Business Model Scenarios for Engendering Trust in Smart City Data

Collaborations

Ruben D’Hauwers

, Nils Walravens

, Pieter Ballon

and Koen Borghys

imec-SMIT-VUB, Pleinlaan 9, Brussels, Belgium

Keywords: Business Model, Data Collaboration, Ecosystem, Smart City.

Abstract: Smart city data has the potential to be used to support evidence -based decision making. Yet, to fulfil this

potential, private data needs to be shared with governments in data collaborations, in which. trust between the

participating actors is a major prerequisite. This paper aims to provide an answer on 1) what the business

conditions and challenges for smart city data providers collaborating to share sensitive data to engender trust

among each other are and 2) what the impact of open and closed business model configurations on the

trustworthiness of smart city data collaborations is. A case study analysis of the Smart Retail Dashboard aims

to set up a data collaboration between smart city data providers and cities to improve the evidence-based

decision making of local retail policy makers. An analysis is made of the data sharing business model

conditions of trustworthiness in an open, closed and hybrid model. The paper concludes with the advantages

and disadvantages of each scenario to engender trust and how these scenarios solve the earlier determined

challenges.

1 INTRODUCTION: DATA

COLLABORATIONS FOR

EVIDENCE-BASED DECISION

TOOLS

City officials aim to make decisions based on

objective and measurable parameters. Technology

and data have some role to play in supporting or

implementing policy (Hollands, 2008), but how that

role should be filled remains unclear and is often the

result of trial and error. The trend towards data-driven

policymaking, which refers to policy decisions made

based on objective empirical and evidence-based

evaluation research about the context, need and

efficacy of different policy programs rather than

subjective intuition (Janssen & Helbig, 2018) is

raising in importance.

As urban policymakers are faced with unique

opportunities, the utilisation of urban big data

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technologies to make advancements towards the

sustainable development of a city becomes more

prevalent in cities (Kharrazi et al, 2016). Indeed,

while data-driven policy making has always been

present to more or lesser extent in policy making, the

availability of vast amounts and new forms of data

introduced by new information and communication

technologies, as well as the increasing ability to

combine data from diverse sources and domains can

provide new types of tools and insights to policy

makers. This data can be captured from Internet of

Things solutions (e.g., sensors in public parking

garages, passer-by sensors), privately owned data

(e.g., transaction data of financial institutions…) or

detailed data on the public domain (e.g., from satellite

imaging).

Local governments have access to open data

sources and their own data, but the access to different

Internet of Things and private data is limited. As the

data is currently owned by different stakeholders,

D’Hauwers, R., Walravens, N., Ballon, P. and Borghys, K.

Business Model Scenarios for Engendering Trust in Smart City Data Collaborations.

DOI: 10.5220/0010522300670075

In Proceedings of the 18th International Conference on e-Business (ICE-B 2021), pages 67-75

ISBN: 978-989-758-527-2

data silos which are not interconnected occur. To

remove these data silos, data sharing between the

different players is required to support the cities to

make the evidence-based decision-making

opportunities a reality. A potential solution are data

collaboratives, which are “cross-sector (and public-

private) collaboration initiatives aimed at data

collection, sharing, or processing for the purpose of

addressing a societal challenge” (Susha, Janssen and

Verhulst, 2017).

Several challenges are preventing data

collaborations between private companies and

governments, as many companies are reluctant to

share data due to a lack of trust (Richter & Slowinski,

2019; Naslund, Kembro, & Olhager, 2017;

Spiekermann, 2019; Dahlberg & Nokkala, 2019;

European Commission, 2018). Data sharing can

cause commercial risk, as companies refrain from

sharing sensitive information with other companies,

which might reduce a competitive advantage

(Martens, 2020 ; Thilo & Verhulst, 2017; Jarman &

Luna-Reyes, 2016; Agahari, 2020). Additionally,

sharing data might cause companies to lose control

over their data and requires redesigning the

governance in inter-organizational relationships

(Abraham, 2019). Therefore, trust is regarded as a

prerequisite for a data ecosystem to survive among

strong competitors (Schreieck et al., 2016; Hein et al.,

2016; Abraham, 2019).

