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Business Model Configurations for Digital Platform Success - Towards a Typology of Digital Platform Business Models

  • Queensland University of Technology (QUT)


Competition between digital platforms is harder compared to non-platform businesses. Fierce platform competition reduces digital platforms' chances of success. Research has identified many aspects of digital platforms and their surrounding ecosystem that influence the success of digital platforms. This research is comprehensive but not integrated. The business model as an activity system provides a lens to orchestrate various dimensions of digital platforms. We conduct a case survey of published case studies on digital platforms and analyze their business models using a multi-value qualitative comparative analysis. The resulting business model configurations reveal how surviving digital platforms combine different value propositions, value capture mechanisms, and value creation strategies. We identify four configurations of digital platform business models (matching, spreading, innovating, and dominating business models) leading to digital platform success (i.e., survival). In our future research, we will identify more detailed business model configurations using a larger case sample.
Thirtieth European Conference on Information Systems (ECIS 2022), Timisoara, Romania 1
Research in Progress
Timo Phillip Böttcher, Technical University of Munich, Garching, Germany,
Valentin Bootz, Technical University of Munich, Garching, Germany,
Norman Schaffer, fortiss GmbH, Munich, Germany,
Jörg Weking, Technical University of Munich, Garching, Germany,
Andreas Hein, Technical University of Munich, Garching, Germany,
Competition between digital platforms is harder compared to non-platform businesses. Fierce platform
competition reduces digital platforms’ chances of success. Research has identified many aspects of
digital platforms and their surrounding ecosystem that influence the success of digital platforms. This
research is comprehensive but not integrated. The business model as an activity system provides a lens
to orchestrate various dimensions of digital platforms. We conduct a case survey of published case
studies on digital platforms and analyze their business models using a multi-value qualitative
comparative analysis. The resulting business model configurations reveal how surviving digital
platforms combine different value propositions, value capture mechanisms, and value creation
strategies. We identify four configurations of digital platform business models (matching, spreading,
innovating, and dominating business models) leading to digital platform success (i.e., survival). In our
future research, we will identify more detailed business model configurations using a larger case
Keywords: Business Model, Digital Platform, Digital Platform Success, Case Survey, QCA.
1 Introduction
Competition between digital platforms is hard. Usually it is harder compared to non-platform businesses.
If you need a taxi anywhere, you do not care about which taxi company operates the particular car, so
you just take the nearest one. Thus, there is enough room for customers to switch easily between different
providers. For digital platforms, competition is different (Van Alstyne et al., 2016, Parker et al., 2016).
If you use a digital ride-hailing platform, such as Uber, you still do not care about the particular driver
as long as the service is fast, cheap, and the drivers raiting is high enough. Uber’s concern is, that you
use the Uber platform, and the Uber platform only. It is easy for customers to switch between digital
platforms such as Uber, Lyft, or Sidecar, leading to fierce competition between digital platforms that
ultimately leads to winner-takes-all or few-takes-all markets, limiting the success and even survival of
digital platforms (Eisenmann et al., 2006, Schilling, 2002). For example, Uber and Lyft are the only
major platforms remaining in the US ride-hailing market, forcing Sidecar to close in 2015.
Ensuring and increasing digital platform usage is critical to the success of digital platforms. In doing so,
digital platforms aim to create network effects, prevent customers and complementors from using
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Thirtieth European Conference on Information Systems (ECIS 2022), Timisoara, Romania 2
multiple platforms, and ultimately, like Uber and Lyft, achieve market dominance (Alt and
Zimmermann, 2019). Therefore, digital platforms leverage several different strategic and operational
measures (Tiwana, 2014). The key challenges any digital platform faces are choosing the right
ecosystem complementors (Evans, 2009), generating cost-exceeding revenue, and cultivating a platform
ecosystem (Cusumano et al., 2020). Network effects and ecosystem dynamics are critical considerations
in digital platform strategy (Cennamo and Santalo, 2013). For example, two-sided network externalities
explain the emergence of dominant platforms due to direct and indirect network effects (Rochet and
Tirole, 2003). However, this first requires attracting complementors or customers to the digital platform
ecosystem. Some digital platforms use different pricing mechanisms, such as asymmetric pricing, where
they charge proportionately less from one side of the platform than the other. Subscription models allow
them to generate recurring revenue and retain customers, but transaction-based pricing offers a
potentially cheaper option for customers. Other digital platforms compete by offering unique features,
products, or services that digital platforms seek to differentiate themselves from their competitors. Thus,
there are numerous opportunities to gain a competitive advantage, that require highly interdependent
management (Helfat and Raubitschek, 2018).
