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The Emergence of Digital Platform Ecosystems: A Problem-Solving Perspective

August 2020
Academy of Management Proceedings 2020(1):17767

August 2020
2020(1):17767

DOI:10.5465/AMBPP.2020.17767abstract

Authors:

Ramya K Murthy

Indian Institute of Management Bangalore

Anoop Madhok

York University

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Overcoming the Early-stage Conundrum of Digital Platform Ecosystem

Emergence: A Problem-Solving Perspective

Ramya K Murthy, Indian Institute of Management, Bangalore

RKMurthy16@schulich.yorku.ca

Anoop Madhok, Schulich School of Business, York University, Toronto

amadhok@schulich.yorku.ca

Abstract

Platform sponsors and complementors co-create value in digital platform ecosystems. But how

does a digital platform ecosystem emerge in the incipient stage, especially in a situation where

value co-creation involves attracting complementors to platform sponsors who are unknown to

one another? We posit that a platform sponsor’s choice of scope signals value co-creation

opportunities and thereby attracts complementors and consumers. We draw upon the problem-

solving perspective, rooted in the knowledge-based view of the firm, to shift the emphasis away

from the actor (‘who’) to the problem at hand (‘what’) and demonstrate how incipient platform

sponsors can align their scope with the problem to stimulate ecosystem emergence. Using fuzzy-

set qualitative comparative analysis on a dataset of crowdfunding campaigns, we identify

multiple pathways and associated propositions for successful emergence of digital platform

ecosystems, notably for innovation, open-source, and information ecosystems. The framework

we conceptualize highlights novel considerations to overcome the early-stage challenge of

attracting participation to an ecosystem that is yet to emerge.

Keywords: Ecosystem emergence, problem-solving, knowledge-based view, platform scope,

digital platforms, fsQCA

Published 14 July, 2021, Journal of Management Studies Special Issue on Corporate Strategy

and the Theory of the Firm in the Digital Age, https://doi.org/10.1111/joms.12748.

Introduction

Digital platform-based ecosystems (hereafter referred to as platform ecosystems) have

proliferated across several industries and geographies. As an organizational form, they have

shifted the locus of value creation from the inner core of the focal firm to co-creation with

external autonomous actors called complementors (Adner and Kapoor 2010; Kapoor 2018).

Much of the research on platform ecosystems has shown keen interest in larger and well-

established platforms like Apple and Amazon, with scholars seeking to understand the sources of

their value creation and growth (McIntyre, Srinivasan, Afuah, Gawer, and Kretschmer 2020). In

contrast, the long tail of platforms that struggle in the incipient stage remains largely ignored

(Dattée, Alexy, and Autio 2018). Moreover, although a shared understanding and agreement of

the scope of activities of the respective actors — in this case platform sponsors and

complementors — is fundamental to co-creation of value (Gulati, Wohlgezogen, and Zhelyazkov

2012), there has been a limited understanding regarding the choice of the scope of the platform

sponsor vis-à-vis complementors (McIntyre et al. 2020). Our paper focuses on the long tail of

platforms in the incipient stage, with a specific focus on the role of the platform sponsor’s scope

on platform ecosystem emergence.

The successful emergence of a platform ecosystem implies that the platform survived the

incipient stage by attracting voluntary participation of complementors and consumers

(Ceccagnoli et al. 2012; McIntyre and Srinivasan 2017). Yet firms in the incipient stage are

faced with a major conundrum. In the case of more established ecosystems, factors such as

superior technology infrastructure (Tiwana 2013; Constantinides, Henfridsson, and Parker 2018),

first mover advantage (Gawer and Cusumano 2014), incentives and subsidies (McIntyre and

Subramaniam 2009; Caillaud and Jullien 2003) and creation of social forums (Fang, Wu, and

Clough 2020), among others, have been shown to attract the contributions of potential

participants to their ecosystems. However, incipient platform firms typically do not have

recourse to these avenues, nor do they possess the resources to create them in order to make the

platform attractive to potential participants.

We propose that a platform sponsor’s scope choices offer a way out of this dilemma.

Platform sponsors have to make key decisions about their scope, at the outset as well as

continually, in order to signal to autonomous complementors potential opportunities for value

creation and capture (Cusumano and Gawer 2002; Kapoor and Lee 2013). The platform

sponsor’s choice of scope is particularly critical at the initial stage to attract participation and

ensure commitment from the autonomous actors to the “de novo ecosystem” (Dattée, Alexy, and

Autio 2018, 467; Hannah and Eisenhardt 2018; Autio and Thomas 2020). Prior research has

suggested that platform sponsors should choose their scope considering factors such as their

dependence on complementors (Cusumano and Gawer 2002), modular design attributes (Tiwana,

Konsynski, and Bush 2010) and the value proposition of the ecosystem (Adner 2017). Although

a useful guideline, these studies do not sufficiently emphasize that complementors are often

unknown ex-ante (Gawer 2011), a scenario particularly relevant for digital platforms. It is thus

not clear how platform sponsors should define their scope at the initial stage to attract potential

participants.

In this paper we ask: How does a digital platform sponsor’s choice of scope facilitate the

emergence of a platform ecosystem? To answer this research question, we base our arguments on

one strain of the knowledge-based theory of the firm, namely the problem-solving perspective

(PSP), which argues that “problem-solving effectiveness is key to superior organizational

performance” (Jeppesen and Lakhani 2010, 1016; Nickerson and Zenger 2004). The PSP posits

that the efficiency of the solution search, at its core, is dependent on the alignment between the

problem dimensions and the governance mode of the search process (Nickerson and Zenger

2004; Macher 2006; Nickerson, Yen, and Mahoney 2012). From this line of argument, the

platform sponsor as the focal economic actor seeks to efficiently solve a problem whose solution,

in the form of complements, creates value for the consumers and is a manifestation of the

commitment of complementors to the ecosystem. Our major premise is that a digital platform

ecosystem emerges when the platform sponsor stimulates an efficient search process through a

choice of scope that accords with the problem they seek to solve. We theorize that, on one hand,

problem dimensions shape the type of search process required to find solutions and, on the other

hand, the platform sponsor scope shapes the extent to which the sponsor can govern the search

process. Platform sponsor scope is comprised of (a) the set of activities that the sponsor chooses

to perform internally and (b) the extent to which the sponsor holds decision rights over the

complementors’ solutions. As the search process moves from being semi-directed by the sponsor

to being undirected, a corresponding reduction in the platform sponsor scope is required for the

search to be efficient and lead to the emergence of an ecosystem (see Figure 2 and theory below).

Our investigation identifies distinct pathways to survive the incipient stage and enable

ecosystem emergence. We adopt abductive reasoning (Mantere and Ketokivi 2013) and fuzzy set

qualitative comparative analysis (fsQCA) to arrive at various configurations of problem

dimensions and platform sponsor scope that lead to ecosystem emergence. Our analysis utilizes a

dataset of campaigns posted on a crowdfunding website to raise funds to launch digital

platforms. Using the fsQCA results and case knowledge, we identify configurations of problem

dimensions and platform sponsor scope for complementary innovation ecosystems, open-source

ecosystems, and information ecosystems. Complementary innovation ecosystems align a semi-

directed search process with a broad sponsor scope. Open-source ecosystems employ a new type

of search that we term as community-directed search with a moderate sponsor scope. Finally,

information ecosystems utilize an orchestrated-undirected search with a narrow sponsor scope.

Our paper makes a number of contributions: First, we shed light on the much-neglected

incipient stage and demonstrate both theoretically and empirically how platform sponsors can

facilitate the emergence of digital platform ecosystems. The framework we provide helps

visualize and better understand the alignment between the problem and scope, a novel set of

considerations to tackle the early-stage challenge of attracting participation to an unknown

platform. Second, in extending the problem-solving perspective to the platform literature and

accordingly shifting the analytical lens from the actors to the problem, we overcome the

difficulty in examining emergence of ex-ante unknown complementors in the ecosystem (Gawer

and Cusumano 2014). In doing so, we also demonstrate how micro-level aspects such as

problem and scope have broader ecosystem-level implications, a finding that can be beneficial to

study broader digital strategy issues. Finally, we bring a configurational approach with abductive

reasoning to the study of digital platforms by developing several propositions for the successful

emergence of incipient ecosystems. The configuration of problem dimensions and platform

sponsor scope highlights equifinality in reaching the outcome and identifies multiple pathways

for successful ecosystem emergence. Here, we empirically identify a distinct solution search

process, i.e., community-directed search, that complements the search processes highlighted in

the PSP literature.

Emergence of Digital Platform Ecosystems

The fundamental tenet of value creation in platform ecosystems is the platform sponsor co-

creating value with autonomous complementors (Ceccagnoli et al. 2012; Kapoor 2018). With the

participation of complementors and availability of valuable complements thereof, consumers are

attracted to consume the ecosystem offerings. Such a positive loop of attraction of actors across

the different sides of the platform drives overall participation and leads to the emergence of an

ecosystem of complementors and consumers around the platform (McIntyre and Srinivasan

2017; Gawer 2014). In the broader ecosystems literature, it has been argued that platform

sponsors can attract participation by identifying a compelling blueprint (Iansiti and Levien 2004)

or value proposition (Adner 2017), balancing cooperation and competition tensions (Hannah and

Eisenhardt 2018) and producing a few complements in-house (Schilling 2002). However, these

strategies may be insufficient when the platforms are built on digital technologies that can

support a variety of visions and complements (Dattée, Alexy, and Autio 2018).

With digital technologies typically characterized by a modular platform architecture,

which allows a diverse set of actors to develop their own products over the platform with little or

no coordination (Zittrain 2005; Cennamo and Santaló 2019; Baldwin and Clark 2006; Tiwana,

2013), digital platforms can be best characterized as a context of distributed assets. The modular

nature of such technologies makes it impossible for a single firm to conceptualize, modify or

extend the technologies in-house to produce all variants of value-enhancing complements.

Importantly, in this context, not only are complementors unknown ex ante but also their

complements are unknown ex ante to the platform sponsor, a condition of unknown unknowns

(Tajedin, Madhok, and Keyhani 2019). The digital context has close similarities to that

characterizing the knowledge setting where “knowledge of the circumstances of which we must

make use never exists in concentrated or integrated form, but solely as the dispersed bits of

incomplete and frequently contradictory knowledge which all the separate individuals possess”

(Hayek 1945, 519). Whereas the economic problem in the knowledge context is to find the best

way to utilize knowledge “not given to anyone in its totality” (Hayek 1945, 520), that in digital

platforms is to find the best way to utilize assets not owned by any single firm in totality, but

rather affiliated with a platform (Hagiu and Wright 2015). Thus, digital platform ecosystems

readily lend themselves to the lens of knowledge-based theories to analyze value creation.

