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When Do Novel Business Models Lead to High Performance? A Configurational Approach to Value Drivers, Competitive Strategy, and Firm Environment

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Abstract

The strategy literature views novel business model design as a universally positive antecedent of high performance in entrepreneurial ventures. Not only do scholars emphasize novelty as a necessity, but they almost consider it a sufficient condition for performance. Challenging this assumption, we hypothesize that novelty can produce high performance only in combination with specific configurations that feature other value drivers such as efficiency, lock-in, and complementarity, which help firms not only create value but also capture more of it. Fuzzy set qualitative comparative analysis (fsQCA) of two samples of Internet-enabled firms largely supports our hypotheses. We find that novelty alone is insufficient for high performance, even when it appears as a necessary condition for very high performance in new technological environments. Our results highlight how novelty combines effectively with other value drivers (in particular with efficiency) and strategies (in particular with differentiation) contingent on the intensity of competition, firm size, and firms' technological environment. Our study contributes to literatures on value creation, business model design, and innovation.
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When Do Novel Business Models Lead to High Performance? A Configurational Approach
to Value Drivers, Competitive Strategy, and Firm Environment
Petteri Leppänen
Imperial College London
Business School
London SW7 2AZ, United Kingdom
p.leppaenen@imperial.ac.uk
Gerard George
Georgetown University
McDonough School of Business
3700 O Street, Washington DC 20057
gerard.george@georgetown.edu
Oliver Alexy
Technical University of Munich
TUM School of Management
80333 Munich, Germany
o.alexy@tum.de
Please cite as:
Leppänen, P., George, G. & Alexy. O. 2021. When do novel business models lead to high
performance? A configurational approach to value drivers, competitive strategy, and firm
environment Academy of Management Journal, DOI: 10.5465/amj.2020.0969
This article has greatly benefited from the guidance of Laszlo Tihanyi and three anonymous
reviewers. The authors would also like to thank Dmitry Sharapov, Ammon Salter, Peer Fiss, and
J.P. Eggers, as well as participants at the Academy of Management Annual Meeting, Strategic
Management Society Annual Conference, DRUID Annual Conference, SEI Consortium, and the
seminars at Imperial College London, New York University, Technical University of Munich,
and Nanyang Technological University, for their valuable feedback.
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When Do Novel Business Models Lead to High Performance? A Configurational Approach
to Value Drivers, Competitive Strategy, and Firm Environment
Abstract
The strategy literature views novel business model design as a universally positive antecedent of
high performance in entrepreneurial ventures. Not only do scholars emphasize novelty as a
necessity, but they almost consider it a sufficient condition for performance. Challenging this
assumption, we hypothesize that novelty can produce high performance only in combination with
specific configurations that feature other value drivers such as efficiency, lock-in, and
complementarity, which help firms not only create value but also capture more of it. Fuzzy set
qualitative comparative analysis (fsQCA) of two samples of Internet-enabled firms largely
supports our hypotheses. We find that novelty alone is insufficient for high performance, even
when it appears as a necessary condition for very high performance in new technological
environments. Our results highlight how novelty combines effectively with other value drivers (in
particular with efficiency) and strategies (in particular with differentiation) contingent on the
intensity of competition, firm size, and firms’ technological environment. Our study contributes to
literatures on value creation, business model design, and innovation.
Keywords: Value creation; Value capture; Novelty; Business model design; Innovation;
Configuration; fsQCA
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INTRODUCTION
A core question of strategy and entrepreneurship research is what drives value creation and
capture, and, thus, firm performance (Amit & Zott, 2001; Brandenburger & Stuart, 1996). Here,
firms have long been known to create value through a variety of elements (Lepak, Smith &
Taylor, 2007), such as unique resources (Barney, 1991), innovation (Schumpeter, 1942), and
networks (Gulati, Nohria & Zaheer, 2000). A recent debate centers on how firms improve
performance by recombining them into systems of interrelated design elementsthe business
model (Massa, Tucci & Afuah, 2017; Zott, Amit & Massa, 2011). As a result, a growing number
of scholars are focusing their attention on how the business model, as the firm’s architecture of
translating an entrepreneurial opportunity into a viable business, may help explain firms’ efforts
at value creation and value capture (Andries, Debackere & van Looy, 2013; Aversa, Furnari &
Haefliger, 2015; Chesbrough & Rosenbloom, 2002; Desyllas & Sako, 2013; George, Merrill &
Schillebeeckx, 2021; McDonald & Eisenhardt, 2020; Snihur, Zott & Amit, 2021; Teece, 2010;
Tidhar & Eisenhardt, 2020; Zott & Amit, 2010).
In the transaction design view that dominates most writings in this area (for reviews, see
George & Bock, 2011; Massa et al., 2017; Zott et al., 2011), a firm may create and capture value
through its business model by considering how it plays toward the key four design themes or
value drivers of business models, defined as “any factor that enhances the total value created by
an [e-]business” (Amit & Zott, 2001 p.494): novelty, efficiency, lock-in, and complementarity
(Amit & Zott, 2001). In turn, with noveltyintroducing new combinations of products and
services, creative methods to generate revenue, and new ways to connect providers, customers,
and partnersseen as the primary driver of firm performance (Zott & Amit, 2007, 2008), an
entire literature on business model innovation and change has emerged (Bock & George, 2018;
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Foss & Saebi, 2017; Hampel, Tracey & Weber, 2020; Shepherd, Seyb & George, 2021; Zott et
al., 2011), centering on how firms can exploit new ideas, technologies, or ways to deliver
products and services through business model redesign. Given how business model innovation is
generally considered to create value and lead to a higher performance (Cucculelli & Bettinelli,
2015; Foss & Saebi, 2017; Zott & Amit, 2007), this literature, as well as management practice
(e.g., Johnson, Christensen & Kagermann, 2008), see novelty as the core business model design
theme enabling business model innovation as an organizational imperative.
We argue that the link between novelty, business model innovation, and performance
may not be as clear cut as it seems. First, novelty and business model innovation should not be
treated synonymously. Logically, the introduction of a new business model element emphasizing
efficiency, lock-in, or complementarity, would similarly qualify as an innovation in the business
model. What seems to be different about those three value drivers is that they seem to be
focusing more strongly, relative to novelty, on value capture rather than value creation (Almeida
Costa & Zemsky, 2021). In turn, it stands to reason that firms may improve their performance by
improving value creation (‘growing the pie’), value capture (‘getting a larger slice’), or both
(Brandenburger & Stuart, 1996; Snihur et al., 2021; Tidhar & Eisenhardt, 2020). In particular,
we note that the original writings on the value of novelty in business models (e.g., Amit & Zott,
2001; Zott & Amit, 2007, 2008) draw on data from a novel firm type (e-businesses) at a time of
environmental turmoil (the dotcom boom and bust) facilitated by an emerging technology (the
Internet). It remains unclear whether the prevailing findings on novelty as a beneficial business
model design attribute would generalize to more established firms or contexts of more mature
technology, with research in other domains suggesting that this may not be the case (e.g.,
Barbosa, Faria & Eiriz, 2014; Park & Mezias, 2005).
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Second, we note that a predominantly transactional view on business models may fall
short of capturing the complementarities inherent in interrelated systems: that a system should
strive to be worth more than the sum of its individual components (Amit & Zott, 2021; Argyres
& Liebeskind, 1999; Ennen & Richter, 2010; Siggelkow, 2011). If a good business model
represents a delicate balance, disrupting this balanceby, say, introducing novelty into business
models centered on efficiency or lock-inmay even lead the firm to lower performance levels
(Siggelkow, 2001). And even if the firm manages to preserve this balance, for business model
innovation to improve performance it will also need to harmonize with firm’s strategy
(Casadesus-Masanell & Ricart, 2010) and its external environment at large (Short, Payne &
Ketchen, 2008). Recent work on business model innovation has begun to acknowledge that firms
need to be careful about how much novelty they should incorporate (Casadesus-Masanell & Zhu,
2013; Desyllas, Salter & Alexy, 2020; Kim & Min, 2015). What is yet missing from this view is
how emphasizing one value driver, novelty, interacts with the other value drivers, the firm’s
business strategy, and its competitive environment in a systemic perspective, which goes beyond
the pairwise interactions of novelty that this literature has mainly examined till date (see, e.g.,
Zott & Amit, 2007, 2008). Such a systemic perspective (see Furnari, Crilly, Misangyi,
Greckhamer, Fiss & Aguilera, 2020; Siggelkow, 2011; Sterman, 2000) may not only highlight
the interdependence between design dimensions, but also point toward potential equifinality in
business model design (Bock, Warglien & George, 2021; Doty, Glick & Huber, 1993): whether
it is really novelty as a singular design dimension that would predict high-value business models
or rather a set of combinations of dimensions under specific circumstances.
Accordingly, to examine how novel business models link to firm performance, we
conduct a full configurational analysis to capture the complete system of interactions between
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business model value drivers, firm strategy, and the competitive environment, both at a point in
time when the underlying technology is emerging and when it is established. To do so, we
perform fuzzy set qualitative comparative analysis (fsQCA; Fiss, 2011) using two samples of
publicly traded Internet-enabled firms that allow us to test our hypotheses under varying market
conditions: when the Internet was new to the world and when it had become more established.
This enables us to investigate how novel business models perform differently over time, and also
provide comparability with studies conducted on data drawn from the late 1990s.
In line with our theorizing, we find that novel business model design alone is insufficient
for high performance, but is effective when used in specific combinations with other value
accretive business model design elements. We highlight that novel and efficient business model
designs can be both complements and substitutes, and that the effectiveness of novelty also
depends on firm size, competition, and the maturity of the technological environment. Finally,
our results reveal novelty as necessary for very high firm performance when the technological
environment is new.
Our study tackles the perceived importance of business model novelty for firm
performance. Our core contribution is that novel business model design is an essential driver of
firm performance, and sometimes even a necessary condition, but that it works only in
combination with other value drivers and strategies. We also advance our understanding of the
complex interdependencies inherent in such combinations and offer theoretical insights and
empirical evidence to show how novel business models can be configured for high performance.
Overall, we find that the relationship of novelty and performance is more nuanced than
previously thought and that the role of other factors and their combinations in creating and
capturing value has been under-emphasized.
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A CONFIGURATIONAL APPROACH TO BUSINESS MODEL DESIGN
A company’s business model describes a set of interconnected choices and mechanisms through
which it pursues to create value for its stakeholders and capture some of that value for itself
(Amit & Zott, 2021; Chesbrough & Rosenbloom, 2002; Lanzolla & Markides, 2020; Teece,
2010; Snihur et al., 2021; Tidhar & Eisenhardt, 2020). While there is consensus on this relatively
abstract view on business models, strategy and entrepreneurship researchers have introduced
numerous approaches to studying business models, often without a common definition (Bigelow
& Barney, 2021; George & Bock, 2011; Massa et al., 2017; Zott et al., 2011). Still, prior studies
largely adopt a shared view of business models as a system of interrelated design elements
representing a firm’s architecture of translating an entrepreneurial opportunity into a viable
business (e.g., Andries et al., 2013; Aversa et al., 2015; Baden-Fuller & Mangematin, 2013;
Bock et al., 2021; Zott & Amit, 2010).
In line with this system-level perspective, the transaction design view by Amit and Zott
(2001) has emerged as one of the dominant perspectives on business models. According to these
authors, a business model is “…content, structure, and governance of transactions designed so
as to create and capture value through the exploitation of business opportunities (Amit & Zott,
2001, p.511).
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To theorize the link between business models and firm performance, the authors
draw from transaction cost economics and theories of Schumpeterian innovation, among others,
to identify four different sources of value creationso-called ‘design themes’ of the business
model: novelty, efficiency, lock-in, and complementarity.
Companies establishing a novelty-oriented business model focus on innovation and hope
to win customers over by providing superior use value. In turn, efficiency-oriented companies
1
See Amit and Zott (2021, p.13) for a more recent and more extensive definition.
