Towards a Governance Framework for Data Platform Ecosystems in

the Construction Industry

Samaneh Bagheri

Department of Information Science, Open University of The Netherlands, The Netherlands

Keywords: Data Platforms, Platform Ecosystems, Governance Framework, Governance Mechanisms, Construction Industry.

Abstract: In today’s digital economy, the potential of using data platforms for secure and trusted business data exchange

between distinct user groups within a data ecosystem becomes extremely significant. The construction

industry is not exempted from the potential benefits of data platform ecosystems (DPEs). While for the

effective orchestration of DPEs, appropriate governance is required, due to specific features of the

construction industry, existing insights on the governance of DPEs may not be directly applicable to the data

platforms in this industry. In this paper, we contribute to our understanding of this phenomenon by developing

a governance framework for DPEs in the construction industry. To this end, we develop a governance

framework by identifying governance mechanisms from the platform literature and investigating if and why

these mechanisms are relevant in the construction industry by conducting a case study. The proposed

framework offers an outline for the analysis of data platform governance and provides first insights about

governance mechanisms that practitioners of the construction industry need to consider especially during the

early stage of the DPEs development.

1 INTRODUCTION

While the construction industry contributes on

average about 8–10% to the economies of countries

around the world (Opoku et al., 2021), this industry

encounters numerous challenges such as low

productivity, poor level of data accuracy, and lack of

data sharing (Ayodele & Kajimo-Shakantu, 2021;

Opoku et al., 2021). Moreover, the fragmented nature

of the construction industry— with many

geographically dispersed actors working together

toward a common goal— leads to inconsistency and

delays in data exchange among actors who

collaborate on construction projects (Lee et al.,

2021). The complex nature of this industry and its

heavy reliance on data exchange require the adoption

of digital technologies and platforms (El Jazzar et al.,

2020). In today’s digital economy, platforms have

been changing the entire landscape of business and

gaining increasing importance and relevance (de

Reuver et al., 2018).

The potential of using platforms for the

improvement of efficiency and competitiveness as

well as for better resource utilization and data-driven

innovative services in the construction sector is

extremely significant (Begić & Galić, 2021; Opoku et

al., 2021). More specifically, data platforms have

immense potential to transform data exchange and

use in the construction industry just like in other

industries. However, the construction industry lagged

behind other industries in the uptake of platforms

(Linderoth et al., 2018; Opoku et al., 2021).

According to a European construction sector report,

few platforms have been widely adopted in this sector

yet (Digitalisation in the construction sector, 2021).

In general, platforms can be defined as a

technological foundation upon which additional

complementary products or services can be

developed (de Reuver et al., 2018; Hein et al., 2019).

Platforms can also act as a mediatory marketplace

that facilitates the transaction between multiple

groups of users (de Reuver et al., 2018). Data

platforms are a subset of platforms that specialize in

secure and trusted data exchange between user groups

(Otto & Jarke, 2019). Similar to other types of

platforms, in Data Platform Ecosystems (DPEs),

legally independent actors, such as platform owners,

data providers, and data users collectively create

value in a complex dynamic network around

platform-based infrastructures and engage in data

exchange and use to leverage data-driven innovation

(Lis & Otto, 2020; Otto & Jarke, 2019). An example

566

Bagheri, S.

Towards a Governance Framework for Data Platform Ecosystems in the Construction Industry.

DOI: 10.5220/0011853100003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 2, pages 566-574

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

of DPEs is a data space which is a data ecosystem of

data providers and users cooperating for data-driven

innovations (Beverungen et al., 2022).