One recent and increasingly prominent strain of the knowledge-based theory of the firm

argues that a focal firm’s effectiveness in problem-solving is vital for superior organizational

performance (Nickerson and Zenger 2004; Nickerson, Silverman, and Zenger 2007; Jeppesen

and Lakhani 2010). The central theme of the problem-solving perspective (PSP) is that the focal

economic actor seeks to solve a problem but is unable to do so efficiently by itself due to

limitations of resources, time, and cognition. Consequently, the actor engages in solution search

in close proximity or at a distance, the choice of which is based on the problem and solution

landscape (Afuah and Tucci 2012; Nickerson and Zenger 2004; Macher 2006). Since the search

for solutions can be afflicted by hazards, such as actors misguiding the search for their own

benefit or misappropriating the value created through solutions, the focal actor chooses a

governance mode that mitigates hazards to facilitate efficient search and value creation.

In the digital context, the problem to be solved constitutes finding valuable complements

that enhance the overall value of the ecosystem. By analyzing the problem as the unit of analysis

from the point of view of a focal actor (Nickerson and Zenger, 2004), the problem-solving

perspective can help overcome a major hindrance in studying emergence of ecosystems – the

difficulty in assessing and following ex-ante unknown complementors and users who are vital

for ecosystem emergence (Gawer and Cusumano 2014). We can assess ecosystem emergence

from the platform sponsors’ perspective by studying the efficacy of problem-solving, which

occurs through the contributions of complementors and thereby participation of consumers.

Thus, analyzing how valuable solutions to a problem, in the form of complements, may be found

leads to assessing the emergence of the ecosystem.

Figure 1 summarizes our theoretical framework. We develop our arguments to explain

the emergence of digital platform ecosystems based on the problem-solving perspective. As we

detail in the following sections, the platform sponsor choice of scope should be aligned with the

problem for an efficient search for valuable complements. We identify configurational

alignments among the different dimensions of problem and scope that manifest as pathways to

successful ecosystem emergence.

--------------------------------

Insert Figure 1 about here

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Problem Solving in Digital Platform Ecosystems

The problem dimensions shape the type of search process required to find solutions (Macher

2006). The problem-solving perspective matches search process with governance forms that

support efficient solution search (see Figure 2). When the problem can be solved independently

by diverse actors without direction from the focal actor, it is more efficient to use an undirected

search for a greater reach (Nickerson and Zenger 2004). Undirected search is a process where

independent actors “sequentially alter one solution design choice at a time, observe whether the

solution value improves or declines in response and then update accordingly” (Felin and Zenger

2014, 916). Such a trial-and-error-driven undirected search that is decentralized is best supported

using a market form of governance (Nickerson and Zenger 2004). Markets use a discovery

process to access multiple agents and mobilize their dispersed knowledge (Hayek 1945) and are

best suited to discover problem-solution pairs (von Hippel and von Krogh 2015) or solve

uncertainty problems where the focal actor does not know what to look for beforehand.

--------------------------------

Insert Figure 2 about here

--------------------------------

In contrast, when the problem is complex and solution design choices have

interdependencies that are poorly understood, direct feedback from independent trial and error is

less useful. Such a problem requires a central actor or group of actors to take a more holistic

approach to “assemble relevant knowledge, then recombine it, and then compose a theory” that

can drive the search (Felin and Zenger 2014, 917). Here, the solution may need to be found in

proximity to the cognitive landscape of the focal actor to allow the use of their heuristics or

theories (Nickerson and Zenger 2004). A hierarchical governance form can best support such a

centrally directed search process. Such a search is suitable to solve uncertainty problems that

have a known starting point in the form of theories, consensus, or heuristics.

In sum, the extreme positions of a centrally directed search and an undirected search are

governed efficiently using the hierarchy and market forms of governance respectively. Next, we

theorize about the search processes between the two extremes, building on problem-solving in

platform ecosystems.

Digital platform ecosystems are argued to offer organizational efficiency relative to other

forms with their ability to solve uncertainty problems while using a discovery procedure that is

orchestrated for the benefit of the focal firm. Here, the “distributed knowledge of a mass of

outside participants is leveraged” and augmented with the firm’s knowledge (Tajedin, Madhok,

and Keyhani 2019, 339). When the focal actor knows the problem, the ecosystem facilitates

finding a solution by broadcasting the problem to a diverse set of actors and enabling efficient

access to cognitively distant knowledge sets (Afuah and Tucci 2012; Jeppesen and Lakhani

2010). Since the starting point of “what to look for” is known, the search process is termed to be

semi-directed (Figure 2), with the market elements employed only on the demand side of

economic value creation. Unlike the centrally directed search process where the relevant

knowledge is assembled by the focal actor, the semi-directed search process involves

“broadcasting the problem in hopes that those with valuable information or valuable solutions

will reveal themselves” (Jeppesen and Lakhani 2010; Felin and Zenger 2014, 917). The semi-

directed search also differs from the undirected search as the discovery procedure is constrained

by the known problem definition.

In contrast, when the focal actor does not know the problem to be solved, then market

elements are employed on both the supply and demand sides of economic value creation. We

term such a search process as orchestrated yet undirected (hereafter referred to as orchestrated-

undirected) search (Figure 2). The search process no longer relies on the focal firm’s knowledge

to define the problem or assemble relevant knowledge. Consequently, the search process is

undirected where the “supply side crowd focuses on a variety of problems specified by the

demand side crowd” (Tajedin, Madhok, and Keyhani 2019, 330). In this scenario, the platform

sponsor’s role is to facilitate the market matching mechanisms through the platform while

orchestrating the search indirectly for their own benefit.

Problem and Scope Alignment

We argued above that digital platform ecosystems employ semi-directed and orchestrated-

undirected search processes to find valuable solutions (Tajedin, Madhok, and Keyhani 2019).

The problem-solving perspective suggests that the search process to solve a problem is efficient

when it is governed by the right governance mode (Nickerson and Zenger 2004). In digital

platform ecosystems, for an efficient search the platform sponsor as the focal actor should have

the required latitude in the governance of the search process. As we detail below, the platform

sponsor scope choices shape the extent to which the sponsor has latitude to govern the search

process within ecosystems. The semi-directed and orchestrated-undirected search processes of

the ecosystem are efficiently governed when the platform sponsor has a broad and narrow scope

respectively (Figure 2). Specifically, we contend that, when the problem requires the platform

sponsor to direct the search, their scope should be broad enough to have latitude over a greater

range of activities and assets. The semi-directed search process requires that the platform sponsor

retain latitude in governance to define the problem, broadcast the problem for a solution search

and select the suitable solution. Thus, the platform sponsor should retain a broad scope to define

the problem as well as absorb the solution complements into existing offerings.

In contrast, when the search process tends to require less direction from the platform

sponsor due to the nature of the problem, their scope should be correspondingly narrower for the

search to be efficient. The limited role of the platform sponsor in facilitating the market matching

mechanisms in an undirected search implies that they choose to retain a narrow scope of value

creation activities and limited latitude in governing the search process. This arrangement of

limited governance may be more conducive to serendipitous discovery of problem-solution pairs

(von Hippel and von Krogh 2015). However, the platform sponsor as the designer of the market

has the ability to orchestrate (Boudreau and Hagiu 2009; Choudary, Alstyne, and Parker 2016)

the search process indirectly for their own benefit (Helfat and Raubitschek 2018).

In sum, platform ecosystems offer a middle ground---i.e., between undirected and

centrally directed---in efficient search processes, such that a known problem can be solved using

semi-directed search whereas an unknown problem may be solved using an undirected but

orchestrated search process. The search is efficient when the problem aligns with the platform

sponsor scope, such an alignment depicting attractive opportunities for value creation and

capture to the potential complementors and consumers. An efficient search would find valuable

solutions in the form of complements, which is a manifestation of the attraction as well as

commitment of complementors and consumers to the ecosystem. Thus, our major premise is that

a digital platform ecosystem emerges when there is an alignment between the problem and

platform sponsor scope.

In the following sections we explicate the elements constituting the problem and platform

sponsor scope. Then, using abductive reasoning we identify practically relevant configurations of

these elements associated with successful ecosystem emergence. We then specify our minor

premises that bring more granularity to our argument of alignment between problem and scope.

Digital Platform Sponsor Scope

The scope of the firm is a major strategic decision for firms and its impact on firm performance

has long been considered a critical issue in strategic management research, with much

scholarship having been dedicated to identify factors that the focal firm has to consider in

making this key decision (Ahuja and Novelli 2017). The choice of firm scope shapes firms’

strategies, likelihood of survival, performance outcomes and its competitive environment

(Zenger, Felin, and Bigelow 2011).

Broadly speaking, there are two aspects defining firm scope: external scope, which refers

to the choice of products and markets in which the firm chooses to compete, and internal scope,

which refers more specifically to which value creation activities the firm chooses to retain within

its boundaries. For our purposes we are more concerned with internal scope. Retaining activities

within their boundaries facilitates firms to maintain control over decision-making regarding

those criteria that, in the ecosystem context, enables them to facilitate coordination with other

actors (Boudreau 2010; Tiwana, Konsynski, and Bush 2010; Tiwana 2013). However, such

hierarchical control is not the only avenue available to firms. An alternate avenue for control

over decision-making is through contracts with complementors, which assigns them decision

rights over select aspects of complements and complementors’ actions, such as timing of release

and integration of the complement with other offerings on the focal platform. Our treatment of

the term scope incorporates both aspects of broader control and more selective decision-rights.

Early work referred to platform scope as the platform sponsor’s choice of which

complements to make internally and which to leave to autonomous complementors (Cusumano

and Gawer 2002). More recent studies have adopted broader definitions of platform scope as the

role played by the platform in the digital markets they enable (Cennamo 2019) and the “vision

that defines the ecosystem value proposition” (Dattée, Alexy, and Autio 2018, 467). We refer to

platform sponsor scope as constituting the activities that the sponsor chooses to perform

internally and the extent of decision rights over complements. Our conceptualization of platform

sponsor scope as the activities a firm chooses to engage in encompasses prior definitions because

at a more granular level activities are what ultimately underpin the delivery of the value

proposition. At the same time, by considering decision rights of complements we address the

firm scope issue from the perspective of control (Boudreau 2010; Gawer and Henderson 2007;

Gawer 2014), which is vital for a collaborative arrangement.