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build transaction-based, scalable business models to remove market imperfections. Lock-in
refers to a company’s attempts to create switching costs to ensure retaining customers and other
business model participants. Complementarity emphasizes products, services, and technologies
that are provided to add value to the core offering. Thus, the four design themes are clearly not
mutually exclusive. Rather, each dimension reflects aggregate evaluations of several
transactional elements of the business model that may well co-exist.
Even though the transaction view acknowledges the potential interdependence of the four
value drivers, the literature reveals (1) a clear focus on novelty and (2) little insight into the
actual system properties and interactions of novelty with other dimensions and key factors
determining firm performance. Rather, the positive valence of novelty has been reified as part of
the burgeoning literature on business model innovation (Casadesus-Masanell & Zhu, 2013;
Hampel et al., 2020; Kim & Min, 2015; Martins, Rindova & Greenbaum, 2015; Snihur & Zott,
2020), focusing on how firms can exploit new ideas, technologies, or ways to deliver products
and services through business model redesign (Foss & Saebi, 2017; George et al., 2021; Zott et
al., 2011). Empirical work that actually tries to capture whether firms that change multiple
aspects of the business model in general (Desyllas et al., 2020) or that try to move toward
integrating more novelty into their existing business model (Kim & Min, 2015) find strong
evidence that any positive effect of business model will likely only result from a better
configuration of all elements, rather than focusing on just one. This systems-based thinking and
assumption of multiple simultaneous interdependencies is at the core of our study.
Configuration theory aptly captures the logic of the business model as a system of
multiple interdependent choices. Here, configurations are multidimensional constellation[s] of
conceptually distinct characteristics that commonly occur together.” (Meyer, Tsui & Hinings,
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1993). Broadly, the configurational approach to studying the strategy-performance relationship
has gained increasing prominence (Furnari et al., 2020; Ketchen, Thomas & Snow, 1993; Miller,
1996; Short et al., 2008). It assumes that organizations are systems of highly interdependent
elements that need to be consistently aligned in order to create internal and external fit, which, in
turn, leads to superior performance (Doty et al., 1993; Siggelkow, 2001), such as when scholars
have explored configurations of strategy, structure, and the environment (e.g., Burns & Stalker,
1961; Miles & Snow, 1978; Siggelkow, 2002).
From a configurational perspective, the question of business model design thus becomes
one of identifying (1) a complementary internal configuration that (2) exhibits external fit
(Miller, 1992; Siggelkow, 2011). First, complementarity constitutes that choices across several
dimensions of a configuration are interdependent, and that specific choices will exhibit different
levels of positive and negative externalities, or synergy (Ennen & Richter, 2010; Milgrom &
Roberts, 1995). To increase firm performance, it is up to management to identify a configuration
that maximizes synergies, which means that choosing an inferior solution to a specific problem
dimension may sometimes be preferred from a systems perspective (Argyres & Liebeskind,
1999). At the same time, many potential configurations that lead to the highest level of synergies
may simultaneously exist (Gresov & Drazin, 1997). Second, the idea of ‘external fit’ (Drazin &
Van de Ven, 1985; Miller, 1992; Siggelkow, 2001) captures whether the chosen configuration is
right given the prevailing external environment. For example, while both the organic and
mechanistic organizations are prototypically optimized internal configurations for innovating
organizations, the organic organization should be applied in volatile environments, and the
mechanistic organization in stable ones (Burns & Stalker, 1961).
The configurational perspective has recently experienced a renaissance as a result of the
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methodological developments that have enabled novel approaches to conjunctural causation,
when an effect depends on a combination of causes, and equifinality, where an end state can be
reached by many potential means (Fiss, 2011; Furnari et al., 2020). This ‘neo-configurational’
perspective (Misangyi, Greckhamer, Furnari, Fiss, Crilly & Aguilera, 2017) allows scholars to
study necessity and sufficiency of theoretically relevant conditions and their combinations for
outcomes of interest. From such a perspective, we propose that the current insights on business
model design and the transaction-based view can be extended using a neo-configurational
analysis to explain novelty as a value driver of firm performance. Rather than looking at novelty,
or even the business model, as an isolated contributor to firm performance, a systemic approach
considers how the firm’s business model design interacts with key contingencies. Here, first, the
firm’s strategy embeds and interacts with the business model to define how the firm competes
and serves its market (Casadesus-Masanell & Ricart, 2010; Massa et al., 2017; Zott & Amit,
2008). Second, strategic factors such as firm size, product market competition, and the maturity
of firms’ technological environment can also be essential elements to understanding how
business models influence firm performance (e.g., Casadesus-Masanell, Zhu, 2013; Zott et al.,
2011). We elaborate on these aspects below.
Insufficiency of Novel Business Model Design for High Firm Performance
Prior literature has established that of the value drivers guiding business model design, in
particular novelty matters for firm performance (Zott & Amit, 2007, 2008). The focus on and
expected positive effect of novelty as a value driver closely connects to a view that sees business
models as translation devices to capture value from technological innovation (Chesbrough &
Rosenbloom, 2002). Technology has played a significant role as an enabler of novel business
model designs (Zott et al., 2011). Indeed, as noted above, the literature on business models
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emerged largely around new Internet-enabled firms that discovered and established novel ways
to generate revenue and connect the activities and participants of a business model. These so-
called e-businesses often attracted significant amounts of funding, achieved rapid growth, and
became publicly traded companies in record speeds. The world saw numerous novel business
model configurations based on cost- and time-efficient online presence and operations that
emphasized fast transactions, automation, and availability of information (Afuah & Tucci, 2003).
We argue that while novelty may well be contributing to firms achieving higher
performance through their business models, novelty alone may be insufficient to do so. First, we
acknowledge the empirical evidence that shows a positive correlation between novelty and firm
performance (e.g., Amit & Zott, 2012; Chesbrough & Rosenbloom, 2002; Cucculelli &
Bettinelli, 2015; Sosna, Trevinyo-Rodriguez & Velamuri, 2010; Zott & Amit, 2007). For
example, Zott and Amit (2007) show how novelty leads to higher firm performance even under
varying environmental munificence. At the same time, as we noted earlier, the context and time
frame in which these studies were conducted may have created unique environmental
circumstances, in which enacting novelty, even if poorly, would positively impact firm
performance, especially if measured by stock market value. Several studies (e.g., Cooper,
Dimitrov & Rau, 2001; Lee, 2001) highlight how any firm, not just e-businesses, could see
tremendous improvements to their stock price merely by adding ‘.com’ to their firm name during
the dotcom bubble years at the end of the 1990’s. In turn, if these circumstances were to change
over time, such market reactions should disappear as well (Alexy & George, 2013). Empirical
studies that actually try to capture broadly whether new business models lead to superior
performance always identify key boundary conditions, such as the firms’ organization design
(Kim & Min, 2015) or technology endowments (Desyllas et al., 2020).
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Second, novelty emphasizes value creation by improving use value (Bowman &
Ambrosini, 2000; Lepak et al., 2007), that is, offering potential customers and partners
something that would satisfy their (possibly unmet) needs in a novel way (Zott & Amit, 2010).
An iconic example is govWorks, immortalized in the movie startup.com. This dotcom-era
venture was amongst the first online platforms trying to digitize government services, such as
paying fees and fines. This novel model aimed at creating value to citizens and businesses, who
would now be able to complete these processes faster, and when and where they wanted it.
Capturing this newly created value, however, is a different matter, and requires firms to
put in place design mechanisms focusing on value appropriation (Almeida Costa & Zemsky,
2021; Barney, 1995; Desyllas & Sako, 2013; Snihur et al., 2021; Teece, 1986, 2010). Here,
novelty’s ability to capture value is much lower than its ability to create it, because novelty per
se need not lead to actual improvements of business model performance: while novelty may
increase the overall ‘size of the pie’, it is unclear why a company could capture a larger ‘slice’ of
that pie, or any slice, for the matter. To continue with the example, govWorks was unable to
deploy a business model to benefit from its idea, as it could not deploy any of the other value
driversefficiency, lock-in, and complementarity, all of which emphasize value capture more
strongly as they lead to actual improvements of the business model’s performance—in a
complementary fashion. govWorks struggled to deploy its technology, so that in the end,
potential efficiency gains to customers were minimal. Competitors were easily able to copy
govWorks, as govWorks had not deployed a way of uniquely locking customers into its service.
Finally, govWorks was unable to create complementarities between the services it offered. As a
result, while it is clear that novelty was instrumental to the business model existing (a point we
return to below), it is unclear how it should translate into firm performance (in particular if
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measured by profits) if no other business model design theme was in place. Indeed, as many
other e-businesses, we suggest that this sole reliance on novelty in business model design
eventually harmed govWorks more than it did them good.
Accordingly, we suggest that while novelty as a business model value driver may well
hold the potential to increase firm performance, it should not be sufficient to do someaning
that a business model that exclusively focuses on this dimension would not enjoy higher
performance. Hence, while we will elaborate how novelty may lead to superior firm performance
in combination with other design elements, we posit as a baseline:
Hypothesis 1: Novelty is insufficient for high firm performance when other
value drivers and strategies are absent from a configuration.
Novel Business Model Design and Efficiency
Having laid out the importance of novelty as a driver of value creation, we now turn to how this
value driver should interact with the business model design elements emphasizing value capture
efficiency, lock-in, and complementarityto co-produce high level of firm performance.
While some studies suggest that a business model embedding new technologies, as in
digital ecosystems, could also leverage those to find ways of locking in customers successfully
or exploring complementarities with existing activities of the firm (Dattee, Alexy & Autio, 2018;
George et al., 2021; Snihur et al., 2021), in most discussions on strategy and organization design,
at least novelty and efficiency would be considered substitutes rather than complements (e.g.,
Burns & Stalker, 1961; March, 1991; Miller, 1996; Porter, 1985, 1996). Similar to business
strategists being advised not to mix differentiation and low-cost strategies (Lee, Hoehn-Weiss &
Karim, 2021; Porter, 1985, 1996), business models should keep novelty and efficiency apart.
Companies establishing a novelty-oriented business model focus on innovation and hope to
provide customers and other participants of the business model with superior use value. In turn,
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efficiency-oriented companies build transaction-based, scalable business models to remove
market imperfections. While taking extreme positions on either of these dimensions should allow
firms to increase performance, Zott and Amit (2007) suggest that novelty and efficiency together
might lead to diseconomies of scope and decrease firm performance.
2
At the same time, however, one could imagine cases in which an efficiency-oriented
business model may require novelty, such as the first-of-a-kind transactional platform, or, more
generally, that technological innovation allows for efficiency-centered process improvements.
For example, Jean-Paul Agon, L’Oréal’s CEO for 15 years until he retired in May 2021, has
received praise for his efforts to digitize his companythat is, to introduce new, digital
technologieswhich first and foremost helped to increase direct sales to the customers for some
key brands to over 27% of revenue (Digital, 2021)with disintermediation being a classic
efficiency-centered business model innovation (Magretta, 2002). In this example alone, similar
configurational arguments may be made for lock-in (having a learning customer profile on
L’Oréal’s website) as well as complementarity (cross-selling). Relatedly, George and colleagues
(2021) suggest that emerging technologies such as tokenization, gamification, and smart
contracting are giving rise to novel business models that work in tandem to make business model
transactions more efficient in conserving natural resource use and tackling climate change.
Accordingly, we put forward that novelty may be complementary with all other business
model design elements, including efficiency. First, for innovative firms, having the value drivers
efficiency, lock-in, and/or complementarity featured in the business models is necessary to
capture the value created by new technologies or services offered (as we have also argued in our
first hypothesis). But second, even for firms that are not focused on new technologies on the
2
Admittedly, the authors note that the negative interaction effect of novelty and efficiency on firm performance was
statistically weak and that this finding should be considered cautiously (Zott & Amit, 2007 p.194).