Appropriate governance arrangements that allow

data exchange and facilitate interaction among

ecosystem actors are key to orchestrating successful

platform ecosystems (Halckenhäußer et al., 2020;

Schreieck, Wiesche, et al., 2017). However, findings

and understandings of platform governance cannot

simply be transferred to emerging data platforms due

to specific charactristics of DPEs such as data

sovereignty, data privacy, and confidentiality

considerations (de Reuver et al., 2022). Moreover,

existing insights on the governance of platform

ecosystems are dominated by examples from high-

tech industries (Hein et al., 2016; Schreieck, Hakes,

et al., 2017). Nevertheless, the construction industry

has special characteristics (e.g., fragmented structure,

project-based nature, high degree of specialization,

complexity and long life span of constructed

products, internet access problems due to remote

sites, unpredictable nature of the project processes)

that differentiate it from other industries (Pulkka et

al., 2016; Regona et al., 2022). Therefore, it is highly

unlikely that the generic platform governance

mechanisms of other industries can be directly

applied to this industry. Further research on the

relevance and applicability of governance

mechanisms of DPEs in the construction industry

context is thus needed.

The goal of this paper is to identify the right

governance mechanisms for DPEs in the specific

context of the construction industry and develop a

governance framework for DPEs in this context.

Specifically, the paper addresses the following

research question:

What Data Platform Governance Mechanisms

should be incorporated into the construction

industry?

We approach this question by adopting a two-step

research approach. First, we identify platform

governance mechanisms through a systematic

literature review (SLR). Second, we investigate if and

how these mechanisms are practically relevant in the

construction industry by conducting a case study.

The outline of the paper is as follows. Related

work is dissuaded in Section 2. Section 3 describes

the research methodology. The results of the literature

review and the case study that led to the proposed

governance framework are presented in Section 4.

Finally, Section 5 presents the discussion and

conclusion.

2 RELATED WORK

Platform governance deﬁnes who makes what

decisions about a platform (ecosystem) (Tiwana,

2013). Governance plays a particularly important role

for platform owners to make deliberate choices about

platform access, ownership, and control to orchestrate

a successful platform ecosystem (Mukhopadhyay &

Bouwman, 2019). While the focus of traditional

views on IT governance is on within-firm or dyadic

inter-organizational relationships, platform owners

are confronted with the complex task of orchestrating

actors (Halckenhäußer et al., 2020). In the context of

platform ecosystems, governance decisions are for

defining the rules to encourage desirable behaviors of

actors and defining how the benefits distributed

among the actors are made by the keystone actors

(Kretschmer et al., 2022; Otto & Jarke, 2019).

The importance of platform governance has been

emphasized by several studies (Mukhopadhyay &

Bouwman, 2019; Schreieck et al., 2016; Tiwana,

2013; Tura et al., 2018). In this regard, for instance,

Tura et al. (2018) emphasize that the health and

longevity of a platform ecosystem depend on the

effective governance of the platform. Furthermore,

various studies have identified different governance

mechanisms for platform ecosystems (Alves et al.,

2017; Halckenhäußer et al., 2020; Mukhopadhyay &

Bouwman, 2019; Schreieck et al., 2016; Tiwana,

2013; Tura et al., 2018). However, they are usually

limited in scope and their resulting governance

frameworks are diverse. For example, Schreieck et al.

(2016) identify and classify different governance

mechanisms for platform ecosystems into roles,

pricing and revenue sharing, boundary resources,

openness, control, and trust. While Halckenhäußer et

al. (2020) categorize governance mechanisms into

cooperation, resourcing, control, and market. In some

other studies, the focus is solely on the data

governance aspect of platform ecosystems (Lis &

Otto, 2021; Nokkala et al., 2019).

Although these studies provide useful insights and

relevant information about possible governance

mechanisms of DPEs, no clear aggregation of these

findings exists yet (Halckenhäußer et al., 2020). A

more comprehensive and systematic view of the

governance of platform ecosystems is thus needed. In

addition, only limited information can be found about

the governance of DPEs in the context of the

construction industry (Alreshidi et al., 2017).

Towards a Governance Framework for Data Platform Ecosystems in the Construction Industry

567

3 RESEARCH METHODOLOGY

The goal of this research is to develop a governance

framework for DPEs in the construction industry. To

this end, firstly, governance mechanisms and

practices of platform ecosystems were identified

through SLR. Secondly, the practical relevance of

these governance mechanisms for the DPEs in the

construction industry was evaluated in a case study.

