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Running Head: THE ADOPTION AND GROWTH OF OPEN API PLATFORMS

Digital Transformation: An Exploratory Research on Factors and Strategies Influencing the

Adoption and Growth of Open API Platforms

Rubén E. Middeljans

Nyenrode Business University

Straatweg 25

3621 BG Breukelen

The Netherlands

Rubix B.V.

Utopialaan 58

5232 CE ’s-Hertogenbosch

The Netherlands

THE ADOPTION AND GROWTH OF OPEN API PLATFORMS 2

Abstract

If we look at the changes happening today and the potential disruptions of tomorrow there is no

denial that the shifts in society, enterprises and technology are massive. These disruptions come

with technological innovations that have a tremendous amount of impact on businesses across

industries. To adapt to these new volatile market conditions and move on in the digital age

businesses will have to rethink their business models and restructure operations to effectively

make use of digital ecosystems and platform models (Choudary, 2013). The concept of a digital

platform – a business that facilitates value-creating interactions between producers and

consumers, viz. the exchange of goods, services or social currency (Rogers, 2016; Parker, Van

Alstyn & Choudary, 2016) – has proven quite successful in the last decade, yet new platforms are

extremely difficult to get off the ground. An open API platform in that sense can be considered

an “exchange platform” (Rogers, 2016), it facilitates the exchange of value units (“information

goods”) between producers and consumers. The objective of this exploratory research paper is to

find factors and strategies influencing the adoption and growth of open API platforms. The

topics that are found theoretically relevant as part of the literature review, viz. network effects,

architecture and design, curation, governance, launch strategies, metrics, monetization strategies, and openness,

could influence the adoption and growth. The main finding of this research is that the vast

majority of the topics seem to have a strong relation with the value-creation process on

platforms, which in turn has a bi-directional relation with the development of network effects,

acting as a flywheel for the adoption and growth. The value-creation process and the

phenomenon of network effects could therefore perhaps be seen as the most important drivers.

That being said, under these circumstances this research paper notices that the key to adoption

and growth of open API platforms could possibly lie in the platform businesses’ ability to

leverage and manage the value-creation process for its users and the development of positive

network effects.

Keywords: Exploratory Research, Adoption, Growth, Open API Platforms


Table of Contents

ABSTRACT ........................................................................................................................................................ 2

DIGITAL TRANSFORMATION: AN EXPLORATORY RESEARCH ON FACTORS AND STRATEGIES INFLUENCING THE

ADOPTION AND GROWTH OF OPEN API PLATFORMS....................................................................................... 5

RESEARCH BACKGROUND ................................................................................................................................... 5

RESEARCH OBJECTIVES ....................................................................................................................................... 7

STRUCTURE OF RESEARCH PAPER ........................................................................................................................ 7

LITERATURE REVIEW ........................................................................................................................................ 8

NETWORK EFFECTS ............................................................................................................................................ 8

ARCHITECTURE AND DESIGN .............................................................................................................................. 9

CURATION ......................................................................................................................................................... 10

GOVERNANCE ................................................................................................................................................... 11

LAUNCH STRATEGIES ........................................................................................................................................ 12

METRICS ............................................................................................................................................................ 14

MONETIZATION STRATEGIES ............................................................................................................................ 15

OPENNESS ......................................................................................................................................................... 16

METHOD ........................................................................................................................................................ 18

RESEARCH APPROACH ....................................................................................................................................... 18

PARTICIPANTS ................................................................................................................................................... 18

MEASURES ......................................................................................................................................................... 19

PROCEDURE ...................................................................................................................................................... 19

DATA ANALYSIS ................................................................................................................................................ 20

RESULTS ......................................................................................................................................................... 21

DISCUSSION ................................................................................................................................................... 28

RESEARCH FINDINGS ........................................................................................................................................ 28

RESEARCH LIMITATIONS ................................................................................................................................... 28


AREAS FOR FURTHER RESEARCH ....................................................................................................................... 29

CONCLUSION .................................................................................................................................................. 30

REFERENCES ................................................................................................................................................... 31


Digital Transformation: An Exploratory Research on Factors and Strategies Influencing the

Adoption and Growth of Open API Platforms

Research Background

If we look at the changes happening today and the potential disruptions of tomorrow

there is no denial that the shifts in society, businesses and technology are massive. This was

already recognized back in 2014 by Westerman, Bonnet and McAfee (2014), “Globalization” and

“Demographics are shifting in ways that will cause deep and lasting changes in markets.” (p. 1).

