Public Officials' Attitudes toward Data-Driven Government: Exploring the Differences between Managers and Practitioners in the South Korean Government

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Public Oicials’ Aitudes Toward Data-Driven Government:
Exploring the Dierences between Managers and Practitioners in
the South Korean Government
Minsu Ra
Department of Public Administration, Kookmin University
rms98@kookmin.ac.kr
Byoung Joon Kim
Department of Public Administration, Kookmin University
kimbj@kookmin.ac.kr
ABSTRACT
is study comparatively analyzes the dierences between man-
agers and practitioners in public ocials’ aitudes toward Data-
Driven Government. While previous research has extensively cov-
ered the technological and institutional aspects of data-driven gov-
ernment, there has been a notable gap in understanding how and
whether data-driven government can be facilitated at an organi-
zational behavior level. is study aempts to ll that gap by
emphasizing the perceptual mechanism toward participation in a
data-driven administration process. It examines the determinants of
public ocials’ aitudes toward a data-driven government, posit-
ing that organizational behavior factors should be signicantly
considered in future research. e study also provides meaningful
results from comparing dierences of aitudes toward data-driven
government between managers and practitioners in the Korean
government. Additionally, it oers policy recommendations and
theoretical implications by integrating UTAUT (Unied eory of
Acceptance and Use of Technology) and additional variables based
on Job Characteristics eory.
CCS CONCEPTS
•Social and professional topics; • Computing / technology
policy; Government technology policy; Governmental regu-
lations;
KEYWORDS
data-driven government, unied theory of acceptance and use of
technology, job characteristics theory
ACM Reference Format:
Minsu Ra and Byoung Joon Kim. 2024. Public Ocials’ Aitudes Toward
Data-Driven Government: Exploring the Dierences between Managers and
Practitioners in the South Korean Government. In 25th Annual International
Conference on Digital Government Research (DGO 2024), June 11–14, 2024,
Taipei, Taiwan. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/
3657054.3657100
1 INTRODUCTION
is study aims to comparatively analyze the dierences between
managers and practitioners in public ocials’ aitudes toward
is work is licensed under a Creative Commons Aribution International 4.0 License.
DGO 2024, June 11–14, 2024, Taipei, Taiwan
© 2024 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0988-3/24/06
https://doi.org/10.1145/3657054.3657100
Data-Driven Government (DDG). Despite the numerous theoretical
discussions on managers and practitioners in the government sector,
there is a lack of comparative studies that consider the dierences
in aributes of each rank of ocials, particularly those between
managers and practitioners in the public sector. is gap in previ-
ous research has led to limitations in understanding the aributes
of each rank of public ocials and organizing the administrative
strategies for the management of public service personnel. In the
current context, understanding the dierences between managers
and practitioners, particularly in aitudes toward DDG, is essential
for reforming the government and making their decision-making
process more scientic.
However, there has been a lack of discussion on the impact of
public ocials’ aitudes and perceptions on the actual use and ac-
ceptance of DDG in perspectives of organizational behavior. ere
have been some discussions in South Korea that have gained posi-
tive international aention from the United Nations e-Government
Development Index, UN Online Participation Index, and OECD Dig-
ital Government Index. South Korean e-government development
has shown signicant improvements in government capacities such
as eciency, transparency, and citizen convenience. South Korean
government strategies for the DDG have been excessively leaning
to the legal, technological, and institutional sides, although they
also have put eorts into the DDG in terms of organizational side
such as operating the commiee for activating DDG at a national
level, CDO policy, and so on. Coulthart & Riccucci (2021) and Kim
(2020) also pointed out that further studies on street-level public
ocials’ use of data are required because they are expected to play
signicant roles in facilitating the DDG at the working level.
In this context, this study focuses on the public ocials’ ai-
tudes toward DDG in terms of organizational behavior, posing the
following research questions: 1) What determines the use of data
in the process of administration tasks and policy? 2) What are
the dierences between managers and practitioners in aitudes
toward DDG? Organizing determinants to nd results of the impact
of organizational behavior variables, this study reviews the previ-
ous literature on UTAUT (Unied theory of acceptance and use of
technology) in terms of digital government and e-government. In
addition, regarding the extension of UTAUT, this study reviews job
characteristic theory, focusing on work autonomy and innovative
culture. Guided by the UTAUT model and Job characteristic theory,
this study organizes determinants of participation in DDG, which
are performance expectancy, facilitating resources, facilitating lead-
ership, performance management, work autonomy, and innovative
culture. To explore the dierences in aitudes toward DDG be-
tween manager-level public ocials and practitioner-level ocials,
this study also conducts two multiple regression analyses from two
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DGO 2024, June 11–14, 2024, Taipei, Taiwan Minsu Ra and Byoung Joon Kim
dierent sets of data, which are classied into two distinct groups:
manager-level and practitioner-level groups.