3.1 Systematic Literature Review

To systematically identify governance practices and

mechanisms of DPEs from the literature, we

conducted an SRL by following the guideline of

Okoli and Schabram (2010). This SLR answered the

following research question:

What Mechanisms Characterize the Governance

of Platform Ecosystems in the Previous Studies?

The following search query was used in our

literature review: ((“data platforms”) OR (“digital

platforms”) OR (“platform ecosystem”)) AND

("governance")).

We sought papers in the digital library of the Open

University, as it gave us the possibility to search

multiple databases, such as ScienceDirect, Springer,

Emerald, and Wiley, simultaneously. Only peer-

reviewed journal and conference papers in the period

2015 to 2020, written in English, with the main

objective of governance mechanisms and practices in

the context of platform ecosystems were included in

this review. Studies in which the main subject is not

on the governance of a platform ecosystem were

excluded. We also excluded studies that are limited to

intra-organizational settings. The literature search

yielded initial hits. We then scanned subsequently the

titles and abstracts of the papers and removed

irrelevant papers. Then the quality of the remaining

papers was assessed based on the quality criteria of

(Dybå & Dingsøyr, 2008). Data extraction from the

selected papers was performed by using content

analysis (Elo & Kyngäs, 2008). This process includes

open coding, creating categories, and abstraction.

Coding was performed by identifying different

aspects of governance that characterize governance

mechanisms and practices in platform ecosystems

and assigning relevant code. Then, coding categories

were created in which the codes were arranged in

categories, based on the similarities which led to the

set of governance mechanisms. Then the relations

between the categories were established which

resulted in governance dimensions.

3.2 Case Study

After identifying platform governance mechanisms,

we investigated if and why those governance

mechanisms are relevant in a real-life DEP in the

construction industry. Besides validation, we were

also looking for explanations and reasoning for the

relevancy to provide a more in-depth understanding

of the research topic. To this end, the case study

research approach was selected as it allows an in-

depth inquiry into a phenomenon within its real-life

setting (Yin, 2014). We focused our analysis on one

type of business in this industry; housing

construction, which is currently faced with a highly

competitive environment due to various customer

needs, and market pressures. As data platforms

enhance accessibility and exchange of data, enable

integrated construction information, and involvement

of all relevant actors in the housing construction

process, these platforms have gained increasing

attention in housing construction projects (Li et al.,

2022).

The selected case was a data platform

ecosystem— and not a single organization—that

focuses on secure data exchange in the development

of housing. Given that in the DPEs multiple

organizations (actors) are involved, their views

should be considered therefore, in the selected

ecosystem-wide case, we collected data from three

main actors; i.e., platform owner, data provider, and

data user as suggested by (Otto & Jarke, 2019). We

evaluated the practical relevance of the governance

mechanisms in the early phase of this DPE

development (i.e., the planning phase), to allow actors

to assess those mechanisms and express their

opinions and reasonings without any prejudice and

influence from the implementation phase.

The data were collected using semi-structured

interviews, as this allows for in-depth questions and

follow-up questions for further explanation. Within

each involved organization, we planned to interview

multiple people from different organizational

positions (e.g., business/project managers, IT

managers) to ensure triangulation. Participants should

be knowledgeable and experienced in either data or

platform governance. In addition, participants should

have a relevant background in governance, and

platforms as well as have higher education for better

abstract reasoning. at the beginning of each interview,

an introduction to the research topic and its purpose

was given. It also contains questions about the

participants and their general view on the subject,

before they see the initial list of governance

mechanisms and be biased by it. In the second part of

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the interview, we validated the initial list of

governance mechanisms by asking respondents if

they are relevant. As we also aimed to get an in-depth

understanding, the interviewees were also asked for

the reasoning behind their answers by asking why

questions. In the closing section, by asking open

questions, we inquired if any further governance-

related aspects have been experienced by the

participants, which were not covered yet.

We conducted 8 interviews with eligible people

from the three actors of the selected DPE (see Table

1) in October 2020 and each interview lasted about

1.5 hours. Due to the Covid-19 pandemic, all

interviews were held online, in a video meeting, and

(with permission of the interviewees) were recorded

and transcribed. The transcription was shared with the

interviewee for final checking and verification.

