
and in what stage of the venture creation process certain kinds of digital technologies provide
the most value.
Third, we develop formal propositions about how variations in the properties of digital
technologies along the two dimensions of specificity and relationality relate to the mechanisms
they provide. Future research can build on these propositions to investigate the sociotechnical
processes to which digital technologies and their mechanisms give rise. That is, in order for the
enabling mechanisms of digital technologies to unfold, human and/or technological actors
must use them. This means that the mechanisms that describe their potential actions are
situated within the relationship between these technologies and the human actors who interact
with them (Leonardi, 2011; Majchrzak & Malhotra, 2013). For example, while social media
can provide expansion mechanisms to new ventures on a sectoral level, such media may not
afford these mechanisms to entrepreneurs who lack the desire to search for information, the
ability to develop trust with potential competitors who could provide advice, or the creativity
to appropriate available knowledge for their own purposes (e.g., Kuhn & Galloway, 2015).
These are ideas for future research to test and develop both within and beyond the IT
14 Entrepreneurship Theory and Practice 00(0)
hardware sector, such as in other sectors or entrepreneurial ecosystems (Autio, Nambisan,
Thomas, & Wright, 2017).
Overall, our theorizing contributes to entrepreneurship research and beyond by providing a
much-desired integrative perspective that draws together research on entrepreneurship and
information systems (Del Giudice & Straub, 2011; Huang, Henfridsson, Liu, & Newell, 2017;
Nambisan, 2016). This integrative perspective has the potential to give new impetus to
research on the nature and effects of digital technologies in entrepreneurship and in related
contexts, such as technology and innovation management (Nambisan et al., 2017).
Implications for Research on the Role of Objective, Actor-Independent Factors in
New Venture Creation
The ‘‘Individual-Opportunity Nexus’’ view of new venture creation launched by Shane and
Venkataraman (2000) and later recast as ‘‘Discovery Theory’’ by Alvarez and Barney (2007)
gives considerable weight to objective, actor-independent factors in new venture creation.
MIT’s 3D printing technology is a prime example (Shane, 2000). However, apart from cogni-
tion-focused, experimental research on ‘‘opportunity recognition’’ (e.g., Gre
´goire & Shepherd,
2012; Wood, McKelvie, & Haynie, 2014), theoretical and empirical progress has been limited
(Shane, 2012). This is at least in part because objective, actor-independent factors have been
cast as preexisting ‘‘entrepreneurial opportunities,’’ a notion that has proven to be elusive and
problematic (Dimov, 2011; Kitching & Rouse, 2017; Klein, 2008).
To avoid the conceptual confusion and stalled theoretical development around the entre-
preneurial opportunities construct, Davidsson (2015) suggested external enabler as a more
workable construct. Our theorizing supports the usefulness of the external enabler construct
and indicates its potential beyond our focus on digital technologies.
We extend Davidsson’s (2015) arguments about external enablers in three important ways.
First, Davidsson (2015) envisioned research that focuses on one or two enablers, but such a
restricted focus would probably have exaggerated the importance of the focal enabler in our
case, and caused us to miss the basis for our sector-level propositions that relate to process
stages. In addition, different types of enablers can combine to enhance the facilitating mech-
anisms, as in the case of 3D printers and online repositories for 3D print files, where the two
technologies feed each other. The importance of analyzing multiple enablers simultaneously is
well worth considering in future research.
Second, we identify a number of mechanisms by which external enablers influence start-up
processes. These mechanisms can be used to describe external enablers and ‘‘increase [the]
specificity of the[ir] assumed effects’’ (Davidsson, 2015, p. 689). Our compression,conservation,
expansion,substitution,combination, and generation mechanisms can help in formulating and
answering questions concerning what enabling mechanisms external enabler X can provide, at
what stage of the process external enabler Y would be of help, what mechanism we should be
looking for in development stage Z, and what digital technology can offer mechanism P,
which venture Q will likely need in the development stage it is about to enter. Extension to
other types of enablers may require the identification of other mechanisms, but the questions
they can answer remain.
Third, our approach also introduces a way to integrate consideration of technologies and
other objective, actor-independent factors into process-oriented theorizing, where systematic
attention to such factors has been lacking (Alvarez, Barney, & Anderson, 2013; Wood &
McKinley, 2010). We found that the Bakker-Shepherd (2017) process model and its prospect-
ing,developing, and exploiting stages works well in addressing process issues in our context
and allowed us to identify external enablers and mechanisms that vary by stage of
Briel et al. 15
development. While advocates have promoted a process perspective on new venture creation
(McMullen & Dimov, 2013; Shane & Venkataraman, 2000; Zahra & Wright, 2011) they
have also voiced doubts about the feasibility of broad process studies based on observational
data. We believe that the combination of the Bakker-Shepherd model’s conceptualization
of stages and the enabling mechanisms we identified makes such studies feasible, and
that future research that applies these tools will be able to test ideas about the associations
among the stages of development, the presence of external enablers and their mechanisms,
and outcomes.