2 THEORETICAL BACKGROUND
2.1 Data-Driven Government (DDG)
Digital transformation has changed how data are produced, stored,
shared, and analyzed, shaping the public and private sector data
revolution [
30
]. During ongoing digital transformation, the adop-
tion of DDG is expected to have an impact on the increase of agility,
openness, and innovativeness of public organizations. OECD (2019)
emphasized that understanding the “DDG” as one of the dimensions
of digital government is necessary to successfully implement digital
government innovation, designing six main characteristics of a dig-
ital government such as government as a platform, open by default,
digital by design, user-driven, proactive, and data-driven public
sector. It is because digital government reform can be implemented
successfully if public organizations structure the data management
system and eectively utilize data for the right purpose in the pub-
lic sector. erefore, many scholars have endeavored to dene,
design, and specify the characteristics of DDG and data-driven
administration.
e DDG or data-driven policy was preceded by data-based ad-
ministration or data-based policy, which have been discussed as
alternate concepts of evidence-based policy in response to the skep-
ticism that the terminology evidence is too ambiguous to dene and
utilize in contexts of academic and practical discussion [
21
]. Many
scholars have tried to dene DDG or data-driven administration
by understanding the meaning of two components of the termi-
nology which are the ‘data-driven’ part and the ‘administration or
government’ part. In the private sector, data-driven administra-
tion has been dened as an enterprise data management system
to help systemize their goal achievement process by standardiz-
ing the data-friendly organization, policies, and instructions and
structuring the data storages and data sharing system between or-
ganizations [
26
,
29
]. Data-driven administration can be understood
to construct data-friendly organizations and services by shaping a
consistent decision-making process to access, analyze, and manage
data so that they can create visible and invisible values such as
maximizing prots and enhancing eciency and transparency in
their organizations.
e strategic use of data can be regarded as one of the most
active roles of government employees at an organizational behav-
ior level. Even though the national and municipal endeavors to
foster the institutions, technologies adoption, and governance have
remained the signicant tasks to be accomplished for DDG, from a
practical point of view, government employees’ actual use of data
can shape, facilitate, and nurture DDG at a working level, which
has been expected to draw substantial and tangible results in the
public sector such as eectiveness, transparency, trust in govern-
ment, eciency and so on. In this context, OECD (2019) and van
Ooijen et al (2019) suggested the government data value cycle, em-
phasizing the use and re-use of data at the strategic level to enable
the government sector to generate public values by public ocials
to actively participate in a data-driven administration as Figure
1 presented below. e value chain of the government data use
in public institutions presented in Figure 1 provides not only the
data-driven administration process but also how the use of data
can generate public values because of the benets from the use
of data such as optimized processes, creation of authoritative data
sources, reduced risks from fraud and error and so on. It can also
provide some valuable implications of the importance of the active
and positive role of public stakeholders in the administration and
policy process in the government sector. e thing to notice delib-
erately in this chain is that this model introduced by OECD (2019)
was presented as a cycle, showing that these kinds of innovative
changes, expected to be a benecial result of DDG, can happen
not in a linear fashion but in inconsistent processes. It can be a
tangible and substantial outcome if government employees in the
public sector proceed with continuous processes through ongoing
feedback loops.
According to Pew Charitable Trusts (2018), the supporting fac-
tors and challenging factors that promoted the use of data in the
public sector were introduced from the Pew Research Center’s in-
terviews with state ocials in the US. ose interviewed answered
that the most supporting factor was leadership, cited by 30% of
respondents and helped use data in terms of state ocials prac-
tically using and citing data. Also, stang, including skills (15%
of respondents), data sharing (11% of respondents), and data ac-
cessibility (10% of respondents), were cited as major supporting
factors in analyzing data for decision-making. On the other hand,
they not only described the scopes of challenges, but they also cited
the elements that were surmountable. State ocials cited stang
issues, including skills, as the most signicant obstacle (43% of
respondents). It was reported that despite the need and demand for
sta to interpret and use data eectively, only a few ocials are
experienced in data analytics. 35% of respondents cited data acces-
sibility as the second-greatest obstacle. To make policy decisions
accurate and evidence-based, various data, including structured
and unstructured data, were required. In terms of the demands of
data, they reported that it was challenging to create and use data for
their work due to the old system and a lack of good data resources
required to generate reports for a beer policy-making process.