Business model literature can shed new light on

the challenges related to trust in data ecosystems. The

way how the business model of a data collaboration

is designed is of high importance for increasing trust,

as misuse or abuse of data is getting more prevalent

(Lee et al, 2017). The structure of data collaborations,

roles, trust, openness, and control are key aspects in

the design of the business model (Schreieck et al.,

2016; Hein et al., 2016; Tiwani et al., 2010).

The strategy of opening or closing an ecosystem

Schreieck et al., 2016; Hein et al., 2016) is an

important decision in the design of the network-level

business model of the data collaboration. A closed

model heavily regulates the access to the platform and

is limited to a selection of partners. An open model is

aimed at a broad and unknown group of participants

(Spiekerman, 2019). Limited research has been done

on what the impact of the openness is on the levels of

trust and the willingness to collaborate between

players in the data ecosystem. In this paper, the

authors aim to give an answer to the following

questions:

• What are the business conditions and challenges

for smart city data providers collaborating to

share sensitive data in order to engender trust

among each other?

• What is the impact of open and closed business

model configurations on the trustworthiness of

smart city data collaborations?

These questions are analysed in this paper through

applying the Data Sharing Business Model

Framework (D’Hauwers et al., 2021) which can be

found in figure 1, on an evidence-based decision tool

being developed in Flanders, Belgium: the Smart

retail Dashboard. First, the Smart Retail Dashboard is

introduced in section 2, followed by an analysis of the

Smart Retail Dashboard ecosystem, covering the first

research question ‘What are the business conditions

and challenges for smart city data providers

collaborating to share sensitive data in order to

engender trust among each other’ in section 3. Next,

different business model scenarios will be presented

for the collaboration between companies in the Smart

Retail Dashboard, answering the question ‘What is

the impact of open and closed business model

configurations on the trustworthiness of smart city

data collaborations?’ in section 4. Finally, the

different business model scenarios are compared and

we explore to which extent they cover the business

conditions and challenges, in the discussion section

of the paper.

2 CONTEXT AND AIM

2.1 Case Study: Smart Retail

Dashboard Project

The increased demand for data-driven policy making

for the local economy led to the ‘Smart Retail

Dashboard’ project, initiated by the Flemish

Governmental Agency ‘VLAIO’ (Vlaams

Agentschap Innovatie en Ondernemen), which is

responsible for innovation and entrepreneurship in

the region. The aim of the Smart Retail Dashboard is

to support policy makers in Flemish cities with

making decisions based on urban data sources

through collaborations between public and private

data sources. Within the scope of this project,

researchers from the Interuniversity Microelectronics

Centre (IMEC), a research and development

organization based withing Flanders, are managing a

feasibility study to assess the need for gathering

different existing data sources both from within the

government (including socio-demographic data, data

on opening hours etc.) as well as from external/

private/smart city data sources and visualizing this

ICE-B 2021 - 18th International Conference on e-Business

data in a Smart Retail Dashboard in order to support

policy makers and eventually retailers in making

decisions based on actual smart city data. The use

cases and required data of the Smart Retail Dashboard

are shown in Table 1.

Table 1: Use cases and data sources of the Smart Retail

Dashboard.

IMEC performs the feasibility study in distinct

phases:

• Phase 1: Defining the needs and challenges

regarding use of data of cities and retailers.

• Phase 2: Defining the requirements of cities and

retailers for a Smart Retail Dashboard.

• Phase 3: Defining the conditions and availability

of private data sources.

• Phase 4: Open call

for smart city data sources and

IT providers to build and pilot the Smart Retail

Dashboard; and

• Phase 5: Run a pilot version of the Smart Retail

Dashboard to test it in two cities.

