Twenty-Ninth European Conference on Information Systems (ECIS 2021), A Virtual AIS Conference. 1 
CONNECTING THE DOTS OF  
DIGITAL PLATFORM ECOSYSTEM RESEARCH: 
CONSTRUCTS, CAUSAL LINKS AND FUTURE RESEARCH 
Research Paper 
 
Rob Jago Floetgen, Technical University of Munich, Munich, Germany, rob.floetgen@tum.de 
Marcus Novotny, University of Cambridge, Cambridge, United Kingdom, man59@cam.ac.uk 
Florian Urmetzer, University of Cambridge, Cambridge, United Kingdom, ftu20@cam.ac.uk 
Markus Böhm, Technical University of Munich, Munich, Germany, markus.boehm@tum.de 
Abstract  
Digital platform ecosystems are at the core of several of the world’s most valuable companies and 
constitute a strongly growing but fragmented research area at the intersection of multiple research 
streams such as IS, economics, marketing, strategy and technology management. To date, prior research 
mainly examines individual constructs and their interrelations in an isolated fashion, with no holistic 
synthesis of the field’s empirical evidence. Addressing this gap, we surveyed 97 empirical studies in top 
IS and management journals, extracting all variables and causal links between them. Variables were 
then aggregated to 51 recurring constructs on seven micro (individual entity) and macro (ecosystem 
and market) levels of analysis and causal links between them were summarised. We contribute a nomo-
logical network of DPE research and present three future research avenues: an emergent multi-level 
perspective, complex dynamics, and studying the heterogeneity of the field to further bridge its isolated 
insights. 
Keywords: digital platform ecosystems, nomological network, constructs, causal links. 
1 Introduction 
Whether it is Apple, Amazon, Alibaba, Tencent, or SAP–several of the world’s most valuable compa-
nies in our largest economies are centred around digital platform ecosystems (DPE). Thereby a digital 
platform as an extensible codebase enables the co-creation of value between autonomous networks of 
complementors and users forming an ecosystem under the orchestration of a platform owner (Hein et 
al., 2019; Tiwana et al., 2010). Enticed by the growth prospects of network effects and low marginal 
costs, and under competitive pressure from new digital players, also companies across traditionally sup-
ply-chain oriented industries such as manufacturing are now faced with questions of adopting, joining 
or building their own platform ecosystems (Jacobides et al., 2019; Urmetzer et al., 2018). Thus, it is no 
surprise that platform ecosystems literature is growing substantially, with its cumulative volume in top 
journals having doubled over the past four years.1  
The IS discipline understands DPEs as socio-technical phenomena, which lie at the intersection of var-
ious fragmented research fields, including IS, economics, marketing, strategy and technology manage-
ment (Hein et al., 2019; McIntyre and Srinivasan, 2017; Schreieck et al., 2016). Naturally, each of these 
fields brings their own foci and lenses to the scene, studying diverse issues such as governance mecha-
nisms and boundary resource design (Floetgen et al., 2020; Karhu et al., 2018), network externalities 
and competition (Katz and Shapiro, 1986; Rochet and Tirole, 2003), electronic word of mouth (You et 
al., 2015), multi-homing (Landsman and Stremersch, 2011), and technology leadership or transitions 
 
1 In our literature search, 654 of 1324 studies within the AIS Senior Scholars' Basket of Journals and the Financial Times 
Research Rank with the platform or ecosystem term in their Abstract, Title or Keywords have been published since 2017. 
Floetgen et al. / Connecting Digital Platform Ecosystem Research 
Twenty-Ninth European Conference on Information Systems (ECIS 2021), A Virtual AIS Conference. 2 
(Kretschmer and Claussen, 2016; Ozalp et al., 2018). However, there is, as yet, no holistic overview of 
the key DPE constructs and their interrelations through causal links that shape their evolution over time. 
This constitutes a missed learning opportunity to aggregate the knowledge across the different research 
areas for the emerging DPE field, as their various constructs are likely connected. 
Consider the following example: A platform owner aims to grow the market size of its ecosystem. This 
growth is based on value co-creation between complementors and users, which the owner can influence 
through governance mechanisms (Schreieck et al., 2016). To attract more complementors, it could de-
velop boundary resources to simplify complement development (Xue et al., 2019), or relax input control 
(Wessel et al., 2017), thus extending the platform’s value proposition and hopefully enticing additional 
users to join through cross-side network effects (Chu and Manchanda, 2016). Yet, it is possible that such 
profound governance changes have effects beyond their intended impact, as a rising number of comple-
ments has also been shown to negatively impact single complement sales (Taeuscher, 2019) or user 
purchasing behaviour (Li and Netessine, 2020). Additionally, the perceived effectiveness of the mech-
anisms from the owner’s point of view will affect its future behaviour as part of a decision-making 
feedback loop (Sterman, 2000). While prior studies have analysed several of these effects during a 
DPE’s evolution in isolation and for varying contexts, their insights have not yet been connected into a 
coherent picture. As such, inducing change at one end of a DPE could have unexpected, non-linear 
effects across the landscape of its complex ecosystem. This is also relevant from a practical perspective, 
as prior research has shown that managers frequently tend to misjudge cause-effect relationships in 
complex systems, leading to unexpected dynamics, policy resistance or even systematic mistakes in 
decision-making (Meadows, 2008; Sterman, 1989, 2000). 
In essence, the DPE field lacks a holistic overview of its constructs and causal links as a summary of its 
established empirical evidence. Prior reviews aiming to holistically survey constructs and causal links 
for specific IS areas have been vital to advancing our understanding of the fields, e.g. of IS success 
(DeLone and McLean, 1992) and management support systems (Clark et al., 2007). Over the past 15 
years, a promising review approach for this endeavour has also been pioneered by Jeyaray et al. (2006) 
and Lacity et al. (2010, 2011, 2016), who systematically aggregated and organised the empirical 
knowledge for the fields of IT innovation and business services sourcing. In line with this research 
approach and the conceptual multiplicity of the research object ‘digital platform ecosystem’, our re-
search question is as follows: Which constructs have been studied empirically at what levels of analysis 
in DPE research, and what are the causal links between them? 
