Content uploaded by Jeannette Stark
Author content
All content in this area was uploaded by Jeannette Stark on Jun 29, 2024
Content may be subject to copyright.
441%+#5+10(130(13/#5+10945'/4441%+#5+10(130(13/#5+10945'/4
.'%5310+%+$3#39'.'%5310+%+$3#39'
31%''&+0)4 6312'#010('3'0%'100(13/#5+10945'/4
60'
7'3%1/+0)'4+45#0%' 0&'345#0&+0)5*'90#/+%41(5*'7'3%1/+0)'4+45#0%' 0&'345#0&+0)5*'90#/+%41(5*'
+)+5#.3#04(13/#5+1031%'44+06$.+%'#.5*+)+5#.3#04(13/#5+1031%'44+06$.+%'#.5*
00#+0#!1.(
'4'#3%*'05'3(130(13/#5+10#0#)'/'05
#00#81.(;/3%&'
'.+0#%*3'+5'3
'4'#3%*'05'3(130(13/#5+10#0#)'/'05
/'.+0#4%*3'+5'3;/3%&'
'#00'55'5#3-
3'4&'0 0+7'34+591('%*01.1)9
,'#00'55'45#3-56&3'4&'0&'
1345'09/#00
0+7'34+591(#93'65*
51345'0'9/#0060+$#93'65*&'
1..185*+4#0&#&&+5+10#.813-4#5*5524#+4'.#+40'513)'%+4
'%1//'0&'&+5#5+10'%1//'0&'&+5#5+10
!1.(00#+0#%*3'+5'3'.+0#5#3-'#00'55'#0&9/#001345'07'3%1/+0)'4+45#0%'
0&'345#0&+0)5*'90#/+%41(5*'+)+5#.3#04(13/#5+1031%'44+06$.+%'#.5*
31%''&+0)4
*5524#+4'.#+40'513)'%+453#%-"2'12.';34553#%-"2'12.';345
*+4/#5'3+#.+4$316)*551916$95*'6312'#010('3'0%'100(13/#5+10945'/4#5.'%5310+%+$3#39
'5*#4$''0#%%'25'&(13+0%.64+10+031%''&+0)4$9#0#65*13+:'&#&/+0+453#5131(
.'%5310+%+$3#39'13/13'+0(13/#5+102.'#4'%105#%5'.+$3#39#+40'513)
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 1
OVERCOMING RESISTANCE: UNDERSTANDING THE
DYNAMICS OF THE DIGITAL TRANSFORMATION
PROCESS IN PUBLIC HEALTH
Completed Research Paper
Anna Lina Wolf, FIM Research Center for Information Management, Bayreuth, Germany
Branch Business & Information Systems Engineering of the Fraunhofer FIT, Bayreuth,
Germany, anna.wolf@fim-rc.de
Melina Schreiter, FIM Research Center for Information Management, Bayreuth, Germany
University of Bayreuth, Bayreuth, Germany, melina.schreiter@fim-rc.de
Jeannette Stark, Technische Universität Dresden, Faculty of Business and Economics,
Dresden, Germany, jeannette.stark@tu-dresden.de
Torsten Eymann, Chair of Information Systems Management, University of Bayreuth,
Bayreuth, Bavaria, Germany, torsten.eymann@uni-bayreuth.de
Abstract
The digital transformation process is a critical yet challenging endeavor in shaping the digital future in
the public health sector. It involves thousands of employees in the public health sector faced with the
choice of embracing or resisting transformation. The collective impact of this choice holds enormous
potential to accelerate transformation or erect barriers. We seek to understand the factors that
contribute to resistance and acceptance among individuals by employing Rogers' diffusion of innovation
theory. We conducted 40 interviews over two years in German public health institutions and contributed
33 factors that shed light on acceptance and resistance to digital transformation. As the importance of
digitalization in the public health sector continues to grow, our research provides valuable insights to
facilitate the acceptance of digital innovations.
Keywords: Public Health Institutions, Digital Transformation Process, Acceptance, Resistance.