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product market, drawing on novelty to strengthen existing value capture mechanisms (i.e.,
finding novel ways to increase efficiency, lock-in, or complementarities) should help them
improve their competitive performance, as it did L’Oréal. While these arguments, in our view,
fully resonate with a logic of business models as well-configured systems, we note that studies
that only examine the effect of specific value drivers or their pairwise combination (e.g., Zott &
Amit, 2007) cannot assess how firms may, as a system of elements, have equifinality in how they
capture the value created through novelty. In contrast, taking a neo-configurational perspective
allows us to study, in parallel, multiple configurations that can all lead to high performance,
which may give insights beyond more conventional correlation-based approaches (Ragin & Fiss,
2017; Misangyi et al., 2017).
3
Since especially the co-occurrence of novelty and efficiency may
seem somewhat contradictory, as explained above, we focus on examining their relationship with
performance. Here, following our reasoning above, we expect that business model designs that
mix elements of novelty and efficiency will be a potent combination for high performance.
Therefore, we predict:
Hypothesis 2: In high-performing configurations, novelty and efficiency will
be complements rather than substitutes.
Novel Business Model Design, Firm Strategy, and Competition
Going beyond the business model, a firm’s competitive strategy describes, broadly speaking,
how it positions itself on the market.
4
In turn, business models are often argued to represent an
3
For example, even if two or more elements were to be found to correlate negatively with each other, they may still
co-occur in various high-performing configurations.
4
Zott & Amit (2008, p.5) define product market strategy as “Pattern of managerial actions that explains how a firm
achieves and maintains competitive advantage through positioning in product markets.” and provide an alternative
definition for the business model that, like the other definition presented earlier in this paper, relates to the
transaction design of firms: “A structural template of how a focal firm transacts with customers, partners, and
vendors. It captures the pattern of the firm’s boundary spanning connections with factor and product markets.
(ibid.). Hence, while both the business model and strategy are activity systems or configurations of multiple
interdependent elements, the elements of each construct differ in that the business model activities relate to the
transaction design of firms while the strategy activities relate to more long-term choices about market positioning.
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implementation consistent with that premise (e.g., Casadesus-Masanell & Ricart, 2010); for
example, a novelty-oriented business model seems more appropriate to enact a differentiation
strategy, because innovation in both transactions and strategy may provide a firm with a position
that increases customers’ willingness to pay if it can deliver unique products in new ways (Grant,
2013; Lee et al., 2021). Moreover, a firm may be able to tap into its full innovative potential by
attracting creative partners and employees with their focus on novelty at both the strategic and
tactical level (Teece, 2010).
Configurational analysis holds the potential to uncover equally valuable hybrid strategies
(e.g., Adner, Ruiz-Aliseda & Zemsky, 2016; Thornhill & White, 2007). That is, while we agree
that a fit between a generic strategy that essentially emphasizes novelty or uniqueness and a
similar business model promises value-accretive synergies, novelty may also hold the potential
to improve a low-cost strategy. Indeed, akin to our arguments for potential synergies between
novelty and efficiency at the level of business model value drivers, new technology has
frequently been identified as a key driver of scaling advantages or learning curve effects that
form the basis of low-cost strategies (Benner & Tushman, 2003; Lee et al., 2021; Porter, 1985;
Schilling, 2019). Hence, we argue that novelty and low-cost strategy can occur simultaneously in
high-performing firms, because firms that innovate their transaction designs may then be better
able to enact processes that contribute to their low-cost position. However, their co-occurrence
could also be realized in reverse order: firms’ efforts as part of their low-cost strategy may lead
to process innovations that also enable innovative transaction designs in terms of their content,
structure, and governance.
Indeed, prior work has shown that novel business model design may well be effectively
combined with both differentiation and low-cost strategy. For example, Zott and Amit (2008)
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drew from the configurations literature to investigate how novel and efficient business model
designs work with product market strategies and early market entry to impact firm performance.
After showing that novelty and efficiency as business model designs are conceptually and
empirically distinct from differentiation and low-cost strategies, they find that novelty interacts
positively with both strategies. Hence, they conclude that novelty and the strategies act as
complements rather than substitutes.
We extend this insight by noting how firm strategy is not made and executed in a
vacuum. Rather, strategy scholars underscore how industry-level factors might have a significant
impact on value creation, capture, and firm performance (Barney, 1991; Doz & Kosonen, 2010;
MacDonald & Ryall, 2004; McGahan & Porter, 1997; Vanneste, 2017). Specifically, the degree
of competition, or competitive threat, has frequently been included in such work (e.g., Almeida
Costa & Zemsky, 2021; Casadesus-Masanell & Zhu, 2013; Teece, 2010; Snihur et al., 2021; Zott
& Amit, 2007). For example, high levels of competition may allow firms to capture less of the
overall value created by a business model, thus leading to lower firm performance (Lepak et al.,
2007). Hence, we suggest that competition drives both innovation and efficiency, which might
also push firms to develop new business model designscompetition, thus, becomes the driving
force of more (business model) novelty. Yet, the risk of imitation in competitive industries is
high, and, as we have argued before, novelty on its own may be insufficient to improve firm
performance. Rather, in competitive industries, novelty in business models should pay off in
particular when it is deployed as part of a clear strategy (Almeida Costa & Zemsky, 2021; Lee et
al., 2021), emphasizing either low cost or differentiation. In contrast, when competitive intensity
is low, we expect to see less novelty as there is less pressure to introduce new sources of value
for customers due to unique industry barriers to entry, such as regulation or license to operate.
18
Similarly, such industries may be more lenient to firms deploying novelty outside of a clearly
defined business strategy (e.g., Hannan, Carroll & Pólos, 2003). We hence posit:
Hypothesis 3: In competitive industries, it is necessary to combine novelty
with either differentiation or low-cost strategy to achieve high performance.
Novel Business Model Design, Firm Size, and Technological Environment
Beyond the firm’s strategy itself, the literature on the strategy-structure fit has often pointed out
that a firms size and how it impacts its ability to acquire and exploit resources efficiently is a
key contingency when studying any effect on firm performance (e.g., Burns & Stalker, 1961;
Donaldson, 2001; Miles & Snow, 1978; Smith, Guthrie & Chen, 1989).
Accordingly, it comes as no surprise that firm size has often been found to constitute a
dividing line in studies on business models. In general, small firms are considered faster and
more flexible than large firms. Limited resources typically push start-ups to find creative
solutions (Baker & Nelson, 2005) while a lack of legitimacy and non-existent customer base
provide freedom to experiment with multiple business models (Andries et al., 2013).
Consequently, studies have frequently looked at start-ups and how they develop innovative
business models, how they change them, and how they perform (e.g., Amit & Zott, 2015;
Andries et al., 2013; Cucculelli & Bettinelli, 2015; Hampel et al., 2020; Snihur & Zott, 2020).
Large firms, in turn, are thought to struggle as a change in the business model might also
require changing the organizational system (Bock et al., 2012), which is why successful business
model innovation typically takes large and more established firms more effort, time, and
leadership skills than small and young ventures (Doz & Kosonen, 2010). Irrespective of how
established firms try to introduce new features into their business models (see, e.g., Casadesus-
Masanell & Tarjizan, 2012; Markides & Charitou, 2004; Kim & Min, 2015), attaining a novel,
working organizational configuration can be hard (Siggelkow, 2001).
19
The perfect storm favoring small firms, thus, should be represented by technologically
disruptive environments that provide new entrants with the opportunity to create a market
(Christensen, 1997). Since small firms build absorptive capacity and innovate at a faster rate
(Zou, Ertug & George, 2018), they are more likely to introduce novel design configurations
enabled by emerging technologies that could potentially help achieve high performance (Massa
et al., 2017). Yet, given their resource constraints, it is likely that the disruptive characteristics
they offer to investors and previously neglected customer groups are a result of ‘simple’
combinations of business model design elements, meaning that high-performing small firms in a
disruptive environment should feature business models that do not necessarily have to include
many elements to capture the value created from novelty. Large firms, in turn, have better access
to resources that facilitate innovation, but their focus typically lies on existing opportunities and
exploitative innovation (March, 1991; Raisch, Birkinshaw, Probst & Tushman, 2009). Given this
established position and distribution of managerial attentionwhich may lead to a slow reaction
to emerging opportunities (Christensen, 1997; Maula, Keil & Zahra, 2013; Ocasio, 1997)they
will likely outcompete faster, more nimble small firms only when it is beneficial to design
complex and resource-intensive configurations that support novelty effectively in creating and
capturing value. This is difficult and requires considerable determination from the established
company or the attempt to create a novel business model may end up being penalized for non-
conformance to established expectations (Alexy & George, 2013; Smith, 2011). Yet, with
substantial effort, some firms such as IBM, have repeatedly succeeded at doing so (King &
Baatartogtokh, 2015; King & Tucci, 2002).
In contrast, the situation is different when the technology and those industries enabled by
it are more mature. Here, too, since small firms have little existing business, they are able to
20
focus their full attention without having to defend their position on the established market
(Greve, 2008; Maula et al., 2013; Ocasio, 2011). Yet, they also lack legitimacy and compete
against established companies that control most of the industry’s resources. Thus, because small
firms can, at least initially, rarely compete on status or economies of scale and efficiency, they
would need to identify ways of interacting novelty with efficiency, lock-in, complementarity,
and/or strategy in resourceful ways to beat incumbents at their own game, to essentially re-invent
an industry (Christensen, 1997). The possibility of such disruption is illustrated by once-small
firms in industries such as video-on-demand, which have combined novelty (e.g., streaming
technology and artificial intelligence) with efficiency (e.g., on-demand services and ‘binge-
watching’), lock-in (e.g., recommendation algorithms), and complementarity (e.g., movie and
TV production; Ansari, Garud, & Kumaraswamy, 2016). In turn, large, established firms enjoy a
higher degree of legitimacy, and should try to combine novelty with simpler configurations that
are appealing to their existing customer base (Smith, 2011) and other business model participants
while exploring new opportunities simultaneously (e.g., Tushman & O’Reilly, 1996).
In sum, we believe that firms of varying size should try to leverage novelty in different
waysthat is, by embedding it into business models with varyingly complex combinations
5
of
value drivers and strategiesin new vs. mature technological environments. Applying more
complex combinations may sometimes hold the key to unlocking higher levels of firm
performance; at the same time, they are difficult to implement and maintain, and often not
necessary. Based on our above reasoning, we expect that, as the enabling technology matures,
small and new versus large and established players with novel business model designs will have
5
Complexity is commonly considered as the number of elements in the configuration multiplied by the number of
interactions between them (e.g., Bock et al., 2021). Because all design elements (business model and strategy)
interact with each other, complexity here refers to the number of elements in the configurations.
21
differently structured combinations of value drivers and strategies to achieve high performance.
While the configurational complexity of design elements (including business model and strategy)
featured in strong small firms should become greater as the enabling technology matures, it
should become lower for large established firms, and vice versa. We predict:
Hypothesis 4a: In mature technological environments, the high-performing
configurations with novelty will feature greater configurational complexity
of complementary design elements in small firms than in large firms.
Hypothesis 4b: In new technological environments, the high-performing
configurations with novelty will feature greater configurational complexity
of complementary design elements in large firms than in small firms.
Necessity of Novel Business Model Design for Very High Performance
While we have argued that novelty alone is insufficient for high performance and effective only
in combination with other business model value drivers and strategies, we believe that novelty
still plays an essential role, especially for very high performance (Fiss, 2011). In particular when
a new technology, such as the above-mentioned Internet enters a market, business models are
also likely to be novel if they try to incorporate the previously non-existing technologies to
address previously unserved customers. These arguments imply that novel business models
should not only be more common in industries that are characterized by novel technology (Massa
et al., 2017; Zott et al., 2011), but that, simply put, to achieve any improvement to firm
performance from these novel technologies, the firm must take the risk of identifying a new way
of value creation in the first place, without knowing whether any of the value will eventually be
captured in a viable form.