Starting from a broad keyword search, we analyse 97 empirical studies from top IS and management 
journals and extract all variables with causal links among them. Through constant comparison, we iden-
tify 51 recurring constructs with distinct causal links across seven micro (individual entity) and macro 
(ecosystem and market) levels of analysis. Aggregating causal links between the levels, we identify foci 
and gaps of the field and connect the repeatedly analysed empirical constructs. Thus, we combine and 
structure the fragmented empirical knowledge across DPE research streams, and connect their dots for 
future theory development. We contribute a nomological network of the DPE field and present three 
future research avenues, highlighting the importance of an emergent multi-level perspective, arising 
complex dynamics, and learning from the heterogeneity of the field to further bridge its isolated insights. 
2 Background 
DPEs are the subject of several disciplines with differing perspectives (Hein et al., 2019; McIntyre and 
Srinivasan, 2017). From a technical point of view, digital platforms are extensible codebases providing 
core functionality that is complemented by an ecosystem of third-party software modules leveraging the 
platform’s interfaces (Tiwana et al., 2010). Following a market-based view, these platforms mediate 
transactions between two or more market sides, laying the focus on pricing and competition instead of 
architecture (Eisenmann et al., 2006; Rochet and Tirole, 2003). Integrating both points of view, the 
socio-technical perspective focusses on how value creation is facilitated in the ecosystem by platform 
owners leveraging governance mechanisms such as input control or the provisioning of boundary re-
sources and incentives (De Reuver et al., 2018). Thereby, the ecosystem is not only seen as a network 
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), A Virtual AIS Conference. 3 
of software modules, but of loosely-coupled autonomous agents creating and implementing innovations 
(Wang, 2021), moving the locus of value creation outside the firm (Parker et al., 2017). In sum, “a digital 
platform ecosystem comprises a platform owner that implements governance mechanisms to facilitate 
value-creating mechanisms on a digital platform between the platform owner and an ecosystem of au-
tonomous complementors and consumers.” (Hein et al., 2019)  
Following Hein et al. (2019), there are three recurring actor roles across DPEs: Platform owners, com-
plementors and users.2 Thereby, platform owners are the focal ecosystem actors, who are responsible 
for the platform’s architecture and facilitate access and value creation through governance mechanisms. 
Ownership may be centralised within a single organisation, such as in the case of Facebook or SAP, 
divided between multiple actors in consortia, as in open source ecosystems, or even decentralised, such 
as in peer-to-peer platforms. Complementors, which can be individuals, organisations or even other 
digital platforms compatible with the DPE, extend the focal platform’s value proposition by providing 
products or services (complements). Users encompass both individuals and organisations that participate 
in the DPE as service beneficiaries. Additionally, actors can take up both complementor and user roles, 
as in the case of user innovators (Ye and Kankanhalli, 2018). Considering this wide range of actor roles, 
there is considerable ambiguity around the concept of DPEs, extending to the utilisation of the overall 
platform term (De Reuver et al., 2018). Thereby, many authors don’t clearly distinguish whether they 
are referring to technological platforms or the marketplaces facilitated by them, e.g. Apple’s iPhone and 
iOS operating system or its iOS App Store (Porch et al., 2015). Throughout our review, we follow the 
IS perspective of DPEs as socio-technical phenomena (De Reuver et al., 2018), gathering empirical 
evidence on interactions amongst actors, and between actors and technology. 
By aggregating DPE constructs and causal links across empirical studies, our approach builds theory 
from prior literature, as constructs and the relationships between them are regarded as central elements 
of theory (Gregor, 2006; Levy and Ellis, 2006; Whetten, 1989). Thereby, constructs are thought of as 
unobservable concepts with a specific scientific purpose, that can be operationalised through one or 
several variables (Bacharach, 1989; Kerlinger and Lee, 2000). Constructs that are analysed in several 
causal links across studies can serve as boundary spanners between theories (Bacharach, 1989), allowing 
us to connect the empirical knowledge of the field in a larger network (Furneaux and Wade, 2009). For 
this purpose, constructs studied as both dependent and independent variables are of particular interest, 
as they can mediate causal link chains across multiple studies. However, a holistic review of empirical 
relationships between constructs for DPE research is missing. Importantly, our approach does not ana-
lyse the theoretical reasoning for causal effects and the boundary conditions cited in original studies 
(Whetten, 1989). Yet, in studying whether and how constructs are interrelated through causal links 
across the different DPE research streams, we aim to showcase an existence or lack of cumulative evi-
dence as a foundation for future research. 
3 Research Approach 
In this descriptive review (Paré et al., 2015), we surveyed the literature on DPEs to aggregate the em-
pirical knowledge of the field. Thereby, we adopt the empirical study as our unit of analysis, and uncover 
prevalent DPE constructs, and causal links through frequency analysis. We followed a systematic liter-
ature review approach (Okoli, 2015) organised into four phases (Figure 1): Planning the review, select-
ing literature, extracting data and synthesising results. In the first step, we developed a review protocol 
within the research team, defining the purpose of the review and ensuring a systematic implementation 
of our approach, including search, data extraction and synthesis strategy.  
Second, literature for the study was selected using the Web of Science and SCOPUS databases. To 
minimise the risk of excluding relevant studies, we started with the broad search string <<platform* OR 
ecosystem*>> in the Abstract/Title/Keywords fields, limiting our search to journals within the AIS 
Senior Scholars' Basket of Journals and the Financial Times Research Rank.  
 
2 Although Hein et al. (2019) refer to users as consumers, we opt for the user terminology in this study, due to the wider range 
of both consumption and content creation behaviours represented by it. 