However, the unheard success of a few platform companies serving as a paragon for firms to launch
new digital platforms (Zhao et al., 2020) or the failure of many others cannot be fully exaplined by
single influencing factors or conditions. Consequently, we have yet to gain generalizable insights into
how the different success and performance mechanisms, identified in the research, should be
orchestrated (McIntyre and Srinivasan, 2017).
Yet, many of the aforementioned measures can be orchestrated in the business model (BM) of a digital
platform (Amit and Zott, 2001, Helfat and Raubitschek, 2018). The BM consists of the value
proposition, the value creation, and the value capture (Teece, 2010, Osterwalder and Pigneur, 2010). It
is proven to be a source of competitive advantage and influence on firm performance and survival
(Böttcher et al., 2021a, Böttcher et al., 2021b, Weking et al., 2019). The attractiveness of a digital
platform is influenced by the value proposition for customers and complementors, such as unique
features. Value creation is achieved through the engagement of complementors and the use of the
ecosystem (Hein et al., 2020, Hein et al., 2019). Asymmetric pricing or subscription models are part of
value creation. Thus, the BM allows holistic and integrative thinking in complex socio-technical systems
such as digital platform ecosystems (Benbya et al., 2020) by creating an activity system that orchestrates
interdependent organizational activities transcending the focal firm and spans its boundaries in its
ecosystem (Zott and Amit, 2010). Through different activity combinations (i.e., BM configurations), the
success mechanisms of digital platforms can be orchestrated in the BM.
Despite enhancing our understanding of managing digital platforms, digital platform research is often
limited to single-industry settings or narrative cases. Thus, digital platform performance may require
attention to a holistic perspective rather than focusing on individual design elements (Zhao et al., 2020).
Existing research on digital platform BMs is largely scattered. Few exceptions are taxonomies of
platform-based marketplaces as BMs (cf. Täuscher and Laudien, 2018) and frameworks to understand
platform BMs from a systemic perspective (cf. Fehrer et al., 2018). Yet, variables are mostly analyzed
in isolation, a holistic approach that enables to understand what BM configurations constitute succesfull
platforms is missing. Hence, we propose the following research question: What are the BM
configurations of surviving digital platforms?
We identify configurations (Fiss, 2011) to platform success from a BM perspective. Therefore,
following Rivard and Lapointe (2012), we combine the case survey methodology (Larsson, 1993) with
qualitative comparative analysis (QCA) (Fiss, 2011, Ragin, 1987). This combination and QCA, in
particular, allow us to identify salient configurations of the different BM design elements that constitute
surviving digital platform BMs. We identify 25 surviving and 7 failed digital platforms in the literature
and analyze them toward the three BM dimensions: value proposition, value capture, and value creation.
Thus, we identify four BM configurations of surviving digital platforms. In our future research, we will
take a multi-method approach. First, we will extend our case sample with additional digital platforms to
refine our configurations. We will collect the data about these additional platforms from empirical
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observations and secondary data, such as firm reports, press releases, and news articles. Second, we will
discuss and analyze the configurations in expert interviews to refine our understanding of why these
configurations manifest surviving digital platform BMs. Finally, we will develop a typology of digital
platform BMs that articulates ideal types of digital platform BMs.
2 Platform Business Models
The literature lacks a general definition of platform BMs (Fehrer et al., 2018), but agrees they can be
conceptualized based on the enablement of different user groups to interact via a platform (Gawer,
2014), to create and derive super-additive value (Clemons, 2018). This follows the three core dimensions
a BM can be divided into: value proposition, value capture, and value creation. The value proposition
dimension is the product or service offered by a firm that addresses the market’s desired value (Al-Debei
and Avison, 2010). The value capture dimension describes how this focal firm captures economic value
from its value proposition (Al-Debei and Avison, 2010). The value-creation dimension articulates the
activities that enhance the total value created by the BM (Amit and Zott, 2001).
