Insights from Big Data Economy of Finnish Company Trust,

Consumer Confidence and Data Exchange: An Empirical Evidence of

Structural Equation Modelling

Sunday Adewale Olaleye

Business School, Jamk University of Applied Sciences, Rajakatu 35, 40200, Jyväskylä, Finland

Keywords: Big Data Economy, Company Trust, Consumer Confidence, Data Exchange, Structural Equation Modelling,

Finland.

Abstract: In the emerging development of the big data economy, data exchange under management information systems

is increasingly relevant in society and organizations. Recent studies have contributed to the literature on data

exchange by investigating security, privacy, errors, and risk issues. However, there is less attention on the

impact of company trust and consumer confidence on data exchange. Interoperability is a panacea to data

exchange, and it refers to the capacity of information systems to share data and information, reducing the

likelihood of information gaps and blind spots. This study fills the gaps in the literature on data exchange and

the big data economy by giving a deeper understanding of data exchange through the influence of company

trust and consumer confidence with the lens of structural equation modelling, thus, consolidating the big data

economy and management information systems. The results from the model tested indicate the direct

relationship between the path coefficient of company trust, consumer confidence and data exchange. The

study emphasized the theoretical contribution and managerial implications and gave future research direction.

1 INTRODUCTION

The earlier research emphasised the importance of

data across different business sectors and discovered

a gap for further investigation of data exchanges

(Elsaify & Hasan, 2021). Data exchange is a tool that

facilitates the sharing of information between

different stakeholders, and it is an offshoot of

information management systems. A data exchange

provides access to data points from all over the world,

which is used to power data-driven marketing and

advertising campaigns worldwide. By gaining access

to a Data Management Platform (DMP), companies

will harness previously outdated or inaccessible data

to power their marketing campaigns. The utilization

of a data exchange offers the companies and

customers a limitless number of information points,

which data stakeholders can use to close any gaps in

their current audience's profile and learn about their

interests even when they are not on their site. On the

other hand, a considerable amount of data

management might assist businesses and their clients

https://orcid.org/0000-0002-0266-3989

in increasing their target audience prospects. Despite

the goodness of data exchange, security, privacy, and

errors are some challenges that call for the data

stakeholders' urgent attention. There is a need for a

standardized, unified privacy computing framework

(Jianpeng, Zheng, Du & Boran, 2022) to have the

maximum benefits of safe data circulation in data

trading marketing.

Interoperability is a panacea to data exchange,

and it refers to the capacity of information systems to

share data and information, reducing the likelihood of

information gaps and blind spots. Interoperability

refers to a method that is both focused and intelligent

in its use of current data to achieve the best practices

in information management while ensuring that all

fundamental rights, those about data protection

regulations, are fully respected. To better secure and

strengthen the company's internal security, the

management of data within companies needs to be

made more effective and efficient while also taking

into consideration fundamental rights.

Olaleye, S.

Insights from Big Data Economy of Finnish Company Trust, Consumer Conﬁdence and Data Exchange: An Empirical Evidence of Structural Equation Modelling.

DOI: 10.5220/0012060100003541

In Proceedings of the 12th International Conference on Data Science, Technology and Applications (DATA 2023), pages 241-246

ISBN: 978-989-758-664-4; ISSN: 2184-285X

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

241

A recent study posits that data relay through

mobile nodes is subject to security attacks due to

openness in vehicular delay-tolerant networks

(VDTNs). The study utilized the proximity-based k-

nearest neighbour (kNN) classification model as a

panacea for trust estimation to calculate the global

trust values (Chourasia, Pandey & Kumar, 2022).

Corroborating the challenges of data exchange

(Ritter, et al. (2022) highlight the risk of silent errors

in the reactor design while engaging with manual data

exchange and proffers transforming the solution of

the digital engineering ecosystem to the engineering

teams. Further, Sukiasyan, Badikyan, Pedrosa &

Leitao (Sukiasyan, Badikyan, Pedrosa & Leitao,

2022) examined the secured data exchange in the

context of the Industrial Internet of Things and

created a threat model as a panacea with spoofing

identity, tampering with data, repudiation threats,

information disclosure, denial of service and

elevation of privileges (STRIDE) approach and

likewise (Freeman & Garcia, 2022) in the context of

Urban Air Mobility Environments.