In short, previous literature regarding DDG has proceeded in
various aspects such as institution, law, and organization. Yet, there
has been a lack of academic discussion on organizational behaviors
that can be observed and analyzed to nurture DDG in the public
sector since governments worldwide have been at the beginning
stage of the adoption of DDG. For this reason, this study focuses on
the perceptual mechanism of DDG at an organizational behavior
level, which can help gure out the determinants of public ocials’
active use of data in the government sector, namely public ocials’
active and positive participation in DDG with an expectation for
lling this gap of the previous literature on DDG.
2.2 Unied eory of Acceptance and Use of
Technology (UTAUT)
Since there are various forms of technologies emerging in the late
20th century and 21st century, UTAUT has been utilized and an-
alyzed to gure out the perceptual and behavioral mechanisms
toward the actual use and acceptance of technologies expected
to be adopted not only by various types of organizations such as
enterprises and public institutions but also by citizens and NGOs
372

Public Oicials’ Aitudes Toward Data-Driven Government: Exploring the Dierences between Managers and Practitioners in
the South Korean Government DGO 2024, June 11–14, 2024, Taipei, Taiwan
Figure 1: e Value Chain of Government Data Use in Public Institutions (OECD, 2019; van Ooijen et al., 2019) Note: Bold and
thin lines on the part OECD presented in the report and dotted lines on the relationship the author added from the contexts of
OECD (2019)
willing to use and accept some of the new technologies in dierent
contexts and situations [
47
,
50
]. is model has been expected to
provide a profound and broad understanding of technology use
and acceptance in contexts of digital government strategies. For
instance, Lnenicka et al (2022) analyzed additional variables such as
voluntariness of use, system quality, information quality, data qual-
ity, and trust, utilizing the UTAUT model to gure out the impact of
determinants of e-government adoption. is study gured out the
signicant relationship between them, as Al-Sha and Weerakkody
(2009) revealed that eort expectancy and social inuence have a
signicant relationship with the adoption of e-government. Also,
various factors, including trust in the internet, trust in government,
awareness, and risk perception, are conrmed as signicant de-
terminants toward the adoption of e-government in developing
countries [1, 35, 63].
Summarizing the results, including the literature mentioned
above, extending the UTAUT model in various domains can provide
valuable lenses to deeply understand the perceptual and behavioral
mechanisms toward the use and acceptance of e-government and
digital government. According to Venkatesh et al (2016), various
forms and types of UTAUT extensions can be conducted in diverse
and broad domains with statistical stability and rich theoretical
backgrounds that support designing models as Figure 2 presents.
Figure 2 also provides that new exogenous mechanisms, new en-
dogenous mechanisms, and new moderation mechanisms can be
included in UTAUT models to design and analyze for the technol-
ogy use and acceptance in the government sector and private sector,
recommending new outcome mechanisms that can be designed as
independent mechanisms of technology use.
2.3 Job Characteristics: Focusing on Work
Autonomy and Innovative Culture
Job characteristics are the concepts related to many other tasks
given by their organizations [
24
]. Commonly, job characteristics
can be dened as the reactivity of their organizational tasks and
organizational aributes consisting of diversity, autonomy, account-
ability, essential knowledge and technology, and interaction with
co-workers and their organization, which is highly correlated to
job satisfaction [
15
,
16
]. Hackman & Lawler (1971) rst suggested
the Job characteristic model to specify the concept of job character-
istic theory. ey tried to classify the job characteristics into ve
types, which are skill variety, task identication, task signicance,
autonomy, and feedback. ose aributes are commonly regarded
as requiring sorts of interaction abilities to develop for their jobs
[
16
]. As the follow-up study, aer almost four years, many other
studies have supported the theory that Hackman & Odham (1975)
and Hackman & Lawler (1971) suggested by empirically analyz-
ing the causal relationships between job characteristics and other
experiences, including the outcome of their jobs [23, 64].
However, there have been criticisms that follow-up studies, pre-
viously conducted to expand the discussion of Job Characteristics
eory suggested by Hackman & Oldham (1975), need to be more
consideration of work environments and working processes [
45
,
65
].