The value proposition of digital platforms can be described in three basic types: transaction platforms,
innovation platforms, and hybrids (Evans and Gawer, 2016). Transaction platforms propose value by
serving as intermediaries for transactions between ecosystem actors, such as exchanging goods or
services between buyers and sellers. For example Airbnb does not offer housing itself; it is the
intermediary bringing the two sides of the market together, which is a two-sided market BM. The value
proposed by innovation platforms is their technological foundation for complementary innovation
(Gawer, 2021). Complementors (e.g., customers or third-party developers) can create innovative
products or services without the need to develop this foundation themselves. For example cloud
platforms offer that type, such as Microsoft Azure, which allows complementors to use computational
power and predefined functions that simplify application development at low cost. In between these two
types, hybrid platforms combine intermediary functions and complementary innovation to integrate
transaction and innovation platforms (Cusumano et al., 2019). For example, Facebook’s social network
itself is a transaction platform enabling communication between users. When it opened the social
network for third-party developers (e.g., through application programming interfaces (APIs)), it became
a hybrid platform (Cusumano et al., 2020).
Value capture strategies for generating cost-exceeding revenue are indispensable for firm success (Teece
and Linden, 2017). However, for digital platforms, competition is often based on the price charged for
the platform's value proposition in order to attract as many customers and complementors as possible to
the platform's ecosystem. The pricing of digital platforms is itself subject to complex interdependencies;
hence, we refer to Rochet and Tirole (2006) for a detailed analysis. The value capture dimension to the
platform BM must balance the profitability of both the platform owner and its complementors without
alleviating incentives to co-create value (Schreieck et al., 2017). To address this challenge, digital
platforms offer subsidized or free services to one side of the platform and capture economic value from
the other (Hagiu, 2015). A digital platform directly captures value from its complementors and
customers mainly via subscriptions or transaction-based pricing (Rochet and Tirole, 2003, Armstrong,
2006, Weyl, 2010). For example, Alibaba offers different subscription plans for sellers to obtain access
to the Alibaba marketplace. Differently, Groupon charges transaction fees based on how many deals
were sold. Further, digital platforms capture economic value from data by either selling the data to
customers and complementors or using the data to improve the digital platform’s operations,
productivity, and products (Najjar and Kettinger, 2014, Gandhi et al., 2019). For example, Google uses
users’ search data to create targeted advertisements sold to complementors at higher prices.
The value creation of digital platforms primarily arises through their ecosystem. For example, eBay
without the products offered by complementors for auctions does not create any value. To create value
for the digital platform, the platform owner must effectively position its BM among its complementors
and competitors (Cusumano and Gawer, 2002). Therefore, the digital platform must propose and create
a differentiating value for its ecosystem participants, especially its complementors. Three strategies to
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create such value can be identified: coring, tipping, and envelopment (Gawer and Cusumano, 2008).
These strategies build on the four sources of value creation (i.e., novelty, lock-in, complementarities,
and efficiency) identified by Amit and Zott (2001). Coring adds complementary functionalities to the
platform itself that fosters value creation by complementors. For example, Apple adds function bundles
(e.g., HomeKit or the ARKit) to iOS that help developers create innovative apps efficiently. It creates
lock-in effects between the digital platform and its complementors, thereby maintaining value creation.
Tipping describes the platform owner’s activities to shape the ecosystem dynamics in favor of its own
platform. This means the platform tries creating and leveraging market momentum for its own
advantage. This strategy includes implementing subsidy mechanisms and incentives to attract
complementors or users and forming coalitions. Uber created momentum by focusing on exclusive ride
experiences that spread by word of mouth, attracting more customers, who in turn attracted more riders,
and so on. Envelopment refers to the strategy of entering adjacent platform ecosystems to create novel
superior value (e.g., higher efficiency) for a shared user base in a multi-platform bundle; as such, a
bundle also creates lock-in effects (Eisenmann et al., 2006, Eisenmann et al., 2011, Böttcher et al.,
2021c). Besides joining forces with other platforms, envelopment also refers to extending own platform
to provide functionalities found in adjacent platform ecosystems. For example, LinkedIn used to be only
a social network focused on professional relationships. Over time, it extended its functionalities to a job
application platform competing with, for example, and a learning platform competing with
Udemy, Coursera, and others.
3 Methodology
Following Rivard and Lapointe (2012), we combine the case survey method (Larsson, 1993) for data
collection with QCA for the data analysis. This integrated approach highlights the strengths of both
methods while simultaneously overcoming their limitations when applied individually. The case survey
method presents a powerful approach for synthesizing qualitative insights into quantitative results
(Larsson, 1993). Much empirical evidence in information system (IS) research is embodied in case
studies; therefore, the case survey is suitable for this research’s holistic, aggregative approach. QCA is
a suitable method for capturing the interdependencies and complexity of digital platform ecosystems
into generalizable insights (El Sawy et al., 2010, Benbya et al., 2020). It allows us to combine different
aspects contributing to digital platforms’ success into a holistic, configurational solution.