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), A Virtual AIS Conference. 4 
 
Figure 1. Research Approach 
We limited the journal set to only include articles which had passed the highest quality standards during 
peer review without further quality appraisal. All resulting studies’ titles and abstracts were then manu-
ally screened for two criteria: (1) the article centrally encompasses a DPE according to the definition by 
Hein et al. (2019) and (2) there has been an empirical analysis of variables and their causal links which 
relate to DPE research. If we could not tell whether a study analyzed a DPE from the abstract, we 
skimmed the full text. This removed 877 studies from our list which did not refer to DPEs (e.g. ideolog-
ical, organisational or internal IT platforms and business ecosystems without an IT focus; n=619), or 
where the platform was not central to the article (n=258). Another 326 articles were excluded as they 
did not report empirical research. Of the remaining 121 studies, 24 had to be dropped from our sample 
during data extraction, as they did not clearly specify their involved variables or the directions of causal 
links between them. Thus, our final sample is composed of 97 studies from 22 journals, which are 
marked with an asterisk (*) in the References section.3 
 
Code Meaning 
Independent Variable Exogenous variable, explains change in the dependent variable. 
Dependent Variable Endogenous outcome variable, is influenced by the independent and moderator varia-ble(s). 
Moderator Variable Exogenous variable, influences the strength of the causal link between an independ-ent and dependent variable. Only defined for some causal links. 
Level of Analysis Level at which each variable was measured (see Table 2). 
Causal Link Directed empirical relationship between an independent and dependent variable. 
Trend 
(positive/negative) 
Coded for each causal link. Refers to whether an increase in the independent variable 
(or the interaction of independent and moderator variable) had a positive/negative ef-
fect on the dependent variable. 
Table 1. Coding Scheme  
Third, we coded every full text to extract all empirically studied causal links and their involved variables, 
as well as the DPE and methodology. Therefore, we created a master list that includes a description of 
the dependent, independent and moderator variables, including their level of analysis, and their relation-
ships to one another expressed as causal links (Table 1). Variables were coded multiple times if utilised 
in several roles by a study, e.g. mediating variables were included as both independent and dependent 
 
3 At least three articles were included for ten journals: Information Systems Research (n=16), MIS Quarterly (n=11), Strategic 
Management Journal (n=11), Management Science (n=10), Journal of Information Technology (n=7), Organization Science 
(n=5), Journal of Management Information Systems (n=5), Marketing Science (n=4), Journal of Marketing (n=4), Journal of 
Marketing Research (n=3). 
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), A Virtual AIS Conference. 5 
variables. Following these guidelines, we extracted a total of 648 variables with 756 causal links be-
tween them. For each causal link, we also coded its trend (negative or positive), along with the involved 
independent, dependent and up to one moderator variable. For quantitatively-studied links, we also doc-
umented whether the original authors deemed them to be significant (true for 670 links), for which the 
significance level varied between 5% and 10% across authors. As we wanted to focus on each study’s 
central insights, we only extracted causal links that were referenced in the text body, omitting control 
variables and auxiliary or non-empirical analyses (e.g. simulation).   
 
Level of Analysis Description 
M
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ay
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Complement Digital artefacts extending the value proposition of the focal platform, including  software applications, product/service listings and user generated content. 
Complemen-
tor 
Suppliers of complementary products and services (complements), including  
developers and sellers. Single actors or organisations. 
Owner Focal platform actor/organisation enabling value co-creation among complementors and users through provision of the technical platform and governance mechanisms. 
Platform Extensible codebase hosting digital complements and mediating interactions  between complementors and users. 
User Service beneficiaries of platform and complements, sometimes provision of user  generated content (complements). Single actors or organisations. 
M
ac
ro
  
La
ye
r Ecosystem 
The socio-technical network of actors (complementors, users, owners) and  
complements spanned up by the focal platform. 
Market Everything outside the respective study’s DPE, including industries and markets with competing or neighbouring DPEs, as well as regulatory institutions. 
Table 2. Description of our constructs’ micro and macro levels of analysis 
Fourth, we synthesised our results by aggregating our dataset to a list of distinct DPE constructs and 
causal links, following grounded theory coding protocols for open coding, axial coding and constant 
comparison (Corbin and Strauss, 2008). As each variable was assigned to a level of analysis, variables 
intended to measure the same construct for a common level were clustered into constructs. For example, 
variables such as a complementor’s revenue (Li et al., 2019), market share (Tanriverdi and Chi-Hyon, 
2008) or IPO likelihood (Ceccagnoli et al., 2012) were grouped into a Performance construct at the 
complementor level. We distinguished constructs at seven levels of analysis (Table 2) according to the 
research objects studied by the original authors, further divided into a micro and macro layer (Bélanger 
et al., 2014). Within the micro layer, levels describe single entities, meaning the individual actors (com-
plementors, users and owners) or technological artefacts (platform, complements) of a DPE. The macro 
layer describes socio-technical collectives that result from interactions between individual entities, span-
ning up the ecosystem level of a DPE and the market-level influences outside of it. Thus, micro levels 
are embedded into macro contexts, whereas macro levels emerge from interactions at the micro levels 
(Kozlowski and Klein, 2000). Clustering was undertaken as an explorative, bottom-up approach without 
an initial coding scheme to avoid a priori judgements. Thereby, a list of 51 recurring DPE constructs 
that were utilised in at least three studies emerged through constant comparison, covering 606 of our 
648 extracted variables.4 This also combined our set of 756 causal links between all variables into 175 
distinct causal links between recurring constructs, covered in one to five studies (excluding moderators). 
Following clustering, we again reviewed all causal links to assure that the relationships between 
 
4 Though the cut-off point at three studies may seem arbitrary, it allowed us to condense our results to the most relevant 
constructs, while still reporting over 93% of our data. Similarly to Furneaux & Wade (2009, p. 5), we recognise that some 
degree of inference is necessary for the task of clustering due to varying naming conventions and partly ambiguous reporting 
in primary studies. However, we aim to increase transparency of the included variables per construct throughout Section 4.2. 
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clustered constructs were still true to the meaning of the underlying variables. We then created a fre-
quency matrix detailing the number of studies that analyse causal links within or between levels of 
analysis. In addition, we built a nomological network (Cronbach and Meehl, 1955) to organize the causal 
links with repeated empirical evidence in a logical and integrated fashion. 
4 Findings 
In the following, we present a descriptive overview of the studies included in our sample, followed by 
two findings: (1) a list of recurring DPE constructs grouped by level of analysis and (2) a frequency 
matrix and nomological network showing causal links between levels of analysis and key constructs. 