Platform Sponsor Activities

Consumers derive value from both platform offerings as well as complements. Consequently,

value creation activities are performed by both the platform sponsor and the complementors

(Adner and Kapoor 2010). The distribution of value creation activities between the platform

sponsor and complementors manifests in the ecosystem structure (Adner 2017), wherein actors

undertake activities to materialize the value proposition by assuming distinct positions within the

ecosystem. Since value propositions often not fully known ex ante (Dattée, Alexy, and Autio

2018), platform sponsors choose at the outset which activities to perform internally depending on

their resource configurations. Such a choice of activities to perform internally is in effect the

choice of the platform sponsor scope. As Adner (2017) rightly highlights, the scope decision puts

forth a “vision of structure and roles [to which] others defer” (p. 48). Thus, the platform

sponsor’s agency in choosing its scope to materialize the value proposition or solve the focal

problem is a first step for the rest of the ecosystem structure to emerge.

The platform sponsor’s choice of activities to perform internally is fundamental at the

initial stage in order to attract complementors and consumers and ensure their commitment to the

de novo ecosystem (Dattée, Alexy, and Autio 2018; Hannah and Eisenhardt 2018). The platform

sponsor’s activities signal its vision for the future ecosystem and the digital market in terms of

how value may be created and the kind of complementors that can participate on the platform.

When complementors and consumers perceive these signals as beneficial, they choose to

participate on the platform that then ultimately leads to the emergence of an ecosystem.

Furthermore, the platform sponsors’ activity choices define the kind of interactions that are

available to prospective complementors on the platform and thereby shapes the type of market

the platform enables (Cennamo 2019; Hagiu and Wright 2019; Jerath and Zhang 2010).

Complement Decision Rights

In addition to defining their scope in terms of value creation activities, platform sponsors can

expand their scope through assuming decision rights over the complements. When they have

decision rights over the complements, the platform sponsors can better control the quality,

variety, and timing of release of the complements and thereby improve their competitive position

(Wareham, Fox, and Cano Giner 2014; Cennamo and Santalo 2013). We contend that the

platform sponsors make a strategic decision about the complement decision rights similar to the

choice of value creation activities to perform internally. The platform sponsors’ authority over

the complement decision rights signals the extent of control complementors would have on their

contributions to the ecosystem and the opportunities for value capture. The potential for value

capture is particularly important in the initial stages to attract participation of complementors.

Platform sponsor scope expansion through complement decision rights may occur

through arrangements such as quality control and review procedures (Wareham, Fox, and Cano

Giner 2014) as well as when the complementors cede complete control of the complements after

producing them, such as in crowdsourcing and innovation contests (Felin and Zenger 2014). In

ecosystems aimed at producing open source hardware and software, the decision rights of the

platform offerings and the complements resides within the community of ecosystem participants

(Jeppesen and Frederiksen 2006). In ecosystems where the sponsor is more like a market

intermediary, the decision rights of the complements remains with the autonomous

complementors (Thomas, Autio, and Gann 2014; Hagiu and Yoffie 2009). In sum, there exists

heterogeneity in who holds the complement decision rights within the ecosystem, which

contributes to alternative platform sponsor scope choices.

Overall, the platform sponsor scope choice shapes the sponsor’s latitude to govern the

search process and, more broadly, the ecosystem. Ecosystems are governed to foster

complementary innovation but “appropriately bound [the] participant behavior” to result in

coherent value propositions (Wareham, Fox, and Cano Giner 2014, 1195). Platform sponsors

govern the ecosystem using strategies such as controlling the core platform modules opened to

complementors (Boudreau 2010; Parker and Van Alstyne 2017), restricting the variety of

complements (Wareham, Fox, and Cano Giner 2014) and selectively incentivizing specific

behavior and products over others (Rietveld, Schilling, and Bellavitis 2019). Such strategies can

be implemented when the platform sponsors have access and control over the corresponding

parts of the value creation process. However, the platform sponsor’s scope choice limits their

access and control, and therefore their latitude to govern, to the value creation activities they

choose to perform internally or to the complements they control.

Problem Dimensions

So far, we have argued that efficient search processes in ecosystems are those where the scope of

the platform sponsor is in accordance with the search process. Facilitating an efficient search

process is particularly important in the early stages of the ecosystem as the platform sponsor as

the entrepreneur tries to discover valuable entrepreneurial opportunities in problem-solution pairs

or at least increase the likelihood of discovering such opportunities (Hsieh, Nickerson, and

Zenger 2007). In this regard, prior research has established that search processes are shaped by

problem dimensions such as structure, context, and complexity (Felin and Zenger 2014;

Nickerson and Zenger 2004; Macher 2006). Thus, we now turn to examine how the different

problem dimensions shape search processes (summarized in Figure 3) in platform ecosystems.

--------------------------------

Insert Figure 3 about here

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Problem Structure

The PSP posits that the solution to a problem is a process of searching for relevant knowledge

sets within a solution landscape (Macher and Boerner 2012; Jonassen 2004). Problem structure

refers to the level of understanding of the interdependencies among knowledge sets and the

availability of formalized processes to reach the solution (Macher 2006). Problems can vary

along a continuum from ill-structured to well-structured, well-structured problems being “those

with well-defined initial states and known elements, explicit approaches for solving, and

accepted end states [whereas] ill-structured problems have poorly defined initial states and

indefinite problem-solving approaches” (Macher 2006, 828). The underlying principle here is

that, when knowledge set interdependencies are well understood, then formalized problem-

solving processes exist that allows the search process to proceed undirected using independent

trial and error-driven feedback mechanisms. In contrast, when the knowledge set

interdependencies are not fully understood, a centrally directed search process driven by the

central actor’s heuristics and theories to fulfill the lack of formalized problem-solving processes.

Extending the above logic to digital platform ecosystems it follows that the problem is

well-structured when formalized problem-solving processes exist within the ecosystem and the

complementors and sponsor recognize initial and accepted end states. Here, the actors operate

independently within the formal processes, a setup that is conducive for an undirected search

involving second-order uncertainty since “all relevant knowledge sets are included and the path

to high value solutions is clear” (Macher 2006, 829). The platform ecosystem as a firm-designed

market (Tajedin, Madhok, and Keyhani 2019) then enables matching and finding problem-

solution pairs among the available knowledge sets. Marketplaces such as eBay are examples of

such ecosystems where the platform matches buyers and sellers of pre-defined products and uses

formalized processes to complete the transactions. Additionally, the platform sponsor

orchestrates the market to ensure enough participation on all sides as well as efficient and

reliable transactions among the participants. Thus, well-structured problems are efficiently

solved using an orchestrated-undirected search where the platform sponsor retains a narrow

scope (Figure 3) to facilitate interaction between the supply and demand sides of the platform.

In contrast, when problems are ill-structured in platform ecosystems, actors may have

poorly defined initial states and indefinite problem-solving processes. The platform sponsor as

the central actor is required to guide the solution search by providing heuristics about the

“probable consequences of search decisions” (Macher 2006, 829). The search guidance may

involve the platform sponsor selecting or specifying the problems that are likely to be more

valuable. The platform sponsor may also need to provide guidance on the accepted end state of

the solution by defining conditions of valuable solutions and their absorption into the ecosystem

(Tajedin, Madhok, and Keyhani 2019). In sum, the platform sponsor plays a larger role of

identifying the problem as well as absorbing the solution. Hence, ill-structured problems can be

solved efficiently using a semi-directed search process where the platform sponsor retains a

broader scope (Figure 3). Innovation platforms of video games are examples of such ecosystems

where the console manufacturers control and orchestrate the game developers using

technological bounds and procedural constraints (Cennamo 2018; Ozalp and Kretschmer 2019).

Problem Complexity

Solving a complex problem involves “highly interdependent elements, choices, and knowledge

sets that must be creatively recombined to compose valuable solutions” (Felin and Zenger 2014,

916). Problem complexity is related to problem structure yet distinct from it. Whereas problem

structure relates to the level of understanding of the interdependencies among knowledge sets,

problem complexity relates to the magnitude of interdependencies between knowledge sets

(Macher and Boerner 2012; Macher 2006). Consequently, as complexity increases, knowledge

transfer and interaction between the actors holding dispersed knowledge sets becomes more

costly. However, it is possible to economize on knowledge transfer costs by choosing an

appropriate communication channel. Communication channel bandwidth refers to the “degree of

intensity of communication among individuals” and can vary between a more intense high

bandwidth channel and less intense low bandwidth channel (Heiman and Nickerson 2004, 404;

Hsieh, Nickerson, and Zenger 2007). Since a complex problem involves higher

interdependencies, the central actor’s “cognitive evaluations of the probable consequences of

particular [search] decisions” are required, which is enabled by greater communication

bandwidth (Macher 2006, 830). In contrast, a simple problem with limited interdependencies

can rely on low bandwidth trial-and-error feedback to guide solution search, thereby making an

undirected search more efficient.

Extending the above argument to the case of digital platform ecosystems, it follows that

when the problem is complex there would be high levels of interdependencies between the

platform and the complements as well as among complements. Thus, for a coherent solution to

take shape, the interdependencies should be resolved through extensive knowledge sharing and

communication. The platform sponsor is required to facilitate such knowledge sharing and high

bandwidth communication channels in addition to cognitive evaluations of potential solutions.

However, in an ecosystem the emphasis is to reach beyond local knowledge sets to diverse

complementors. Hence, a semi-directed search where the platform sponsor balances the need to

direct the search whilst using market mechanisms to alleviate knowledge constraints is most

efficient to find valuable solutions. The need for cognitive inputs from the platform sponsor

during the search process implies that the sponsor should retain a broad scope (Figure 3). For

example, platform sponsors like Apple enable solving complex problems through the

development of apps by providing guidance on their platform, evaluating apps for performance,

and enabling wider reach to diverse developers through easy-to-use software development kits.

In contrast, simple problems entail a lower magnitude of interdependencies between the

platform and complements as well as among complements. The actors within the ecosystem may

not need extensive knowledge sharing in developing their offerings and thus require only low

bandwidth communication channels. Consequently, the complements are often not only highly

fungible within the ecosystem but also can be made available on competing platforms, a scenario

termed multi-homing (Cennamo, Ozalp, and Kretschmer 2018). In terms of problem-solving, the

solution search in such a scenario needs little guidance from a central actor and can thus proceed

undirected but orchestrated to achieve solutions that benefit the sponsor and ecosystem at large.