We collected our sample of case studies following the search process for systematic literature reviews
(Webster and Watson, 2002). Our search query was centered around the terms “case,” “business model,”
and “digital platform” in the title, abstract, or keywords of peer-reviewed scientific articles. We also
accounted for interchangeably used terms (e.g., ecosystem and market). The search query was defined
as: TS=(((digital OR *sided) NEAR/2 (platform OR market OR ecosystem)) AND case AND “business
model*”). We run our search query in three scientific databases: the AIS eLibrary, Web of Science, and
Scopus. The search was performed in December 2020. The databases returned 228 articles (Scopus: n
= 110, Web of Science: n = 58, AIS: n = 60). After removing 36 duplicates, 192 unique articles remained.
We defined inclusion and exclusion criteria to select cases that address our research goal and ensure
sufficient detail (Larsson, 1993). The criteria addressed the articles’ methodology, their unit of analysis,
and their description of the platform’s BM. Table 1 presents the applied inclusion and exclusion criteria.
In the first step, we excluded 94 articles based on their title and abstract. In a second step, we evaluated
the cases based on the full texts of the articles. Therefore, we summarized the information of the cases
covered in multiple articles, such as Microsoft Azure in Gustavsson and Ljungberg (2019) and Harmon
and Castro-Leon (2018) and differentiated the individual cases from multiple case studies, such as
Constantiou et al. (2017). This eliminated an additional 62 cases. For the final sample of 32 cases, from
32 articles, we aimed for data triangulation by enhancing the case study data with publicly available
information from platform owners’ press releases, articles in relevant newspapers, and public interviews
with informed experts.
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The research design is centered around a systematic analysis of a particular case in
considerable depth i.e. a case study
The ability to derive universally applicable knowledge is limited (lack of
Unit of
The case study examines one or multiple cases where digital platforms are the primary
unit of analysis
The unit of analysis does not classify as a (digital) platform as commonly defined in IS
and related subject areas
The digital platforms are linked to adequatly detailed business models or aspects of it
Highly contextual or narrow analysis are incompatible with a holistic configurational
perspective on business models
Table 1. Inclusion and exclusion criteria.
Value Proposition
Transaction platforms serve as intermediaries for exchanges of
goods, services, or information (Cusumano et al., 2019)
Innovation platforms facilitate the development of
complementary products or services that add functionality or
assets to the platform (Cusumano et al., 2019)
Hybrid strategies combine intermediary function and
complementary innovation to integrate transaction and
innovation platforms (Cusumano et al., 2019)
Value Capture
Subscription models capture value through lump-sum fees for
market access (Armstrong, 2006)
Interaction-based models capture value through fixed or
proportional fees per interaction (Weyl, 2010)
Data monetization strategies use the intangible value of data as
a primary asset by selling it, converting it into other tangible
benefits, or avoiding cost (Najjar and Kettinger, 2014)
Value Creation
Coring implements elements (technology, product, or service)
in the platform’s core that solve problems of complementors or
customers (Gawer and Cusumano, 2008)
Tipping builds momentum by developing unique and hard-to-
imitate features (Gawer and Cusumano, 2008)
Platform envelopment extends the platform’s original
functionality to enter an adjacent market to bundle
functionalities on one platform (Eisenmann et al., 2011)
Survival (1)
Survival is defined as the persistence of the digital platform
Failure (0)
Failure is defined as a discontinuance, bankruptcy, or
retrenchment of the platform.
Table 2. Coding scheme.
We designed the coding scheme to describe the core elements of the BM based on extant literature. We
did not adapt existing taxonomies on digital platform BMs, such as Täuscher and Laudien (2018),
because their level of detail conflicts with the number of conditions that can be used in a QCA with our
sample size. Thus, the coding scheme focuses on the aforementioned three core dimensions of the BM
(i.e., our conditions for the QCA). Each condition can take on one of three values. These values are
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mutually exclusive and non-hierarchical. Based on the collected case information, we have assigned
exactly one value to each case for each condition. For the outcome (i.e., the dependent variable), we
define whether a digital platform survived or failed. Survival means that the digital platform was still
active and online at the time of our analysis. Failure means that the digital platform was no longer active
for some reason. Hence the outcome is coded binary (i.e., “1” indicating survival, “0” indicating failure)
Table 2 shows our coding scheme and includes a definition, example, and number of cases (n) of each
possible value that a condition can take on. Two authors coded the cases independently and discussed
discrepancies afterward until a mutual agreement was reached.