1 Introduction
While researchers in the field of information systems (IS) have extensively researched the acceptance
of digital innovations in various contexts (Carroll et al., 2023), there has been limited attention given to
the acceptance of digital innovations in the public health sector (Joukhadar et al., 2023). Notably, the
COVID-19 pandemic has thrust public health institutions into the spotlight, emphasizing the importance
of their digital transformation for future crisis preparedness and meeting the daily healthcare needs of
the population (Doctor et al., 2023). Digital transformation is defined as an organizational approach
involving comprehensive digital restructuring of institutions, influenced by external factors such as the
adoption of new technologies in public administration (Mergel et al., 2019). Digital transformation in
the public health sector is characterized by many challenges. Staff shortages restrict the capacity and
time available for experimenting with digital solutions, often leading to the perception of digitalization
as a threat rather than an opportunity (Iyanna et al., 2022). Additionally, public health institutions are
frequently led by medical professionals who may not inherently possess the necessary focus and
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 2
expertise for digitalization, unlike managers with IT backgrounds (Anggraeni, 2020). Furthermore, the
presence of federal structures in countries such as Germany, Austria, or Spain introduces a variety of
distinct legal regulations. These legal intricacies can impede collective efforts toward digitalization,
primarily due to conflicts of interest stemming from the constitutional separation of powers (Jaeger,
2002). Consequently, digitalization often occurs in isolated areas, leading to varying levels of progress
in different regions. In such scenarios, local health institutions face challenges in harmonizing their
digitalization strategies (Doctor et al., 2023).
Because of these challenges, digital transformation in the public health sector remains at an early stage
in numerous European countries . This early stage often coincides
with high failure rates in digital transformation projects, resulting in missed opportunities to address
existing problems and significant costs (Carroll et al., 2023; Jasperson et al., 2005). Despite the
challenges confronting European public health institutions, the digital transformation of this sector is of
paramount importance as it enhances the efficiency and effectiveness of healthcare delivery, streamlines
administrative processes, and improves data management, ultimately leading to better patient care
(Wang et al., 2021). However, according to Iyanna et al. (2022), the majority of those working in the
field of public health institutions have not fully embraced digital transformation, leading to resistance
against it. To help overcome resistance and hence to allow for shaping the digital futures together with
those who work in public health institutions, this paper aims to understand what leads to resistance and
acceptance by answering the following research question:
What factors contribute to resistance and acceptance in the digital transformation process of public
health institutions?
We employ Rogers' diffusion of innovation (DOI) theory (2003) as the theoretical lens to address this
research question. This theory delves into the acceptance of innovation diffusion, making it a suitable
framework for this paper. We apply DOI theory by analyzing 40 expert interviews conducted from 2021-
23 in the context of a Germany-
maturity model for public health institutions, now referred to as the Public Health Agency Maturity
Model (PHAMM; Doctor et al., 2023), which numerous agencies have applied to assess their level of
digitalization and strategize steps for digital transformation (Kauffmann et al., 2023). Our contribution
extends to both theory and practice. Our theoretical contribution consists of 33 factors to shed light on
the reasons behind the resistance and acceptance of digital transformation within public health
institutions. For practical contribution, we discuss how these factors can be tackled to facilitate a more
favorable stance toward digital transformation.
2 Conceptual Framework
In the area of technology acceptance and innovation diffusion in healthcare, numerous models and
theories are used to investigate the decision-making process. Two of the most frequently used theories
are the technology acceptance model (TAM) and the unified theory of acceptance and use of technology
(UTAUT) (AlQudah et al., 2021; Heinsch et al., 2021). TAM focuses mainly on individual user intention
and explains technology acceptance based on two main factors: perceived usefulness and perceived ease
of use (Davis et al., 1989). In contrast, UTAUT is an extended theory that integrates several influencing
factors, including performance expectancy, social influences, effort expectancy, and facilitating
conditions (Venkatesh et al., 2003). While these models have provided valuable insights, they also
exhibit limitations. In the field of healthcare, the explanatory power of TAM and UTAUT varies across
studies and contexts, typically spanning a range from 20% to 50% (AlQudah et al., 2021). This
variability can be attributed to the complexity of technology acceptance and the numerous influencing
factors within healthcare. Moreover, these models tend to overlook the socio-organizational and cultural
aspects of technology acceptance (Heinsch et al., 2021; Ammenwerth, 2019). For instance, TAM is
frequently scrutinized for its relevance among physicians and other highly competent groups, as one of
its primary constructs, user-friendliness, is perceived as of limited importance in such contexts (Chau
and Hu, 2002; Yi et al., 2006). Likewise, the UTAUT falls short of addressing the various stages
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 3
individuals go through in the decision-making process (Kiwanuka, 2015). These limitations leave
researchers facing what can be characterized as
confusion since there is no commonly accepted adoption model in IS," as articulated by Benbasat and
Barki (2007, p. 214). Consequently, there has been a growing demand for research focused on multistage
models encompassing the diverse phases of adoption and the associated influencing factors with these
phases (Benbasat and Barki, 2007; Blut et al., 2022).