Recent studies have contributed to the literature

on data exchange by investigating security, privacy,

errors, and risk issues. However, there is less attention

on the impact of company trust and consumer

confidence on data exchange. This study fills the gaps

in the literature on data exchange and the economy by

giving a deeper understanding of data exchange

through the influence of company trust and consumer

confidence in the Finnish environment. This study

answers the following research question: what impact

do company trust and consumer confidence have on

Finnish data exchange? The second part of the study

reviewed the literature on company trust and

consumer confidence and formulated two hypotheses

that connect data exchange. The third method

discussed the methodology employed, the fourth part

discussed the data analysis and results, and the fifth

part concluded with the study implications,

limitations, and future studies.

2 THEORETICAL MODEL AND

CONSTRUCT DEFINITION

An understanding of the company's target audience

and potential new segments is gained through this

study, which benefits both the companies and the

customers involved. Through meaningful data

exchange, it is possible to identify and exploit

behavioural patterns, demographic similarities,

frequently used devices, and interests or affinity

groups among a company's customers. This pattern

allows a company to understand its target customers

better and extrapolate potential customers based on its

data and exchanges with other companies or

customers. It is advised that businesses use data

exchanges to ensure that their data is reliable and of

high quality to ascertain that their efforts yield

tangible results and an outstanding return on

investment.

Data exchange is a critical resource for publishers,

advertisers, agencies, and others who either lack

sufficient first-party data to make broad campaign

decisions or seek additional information on their

existing consumers. Rather than that, companies can

rely on data exchange to provide them with the

information necessary to optimize their strategy and

go forward confidently. Here are a few ways

agencies, marketers, publishers, and other data

stakeholders can maximize their outcomes by

utilizing a data exchange. A data exchange helps the

stakeholders rise above the competition, target

potential customers, find new customers, learn about

the target audience, extend reach, and increase

conversions. It also helps companies develop

personalized product offers from their DMP with data

exchange. Data Exchange enables the company to

manage and restrict business data internally and

externally with suppliers, partners, and customers. It

is critical to grant and revoke access to data via

standard and personalized listings.

This section focuses on the proposed theoretical

model and the construct definition.

2.1 Company Trust

A company is like a leader with whom people wish to

connect. Based on this analogy, a company expects to

live an exemplary life for the customer and the

consumer. For companies to act in a leader's capacity,

it is essential to develop trust stores with the core three

drivers of authenticity, logic, and empathy (Frei &

Morriss, 2020). Company trust is multidimensional.

Malkamäki, Hiltunen & Aromaa, (2021) examined

trust as a strategic management process in the context

of grocery trade business based on the dimensions of

trust of ability and competence, benevolence,

integrity, affective-based and cognitive-based trust.

The authors indicate the prominence of trust at the top

management level to pave the way for innovation,

engagement, and efficiency in company strategic

implementation.

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2.2 Consumer Confidence

Consumer confidence is an essential indicator that

explains the possible amount the consumer is willing

to spend. Between 1995 and 2022, there is a history

of declining consumer confidence in Finland, and

there was negative consumer confidence of -10.5 in

March 2022, according to Statistics Finland (2022).

Consumer confidence measures either optimistic or

pessimistic consumer financial expectations. Juhro &

Iyke, (2020) investigated the consumption-

confidence relationship of the Indonesians based on

combined data on consumer and business confidence

and found that sentiments contribute to the forecast

accuracy of consumption between four and thirteen

per cent. Similarly, Zorio-Grima & Merello, (2020)

examined the causality relationship between

consumer confidence and the economic information

ecosystem in Spain and discovered a causality

influence of consumer confidence and financial

information on each other. Also, Vanlaer, Bielen &

Marneffe, (2020) worked on the financial information

of the global financial crisis for thirteen years and

probed how the European financial crisis can defect

consumer confidence. The duo results from the study

show that confidence in the household financial

situations had a more significant effect on household

savings than confidence in the general economic

situation.