Although Westerman et al. (2014) emphasize the importance of these changes in society and

enterprises as being “big deals”, they also state that technology shifts are even bigger because in

“(…) recent progress in all things, digital is removing constraints and creating exciting new

possibilities that affect everyone’s lives and enterprises.” (p. 1). Think about the rise of social

media, mobile, big data, the importance of smart phones and tablets in our daily lives, the

emergence of cloud computing and platforms such as Airbnb, Facebook, Uber, WhatsApp, and

dozens more. Also, blockchain, machine learning, artificial intelligence, the internet of things

(IoT) and self-driving vehicles will create endless new possibilities by leveraging digital

ecosystems. All these mentioned technologies are digital, connected and some of these are

already changing the ways businesses innovate. Back in 2014, Westerman et al. compared the

enormous amount of technological innovation hitting the business world with the amount of

technological innovation back in the Industrial Revolution. Schwab (2017), Founder and

Executive Chairman of the World Economic Forum, even claims that digitization as part of the

fourth Industrial Revolution - driven by technological innovations and in particular characterized

by the speed of its development and diffusion - will revolutionize “everything” and predicts that

the “science fiction” of yesterday may become reality in today’s products and services. These

technological innovations have a tremendous amount of impact on businesses across industries

which are affected by shifted customer expectations, data-enhanced products, new forms of

collaboration, and the transformation of operating models into new digital models (Schwab,


2017). These changes are equally shaping the competitive landscape of industries. This

phenomenon is referred to by McGrath (2012) as the arena-concept: a place without defined

industry boundaries which is characterized by customers and solutions, not by industries or

competing products. In this arena, competition - including disruptive tech entrants and other

asymmetric threats - is emerging from unexpected places and in such a fast pace that existing

markets are being disrupted by new business models driven by technological innovations, and

hereby displacing well-established market leaders (Choudary, 2013). To adapt to these new

volatile market conditions and move on in the digital age businesses will have to rethink their

business models and restructure operations to effectively make use of digital ecosystems and

platform models (Choudary, 2013). Rogers (2016) has a similar claim, arguing that businesses will

have to rethink competition and “build platforms, not just products”.

The concept of a digital platform – a business that facilitates value-creating interactions

between producers and consumers, viz. the exchange of goods, services or social currency

(Rogers, 2016; Parker et al., 2016) – has proven quite successful in the last decade, yet new

platforms are extremely difficult to get off the ground. Although there are a numerous examples

of successful platform businesses (e.g. Airbnb, Facebook, Uber, WhatsApp), many of them could

not reach a critical mass “in spite of investing in technology because they fail to generate supply”

(Choudary, 2013). An open API platform in that sense can be considered an “exchange

platform” (Rogers, 2016), it facilitates the exchange of value units (“information goods”) between

producers and consumers. These value units are leveraged by so-called data-powered business

models, also known as API products1. These API products share a wealth of information that

could be highly valuable for different types of users: individual users, partners, or even entire

ecosystems. The purpose of API products varies but are considered as the functional building

blocks for digital business models, co-creation, collaboration, and open innovation (Iyer &

1 not to be confused with the low-level application programming interface (API) in software engineering.


Subramaniam, 2015; Iyengar, Khanna, Ramadath, & Stephens, 2017). But as with all platforms,

without producers, there is simply no value for users. On the other hand, when there are no users

on a platform, there is no incentive for producers. The chicken-and-egg dilemma, where to start?

Research Objectives

The objective of this exploratory research paper is to find factors and strategies

influencing the adoption and growth of open API platforms. The research approach is twofold:

firstly, to find the factors and strategies that could influence the adoption and growth of digital

platforms in general (literature review); and secondly, to validate and extend the theory by

comparing and mapping the outcome on an open API platform (single-case study), noting key

differences, anomalies, and perhaps contradictions in addition to the theory.

Structure of Research Paper

This research paper consists of five chapters, below is a brief description of each chapter.

Literature Review – In this chapter, the literature used to conduct the research is systematically

presented and summarized by topic. These topics have been selected because they were

repeatedly found relevant in various literature and other sources and therefore of potential

interest for the research.

Method – Elaboration on the research approach, strategy, and methods for conducting the

research.

Result – This chapter contains the result of the conducted research (theoretical framework).

Discussion – Discussion about the findings, research limitation, and areas for further research.

Conclusion – A brief conclusion on the conducted research.


Literature Review

Network Effects

Network effects seem to be a very important phenomenon and topic in the adoption and

growth of platforms. Parker et al. (2016) refers to network effects as to “(...) the impact that the

number of users of a platform has on the value created for each user” (p. 17). Rogers (2016)

emphasizes that this phenomenon is even one of the key features of platforms. These effects – a

flywheel that helps platforms grow larger is comparable with the creation of monopolies back in

the twentieth-century industrial era (Parker et al., 2016). The growth of these monopolies was

based on the so-called supply economy of scale. The larger the scale of production and higher

amount of quantities produced, the lower the unit cost of creating a product or service. This led

to a positive spiral by giving the largest company in an industrial economy a cost advantage over

competitors that was extremely difficult to overcome. Today comparable monopolies are instead

created by demand economies of scale whereas the value of a product or service increases in

accordance with the number of users (Parker et al., 2016). For platform businesses it is safely to

assume that the number of users in general positively impacts its value, in turn attracting more

users and increasing the cost-efficiency of platforms to “near-zero” (McAfee & Brynjolfsson,

2017). If these users reside on the same side of a platform - for example as consumers - these

effects are called direct network effects (Rogers, 2016), also known as same-side effects (McAfee &

Brynjolfsson, 2017). A clear, contemporary example has been seen with the introduction of

WhatsApp, many mobile users with switched from using texting (SMS) to WhatsApp because

many of their contacts started using WhatsApp (McAfee & Brynjolfsson, 2017). McAfee and