In closing this section we note that much of our theorizing about enabling mechanisms may
be valid for other technologies and for other types of external enablers that may operate in
part through the same and other mechanisms than those discussed here. At the very least, the
sector-level Propositions 7–9, while derived from the context of digital technologies and the IT
hardware sector, are likely to apply to other sectors and other types of enablers.
Contributions Regarding the Role of Context in Entrepreneurship and
Entrepreneurship Research
We have responded in two ways to the multiple calls for the entrepreneurship research to pay
more attention to the role of context (e.g., Welter, 2011; Zahra & Wright, 2011; Zahra et al.,
2014). First, we theorized in context. Our analysis of the IT hardware sector should provide
insights that are more valid for that sector and for similar industries than would insights that
emanate from an attempt at ‘‘contextless’’ theorization. We thus support Nambisan’s (2016)
and Zahra and Wright’s (2011, p. 73) contention that this type of focus can be fruitful. Our
application, combination, and extension of a set of theoretical tools allowed us to provide a
detailed portrayal of start-up activities in the IT hardware sector. For example, per
Proposition 9, the combination of these enablers and their mechanisms across all three
stages of new venture creation arguably provides a major part of the explanation for the
recent surge in start-up activity in the IT hardware sector.
Second, we show how focusing on a particular context—IT hardware and digital techno-
logies—can facilitate theorizing that has value beyond that context (e.g., Bakker & Shepherd,
2017; Ozcan & Eisenhardt, 2009; Vissa & Bhagvatula, 2012). Just as the structure and sim-
plification of good theory can safeguard against being dazzled by the complexity of the social
realities one studies (Bacharach, 1989), a specified context can provide a sound setting for
developing theoretical ideas in the first place. The choice of the IT hardware sector, with its
traditionally high barriers, made the enablers and their mechanisms more visible; identifying
them and locating them in the process might have been an insurmountable challenge without
a focus on a particular setting, not least because enablers and stage markers manifest them-
selves differently across different contexts.
That said, in the context of this special issue and with a set of mechanisms at the center of
our theorizing, the implications of our theorizing for industry-specific entrepreneurship across
sectors is worthy of further elaboration. The traditionally high resource intensity and slow
process speed of IT hardware make it likely that compression, conservation, and expansion
mechanisms are particularly important in increasing independent start-up activity in this
sector. The provision of substitution mechanisms is similarly important in IT hardware
because providing alternatives to complementary assets and their providers reduces or elim-
inates the external dependencies that traditionally characterize the sector. Finally, the IT
hardware sector faces rigidity challenges, so digital technologies that provide combination
and generation mechanisms that increase the flexibility of the products offered can be dispro-
portionately important for start-up activity in this sector.
16 Entrepreneurship Theory and Practice 00(0)
These mechanism-based insights may apply to a considerable extent to other industry
sectors that share similar characteristics, such as most medium- and high-tech manufacturing
sectors, where start-up processes tend to be complex and of long duration. In the biotech
sector, for instance, new gene-sequencing technologies offer conservation mechanisms, and
big data technologies offer compression mechanisms that significantly reduce the costs and
time that are required to discover new compounds. However, neither of these industries
appears to have benefitted from a combination of enablers and mechanisms across stages
to the same degree that the IT hardware sector has. Our Proposition 9 suggests that it would
take simultaneous enablement across stages to give rise to a similar surge in independent start-
up activity in these other industries.
While the specific technologies that enable entrepreneurial activity likely vary by industry
context, we are confident that our six enabling mechanisms provide a valuable analytical tool
across similar contexts that can guide further industry-specific theorizing. Several of the
mechanisms may still be important in industry contexts that share fewer of the IT hardware
sector’s characteristics, although some may be of minor importance while other mechanisms
not covered here might be more important. To guide further industry-specific theorizing,
Table 3 compares the characteristics of the IT hardware sector with a few other medium-
and high-tech manufacturing sectors. With the similarities and differences elucidated, it
should be possible to adapt our theorizing such that specific propositions can be derived
for other sectors. For example, we believe that the associations between digital technology
characteristics and mechanisms (Propositions 1–6) are generalizable across sectors. Specificity
and relationality are two fundamental characteristics of digital technologies through which
they influence agency and boundaries of entrepreneurial processes, so these characteristics are
closely linked to their action potential and, thus, to the mechanisms they can provide.