The limited role of the platform sponsor allows them to retain a narrow scope (Figure 3).

Marketplaces like eBay are examples of ecosystems where sellers seek to sell largely

standardized products and often sell same products on multiple marketplaces simultaneously.

Problem Context

Recent work in the PSP literature has argued that the problem context shapes the cost of

experimenting to find valuable solutions and has implications for the search process (Furr,

Nickerson, and Wuebker 2016; Nickerson, Silverman, and Zenger 2007). A high cost to

experiment implies that the search for solutions requires costly resources or longer periods of

time, resulting in a more constrained problem-solving process. Whereas cost of experimentation

is vital, especially in the early stages, problem context can also include other factors that

constrain problem-solving, such as the regulatory environment, that in effect increases the costs.

The focal firm’s choice of the problem implicitly includes the context since that often cannot be

changed. Thus, similar to the other problem dimensions discussed above, the context should be

considered in choosing the governance form for efficient solution search.

A high-cost problem context involves costly experimentation to find valuable solutions.

In such a context, the actors would plausibly prefer to invest their resources judiciously in design

choices that could yield potentially valuable solutions. When the problem context is resource

intensive or more constraining, and thus costly, then outcomes of experimental search are viewed

through a risk averse lens (Furr, Nickerson, and Wuebker 2016). However, identifying what

constitutes a valuable solution requires knowledge from prior trials and a broader vision of

competing solutions, or simply put heuristics and theories. In digital platform ecosystems,

solving a problem in a high-cost context would deter complementors from investing in

developing complements around the de novo platform. The platform sponsor’s guidance on the

design choices for potentially valuable complements are key to overcome such deterrence in

experimentation. Thus, the platform sponsor would require a broad scope to provide such

guidance to aid the search (Figure 3). At the same time, the platform sponsor should allow room

for innovation from diverse actors. The balance between guiding the search and fostering

innovation (Boudreau 2010; 2012) is attained using a semi-directed search process in platform

ecosystems (Tajedin, Madhok, and Keyhani 2019).

In contrast, a low-cost problem context is less expensive to experiment and find valuable

solutions. Here, the problem solvers can perform independent trial-and-error based search and

rely on feedback from their own trials to proceed with the search process, a scenario of

orchestrated-undirected search. In digital platform ecosystems, a low-cost problem context could

attract diverse set of complementors to experiment since the downsides are not significant.

Moreover, problems with second-order uncertainty may be more conducive for such a search

since platform sponsors’ guidance is not required to identify valuable problem-solution pairs.

Hence, the platform sponsor may choose to retain a narrow scope (Figure 3).

In sum, each of the problem dimensions shapes the extent to which the search for

solutions requires guidance and direction of the platform sponsor and consequently the choice of

the search process. However, the problem dimensions and the corresponding search process are

rarely dichotomous and, as depicted in Figure 2, we expect that the search process varies over a

continuum with centrally directed and undirected as the two ends.

Analytical Approach

To reiterate, each of the problem dimensions impacts some aspect of the solution search process.

However, and importantly, the efficiency of the solution search depends not just on the focal

problem dimension but also on the other dimensions that co-occur. Even though a problem has

various distinct dimensions, such as structure, complexity, and context, these are interconnected

and occur together as they describe different facets of the problem. Whereas each problem

dimension may require a particular type of search process to find valuable solutions efficiently, it

is plausible that the problem dimensions may require conflicting search processes. For example,

consider a problem with high complexity in a low-cost context. Whereas high complexity would

suggest a semi-directed search to be efficient, a low-cost context allows for independent

experimentation and therefore an undirected search. In such scenarios, it is not entirely clear

which dimensions become important in determining the search process. Moreover, it is futile to

determine the relative importance of problem dimensions since they co-occur and may not be

effectively altered. Hence, it is necessary to consider the problem dimensions holistically in

determining the efficient search process for the problem.

Furthermore, it is essential to consider both the elements of platform sponsor scope –

platform sponsor activities and complement decision rights – in determining the most suitable

governance form for an efficient search. The two elements co-occur in the context of platform

ecosystems yet are distinct and shape the governance of the search process in different ways. The

platform sponsor’s choice of activities shapes the extent to which the sponsor can choose and

define the problem, facilitate knowledge sharing and communication, as well as select and

absorb solutions. The decision rights on complements defines who determines the value of

solutions and how those solutions would be absorbed into the value propositions.

As we detail in the following sections, to address the above issues we employ a

configurational perspective that relies on abductive reasoning as the mode of inquiry. The

configurational perspective enables us to analyze the problem dimensions and platform sponsor

scope elements more holistically, the alignment between the two for successful ecosystem

emergence being our major premise. The abductive reasoning mode helps identify minor

premises encompassing practically relevant configurations of the different elements of problem

and scope, a vital step to bring more granularity to our major premise.

Configurational Perspective

The configurational approach constitutes a holistic mode of inquiry examining

“multidimensional constellations of conceptually distinct characteristics that commonly occur

together” (Meyer, Tsui, and Hinings 1993, 1175). Fundamental to the configurational approach

is the focus on identifying complex causal relationships, in the form of patterns or profiles of

conditions related to an outcome of interest, rather than individual variables to identify the

combined effects of causal conditions (Meyer, Tsui, and Hinings 1993; Ragin 2009; Furnari et

al. 2020). In our context, using a configurational approach helps identify patterns of problem

dimensions and platform sponsor scope associated with successful emergence of ecosystems.

The configurational approach facilitates examining conjunctural causation of the various

causal conditions, equifinality in outcomes, and asymmetry in causal relationships. Conjunctural

causation is helpful to examine causal complexity where the outcomes “result from the

interdependence of multiple conditions” (Misangyi et al. 2017, 256) and thus the effect of a

condition may vary based on the other co-occurring conditions, such as our scenario of co-

occurring problem dimensions. Equifinality allows for the possibility that there can be “more

than one pathway to a given outcome” (Misangyi et al. 2017, 256), which is consistent with our

context where ecosystems emerge in many formats. Finally, asymmetry of causal relationships

allows the possibility that the values of a particular problem dimension in one configuration may

be unrelated in another configuration or may have very different values.

Abductive Reasoning

While a configurational approach “facilitates the exploration of complex models, however,

complex configurational models are difficult to specify a priori” (White et al. 2020, 6; Park and

Mithas 2020). Besides, as with most social phenomena, not all configurations are practically

possible and relevant, a scenario referred to as limited diversity (Ragin 2009). We use abductive

reasoning to elaborate the theory we have described so far by identifying data-driven practically

relevant patterns or configurations of the different problem dimensions and platform sponsor

scope that enable successful ecosystem emergence. Such an approach to theory building is

argued to be a “practical compromise of induction and deduction [that] more realistically

captures the authentic process by which theorizing occurs” (Shepherd and Suddaby 2017, 79).

Abductive reasoning involves forming a conclusion from known information or

“inference to the best explanation” (Kathuria, Karhade, and Konsynski 2020, 418) when the

“major premise is evident but the minor premise and therefore the conclusion are only probable”

(Merriam-Webster Dictionary, 2020). Abductive reasoning can transparently select the

configurations of interest from alternatives (Mantere and Ketokivi 2013; Van Maanen, Sørensen,

and Mitchell 2007). We employ abductive reasoning at two stages – first, we employ fuzzy-set

qualitative comparative analysis (fsQCA) as the empirical basis (Douglas, Shepherd, and

Prentice 2020; Ragin 2009) to reveal configurations of problem and scope elements. Second, we

abduct away (Kathuria, Karhade, and Konsynski 2020) from the fsQCA results and abstract to

generate propositions encompassing configurations of problem and scope elements associated

with successful ecosystem emergence. Such a two-staged process enables iterating between

theory and empirical evidence and progressively develop theory through abductive discovery.

Methods

fsQCA technique

In brief, fuzzy-set qualitative comparative analysis (fsQCA) seeks to identify configurations of

causal conditions that are related with the outcome of interest based on subset relations between

the two across multiple cases (Fiss 2011; Greckhamer et al. 2008; Ragin 2009; see Greckhamer

et al. (2008) for an in-depth explanation of fsQCA). Using set theory and Boolean minimization,

fsQCA can identify the combination of theoretically relevant attributes or causal conditions for

the occurrence or non-occurrence of the outcome of interest (Greckhamer et al. 2018). In our

context, the procedure helps identify the combination of different problem and platform sponsor

scope dimensions for the occurrence or non-occurrence of successful ecosystem emergence. As

we explain in the subsequent sections, the initial steps in the procedure involve selection of

theoretically relevant cases as samples and the calibration of their degree of membership in the

sets of the causal conditions and the outcome.

Research Setting

Our dataset comprises of crowdfunding campaigns to launch digital platforms posted on

Kickstarter, the largest crowdfunding site. This dataset is well-suited to answer our research

question for a number of reasons. As a repository of both successful and unsuccessful

campaigns, Kickstarter serves as an excellent source of counterfactuals, which is typically

difficult to find in the early stages of firms (Mollick 2014). The detailed campaign information

provides insight into how aspiring platform sponsors frame their perspective of the problem to be

solved and their choice of platform scope. Furthermore, it has been established that fundraising

campaigns on crowdfunding websites like Kickstarter also serve to validate the feasibility of the

idea and understand market potential (Short et al. 2017; Elia, Margherita, and Passiante 2020). In

our context, such validation signals the perception of potential complementors and consumers,

who as backers indicate their preferences through their funding pledges to the campaigns that

propose to launch the platform.

We selected our cases using the following criteria: First, we chose campaigns under the

category of ‘Technology’ and sub-categories of Apps, Web, Hardware and Software that were

active during 2016-17. The campaigns listed under the technology category are argued to have a

significant technological and scientific component and higher funding goals, both of which make

them attractive to professional investors (Roma, Messeni Petruzzelli, and Perrone 2017).

Furthermore, we selected only those campaigns that proposed to use digital technologies.

Whereas the apps and web categories clearly leverage the technologies of established digital

platforms (internet, Apple iOS, Android), the software and hardware categories were manually

verified to encompass generativity in their proposed technologies i.e., if they were modular and

extendable without affecting the core modules. Second, we selected campaigns that self-

described as a platform and met the accepted academic definition of a platform as enabling direct

interactions between two or more distinct sides and where each side is affiliated with the

platform (Hagiu and Wright 2015). Third, we selected campaigns that had at least ten backers.