Table 1: Profile of the interviewees.

Role of the

organization

in the DPE

Participant

Position

Work

Experie

nce

Education Int. #

Data user

Manager

digital

latfor

2 years Academic

Int#1

Director

development

& realization

11 years Bachelor

Int#2

Data

provider

Product

manager &

commercial

mana

2 years Bachelor

Int#3

Project

manager &

commercial

directo

3 years Bachelor

Int#4

IT manager

12 years Bachelor

Int#5

Platform

Owner

Manager DCC

4 years Bachelor

Int#6

IT Director

3 years Academic

Int#7

Data Manager

5 years Academic

Int#8

We used a content analysis method suggested by Elo

and Kyngäs (2008) to analyze the data. The initial list

of governance mechanisms identified from the

literature was used as starting point for the analysis.

We began the data analysis by open coding to recap

each interview’s key statements. After the coding

process, we synthesized the outcome by removing

duplicates and classifying similar governance

mechanisms into one group. A difference between

empirical and theoretical governance mechanisms

can be a refinement of an existing element or a new

element. This data analysis process led to a

governance framework of a DPE.

4 RESULTS

In this section, we, first, present the results of the

SLR. Then, the results of the case study are presented.

We executed our search and identified 101 papers

of which nine were duplicates. We then screened the

92 papers based on our inclusion and exclusion

criteria of which 15 were selected for a full-text

assessment. During this assessment, we excluded

another five papers. We reviewed 11 papers in detail

and extracted relevant data. The data synthesis

process resulted in an initial list of 15 governance

mechanisms under six dimensions (see Table 2).

In the evaluation phase of this study, to provide an

in-depth understanding of the relevance of

governance mechanisms in the real-life DPE in the

construction industry, 8 semi-structured interviews

were conducted within the three organizations from

the selected DPE in the housing sector. In general, the

interview process went as planned. The participants

were able to provide sufficient data and had an in-

depth understanding of the topic.

A definition of each governance dimension and

example participants’ quotes for the relevance of the

associated governance mechanisms are provided in

the following.

Governance Structure decides the ownership

and decision rights in the platform ecosystem. This

can either be formal or informal, depending on the

needs of the ecosystem. (Abraham et al., 2019; Katz

et al., 2019; Schreieck, Wiesche, et al., 2017; Wang

et al., 2017).

Three participants prefered an informal

governance structure as, in their opinion, it would

bring trust and speed, and ease in development.

“From our experience: informal governance is good

for the start-up phase. Formal governance often

holds heavy contracts, which slows adoption and

innovation.” (Int#3). The other five stakeholders,

however, suggested a formal governance structure;

their reasons are security, the legal value of data, and

avoidance of discussion. “Always go for a formal

governance, that way you avoid discussion.

Stakeholders who participate will know what to

expect, it will give clarity.” (Int#7). The governance

structure of this DPE thus forms a continuum ranging

from informal to formal.

The case study shows that the ownership status is

important. “it must be clear who is the owner of a

certain element of the ecosystem” (Int#5). “The

ownership of a platform is always crucial, but the

ownership of data is important as well.” (Int#8).

However, the participants have mixed opinions

regarding the centralization of the ownership of a

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DPE. While the participants from the platform owner

think a central approach should be taken, the other

actors prefer a decentral approach. For example

participant (Int#6) stated “the ownership of the data

platform should be centralized and owned by one

company which is composed of tooling, data storage,

and master data management.”

Regarding decision rights, as the second

mechanism of governance structure, all participants

believed that all actors should, in some form, have

decision rights and be able to influence decision-

making for the platform, and most of them agree that

the decision-making responsibility remains with the

platform owner. “It would be an advantage for the

data source to have influence in the decisions on the

platform, and which data is available.” (Int#4)

Accessibility and Control of the platform are

linked to the formal or informal structure of the

governance. The control can be formal, using the

input and output control mechanisms, or informal

using the self-control and clan control mechanisms.