Regarding systemic consideration of the expansion of work char-
acteristics, Parker et al (2001) suggested the elaborated model of
work design that Figure 3 presents. eir systematic analysis also
included the links between diverse organizational initiatives and
outcomes, such as various forms of performance at the individual
373

DGO 2024, June 11–14, 2024, Taipei, Taiwan Minsu Ra and Byoung Joon Kim
Figure 2: Types of UTAUT Extensions (Venkatesh et al, 2016)
Figure 3: Elaborated Model of Work Design (Parker et al, 2001)
and organizational levels. Based on this perspective, the impact of
work autonomy on outcomes such as creativity, active participation
in making their job performance, customer satisfaction, and innova-
tion were included in this model. Furthermore, emotional demands,
organizational culture, and interdependence were also captured
in this model as parts of the relationships between antecedents.
ey also expanded work characteristics, empirically presenting
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Public Oicials’ Aitudes Toward Data-Driven Government: Exploring the Dierences between Managers and Practitioners in
the South Korean Government DGO 2024, June 11–14, 2024, Taipei, Taiwan
individual perceptions such as work autonomy and organizational
factors such as innovative culture.
In short, as Figure 3 shows, there has been discussion on the
structures of the work design model expanded Job Characteris-
tics eory, explaining various dimensions of mechanisms related
to job characteristics. From the rich explanation and analyses of
Job Characteristics eory, the causal relationship between active
aitudes toward their work and job characteristics such as work
autonomy and innovative culture can be constructed as hypotheses
of empirical analyses. In this context, this study conrms the rela-
tionship between public ocials’ active participation in DDG and
job characteristics such as work autonomy and innovative culture
in perspectives of integrating UTAUT and job characteristic theory.
3 RESEARCH MODEL AND HYPOTHESIS
DEVELOPMENT
As mentioned above, this study designed the research model con-
sidering UTAUT and Job Characteristics eory (JCT) as a view
of the expansion of UTAUT. Furthermore, this study incorporated
variables of UTAUT, such as performance expectancy, facilitating
resources, facilitating leadership, and performance management
(organizational/social inuence), and the additional variables from
Job Characteristics eory, such as work autonomy and innovative
culture. Meanwhile, the actual use of data in the administration
process or policy process reported by public ocials is considered
the dependent variable, and it is dened as participation in DDG in
this study. e perspective of this relationship is introduced, and
the proposed model is presented below.
3.1 Characteristics of Participation in DDG
between Dierent Ranks of Public Ocials
In the context of the public sector in South Korea, the characteris-
tics of public aairs can be classied by the ranks of public ocials
[36]. According to previous literature in South Korea, many other
scholars tend to classify the ranks of public ocials into two groups
which are 5th rank and over (5th, 4th, 3rd, 2nd, 1st), and up to 6th
rank (6th, 7th, 8th, 9th) according to the ministry of personnel
management in South Korea [
49
]. is study also follows this stan-
dardization. is study’s respondents (public ocials) were divided
into two groups: managers and practitioners. is study denes
public ocials in the 5th rank and over as managers and public
ocials up to the 6th rank as practitioners. According to the report
on data-based administration released by the Korean Development
Institute (KDI), there are dierences in the types of work between
practitioners and managers in the public sector. For instance, prac-
titioners tend to be involved in the public service delivery process
rather than the policy decision-making process. In contrast, man-
agers tend to be involved in the public policy decision-making
process rather than the other. In this context, this study establishes
hypotheses that the impact of determinants on participation in
DDG will dier between managers and practitioners because the
roles and tasks of the two groups can be dierent. e hypotheses
of this study are displayed below.
H1. e impact of performance expectancy on participation in
DDG will dier between managers and practitioners.
H2. e impact of facilitating resources on participation in DDG
will dier between managers and practitioners.
H3. e impact of facilitating leadership on participation in DDG
will dier between managers and practitioners.
H4. e impact of performance management on participation in
DDG will dier between managers and practitioners.
H5. e impact of work autonomy on participation in DDG will
dier between managers and practitioners.
H6. e impact of innovative culture on participation in DDG
will dier between managers and practitioners.
3.2 Research Model
Guided by the UTAUT and Job characteristic theory, an empirical
study was conducted to identify participation in the DDG of the
Korean government. DDG acceptance can be determined by two
factors such as aitude toward implementing the policy process
or the administrative tasks by using data actively and behavioral
intention to use data in the policy process. In this study, participa-
tion in DDG was analyzed as the dependent variable to identify the
use and acceptance of DDG. On the other hand, six factors were
considered as the dependent variables: performance expectancy,
facilitating resources, facilitating leadership, performance manage-
ment, work autonomy, and innovative culture (Figure 3). Perfor-
mance expectancy, facilitating resources, facilitating leadership, and
performance management were considered independent variables,
commonly dened and utilized in previous quantitative studies
using the UTAUT model, and expected to aect participation in
DDG based on the UTAUT model in this study. In addition, in
perspectives of extending UTAUT, this study tended to examine
the impact of aributes related to job characteristics and work en-
vironment. Work autonomy and an innovative environment based
on Job Characteristics eory were added to the research model in
this context.