Brynjolfsson (2017) emphasize that that these network effects arose because of an explicit choice

made by platform businesses. If the creator had decided to make their product compatible (more

open) with the already established SMS texting, users would not have been that eager to switch if

it weren’t for cost reasons. In contrast, if users from one side of a platform impacts the numbers

of users on the other side - for example when more consumers will likely attract more producers


- these effects are called indirect network effects (Rogers, 2016), also known as cross-side effects (McAfee

& Brynjolfsson, 2017). Both types of network effects can also exist in negative form if the growth

or shrinkage instead reduces the value created for each user. It is important to understand that

positive network effects should not as per definition be associated with the growth of users, on

certain platforms less users could instead increase the value for each user. Parker et al. (2016)

claim that “(...) understanding the value creation via positive network effects is essential first

step” (p. 18) and that a growing platform business must therefore be able to manage all four of

them in order to be successful.

Architecture and Design

As Parker et al. (2016) mention, a poorly designed platform that creates little or no value

for users will likely generate weak network effects. One could say that a platform initially

establishes the foundation, tools and the right ingredients for an ecosystem in which desirable

value-creating interactions are facilitated and stimulated. Parker et al. (2016) elaborate that a

platform in the basis must be able to perform three fundamental functions to support the value-

creation process, or core interaction: pull, attract users to a platform and keep them (happy) on

the platform by leveraging techniques such as (multi-user) feedback loops; facilitate, make it easy

for users to exchange value units, encourage easy and desirable value-creating interactions by

platform curation and governance, and lower the barriers for the use of a platform; match, collect

data about the users and the overall usage of the platform to create matches between users and

the most relevant value units to them. McAfee and Brynjolfsson (2017) add to this that to

provide easy value-creating interactions, successful platforms work “obsessively” on the user

interface and user experience, to make it as appealing and intuitive as possible. It is needless to say

that a better user experience will likely increase the adoption of a platform. Rogers (2016) argues

that a platform should include unique features and benefits to attract users. Apple, for example,

attracts users to its platform with the App Store or the Siri personal assistant. This is particularly


important if the value provided by the users is not enough to be competitive and generate

positive network effects.

Curation

The purpose of a platform is to facilitate value-creating interactions between users by

matching supply and demand. Once a platform starts growing beyond the startup and early

growth phase, difficulty might emerge in creating the best matches between users (demand) and

most relevant value units to them (supply). When users are not creating enough value of

platforms, they might walk away, which could contribute to negative network effects, and in

worst case leading to the exodus of users. This can also be the case when both sides of a platform

are not balanced, Parker et al. (2016) use the example of Uber: if there are too many drivers

(producers) relative to the number of passengers (consumers), driver downtimes will go up. If

Uber attracts too many passengers (consumers), waiting times will go up. In both situations users

might walk away. To avoid this dilemma and potential negative network effects, an effective

solution could be curating, which is the process of filtering, controlling, and limiting the access of

users to a platform, the activities they participate in, and the connection they form with other

users (Parker et al., 2016, p. 26). When a platform is effectively curated, it is much easier for users

to engage in value-creating interactions. Rogers (2016) refers to curation as a collection of

integration tools, to be able to provide users with the best tools to find the best match and enable

the interaction between users. McAfee and Brynjolfsson (2017) are less subtle about this topic

and simply state that curation is the solution for better platforms, by “keeping out the bad, and

encouraging the good” (p. 165). Although McAfee and Brynjolfsson (2017) do not explicitly

describe the influence of curation on network effects they do mention that platforms should

“curate contributions from outside to maintain standards” (p. 175) because “chaotic, unsafe, or

fraudulent contributions” (p. 175) could have influence on the value-creation process, which

could harm the network effects.


There are several ways for curation. By collecting data and implementing algorithms as

part of the design of (algorithmic curation), a platform could be able to effectively match both sides

of a platform based on the user’s data (interests, behavior), which in turn will likely attract more

users, and with this more user data. This is how the Netflix recommendation system works. The more

users, the more data, the better curation. This is a phenomenon that Parker et al. (2016) refer to

as data-driven network effects. A simpler form of curation, but not less effective, is the social

curation system of Uber or Airbnb in which users on one side of a platform rate, vote or review

users on the other side, thereby improving the quality of the value units on a platform. Choudary

et al. (2013) consider social curation and reputation systems – by employing “the collective intelligence

and judgment of users” – one of the three factors transforming industries that platforms should

embrace. Rogers (2016) has a similar claim and states that reputations systems can be seen as a “trust

enabler”, being one of the five areas of value in which platforms tend to compete.

Governance

Governance is a crucial topic for platforms, particularly given the open nature of many of

them. Restrictions and rules could give a platform manager the right level of control to ensure

that the value created between users is fair and satisfactory and market failures such as

“information asymmetry, externalities, monopoly power, and risks” are prevented and mitigated

(Parker et al, 2016, p. 162). These market failures, if unmanaged, could influence the user’s

satisfaction and harm the network effects.