One of the theories integrated into the development of UTAUT, specifically the DOI, already
encompasses the stages individuals go through in their decision-making process. The DOI, alongside
the UTAUT and TAM, stands as one of the most prominent and frequently used theoretical frameworks
in IS research (Guidolin and Manfredi, 2023). DOI is applied in various domains to understand the
diffusion process of innovations, such as in the exploration of the diffusion of different channels (e.g.,
Overby and Ransbotham, 2019), as well as in healthcare to comprehend the dissemination of medical
innovations (e.g., Colosio, 2022). Rogers (1962) developed the DOI in the early 1960th and provided a
more comprehensive perspective on the adoption of innovations by viewing the decision to adopt an
innovation not as an "instantaneous act" (Rogers, 2003, p. 163) but as a process consisting of a series of
stages. The DOI theory offers insights into various levels of adoption research, integrating the individual
level of innovation adoption (micro-level) into the spread of innovations within a social system (macro-
level). A key point at the macro-level is the assumption that innovation spreads within the social system
when a critical mass has adopted it. Since it is necessary in our context to first understand how the
individual perceives the innovation, we focus on the micro level and specifically look at prior conditions,
the knowledge, and the persuasion stage, which include characteristics influencing the digital
transformation (see Figure 1).
Figure 1. Decision-Making Process. Source: Own Illustration based on Rogers (2003).
According to Rogers (2003), the adoption of innovation is determined by prior conditions, which
encompass previous practice, felt needs or problems, innovativeness, and norms of the social system.
For example, felt needs or problems and an individual's innovativeness may play a pivotal role in
initiating an individual's exploration of an innovation. Furthermore, previous practice can impact how
one evaluates an innovation as individuals assess it in relation to their established practices. The
innovation decision-making process begins with the knowledge stage, where individuals first encounter
the innovation and develop an understanding of how it works. Knowledge is divided into awareness-
knowledge (knowledge of the innovation's existence), which must be present to motivate individuals to
seek knowledge about how to use the innovation (how-to-knowledge) and knowledge of how and why
the innovation works (principles-knowledge). This type of information-seeking primarily occurs in this
initial stage but may also occur in the following stage. According to Rogers, the decision-making unit's
characteristics significantly influence the individual's journey through the decision-making process,
particularly from the knowledge stage to the persuasion stage. Rogers assumes that specific
characteristics of an individual (e.g., higher education or higher social status) positively influence the
knowledge stage but do not necessarily lead to persuasion. In the persuasion stage, individuals either
form a positive or negative attitude toward the innovation, which can provide a tendency for the
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 4
subsequent decision. The persuasion stage is determined by five key characteristics of innovation: (1)
Relative advantage refers to the extent to which an innovation is perceived as better than the idea it
replaces. The perceived advantages can manifest in various ways, such as economic profitability, status
gain, or immediacy of reward. (2) Compatibility refers to the extent to which an innovation is perceived
as consistent with existing values, prior experiences, and needs. (3) Observability encompasses the
extent to which the results of an innovation are visible to others. (4) Complexity is the extent to which
an innovation is perceived as relatively difficult to understand and apply. (5) Trialability involves the
extent to which an innovation can be experimented with on a limited basis.
3 Method
This study examines the phenomenon of digital transformation in the public health sector. Given the
lack of well-established studies on how digital innovations spread in the public health sector (Joukhadar
et al., 2023), we opted for an exploratory research design (Myers and Newman, 2007). Utilizing
interviews, we seek to uncover the prevailing experiences, opinions, and sentiments of experts in the
public health sector in digital transformation. Applying the DOI to public health institutions, we gain
insights into the dynamics of resistance and acceptance throughout the digital transformation process.
A semi-structured interview guide was designed for data collection, following Schultze and Avital
(2011). A total of 40 interviews were conducted in the period from February 2021 to August 2023.