Also, multiple regression analyses were conducted to compare
the inuence of aributes explaining public ocials’ perception
of a data-driven policy process between managers and practition-
ers. e rst part of the analysis examined manager-level public
ocials’ participation in DDG to examine if manager-level public
ocials tend to participate in the DDG by actively using data in the
process of task execution and policy process when they positively
think of performance expectancy, facilitating conditions and orga-
nizational inuence related to a DDG and job characteristics. e
second part of the analysis examined which variables determine
practitioner-level public ocials’ participation in DDG. Multiple
regression analyses were conducted twice, based on UTATU and
Job characteristic theory, to empirically analyze and compare the
results of those two parts of the models.
4 METHODOLOGY
4.1 Measure and Reliability Analysis
is study denes one dependent variable and six independent
variables for the analyses. Participation in DDG (PDDG) reects
public ocials’ reported actions regarding the frequency of using
structured and unstructured data in the process of task execution
and decision-making. Performance Expectancy (PE) reects public
375

DGO 2024, June 11–14, 2024, Taipei, Taiwan Minsu Ra and Byoung Joon Kim
Figure 4: Proposed research model based on UTAUT and Job characteristic theory Note: Sample sizes of each group used for
the study are added in parentheses
ocials’ reported perceptions of the expected performance of im-
plementing data-driven administration. Facilitating Resources (FR)
reects public ocials’ reported perceptions of data preparedness
to analyze for task execution and decision-making in their organiza-
tions. Facilitating Leadership (FL) reects public ocials’ reported
perceptions of the subjective degree to which their leadership fa-
cilitates ocials’ use of data in the process of public policy and
administration execution. Performance Management (PM) reects
public ocials’ perceptions of the subjective degree to which their
organization demonstrates the performance of the data used for
the policy process and administration execution in their perfor-
mance management. Work Autonomy (WA) reects public ocials’
perceptions of autonomy in their job environments. Innovative
Culture (IC) reects public ocials’ perceptions of their organi-
zations’ innovativeness and risk-taking tendencies. All variables
are constructed as Table 1 presents, and indicators of explanatory
variables are coded in 5-point scales (scale: 1
=
not agree at all, 2
=
not agree, 3
=
neutral, 4
=
agree, 5
=
strongly agree). Table 1 also
provides the results of the reliability tests of each variable.
4.2 Data Collection
is study is based on data taken from a national survey of public of-
cials working in the South Korean central government, which was
produced by the Korea Institute of Public Administration (KIPA).
is study makes use of research material produced by the Korea In-
stitute of Public Administration (KIPA), which has been authorized
for use according to KIPA’s regulations on the ownership and use of
said research material. e survey (structured questionnaire) was
called “Data-based Administration Survey in the Era of Cloud Data
Transformation.” It measured perceptions and aitudes regarding
DDG, including participation in DDG, performance expectancy,
facilitating resources, facilitating leadership, performance manage-
ment, work autonomy, and innovative culture. e survey targeted
adult men and women aged 20 to 59 years who are working in the
Korean central government. A total of 500 people were surveyed
through an online panel survey method. Table 2 shows the descrip-
tive statistics of respondents by gender, age, education, years of
service, and grades.
5 RESULTS
5.1 Correlation Analysis
is section presents the results and ndings of the correlation
analysis and multiple regression analyses. Some of the correlation
coecients listed in Table 3 are meaningful as pre-regression evi-
dence. Facilitating leadership is highly correlated with facilitating
resources (r
=
0.623), performance management (r
=
0.630), and
innovative culture (r
=
0.638). Years of service are also highly cor-
related with Age (r
=
0.753) because of the bureaucratic personnel
system of public organizations. While the correlation between par-
ticipation in DDG and facilitating leadership (r
=
0.410) is higher
than the other outcome-determinant correlations, none of the out-
come variables correlate highly with all determinants.
5.2 Regression Analysis
Table 4 presents the results of the multiple regression analyses of
public ocials’ participation in DDG, dividing into two dierent
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Public Oicials’ Aitudes Toward Data-Driven Government: Exploring the Dierences between Managers and Practitioners in
the South Korean Government DGO 2024, June 11–14, 2024, Taipei, Taiwan
Table 1: Indicators of Variables
Variables Indicators Scale Reliability(Cron-
bach’s 𝛼)
Participation in DDG
(PDDG)
I actively use a variety of structured and unstructured data in the
process of task execution and decision-making.