Whereas successful campaigns under the Technology category are known to have an average

number of backers higher than this threshold (Mollick 2014), we chose the above threshold as it

allows us to capture both successful and unsuccessful campaigns yet include only those that are

substantial enough to gather the interest of at least ten distinct backers. We arrived at a dataset of

52 cases or campaigns based on the above criteria.

Measures

The standard fsQCA procedure involves transforming conventional measures (dependent and

independent variables) into fuzzy set membership scores by calibrating them against three

qualitative thresholds: full membership, the crossover point, and full non-membership. We set

thresholds for each measure, based on extant theory and substantial knowledge of the context.

Following Ragin (2009), we used the direct method of calibration available in the fsQCA

software. Table 1 summarizes the measures, the fuzzy sets, and their calibration thresholds.

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Outcomes

Campaign Funding Success

We measure campaign success as the ratio of funds raised to the funding goal of the campaign.

The primary objective of starting campaigns on Kickstarter is to raise funds in the form of

pledges from backers who are then rewarded for their contribution through early access to the

platform, products, or other such perks. However, the growing literature on crowdfunding has

established that in addition to raising funds, crowdfunding campaigns such as those on

Kickstarter enable entrepreneurs to conduct an open search for ideas (Stanko and Henard 2017),

engage backers in innovation and product development (Eiteneyer, Bendig, and Brettel 2019),

and collect information on the potential interest of consumers on the product (Viotto da Cruz

2018). A recent study has thus argued that crowdfunding campaigns help in the “transition from

an abstract idea to a concrete social entity” (Clough et al. 2019, 241; Soublière and Gehman

2020). Hence, with campaign funding success as an outcome variable, we are measuring not just

the entrepreneur’s success in raising funds but also the interest of potential consumers and

complementors, their willingness to suggest ideas and innovation and eventually the creation of a

social entity in the form of an ecosystem.

Kickstarter considers a campaign successful when it raises funds equal to or higher than

its goal. We calibrated membership in the set of successful campaigns using the following

thresholds: campaigns that raised funds equal to or higher than their set funding goal were coded

as “fully in” the set of successful campaigns; campaigns that did not raise any funds (i.e., 0% of

the set funding goal) were coded as “fully out” of the set of successful campaigns; and the

halfway mark was used as crossover point (i.e., campaigns that raised 50% of the set goal).

Ecosystem Emergence

Although Kickstarter campaigns are argued to serve the purposes of early validation, idea

generation, and creation of a social entity in addition to raising funds, we supplement our

analysis of campaign success with a different outcome variable that explicitly captures

ecosystem emergence. Ecosystem emergence is the outcome variable that captures if the

ecosystem came into being after and as a result of the Kickstarter campaign. We collected data

from announcements on Kickstarter and social media pages of the respective campaigns. The

ecosystem existence variable is coded 1 if the platform was launched and gained traction through

participation of complementors and consumers within one year of the campaign and coded 0

otherwise. We calibrated the set of ecosystem emergence using the following typical thresholds

of crisp sets: the campaign was coded as “fully in” when the score was at 1, “fully out” at 0, and

crossover point at 0.5 where the membership was neither fully in nor fully out.

Causal Conditions

Problem Structure: The understanding of interdependencies between components of the platform

and the complements manifests through the sponsors’ identification of initial states, problem-

solving approaches and end states that collectively contribute to the dimension of problem

structure. Since Kickstarter allows the campaigns to have free text describing their projects, we

captured the presence or absence of the above three elements ⎯ initial states, problem-solving

approaches, and end states ⎯ in the campaigns by posing questions that had binary responses

(Yes/No), as summarized in Table 2. We calculated the problem structure for each campaign as

the ratio of the sum of scores of the above questions to the maximum possible total score, i.e., all

questions receive ‘Yes’ response). We then calibrated the set of well-structured problem using

the following thresholds: the campaign was coded as “fully in” when the score was at or above

0.8, “fully out” at 0.4, and crossover point at 0.6 where the membership was neither fully in nor

fully out. We chose these thresholds because a score of 0.8 (4 of the 5 questions received ‘Yes’)

indicates that all three elements have been addressed as at least one question in each element has

received a ‘Yes’ response and a score of 0.4 (2 of the 5 questions received ‘Yes’) indicates at

least one element has not been addressed.

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Insert Table 2 about here

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Problem Complexity: The magnitude of interdependencies between the platform components and

the complements drives the nature of communication between actors and thus the corresponding

communication channel bandwidth. Following recommendation from Hsieh et al. (2007), we

operationalize problem complexity as the magnitude of bandwidth of the communication channel

proposed in the Kickstarter campaign to enable interaction among the platform sponsor and

complementors. We coded the campaigns using a four-value fuzzy set of high bandwidth

channel as follows: campaigns that sought to use in-person or face-to-face or phone calls were

coded as fully in this set ( = 1); campaigns that were based on open-source licensing and

proposed to follow open-source community practices were coded as more in than out in the set

(0.66); campaigns that proposed offering application programming interfaces (APIs) and design

manuals to complementors were coded more out than in the set (0.33); and campaigns that only

offered online transactions were coded as fully out of the set of high bandwidth channels (0).

Problem Context: The complementors engage in experimentation to find valuable complements

and thus encounter costs of experimentation that are shaped by the problem context. We

measured problem context as the least cost incurred by the complementors to access all available

features of the platform to experiment and produce complements. We calibrated membership in

the set of high-cost context using the following thresholds: Since according to Kickstarter the

average pledge amount across all categories is about $80 (Kickstarter 2019), we coded

campaigns that proposed to charge the complementors equal to or more than $80 as fully in the

set; campaigns that proposed to charge the complementors less than $20 as fully out of the set;

and campaigns that proposed to charge the complementors $50 as the crossover point.

Platform Sponsor Activities: One of the elements of platform sponsor scope relates to the

activities that the sponsor chooses to perform internally. The extent to which these activities are

organized within the firm reflects in the different types of markets that these platforms seek to

enable, namely information markets, multisided transaction markets and complementary

innovation markets (Cennamo 2019). Although digital platforms can encompass a combination

of these markets, the distinct types can help identify the distinct activities of the platform. In

enabling information markets, the platform serves as an “information channeling infrastructure

that enables the categorization and search of relevant information, and facilitates users’ exchange

of information and matching” (Cennamo 2019, 8). We identified that the activities underpinning

information markets are information exchange and matching or categorization of information. In

multisided transaction markets, the platform provides the “infrastructure to connect providers of

goods and services with final customers, and facilitate value-exchange transactions among them”

(Cennamo 2019, 6). Thus, the activities underpinning multisided transaction markets are trading,

matching demand and supply, and enabling competition. In complementary innovation markets,

the platform provides a “common assets’ infrastructure for innovation, making sure that

complementarity and product system integration are achieved ex ante” to enable the

complementors to extend the platform functionality (Cennamo 2019, 7). Consequently, the

primary activities underpinning complementary innovation markets are group-level coordination

of complementors to generate and commercialize innovation.

Overall, we identified the key activities underpinning each of the above three markets as

– information exchange, matching, trading, competition, and group-level coordination. When

most of these above-mentioned activities are encompassed within the platform rather than being

internal to the sponsor firm, then it follows that the platform sponsor has retained few activities

to be performed internally (Hagiu and Wright 2015) and therefore has a narrow scope. We coded

each campaign for the activities they propose to perform from the above list. The presence of an

activity was coded as 1 or 0 otherwise. We then calculated the platform sponsor scope as the

ratio of the sum of the activities performed by the platform to the maximum score of 5 when all

activities are proposed. Next, we calibrated the set of narrow scope of activities using the

following thresholds: the campaign was coded as “fully in” when the score was at or above 0.6,

“fully out” at 0.2, and crossover point at 0.4 where the membership was neither fully in nor fully

out. We chose the above conservative threshold for full membership in the set of narrow scope

of activities as the combination of any three activities requires the platform to forego control over

key parts of the value creation process.

Complement Decision Rights: The decision rights of the complements can remain with the

complementors, within a subgroup or community, or with the platform sponsors. The campaigns

on Kickstarter provide this information as it indicates the possible ways backers can capture

value in addition to the rewards laid out as part of the campaign. We coded the campaigns using

a four-value fuzzy set of platform sponsors’ decision rights on complements as follows:

campaigns that proposed to centrally hold decision rights over complements were coded as fully

in this set ( 1); campaigns where the decision rights of the complements resided within platform

sponsor-identified clusters or sub-groups were coded as more in than out in the set (0.66);

campaigns that proposed distributing the decision rights within the community were coded more

out than in the set (0.33); and campaigns that allowed the complementors to retain decision rights

were coded as fully out of the set of platform sponsors’ decision rights on complements (0).

Data Analysis

We used the standard fsQCA software 3.0 to perform our analyses. As a first step we sought to

identify any necessary conditions, which are the causal conditions that must be present for an

outcome to occur. We conducted necessity analyses of all conditions and their negation using the

recommended benchmark of 0.9 for consistency scores (Ragin 2009; Greckhamer et al. 2018).

We did not find any necessary conditions from our dataset. Next, we conducted sufficiency

analyses using the Quine–McCluskey algorithm to logically minimize from all possible

combinations. The results of the sufficiency analyses identified configurations of conditions

consistently linked to an outcome. Following recommended guidelines for an intermediate-N

dataset like ours, we chose a minimum frequency threshold for a configuration’s inclusion in

causal analyses as 1, which included 80% of our cases (Greckhamer, Misangyi, and Fiss 2013).

We applied a consistency threshold of  0.8 and a PRI (proportional reduction in inconsistency)

of  0.7, as recommended for analyses involving fuzzy sets. We performed the sufficiency

analyses for both the outcome and non-outcome using the same thresholds and cut-offs.

Results

Table 3a, 3b, and 4 summarize the results of the fsQCA analyses using the standard notation

(Ragin 2009) for the occurrence and non-occurrence of the outcomes. In each configuration, the

full circles indicate the presence of a condition, and the crossed-out circles indicate the absence

of a condition. Further, the larger circles indicate core conditions that occur in both the

parsimonious and intermediate solutions and thus indicate strong causal relationship. The smaller

circles indicate peripheral conditions that occur only in intermediate solutions and thus indicate

weak causal relationships (Fiss 2011).