Interviews lasted about 54 minutes on average. Either one, two, or three experts took part in each
interview. The experts were recruited from diverse sectors within the public health sector, including
federal state ministries of health, higher-level public health institutions and projects, IT service
providers, non-governmental organizations, and practitioners from public health institutions across
different federal states in Germany. To ensure diverse perspectives, we carefully considered including
various genders, ages, and professions of different institutions in the sample selection process. All
interviews were conducted via video conference. The researchers used open-ended questions to avoid
-Montoya
Where do you see the biggest challenges with regard
to digitalization public health institutionWhat
went well and less well in past digitization initiatives?.
recording and transcription were conducted to perform a complete data analysis using MAXQDA. To
ensure the confidentiality and privacy of the participants, all personal data were pseudonymized.
For the analysis of interview transcripts, we employed qualitative content analysis (QCA) (Mayring,
2000). Following Mayring's (2000) recommendations for mixed inductive and deductive category
building, we initiated inductive, data-driven category development, setting two requirements for these
categories. First, based on the research question, the categories should include factors that affect the
digital transformation of public health institutions. Second, to achieve a certain degree of transferability
of the results, the categories aim to include content that is not unique to the interview participant but is
also transferable to others. Once the requirements were formulated, the interview transcripts underwent
an analysis process involving the development of inductive categories. Initially, we established
preliminary categories and documented pertinent information in memos. The initial round of interviews
was coded by one author, with two authors cross-checking the result to address potential
misunderstandings. This approach lets the authoring team draw connections with the decision-making
process of DOI theory, enabling the utilization of DOI-based categories for the subsequent deductive
coding process. For instance, during the inductive coding process, topics such as the "waterfall model"
emerged, which were then assigned to the DOI's deductive category "sequential procedure in
digitalizing". Subsequently, we applied deductive codes to all the interview transcripts. The initial
categories evolved into 33 factors, grouped into 15 overarching categories. Within the content-analytical
category system, we registered how often a factor occurs (as coded text segment and as code in
interviews) to add weight to its meaning and derive a trend for the importance of the topic.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 5
4 Results
In accordance with our research aim, we identified 33 factors that are relevant to resistance and
acceptance within the digital transformation process in the public health sector. These factors have been
categorized into four overarching core elements of the DOI theory: prior conditions, characteristics of
the decision-making unit, knowledge, and persuasion.
4.1 Prior Conditions
The initial element of the DOI theory is "prior conditions," encompassing the categories: norms of the
social system, previous practices, felt needs/problems, and innovativeness. Table 1 summarizes the
factors derived from QCA, along with the corresponding number of coded text segments and the number
of interviews in which the factor was a topic.
Prior conditions
Factors in public health institutions
# coded text
segments
in #
interviews
Norms of the social
system
Federal system
36
24
Public authority context
23
18
Diversity of location and sizes
14
13
Previous practices
Sequential procedure in digitalizing
10
8
Felt needs/problems
Data protection
26
15
Crisis situations
25
17
Compulsion
22
16
Modernization delay
18
14
Skilled labor availability
11
9
Public image
6
6
Innovativeness
Low digital competencies
41
20
Ambivalent employee sentiments in regard to
(digital) transformation
26
16
Employee engagement
15
10
Lack of innovativeness IT solution providers
13
10
Table 1. Results for the DOI Element Prior Conditions.
Norms of the social system. The DOI category "norms of the social system" encompasses three factors
identified through QCA ,
with 14 coded text segments in 13 interviews. According to the interviewees, the German public health
sector operates within a "federal system, " which leads to unclear responsibilities, a lack of overarching
planning, and slow implementation of innovations as decisions are made at several federal levels. One
interviewee encapsulated this situation by saying, "From what I've observed, it always delays, and I
imagine that it might look somewhat different in ten years. But until then, I believe I'll often have to rely
on myself.". While the federal organization of public health institutions brings its own challenges,
additional complexities arise from the "public authority context". The interviewees perceive the
pressure to perform is less intense than in market-oriented institutions. According to one interviewee,
"Public administration tends to function more as a monopolist, and citizens don't have the option to
choose whether to use its services or not.". Another aspect of this factor is the typically lower funding
available in public health institutions than market-oriented ones. Especially in Germany, public health
institutions have experienced decades of underfunding, resulting in inadequate digital infrastructure.