However, consistent with our use of mechanisms as ‘‘an intermediary level of analysis
in-between pure description and storytelling on the one hand, and universal social laws on
the other’’ (Hedstro
¨m & Swedberg, 1996, p. 281), we do not expect these relationships to be
deterministic. For example, we do not expect every digital technology with high specificity to
afford compression and/or conservation mechanisms (Proposition 1), nor do we exclude the
Table 3. Comparison of the IT Hardware Sector with Other Medium- and High-Tech Manufacturing
Sectors
Differentiators IT Hardware Automotive Bio-tech Chemicals
Time intensity Time to market Medium-long Long Long Long
Reproduction time Medium-long Long Short-medium Short
Distribution time Medium Long Medium Medium-long
Resource intensity Knowledge intensity High High High High
Equipment intensity Medium-high High Medium-high Low-medium
Capital intensity Medium-high High High High
External dependencies Value chain activities Full Full Full Full
Vertical specialization High High Medium-high Medium-high
Solution complexity Medium-high High Medium-high Low-medium
Firm flexibility Product lock-in High High High Medium
Economies of scale High High High Medium
Market responsiveness Medium Low Low Low
Briel et al. 17
possibility that digital technologies with low specificity will occasionally offer these mechanisms.
Therefore, in line with the promising lens on affordances and constraints of technological objects
(e.g., Leonardi, 2011; Majchrzak & Malhotra, 2013; Nambisan et al., 2017), the digital technol-
ogy characteristics-mechanism links we propose may be relational and probabilistic yet of high
generalizability across sectors (and other context dimension; see Zahra et al., 2014).
The associations between mechanisms and the stage of venture creation observed in the
context of the IT hardware sector (Table 2) may or may not be generalizable to other sectors.
However, our propositions (7–9) about stage-specific enablement and sector-level start-up activ-
ity should not be limited to IT hardware and digital technologies but should apply also to other
sectors and types of enablers. In all, the assessment clearly suggests that our theorizing has value
well beyond understanding recent developments in the IT hardware sector.
Besides its implications for research on industry-specific entrepreneurship, our theorizing
has the potential to provide impetus for emerging research on entrepreneurial ecosystems.
Entrepreneurial ecosystems are industry clusters that are influenced by spatial proximity and
the availability of digital technologies (Autio et al., 2017). While the IT hardware sector is not
subject to spatial proximity or delineated by spatial boundaries, our analysis suggests that
digital technologies have played an important role in enabling entrepreneurial activity in the
sector. Moreover, while startups in the IT hardware sector use similar inputs and technologies
to create their market offerings, the offerings themselves typically transcend traditional indus-
try and market boundaries by evolving around generic business model innovation, so they are
similar to the offerings created by startups in entrepreneurial ecosystems (Autio et al., 2017).
For these reasons, we expect that both the associations between digital technology charac-
teristics and mechanisms (1–6) and the sector-level propositions (7–9) we propose can also
serve future research on entrepreneurial ecosystems.
Conclusion
In this paper we developed an effective way of theorizing the role of digital technologies as
objective, actor-independent factors within a process view of new venture creation. We com-
bined five conceptual tools—the external enabler construct, the distinction between bound-
aries and agency as a perspective on digital technologies, the analytical construct of
mechanisms, the Bakker-Shepherd process model, and literatures on decision-makers’ focus
of attention—using the IT hardware sector to provide inspiration, direction, and focus for our
conceptual development. The result is theoretical tools for analyzing how digital technologies
enable new venture creation.
If, as we hope, our theorizing stimulates future research on entrepreneurship, flaws and
omissions in our theorizing will doubtless be revealed. However, such studies will provide
empirical evidence and conceptual extensions along the same path that will serve business
and teaching practice and the scholarly community well by providing useful and validated
tools.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: The authors were supported by a grant from the Institute for Future
18 Entrepreneurship Theory and Practice 00(0)
Environments (IFE) at Queensland University of Technology (QUT). The IFE also kindly sponsored the
open access fees to make this article freely available. In addition, Dr Recker’s contributions were
partially supported by a grant from the Australian Research Council (DP160103407).