We analyze our coded dataset using multi-value QCA (mvQCA) (Ragin, 1999, Cronqvist and Berg-
Schlosser, 2009), since our coding scheme implies a nominal scale of three non-hierarchical conditions.
For the application of the mvQCA we follow the guidelines provided by Mattke et al. (forthcoming).
We analyzed our data for necessary conditions using a consistency threshold of 0.90 and a coverage
threshold of 0.60 (Mattke et al., forthcoming), but found none. Consistency describes how well a
solution represents the cases. Coverage describes how many cases are represented by the solution. To
identify sufficient configurations, we set the consistency threshold to 0.70 and the minium cases to be
included in a configuration to n = 2 (Schneider and Wagemann, 2010, Mattke et al., forthcoming). We
deducted the intermediate and the parsimonious solutions to identify core and peripheral conditions in
our configurations (Fiss, 2011). We then qualitatively analyzed the resulting configurations by revisiting
the case information and associated theory to understand the configurations and explain their success
factors (Park et al., 2020).
4 Results
Surviving digital platforms (n = 25)
Failed digital platforms (n = 7)
Apple iOS
Beam Wallet
ResQ Club
Take Eat Easy
Watson Health
Table 3. Overview of case sample.
Table 3 presents the digital platforms included in our final case sample. The mvQCA revealed four
configurations explaining surviving digital platform BMs (Table 4). The configurations show which BM
dimensions are combined by surviving digital platforms. In sum, the configurations explain 84% of the
variance in our dataset. The overall solution has a consistency of 0.884, which is above the suggested
threshold of 0.80 and thus expresses a robust empirical foundation in our case sample (Ragin, 2009).
Hence, our solution quality is comparable to other IS and strategy research, for example, Park et al.
(2017), Fiss (2011), and Lee et al. (2019).
After qualitatively analyzing the resulting configurations, we gave them a name to describe their BM
configuration. Matching BMs are transaction platforms implementing a tipping value creation strategy.
They facilitate transactions between different ecosystem actors, such as the exchange of goods or
services. Moreover, these models differentiate through the provision of unique features on the platform,
such as unique products. Spreading BMs spread across adjacent markets and envelop multiple platforms
in one ecosystem. These BMs capture their value per interaction on their digital platforms, such as per
ride or food order. This is common for transaction platforms. However, for matching BMs, this is not a
core condition. Surviving matching BMs follow different value capture strategies. The envelopment
strategy for value creation is another difference between matching and spreading BMs. An innovating
BM provides technological affordances on a digital platform for complementors to engage in innovation.
Thus, coring creates more value to the digital platform by adding more elements fostering innovation.
Moreover, interaction-based value capture is a peripheral condition. Many of these BMs charge users
and complementors based on the actual use of the digital platforms’ features (i.e., per interaction with
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the platform’s elements). Dominating BMs rely on a hybrid value proposition. The value proposition is
the only core condition for success, whereas monetizing data and value creation by envelopment are
only peripheral conditions. A dominant BM is supported by envelopment by creating a digital platform
ecosystem that combines solutions from the best of different worlds for many customer-related
problems. The dominance then leads to a vast amount of data created on the digital platform that can be
leveraged for value capture.
Data Monetization
Solution Coverage
Unique Coverage
Overall Solution Consistency
Overall Solution Coverage
Big circles “” indicate core conditions, and small circles “” indicate peripheral conditions.
Table 4. Business model configurations sufficient for digital platform survival.
5 Discussion
The mvQCA reveals four BM configurations (i.e., matching, spreading, innovating, and dominating) of
surviving digital platforms. Matching BMs are efficiency-centered and hence designed to achieve
greater efficiency by reducing transaction costs. This often introduces novelty through the adoption of
new activities and new ways of linking and governing the activities. Thus, matching platforms benefit
from first-mover advantages in new markets such as hospitality (e.g., Airbnb) or group-buying (e.g.,
Groupon). However, matching BMs like Groupon becomes vulnerable to imitators (e.g., later acquired
CityDeal and LivingSocial) and envelopment attacks (e.g., the launch of Google Offers after Google’s
$6 billion bid to acquire Groupon in 2010) because of low technological innovation. Hence, using
tipping strategies such as engaging in mergers and acquisitions, boosting growth through heavy
marketing spending, and investing in platform design to tip the market in their favor is key to platform
success (Zhou et al., 2020).