4.1 Overview of our sample 
Our empirical studies cover a large variety of different platform ecosystems that all fit the definition of 
DPEs as extensible codebases enabling value co-creation between complementors and consumers, gov-
erned by a platform owner (Hein et al., 2019; Tiwana et al., 2010). We sketch an overview of the plat-
forms included in our sample according to the transaction and innovation platform typology developed 
by Cusumano et al. (2019): Thereby, transaction platforms primarily serve as intermediaries for ex-
changes of products, services, or information, whereas innovation platforms provide a technical foun-
dation for which complementors can develop software extension. Our sample includes 52 studies 
covering transaction platforms such as multi-sided marketplaces (Amazon, Taobao) or social networks 
and online communities (Facebook, Wikipedia, or TripAdvisor), and 47 studies analyzing innovation 
platforms such as smartphone operating systems (Android, iOS), video game consoles (Microsoft Xbox, 
Sony Playstation) or other software platforms (SAP, Mozilla Firefox). Some platforms take up a hybrid 
role in that they offer complementors both an extensible codebase to generate and a marketplace to 
distribute new innovations (e.g., the iOS and Android smartphone app stores). 
Regarding methodology, included articles relied predominantly on quantitative data analysis, with most 
studies utilising econometric analyses (n=79), structural equation modeling (n=7), dynamic modeling 
approaches (n=4) and meta-analysis (n=3). Four studies followed a qualitative case study approach. 
Additionally, the sample incorporates recent knowledge, as half of the studies in our final set have been 
published since 2017 (n=58). 
4.2 Recurring Digital platform ecosystem constructs 
We clustered 51 recurring DPE constructs that were analysed in at least three studies, grouped by their 
layer and level of analysis (Table 3). For each level, constructs are ranked by the number of studies 
employing them, which is shown first in parantheses. The three following numbers indicate the subsets 
of studies operationalising the construct as a dependent, independent or moderator variable, subse-
quently also referred to as a construct’s role. As studies may utilise constructs in a number of these roles, 
the subset sizes do not necessarily add up to the total study count. In the following, we introduce each 
construct and detail its predominant operationalisations with references to exemplary studies. 
Regarding layers of analysis, almost all studies in our sample (n=92) utilise constructs within the micro 
layer, while over half of studies (n=57) examine constructs within the macro layer. In the micro layer, 
studies analysed constructs with individual complements (n=42), complementors (n=46), owners 
(n=28), platforms (n=27) or users (n=25) as their level of analysis. Two thirds of all studies with con-
structs at the complement level measured its Performance through sales (Rietveld et al., 2019), sales 
ranking (Yin et al., 2014) or usage and demand measures such as downloads (Wang et al., 2018), pri-
marily operationalised as dependent variables. Word of Mouth, Architecture and Updates were opera-
tionalised with both independent and dependent variables, respectively measuring perceived quality 
through volume and/or valence of user review scores (Eckhardt et al., 2018), technical attributes, such 
as modularity or standardisation (Tiwana, 2015a, 2015b), and version evolution (Yin et al., 2014). The 
remaining constructs were operationalised as independent or moderator variables, including a comple-
ment’s Price, Age, availability on multiple platforms (Multi-Homing), the Information available to users 
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prior to purchase (e.g. descriptions), and its Type, describing the impact of its e.g. app categories and 
business models (Ghose and Han, 2014). Ghose and Han (2014) provide a comprehensive example of a 
complement-level study, analysing drivers on the Performance of smartphone applications. 
 
Level of Analysis Constructs 
M
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Complement  
(42) [30/26/20] 
Performance (28) [26/6/3], Type (17) [0/6/15], Word of Mouth (12) [3/9/3],  
Price (8) [0/8/2], Architecture (7) [4/5/3], Updates (6) [2/6/0], Age (6) [0/4/2],  
Multi-Homing (4) [0/4/1], Information (3) [0/3/0] 
Complementor  
(47) [31/32/19] 
Platform Engagement (18) [13/6/2], Performance (13) [12/3/0], Type (11) [0/4/6], 
Strategy (10) [5/6/3], Portfolio Size (6) [1/4/1], Portfolio Composition (6) [1/4/1],  
Experience (6) [0/6/0], Reputation (5) [1/4/4], Capabilities (4) [2/2/2],  
Generativity (3) [3/0/0], Perceptions (3) [1/2/1], Multi-Homing (3) [1/2/1] 
Owner  
(28) [11/24/8] 
Governance Mechanisms (16) [2/15/5], Performance (8) [8/0/0],  
Market Entry (7) [2/5/0], Strategy (5) [1/4/2] 
Platform  
(27) [4/21/6] 
Architecture (9) [2/8/0], Type (8) [0/2/5], Openness (6) [1/5/1],  
Word of Mouth (6) [2/4/1], Features (4) [0/3/1] 
User  
(25) [23/16/10] 
Platform Usage (14) [11/3/1], Purchasing (11) [11/2/0], Type (9) [0/5/4],  
Perceptions (8) [3/7/3], Expectations (7) [2/5/0], Content Creation (6) [6/4/1],  
Satisfaction (4) [4/4/1], Search Effort (4) [3/1/1] 
M
ac
ro
 L
ay
er
 
Ecosystem  
(51) [34/31/17] 
Complement Base Volume (17) [10/12/2], User Base Volume (13) [6/10/1],  
Performance (12) [11/3/1], Maturity (8) [0/3/6],  
Complement Base Variety (7) [2/6/1], Complementor Competition (6) [2/4/2],  
Complement Performance (6) [5/1/1], Complementor Base Volume (5) [2/3/0],  
Complementor Generativity (4) [3/1/0], Complement Base Multi-Homing (3) [1/2/1], 
Community Attention (3) [2/3/0] 
Market (10) [2/5/3] Performance (3) [0/1/2], Competing DPE Performance (3) [2/1/1] 
Table 3.  Recurring constructs by level of analysis. (Total study count). [Study counts for usage 
as dependent/independent/moderator variable]. 