Notes
1. Agency is the ‘‘capacity for action’’ (Giddens, 1984). Because digital technologies can be assigned
functional capacity for action, they can have material agency (Faulkner & Runde, 2009; Leonardi,
2011; Orlikowski & Scott, 2008). Whereas human agency refers to actions that humans perform
intentionally, material agency refers to actions that digital technologies perform without users’
direct or complete control (Leonardi, 2011).
2. Our use of the term specificity is in alignment with some ideas concerning asset specificity in trans-
action cost economics (Williamson, 1985) in that a digital technology’s specificity reflects the degree
to which it is specialized to performing a unique task and is not redeployable to other tasks.
3. Readers who are familiar with the literature on mechanisms will recognize that we use a simplified
conceptualization that focuses on commonalities among prevailing conceptualizations. We are inter-
ested in generalizable theorizing about what kind of mechanisms exist, rather than engaging in eclectic
discussions about how some mechanisms might behave. Overall, we follow Hedstro
¨m and Swedberg
(1996, p. 281) in using mechanisms as ‘‘an intermediary level of analysis in-between pure description
and storytelling on the one hand, and universal social laws on the other.’’
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Author Biographies
Frederik von Briel is a Postdoctoral Research Fellow in the School of Management at QUT
Business School, QUT, Brisbane, QLD, Australia.
Per Davidsson is Director & Talbot Family Foundation Professor in Entrepreneurship at the
Australian Centre for Entrepreneurship Research (ACE) at QUT Business School, QUT,
Brisbane, QLD, Australia, and is Professor of Entrepreneurship at Jo
¨nko
¨ping International
Business School, Sweden.
Jan Recker is a Professor in the School of Management at QUT Business School, QUT,
Brisbane, QLD, Australia.
Briel et al. 23
... Accordingly, the scholarly focus on implications of digital transformation is inclined towards pure digital entrepreneurs (Nzembayie et al., 2019) in the technology-intensive environment (Beckman et al., 2012;Elia et al., 2020;Zupic, 2014). Specifically, the extant literature enlightens our understanding of implications from the perspective of digital business model innovation (DBMI), platform strategies, digital entrepreneurship, and digital ecosystem (Hsieh & Wu, 2019;Huang et al., 2017;Rachinger et al., 2019;Rayna et al., 2015;Richter et al., 2017;Srinivasan & Venkatraman, 2018;von Briel et al., 2018) at the individual and organisational level (Bharadwaj et al., 2013;Nambisan et al., 2019). Nevertheless, evidence from recent research in the area of digital innovation von Briel et al., 2018), open innovation (Chesbrough & Bogers, 2014;Nambisan et al., 2018), sharing economy (Fournier et al., 2013;Richter et al., 2015), platformisation (Gawer, 2014;Nambisan et al., 2018;Parker, Van Alstyne, & Choudary, 2016) advocates the broader implication of digital transformation beyond digital businesses. ...
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Digital ventures, start-ups growing by drawing on and adding to digital infrastructures, can scale their business at an unprecedented pace. We view such rapid scaling as a generative process by which a venture’s user base increases significantly between two points in time through digital innovation. We studied WeCash, a Chinese digital venture, nearly doubling its user base monthly, to learn more about this generative process. We trace three contingent mechanisms underpinning rapid scaling: data-driven operation, instant release, and swift transformation. We explain these mechanisms and how they interact in the rapid scaling of digital ventures. The research offers an agency perspective on scaling of digital ventures that speaks to the digital innovation literature.
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Digital ventures, start-ups growing by drawing on and adding to digital infrastructures, can scale their business at an unprecedented pace. We view such rapid scaling as a generative process by which a venture’s user base increases significantly between two points in time through digital innovation. We studied WeCash, a Chinese digital venture, nearly doubling its user base monthly, to learn more about this generative process. We trace three contingent mechanisms underpinning rapid scaling: data-driven operation, instant release, and swift transformation. We explain these mechanisms and how they interact in the rapid scaling of digital ventures. The research offers an agency perspective on scaling of digital ventures that speaks to the digital innovation literature.
Article
New digital technologies have transformed the nature of uncertainty inherent in entrepreneurial processes and outcomes as well as the ways of dealing with such uncertainty. This has raised important questions at the intersection of digital technologies and entrepreneurship—on digital entrepreneurship. We consider two broad implications—less bounded entrepreneurial processes and outcomes and less predefined locus of entrepreneurial agency—and advance a research agenda that calls for the explicit theorizing of concepts related to digital technologies. In articulating the promise and value of such a digital technology perspective, we consider how it would build on and enrich existing entrepreneurship theories.