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Insert Table 3a and 3b about here

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We report in Table 3a, 3b, and 4 standard measures of consistency, raw coverage, and unique

coverage for each of the configurations as well as overall consistency and coverage for the

solution formula. The consistency score is a measure of the number of cases consistent with the

outcome and is calculated as the ratio of number of cases that exhibit the configuration of causal

conditions and the outcome to the number of cases that exhibit the configuration of causal

conditions but not the outcome (Ragin 2009). The coverage score is a measure of the empirical

importance of a configuration and is calculated as the percentage of cases that follow a given

pathway to the outcome (Fiss 2011). Our results show that the overall solution consistency is

0.86 (coverage of 0.48) for the outcome of successfully funded campaigns and 0.78 (coverage of

0.36) for ecosystem existence, both exceeding recommended threshold for consistency scores.

We first present the various results, following which we derive select insights and abduct away

(Kathuria, Karhade, and Konsynski 2020) to offer propositions.

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Insert Table 4 about here

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Configurations of Successfully Funded Campaigns

Solutions represented in the columns of Table 3a represent the configurations related to the

outcome of successful campaigns. Solution 1a (consistency score of 0.87 and raw coverage of

0.14) shows that campaigns were successfully funded when they proposed to solve problems that

were not well-structured in a high-cost context as long as they also proposed to retain both a high

platform sponsor scope and decision rights over complements. Solution 1b (consistency score of

0.87 and raw coverage of 0.15) shows that campaigns were successfully funded when they

proposed to solve problems that were not well-structured and highly complex in a high-cost

context as long as the platform sponsor retained a broad scope of activities. In both solution 1a

and 1b the conditions of not well-structured problems and high-cost context are core conditions.

Solution 2 (consistency score of 0.92 and raw coverage of 0.22) shows that campaigns

were successfully funded when they proposed to solve problems that were highly complex in a

low-cost context when the platform sponsor scope of activities was broad, but the sponsor did

not exert decision rights over the complements. All the conditions appeared as core conditions.

Solution 3 (consistency score of 0.9 and raw coverage of 0.23) shows that campaigns were

successfully funded when they proposed to solve high complexity problems that are not well-

structured and combined them with distributed decision rights while retaining a high share of

activities. Finally, solution 4 (consistency score of 0.82 and raw coverage of 0.14) shows that

campaigns were funded when they proposed to solve well-structured and simple problems but in

a high-cost context as long as the platform sponsor retained a narrow set of activities.

Configurations for Successful Ecosystem Emergence

Solutions 5 to 7 in Table 3b depict the outcome of successful ecosystem emergence, as distinct

from successful funding. Solution 5 (consistency score of 0.78 and raw coverage of 0.235)

represents a configuration that is identical to solution 2 in Table 3a, where the outcome was

successful funding of the campaign. Solution 6 (consistency score of 0.77 and raw coverage of

0.25) is similar to solution 3 in Table 3a, the one difference being that the former depicts all

conditions as core conditions whereas the latter had all peripheral conditions. Finally, solution 7

is similar to solution 4 with an additional condition of the platform sponsor exerting decision

rights of complements as a core condition of the causal recipe. Such tight overlap of the solutions

across the two outcomes validates the argument that a successful funding may also indicate

progress in the emergence of the ecosystem.

Not successful campaigns

Table 4 summarizes the configurations related to the non-occurrence of the outcomes of

successful funding of campaigns and ecosystem emergence. Solution 8 (consistency score of

0.83 and raw coverage of 0.22) shows that campaigns were not successfully funded when they

proposed to solve problems that were well-structured and highly complex in a high-cost context

and the platform sponsor proposed to not exert decision rights over the complements. Solution 9-

13 (consistency score of 0.88 and raw coverage of 0.48) shows the configurations where the

campaigns failed to launch platforms and facilitate ecosystem emergence. These configurations

do not have any overlap with the configurations for successful funding and ecosystem emergence

depicted in table 3a and 3b. Furthermore, as we argued in our theory, the configurations in

solutions 9-13 of Table 4 depict a mismatch between the problem dimensions and platform

sponsor scope. Whereas solutions 11, 12, and 13 show recipes of the platform sponsor retaining a

narrow scope and exerting decision rights over complements, the problem dimensions move the

search towards a more semi-directed process, thereby creating a misalignment. Solution 10 also

suffers from a misalignment between a broad scope with complement decision rights and

problem dimensions requiring an undirected search process. In contrast, solution 9 suffers from a

misalignment between a narrow scope and problem requiring cognitive guidance of the sponsor.

Finally, we conducted a number of sensitivity analyses to examine the robustness of our

findings (results available on request). We considered alternative crossover points by varying the

crossover points for all measures by +/- 25 percent. Although minor changes appear in the

solution in the form of the number of solutions and sub-solutions, the interpretation of the results

remain unchanged indicating the robustness of the findings.

Pathways to Ecosystem Emergence

We expected an alignment between the problem dimensions and platform sponsor scope to

enable ecosystem emergence but relied on abductive reasoning to identify the exact

configurations of problem dimensions and scope. In the results section above, we discussed such

configurations where the campaign was successfully funded and led to ecosystem emergence as

well as those that did not receive sufficient funding and did not emerge as an ecosystem.

Following prior studies (Kathuria, Karhade, and Konsynski 2020), we now abduct away from the

individual configurations to offer generic propositions encompassing pathways to ecosystem

emergence. We employ a taxonomy of ecosystems as our baseline for interpreting the empirical

results and draw on substantial and case knowledge (Ragin 2009) to abstract from the

configurations and highlight alignment between platform sponsor scope and search process.

Complementary innovation ecosystems

Platform ecosystems have been exhaustively studied as innovation engines “providing the core

technological architecture other firms build upon to create new products that extend the core

functionality and reach of the platform to final users” (Cennamo 2019, 7; Gawer 2014).

Examples of such ecosystems include the Apple iOS ecosystem and SAP NetWeaver computing

ecosystem. The primary source of value in such platforms comes from the platform offerings that

are then enhanced by complementing products (McIntyre and Srinivasan 2017). As a result, the

platform sponsors play a wider role in the value creation process and exert considerable

influence on the solutions to the problems. Such broad scope of activities allows the platform

sponsors to better understand the interdependencies among the different modules and thereby

become better equipped to solve ill-structured problems. The interdependencies sometimes

requires that the sponsor retain decision rights over the complements for better absorption into

the value propositions. The search process in solving such problems would tend to be a more

semi-directed one that is aligned with a broad platform sponsor scope, as depicted in Figure 2.

Also, the platform sponsors can charge the complementors a higher price for allowing access to

the core infrastructure and reach to final users, which is often valuable to the complementors,

and thereby make the problem context more constrained for experimentation (Weyl 2010). These

observations are found in the configurations of solution 1a and 1b (Table 3a) in our results and

can be summarized as in the proposition below:

Proposition 1: The emergence of complementary innovation ecosystems is associated

with solving ill-structured, complex problems in a high-cost context where the

platform sponsor retains a broad set of activities to perform internally and holds

decision rights over the complements.

Open-source platform ecosystems

Comparing the configurations of solution 2 and 3 (Table 3a) as well as solution 5 and 6 (Table

3b) with the cases representing these configurations, we found that both these configurations

correspond to campaigns aimed at building open-source platforms. Digital technologies have

enabled the rise of open-source hardware and software platforms that foster open innovation,

which is a “distributed innovation process based on purposively managed knowledge flows

across organizational boundaries” (Chesbrough and Bogers 2014, 17). Examples of such

ecosystems include Mozilla and Linux open-source software ecosystems. Value creation in the

ecosystems around such platforms is dependent on the contribution of participating

complementors as well as on the management of these contributions to form a coherent and

valuable solution to the problem. Hence, a less-constrained problem context is fundamental to

such ecosystems so that complementors can experiment easily and contribute to the solution.

The platform sponsors retain a broad scope of activities to enable the creation of coherent

solutions from the contributions of complementors. Notably, the underlying premise of these

ecosystems is that the decision rights to the solutions rest within the community of contributors

and the solutions are often free for everyone to use (Bogers et al. 2017). Thus, the platform

sponsor scope is less broad than that in the complementary innovation ecosystem discussed

above. With such a scope choice, the platform sponsors open the core infrastructure and seek

solutions to complex ill-structured problems. We refer to the search process for solving such

problems as a community-directed search (Figure 2) that is less dependent on the sponsor yet not

undirected. Thus, as shown in the configurations of solution 2 and 3, open-source ecosystems

depict the problem and scope configurations as summarized in the following proposition:

Proposition 2: The emergence of open-source ecosystems is associated with solving

highly complex and ill-structured problems in a low-cost context where the platform

sponsor retains a broad set of activities to perform internally but does not exert

decision rights over complements.

Information ecosystems

The configurations of solution 4 (Table 3a) and solution 7 (Table 3b) in the results represent

cases that can be described as information ecosystems, where the platform primarily serves as an

“information channeling infrastructure that enables the categorization and search of relevant

information, and facilitates users’ exchange of information and matching” (Cennamo 2019, 8).

Examples of such ecosystems include Google’s search engine, LinkedIn, and Slack messaging

platforms. The primary sources of value in these ecosystems are: one, the platform infrastructure

that allows for a reliable and accurate matching of relevant information; and two, the information

that is often generated by the complementors of the platform. The complementors of information

ecosystems include, on one hand, advertisers and content creators who rely on the platform

infrastructure to match with the right target users and, on the other hand, the users who generate

information for other users on the platform. Since the complementors are engaged in solving the

problem of targeting the right users with right information and the platform sponsors provide the

tools to filter and match relevant information, the problem being solved is a well-structured one

with known initial and end states and formalized problem-solving procedures. Thus, the search

process may be undirected yet orchestrated by the platform sponsor (Figure 2).

At the same time, the sponsor retains control over the information generated to enhance its

matching tools and provide better targets to the complementors. In our terminology, this shows

that the platform sponsors exert decision rights over the complements. Though the platform

sponsors provide the core infrastructure for matching, they do not engage in activities of creating

the information or in disseminating them. Hence, the platform sponsors can retain a narrow

scope of activities and facilitate the complementors to perform other activities for value creation

(Figure 2). However, despite their narrow scope the platform sponsors can charge a high price

and constrain access to the platform, the information, and the users as they exert decision rights

on the overall solution. The above findings may be summarized in the following proposition:

Proposition 3: The emergence of information ecosystems is associated with solving

well-structured and simple problems in a high-cost context where the platform sponsor

retains a narrow set of activities to perform internally and holds decision rights over

complements.