The accessibility is governed by entry rules

(Goldbach et al., 2018; Katz et al., 2019; Lis & Otto,

2020; Schmeiss et al., 2019; Schreieck, Wiesche, et

al., 2017; Thies et al., 2018).

All participants believed input control is a

relevant mechanism in the governance of their DPE

because it protects the level of quality in the

ecosystem and allows verifying “what” and “who”

enter the ecosystem.

“it checks if the standards are

met and prevents pollution in the platform.” (Int#7)

Output control was seen as a relevant mechanism

by all participants in order to verify the quality of the

output and check compliance with regulations

“Output control is important to not lose track of data,

to meet regulation and to secure rightful access.”

(Int#6).

Four participants believed that self-control is

relevant in combination with formal control

mechanisms. “Solely self-control will not be sufficient

in the ecosystem.” (Int#1). Two participants conclude

that it is relevant to check the quality of data or to ask

verifying questions to platform users or data

providers.

Finally, six participants argued that due to

competition in the ecosystem clan control is a

mechanism with limited possibility, and

“safeguarding privacy is required” (Int#6). While all

eight interviewees corroborated the formal control

mechanisms (i.e., input and output control), a smaller

number of participants stated that informal control

mechanisms (i.e., self-control and clan control) are

relevant if they are along with formal control. These

findings are in line with generic platform studies that

suggest that formal control is in use in the early stages

of platform ecosystem development, while formal

control is in use during implementation (de Reuver &

Bouwman, 2012; Hodapp et al., 2019).

Besides the control aspects, all participants

argued that entry rules which regulate entrance to the

ecosystem are a relevant and necessary mechanism

for the governance of their DPE in order to protect the

competitive position of data providers. “data

providers with the same products should not be able

to see their competition.” (Int#2). According to

Schmeiss et al. (2019) control mechanisms, like entry

rules, require a clearly articulated set of values that

allows competing stakeholders to collaborate. Five

participants stated that the platform owner should set

up these rules, with input from platform users.

Trust and Values aspect of governance means

that all parties in the ecosystem should have a shared

set of values and build trust in the reliability and

continuance of the platform (Huber et al., 2017;

Schmeiss et al., 2019; Schreieck, Wiesche, et al.,

2017).

Seven participants concluded that trust is a

relevant dimension for the governance of a DPE, with

three interviewees pointing out it is crucial. “No trust,

no trade. Trust is the basis, and always is part of a

transaction.” (Int#6). They also agreed that trust

should be complemented with regulations, or that

regulations even enforce trust in their DPE.

Seven interviewees argued that shared values are

relevant, “Someone’s shared values will be the basis

for how they collaborate. It does not have to be a

written set of values, but more if a person’s behavior

is trustworthy.” (Int#2). When a shared value is

established among ecosystem partners, informal

control can be more effective than formal controls

(Mukhopadhyay & Bouwman, 2019). In contrast, one

participant (Int#3) believed that “shared values are

unnecessary as rules and regulations will

automatically enforce the trust.”

Incentives govern the way value is shared in the

ecosystem. This can be done by a pricing mechanism

in which parties pay for access or get a fee for their

data (Lis & Otto, 2020; Schmeiss et al., 2019;

Schreieck, Wiesche, et al., 2017). Both monetary and

non-monetary rewards were mentioned by the

participants. “Data sources should be paid for the

worth of their data within the total consolidation.”

(Int#1). “receiving relevant data is an incentive for us

as it helps us better serve our clients and get insights

(Int#8).

Boundary Resources

refers to technical tools

(such as APIs and SDKs) and documentation . These

tools are complemented with documentation and can

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570

be standardized or personalized, depending on the

needs of the collaboration. They support the platform

ecosystem and internal collaborations (Foerderer et

al., 2019; Huber et al., 2017; Schreieck, Wiesche, et

al., 2017).

Six participants argued that boundary resources

keep the ecosystem dynamic and working properly.