Parker et al. (2016) – Inspired by the constitutional law scholar Lawrence Lessig – point

out there are four system of controls or tools that platforms could use as part of governance:

laws, the “explicit rules” of platforms (e.g. Spotify does not allow the concurrent use of one

account without family plan); norms, “behavior design” of the platform, shaping a culture through

informal codes; architecture, “architectural control” to prevent and mitigate market failures (e.g.

eBay uses systems for spelling assistance), and markets, “govern behavior” by the use of market


mechanisms and incentives, such as social currency to reward good behavior (e.g. endorsements,

likes, followers).

Parker et al. (2016) also elaborate on the importance of self-governance for platform

businesses. By providing all business divisions, partners and users with internal transparency

across the platform, a higher level of consistency could be achieved. This platform consistency

could increase the comprehensibility, development, and use of the platform itself and related

resources. By additionally involving external partners and users in the internal decision-making

process (external participation), platform businesses could prevent making decisions that are not

supported by the outside partners and users (Parker et al., 2016).

Launch Strategies

Launching a platform from scratch can be quite challenging due to the chicken-and-egg

dilemma. Without producers, there is simply no value for users. On the other hand, when there

are no users on a platform, there is no incentive for producers. To overcome this dilemma there

are several seeding strategies written in the literature that platform businesses could take or

combine to stimulate the initial adoption and growth of a platform. All of these are based on

providing a bait to initial users, which in turn could act as a bait for the other side (Choudary,

2012). The bait used to attract the first group of users on a platform is typically the first value

unit and is arranged or created by the platform businesses itself.

Follow the rabbit strategy (Parker et al., 2016). The most convenient way to launch a

platform is by making use of an existing non-platform product business. By using a

demonstration project and showing its potential benefits, platform businesses could possibly

attract users to a platform. Parker et al. (2016) emphasize that this could be only effective if the

business already has a user base which they can attract. An example is Amazon, once they had a

customer base of considerable size they launched Amazon Marketplace, and with it opening their

platform for external producers (Parker et al., 2016).


The piggyback strategy (Edelman, 2015; Parker et al., 2016). “The piggyback

strategy” involves reusing an existing user base from a different platform as a way to recruit new

participants on the new platform. For example, by offering unique platform value that might be

of interest to users of other platforms. This way PayPal used eBay’s users as initial participants on

their platform (Parker et al., 2016).

The seeding strategy (Choudary, 2012; Edelman, 2015; Parker et al., 2016). A

platform business could be acting as the first producer on a platform. This producer-first strategy

was also taken by Apple when launching the first iPhone back in 2007. Apple acted as the first

producer and created (built) the first value unit (apps) on the App Store, which attracted users

(consumers) on the other side of their platform, which in turn attracted producers (app

developers) (Choudary, 2012).

The marquee strategy (Choudary, 2012; Edelman, 2015; Parker et al., 2016). By

providing an incentive, a platform is theoretical able to “buy” their initial users (or organizations)

hoping that these in turn will attract other users. These initial users are so valuable for the success

of a platform that they are incentivized.

The single-side strategy or stand-alone mode (Choudary, 2012; Parker et al., 2016).

This strategy involves in first creating stand-alone value to users on one side of a platform by

providing tools, products, or software and later on connecting the other side. This strategy could

be combined with the marquee strategy by offering complementary tools, products, or software

free of charge. This strategy was taken by the Dutch bank ABN Amro. When ABN Amro

launched the payment platform “Tikkie” they first facilitated the easy and direct wiring of

payments between users, at no charge, independent of their banks. After the startup and growth

phase of their platform they connected the other side, and with this allowing organizations to

make use of their platform, thereby charging for each transaction.

The producer evangelism strategy (Choudary, 2012; Parker et al., 2016). The

“producer evangelism strategy” is as simple as it sounds, it involves in attracting producers who


can bring their own customers to become users. This will probably only work when there is an

incentive involved for the producer.

The big-bang adoption strategy (Parker et al., 2016). “The big-bang adoption”

strategy involves in using all the power that traditional opportunistic marketing can offer to

create attention for a platform, hoping that this will trigger the adoption and growth.

The micro market strategy (Choudary, 2012; Edelman, 2015; Parker et al., 2016). In

contrast to the big-bang adoption strategy “the micro market strategy” aims on targeting on a

small niche market and small group of users and later on expanding to a larger market. This is the

strategy that Facebook took when they launched the social network platform exclusively in the

closed community of Harvard University (Parker et al., 2016).

Metrics

Although metrics is not an essential topic that directly influences the growth and

adoption of platforms (merely collecting data and statistics), it can be used as an important key

indicator that is derived from the rate of interaction success or failure on platforms. Parker et al.

(2016) elaborate on this subject by arguing the strong relation between the interaction success

and the development of positive network effects. Metrics could therefore be of importance as

input for effectively managing the value-creation process and the development of network

effects.

Parker et al. (2016) recognize different types of metric throughout the lifecycle of a

platform. During the startup phase Parker et al. (2016) elaborate on the importance of the

fundamentals: liquidity, measuring the number of (active) users compared to the percentage of

successful interactions; matching quality, measuring the accuracy and effectiveness of curation by

tracking the sales conversion rate; and trust, the degree to which users feel comfortable creating

and exchanging value units on a platform. Trust is also recognized by Rogers (2016) as being one

of the five areas of value in which platforms tend to compete, for example by offering better


trust-enabling methods among users (e.g. social logins through Facebook, Google, Twitter, or

LinkedIn).