Discussion

In this paper we demonstrated how a digital platform sponsor’s choice of scope vis-à-vis

complementors can facilitate the emergence of an ecosystem around its platform. The context of

digital platforms being similar to that of knowledge, we extended the problem-solving

perspective to explain how two distinct decisions – problem to be solved and platform sponsor

scope – have implications for the successful emergence of an ecosystem of complementors and

consumers. Since the configuration of problem dimensions influence aspects of the search

process, as the PSP suggests, governing such a search process suitably to overcome hazards of

value creation is essential to identify valuable solutions efficiently. However, the governance of

the search is constrained by the scope of the platform sponsor. Hence, we considered both

problem dimensions and platform sponsor scope in our analysis of ecosystem emergence. Using

a configurational perspective, we showed that an alignment between the problem dimensions and

the platform sponsor’s scope facilitates an efficient search and thereby attract autonomous

complementors and consumers, leading to the emergence of an ecosystem.

Our paper makes a number of theoretical and empirical contributions to both the platform

and the problem-solving perspective literatures. First, it sheds light on the underexplored topic of

how platform sponsors can facilitate the emergence of digital platform ecosystems in the

incipient stages. Through its distinct focus and logic, the paper complements the stream of

studies arguing that managers aspiring to build ecosystems must consider the benefits for

ecosystem users as a way of attracting them to participate (Gawer 2011; Thomas, Autio, and

Gann 2014). Our findings highlight a novel set of considerations ⎯ problem dimensions and

platform sponsor’s scope choices ⎯ to tackle the early-stage challenge of attracting autonomous

actors to participate and contribute to an incipient less-known platform ecosystem. Our

theoretical framework of alignment between problem and scope for efficient search, supported

by empirical evidence encompassing distinct configurations of these two dimensions,

demonstrates how platform sponsors can attract participation and thus enable ecosystem

emergence. We also contribute to the firm scope literature by demonstrating that, in the context

of digital platform ecosystems, selective decision rights over complements are a facet of scope

choice in addition to ownership.

Second, by establishing that the context of digital platforms is similar to that of

knowledge, we extend the problem-solving perspective to platform ecosystems. As we have

demonstrated in this paper, the problem-solving perspective is useful to examine how the micro-

level aspects of problem and sponsor scope link with broader ecosystem-level considerations

(Felin and Zenger 2014). The problem-solving perspective and problem as the unit of analysis is

helpful to address innate challenges in studying digital platform ecosystems and more broadly

digital strategy. For example, the difficulty in tracking ex-ante unknown complementors (Gawer

and Cusumano 2014) is a major reason why research on ecosystem emergence has remained

scant (Dattée, Alexy, and Autio 2018). The focus on the value proposition (Adner 2017) also

suffers from a similar drawback as it assumes that the value proposition is known ex-ante

whereas in most cases the value proposition evolves as more complements are offered to

consumers. We demonstrate that applying ‘problem’ as a unit of analysis in ecosystems helps

overcome the challenges in explaining emergence of ex-ante unknown actors in the ecosystem as

it is not required to know ‘who’ solves the problem, rather it is sufficient to know ‘what’ problem

is being solved. Our approach shows how we can study issues pertinent to digital strategy

without knowing or tracking these multiple actors.

Third, our study is among the few to employ abductive reasoning for theory development

(for a detailed discussion see Kathuria et al. 2020 and Shepherd and Subbady 2017) in

combination with a configurational approach (Misangyi et al. 2017). In empirically identifying a

new search process – community-driven search – we demonstrate the benefits of such an

analytical approach where abductive logic can refine and bring granularity to theory using

empirical evidence. By using a configurational approach to understand the implications of the

problem dimensions and platform sponsor scope choice, we established that there are multiple

pathways to successful emergence so long as there is an alignment between the problem and

scope. The result of equifinality in outcomes underscores that digital platform ecosystems can

emerge in multiple forms and types. Fundamental to our configurational alignment argument is

the inherent trade-offs that managers face at the outset in choosing their scope in accordance

with the problem dimensions. In identifying the configurations of the three distinct ecosystem

types, our study provides exemplars of such trade-offs and thereby the pathways to ecosystem

emergence. Furthermore, by employing an analytical strategy combining a configurational

approach alongside problem-solving perspective, we have demonstrated the importance of

studying different problem dimensions holistically as they shape the search process together, a

finding that has implications for the problem-solving perspective.

Our study is not without limitations. Firstly, we coded our measures based on the free-

form text of the campaigns. Although care has been exercised to use objective criteria for coding

the conditions, there is scope to use a dataset that provides more objective measures for problem

structure and platform sponsor scope to further test our arguments. Whereas the QCA technique

allowed us to handle the subjectivity involved in these measures, future studies may benefit by

considering the implications of subjective measures if a large sample is involved. Relatedly,

although we have employed a crisp set of successful ecosystem emergence outcome in addition

to the fuzzy set of campaign funding success, the outcomes may not fully capture the potential

complementors’ interest in the platform ecosystem as they are closely aligned to success in

raising funds through crowdfunding. Future studies can address this limitation by employing

objective measures of ecosystem emergence such as number of participants on each side of the

platform or number of complements. However, such attempts should employ measures that are

comparable across the different types of platform ecosystems under study. Furthermore, we

restricted our analysis of ecosystem emergence to one year after the end of the Kickstarter

campaign in order to capture the impact of problem dimensions and scope alignment proposed in

the campaign. Future work may examine these relationships over a longer period of time and in

particular examine the evolution of these configurations.

Secondly, although we expected an alignment between problem dimensions and platform

sponsor scope, we relied on fsQCA analyses to identify the ideal configurations. Consequently,

we did not find consistent configurations for multi-sided transaction platforms from our dataset.

A closer examination of the dataset revealed that the inconsistencies emerged from the apps and

web categories that had a very low-cost context. Future studies can explore the reasons for varied

problem configurations for multi-sided transaction platforms and the underlying reasons for the

inconsistencies in low-cost contexts. Thirdly, the scope of this study was limited to explaining

the scope decisions of platform sponsors of ecosystems that emerge as a deliberate action. Future

work can contrast such ecosystems with ones that emerge in a non-deliberate manner or as

communities. Furthermore, our study focused only on governance of search processes and thus

restricted itself to scope in terms of activities and decision rights. There is also room to consider

other governance aspects such as monetization methods, incentives, and subsidies. Finally, our

study focused on the early stage of ecosystem emergence. Future work can identify how these

ecosystems survive and grow over a period of time and how problem configurations and scope

choices change with time.

In conclusion, with the proliferation of platform ecosystems, there is a need for more

research that focuses on the incipient stage to better understand how platform sponsors can

enable the emergence of platform ecosystems. As we demonstrated, the problem-solving

perspective has immense potential to deepen insight into the strategic choices available to lesser

known platform sponsors and their implications. We see much promise in this stream of research

to advance theory at the intersection of platform ecosystems and entrepreneurship, as well as

provide practically relevant knowledge.

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Figure 1

Emergence of Digital Platform Ecosystems

Problem-solving and configurational perspective

Problem-solving perspective

Problem dimensions

1. Problem structure

2. Problem complexity

3. Problem context

Platform sponsor scope

Platform sponsor activities

2. Complement decision rights

Type of search

process

Governance of

search process

Configurational

alignment for

efficient search

Emergence of

digital platform

ecosystem

Alignment

for efficient

Configurational

Perspective

Figure 2

Search Process and Scope alignment

Scope of economic actor

Narrow

platform

scope

Broad

platform

scope

Markets

Community

directed

Semi-

directed

Centrally

directed

Hierarchies

Orchestrated

-undirected

Undirected

Solution search process

Complementary

innovation ecosystem

Open-source

ecosystem

Information

ecosystem

Figure 3

Problem dimensions, Search Processes, and Platform Sponsor Scope Alignment

Broad Platform

Sponsor Scope

Semi-directed

Narrow Platform

Sponsor Scope

Orchestrated-

undirected search

Well

structured

Problem Structure

Level of understanding of

interdependencies

Low

complexity

Problem Complexity

Bandwidth of

communication channels

Low-cost

context

Problem Context

Cost of experimentation

Ill structured

High

complexity

High-cost

context

Problem Dimensions

Table 1

Set Calibrations and Descriptive Statistics

Fuzzy Set Calibrations

Measure Descriptives

Measure / Fuzzy Set

Fully In

Crossover

Fully Out

Mean

Max

Min

Well-structured problem

0.8

0.6

0.4

0.72

0.19

0.25

High bandwidth channel

0.66/0.33

0.30

0.38

High-cost context

70.6

117.9

500

Complement decision rights

0.66/0.33

0.26

0.34

Narrow scope of sponsor activities

0.6

0.4

0.2

0.48

0.18

0.25

Table 2

Coding scheme for Problem Structure

Problem structure

elements

Questions (Yes/No)

Initial state

Is a gap/need identified?

Have all the sides of the platform been identified?

Problem-solving

approaches

Are the activities to be performed by each of the sides identified?

Does the campaign refer to existing/established platforms or

business models?

End state

Is a working solution or prototype provided?

Table 3a

Configurations of Successfully Funded Campaigns

Taxonomy of Digital Platform

Ecosystems

Complementary

Innovation Ecosystems

Open Source

Ecosystems

Information

ecosystems

Search process and platform

sponsor scope alignment

(Semi-directed search and

broad scope)

(Community-directed search

and less broad scope)

(Orchestrated-

undirected search

and narrow

scope)

Solution

Problem dimensions

Well-structured problem

High complexity

High-cost context

Platform sponsor scope

Complement decision rights

Narrow scope of activities

Consistency

0.87

0.92

0.90

0.82

Raw Coverage

0.14

0.15

0.22

0.23

0.14

Unique Coverage

0.02

0.01

0.04

0.01

0.21

Overall Solution

Consistency

0.86

Overall Solution Coverage

0.48

Note: Full circles indicate the presence of a condition. Crossed-out circles indicate the absence of a condition.

Large circles indicate core conditions and small circles indicate peripheral conditions.

Table 3b

Configurations for Ecosystem Emergence

Taxonomy of Digital Platform

Ecosystems

Open Source

Ecosystems

Information

ecosystems

Search process and platform sponsor

scope alignment

(Community-directed search and less

broad scope)

(Orchestrated-

undirected search

and narrow scope)

Solution

Problem dimensions

Well-structured problem

High complexity

High-cost context

Platform sponsor scope

Complement decision rights

Narrow scope of activities

Consistency

0.78

0.77

0.91

Raw Coverage

0.23

0.25

0.13

Unique Coverage

0.05

0.06

0.05

Overall Solution Consistency

0.78

Overall Solution Coverage

0.36

Note: Full circles indicate the presence of a condition. Crossed-out circles indicate the absence of a condition.

Large circles indicate core conditions and small circles indicate peripheral conditions.