6
Accordingly, we would expect that as a new technology emerges, novelty should indeed
6
As laid out for H1, our argument also captures one of the problems now apparent from the dotcom boom era: that
companies were evaluated based on sales growthin the hope that substantially increased sales would at some point
turn positive the fairly common negative profit margins seen at most dotcom era darlings.
22
be a key value driver to the company. Yet, such early phases are uncharacteristically volatile, and
it is unlikely that the same behavior, even if exhibited by similar (if older) firms in the same (if
more established) industries would result in the same market performance as the technology
matures (Alexy & George, 2013). For example, as we argued above, studies reveal that a firm’s
name change to include “.com” or to announce relatively straightforward alliance deals had
substantial effects on market valuation (e.g., Cooper et al., 2001; Lee, 2001; Park & Mezias,
2005), which we would not expect to see this day. Rather, these kinds of bandwagon effects
(Abrahamson & Rosenkopf, 1993; Fiol & O’Connor, 2003; McNamara, Haleblian & Dykes,
2008)a hype that results in irrational exuberance and overvalued assets (Shiller, 2000)
should fade out over time, with firm performance increasingly hinging less on promises to create
value someday, but actually hard evidence that they can capture some of it (Snihur et al., 2021).
Accordingly, we expect novel business model design to play a vital role in firms enabled by a
novel technology, like the Internet. Yet, as this technology matures, firms relying on novelty as
the key, or even sole, value driver of their business model will likely struggle to perform
competitively. Hence:
Hypothesis 5: Novelty is necessary for very high performance when a
business model is enabled by new technology, but not when the technology
is mature.
DATA AND METHOD
Samples
We test how novel business models combine effectively (and ineffectively) with other value
drivers and strategies to affect firm performance under varying conditions. We chose to study
Internet-enabled firms for two reasons. First, the Internet provides an ideal setting for comparing
business model designs and firm performance between (1) when there was a rapidly increasing
23
number of firms adopting the technology at the end of the 1990’s and creating a wide-spread
hype around the novel type of business, and (2) when the Internet had become an established
technology and accessible to almost any business 15 years after its initial boom. Second, in this
context, we can draw on established scales (Zott & Amit, 2007, 2008) developed specifically to
capture the business model design and product market strategies of e-businesses. In this way, we
build on existing literature and allow for comparability across studies in this domain.
Our sampling strategy is identical to Zott and Amit (2007, 2008). We considered a firm
Internet-enabled if it generated sales via online channels and was actively promoted as an e-
business either by itself or industry experts. For the context of new technological environment,
we looked for Internet-enabled firms that had gone public between (and including) 1996 and
1999 on the five largest stock exchanges in North America and Europe.
7
From the total of 384
firms we found initially eligible for the study, we drew a random sample of 201 firms. The data
on business models, strategies, competition, and the firms’ financials in this dataset are from the
year 1999.
The second sample, representing a context with now-established Internet technology, is
from 15 years later (data were collected from the year 2014). As the Internet technology
matured, the number of IPOs decreased. Therefore, and in line with our hypotheses, we
expanded our search to include new and more established firms by considering public e-
businesses regardless of the year they went public, while otherwise applying the same sampling
criteria as for the first sample. The original purpose of studying new IPO firms was to avoid
firms with complex, multiple business models (Zott & Amit, 2007). Large firms typically have
diversified businesses and the inclusion of such firms would potentially bias the results regarding
7
We considered NASDAQ, NYSE, Euronext, London, and Frankfurt.
24
the link between a business model and firm performance. Therefore, we carefully assessed the
business models of all potential firms and selected only firms that generated a clear majority of
their revenues by means of one main business model. For example, although Google was
involved in several businesses in 2014, it still yielded roughly 90% of its revenue via the search
engine businessalmost all other services were free of charge or under development. In those
relatively rare cases where firms did not report the proportion of their online sales or it was
unclear whether they were promoted as an e-business, we relied on discussions with colleagues.
Subsequently, after we had initially found and assessed the business models of more than 300
potentially eligible firms, the sample consisted of 173 e-businesses.
Due to unclear ownership structures (and therefore unclear link between the business
model and firm performance), limited availability of data, or lack of access to it, the final number
of firms in our first sample decreased to 125, while the second sample resulted in 169 firms.
Data Collection
We measure firm performance by using Tobin’s Q (Brainard & Tobin, 1968; Tobin, 1969).
Opposite to merely realized (such as RoA or EBIT%) or perceived (such as market value)
performance measures, Tobin’s Q, calculated here as the ratio of a firm’s market value and its
total assets, combines these two (Ceggangoli, 2009; Visnic, Weingarten & Neely, 2016). If
Tobin’s Q is greater than 1.0, the firm’s market value is greater than the value of its assets, and
vice versa. In other words, the higher the Tobin’s Q, the higher the firm’s performance. We
collected data on Tobin’s Q from Compustat and the sample firms’ annual reports.
Zott and Amit (2007, 2008) developed scales for the business model designs and product
market strategies. These items are listed in Appendix A. For novelty, efficiency, differentiation,
cost leadership, we followed Zott and Amit (2008), while the items on the business model design
25
themes complementarity and lock-in were derived from a working paper (Zott & Amit, 2002).
Since the four items on the degree of competition were not publicly available, we developed our
own scale based on existing literature (e.g., Porter, 1980). The data were collected mostly from
the firms’ annual reports and IPO prospectuses complemented by data from press releases, news
articles, industry analyses and reports, company websites (e.g., through Google Cache and
Wayback Machine), and other SEC filings. Overall, we used 13 items to measure novelty, 11 for
efficiency, 11 for lock-in, nine for complementarity, three for differentiation strategy, four for
low-cost strategy, and four for competition.
8
A considerable proportion of the survey items is based on relatively subjective
assessments, such as “The business model enables fast transactions” and “The business model
links participants to transactions in novel ways. We attempted to address this issue in two ways.
First, we created definition rules for the assessments. For example, we defined novelty as
something that was novel to the world in that it was first seen two or three years before our
measurement year (1999 and 2014). The next highest level of novelty was defined as something
new that was first seen three to five years before the measurement year, then five to ten years,
and, finally, novelty would receive the lowest Likert scale assessment if something were done
ten or more years before the measurement year. Moreover, we discussed different comparison
points for the assessments. For example, we defined new airports and powerplant construction
business as examples of the least efficient in terms of transaction design, while the most efficient
business in this sense would be sales of an online software, such as apps. These definitions of
extremes and averages helped our raters to orientate in their assessments.
8
Cronbach alphas were: novelty 0.7 and 0.7; efficiency 0.62 and 0.63; lock-in 0.64 and 0.66; complementarity 0.73
and 0.66; differentiation strategy 0.54 and 0.64; low-cost strategy 0.77 and 0.69; competition 0.64 and 0.67 for the
data from 1999 and 2014, respectively. We were able to improve the alphas substantially by dropping some of the
items. Notably, our key results hold across different combinations of items.
26
Second, we assigned eight students to collect and rate the data (see also Howell &
Higgins, 1990; MacCormack, Verganti & Iansiti, 2001; Zott & Amit, 2007, 2008). Before the
data collection, the students took part in a class on business models and were trained for data
collection and provided with in-depth exercises, regular discussions with one of the authors, as
well as written guidance and instructions to ensure a required quality standard. Each student
collected data independently on 60 firms on average and then compared their data with another
student who had collected the same data. We supervised this process by performing sanity
checks and acting as a referee when students disagreed on the items. Average inter-rater
consistency between the students in the first round was 0.71 for the 1999 data and 0.79 for the
data from 2014, measured by the Pearson correlation coefficient (Cohen’s kappa 72% and 78%,
respectively). After one to three rounds of discussion we derived consensus and reached an
agreement of 100% on all items.
Fuzzy Set Qualitative Comparative Analysis (fsQCA)
To test our hypotheses, we draw from the neo-configurational approach (Misangyi et al., 2017)
that emphasizes causal complexity and the use of fuzzy set qualitative comparative analysis
(fsQCA; Fiss, 2011; Ragin, 2000, 2008). FsQCA is a set-theoretic method that links
configurations of theoretically relevant elements (here: business model, strategies, contingencies)
to an outcome (here: performance) by calculating scores of necessity and sufficiency for each of
the individual elements and their combinations. It assigns empirical cases (here: firms) to
configurations based on their membership degree in each of the theoretically relevant element.
FsQCA has been used across management research (e.g., Bell, Filatotchev & Aguilera,
2014; Campbell, Sirmon & Schijven, 2016; Crilly, Zollo & Hansen, 2012; Garcia-Castro &
Francoeur, 2014; Greckhamer, 2016; Misangyi & Acharya, 2014) as well as further developed
27
and tested as a method (e.g., Fiss, 2007, 2011; Fiss, Sharapov & Cronqvist, 2013; Greckhamer,
Misangyi, Elms & Lacey, 2008; Greckhamer, Misangyi & Fiss, 2013; Misangyi et al., 2017).
Greckhamer and colleagues (2008) concluded that fsQCA is a viable method in strategy and
management research and provides substantial benefits especially when examining
configurations and complexity. Fiss (2011) showed that fsQCA demonstrates several potential
advantages over correlational interaction methods, cluster analysis, and deviation scores, when
building configurational theories with causal core and periphery.
We use fsQCA for several reasons. First, rather than estimating coefficients or bi-variate
interactions on a correlational basis, we study how sufficient and how necessary novelty is as a
business model design element alone and in combination with other key factors (Ragin, 1987;
Ragin & Fiss, 2008; Rihoux & Ragin, 2009). FsQCA is a suitable analytical method for problem
statements like this as it accounts for conjunctural causation, i.e., outcomes are often results of
combinations of two or more conditions. Second, we seek to understand how there may be
several effective ways to combine novelty with other crucial elements. FsQCA reports equifinal
paths and even allows for a distinction between first-order equifinalitywhen configurations
differ in their core elementsand second-order equifinalitywhen configurations with the same
core elements differ in their peripheral elements (Fiss, 2011). Third, fsQCA supports
investigations of causal asymmetry where causally relevant conditions may be relevant for the
presence of an outcome (e.g., high performance) but not for its absence (e.g., low performance;
Misangyi et al., 2017). This is advantageous as we can generate additional insights regarding the
role of novelty by examining how it links to low performance.
Calibration
An important step prior to a fuzzy set analysis is a calibration of the variables into set
28
memberships (Ragin, 2000). The researcher uses three thresholds, or anchor points, that
determine the cases degree of set membership in each causal and outcome condition used in the
study. The membership scores fall between 0 and 1, where (close to) 0 implies full non-
membership and (close to) 1 full membership. For the calibration of any condition the researcher
is required to have theoretical or substantial knowledge of the cases for being able to define
meaningful thresholds (Ragin, 2008). Often, however, such as here, there is little or no
theoretical or substantial knowledge about meaningful thresholds that apply in socially complex
phenomena. Hence, scales and other similar measurement instruments can provide practical help
in calibration (Misangyi et al., 2017; Rihoux & Ragin, 2009; Schneider & Wagemann, 2012).