“It is good to get ecosystem-wide standards, agreed

to by all stakeholders” (Int#7). While only one

participant argued that it depends on the value of the

data (“Platform owners should decide if the data

providers are valuable enough to be facilitated for

free.” (Int#6)) the others stated that boundary

resources should be freely available in the DPE. The

data platform owner should facilitate the technical

platform to put different data together— this includes

data exchange, storage, and integrations— with any

necessary APIs, and reporting capabilities (such as

documentations) (Int#7).

Data Governance in the platform ecosystem

refers to defining, applying, and monitoring the

patterns of rules and authorities for ensuring

accountability for the entire data lifecycle (creating,

processing and sharing, and using) (Janssen et al.,

2020). It encompasses three types of governance

mechanisms. The procedural mechanisms encompass

strategy, policies, contractual agreements,

performance management, and compliance

monitoring. The structural mechanisms encompass

the roles and responsibilities, and location of

decision-making authority. The relational

mechanisms encompass communication, training,

and coordination of decision-making. Data

governance also encompasses six data decision

domains: data quality, data security, data privacy,

data architecture, data lifecycle, and metadata

(Abraham et al., 2019; Katz et al., 2019; Lis & Otto,

2020).

As confirmed by seven participants data quality,

data security, and data privacy are the most important

data decision domains for the governance of the DPE.

“Security, privacy, and quality are the core of data

governance, if these are not good the other aspects

(e.g., data lifecycle, and metadata) do not matter.”

(Int#6).

The participants agreed that data quality should be

part of the governance framework; “To protect the

level of data quality, unified quality standards could

be part of the DPE entry rules.” (Int#7). As stated by

participant (Int#1)“The platform owners should make

sure that the data in the ecosystem is on a qualitative

level to provide information for all the stakeholders.”

In addition, all participants believed that data

providers should be responsible for their data quality,

while the platform owner is responsible for the data

quality on the platform.

Seven participants argued that data security

should be governed with an ecosystem-wide

approach because “in this way, all actors have a good

security basis.” (Int#4). Most of them suggested a

split in responsibility: the platform owner should

arrange the security of the platform and set up a

minimum standard for the ecosystem, while the other

actors are responsible for their own data security

within these minimum standards.

In terms of data privacy, the interviews stated that

“with sensitive personal data, privacy is a big

governance component”(Int#8). The seventh

participant noted that all data privacy concerns should

be handled before data arrives on the platform.

Furthermore, multiple privacy measures were

mentioned by the interviewees. In this regard, for

instance, the participant (Int#8) stated “As a platform

ecosystem you should have a data privacy officer, an

application to track sensitive data, a privacy-by-

design framework, and a DPIA (Data Protection

Impact Assessment) framework.

We compile the discussions with eight

participants with the identified governance

mechanisms from the literature to propose a

governance framework for DPE in the construction

sector as illustrated in table 2.

Table 2: A governance framework for DPEs in the

construction industry.

Governance structure

Ownership Decision rights

Accessibility and control

Input control

Output control

Informal control

Entry rules

Trust and values

Trust building Shared values

Incentives

Pricing mechanisms Non-monetary rewards

Boundary resources

Software tools Documentation

Data governance

Data decision domains

Structural mechanisms

Procedural mechanisms

Relational mechanisms

5 DISCUSSION AND

CONCLUSION

In this section, we first discuss the main findings of

this study and then conclude with limitations and

suggestions for future studies.

Towards a Governance Framework for Data Platform Ecosystems in the Construction Industry

571

In the introduction section of the interviews, the

participants were asked to name the governance

mechanisms prior to discussing the theoretical list.

They suggested six governance mechanisms: security

and privacy, data ownership, data access, API

connection, open standards, and legal consideration

for the protection of personal data. Although most of

these suggestions could easily be placed into the

initial list of governance mechanisms, they provide

further confirmation of the relevance of these

mechanisms besides their corroboration in the second

part of the interviews.

In the confirmatory part of the interviews, the

initial list of governance mechanisms was used

explicitly. All but one (i.e., clan-control) mechanisms

were recognized at least by six interviewees as

relevant mechanisms for governing their DPE.