The project is in phase 4 at the time of writing, where

an open call for smart city data sources and IT

providers to build and pilot the Smart Retail

Dashboard is developed. Based on the outcomes of

research phase 3, the conditions and availability of

private data sources, a business model analysis was

performed which is the subject of this paper.

2.2 Methodology

In section 3 of the paper, an analysis of the smart city

data provider ecosystem for the Smart Retail

Dashboard will be performed employing a case study

methodology. The scope of a case study is “an

empirical inquiry that investigates a contemporary

phenomenon within its real-life context, especially

when the boundaries between phenomenon and

context are not clearly evident” (Yin, 2014). The data

sharing business model framework (D’Hauwers,

Walravens, 2021), is used as a tool to analyse data

sharing ecosystems in a research setting. It also

provides the building blocks to design business

models for data sharing on a network level. It is based

on four distinct factors:

• Value: How is value created and captured through

financial models?

• Data governance: How is the quality of data

ensured?

• Ecosystem trust: How is the trust in the ecosystem

ensured?

• Data trust: How is the trust in the data ensured?

Each building block is made up of a different factor.

For example, the building block “value” is

determined by ecosystem value, value proposition,

etc. For each factor, there are different choices which

need to be made to determine a business model

configuration, which means that ecosystem value can

be transaction-centric or data-centric. A combination

of different choices is the foundation of a business

model configuration, as the distinct factors are likely

to influence each other. These factors constitute the

business model choices that will need to be made,

which results in the business model configuration of

a data ecosystem.

The framework serves as a useful tool to develop

a topic list to perform and analyse interviews. The

framework is applied in a research setting to identify

the status of the Smart Retail Dashboard. The analysis

is based on interviews with 11 companies, selected

based on their current activities in providing

evidence-based decisions tools, as well as based on

the analysis of the data needs of cities, which was an

outcome of phase 1 and 2 of the Smart Retail

Dashboard research. Based on the analysis, the main

challenges for developing the business model of the

Smart Retail Dashboard are identified.Business

model scenarios are developed based on the data

sharing factors of an ‘open model’, ‘hybrid model’

and ‘closed model’ as developed by (Spiekerman et

al., 2019) and analysed based on the interviews and

on workshops with the agency VLAIO with the

market data collaboration conditions in the smart

retail dashboard

Based on the data sharing business model

framework (Table 2), an analysis is made of the data

sharing ecosystem of the Smart Retail Dashboard.

Below, an explanation is given based on the four main

components of the data sharing business model

framework: value, data governance, ecosystem trust

and data trust.

Business Model Scenarios for Engendering Trust in Smart City Data Collaborations

Table 2: Data sharing business model Smart Retail

Dashboard.

2.3 Value

The Smart Retail Dashboard requires data-centric

services (services on data-analysis and data

visualisations) from different stakeholders, such as

banks, telecom providers, passer-by data providers

(e.g., from WIFI-sniffers or Bluetooth beacons) and

other service providers in order to provide

information services to cities in the form of a retail

dashboard. The ecosystem value of the Smart Retail

Dashboard comprises of ensuring that public

authorities can make decisions based on actual data.

In order to provide this value, the different actors

combine smart city information and data such as

transaction data, passer-by, visitor profiles and so on,

to support cities to make decisions regarding retail

plans, mobility plans, events, city marketing. As the

policy demands might shift over time, the required

data sources might also change. This requires

flexibility in terms of the service offering of the Smart

Retail Dashboard.

The revenue model for the different actors ensures

monetary incentives to share data and is based on

license fees and usage fees. The interest of the

different cities and the ecosystem value is high, as it

can enable cities to ensure the sustainability of the

local economy, especially after shops in Flanders

were closed for a significant amount of time after the

COVID-19 crisis. Yet, the budgets the cities must

allocate to pay for the Smart Retail Dashboard are

limited (10.000EUR up to 30.000EUR per year).