Another difficulty in the social system of public health institutions is the "diversity of location and
seizes". As one interviewee noted, "Digitalization demands personnel, expertise, and financial resources.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 6
And particularly in smaller districts, there's a scarcity of all these elements.". This insight underscores
the resource constraints faced by smaller public health institutions. Additionally, the location plays a
pivotal role as an interviewee remarked: "Especially in rural public health institutions, there is less
funding available, and the leadership often holds lower salary grades.".
Previous practices. In the DOI category "previous practices", the factor sequential procedure in
digitalizing was identified with ten codes. As public health institutions belong to the context of public
authority, they may not feel the same pressure as market-oriented institutions, which can lead to outdated
problem-solving approaches. One interviewee emphasized this concern: "I have seen so many inefficient
working groups and expert committees that I'm slowly losing faith that anything will change.".
Additionally, public health institutions have not yet established agile methodologies and practices.
According to another interviewee, these institutions adhere to the waterfall approach, characterized by
heavy phase sequencing.
Felt needs/problems. The DOI category "felt needs/problems" comprises five factors. Data protection
(n=26) and crisis situations (n=25) received the highest number of coded text segments, followed by
compulsion with 22 codes. The remaining factors, delay in modernization, skilled labor shortage, and
public image, received 18,11 and 6 coded text elements. An aspect that has surfaced in several interviews
data protection
protection for personal medical data, they were concerned that data protection is often a hindrance. In
this regard, one interviewee remarked, "In Germany, we seem always to aspire to find not just 100%,
but 120% solutions, which can never truly work because it's utopian. Perhaps it's time to consider
accepting that we have 99.9% security.". This concern was expressed when discussing data protection,
meaning that many interviewees found it challenging to deal with the requirements of data protectors.
This perspective highlights the ongoing debate surrounding the balance between stringent data
protection and the practicality of implementation. Also, the topic "crisis situations" is prominent. The
factor characterizes pivotal moments that challenge the resilience of the public health sector. As one
interviewee noted, "These crises led to a period where certain aspects appeared overwhelming, leaving
us with the sensation that we couldn't manage our workload." While most interviewees mentioned the
massive increase in workload, some also added the mental stress that resulted from the crisis and affected
their work experience. On the other hand, "The COVID-19 pandemic was a catalyst for digitalization.",
as another participant emphasized. Another frequently discussed factor was "compulsion," which
pertains to the mandatory use of specific digital tools, which has introduced substantial challenges within
the public health system and has often been met with resistance. As one respondent noted, "It absolutely
doesn't align with my thinking that one would go and say, 'You have to do this now!'. Even though, of
have identified is "modernization delay". Over the years, chronic underfunding has led to a substantial
lag in modernizing the public health sector. This prolonged delay has left institutions burdened with
outdated skilled labor
which for the public health institutions in Germany may be described as a skilled labor
shortage. The challenge of attracting and retaining qualified workers has added another layer of
complexity to the digitalization efforts. The "public image" factor has proven critical in the public health
sector, particularly in times of crisis when organizations are under increased media scrutiny. One
interviewee noted: "Public health institutions are currently the focus of the media, and much of what is
reported does not match our first-hand experience."
Innovativeness. The DOI category "innovativeness" encompasses four factors: igital
competencies received the highest number of coded text segments, totaling 41, followed by
mbivalent employee sentiments in regard to (digital) transformation with 26 codes. Employee
engagement has 15 codes, and 13 are related to the innovativeness of IT solution providers. In public
health institutions, innovation capacity is a complex interplay of various factors, one facet being the
low digital competencies (with some variations) of the personnel. Public health institutions host a
diverse workforce, encompassing individuals from medical to administrative backgrounds, each with
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 7
varying levels of digital proficiency. Interviewees emphasized the crucial need for support, especially
in managing software updates within public health institutions, which exposed a widespread issue of
inadequate IT expertise among the staff. In addition to digital competencies, ambivalent employee
sentiments in regard to (digital) transformation crucial factor for (digital)
transformation. Throughout the interviews, the employees' sentiments showed an ambivalence regarding
(digital) transformation, marked by a blend of caution and enthusiasm. Many employees cling to familiar
but inefficient systems and are concerned about the challenges that may arise despite recognizing the
defined objectives. Another prominent theme in this factor underscores the imperative for a shift in
mindset. It's not merely a matter of adopting new technology; it entails cultivating a workforce that is
genuinely enthusiastic and invigorated by the prospects of digitalization However, a significant
obstacle that emerges is resistance to change, although there are exceptions, where some individuals are
genuinely enthusiastic and eager to participate actively in the digitalization journey. Another essential
employee engagement, .