3 METHODOLOGY

The term "data economy" refers to an economy in

which several different operators work together in the

same environment to ensure the accessibility and

usability of data, as well as to make use of data and

use it as a basis for the creation of new applications

and services. This concept is widely accepted and

growing steadily. The data of this study was collected

as an online questionnaire. It was commissioned by

Sitra through an international IHAN project and made

available as open data. Innolink, an international data

consultant, undertook the data collection as a business

decision-maker panel between April and May 2019.

Large enterprises and SMEs in Finland, France,

Germany, and the Netherlands participated in the

survey. The study sample size accounts for n=1667

responses, and the questionnaire focuses on

companies' awareness, attitudes, and commitment to

business potential enabled by a fair data economy.

The study focused on the Finnish data economy, one

of the leading global data economy countries. Only

Finnish data was extracted from the total responses,

and the sample size for Finnish data accounts

for n=427. The questionnaire captured the company's

descriptive information such as the accompanies

attitudes towards data sharing, capabilities for using

data economy, the margin between attitudes and

commitment to fair data principles, data economy

challenges and its potential. Through the data

cleaning process of the data, six variables emerged as

independent and dependent variables (variable

pricing, customer experience, company trust,

consumer confidence, privacy, and data exchange).

Subject to more rigorous Structural Equation

Modelling (SEM) assumptions such as common

method bias, multivariate normality,

multicollinearity, linear relationship between the

observed variables and their constructs, and no

missing data, only three variables met the criterion of

SEM. This study used company trust and consumer

confidence as independent variables to predict data

exchange as a dependent variable. The study utilized

Warp Version 7.0 to conduct SEM analysis.

3.1 Hypotheses Development

A belief in another person or entity's dependability,

integrity, and honesty is referred to as trust. It is

essential to social capital and promotes teamwork,

collaboration, and cooperation between people and

groups.

Trust is the cornerstone of intimacy and the secret

to creating lasting bonds in interpersonal interactions.

Trust is essential for partnerships and transactions to

succeed in business since it establishes credibility and

reduces uncertainty. In societal and institutional

environments, trust is also crucial. People depend on

institutions like governments, legal systems, and

financial systems for stability and security. People are

more likely to obey these institutions if they have

their trust.

The study of Malkamäki, et al. (2021; Fregidou-

Malama & Hyder, 2021), shed more light on

multilevel trust as a means of strategic international

marketing for healthcare services, and the study

culminates multilevel trust as individuals, company

performance, and context. This multilevel trust will

help companies increase their business relationships

and achieve consumer acceptance. Resolving

consumer sentiments is one of the ways of building a

company's trust. Istanbulluoglu & Sakman, (2022)

spotlights five dimensions of handling based on

timeliness, redress, apology, credibility, and

attentiveness with consumer's trust mediation. The

authors conclude that the credible perception of

consumers about the company will be high in

Insights from Big Data Economy of Finnish Company Trust, Consumer Conﬁdence and Data Exchange: An Empirical Evidence of

Structural Equation Modelling

243

handling the complaint, and this situation determines

the consumer's repurchase intention. Based on the

literature reviewed, a company's trust is an antecedent

of data exchange. This study hypothesized that (H

the lower the company's trust, the lower the intention

for data exchange; (H

): the higher the company's

trust, the higher the intention for data exchange.

On the other hand, the construct of confidence

refers to an individual's belief in their abilities, skills,

and competencies to accomplish a particular task or

achieve a specific goal. It is a psychological concept

that reflects a person's self-assurance and conviction

in their abilities. It can manifest in various domains

of life, such as personal, social, academic, and

professional contexts.