The most prevalent constructs for the complementor level were Platform Engagement and Perfor-
mance, which were both generally studied with dependent variables. Platform Engagement relates to a 
complementor’s participation in a DPE by offering (Venkatraman and Lee, 2004; Wang and Miller, 
2020) or not removing (Tiwana, 2015b; Zhu and Liu, 2018) products and services, as well as contrib-
uting code (Moqri et al., 2018). Performance was mostly operationalised through financial measures 
such as revenue or market share (Ceccagnoli et al., 2012; Li et al., 2019; Tanriverdi and Chi-Hyon, 
2008). Solely operationalised with dependent variables, Generativity measured innovation efforts, e.g. 
through app updates (Boudreau, 2012; Foerderer et al., 2018). Commonly utilised independent variables 
related to a complementor’s Portfolio Size (He et al., 2019), Portfolio Composition (Rietveld et al., 
2019) or Experience, measured as number of prior releases (Yin et al., 2014) or active time in the eco-
system (Boudreau, 2012). Strategy encompassed competitive pricing (Zhu and Liu, 2018), marketing 
(Sun et al., 2020) or portfolio choices (Wen and Zhu, 2019) and was studied with both independent and 
dependent variables. Complementor Reputation (Sun et al., 2020) and Type were operationalised 
through independent or moderator variables. Thereby, Type was utilised across studies to explain heter-
ogeneity within a group of complementors, e.g. by country of origin (Hong and Pavlou, 2017) or organ-
isational size (Miric et al., 2019). The remaining constructs described participation behaviour on 
multiple platforms (Multi-Homing, Landsman and Stremersch 2011), Perceptions of platform attrac-
tiveness and governance (Benlian et al., 2015) and Capabilities such as intellectual property and mar-
keting (Huang et al., 2013). Studies at the complementor level included Ceccagnoli et al.’s (2012) 
analysis of how an independent software vendor’s participation in SAP’s platform ecosystem (Platform 
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Engagement) together with its IP protection and marketing Capabilities influences Performance 
measures such as sales and IPO likelihood.  
At the owner level, only Performance was primarily analysed as a dependent variable, comprising fi-
nancial measures such as revenue, market share or firm survival (Chakravarty et al., 2014; Dushnitsky 
et al., 2020). Governance Mechanisms such as boundary resource provision (Karhu et al., 2018), input 
control (Thies et al., 2018) and complement endorsement (Rietveld et al., 2019) were mostly operation-
alised with independent variables. Similarly, other constructs explained the effects of Market Entry 
through first-party complement distribution (Foerderer et al. 2018; Zhu and Liu 2018) and marketing, 
collaboration or transaction Strategy (Dushnitsky et al., 2020; Gnyawali et al., 2010). An exemplary 
study at the owner level by Dushnitsky et al. (2020) analyses the effects of a platform firm’s transaction 
Strategy and differentiation on its downside Performance, measured as firm dissolution. 
The platform level contained mostly constructs studied as independent variables, concerning technical 
aspects (Architecture, Type and Features), access (Openness) or quality (Word of Mouth). Architecture 
was measured with generational platform transitions or development complexity (Cennamo et al., 2018; 
Kapoor and Agarwal, 2017; Ozalp et al., 2018), while Features referred to affordances such as machine 
translation (Brynjolfsson et al., 2019) or social network integration (N. Huang et al., 2017). Type was 
utilised mostly as a moderator to explain heterogeneity across platform specialisations, e.g. in meta-
analyses (You et al., 2015). Lastly, Openness described technical interoperability with outside platforms, 
utilisation of open standards and the degree of integrating third-party complementors (Boudreau, 2010; 
Ondrus et al., 2015), while Word of Mouth referred to effects of integrated reviews (Babić Rosario et 
al., 2016; N. Huang et al., 2017). Most platform studies also included the owner level. An example of 
this is the study of Karhu et al. (2018), which explores how the provisioning of boundary resources 
(Owner Governance Mechanisms) influences a platform’s Architecture and Openness. 
At the user level, constructs operationalised as dependent variables focussed on Platform Usage, Pur-
chasing or Content Creation behaviour. Thereby, Platform Usage was measured through platform adop-
tion, visits or content consumption (Ahn et al., 2016; Albuquerque et al., 2012; Katona et al., 2011), 
whereas Purchasing was quantified through purchase rates or expenditures (N. Huang et al., 2017; 
Zhang et al., 2020). Similarly, Content Creation was determined through contribution likelihoods and 
volume (Ahn et al., 2016; Chen et al., 2018). A less utilised construct, Search Effort measured the con-
sideration sets of buyers on their order journey (Dinerstein et al., 2018; Li and Netessine, 2020). Per-
ceptions, Expectations and Satisfaction were analysed in varying roles, respectively referring to 
perceived ease of use, gains and risks (Krasnova et al., 2010; Xu et al., 2010), anticipations, intentions 
and goals (Albuquerque et al., 2012; Kankanhalli et al., 2015) and usage satisfaction or motivation (Chen 
et al., 2018; Q. Huang et al., 2017). Lastly, Type explained heterogeneity between users as independent 
or moderator variables, e.g. gender, age or personality (Katona et al., 2011). An exemplary study at the 
user level by Albuquerque et al. (2012) analyses Expectations and prior behaviour as drivers of future 
Platform Usage, Purchasing and Content Creation. 