During the growth phase, platforms should lie the focus on metrics that are likely to impact

the growth and/or value-creation, for example the active user participation, consumer/producer

balance, interaction failures, frequency of consumption, and interaction conversion rate (Parker

et al., 2016).

Once a platform survives the growth phase and enters the maturity phase new challenges

may emerge to which platforms need to respond. Parker et al. (2016) emphasize that a platform

must be able to adapt to change as required by users or environment (competitive and

regulatory). Metrics to drive innovation could for example help to find new functionality or

features that could enhance the value-creation process on platforms (Parker et al., 2016), which in

turn could attract new users. Additionally, metrics that can identify threats from competitors

could be used in this phase to determine the competitive position of a platform.

Monetization Strategies

To monetize, and whom to monetize are important decisions that could have widely

differing network effects. When users are suddenly charged for access to a platform, they could

simply decide to leave and with this harming the network effects. Choudary et al. (2013) even

warn that “charging every user can destroy network effect”. This makes monetization one of the

most difficult issues that any platform business must address (Parker et al., 2016), the best

approach could be found in the answer to the question on how to generate revenues of a

platform without harming, damaging or destroying the positive network effects.

One way to monetize users and not discourage them from joining a platform, is by

charging a transaction fee (Parker et al., 2016). Because users are only charged when actual value is

created, it is less likely that network effects will be harmed. Although it is arguable that this could

impact the rate of active participants on a platform, particularly when the transaction fee is too

high for the amount of value created.


Charging for access or charging for enhanced access/curation (Parker et al., 2016) could be an

effective way to charge producers or third-parties on a platform to get access to a community of

users (e.g. LinkedIn Premium) or for users to respectively get access to additional value (e.g.

Dropbox). Since in the basis the entry-level participation is free of charge this strategy will

generally not harm the network effects (Parker et al., 2016). McAfee and Brynjolfsson (2017)

elaborate on this “freemium” approach by stating that “smart companies realize that the free goods

can be a complement, not a substitute, for more expensive versions (...)” (p. 162). By generating

ad revenues (charging third-parties) many platform apps could be offered to users at no charge.

It is important to understand that monetization on a platform is not always relevant.

McAfee and Brynjolfsson (2017) argue that the domains of customer and public services could

be a valid use-case to develop platform apps that are free of charge to participants. This is also

typically the case for platforms that are paired (or bundled) with digital products such as the

Fitbit. Providing free platform access to consumers who buy a Fitbit could be the easiest way to

launch a platform and generate network effects (seeding strategy, Choudary, 2012; Edelman,

2015; Parker et al., 2016). It is however worth mentioning that providing free access to users (as

consumers) could also be part of the single-side strategy or stand-alone mode (Choudary, 2012;

Parker et al., 2016).

Openness

The openness, and in particular the degree of openness of a platform is one of the most

complex and critical decisions that platforms businesses must make (Parker et al., 2016). The

decision affects nearly all other topics such as network effects, curation, governance, and

monetization. Opening up a platform could in certain cases provide significant benefits, and as

McAfee and Brynjolfsson (2017) point out it could bring “a greater volume and variety of

contributions, motivations, and ideas than the owner alone ever could have mustered” (p. 164).

On the other hand, making a platform too open could open the door to undesirable interactions

(McAfee and Brynjolfsson, 2017; Parker et al., 2016). Having a platform that is too closed could


in turn however discourage platform participation and harm network effects (Parker et al, 2016),

but will enable platforms to retain a certain level of control. It is clear as day that the level of

openness is a tradeoff (McAfee and Brynjolfsson, 2017; Parker et al., 2016).

Parker et al. (2016) argue there are three kind of openness decisions: decisions regarding

manager and sponsor participation, deciding whether a platform is based on a proprietary model (one

platform manager, one sponsor, e.g. Alibaba) or a shared model (multiple platform managers,

multiple sponsors, e.g. Linux OS) and everything between; decisions regarding developer participation,

deciding whether or not to open up a platforms infrastructure and collected data to external

developers to create additional extensions, features or other enhanced value (e.g. Facebook app

developers); and decisions regarding user participation, allowing users, for example by side-switching or

social curation, to freely add content and other value to a platform (e.g. upload media to

YouTube). Rogers (2016) elaborates that by providing more-open and easier-to-use standards user

participation can be further stimulated in an open platform. These standards could make the life of

users easier and at the same time act as a control point from governance perspective (Rogers,

2016) to leverage the curation process.

Each decision defines what platform users can and cannot do and could implicate the use

of necessary measures to encourage desirable interactions and discourage undesirable ones (e.g.

curation and governance), “keeping out the bad, and encouraging the good” (McAfee and

Brynjolfsson, 2017). Openness is therefore not a choice between black and white, instead it a

choice between many shades of grey with pro’s and con’s that could have contrasting effects on

the network effects, thereby possibly influencing the adoption and growth.