For the calibration of our outcome condition, Tobin’s Q, we used both theoretical and
substantial information. As explained above, Tobin’s Q equals 1 if firms’ perceived value (based
on the market’s expectations) is the same as the value of its total assets. We consider this a
threshold that distinguishes between high and low performance. Therefore, we set the crossover
point at 1. Due to a lack of theoretical knowledge of further thresholds for Tobin’s Q, we turned
to the Compustat database and compiled a dataset containing all public firms listed on any North
American stock exchange between 1998 and 2019. Like prior studies that use fsQCA (e.g., Fiss,
2011; Misangyi & Acharya, 2014), we sought to use the population-level median to distinguish
between fully out and more out than in the set of high-performing firms. The yearly median
Tobin’s Q ranged from 0.45 to 0.99. We set the lower threshold between these two at 0.70 (just
below the ‘middle value’ between 0.45 and 0.99, which would have been 0.72; our results
remain unchanged if we choose 0.72 instead). For the upper threshold to distinguish between
more in than out and fully in the set of high-performing firms, we used Tobin’s Q of 2. This
indicates that a firm’s perceived market value is twice as high as the value of its total assets. This
29
value is also very close to the 75th percentile at the population level (e.g., Garcia-Castro &
Francouer, 2016; Misangyi & Acharya, 2014). As we have done in Hypothesis 5, our
configurational approach allows us to study patterns resulting from causal asymmetry, i.e.,
configurations leading to high performance may be different from those leading to other levels of
(high) performance (Fiss, 2011). Hence, for very high performance, we set the thresholds at 2 for
fully out and 5 for fully in to correspond to the 75th and 90th percentiles, respectively, while
setting the crossover point between these two thresholds at 3.5.
We calibrated all four business model design themes, the two strategies, and competition
based on the scales used in the data collection. Since all these constructs had at least three items,
each of which was assessed along a Likert scale ranging from 0 to 1 or 1 to 5, the minimum and
maximum aggregated values of the individual constructs rarely, if ever, reached close to the
theoretical ends (e.g., the maximum mean value for novelty was 0.76 out of 1 in the data from
1999 and 0.85 in the data from 2014). Therefore, we set the thresholds at 0.75 (fully in), 0.50
(crossover point), and 0.25 (fully out) when the scale was 0-1. Similarly, we used 4, 3, and 2,
respectively, when the scale was 1-5. All items were assessed such that the global average of all
firms from all industries should be close to the middle point of the scale. Hence, for instance, the
average efficiency of Internet-enabled firms would be expected to exceed the global average as
their transactions are inherently more efficient than those of firms in most other industries.
Lastly, we calibrated firm size using the number of employees as thresholds. We based
our calibration on the commonly used definition of small and medium-sized enterprises (SME),
and hence set the crossover point at 250. We used 50 as the lower threshold for fully out of the
set of large firms and 1,000 as the threshold for firms being fully in the set. The calibration
thresholds and summary statistics of the raw values are presented in Table 1 and Table 2.
30
---------------Insert Table 1 and Table 2 about here---------------
Procedure
After the calibration, fsQCA proceeds in three steps (Fiss, 2011). First, the researcher generates a
truth table that displays all theoretically possible combinations of the causal conditions used in
the analysis (Ragin, 1987, 2000). It also shows how the studied cases distribute across the
combinations and to what extent those combinations lead to the desired outcome.
Second, two further thresholds are set for the analysis, namely, consistency and
frequency thresholds. Consistency refers to the degree to which a combination of causal
conditions produces an outcome in question (e.g., high performance) and frequency simply
depicts the number of cases (sample firms) that follow a given configuration. While a
consistency score of 1 indicates a perfect subset relation (i.e., this configuration would always
lead to the outcome of interest), low consistency may produce unreliable results. Consistency is
recommended to be set at least at 0.75 (Ragin, 2008), however, we followed recent studies (e.g.,
Campbell et al., 2016) and set it higher at 0.80 to avoid inconsistencies in the analysis. We also
kept the proportional reduction in inconsistency (PRI) above 0.75 in all analyses (e.g., Misangyi
& Acharya, 2014). PRI captures the consistency level after eliminating cases that appear among
the configurations of both high and low performance (Fiss, 2011), thus further helping us to
avoid inconsistent results. Lastly, we set the frequency threshold at two cases per configuration
and were hence able to always include at least the recommended 75% of the sample firms in the
analysis (Ragin, 2017).
In the third step, the researcher makes assumptions about how the causal conditions are
expected to relate to the outcome of interest. This is done for the counterfactual analysis that
results due to a limited diversity (Soda & Furnari, 2012; Ragin, 2000), meaning that all
31
theoretically possible configurations may not be observed in the real world, which is why the
researcher’s knowledge is required as input. We therefore assumed that the presence of each
business model value driver positively contributes to firms’ high performance. In turn, we did
not make specific assumptions for the other four conditions used in this study.
RESULTS
We started out by first conducting a necessary condition analysis that reveals to what degree the
individual causal conditions are necessary for high and very high performance. The degree of
necessity is expressed as a score between 0 and 1; the higher it is, the stronger the evidence of
necessity. A causal condition obtaining a score of 0.90 or higher can be considered necessary for
the outcome of interest (Ragin, 2008). In our analysis, the necessity score of novelty is 0.47 in
1999 and 0.54 in 2014 implying that novel business model design is not a necessary condition
for high performance. For very high performance, the score is 0.57 in 1999 and 0.58 in 2014,
indicating that novel business model design is not necessary for very high performance either.
9
In contrast, we find that the necessity score of efficiency in 1999 is 0.895 for high
performance and 0.916 for very high performance. The same score in 2014 is 0.899 for high
performance and 0.937 for very high performance. In addition to efficiency, competition obtains
a necessity score of 0.927 for very high performance in 2014. Since neither condition was fully
necessary across the samples or the different definitions of firm performance, we keep them in
the model for further analysis (cf. Greckhamer, 2016; Misangyi & Acharya, 2014).
Hypothesis Tests
To test our hypotheses, we ran the standard sufficiency analysis for high and very high
performance both when the enabling technology was new (year 1999) and when it had become
9
These tests do not assume a frequency threshold, i.e., they consider all configurations, including rare ones.
32
more mature (year 2014). We find consistently high- and very-high-performing configurations
that are presented in Table 3 and Table 4. We did not identify any configurations for very high
performance when the Internet technology was mature. Efficiency appears as a necessary
condition for high and very high performance; this is somewhat expected given our focus on e-
businesses that have inherently efficient transaction designs.
---------------Insert Table 3 and Table 4 about here---------------
As shown in Table 3 and Table 4, we find high- and very-high-performing configurations
where novelty is either present (HP1, HP11, and VHP1-VHP3), absent (HP5-HP8 and HP10), or
does not matter (HP2-HP4 and HP9a/b). We find no evidence of novelty alone being sufficient
for high performance: it seems rather clear that firms must combine it with other value drivers
and strategies to achieve high performance. Any configuration where novelty is or can be present
features at least one additional value driver. When the enabling technology is novel, the business
model must also be efficient, and the firm should follow a differentiation strategy. When the
enabling technology is more mature, novelty must be combined with at least efficiency and
either lock-in or complementarity. Thus, we find strong evidence for our baseline prediction,
Hypothesis 1.
For Hypothesis 2where we predicted that novelty and efficiency are complements
rather than substituteswe find two configurations (HP1, HP11) where both novelty and
efficiency are present. However, configurations HP5-HP8 and HP10 show how only efficiency is
present and novelty is absent. In turn, we also find several configurations (HP2-HP4, HP9a/b)
that require the presence of efficiency, while novelty does not matter. This creates an interesting
situation where it seems that novelty and efficiency can be both complements and substitutes.
Moreover, whenever novelty is present (i.e., excluding situations where novelty does not matter),
33
there must be efficiency as well. We can see this also in all three very-high-performing
configurations (VHP1-VHP3). This provides clear support for the argument of complementarity.
The presence of efficiency, however, does not always require novelty as explained above.
Consequently, we conclude that novelty and efficiency can be both complements and substitutes
for high performance, but that they are complements for very high performance when the
enabling technology is new.
To gain more insights into when efficiency is likely to occur with and without novelty,
we investigated the configurations that include both novelty and efficiency and compared them
with configurations that featured efficiency and the absence of novelty. Although this did not
offer a clear-cut answer, we find that if novelty and efficiency are present, they always co-occur
with differentiation and intense competition. This is also true for configurations HP2-HP4 where
novelty can but does not have to be present, but not for HP9a/b, which appear only when the
enabling technology is mature. In turn, when novelty is absent and efficiency present,
differentiation is absent in configuration HP7 and does not matter in configurations HP5 and
HP10it is present only in HP6 and HP8. At the same time, for configurations HP6 and HP8 it
does not matter whether they operate on markets with high or low level of competition.
Following these findings, we believe the combination of novelty and efficiency may be more
important for performance when firms combine them with a differentiation strategy and are
facing intense competition from direct and indirect rivals, especially in situations where the
enabling technology is new.
In Hypothesis 3, we predicted that novel business model designs in competitive industries
must be combined with at least one of the two strategies to lead to high performance. We have
already discussed above how novelty, when it is present, is always combined with intense
34
competition and differentiation strategy, regardless of firm size and technological environment.
In fact, like with efficiency, novelty appears only when differentiation is present, too, while
differentiation occurs also when novelty is absent. In addition, configuration HP1 (and also
VHP3) illustrates how novelty and differentiation can be combined with low-cost strategy in
large firms. Even configurations HP2-HP4, which include both large and small firms, can
contain all three elements and competitive markets. The high-performing configuration HP1 and
very-high-performing configuration VHP3 also reveal an interesting pattern: these are firms that
have novel and efficient business models combined with both differentiation and low-cost
strategies. However, configurations HP9a/b in a maturing technological environment can feature
novel business model design yet they do not have a clear strategy (both strategies are absent). In
these configurations, novelty does not matter (may be present or absent) implying that high
performance is driven by other value drivers. Hence, the analysis supports Hypothesis 3.
Our data provide full support for Hypothesis 4a and partial support for Hypothesis 4b,
which stated that large firms with a novel business model design will have more complex
configurations than small firms to achieve high performance when the enabling technology is
new, and vice versa. When it is new, we observe two configurations with large firms that have,
or can have, novelty: HP1 combines novelty with efficiency and both strategies; HP4 does not
require novelty, but when it is present, it combines with efficiency, lock-in, complementarity,
and differentiation strategy. The only configuration with small firms, HP2, does not require
novelty either, but when it is present, it needs only efficiency and differentiation strategy to
achieve high performance. Hence, it seems to be ‘easier’ for novel small firms to achieve high
performance in this context. When the enabling technology is mature, the only high-performing
configuration of small firms, HP11, combines all four business model design elements and
35
differentiation strategy. In turn, the large-firm configurations HP9a/b contain only two other
business model design elements in addition to novelty when it is present. Therefore, this context
seems ‘easier’ for novel large firms to achieve high performance.
Lastly, as noted earlier, we observe the presence of novelty in all three very-high-
performing configurations (VHP1-VPH3) where the enabling technology is new. Moreover,
novelty is a core condition in these configurations implying its key contribution to very high
performance. This finding supports Hypothesis 5. We noted earlier how novelty’s necessity score
for very high performance was only 0.57 out of 1 and that it could therefore not be considered
necessary. Yet, the necessity analysis of individual elements differs from the standard
configurational analysis in that the latter considers only those configurations that are observed
empirically at least in two firms since that is our frequency threshold to help maintain a healthy
reliability level of the results. Consequently, configurations that were observed only once were
left out from the standard analysis, which explains the necessity score of novelty. We discuss
rare configurations briefly in our robustness tests. The second part of our hypothesisthat
novelty is not necessary for very high performance when the enabling technology is mature
could not be confirmed, because we did not find any very-high-performing configurations in this
setting.
The results of the hypothesis tests are summarized in Table 5.
---------------Insert Table 5 about here---------------
Low Performance
Causal asymmetry implies that the configuration of causal conditions leading to the presence of
an outcome (e.g., high performance) is not necessarily the inverse of the configuration that
produces the absence of the same outcome (e.g., low performance) (Misangyi et al., 2017; Ragin,
36
2000). Results from such analysis may provide further support to our hypotheses or other
relevant insights into situations where novel business model design results in low performance.