Within the macro layer, studies analysed constructs pertaining to the study’s platform ecosystem and 
its encompassing market. At the ecosystem level, constructs described the networks of complementors 
and users interacting by providing or utilising complements on the platform. The only exception is posed 
by the ecosystem’s Maturity, which is measured via a platform’s age (Landsman and Stremersch, 2011) 
and employed as an independent or moderator variable. As with the micro layer, success measures, such 
as the ecosystem’s Performance, its Complement Performance or Complementor Generativity, are 
mostly studied as dependent variables. Thereby, ecosystem Performance is measured through sales, 
market share, usage or transaction volume (Cennamo, 2018; Dushnitsky et al., 2020; Landsman and 
Stremersch, 2011). Similarly to their micro counterparts, the ecosystem’s Complement Performance and 
Complementor Generativity were assessed with ecosystem-wide sales measures (Brynjolfsson et al., 
2019) and measures of innovation efforts, such as app releases (Wen and Zhu, 2019). Further dominant 
constructs simply described the installed base of actors and artefacts on the platform (Complement Base 
Volume, User Base Volume, Complementor Base Volume), which were commonly studied with both 
dependent and independent variables to analyse dynamic same- and cross-side network effects 
(Boudreau and Jeppesen, 2015; Chu and Manchanda, 2016; Song et al., 2018; Thies et al., 2018; Zhu 
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and Iansiti, 2012). The remaining constructs described the Complement Base Variety (Boudreau, 2012; 
Taeuscher and Rothe, 2020), as well as Complementor Competition (Cennamo and Santaló, 2013; 
Venkatraman and Lee, 2004) and Complement Base Multi-Homing (Landsman and Stremersch, 2011) 
at the ecosystem level. Lastly, some online community studies also utilised a Community Attention con-
struct to measure effects of peer recognition (Chen et al., 2020; Q. Huang et al., 2017). As an exemplary 
study at the ecosystem level, Chu & Manchanda (2016) analyse cross and direct network effects on 
Taobao.com (User Base Volume, Complementor Base Volume). 
Only ten studies considered the market level, with two constructs studied three times. Market Perfor-
mance was analysed as an independent or moderator variable to describe the influences of industry 
growth and demand on an ecosystem’s actors (Wang and Miller, 2020). Competing Platform Perfor-
mance was studied in all three roles, analysing its dynamic interplay with a focal DPE’s performance 
(Krijestorac et al., 2020). The study of Li & Agarwal (2017) incorporates the market level by analysing 
the effect of Facebook’s integration of Instagram on the wider photo-sharing ecosystem. 
In summary, we make three observations: First, there are archetypal constructs that were analysed for 
several levels of analysis, including Performance, Word of Mouth/Reputation, Maturity/Experience, Ar-
chitecture, Strategy, Multi-Homing and Type. These are covered in a large part of our sample, with 
Performance constructs alone occurring across 60 out of 97 studies. Second, most constructs were pre-
dominantly analysed in a focus role, either with dependent (Performance, Platform Engagement, Plat-
form Usage, Purchasing), independent (Governance Mechanisms, Architecture, Price, Perceptions) or 
moderator variables (Type, Maturity/Experience). Constructs studied in the field of network effects seem 
to pose an exception, as they were often analysed with dependent and independent variables to examine 
their interrelation at the ecosystem level (Complementor Base Volume, Complement Base Volume and 
User Base Volume). Third, despite this focus on certain roles, over two thirds of constructs (39) are 
analysed at least once with both dependent and independent variables, creating an opportunity to be 
studied as mediators of longer causal link chains spanning more than two constructs.  
4.3 Causal links between levels of analysis 
In the following, we present our analysis of causal links, starting with a frequency matrix detailing the 
number of studies analysing links within and between levels of analysis (Table 4). While moderated 
links are included, the moderator’s level of analysis is not incorporated for simplification. As studies 
may analyse links between several levels, cells do not need to add up to their row or column totals. 
 
Independent  
Variable Level 
Dependent  
Variable Level 
Micro Macro 
Total Comple 
ment 
Comple 
mentor Owner Platform User 
Eco 
system Market 
M
ic
ro
 
Comple 
ment 17 10 9 2 / 5 1 30 
Comple 
mentor 5 18 5 4 1 6 2 31 
Owner 2 2 6 4 / 3 / 11 
Platform / / 3 2 / / / 4 
User 2 2 3 2 15 8 1 23 
M
ac
ro
 Eco 
system 2 1 8 12 1 16 2 34 
Market / / 1 1 / / / 2 
Total 26 32 24 21 16 31 5 97 
Table 4. Number of studies analysing causal links between levels of analysis. ‘/’ equals zero. 
Independent variable level of analysis in column, dependent variable level in row. 
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We make three observations: First, some levels of analysis exhibit a clear tendency to be studied more 
often with either independent or dependent variables. Both owner and platform are analysed mostly with 
independent (24 and 21 studies) compared to dependent variables (11 and 4 studies), meaning research-
ers predominately use their constructs to explain effects on other levels. On the other hand, the user is 
represented with dependent variables in 23 studies and independent variables in only 16 studies, indi-
cating that researchers generally tend to explain user behaviour rather than inferring effects from it on 
other levels. Second, levels of analysis can be split into those utilised to explain intra-level and extra-
level behaviour. For complement, complementor, user and ecosystem, over half of studies employing 
independent variables at their level use these to explain their own behaviour. In contrast, only one fourth 
of owner (6 out of 24) and one tenth of platform studies (2 out of 21) utilise their independent variables 
to explain intra-level behaviour, though they show causal links to all other levels. Third, only about half 
of our studies bridge the two layers of analysis, thus analyzing how individual DPE actors and artefacts 
shape their ecosystem or market, and vice versa. While we noted in the prior subsection that 57 and 92 
studies employed constructs at the macro and micro layer respectively, an auxiliary analysis showed 
that 43 studies analysed links across the two layers, though this increases to 49 when including moder-
ators. Bridging studies include the study of owner and platform constructs driving ecosystem behaviour 
(Wessel et al., 2017; Xue et al., 2019), and ecosystem constructs affecting constructs on the complement 
(Eckhardt, 2016), complementor (Boudreau, 2012), owner (Chakravarty et al., 2014) or user level (Ahn 
et al., 2016). 
Finally, we visualised the causal links (edges) between individual constructs (nodes) by plotting them 
as a nomological network (Figure 2). To simplify interpretation, we only include direct empirical rela-
tionships found to be significant in at least two studies, ommitting moderated links. As a result, 49 causal 
links (out of 175), including 36 of our 51 identified constructs are shown here. While both layers of 
analysis are covered, the market level had no repeated causal links and is thus excluded. 
 
Figure 2. Nomological Network of causal links with significant effects in at least two studies. 