Table 4

Configurations for Negation of Outcomes

Outcome

Not

funded

No Ecosystem existence

Solution

Problem dimensions

Well-structured problem

High complexity

High-cost context

Platform sponsor scope

Complement decision rights

Narrow scope of activities

Consistency

0.83

0.84

0.83

0.90

0.87

0.88

Raw Coverage

0.22

0.17

0.15

0.22

0.14

0.11

Unique Coverage

0.22

0.07

0.06

0.10

0.06

0.04

Overall Solution Consistency

0.88

Overall Solution Coverage

0.48

Note: Full circles indicate the presence of a condition. Crossed-out circles indicate the absence of a condition.

Large circles indicate core conditions and small circles indicate peripheral conditions.

Assessing Performance Impact of Digital Transformation for Instructors in the Covid-19 Era

Article

Full-text available

Dec 2022

Digital transformation has evolved as the main issue for higher education institutions (HEIs) across the globe due to the Covid-19 outbreak. The purpose of this study is to investigate the performance impacts of digital transformation for instructors of HEIs during the pandemic. The technology-to-performance chain (TPC) model pursues to predict the influence of an information system on the performance of an individual user. Hence, TPC model is used to evaluate the performance of instructors due to digital transformation in the institutions during the Covid-19 pandemic. The data is collected from instructors of higher educational institutions. Recently, partial least squares path modeling (PLS-PM) has found extensive application for causal information systems (IS) research. Hence, Smart-PLS is used to analyze the data. Five constructs (task-technology fit, social norms, facilitating conditions, utilization, and performance impacts) are used to develop the model. The results showed that task technology fit (TTF) has a significant effect on performance impacts whereas utilization does not have an influence on performance impacts. Facilitating conditions and TTF are found to have an influence on the utilization whereas subjective norms do not have influence. This study contributes to the knowledge on the factors that matter to instructors of HEI in improving their teaching productivity, communication, and coordination during the Covid-19 pandemic.

Platform Diffusion at Temporary Gatherings: Social Coordination and Ecosystem Emergence

Article

Full-text available

Aug 2020

Research Summary Software platforms create value by cultivating an ecosystem of complementary products and services. Existing explanations for how a prospective complementor chooses platforms to join assume the complementor has rich information about the range of available platforms. However, complementors lack this information in many ecosystems, raising the question of how complementors learn about platforms in the first place. We investigate whether attending a temporary gathering—a hackathon—impacts the platform choices of software developers. Through a large‐scale quantitative study of 1,302 developers and 167 hackathons, supported by qualitative research, we analyze the multiple channels—sponsorship, social learning, knowledge exchange, and social coordination—through which hackathons serve as a social forum for the diffusion of platform adoption among attendees. Managerial Summary A software platform such as Windows, iOS, or Amazon Web Services relies on third‐party developers to create applications that complement the platform and make it valuable for end users. However, developers face a wide range of possible platforms, and they may have limited information about which platforms would be worthwhile to develop for. A software platform business can educate and encourage developers to adopt their platform by supporting in‐person software development competitions, known as hackathons. Developers learn about prospective platforms that advertise at the hackathon. Developers also learn whether and how to use a platform by observing and teaching one other. Hackathons are particularly useful for spreading platform technologies: developers prefer to adopt widely‐used platforms, and hackathons permit developers to identify and join fashionable platforms.

Hybrid Context, Management Practices and Organizational Performance: A Configurational Approach

Article

Full-text available

Jun 2020

Employing a configurational approach we explore how “hybrid context” shapes organizations’ adoption, and performance implications, of management practice. We do because hybrid contexts have been a policy aim of many governments seeking to blurr the distinction between the public, private and not‐for‐profit sectors. To conceptualize hybrid contexts we employ the dimensions of market authority and (the multiple) political authority. Employing data from UK care homes, our findings suggest that: (i) the adoption, and performance effects, of management practices are conditioned by dimensions of hybrid context; (ii) there is significant variation across the configurations in terms of the mix of management practices that lead to high and low performance; and (iii) there is a high degree of symmetry between high and low performance, with good management practices being a necessary condition for high performing as compared to low performing organizations.

Organized Complexity of Digital Business Strategy: A Configurational Perspective

Article

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Jan 2020

Platform Strategy: Managing Ecosystem Value Through Selective Promotion of Complements

Article

Full-text available

Sep 2019

Platform sponsors typically have both incentive and opportunity to manage the overall value of their ecosystems. Through selective promotion, a platform sponsor can reward successful complements, bring attention to underappreciated complements, and influence the consumer’s perception of the ecosystem’s depth and breadth. It can use promotion to induce and reward loyalty of powerful complement producers, and it can time such promotion to both boost sales during slow periods and reduce competitive interactions between complements. We develop arguments about whether and when a platform sponsor will selectively promote individual complements and test these arguments on data from the console video game industry in the United Kingdom. We find that platform sponsors do not simply promote “best in class” complements; they strategically invest in complements in ways that address complex trade-offs in ecosystem value. Our arguments and results build significant new theory that helps us understand how a platform sponsor orchestrates value creation in the overall ecosystem.

In the Realm of Hungry Ghosts: Multi-Level Theory for Supplier Participation on Digital Platforms

Article

Jun 2020

Digital platforms have transformed various industries, with profound changes witnessed in settings characterized by repeated, low value, and novel transactions, such as ride sharing, household services, and food delivery. Platform providers need to understand the factor(s) that suppliers consider before choosing to participate on their platforms. We argue that multi-level theorizing is necessary to explain the patterns of decision criteria that constitute the complex, yet boundedly-rational decision of platform participation. We draw on multiple theoretical perspectives which include predictors from firm-level strategic behavior, firm-level digital predictors, institutional predictors, and platform level, competition and contextual (environmental) predictors to model restau-rant's decision to participate on food delivery platforms, which are an exemplar of platforms that digitize transactions of ubiquitous and epi-sodic nature. The population of 95,735 restaurants, serving a total of 135 different cuisines, located in the 37 largest cities of India, forms the dataset for developing our multi-level theory. Our decision tree induction methodology, which employs high levels of pruning, empowers us to discover context-specific rules that serve as credible approximations of the partial ordering of decision flows tacitly used by restaurants in deciding to participate on platforms. We identify six combinations of predictors, that is, decision rules, which are situated in at least four theoretical perspectives, to succinctly explain supplier participation on digital platforms. Finally, we abduct away from these specific decision rules to develop generalizable theoretical propositions. The decision trees, context-specific rules, general forms of the rules, and generalized propositions together form our multi-level theory for supplier participation on digital platforms. Our findings aid platform providers identify suppliers who can be the focus of efforts to increase platform participation , and help suppliers identify the participation status of competitors. For policymakers, our findings imply that incentives at the ends of the pricing spectrum can increase supplier participation on digital platforms.

Capturing causal complexity: A configurational theorizing process

Article

May 2020

Multisided Platforms as New Organizational Forms

Article

May 2020

Multi-sided platforms (MSPs) have recently drawn considerable research attention, as the influence of platforms is increasingly relevant across a wide array of industries. However, despite theoretical and empirical advances spanning multiple disciplines, several core questions remain about MSPs. This article offers novel insights around platform business models, platform scale and scope, characteristics of platform “sides”, and the role of technology in platform evolution. We hope that these insights provide a useful framework for integrating and advancing various perspectives on the emergence and management of MSPs.

Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship

Article

Jan 2020
J BUS VENTURING

Entrepreneurship theory has largely been developed and tested using symmetrical correlational methods, effectively describing the sample-average respondent and subsuming individual differences. Such methods necessarily limit investigation of asymmetries that are evident in entrepreneurship, and provide only a single explanation that belies the multiple pathways to entrepreneurship observed in practice. This paper employs a case-based approach—fuzzy-set Qualitative Comparative Analysis (fsQCA)—to identify configurations of antecedent attributes of individuals in groups within samples, thereby revealing asymmetries and multiple entrepreneurial pathways that are otherwise hidden in the data. We explain the application of fsQCA to reveal these common issues in entrepreneurship; demonstrate how fsQCA complements correlational methods and offers finer-grained understanding of individual entrepreneurial behavior; and offer a comprehensive research agenda to build new entrepreneurship theory.

Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process

Article

Jan 2020
TECHNOL FORECAST SOC

Digital technologies have nowadays a significant impact on how new business ventures are imagined and created. The arising technology paradigm is leveraging the potential of collaboration and collective intelligence to design and launch more robust and sustainable entrepreneurial initiatives. However, although the topic of digital entrepreneurship is relevant and timely, there is a limited literature discussion on the real impact of digital technologies and collaboration on the entrepreneurial process. Further research is needed to describe the nature and characteristics of the entrepreneurial ecosystem enabled by the new socio-technical paradigm. Based on extant literature, this article proposes a definition of digital entrepreneurship ecosystem by highlighting the integrated digital-output and digital-environment perspectives. A collective intelligence approach is then adopted to define a descriptive framework and identify the distinguishing genes of a digital entrepreneurship ecosystem. Four dimensions associated to digital actors (who), digital activities (what), digital motivations (why), and digital organization (how) are defined and discussed. The framework was also applied to describe 9 real cases of companies and initiatives, which are analyzed as digital entrepreneurship ecosystems along the four key dimensions presented. The article ends with a discussion about the results and a research agenda for future studies.

A Theory of Digital Firm-Designed Markets: Defying Knowledge Constraints with Crowds and Marketplaces

Article

Dec 2019

In this paper, we investigate the ways in which new forms of organization enabled by digital technologies, such as crowdsourcing and digital marketplaces, are allowing firms to circumvent and defy traditional knowledge constraints. This is part of the broader question of when and why these forms of organization are more efficient relative to alternatives, given that some firms simultaneously utilize crowdsourcing, marketplaces, and traditional forms of organization. We observe that an important cluster of these new organizational forms are able to circumvent knowledge constraints, because they combine elements of market and hierarchical organization in firm-designed hybrid arrangements. We further categorize these firm-designed markets into one-sided market arrangements (crowds) and two-sided market arrangements (marketplaces). To explain their efficiency relative to hierarchies and relative to each other, we take a knowledge-based perspective and review ways in which firm-designed markets reduce or remove both first-order (known unknown) and second-order (unknown unknown) knowledge constraints compared with hierarchies. Our argument hinges on the notion that firm-designed markets provide semidirected and undirected search and generativity mechanisms that allow firms to go beyond what is possible with centrally directed search.

The Emergence of Digital Platform Ecosystems: A Problem-Solving Perspective

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