Additionally, in Belgium between 20 and 50 cities

might be able to be willing to pay for the Smart Retail

Dashboard, thus the total addressable market is small.

The costs of data processing and standardization in

the case of combining a lot of data might be high,

which poses a challenge to make the development of

the Smart Retail Dashboard realistic.

Given the societal value of the Smart Retail

Dashboard, governmental support of higher

governments (on the Flemish level) is required. An

important criterion for choosing an appropriate

business model will be to ensure that overall costs are

not too high, thus still making the Smart Retail

Dashboard an interesting opportunity for

participating data platforms and data providers.

2.4 Ecosystem Trust

There is a limited ecosystem trust due to commercial

risk, especially between competitors. Additionally,

there is no trust that certain players might reshare the

data with external stakeholders, which might cause

the companies to lose commercial value.

Additionally, the privacy risk is high in the

ecosystem.

Data ownership is decentralized with a limited

number of dominant players, such as banks and

telecom providers, or by public entities such as the

city councils. As the data in the smart retail dashboard

concerns personal information (transaction data,

passer-by, and visitor profiles), the data is subject to

the GDPR legislation and might only be shared after

several privacy check-ups. Additionally, companies

might refrain from data sharing due to the public

opinion regarding data sharing, as in e.g., Kortrijk (a

mid-sized city in Flanders) a data-sharing

collaboration between the city council and a telecom

provider came negatively in the media (Datanews,

2019). As the data also concerns proprietary data that

companies do not wish to share with competitors or

with cities, as the commercial value might be lost.

The customer relationships are in some cases

direct (for telecom providers and banks) but could

also be indirect (passer-by data owned by the city

council). Ethical and legal questions might arise on

whether the citizens need to provide consent in order

to be able to share the data, which is currently not the

case in the case of passer-by data.

2.5 Data Governance

Privacy infringement is a considerable risk in the

Smart Retail Dashboard, especially in the case when

data from different data providers is combined. The

data owner and the data platform have the role to

ensure processes to avoid the risk of re-identification

of anonymized data. The Smart Retail Dashboard

needs to conform with the GDPR legislation

(European Commission, 2018), which might

contradict with the PSD II legislation (European

ICE-B 2021 - 18th International Conference on e-Business

Commission , 2018), which supports the sharing of

data between financial providers and open data

directives. On one hand, the personal data needs to be

protected, but on the other hand, the Smart Retail

Dashboard data concerns public data which might

need to be shared when possible.

To ensure the quality of the data, different quality

standards are required, such as the

‘Definitiehandboek Drukte in de Stad’ (Smart

Flanders, 2018), a definition handbook in which

contains arrangements between Flemish cities on

structured definitions of profiles and characteristics

that can be used when measuring crowdedness in the

city. The quality of the data will need to be monitored

by the data platform, and the quality of data needs to

be ensured by the individual data providers.

2.6 Data Trust

The trust in the data is low, especially with the city

councils involved, mainly because different standards

were used in the past, which led to a low historical

and geographical comparability of crowdedness in

the city.

As a result, the Smart Retail Dashboard needs to

comply with standards in the “Definitiehandboek

Drukte in de Stad” (see above). Additionally, OSLO

standards (open data standards) (Vlaamse Overheid ,

2012) and OASC Minimal Interoperability

Mechanisms (OASC, 2019) can ensure

interoperability between the different companies.

These standards can make sure the cities and the

companies will be able to share data in a comparable

and trustworthy way. Additionally, the data is

currently not traceable by the owners of the data, and

thus companies have no control over who can use the

data and whether the data can be reshared. Using

licenses, companies exercise more control over who

can use the data. In this case, licences need to allow

the consultation and re-use of the data, but not to

reshare the data.

2.7 Main Challenges

Based on the analysis of the data collaboration

conditions, the following challenges are the major

barriers for a Smart Retail Dashboard data

collaboration:

• Due to the competitive nature of the market, there

is a limited trust in the ecosystem, resulting in a

low willingness to share data.