The nomological network allows us to analyse the most frequently studied causal links and explore 
whether they had a common positive or negative trend (edge colour), for which we require at least 75% 
of underlying studies to evidence the trend. We find that causal links explaining constructs that ulti-
mately drive economic DPE success (Performance, Platform Engagement, Platform Usage, Purchasing 
and Content Creation) are predominant as these make up 40 of the 49 causal links.  
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Even though all of our extracted significant causal links have been coded as either positive or negative, 
only 25 edges in the nomological network are shown with an aggregated positive or negative trend. 
These include e.g. the negative effects of Price and the positive effects of Word of Mouth and Updates 
on Performance at the complement level across a range of DPE categories, such as app stores (Ghose 
and Han, 2014; Tiwana, 2015a), video game consoles (Cennamo and Santaló, 2019) and social media 
and online communities (Lee et al., 2015; Lu et al., 2013; Zhu et al., 2020). As another example, we 
find repeated evidence of positive self-reinforcing effects for an ecosystem’s Complement Base Volume 
(Ahn et al., 2016; Thies et al., 2018; Xue et al., 2019) or user’s Content Creation (Ahn et al., 2016; 
Albuquerque et al., 2012; Chen et al., 2018), together with mutually reinforcing effects of a Comple-
mentor’s Performance on its Platform Engagement and vice versa (e.g. Ceccagnoli et al., 2012; Wang 
and Miller, 2020). However, even for causal links with repeated evidence for a common trend, such as 
an ecosystem’s User Base Volume positive effect on its Complement Base Volume (Boudreau and 
Jeppesen, 2015; Gretz et al., 2019; Zhu and Iansiti, 2012), there can be single cases for which these did 
not hold (Thies et al., 2018), making an inference of generalisability premature. Additionally, trends 
should be interpreted cautiously due to the high level of abstraction: Even though multiple studies 
showed that an owner’s Strategy can positively influence its Performance (Chakravarty et al., 2014; 
Fang et al., 2015; Gnyawali et al., 2010), the underlying variables represent very different practices, 
from which one cannot infer that following a strategy is generally beneficient to performance.  
The remaining 24 edges show no trend due to conflicting evidence. For example, while we found two 
studies with a positive effect of a complement’s Multi-Homing on its Performance (Ghose and Han, 
2014; Krijestorac et al., 2020), a third study found a negative effect (Pervin et al., 2019), leading us to 
infer no trend. As two studies (Ghose and Han, 2014; Pervin et al., 2019) were even conducted in the 
same DPEs (Apple iOS and Google Android), we cannot yet tell where this heterogeneity emerges from.  
In closing, while the nomological network provides a condensed overview of recurring causal links and 
constructs, only 49 out of 175 distinct causal links (28%) and thus 337 out of 670 significant extracted 
causal links (50%) from our data are represented here. This means that there is a long tail of 126 distinct 
causal links between recurring constructs which were not replicated in our sample, representing a large 
body of knowledge that cannot be effectively summarised at the time. 
5 Discussion 
By presenting a synthesis of DPE constructs and causal links, we provide a current integrative overview 
of the field. We interpret and discuss our findings along three avenues for future research. 
Connecting the isolated parts for an emergent multi-level perspective. Surveying empirical relation-
ships across the field, we recognise that most studies analysed isolated parts of a DPE, such as comple-
ment(or) performance or user behaviour, often without considering these actors’ environments. Yet, 
while most articles reduce the complex DPE phenomenon to small subsets of distinct constructs, our 
synthesised nomological network reveals manifold connections and dependencies between them (Figure 
2). As platform ecosystems are inherently dynamic and multi-level systems, not incorporating multiple 
levels of analysis during research may lead to common fallacies that impair construct and internal va-
lidity (Burton-Jones and Gallivan, 2007). As a result, we call for future research to leverage these con-
nected parts for an emergent multi-level perspective of DPE research: A promising direction could be 
to study the emergence of collective constructs in DPE, which result from an ongoing series of events 
and interactions of their individual entities (Morgeson and Hofmann, 1999). Recent research on ‘DPE 
resilience’ already emphasized the explanatory power of collective constructs to explain the success of 
DPE emerging from joint series of (inter-)actions by leveraging the platform-based nature and the eco-
system (Floetgen et al., 2021). Similarly, our nomological network provides a novel basket of opportu-
nities to analyze not only emergent multi-level, but also multi-dimensional constructs: For example, 
DPE Performance might be more meaningful than just the sum or average of its actors’ performances, 
as it was commonly studied in our sample (e.g., overall transaction volume). Thus, an emergent per-
spective of DPE Performance should represent more than mere financial measures, as success is not a 
property shared across actors (Wang, 2021). Similar to DeLone and McLean (1992) with their IS 
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Success model, future research could thus propose an interdependent and multi-dimensional view on 
the collective phenomenon of DPE Performance.  
Looking ahead: Complex dynamics in DPEs. Our study shows that only few authors seem to look for 
longer causal link chains, with most studies trying to identify individual constructs that drive the eco-
nomic performance of the ecosystem and its actors. While this reductionism is vital in achieving robust 
results, it tends to ignore the function of DPEs as a whole, where one effect can quickly become a new 
cause. A laudable example of a study ‘looking ahead’ is the analysis of Amazon’s entry into the product 
spaces of its marketplace vendors by Zhu and Liu (2018), who find that Amazon is more likely to com-
pete with sellers that offer successful products on the platform, which then negatively affects their future 
growth, thereby even closing a feedback loop. While only few comparable loops (e.g. Complementor 
Performance ßà Complementor Platform Engagement) are currently evident in our nomological net-
work (Figure 2) due to its high level of abstraction, one could extend it with further constructs and causal 
links outside our sample, possibly also by engaging industry experts, to uncover more avenues for emer-
gent dynamics. As an example, possible additions include logically-inferable connections, such as a 
positive link between user Purchasing and ecosystem or complementor Performance, as well as the 
positive effects of developer-friendly Architecture on the ecosystem’s Complement Base Volume (Ozalp 
et al., 2018) or changes in actor’s behavior (e.g., complementor Platform Engagement) based on the 
ecosystem’s Performance (Venkatraman and Lee, 2004). Broadening our horizon this way, a multitude 
of new and longer feedback loops emerge, of which we will consider one example: Owner Governance 
Mechanisms à Platform Architecture à Ecosystem Complement Base Volume à Ecosystem Perfor-
mance à Owner Governance Mechanisms. In prose, platform owners may develop boundary resources 
to simplify their platform’s architecture, which hopefully leads to a rise in complements developed for 
the platform, extending the DPE’s value proposition and further increasing its transaction volume. The 
effectiveness of this approach will then affect the owner’s future governance behavior and boundary 
resource development. In sum, we propose that the connected DPE constructs span new and longer 
feedback loops beyond established network effects, which may have profound implications for the pre-
diction of future system behaviour (Benbya et al., 2020; Sterman, 2000) and our understanding of DPEs 
as a whole (Wang et al., 2021). Similarly to Clark et al. (2007) for management support systems or Fang 
et al. (2018) for overall IS research, we propose to analyse DPEs with a systems approach to consider 
their emergent dynamics and function as a whole, an appeal which is also in line with calls to further 
integrate a complexity perspective into platform ecosystems research (Phillips and Ritala, 2019). 