Confidence varies from situation to situation. For

example, an individual may feel confident in their

ability to give a presentation but need more

confidence in their ability to perform well in a sports

competition. Factors such as past experiences,

feedback, social comparison, and personal beliefs can

also influence confidence.

Confidence is often associated with positive

outcomes such as high motivation, resilience, and

effective performance. On the other hand, low

confidence or self-doubt can have negative

consequences such as anxiety, avoidance, and

underperformance. Confidence is a complex and

multidimensional concept that significantly shapes an

individual's thoughts, feelings, and behaviours in

various situations.

Macready, et al. (2020) combined consumer trust

and consumer confidence to assess the food value

chain in five countries across Europe and discovered

that consumer trust beliefs predict confidence in food

and technology integrity. Diffey, (2020) reflected on

the possibility of sunscreen product development

jeopardizing consumer confidence and concluded

that retention of consumers' confidence in the

veracity of products is paramount. The synthesized

literature shows that consumer confidence could be

optimistic or pessimistic, and this study hypothesized

that (H

): the lower the consumer confidence, the

lower the intention for data exchange; (H

) the higher

the consumer confidence, the higher the intention for

data exchange.

3.2 Data Analysis

This study tested the hypothesis showcased in the

proposed conceptual framework in Figures 1 and 2

with WarpPLS 7.0 statistical software. WarpPLS is

suitable software for Variance Structural Equation

Modelling (VSEM). This software has been used in

earlier studies (Sanusi, Olaleye, Agbo & Jatileni

(2021; Olaleye, Sanusi, Mark & Salo, 2020).

3.3 Measurement Model

This section ascertains the quality criterion of the

tested model. The variables adopted reliability and

validity were tested, and the model fit was evaluated,

and the results are shown in Table 1. The results were

satisfactory based on the algorithm of WarpPLS

(Kock, 2020). Further, the Composite Reliability

(CR), Cronbach Alpha (CA) and Average Variance

Extracted (AVE) of all the variables were found

satisfactory in comparison to the thresholds of 0.7 and

0.5. Similarly, the factor loading of all the items was

more significant than 0.5 (See Table 2). Variance

inflation factor (VIF) that measures the level of

multicollinearity is below 3.5, which conforms to the

acceptable thresholds and variance inflation factors

(VIFs) that ascertain the extent of correlation between

one predictor and the other predictors are less than

1.5, which certify the acceptable boundary. All these

criteria certify the model’s internal consistency,

reliability, and proper loading of the variable’s items.

This statistical model shows the relationship

between trust and confidence as independent

variables and data exchange as dependent variables.

Further, the model provides valuable information for

understanding the causal relationships between key

data variables.

4 RESULTS

This study confirmed that the adopted variables and

their items align with their thresholds as specified in

the existing literature. The study formulated two

hypotheses to understand the relationship between

Company Trust, Consumer Confidence, and Data

Exchange. The results from the model tested indicate

the direct relationship between the path coefficient of

company trust, consumer confidence and data

exchange. Hypothesis (H

) company trust → data

exchange ( β = 0.48, t = 10.62, p <0.001). H

consumer confidence → data exchange (β = 0.17, t

= 3.58, p <0.001). The null hypotheses (H

0a,b

) were

rejected for trust, confidence, and data exchange

against the alternative hypotheses (H

1a,b

). Company

trust is the highest predictor of data exchange and has

the largest effect size of 0.25. The entire model

explained R

= 30% variance, while 70% could not be

explained. This R

is higher than the weak threshold

of 0.25.

DATA 2023 - 12th International Conference on Data Science, Technology and Applications

244

Table 1: Data Quality Assessment.