5 -
Performance Expectancy
(PE)
Data-driven administration will enhance the external
persuasiveness of government decision-making.
Data-driven administration will further promote the development
and provision of administrative services needed by the citizens.
Data-driven administration will enhance the transparency of
administration through the accumulation and openness of data and
records.
Data-driven administration will enhance the accountability of
administration through the accumulation and openness of data and
records.
5 0.836
Facilitating Resources (FR) My organization possesses enough data necessary for analysis and
utilization.
My organization has an ample supply of data essential for data
analysis and utilization, including unstructured data (e.g.,
video/photos/les), external data (e.g., data held by private
companies/non-prot organizations), and sensitive data (e.g.,
personal information).
My organization securely provides the data needed for analysis and
utilization without concerns about security.
My organization can readily obtain the necessary data for analysis
and utilization when needed.
5 0.863
Facilitating Leadership (FL) e head of my organization actively embraces data-driven
administration.
My organization is exploring strategic tasks and projects for the
utilization of data.
Data-driven administration is integrated into the vision, strategy,
tasks, and performance goals of my organization.
My organization encourages decision-making based on data
analysis and utilization in the course of performing tasks.
5 0.891
Performance Management
(PM)
My organization incorporates data-driven administration into
performance management to encourage data analysis and utilization.
My organization reects achievements related to data analysis and
utilization in performance management as a basis for recognizing
them as incentives.
5 0.877
Work Autonomy (WA) I can participate in decisions that aect the content of the work.
I have the autonomy to choose the method and procedures for
performing tasks.
I can regulate the pace of task execution and deadlines.
I can determine the sequence and priority of task execution
5 0.882
Innovative Culture (IC) My organization innovatively responds to changes in both internal
and external environments, as well as government policies.
My organization is open to information exchange and
communication
My organization conducts its operations focusing on meeting the
demands of policy stakeholders and customers.
My organization tolerates a certain level of risk-taking for the sake
of innovation.
e changes in my organization generally yield positive eects
5 0.909
Gender Man (=0), Woman (=1) 2 -
Age 20s (=1) 30s (=2) 40s (=3) 50s (=4) 4 -
Education High school (=1) undergraduate (=2) bachelor (=3) completion of
master’s degree program (=4) master (=5) completion of PhD
program (=6) PhD (=7)
7 -
Years of services Up to 5 years (=1) 6 years and over, but less than ten years (=2) 10
years and over, but less than 20 years (
=
3) 20 years and over but less
than 25 years (=4) 25 years and over (=5)
5 -
377

DGO 2024, June 11–14, 2024, Taipei, Taiwan Minsu Ra and Byoung Joon Kim
Table 2: Descriptive Statistics
Demographics Categories N %
Gender Male 303 60.6
Female 197 39.4
Age 20s 26 5.2
30s 118 23.6
40s 234 46.8
50s 122 24.4
Education High school graduate 7 1.4
Undergraduate 30 6.0
Bachelor’s degree 345 69.0
Completion of Master’s course 16 3.2
Master’s degree 80 16.0
Completion of Doctoral course 6 1.2
Doctoral degree 16 3.2
Years of services Up to 5 years 95 19.0
6 years and over, but less than 10 years 77 15.4
10 years and over, but less than 20 years 193 38.6
20 years and over but less than 25 years 55 11.0
25 years and over 80 16.0
Grades of public ocials Manager-level ocials
(Grade 5, 4, 3, 2, 1)
370 74.0
Practitioner-level ocials
(Grade 9, 8, 7, 6)
130 26.0
N 500 100.0
Table 3: Correlation matrix
a b c d e f g h i j k
a. Participation in DDG 1.000
b. Performance expectancy
0.345**
1.000
c. Facilitating resources
0.325** 0.220**
1.000
d. Facilitating leadership
0.410** 0.366** 0.623**
1.000
e. Performance management
0.267** 0.214** 0.457** 0.630**
1.000
f. Work autonomy
0.369** 0.299** 0.345** 0.378** 0.276**
1.000
g. Innovative culture
0.339** 0.265** 0.485** 0.638** 0.487** 0.455**
1.000
h. Sex -
0.089*
0.033 -0.057 -0.041 -0.056 -0.011 -0.076 1.000
i. age 0.075 0.093* -0.011 0.028 -0.011 0.093*
0.128**
-0.005 1.000
j. Education
0.175** 0.126**
0.056 0.091* 0.048 0.078 0.034 -0.034 0.058 1.000
k11. Years of services 0.053 0.107* -0.003 0.025 -0.011
0.116** 0.143**
0.097*
0.753**
-
0.106*
1.000
** p<0.01, * p<0.05
groups, manager-level public ocials and practitioner-level public
ocials, to explore the dierences in the results of the analyses
regarding variables related to a DDG in an organization-behavior
level, respectively. Regarding manager-level perceptions, perfor-
mance expectancy, facilitating leadership, and work autonomy
increased participation in DDG. e facilitating resource, perfor-
mance management, and innovative culture were not statistically
signicant in participation in DDG. Among the control variables,
the higher the level of education, the more likely individuals are to
participate in DDG. e results also showed that women were less
likely to participate in DDG than men, which required serious cau-
tiousness not to make the fallacy of hasty generalization. e age
and years of service were not signicant in participation in DDG. In
contrast, dierent results were examined in the case of practitioner-
level public ocials. For practitioner-level ocials, performance
management and work autonomy were statistically signicant in
participation in DDG. None of the variables are signicant without
those two variables.