• Due to the nature of the data (personal data), the

collaboration needs to ensure trust can be created

in the processing and gathering of the data.

The following two points are important preconditions

for the business model to be successful:

• Develop a value proposition which is easily

adaptable to the changing city needs.

• Due to the limited total addressable market, a

realistic revenue model and collaboration

model needs to be identified.

3 BUSINESS MODEL

SCENARIOS

The market conditions show that many actors operate

in the ecosystem in a competitive environment. In

order to overcome this lack of trust in the ecosystem,

collaboration models need to be determined.

The factor utilised to develop scenarios are the

data sharing model parameters ‘open, hybrid or

closed’ (Spiekerman et al., 2019). Based on these

parameters, the major questions that can be raised is

whether a model should be established which is:

• Open: a collaboration where all data owners can

join the data collaboration,

• Closed: a closed consortium with only selected

data owners,

• Hybrid mode: a closed consortium with the

possibility to add data owners when required.

Below, the three scenarios are discussed, showcasing

the possible value networks of the data collaboration

with different positive and negative factors related to

the scenarios.

3.1 Open Model

In the open model, all data providers are allowed to

contribute to the Smart Retail Dashboard if they fulfil

basic criteria, and receive a fee based on the

percentage of their data contribution to the final

offering. Additionally, the city could add data of their

own, acquired from third-parties or gathered by

themselves, on the data platform. That way, the data

platform can utilize the city data to enrich the data

insights gathered on the data platform. Also, other

data platforms could be integrated with the Smart

Retail dashboard platform (e.g., legacy systems,

governmental dashboards…).

The data platform is a neutral player, who can be

trusted by all the parties. The neutral player could be

a trusted governmental player or a trusted private

partner depending on which entity is trusted by the

ecosystem. In the case of the Smart Retail Dashboard

this needs to be a player who cannot get personal

benefit from selling the data, and thus cannot be a data

provider him or herself to be independent. Thus, a

Business Model Scenarios for Engendering Trust in Smart City Data Collaborations

data provider cannot play the role of a data platform

as they might have individual objectives. The data

platform is the final responsible for the data

governance, which concerns the data quality and

compliance to (privacy) regulations. It has a broker

role, ensuring to gather the data with the use of APIs

and to standardize the data. The data platform

combines the data, while managing re-identification

risk and ensures encrypting data where needed.

Figure 1: Open model Smart Retail Dashboard.

Additionally, the data platform coordinates the

data providers by allowing who can contribute their

data, based on predetermined criteria. Additionally,

as potentially different data providers are

competitors, the data platform keeps the data

separated in silos, so the different data providers do

not have access to data of its competitors. The trusted

data platform should set up governance rules on who

can access the data, who can contribute to the data

platform and what are the guidelines for

collaborating.

3.2 Closed Model

In the closed model, a limited amount of data

providers creates a consortium. The consortium is

composed of complementary players, who do not

compete but collaborate. The data providers receive a

fee based on the percentage of their data contribution

to the final offering to the cities, as is negotiated in

the beginning of the collaboration model. Adding

data by the cities is not possible in this model.

The data platform can also be a data provider, if

this player has the technological and coordination

capacities to fulfil this role. Also, the data platform

needs to be trusted by the other participants of the

consortium to play this role. As it concerns a closed

ecosystem, the trust is created through agreements

and contracts between the closed consortium prior to

setting up the platform. As the different participants

have full control over who is part of the ecosystem,

trust is a precondition to enter the closed consortium

at the beginning of the collaboration. As the partners

have mutual benefits of entering in this partnership,

trust arises out of this discussion.

Figure 2: Closed model Smart Retail Dashboard.

The data platform is the final responsible for the

data governance, thus the data quality and compliance

to (privacy) regulations, yet all the partners have an

important responsibility. The data platform thus

combines the data, while managing re-identification

risk and ensures encrypting data where needed.