Consequently, we analyzed configurations leading to low performance, as well as
configurations that did not lead to high or very high performance. The analysis is similar to the
analysis presented above, but here we use different outcome definitions. First, we re-calibrated
the outcome variable to correspond to the 25th, 32.5th, and 50th percentiles of the lowest
performing firms in the Compustat database and ran the analysis for both of our samples. We did
not identify any consistent low-performing patterns. Then, similar to past work (e.g., Bell et al.,
2014; Campbell et al., 2016; Misangyi & Acharya, 2014), we used the inverse of high
performance (i.e., not-high performance) and very high performance (i.e., not-very-high
performance) as the outcome condition and ran the analysis again. We did not identify any
configurations that would consistently lead to low or the inverse of high performance in our first
sample, where the enabling technology is new. The consistency levels were much lower from the
recommended 0.75. This indicates that, instead of a clearly visible pattern, there may be many
ways to underperform. However, when the enabling technology is mature, we identified a set of
configurations that did not achieve very high performance.
---------------Insert Table 6 about here---------------
The results are reported in Table 6. First, regarding the role of novel business model
design, Configuration LP3 shows that when novelty is the only value driver present in the
configuration, it is sufficient to prevent firms from achieving very high performance, regardless
of firm size. This provides further support to our Hypothesis 1. Second, regarding the role of
efficient business model design, all solutions indicate the importance of efficiency by providing
evidence that the absence of it is likely to not lead to very high performance. Finally, regarding
37
all business model designs, these configurations, especially LP1a and LP2, show that there are
many ways to fail to achieve very high performance, even with seemingly strong combinations
of business models and strategies. They also show, however, that if firms fail to create and
capture value with their business model, they are more likely to underperform, even with a
strong strategy.
Robustness Tests
We ran several additional fsQCA-specific analyses to ensure robust results (Skaaning, 2011).
First, we ran the same analysis with different thresholds for the set memberships. We re-
calibrated all causal conditions representing business model and strategy. For example, we
altered the upper and lower thresholds from 0.75 and 0.25 to 0.70 and 0.30; 0.80 and 0.20; 0.85
and 0.15; as well as 1.00 and 0.00 (same relative changes with strategy variables with scale 1-5).
The results remained qualitatively unchangedmainly new neutral permutations took place and
the number of (sub-)configurations changed insubstantially, but the same observed core elements
and the interpretation of the solutions persisted. Second, we changed the consistency threshold
from 0.80 to 0.75 and 0.90. Expected changes regarding the number of configurations in the final
solution took place while the key findings remained same. Third, we changed the frequency
threshold from two to three cases which resulted in fewer high-performing configurations and a
lower overall coverage score, but also a more parsimonious overall solution with only two high-
performing configurations in the year 2014. However, again, we find similar patterns, so that our
key findings hold. By changing the frequency threshold to one, we learnt that there were a few
rare (very-)high-performing configurations without novelty and/or without efficiency. This
shows that it is indeed possible to gain high or very high performance without expected design
patterns in new technological environments.
38
We performed further, more general robustness checks. First, given the similarity to their
work, we set up a regression design directly replicating Zott and Amit’s (2007) study. We were
successful in reproducing their key findinga positive effect of noveltyshowing how our
configurational approach truly generates insights beyond what regression analysis may. Second,
in the main analysis reported above, we drew on the exact same scale items as Zott and Amit
(2002, 2007, 2008). We further subjected these scales to factor analysis and reduced the number
of scale items following good practice of construct validity and reliability (e.g., Hair, Black,
Babin, Anderson & Tatham, 2005). Still, our fsQCA results remain qualitatively unchanged.
Finally, since prior work has also used market valuation to measure firm performance (e.g., Zott
& Amit, 2007, 2008), we ran our analysis with that as the outcome condition. Similar to when
using our main measure, the hypotheses were fully or partially supported.
DISCUSSION
We adopt a configurational perspective of business models and seek to understand how novelty
interacts with other value driversefficiency, lock-in, and complementarity—the firm’s
strategy, and its environment. We highlight how novelty, which is usually seen as key to a
business model contributing to firm performance, primarily centers on value creation. In line
with our expectations, we find that novelty alone falls short of predicting a successful business
model sufficiently, but that it is a necessary ingredient for very high performance in emergent
technological environments that typically favor novelty. At the same time, we showcase how
novelty may be successfully recombined with other value drivers in varying, previously
unexplored configurations, and thus highlight the importance of our system-level approach.
In some presentations of the QCA method, scholars label configurations to allow ease of
interpretation and recall given the complexity underlying them (Furnari et al., 2020). In our
39
study, we found 20 configurations based on varying states of novelty in the business model. In
Table 7, we classify these configurations into five types for discussion purposes: (1) novelty-
centric configurations where novelty is present as a core condition; (2) novelty-enhanced
configurations where novelty is present as a peripheral condition; (3) novelty-neutral
configurations where novelty can be present or absent; (4) novelty-averse configurations where
novelty is absent as a peripheral condition; and (5) novelty-phobic configurations where novelty
is absent as a core condition.
---------------Insert Table 7 about here---------------
This categorization of business models allows us to elaborate on the relationship between
business models and firm performance by the role that novelty plays in the configurations. First,
we see that firms can achieve high and low performance regardless of the state of novelty.
However, while novelty alone is insufficient for high performance across all five categories, it is
a necessary condition for very high performance when the enabling technology is new. Still,
overall, we find that novelty alone is not the decisive driver of firm performance it is often
portrayed. Second, looking at novelty-centric, novelty-enhanced, and novelty-neutral
configurations, we see that while novelty can be combined in many ways for high and very high
performance, it is particularly effective with efficiency and differentiation in competitive
industries. Relatedly, third, important contingencies such as firm size and technological
environment highlight how novel business models may face completely opposite requirements
for high performance in different contexts. In fact, we note how the high-performing
configurations in all five types are more complex than those performing less well, which
highlights that firms need to (1) implement elements for both value creation and value capture
and (2) manage the resulting complexity of their business model design as a part of the overall
40
configuration. Finally, in configurations labelled as novelty-neutral, novelty-averse, and novelty-
phobic, a plurality of high-performing business model designs without novelty exist, focusing on
efficiency and complementarity. While we do not emphasize these non-novel configurations in
our discussion, they support our insight that novelty is not a necessary condition for high
performance and highlight alternative business model designs that exclude novelty.
Based on these findings, we present three insights to literatures on value creation,
business model design, and innovation.
Novelty Alone Is Not Enough
We contribute to conversations in the nexus of organization design, strategy, and
entrepreneurship. We find that novel business model design is an important determinant of firms’
success and even a necessary condition for very high performance when the enabling technology
is new. However, our analysis clearly reveals that a business model design emphasizing novelty
alone is not sufficient for superior firm performance. Although scholars have repeatedly found
that novel business models positively affect performance (Amit & Zott, 2012; Chesbrough &
Rosenbloom, 2002; Cucculelli & Bettinelli, 2015; Sosna, Trevinyo-Rodriguez & Velamuri,
2010; Zott & Amit, 2007, 2008), these effects are, as is common to linear regression, about
changes at the mean. Indeed, when we attempt to replicate this linear regression approach
directly, we find structurally equivalent results. However, our configurational theorizing and
empirics allow us to scrutinize the (joint) effect of novelty for high-performing systems.
Building on work that emphasizes value creation as use value and perceived value
(Bowman & Ambrosini, 2000; Lepak et al., 2007), we argued that novelty focuses on creating
valuethat is, growing the overall size of the pie’—but unlike the other value drivers, it does
less to help business model innovators capture that value (Almeida Costa & Zemsky, 2021).
41
Even for emergent technological fields in which novelty may seem a necessity, we find that
firms need to include elements emphasizing efficiency, complementarity, and/or lock-in. As
such, we conclude that even when novelty might help firms attract new customers, penetrate new
markets, or leverage new technologies, firms that focus exclusively on novelty as a design
attribute are unlikely to achieve high performance.
A Micro-Configurational Perspective on Business Model Design
Our findings place a renewed emphasis on configurational thinking in business model design and
innovation. Most work linking business model design and firm performance falls short of
examining business models as systems of interconnected elements (Bock et al., 2021; Desyllas et
al., 2020). Accordingly, we see our findings as an encouragement to look granularly at the
content of business models and how these are assembled (see, e.g., Amit & Zott, 2021), rather
than for example at average effects of broad-brush design themes or (changes across) categories
of firm activities. As a first clear indication for that, we propose that it is unlikely that higher-
order categories such as novelty and efficiency, are per se incompatible with each other or the
other business model design choices. In fact, in all the high- and very-high-performing novelty-
centric and novelty-enhanced configurations (where novelty is present as a core or peripheral
condition), novelty and efficiency are complements.
In contrast, in the novelty-averse and novelty-phobic configurations, novelty and
efficiency seem to be substitutes. This leads to two specific considerations. First, while more
work is needed to ascertain this claim, it appears that some efficiency-centered business models
do not universally benefit from novelty, as novelty may create diseconomies of scope that reduce
performance (Zott & Amit, 2007). While this study is focused on novelty, we could potentially
see how efficiency itself could drive value creation, for example, in inclusive innovation where
42
business models that drive down cost can make products accessible and affordable (e.g., George,
McGahan & Prabhu, 2012; Reinhardt, Gurtner & Griffin, 2018). Second, while some firms may
be able to address this incompatibility with ambidexterity (Raisch & Birkinshaw, 2008; Tushman
& O’Reilly, 1996), our findings indicate that the nature of the relationship between novelty and
efficiency may depend on whether or not firms combine them with a differentiation strategy in
the prevalence of intense competition.
What is apparent from our findings is that the business model is not about discrete
choices of design elements, but rather a set of compounding decisions with a multiplicative
effect resulting in configurational designs that best capture the increased value being created (see
also Amit & Zott, 2021; Desyllas & Sako, 2013; McDonald & Eisenhardt, 2020; Siggelkow,
2011; Snihur et al., 2021; Teece, 2010; Tidhar & Eisenhardt, 2020). As such, we call for future
research studying business models, and business model innovation in particular, to look at what
firms actually do to create and capture value, and whether these specific choices form an
internally consistent system, rather than asking whether or not firms changed aspects of their
business model. While taking such a perspective may also mandate more conceptual clarity on
business model designfor example, would introducing a new technology to improve efficiency
count as a business model change in novelty, efficiency, or both?we believe that it is only
through such a configurational micro-perspective that we can arrive at better explanations of how
business models link to firm performance.
A Macro-Configurational Perspective on Business Model Design
Finally, our study speaks to recent literature on the link between business strategy as the firm’s
choice of which markets to enter with what general direction and the business model as the
deployed activity system to execute on that strategy (Casadesus-Masanell & Ricart, 2010; Massa
43
et al., 2017; Zott & Amit 2008, 2010). Our results indicate that these two constructs may be
considered as separate. Specifically, somewhat similar to Fiss’ approach (2011), who finds
consistency between the Miles and Snow typology (1978) and firm’s business strategy, we find a
clear, high-performing pattern that works across different technological environments and for
different firm sizes: novelty with efficiency and differentiation strategy in competitive
environments. This pattern is always present in novelty-centric and novelty-enhanced
configurations that perform well. Sometimes, lock-in and complementarity, and even low-cost
strategy, are also part of this configuration. Hence, while Novelty*Efficiency*Differentiation*
Competition seems to be a consistently high-performing combination, we also find strong
indication of equifinality (Bock et al., 2021): that varying systems of internally consistent
business model designs may be appropriate to enact the same strategy.
At the same time, our findings highlight that what precisely is a good internally
consistent system will still, in most cases, depend on the firm’s external environment: across
firms of different size, and different levels of technological maturity. Here, we offer theoretical
explanations and empirical validation of how small firms with a simple but novel business model
may have an advantage against more established firms in new technological environments, even
when they are constrained by limited resources and lack of legitimacy. Yet, these small firms
with novel business models have a disadvantage in mature technological environments where
they need to implement innovative but complex configurations to achieve high performance
(Christensen, 1997; Greve, 2008; Maula et al., 2013; Ocasio, 2011). We offer managerial
attention, ambidexterity, legitimacy, and access to resources as combinations of mechanisms that
underlie these outcomes.