378

Public Oicials’ Aitudes Toward Data-Driven Government: Exploring the Dierences between Managers and Practitioners in
the South Korean Government DGO 2024, June 11–14, 2024, Taipei, Taiwan
Table 4: Regressions of Public Ocials’ Participation in Data-Driven Government
Manager-level public ocials Practitioner-level public ocials
Performance Expectancy 0.224*** (0.061) -0.024 (0.120)
Facilitating Resources 0.051 (0.062) 0.156 (0.094)
Facilitating Leadership 0.192** (0.073) 0.095 (0.128)
Performance Management -0.070 (0.050) 0.260** (0.093)
Work Autonomy 0.191*** (0.061) 0.182* (0.122)
Innovative Culture 0.081 (0.074) -0.089 (0.126)
Gender (women =1) -0.092** (0.076) -0.040 (0.142)
Age 0.012 (0.069) 0.086 (0.127)
Education 0.175*** (0.038) 0.023 (0.051)
Years of services -0.015 (0.048) 0.092 (0.071)
Constant 0.072 (0.315) 1.284 (0.558)
N 370 130
F 14.967*** 4.317***
𝑅20.294 0.266
Adjusted 𝑅20.275 0.205
Note: Standardized coecients on explanatory variables and control variables, unstandardized coecients on constant variables, and
standard errors in parentheses
***p<0.001, ** p<0.05, * p<0.1
Table 5: Summary of Findings
Managers Practitioners Research
PropositionResults
[H1] Performance Expectancy →Participation in DDG + NS Supported
[H2] Facilitating Resources →Participation in DDG NS NS Not supported
[H3] Facilitating Leadership →Participation in DDG + NS Supported
[H4] Performance Management →Participation in DDG NS + Supported
[H5] Work Autonomy →Participation in DDG + + Not Supported
[H6] Innovative Culture →Participation in DDG NS NS Not supported
Note: ‘NS’ =Not Signicant; ‘+’=Positively Signicant.
In conclusion, these analyses found that performance expectancy
and facilitating leadership maered to positively increase the use of
data in the process of administration tasks and policy for manager-
level public ocials, while performance management maered
to positively increase only the practitioners’ use of data, not the
managers’ use of data. Work autonomy was statistically signicant
in increasing participation in DDG, not only for managers but also
for practitioners, while the relationship between the independent
variable and innovative culture was examined as not statistically
signicant.
Table 5 presents a comprehensive overview and succinct sum-
mary of the study’s outcomes. Notably, among the ve hypotheses
investigated, H1, H3, and H5 reveal statistical signicance within
the cohort of manager-level public ocials, while H4 and H5 demon-
strate statistical signicance among practitioner-level public o-
cials. Consequently, the ndings provide support for hypotheses
H1, H3, and H4.
6 DISCUSSION AND LIMITATION
Expanding previous works on perceptual mechanisms toward digi-
tal government, such as e-government and technology use in the
government sector, this study examined whether and how the im-
pact of individual perceptions on participation in DDG diered
between managers and practitioners in the public sector. e study
aempted to extend UTAUT with additional variables related to job
characteristic theory, which can reect organizational aributes
and help gure out the directions on how to construct DDG at
the organizational behavior level. e important eects of theory-
based determinants on the self-reported level of participation in
DDG also emphasize organizational seing and atmosphere over
individual perceptions of work autonomy, facilitating leadership,
performance expectancy, and performance management, which are
dicult to construct in the short term.
In this study, performance expectancy is positively related to
participation in DDG, not for the practitioners but for the managers.