Method

Research Approach

For this exploratory research, a holistic single-case study strategy was taken with a

qualitative multi method research approach. A case study strategy was specifically taken: firstly, to

be able to apply and validate the theoretical outcome of the literature review in practice, gaining

better understanding of the theory; and secondly, to be able to find key differences, anomalies,

and perhaps contradictions if present in addition to the theory.

Participants

The case for this research was the digital transformation of one of Europe’s largest

airports in becoming the best digital airport in the world: by offering a smooth and cost-efficient

operation in the airport to airlines; and, to bring a seamless passenger journey to the passengers.

This case was particularly of interest for the research because – as part of the digital

transformation – the airport had recently launched a platform to create and share value on via

API products (e.g. Flight API). The adoption and growth of this “open API business” as a

platform was therefore the main unit of research and in particular the involved factors and

strategies.

For the interviews two participants (research interviewees) were selected that were highly

involved in the launch of the open API platform and would be able to provide valuable

information and insights.

- Participant A – Technical Product owner API Support Team;

- Participant B – Technical Product owner API Support Team;


Measures

Document review. The literature review for preparing the conceptual foundation and

theory of the research, was an inherent part of the structured data collection. Hence, existing

literature was examined as a measure to identify, analyze, code and describe relevant topics that

could influence the adoption and growth of platforms, applying triangulation of the theory where

possible.

In-depth interviews. In-depth interviews were taken as a measure to collect

unstructured data with the aim of gaining practical understanding, information and insights on

the case study of one of Europe’s biggest airports. For the interviews a semi-structured approach

was followed with pre-defined questions (see Appendix A for the list of the pre-defined interview

questions).

Procedure

The procedure for conducting the research involved the following sequential steps:

1. Existing literature was examined as a measure to identify, analyze, code and describe relevant

topics that could influence the adoption and growth of platforms, applying triangulation of

the theory where possible.

2. Based on the outcome of the literature review the relevant topics were organized, and listed

in a theoretical framework proposal, this step was an inherent part of the data analysis;

3. In preparation for the in-depth interviews a set of questions were pre-defined. These

questions were open and mainly derived from the theoretical framework to make sure the

outcome of the literature review was entirely covered, but with flexibility left for any

additions to the theory. The set of questions were sent to the research interviewees by e-mail

for a thorough preparation;

4. In-depth interviews were taken and recorded to gain practical understanding, information and

insight on the case study subject. The case study and interviewees were already selected in a

preliminary phase of the research.


Data Analysis

The process of data analyses followed a simple deductive approach. Based on the

outcome of the literature review the relevant topics were organized, described, and listed in a

theoretical framework. This theoretical framework was then used for pre-defining the questions

for the in-depth interviews. The results of the in-depth interviews were manually analyzed,

grouped, compared, and coded by topic, noting key differences, anomalies, and contradictions in

addition to the theory. All new findings were added to the theoretical framework.

Running Head: THE ADOPTION AND GROWTH OF OPEN API PLATFORMS

Results

This chapter contains the results of the conducted research, summarized in Table 2. Each topic that could influence the adoption and growth of platforms is clearly described, followed by the subtopic, corresponding code, method of data collection, and original source. Table 2. Results

Topic Description Subtopic Code Data Collection

Method

Original Source

Network Effects The impact that the number of

users of a platform has on the

value created for each user, which

in turn could attract or repel more

users. This positive or negative

spiral could influence the adoption

and growth of platforms

significantly.

- Positive same-side (direct) network effects;

- Negative same-side (direct) network effects;

- Positive cross-side (indirect) network effects;

- Negative cross-side (indirect) network effect;

NET01

NET02

NET03

NET04

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McAfee and Brynjolfsson,

(2017); Parker et al., (2016);

Rogers, (2016).

Parker et al., (2016).

Rogers, (2016); Parker et al.,

(2016);

Participant B (Personal

Communication, July 6,

2018).


Architecture and

Design

Launching a platform that benefits

from positive network effects and

stimulates the adoption and growth

could require certain design

considerations from architecture

and design perspective. A well-

designed platform could stimulate

the value-creation process.

- Pull, attract users to a platform and keep

them (happy) on the platform by leveraging

techniques such as (multi-user) feedback

loops;

- Facilitate, make it easy for users to exchange

value units, encourage easy and desirable

value-creating interactions by platform

DES01

DES02

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In-depth Interview


Parker et al., (2016);

Participant A (Personal


2018); Participant B


curation and governance, and lower the

barriers for the use of a platform;

- Match, collect data about the users and the

overall usage of the platform to create

matches between users and the most relevant

value units to them;

- User interface/user experience, ease-of-use

platform and a smooth onboarding of users;

- Unique features and benefits to attract users,

this is particularly important if the value

provided by the users is not enough to be

competitive and generate positive network

effects;

- Developer experience, smooth onboarding of

developers on the platform;

DES03

DES04

DES05

DES06

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(Personal Communication,

July 6, 2018).



(2017);





July 6, 2018).

Rogers, (2016).



2018).

Curation The process that encourages easy

and desirable value-creating

interactions between users by the

use of filtering, controlling, and

limiting the access of users to a

- Algorithmic curation (recommendation

systems);

- Social curation and reputation systems (rate,

vote, review);

CUR01

CUR02

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Rogers, (2016); Parker et al.,

(2016).