In the closing part of the interview, we asked the

participants if a subject was missing in the

governance framework. While six participants think

it is complete, the other two participants missed an

aspect related to the human side, the culture within

the ecosystem, and the soft side of governance. This

is subject to further investigation in future research.

In the closing part, we also asked about the usefulness

of this governance framework to be used in the

planning phase of the DPE development. Six

interviews concluded that this governance framework

is useful in the planning of a DPE, because “The

framework provides us with a list we usually do not

think about explicitly. If we want a good and safe

platform, this is very important.” (Int#3). The

participants also noted that it will help thinking

broader than the current business case. “It gives you

the opportunity to do a holistic analysis, to not only

focus on the business case” (Int#1), and “It will help

to design the data platform ecosystem in a good way,

in accordance with laws and regulations” (Int#8).

As a concluding question, we asked if the

participants are going to use this governance

framework in their own DPE. Four participants stated

they are going to use it. Six of our eight participants

were asked to receive the proposed governance

framework, to help them and their partners to enhance

the design and outline the governance for their DPE.

This gives an indication of the usefulness of the

proposed governance framework.

As the focus of most prior studies is on other types

of platform ecosystems, the data governance

mechanisms have not been introduced by those

studies as a distinct aspect of platform governance

(Hein et al., 2016; Mukhopadhyay & Bouwman,

2019), while in some other studies their focus is

solely on data governance aspect of platform

ecosystems (Lee et al., 2018; Lis & Otto, 2020;

Nokkala et al., 2019). This paper is a first step

towards closing this research gap by developing a

theoretically founded and practically relevant

governance framework for DPEs. To this end, by

conducting SLR, we identify a set of governance

mechanisms. We then provide empirical evidence on

the relevance as well as the reasoning for the

relevancy of almost all governance mechanisms by

performing 8 interviews of a single data platform

ecosystem in a housing construction sector.

The proposed governance framework for DPEs in

the construction industry contains six dimensions—

governance structure, accessibility and control, trust

and values, incentives, boundary resources, and data

governance— and their associated governance

mechanisms. Compared to the existing governance

frameworks of platform ecosystems, our proposed

framework provides a more comprehensive and

integrative view of the governance of data platform

ecosystems. We contribute to the platform literature

by developing a theoretically grounded and

practically relevant governance framework for DPEs

in the construction industry. We also add to the

existing knowledge by providing empirical insights

into the governance mechanisms that are relevant for

the early stage of DPEs in the construction industry.

The results of this study provide first insights into

governance mechanisms that practitioners in the

construction industry need to consider especially

during the early stage of the DPEs development (i.e.,

the planning process). The proposed governance

framework can be used by decision-makers of DPEs

in the construction industry to make more-informed

governance decisions as well as to evaluate and

improve the governance mechanisms of their DPEs.

Despite the contributions of this study, it is

constrained by limitations. The first limitation is that

while we have followed a systematic review process

to identify governance mechanisms of DPEs, due to

potential bias in the coverage of the literature, we do

not claim that the set of identified governance

mechanisms is complete or exhaustive. Future

research may identify further governance

mechanisms and thus can improve our proposed

governance framework. For instance, the human side

was mentioned by multiple participants as a missing

element; this is a subject to future research for further

validation. Second, we executed a single case study

on the ecosystem level in a housing construction

sector. Therefore, the generalizability of the results is

limited to other similar DPEs in the same sector.

Future studies could examine this governance

framework in other contexts to improve the

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generalizability of the results. Third, the case study

shows different opinions about the relevance of clan

control, as most of the participants see it as not

relevant. But before we can decide on the relevance,

and its inclusion in the framework, further research

into clan control is necessary. The fourth limitation is

that we evaluated the relevance of the identified

governance mechanisms in the planning phase of a

DPE in the selected case. The proposed governance

framework can serve as a starting point for future

empirical work on the governance aspect of DPEs in

the construction industry as they mature. To examine

how governance mechanisms might evolve over time

further research should examine the proposed

governance framework in the other phases of DPEs

development by conducting a longitudinal study.

ACKNOWLEDGEMENTS

The author wishes to thank Sander van Dienst for his

invaluable help in performing this study.

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