Our results are encouraging for the study of business models and firm performance
44
through a configurational lens (Donaldson, 2001; Miller, 1996; Misangyi et al., 2017;
Siggelkow, 2011). They should also extend to areas of interest in the strategy, innovation, or
entrepreneurship literature. For example, innovation ecosystems (Adner, 2012; Jacobides,
Cennamo & Gawer, 2018), similar to business models, are often described as a well-balanced
system of interconnected elements. Empirically, as in our context, much of this work does not
account fully for a systems perspective, and the struggle that achieving an internally consistent
system balancing value creation and capture applies (e.g., Dattée et al., 2018). Future research
using a macro-configurational perspective may be able to better uncover the systems dynamics.
Limitations
Our study does not come without limitations. First, fsQCA researchers need to be judicious about
choosing conditions for their model (Misangyi et al., 2017), because model complexity increases
exponentially by each new element. Hence, the ability to study, simultaneously, 2k combinations
of k elements in the system comes at the cost of potentially missing relevant firm- and industry-
level “control” conditions. Second, although there is an increasing number of studies testing
configurational hypotheses with fsQCA (e.g., Bell et al., 2014; García-Castro & Francoeur,
2016), scholars are still debating the method’s inferential power and ability to generalize findings
beyond samples (Misangyi et al., 2017). Third, we follow a sampling strategy that might
introduce survival bias in both of our samples. While we acknowledge this limitation, we also
note that fsQCA does not rely on assumptions of probabilistic distributions (Fiss, 2011, p.402),
which might alleviate some of this concern. Finally, even though we conducted our analysis
across two different points in time, the fact that we solely draw from firms in the IT sector
which is not only lower in capital intensity, but also renowned for its friendliness toward
innovationmay limit the generalizability of our findings. While we expect that taking a
45
configurational lens will still add value in any industrial sector, in line with our own theoretical
conclusions, we expect that the precise configurations that turn out to be value accretive might
still widely vary by industry. In addition to novelty, we could imagine that efficiency may take a
less prevalent role outside our context of e-businesses, which often thrive on the transactional
efficiency they can offer compared to the offline world.
Future Research and Implications for Practice
Beyond the recommendations stated above, we see several fruitful avenues through which our
insights might be extended. From a methodological perspective, drawing on content analysis or
machine learning techniques such as natural language processing (George, Osinga, Lavie &
Scott, 2016; Puranam, Shreshtha, He & von Krogh, 2020) to analyze IPO prospectuses or annual
reports would allow to circumvent potential issues with manual ratings and inter-rater reliability
for coding strategies and designs. In this way, the data collection process could potentially be
more efficient, accurate, and consistent, which would enable scholars to use longitudinal
research designs for studying the relationship between firms’ business model design and
performance over time. Efficient techniques of data collection can also allow for large-scale
datasets for improving both reliability and validity of the business model design constructs.
From a conceptual perspective, in turn, there is significant value in studying changes of
business model configurations in industries over time. For example, an assimilation of others’
business models through vicarious learning (Baum, Li & Usher, 2000) or external pressure to
conform (DiMaggio & Powell, 1983) should eventually make firms more similar on the outside.
In our view, it would be interesting to see under which conditions this would also lead firms to
perform more similarly, as posited by the Red Queen effect (Barnett, 2008), and when the actual
mechanics of the business model may be difficult to imitate because of, for example, causal
46
ambiguity (Rumelt, 1984). This question, in our view, should be at the core of efforts to examine
what specific contributions the business model can make toward superior performance.
Finally, for practice, our study contains two key insights. First, we think that managers
must pay attention not to fall into a ‘novelty trap’ by following the consistent positive narrative
about business model innovation. Including new customers, markets, and technologies is of
course something that should help managers to create value; however, such efforts are more
likely to pay off when firms can connect them to a clear means of value capture through business
model design. Second, we propose that clever business model innovation, more often than not,
implies looking at complementarities between the business model elements chosen to build up
the proverbial “operating system.” At the same time, our results suggest that managers re-
evaluate design choices when the broader environment changes. Yet, any adaptation would again
need to go beyond optimizing on a single business model attribute; rather, managers will need to
ensure that any novel configuration remains more valuable than the sum of its parts.
Conclusion
We examine the link between novel business models and firm performance by extending the
linear inquiry predominant in extant work to a more holistic configurational analysis. We use
two samples of Internet-enabled firms to account for the maturity of firms’ technological
environment and perform a fuzzy set analysis to test our hypotheses. Our results highlight that
novelty is neither necessary nor sufficient for high firm performance, but that it plays an
important role as a configuration with other business model designs, strategies, and
environments. This underscores our assumption that novelty does matter for firm performance,
but it does so only in combination with other value drivers and strategies. Our findings also
dispel traditional myths that novelty- and efficiency-based designs are necessarily orthogonal
47
approaches, and indeed we find that they can be complements and substitutes to achieve high
performance. These insights create pause for scholars and practitioners alike to revisit their
recommendations on business model design and venture growth.
48
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TABLES AND FIGURES
Table 1: Calibration Thresholds
Variables
Fully out
Crossover point
Fully in
High performance
(Tobin’s Q)
0.70
1.00
2.00
Very high performance
(Tobin’s Q)
2.00
3.50
5.00
Novelty
0.25
0.50
0.75
Efficiency
0.25
0.50
0.75
Lock-in
0.25
0.50
0.75
Complementarity
0.25
0.50
0.75
Differentiation
2.00
3.00
4.00
Low-cost
2.00
3.00
4.00
Competition
0.25
0.50
0.75
Large size (employees)
50
250
1,000
a We used 50th, 75th, and 90th percentiles derived from population-level data.
b We used 75th for the lower and 90th percentile for the upper threshold derived from population-level data.
c Small and medium-sized enterprises.
Table 2: Summary Statistics
Sample 1 (year 1999): N=125
Sample 2 (year 2014): N=169
Variable
Mean
S.D.
Min
Max
Mean
S.D.
Min
Max
Tobin's Q*
10.86
25.12
0.19
232.12
2.41
1.98
0.00
9.05
Novelty
0.46
0.14
0.12
0.76
0.50
0.16
0.12
0.85
Efficiency
0.73
0.12
0.41
0.98
0.72
0.12
0.39
1.00
Lock-in
0.42
0.15
0.09
0.70
0.48
0.14
0.16
0.77
Complementarity
0.52
0.19
0.03
0.86
0.66
0.16
0.22
0.94
Differentiation
3.39
0.71
1.67
5.00
3.14
0.79
1.67
5.00
Low-cost
2.84
0.77
1.00
4.50
2.58
0.90
1.00
4.75
Competition
0.74
0.20
0.25
1.00
0.69
0.16
0.25
1.00
Employees
734
1,362
34
7,600
5,665
18,298
23
160,518
*Our robustness checks showed that outliers do not pose a threat to our findings.
56
Table 3: High and Very High Performance When Enabling Technology Is New
High Performance
(HP)
Very High Performance
(VHP)
HP1
HP2
HP3
HP4
HP5
HP6
HP7
HP8
VHP1
VHP2
VHP3
Business model
Novelty
Efficiency
Lock-in
Complementarity
Strategy
Differentiation
Low-cost
Contingencies
Competition
Large size
Consistency
0.94
0.99
0.91
0.93
0.91
0.94
0.98
0.89
0.84
0.82
0.83
Raw Coverage
0.20
0.32
0.29
0.19
0.32
0.16
0.11
0.16
0.29
0.22
0.18
Unique Coverage
0.02
0.04
0.03
0.01
0.09
0.01
0.02
0.01
0.08
0.02
0.05
Overall Solution Consistency
0.91
0.80
Overall Solution Coverage
0.58
0.37
indicates presence of a condition, indicates its absence. Large characters indicate core conditions, small
characters indicate peripheral conditions. Blanks indicate “does not matter”.
Table 4: High Performance When Enabling Technology Is Mature
High Performance
(HP)
HP9a
HP9b
HP10
HP11
Business model
Novelty
Efficiency
Lock-in
Complementarity
Strategy
Differentiation
Low-cost
Contingencies
Competition
Large size
Consistency
0.87
0.83
0.83
0.84
Raw Coverage
0.23
0.31
0.35
0.09
Unique Coverage
0.02
0.02
0.07
0.02
Overall Solution Consistency
0.83
Overall Solution Coverage
0.43
indicates presence of a condition, indicates its absence. Large characters indicate core conditions, small
characters indicate peripheral conditions. Blanks indicate “does not matter”.
57
Table 5: Summary of Hypothesis Tests
Hypothesis
Result
Evidence
Details
H1: Novelty is insufficient for
high firm performance when
other value drivers and
strategies are absent from a
configuration.
Supported
All configurations
No evidence of (very-)high-performing
configurations with novelty but without
other value drivers and strategies.
H2: In high-performing
configurations, novelty and
efficiency will be complements
rather than substitutes.
Partly
supported
HP1, HP11
(VHP1-VHP3)
If the configurations feature both novelty
and efficiency (i.e., excluding blanks), it will
be high- or very-high-performing. However,
novelty requires efficiency, but efficiency
does not require novelty for high
performance. This relationship may be
subject to competitive strategy and the
intensity of competition.
H3: In competitive industries, it
is necessary to combine novelty
with either differentiation or
low-cost strategy to achieve
high performance.
Supported
HP1, HP11
(VHP1-VHP3)
When novelty is present, the data support
this hypothesis in 5/5 configurations. When
novelty does not matter, the hypothesis is
supported by 3/5 configurations (see HP2-
HP4 and HP9a/b).
H4a: In mature technological
environments, the high-
performing configurations with
novelty will feature greater
configurational complexity of
complementary design elements
in small firms than in large
firms.
Supported
HP11
(HP9a/b)
HP11 features all four value drivers and
differentiation (small firms). There is no
high-performing configuration of large firms
with novelty, but HP9a/b can have it: these
configurations are simpler with only two
value drivers (in addition to novelty).
H4b: In new technological
environments, the high-
performing configurations with
novelty will feature greater
configurational complexity of
complementary design elements
in large firms than in small
firms.
Partly
supported
HP1
(HP2-HP4)
HP1 features efficiency and both strategies
in addition to novelty. There is no high-
performing configuration of small firms with
novelty, but HP2 can have it: this
configuration is simpler than HP1 and HP4.
However, HP3 is relatively complex (both
small and large firms).
H5: Novelty is necessary for
very high performance when a
business model is enabled by
new technology, but not when
the technology is mature.
Partly
supported
VH1-VHP3
Novelty is present in all three very-high-
performing configurations. However, we do
not identify any very-high-performing
configurations enabled by mature
technology (non-results).
58
Table 6: Low Performance When Enabling Technology Is Mature*
Low Performance
(LP)
LP1a
LP1b
LP2
LP3
LP4
LP5
LP6
Business model
Novelty
Efficiency
Lock-in
Complementarity
Strategy
Differentiation
Low-cost
Contingencies
Competition
Large size
Consistency
0.89
0.90
0.84
0.87
0.95
0.82
0.85
Raw Coverage
0.11
0.12
0.34
0.29
0.08
0.29
0.13
Unique Coverage
0.01
0.03
0.18
0.03
0.01
0.02
0.01
Overall Solution Consistency
0.83
Overall Solution Coverage
0.59
indicates presence of a condition, indicates its absence. Large characters indicate core conditions, small
characters indicate peripheral conditions. Blanks indicate “does not matter”.
*Inverse of very high performance (i.e., configurations that do not achieve very high performance).