3.3 Hybrid Model

In the hybrid model, a limited amount of data

providers creates a consortium. The consortium is

composed of complementary players, who do not

compete but collaborate. The data providers receive a

fee based on the percentage of their data contribution

to the final offering to the cities, as is negotiated in

the beginning of the collaboration model. Yet,

through subcontracting additional data providers

could be added to the consortium, either on a short

term – or long-term basis. The data platform decides

which subcontractors can be added to the consortium,

based on requests of the cities. Additionally, the city

could add data of their own (acquired from third-

parties or gathered by themselves) on the data

platform. That way, the data platform can utilize the

city data to enrich the data insights gathered on the

data platform. Also, other data platforms could be

integrated with the Smart Retail dashboard platform

(e.g., legacy systems, governmental dashboards).

Like the closed model, the data platform can also

be a data provider, if this player has the technological

and coordination capacities to fulfil this role. Also,

the data platform needs to be trusted by the other

participants of the consortium to play this role.As it

concerns a closed ecosystem, the trust is created

through agreements and contracts between the closed

consortium prior to setting up the platform. As the

different participants have full control over who is

part of the ecosystem, trust is a precondition to enter

the closed consortium at the beginning of the

collaboration. As the partners have mutual benefits of

entering in this partnership, trust arises out of this

ICE-B 2021 - 18th International Conference on e-Business

discussion. As it concerns a model where the core

consortium members decide on which external

partners can enter the consortium, they can shape the

guidelines and conditions for entering the partnership

and thus can base this decision on whether they trust

the potential new entrant.

Figure 3: Closed model Smart Retail Dashboard.

The data platform is the final responsible for the

data governance in a similar role as in the open model,

as it needs to ensure the data quality and compliance

to (privacy) regulations. It has the role of integrating

and gathering data (from consortium members

subcontractors and of the city), ensuring to gather the

data with the use of APIs and to standardize the data.

The data platform thus combines the data, while

managing re-identification risk and ensures

encrypting data where needed.

4 DISCUSSION

The research questions guiding this paper were 1)

What are the business conditions and challenges for

smart city data providers collaborating to share

sensitive data to engender trust among each other?

and 2) What is the impact of open and closed business

model configurations on the trustworthiness of smart

city data collaborations. Based on the challenges

determined in the third section of this paper, the

different models were analysed, resulting in the pro-

and contra arguments discussed below.

The different options are analysed using input

from interviews with potential data providers, a

workshop with data providers and with the Flemish

Governmental Agency ‘VLAIO’ (Vlaams

Agentschap Innovatie en Ondernemen), based on the

following factors:

• Ecosystem trust: to which extend is the low trust

in the ecosystem due to the competitive nature of

the market solved?

• Data Trust: to which extend is the privacy risk

solved?

• Value proposition: to which extend is the value

proposition flexible to adapt to the needs of the

cities?

• Cost model: to which extend is the cost model

affordable and kept on a sustainable way

Figure 4 shows the main differences between the

open, closed and hybrid models, as discussed below.

Figure 4: Open vs. hybrid vs. closed model.

The open model is a model with a high

complexity, as it requires competitive companies to

collaborate with each other. As it concerns an

ecosystem approach with a multitude of partners, an

ecosystem governance to maintain trust is required.

Technological solutions might tackle this issue by

keeping the data in different silos, as well as a model

with a trusted third party with no stakes in selling the

data. Yet, this can still cause trust issues, as

competitive companies might not want to collaborate,

and fear that it would lower their competitive

advantage. In terms of data, it also requires a more

complex technological solution to combine data, as

more data sources are included. Thus, a higher risk

for re-identification exists, which might cause

anonymized personal data to be ‘re-identified’.

Additionally, to engender data trust technologies and

agreements between the partners are required to

ensure the data is traceable to ensure provenance.