Rogers, (2016); Choudary et

al., (2013); Parker et al.,

(2016).


platform, the activities they

participate in, and the connection

they form with other users.

- Individual curation (filters, search queries);

- Simple user feedback could improve the

quality of the API products, thereby

stimulating the value-creation process;

- Improve the value units exchanged by

manually analyzing the usage data and

recomposing new API products;

CUR03

CUR04

CUR05

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In-depth Interview

In-depth Interview

In-depth Interview


(2017); Parker et al., (2016);





July 6, 2018).





July 6, 2018).

Participant B (Personal


2018).

Governance Governance is a crucial topic for

platforms, particularly given the

open nature of many of them.

Restrictions and rules could give a

platform manager the right level of

control to ensure that the value

created between users is fair and

satisfactory and market failures are

prevented and mitigated. These

market failures, if unmanaged,

could influence the user’s

- Platform laws, the “explicit rules” of

platform;

- Platform norms, “behavior design” of the

platform, shaping a culture through informal

codes;

- Platform architecture, “architectural control”

to prevent and mitigate market failures;

GOV01

GOV02

GOV03

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July 6, 2018).




satisfaction and harm the network

effects.

- Platform markets, “govern behavior” by the

use of market mechanisms and incentives,

such as social currency to reward good

behavior;

- Self-governance, by providing all business

divisions, partners and users with internal

transparency across the platform, a higher

level of consistency could be achieved;

- External participation, involving external

partners and users in the internal decision-

making process;

GOV04

GOV05

GOV06

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In-depth Interview

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July 6, 2018).


Launch Strategies To overcome the chicken-and-egg

dilemma of platform, there are

several seeding strategies written in

the literature that platform

businesses could take to stimulate

the initial adoption and growth of a

platform. All of these are based on

providing a bait to initial users,

which in turn could act as a bait for

the other side (Choudary, 2012);

- Follow the rabbit strategy;

- The piggyback strategy;

- The seeding strategy;

- The marquee strategy;

- The single-side strategy or stand-alone mode;

- The producer evangelism strategy;

LAU01

LAU02

LAU03

LAU04

LAU05

LAU06

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Edelman, (2015); Parker et

al., (2016).

Choudary, (2012); Edelman,

(2015); Parker et al., (2016).


(2015); Parker et al., 2016).


(2015); Parker et al., 2016).


(2015); Parker et al., (2016).

Choudary, (2012); Parker et

al., (2016).



- The big-bang adoption strategy;

- The micro market strategy;

- Launch an open API platform in a controlled

internal environment as a minimal viable

product (MVP) before opening up for public

(variant on the micro market strategy);

- Organize hackathons as a mean to create

attention and awareness for an open API

platform, which is also a good method to

receive feedback on the value-creation

process;

LAU07

LAU08

LAU09

LAU10

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In-depth Interview

In-depth Interview


2015; Parker et al., 2016).





July 6, 2018).





July 6, 2018).

Metrics Although metrics is not an

essential topic that directly

influences the growth and

adoption of platforms (merely

collecting data and statistics), it can

be used as an important key

indicator that is derived from the

rate of interaction success or

failure on platforms. Metrics could

therefore be of importance as input

for effectively managing the value-

creation process and the

development of network effects.

- Startup phase metrics (e.g. liquidity, matching

quality, trust);

- Growth phase metrics (e.g. active user

participation, interaction failures, frequency

of consumption, and interaction conversion

rate);

- Maturity phase metrics (e.g. innovation,

threats of competition);

MET01

MET02

MET03

Document Review

In-depth Interview

Document Review

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Parker et al., (2016); Rogers,

(2016);





July 6, 2018).




Monetization

Strategies

To monetize, and whom to

monetize are important decisions

that could have widely differing

network effects. When users are

charged for access to a platform,

they could simply decide to leave

and with this harm the network

effects. This makes monetization

one of the most difficult issues that

any platform business must

address (Parker et al., 2016), the

best approach should be to answer

the question on how to generate

revenues of a platform without

damaging or destroying the

positive network effects.

- Charging a transaction fee;

- Charging for access;

- Charging for enhanced access, freemium

approach;

- Charging for enhanced curation;

- Free platform access;

MON01

MON02

MON03

MON04

MON05

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In-depth Interview




(2017); Parker et al., (2016).



(2017);





July 6, 2018).

Openness The openness, and in particular the

degree of openness of a platform is

one of the most complex and

critical decisions that platforms

businesses must make (Parker et

al., 2016). Each decision defines

what platform users can and

cannot do and could implicate the

use of necessary measures to

encourage desirable interactions

- Manager and sponsor participation;

- Developer participation (core developers,

extension developers and data aggregators);

- User participation (producer openness) could

foster innovation but will require more effort

on curation and governance to prevent

undesirable interactions;

OPE01

OPE02

OPE03

Document Review

Document Review

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In-depth Interview

Parker et al. (2016).

Parker et al. (2016).