Learning from heterogenity: Towards new insights from generalizing across DPE contexts. Prior 
reviews of the DPE field include diverse examples of DPEs, ranging from Microsoft’s Xbox and SAP’s 
cloud ERP ecosystem to Facebook and Wikipedia (Hein et al., 2019; McIntyre and Srinivasan, 2017; 
Schreieck et al., 2016). Similarly, our sample contains a multitude of profit and non-profit platforms 
with wildly different business models that fit the  Hein et al. (2019) definition of a DPE. For instance, 
both Microsoft Xbox and Wikipedia are digital platforms that derive their value largely from their com-
plements, yet in one case these are professionally-developed video games, while in the other they are 
crowd-sourced pieces of information. Interestingly, we still find the same themes analysed across DPE 
contexts, as we managed to attribute 606 of our 648 extracted variables to recurring constructs. How-
ever, as we fail to find repeated trends for many causal links, such as a complement’s Multi-Homing on 
its Performance (Ghose and Han, 2014; Krijestorac et al., 2020; Pervin et al., 2019), this heterogeneity 
might unlock a novel approach of differentiation for DPEs: While a clearly positive or negative trend of 
a causal link could indicate a genrealisable “core causal link” valid across different DPE contexts, causal 
links with conflicting evidence might provide a promising starting point to differentiate DPEs based on 
their unique empirical relationships. This method to conceptualize their differences would go beyond 
established distinctions of digital platforms types based on value creation strategies, such as innovation 
and transaction (Cusumano et al., 2019), by inductively leveraging the empirical body of knowledge in 
DPE research. Moreover, we hope to inspire future research to analyse even further sources of hetero-
genity across DPEs (e.g., maturity stages, complementor types: in-house, 3rd party, etc., or even the 
combination of different sources) to gain novel and deeper understandings of DPEs and bridge their 
isolated insights. 
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), A Virtual AIS Conference. 13 
6 Conclusion 
Empirical research on DPEs is fragmented across different research streams, lacking a holistic overview. 
However, this body of academic literature constitutes the most comprehensive, rigorous, and reliable set 
of evidence on the field. Going with Steve Job’s famous quote: “You can’t connect the dots looking 
forward – you can only connect them looking backwards”, we analyse 97 empirical studies in top IS 
and management journals. Thereby, we synthesised this body of academic literature into 51 recurring 
constructs with distinct causal links across seven micro and macro levels of analysis, showing existing 
foci and gaps and contributing towards bridging different research streams of DPE. As such, this paper 
shifts the focus from finding what autonomous actors in DPE interact (Riasanow et al., 2018; Riasanow 
et al., 2020) towards a deeper understanding of how the actors in DPE interrelate. 
Naturally, our approach is not without limitations, leading us to critically discuss three aspects. First, 
despite our large sample of 97 empirical studies, our set of constructs and causal links cannot be con-
sidered exhaustive, as we limited our keyword search to a set of top journals without forward/backward 
search, excluding other refereed publications and top conferences. While a sole focus on top journals 
has been criticised in the past for reasons such as lack of comprehensiveness and publication bias, we 
believe it to be justifiable to ensure the inclusion of only the most rigorous empirical evidence and the 
explicitness and reproducibility of our approach. Still, future research could consider an even larger 
body of literature, also including further research areas such as software engineering. Second, while 
knowledge about the existence of positive or negative trends for significant causal links is valuable, it 
does not allow for detailed comparison. In particular when two causal links influence a focal construct 
in diverging directions, we cannot judge whether one outweighs the other without knowing their effect 
sizes. Thus, uncovering the strength of causal links across DPE contexts is another avenue for future 
work. Third, our review may suffer from issues of generalisability. On one hand, grouping variables into 
constructs is a partly-subjective task, which simplifies the results of studies to make them comparable, 
potentially losing granular insights in the process. We addressed this subjectivity by following grounded 
theory guidelines and discussing our clustering within the author team. On the other hand, generalisa-
bility of causal links to other DPE contexts was a regularly-cited limitation in our study sample. Thus, 
one should carefully evaluate whether a specific causal link is transferable to one’s own research or 
business, which may require further analysis. 
Our findings have profound implications for both DPE research and practice. From a theoretical per-
spective, we developed a holistic and connected overview of constructs and causal links in a quickly 
evolving and fragmented field (i.e. the ‘What’s’ and ‘How’s’ of theory, Whetten, 1989). Thus, we com-
bine empirical knowledge across largely unconnected areas and showcase boundary constructs that can 
bridge theories. By formulating three aveues for the future of DPE research, we show how our perspec-
tive on the DPE research field can contribute to future theory development. From a practical perspective, 
we shed light on cause-effect relationships and effect chains in DPE. Prior research has shown that 
managers are often unable to correctly judge outcomes in complex socio-technical systems (Sterman, 
1989, 2000). Demonstrating the connectedness of constructs within DPE, we enable managers to antic-
ipate possible implications of their actions. This removes ambiguity about the effectiveness of interven-
tions and improves decision-making, informing the future design and management of successful DPE. 
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