Agreements need to be made between the different

partners regarding interoperability, which require

collaboration between the entire ecosystem. The

advantage of this model is the highly flexible

solution, as it would be easy to allow additional data

providers to enter which would enable it to adapt to

the needs of cities. However, due to the more complex

technological solution and the amount of

coordination that would be required, the costs might

be higher.

The closed model only consists of

complementary players which are not competitive. As

it concerns a bilateral collaboration with a limited

number of partners, a more traditional business

relationship can be maintained. Thus, the closed

Business Model Scenarios for Engendering Trust in Smart City Data Collaborations

model relates to higher trust in the partnership as

agreements are made prior to entering the

collaboration. Additionally, as there is a limited

amount of data which needs to be combined, lower

risks of re-identification occur, which results in a

lower coordination effort. There is also a lower

degree of technological solutions required to ensure

provenance, due to smaller number of partners.

Agreements need to be made between the different

consortium member interoperability, which require

bilateral discussions. The coordination and technical

costs will be significantly lower compared to the open

model. The disadvantage is the limited flexibility vis-

à-vis the needs of cites as less data sources are

included.

The hybrid model is a combination of the open

and closed model, as only complementary players are

part of the collaboration. Similar to the open model, a

more traditional business relationship can be

maintained as it concerns bilateral agreements. Thus,

the closed model relates to higher trust in the

partnership as agreements are made prior to entering

the collaboration. Additionally, there is a limited risk

of re-identification resulting in a less complex model.

There is also a lower degree of technological

solutions required to ensure provenance compared to

the open model, due to smaller number of partners,

yet for the subcontracting partners province solutions

are required. Trust is created through contracts and

agreements prior to the partnership, and new entrants

can enter based on whether they are trusted by the

core consortium members. As subcontractors are

allowed into the model, additional data sources could

be added upon request, increasing the flexibility of

the Smart Retail Dashboard, while still being able to

reduce the costs significantly compared to the open

model.

5 CONCLUSION

To develop a trustworthy business model, different

challenges need to be overcome regarding trust in

the ecosystem (Do the different partners trust each

other?) and trust in the data (Can privacy

requirements be maintained? Can provenance of the

data be ensured? Can interoperability between the

partners be guaranteed?). When overcoming these

challenges in a data collaboration, important

preconditions are the value proposition and the cost

model occur. In the design of data collaborations, the

openness of the data collaboration is a crucial factor

which influences the trustworthiness of the business

model. Thus, important decisions need to be made in

the design phase of the data collaboration. The level

of openness determines whether the collaboration

will consist of bilateral relationships or networked

relationships in an ecosystem.

To engender trust in the open model, where the

collaboration operates in an ecosystem, several

complexities need to be overcome. The collaboration

between the companies might include trusted

intermediaries and needs to consider the neutrality of

the partners. The number of agreements that need to

be made regarding standardisation, provenance and

interoperability are numerous, and technological

solutions are required to ensure that the data cannot

fall into the wrong hands or to ensure privacy issues

cannot prevail. Thus, the cost will be higher, whilst it

might generate a more appealing value proposition

and will enable the ecosystem to innovate faster in the

case of changing customer needs. In a closed and

hybrid model, the complexities are of a lower

degree, as it consists of fewer relationships, which

can be solved through mutual trust and agreements,

and the data interactions are less complex. Thus, the

cost will be lower, but the model will be less able to

provide an appealing and flexible value proposition.

When deciding which model is the most beneficial for

a data collaboration requires a trade-off between

complexities and related costs, with the desired value

proposition and flexibility it wants to provide.

In further research, the different degrees of

openness in the trustworthy data collaborations will

be analysed and validated with a more concrete

division of roles, as well as a real-life trade-off will

be made of the decisions related to the business model

of the Smart Retail Dashboard.

ACKNOWLEDGEMENTS

The Smart Retail Dashboard is funded by VLAIO

‘Vlaams Agenschap Ondernemen en Innovatie’

https://vlaio.be/nl.

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