McAfee and Brynjolfsson

(2017); Parker et al. (2016);





July 6, 2018).


and discourage undesirable ones

(e.g. curation and governance),

“keeping out the bad, and

encouraging the good” (McAfee

and Brynjolfsson, 2017).

- Platform openness, by providing more-open

and easier-to-use standards user participation

can be further stimulated in an open

platform;

OPE04 Document Review

In-depth Interview

Rogers (2016); Parker et al.

(2016);





July 6, 2018).


Discussion

Research Findings

The vast majority of the topics that were found theoretically relevant as part of the literature

review, viz. network effects, architecture and design, curation, governance, launch strategies, metrics, monetization strategies,

and openness, seem to have a strong relation with the value-creation process on platforms, which in turn

has a bi-directional relation with the development of network effects, acting as a flywheel for the

adoption and growth. The relation between these topics, the value-creation process and the phenomenon

of network effects seem to be positive correlative, although given the qualitative nature of the research

cannot be supported by lack of quantitative evidence. The single-case study validates the importance of

these topics, but merely from a value-creation perspective although potential network effects were

acknowledged. The study subject launched the open API platform in a controlled internal environment as

a minimal viable product (MVP) before opening up for public, which can be seen as a variant on the

micro market strategy. The adoption and growth of the platform was not the primary goal, but by

focusing on the value-creation process this can still be one of the desired outcomes.

Research Limitations

This research has a number of limitations that should be considered. Firstly, due to timing

constraints the theoretical framework might not contain all the possible topics influencing the

adoption and growth of platforms. The scope of the data collection was limited to a relatively

small, though representative selection of literature and other sources as well as the in-depth

interviews. Secondly, due to the same timing constraints a holistic single-case study strategy was

taken with solely two in-depth interviews to collect primary data and validate the theory. The

small sample size (one) implicates that it is not possible to generalize or replicate the results and

findings of this research with great certainty to other cases. And lastly, this research could contain

a slight personal bias towards the validation of the theory, which could have possibly influenced

both data collection and analysis.


Areas for Further Research

Although this research produced interesting findings, additional research is needed to

validate, expand and enrich the theoretical framework. Future research could clarify and quantify

the effects of these topics on the value-creation process on platforms and the development of

network effects. Additionally, an embedded single-case study or a holistic multi-case study could

either provide a higher level of detail on the topics or a more representative sample size,

respectively.


Conclusion

The objective of this exploratory research paper is to find factors and strategies

influencing the adoption and growth of open API platforms. The topics that are found

theoretically relevant as part of the literature review, viz. network effects, architecture and design,

curation, governance, launch strategies, metrics, monetization strategies, and openness, could influence the

adoption and growth. The main finding of this research is that the vast majority of the topics

seem to have a strong relation with the value-creation process on platforms, which in turn has a

bi-directional relation with the development of network effects, acting as a flywheel for the

adoption and growth. The value-creation process and the phenomenon of network effects could

therefore perhaps be seen as the most important drivers. That being said, under these

circumstances this research paper notices that the key to adoption and growth of open API

platforms could possibly lie in the platform businesses’ ability to leverage and manage the value-

creation process for its users and the development of positive network effects.


References

Choudary, S. (2012, August 10). Platform Seeding Strategies. Retrieved from Pipes to Platforms:

http://platformed.info/seeding-two-sided-businesses-strategy-chicken-and-egg-problem

Choudary, S. (2013, January 12). Platform Thinking: A Brief Guide to Leveraging Networks for Business.

Retrieved from Pipes to Platforms: http://platformed.info/platform-thinking-networks-

online

Choudary, S., Parker, G., & Van Alstyne, M. (2013, October 19). The Rise of the Platform: A Seismic

Shift in Business Models. Retrieved from Pipes to Platforms:

http://platformed.info/platform-economics

Edelman, B. (2015, April 1). How to Launch Your Digital Platform. Retrieved from Harvard Business

Review: https://hbr.org/2015/04/how-to-launch-your-digital-platform

Iyengar, K., Khanna, S., Ramadath, S., & Stephens, D. (2017, September 12). What it really takes to

capture the value of APIs. Retrieved from McKinsey & Company: Digital Insights:

https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/what-it-

really-takes-to-capture-the-value-of-apis

Iyer, B., & Subramaniam, M. (2015, January 7). The Strategic Value of APIs. Retrieved from

Harvard Business Review: https://hbr.org/2015/01/the-strategic-value-of-apis

McAfee, A., & Brynjolfsson, E. (2017). Machine - Platform - Crowd. New York, NY: W.W. Norton

& Co.

Mcgrath, R. (2012). The End of Competitive Advantage. Boston, MA: Harvard Business Review Press.

Parker, G., Choudary, S., & Van Alstyne, M. (2016). Platform Revolution. New York, NY: W.W.

Norton & Co.

Rogers, D. (2016). The Digital Transformation Playbook. New York, NY: Columbia University Press.

Schwab, K. (2017). The Fourth Industrial Revolution. New York, NY: Crown Business.

Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital. Boston, MA: Harvard Business

Review Press.


Due to anonymity reasons the transcripts of the interviews cannot be publicly published. If you

are interested in receiving these, please send an email to the author: [email protected]

mailto:[email protected]

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