Whereas Matching BMs try developing unique digital platforms, spreading BMs follow an envelopment
strategy: They spread their platforms across multiple markets. Lock-in effects of their core platform
enable them to envelop adjacent markets and thus provide complementarities to customers and
complementors. For example, Uber built a digital platform for luxury rides, expanding the value
proposition to any type of ride-hailing. Then, leveraging the extensive network of locked-in drivers and
customers, Uber enveloped a food delivery BM (i.e., UberEats). Although some failed cases for
matching BMs have been observed, a surviving digital platform BM was created via a platform with the
power to create additional value through platform envelopment.
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Innovating BMs create the technological foundation for complementary innovation. They solve business
problems and enable add-ons to the platform’s core through third parties as an alternative to developing
the foundation themselves. Innovation platforms, such as Microsoft Azure, further offer scalability and
low barriers to entry with flexible pricing plans. They combine ready-to-use platform features (e.g.,
Azure Cognitive Services for artificial intelligence) and APIs for custom solutions to attract customers.
Microservice architectures and pay-as-you-go pricing enable value capture on a per-interaction basis.
Strategic focal points are structuring the platform core and periphery and governing external partners
(Böttcher et al., 2021c). The technological measures of BMs designed to create high switching costs are
intellectual property protection in the platform core and maintaining interdependencies between the
platform and complementors (Gawer and Cusumano, 2008, Zott and Amit, 2010).
Dominating BMs very successfully combine transaction and innovation platforms in a complex two-in-
one value proposition. This requires adequate resources and capabilities. Once successful, they dominate
their industry, such as the duopoly of Apple iOS and Google Android operating systems with little space
for competing platforms, such as Windows Phone or Blackberry. An envelopment strategy supports this
dominance. For example, Apple creates a seamless integration between its platforms iOS, Apple Music,
AppleTV, and Apple Arcade with powerful lock-in effects. Value capture through data monetization is
a side product as the amount of data collected on these hybrid platforms is enormous.
5.1 Initial Contributions and Future Research
Although this research is still in progress, we make initial contributions to research and practice. We
identify BM configurations of surviving digital platforms. The configurations show how different value
propositions are better combined with specific value creation strategies. The study further extends the
correlation between BM design and product market strategies (Zott and Amit, 2008) toward digital BMs.
In contrast to contexts, such as mobile app business models, where the value capture element is a source
of competitive advantage (Tidhar and Eisenhardt, 2020, Rietveld, 2018), the configurations reveal that
value capture may be less important in digital platform BMs, implying the incapacity of digital platforms
to differentiate based on the value capture. Value capture is often not a core condition for platform
success. Our chosen methodology addresses calls in recent research for generalizable insights into BMs
of digital platforms (Zhao et al., 2020). For practice, the findings imply to design their strategy based
on their BM (Lanzolla and Markides, 2021). Depending on the type of BM the strategy for growth and
competitive advantage shall differ.
In future research, we will develop a typology of digital platform BMs. To do this, we will use a multi-
method approach. We will expand our case sample with additional cases based on empirical observation
and secondary data. Currently, our sample consists only of cases published in academic articles. This
limits our sample size and introduces bias that is common in research articles on surviving digital
platforms. With additional cases, we can balance the dataset between surviving and failed cases. This
will help identify the BM characteristics and configurations that differentiate successful and failed
digital platforms, thus develop and explain ideal types of digital platform BMs. In addition, mvQCA
only allows for as many conditions as can theoretically be represented as configurations in the case
sample. With a larger case sample, more conditions can be included in the mvQCA, such as value
delivery, the role of the digital platform ecosystem, and control variables such as industry and digital
platform maturity. The resulting BM configurations will be examined for configurations of ideal types.
Through interviews with experts from digital platform companies, we will refine these ideal types to
better understand why these configurations represent ideal types of successful BMs on digital platforms.
In combination, this will enable the development of a typology of digital platform BMs.
6 Acknowledgements
The authors would like to thank the track chairs, editors and all anonymous reviewers for their helpful
comments and suggestions. We thank the German Federal Ministry for Economic Affairs and Energy
for funding this research as part of the project 01MK20001B (Knowledge4Retail).
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