Performance expectancy was dened to examine the expectancy
379

DGO 2024, June 11–14, 2024, Taipei, Taiwan Minsu Ra and Byoung Joon Kim
of DDG toward public performance, such as transparency, eec-
tiveness, and responsiveness of their public institutions, and those
indicators of performance expectancy are the elements that can be
related to the public values generated by government capacities
regarding DDG. It can highlight the complexity of incentivizing
the use of data across dierent organizational roles in the context
of public administration. While managers are more aware of the
inherent values in building DDG for the public good, practition-
ers may require additional support or incentives to recognize and
understand its importance. erefore, the government needs to
make detailed strategies to address the dierent perspectives and
needs of both managerial and practitioner levels, fostering a data-
friendly culture in the public sphere to make the public process
more transparent, eective, and responsive.
Performance management can be one of the signicant elements.
It was observed as a signicant determinant enhancing the practi-
tioners’ self-evaluated level of active participation in DDG, which
can be understood in contexts where performance management
and promotion can be the great motivation for actively implement-
ing their work, such as participating in DDG [
66
]. It is because
individuals tend to actively participate in their jobs if they believe
that their hard work can meet their needs. In contrast, the results
show that managers tend to use various forms of data in their work
when they are more highly aware of the expected performance of
DDG in terms of public values such as transparency, eectiveness,
and responsiveness than performance management.
Work autonomy was revealed as a signicant variable that posi-
tively aects active participation in DDG both for the manager-level
group and for the practitioner-level group. It can also be found
that public ocials tend to actively dive into their work if they can
have work autonomy in their organizations [
3
,
7
]. Similar empirical
results can also be found in Lee & Ahn (2019), which examined
the positive relationship between active public administration and
work autonomy. In short, it suggests the signicance of fostering
work environments that encourage employees’ autonomy across
all levels of organizational hierarchy. is implies that empowering
both managers and practitioners with work autonomy can be a key
strategy for promoting active participation in DDG. erefore, pub-
lic organizations should prioritize initiatives to ensure employees’
work autonomy to facilitate their eective use of data for public pol-
icy agenda-seing, policy decision-making, policy implementation,
and policy feedback.
e results also show that facilitating resources is not statistically
signicant in participation in DDG for both managers and practi-
tioners, even though each coecient is positive. According to Pew
Charitable Trust (2018), US state ocials cited data preparedness
as a supporting factor in actively using data in the policy process
and administration tasks. erefore, there can be the presumption
on why facilitating resources, namely preparedness of data, are not
in a statistically signicant linear relationship with participation in
DDG, which there are yet fewer ways to nd and use appropriate
data for them easily. With an understanding of the role of pub-
lic ocials who are major actors fostering DDG, the government
should design the relationship between institutions that can help
foster collaborative governance to establish data-sharing systems
and online platforms that can help easily nd the appropriate data
for their public aairs so that the positive perceptions of perfor-
mance expectancy and facilitating resources can lead to the high
frequencies of eective use of data in the government sector. In
addition, the government needs to provide public ocials with
various opportunities to learn and experience making policies or
implementing administration tasks by eectively using and analyz-
ing data so that not only the managers but also the practitioners can
positively understand the performance of DDG by providing dier-
ent types of education training programs between the practitioners
and managers.
is study conrmed that facilitating leadership is positively
associated with participation in DDG only for managers, not for
practitioners. e results underscore the necessity for tailored ap-
proaches in facilitating public ocials’ data-driven behavior across
dierent organizational levels. While leadership encourages the
use of data, which tends to inuence managers’ active behavior
directly, additional factors might inuence practitioners’ active use
of various forms of data in their work. e government should
consider implementing targeted strategies to enhance data literacy
and provide structured supporting tools that meet the needs of
practitioners, ensuring the comprehensive adoption of DDGs by all
public organizations. For instance, adopting the CDO (Chief Data
Ocer) policy can help public ocials enhance the actual adoption
of DDG at a strategic level.
Despite signicant ndings and evocative practical implications,
this study has some limitations. First, the relationship between
variables used in this study can be veried by various types of
analyses, such as structural equation models or logistic regression,
to seek more causal relations that can explain the dierences among
managers and practitioners. Another limitation of this study is
the generalizability of perception-based results. is study used a
specic sample and conditions, which examined the perceptions of
public ocials in the central government of South Korea. erefore,
it should be cautious to generalize the ndings to others in dierent
countries. Moreover, because this study only dened participation
in DDG as the active use of data, future studies are required to reect
the various dimensions of public ocials’ use and acceptance of
DDG and provide empirical evidence by puing diverse variables
not considered in this study.
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