59
Table 7: Configurations by State of Novelty
Label
State of novelty
Observed
configurations
Elements present in the
configurationa
Elements absent in the
configurationa
Enabling
technology
Performance
Comments
Novelty-
centric
Present as a core
condition
VHP1
VHP2
VHP3
HP11
LP3
N*E*DI*CO
N*E*L*C*DI*CO
N*E*DI*LC*CO*LS
N*E*L*C*DI*CO
N*CO
~LC*~LS
~LS
~L*~C
~LC*~LS
~L*~LC
New
New
New
Mature
Mature
Very high
Very high
Very high
High
Lowb
Novelty is a central part of the
configuration, but it helps firms
achieve (very) high performance only
if it is combined with other value
drivers (especially efficiency) and
strategies (especially differentiation).
Novelty-
enhanced
Present as a
peripheral condition
HP1
N*E*DI*LC*CO*LS
New
High
Novelty adds value as a peripheral
element in the configuration. As in
novelty-centric configurations, novelty
combines effectively with efficiency
and differentiation strategy.
Novelty-
neutral
Can be present or
absent
HP2
HP3
HP4
HP9a
HP9b
LP1a
LP2
LP5
E*DI*CO
E*C*DI*LC*CO
E*L*C*DI*CO*LS
E*L*CO*LS
E*C*CO*LS
DI*CO
LC*CO*LS
DI*CO
~L*~LS
~DI*~LC
~DI*~LC
~LC*~LS
~L*~LC
New
New
New
Mature
Mature
Mature
Mature
Mature
High
High
High
High
High
Lowb
Lowb
Lowb
Novelty does not matter for the
performance of the configuration.
Novelty-
averse
Absent as a
peripheral condition
HP5
HP6
HP7
HP8
LP1b
LP4
E*CO
E*C*DI
E*L*C*CO
E*C*DI*LC*LS
CO
LS
~N*~L*~LC
~N*~L*~LC*~LS
~N*~DI*~LS
~N*~L
~N*~DI*~LS
~N*~L*~C*~DI*~LC*~CO
New
New
New
New
Mature
Mature
High
High
High
High
Lowb
Lowb
Novelty is absent as a peripheral
condition. Together with novelty-
neutral configurations, these provide a
wide range of alternatives to
innovative business model designs by
focusing on efficiency and
complementarity in particular.
Novelty-
phobic
Absent as a core
condition
HP10
LP6
E*C*CO*LS
DI*CO
~N*~LC
~N*~C*~LC
Mature
Mature
High
Lowb
Even when novelty is absent as a core
condition, large firms with an efficient
business model design can achieve
high performance.
a N=novelty; E=efficiency; L=lock-in; C=complementarity; DI=differentiation; LC=low-cost; CO=competition; LS=large firm size;
~ =element is absent; *=and; Letters in bold imply that the element is a core condition.
b Inverse of very high performance (i.e., configuration does not achieve very high performance).
60
APPENDIX
Appendix A: Survey Items
Item
Statement
Scale
Zott &
Amit,2007
Zott &
Amit,2008
This study
(both samples)
efficiency1
Inventory costs for participants in the business model are reduced
1; 0.75; 0.25; 0
yes
yes
yes
efficiency2
Transactions are simple from the user's point of view
1; 0.75; 0.25; 0
yes
yes
yes
efficiency3
The business model enables a low number of errors in the execution of
transactions
1; 0.75; 0.25; 0
yes
yes
yes
efficiency4
Costs other than those already mentioned for participants in the
business model are reduced (e.g., marketing and sales, transaction
processing, communication costs)
1; 0.75; 0.25; 0
yes
yes
yes
efficiency5
The business model is scalable (e.g., can handle small as well as large
number of transactions)
1; 0.75; 0.25; 0
yes
yes
yes
efficiency6
The business model enables participants to make informed decisions
1; 0.75; 0.25; 0
yes
yes
yes
efficiency7
Transactions are transparent: flows and use of information, services,
goods can be verified
1; 0.75; 0.25; 0
yes
yes
yes
efficiency8
As part of transactions, information is provided to participants to
reduce the asymmetric degree of knowledge among them regarding the
quality and nature of the goods being exchanged
1; 0.75; 0.25; 0
yes
yes
yes
efficiency9
As part of transactions, information is provided to participants about
each other
1; 0.75; 0.25; 0
yes
yes
yes
efficiency10
Access to a large range of products, services and information, and other
participants is provided
1; 0.75; 0.25; 0
yes
no
no
efficiency11
The business model enables demand aggregation
1; 0
yes
no
no
efficiency12
The business model enables fast transactions
1; 0.75; 0.25; 0
yes
yes
yes
efficiency13
The business model, overall, offers high transaction efficiency
1; 0.75; 0.25; 0
yes
yes
yes
novelty1
The business model offers new combinations of products, services and
information
1; 0.75; 0.25; 0
yes
yes
yes
novelty2
The business model brings together new participants
1; 0.75; 0.25; 0
yes
yes
yes
novelty3
Incentives offered to participants in transactions are novel
1; 0.75; 0.25; 0
yes
yes
yes
novelty4
The business model gives access to an unprecedented variety and
number of participants and/or goods
1; 0.75; 0.25; 0
yes
yes
yes
novelty5
The business model links participants to transactions in novel ways
1; 0.75; 0.25; 0
yes
yes
yes
novelty6
The richness (i.e., quality and depth) of some of the links between
participants is novel
1; 0.75; 0.25; 0
yes
yes
yes
novelty7
Number of patents that the focal firm has been awarded for aspects of
its business model
1; 0.66; 0.33; 0
yes
yes
yes
novelty8
Extent to which the business model relies on trade secrets and/or
copyrights
1; 0.66; 0.33; 0
yes
yes
yes
novelty9
Does the focal firm claim to be a pioneer with its business model?
1; 0
yes
yes
yes
novelty10
The focal firm has continuously introduced innovations in its business
model
1; 0.75; 0.25; 0
yes
yes
yes
novelty11
There are competing business models with the potential to leapfrog the
firm’s business model
1; 0.75; 0.25; 0
yes
yes
yes
novelty12
There are other important aspects of the business model that make it
novel
1; 0.75; 0.25; 0
yes
yes
yes
novelty13
Overall the company's business model is novel
1; 0.75; 0.25; 0
yes
yes
yes
lockin1
The incentives offered to participants by loyalty programs to engage in
repeat transactions are strong
1; 0.75; 0.25; 0
n/a
no
yes
lockin2
Business model participants can customize products, services, or
information to their needs
1; 0.75; 0.25; 0
n/a
no
yes
lockin3
State the methods used by the e-commerce company to personalize
goods (check box)
n/a
n/a
no
no
lockin4
This personalization is effective in attracting and maintaining
participants
n/a
n/a
no
no
lockin5
The business model promotes transaction safety and reliability
1; 0.75; 0.25; 0
n/a
no
yes
lockin6
Methods adopted that promote trust by giving customers control over
the use of personal information (check box)
n/a
n/a
no
no
lockin7
Other methods adopted that promote trust (check box)
n/a
n/a
no
no
lockin8
The focal firm has a dominant design (i.e., a proprietary standard that it
developed for its business model)
1; 0.75; 0.25; 0
n/a
no
yes
lockin9
The concept of "virtual community" plays an important role in the
business model
1; 0.75; 0.25; 0
n/a
no
yes
lockin10
Affiliate Programs, which are designed to enable transactions
originating from the company’s partners, play an important role
1; 0.75; 0.25; 0
n/a
no
yes
lockin11
The business model exhibits important direct network externalities;
participants benefit from increasing numbers of similar participants
1; 0.75; 0.25; 0
n/a
no
yes
61
lockin12
The business model exhibits important indirect network externalities:
participants from one group benefit from increasing numbers of
participants from another group
1; 0.75; 0.25; 0
n/a
no
yes
lockin13
Site users must make considerable site-specific investments of time and
effort in order to learn how to use the site
1; 0.75; 0.25; 0
n/a
no
yes
lockin14
Site users must have specialized assets (like customized software) in
place in order to use the site
1; 0.75; 0.25; 0
n/a
no
yes
lockin15
Overall, the business model succeeds in creating lock-in
1; 0.75; 0.25; 0
n/a
no
yes
comple1
There are complementarities between online and offline elements of the
transaction in the business model
1; 0.75; 0.25; 0
n/a
no
yes
comple2
The business model enables complementarities among activities of
participants (e.g., supply chain integration)
1; 0.75; 0.25; 0
n/a
no
yes
comple3
The business model enables complementarities between the company's
technologies and technologies provided by others
1; 0.75; 0.25; 0
n/a
no
yes
comple4
The business model offers customers a wide range of complementary
services and products from various participants to the business model
1; 0.75; 0.25; 0
n/a
no
yes
comple5
The business model offers customers a wide range of complementary
services and products from the firm whose business model is discussed
itself
1; 0.75; 0.25; 0
n/a
no
yes
comple6
Cross-selling of products/services is important to the business model
1; 0.75; 0.25; 0
n/a
no
yes
comple7
There are strong vertical complementarities in terms of product/service
offerings (e.g., after sales service)
1; 0.75; 0.25; 0
n/a
no
yes
comple8
There are strong horizontal complementarities in terms of
product/service offerings (e.g., hardware and software, one stop
shopping)
1; 0.75; 0.25; 0
n/a
no
yes
comple9
Overall, the bundling of complementary products/services are
important to the business model
1; 0.75; 0.25; 0
n/a
no
yes
lowcost1
Offering products/services at low prices/prices lower than competition
5; 4; 3; 2; 1
no
yes
yes
lowcost2
Minimizing product-related expenditures, in particular through process
innovation
5; 4; 3; 2; 1
no
yes
yes
lowcost3
Emphasizing economies of scale and scope with products and services
5; 4; 3; 2; 1
no
yes
yes
lowcost4
Low-cost strategy
5; 4; 3; 2; 1
no
yes
yes
differ1
Importance and use of productservice-related patents
5; 4; 3; 2; 1
no
yes
yes
differ2
Importance of new product development, innovation and R&D activity
5; 4; 3; 2; 1
no
no
no
differ3
Emphasis on growth by acquiring, or merging with R&D/technology
intensive firms
5; 4; 3; 2; 1
no
no
no
differ4
Branding and advertising as part of firm's marketing strategy/approach
5; 4; 3; 2; 1
no
yes
yes
differ5
Differentiation strategy
5; 4; 3; 2; 1
no
yes
yes
comp1
The company competes with several direct competitors
1; 0.75; 0.25; 0
n/a
n/a
no
comp2
The company competes with several indirect competitors
1; 0.75; 0.25; 0
n/a
n/a
yes
comp3
Competition in the company's industry is intense
1; 0.75; 0.25; 0
n/a
n/a
yes
comp4
The company's industry is very innovative compared to other industries
1; 0.75; 0.25; 0
n/a
n/a
no
comp5
The company's industry is easy to enter (and it is easy to become an
established player)
1; 0.75; 0.25; 0
n/a
n/a
yes
comp6
The company's customers can easily change their provider
1; 0.75; 0.25; 0
n/a
n/a
yes
*Items for lock-in and complementarity were the same as in Zott and Amit (2002).
62
Petteri Leppänen (p.leppaenen@imperial.ac.uk) is a Postdoctoral Research Associate at
Imperial College Business School. He received his PhD from the Technical University of
Munich. His research focuses on the emergence, adaptation, and performance of
organizational configurations.
Gerard (Gerry) George (gerard.george@georgetown.edu) is Brown Family Chair in
Entrepreneurship and Innovation and Director of the Georgetown Entrepreneurship Initiative
at McDonough School of Business at Georgetown University. His research focuses on
innovation and entrepreneurship, with an emphasis on organization design, governance,
social inclusion, and sustainability.
Oliver Alexy (o.alexy@tum.de) is professor of strategic entrepreneurship at the TUM School
of Management, Technical University of Munich, where he also received his doctorate. His
current research focuses on effective organization designs for high-uncertainty environments.
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