Avera

ath coefficient

(

APC

)

=0.326, P<0.001

Avera

e R-s

uared

(

ARS

)

=0.298, P<0.001

Average adjusted R-squared (AARS)=0.295,

P<0.001

Average block VIF (AVIF)=1.052, acceptable if <=

5, ideally <= 3.3

Average full collinearity VIF (AFVIF)=1.287,

acce

table if <= 5, ideall

<= 3.3

Tenenhaus GoF (GoF)=0.443, small >= 0.1,

medium >= 0.25, large >= 0.36

Sympson's paradox ratio (SPR)=1.000, acceptable if

>= 0.7, ideally = 1

R-squared contribution ratio (RSCR)=1.000,

acce

table if >= 0.9, ideall

= 1

Statistical suppression ratio (SSR)=1.000,

acce

table if >= 0.7

Nonlinear bivariate causality direction ratio

(NLBCDR)=1.000, acceptable if >= 0.7

Note: APC - Average path coefficient; ARS - Average R-squared; AARS -

Average adjusted R-squared; AVIF - Average block VIF; AFVIF - Average

full collinearity VIF; GoF - Tenenhaus GoF; SPR - Sympson's paradox ratio;

RSCR - R-squared contribution ratio; SSR - Statistical suppression ratio;

NLBCDR - Nonlinear bivariate causality direction ratio; A - Acceptable.

Table 2: Measurement Quality Assessment.

Note: DE – Data Exchange; CT – Company Trust; CC – Consumer

Confidence; ES – Effect Size

5 CONCLUSIONS

This study combined the theories of fragments of

company trust and consumer confidence to examine

the viability and the impact of data exchange in

Finnish context. Finland is one of the countries in the

world that is leading in data economy hence the study

focusses on Finland as a case country. The study

shows that company trust and consumer confidence

have very strong impact in data exchange. To be more

specific the higher the perception of the company

Figure 1: Proposed Model Hypotheses.

Figure 2: Data Exchange Model Path-Coefficient Results.

Table 3: Tested Hypotheses Results.

Path

Connection T Ratio

Value

HYP

Results Remark

CT → DE 10.623 0.001 Significan

Accepted

CC → DE 3.579 0.001 Significan

Accepted

Note: DE – Data Exchange; CT – Company Trust; CC – Consumer

Confidence

trust, the higher the data exchange and the perception

of consumer confidence also increases the data

exchange. Earlier researchers examined the data

exchanges among firms and found that data

exchanges took place between firms in similar

industries with relevant data science capabilities

(Elsaify & Hasan, 2021). This study only focused on

bilateral data exchanges without considering

company trust and the consumer confidence.

Similarly, to this result, the study of Nicolaou &

McKnight, (2006) confirmed the significant

relationship of trust and the intention to use exchange

data. This study theoretically contributes to the

literature of data exchange and the emerging data

economy literature by explaining how company trust

and consumer confidence impact the data exchange.

This result is consistent with the existing study that

mentioned that trust is a key predictor for customer

retention (Olaleye, et al. 2020).

The exchanging of data between different systems

is what data exchange is all about. It establishes an

instance of target data based on the data received after

Insights from Big Data Economy of Finnish Company Trust, Consumer Conﬁdence and Data Exchange: An Empirical Evidence of

Structural Equation Modelling

245

it has been delivered from the source and obtained

through a data link. The enormous amount of data

produced due to the modern digital transformation is,

for the most part, locked away in proprietary silos. Data

exchange has become increasingly important as a

means of accomplishing effective information sharing

and fostering the efficient use of valuable data because

of the tremendous improvements that have been made

in information and computing technologies. Sharing

information in a trusted, compliant, secure, auditable,

and accessible way is necessary for a successful data

exchange. Managerially, suggest that the data

stakeholders should pay close attention to trust and

confidence through security assurance, privacy

concern data ethics.

This study is not without a limitation. The

coefficient of determination of this study indicates

30% variance. The future research should extend this

study by adding and tested more relevant variables

hypothetically. Also, the future researchers combine

the artificial intelligence and blockchain to examine

trustworthiness of data exchange. Further, the future

researcher should examine the blockchain-based data

economy.

ACKNOWLEDGEMENTS

This work was supported by the Foundation for

Economic Education (Liikesivistysrahasto), Finland

[grant numbers: 16-9388], and [18-10407].

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