Participation can be particularly effective in fostering acceptance of new digital solutions for the
individuals directly involved in shaping them and their immediate colleagues. Unfortunately, not
everyone is receptive to participation, as one interviewee highlighted, saying, "But it has also been
shown that you can't involve everyone. So, you have to push ahead with some because some simply
don't want to or for various reasons, they have tendencies to resist.". Innovativeness is not only restricted
to employees but also to a lack of innovativeness of IT solution providersThe factor highlights the
challenges organizations face when dealing with IT solution providers. Interviews reveal that most of
these providers are medium-sized companies, and many public health institutions work with outdated
software manufacturers that haven't kept up with modern needs. When things do not work as expected,
employees tend to use their personal tech solutions to fill the gaps. For those who are digitally savvy,
having the right tools, particularly robust and up-to-date software, is crucial for effective digitalization,
and unfortunately, it appears that this is lacking in many cases
4.2 Knowledge Stage
The DOI element, knowledge characterizes a state where individuals first encounter the innovation
and develop an understanding of how it works. In accordance with Rogers' DOI theory (2003), we
identified the categories of wareness-knowledge rinciples-knowledge and ow-to-knowledge,
which are summarized in Table 2 and elaborated on in the following.
Knowledge stage
Factors in public health institutions
# coded text
segments
in #
interviews
Principles knowledge
Principles knowledge
14
9
How-to-knowledge
How-to-knowledge
20
13
Awareness-knowledge
Awareness-knowledge
9
6
Table 2. Results for the DOI Element Knowledge.
Principles-knowledge about an innovation is not considered crucial for its acceptance (Rogers,
2003). Within public health institutions, principles-knowledge is sometimes lacking in fundamental
aspects, including switching from handwritten lists to digital records and mastering everyday office
programs. This deficiency can be notably problematic, as emphasized by an interviewee who noted,
"For instance, the lab has completely transitioned away from handwritten lists and records. It pertains
to the fundamental aspects and the day-to-day operation of office programs.". Moreover, employees'
receptivity to new technologies can be impeded by their limited background knowledge, which hampers
their ability to fully grasp digital tools' potential. This is a recurring theme among interviewees who
indicate that while individuals may have an open mindset, their understanding remains incomplete due
to the absence of a comprehensive background in digitalization. how-to-knowledge
category, one participant noted, "Digitalization requires personnel, know-how, and money. Clearly. And
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 8
especially when you have a smaller group, there's a shortage of everything.". What's noteworthy is the
positive experience shared by one interviewee about service providers who personally visited to assist
with digitalization efforts. This hands-on approach was seen as distinct from remote digital presentations
that can be challenging, given the impracticality of expecting half of German
institutions to tune in. Awareness-knowledge is pivotal in successfully transitioning from manual to
digital processes in public health institutions. As one interviewee aptly pointed out, The earlier you
involve them in the process, the more likely they are to embrace it and encounter fewer issues during
the transition from manual to digital.". It's not merely about announcing that something new is coming
but explaining its compelling reasons, emphasizing the substantial advantages.
The - a pivotal role in shaping the individual's
progression from the knowledge stage to the persuasion stage. This element comprises three key
categories of the DOI theory as summarized in Table 3: personality variables, socio-economic
characteristics, and innovation friendliness.
Characteristics of the
decision-making unit
Factors in public health institutions
# coded text
segments
in #
interviews
Personality variables
High average age
13
9
Lateral entry
2
2
Socio-economic
characteristics
Emerging generational shift
6
6
Communication behavior
Lack of communication
14
8
Table 3. Results for the DOI Element Characteristics of the Decision-Making Unit.
he factors high average age with 13
coded text elements lateral e with two coded text elements. In public health institutions, the
high average age of employees significantly impacts the acceptance of new digital tools.
the average age in these institutions has been around 60 or older. This older
demographic often leads to resistance when embracing new software and digitalization. A second factor
"lateral entry". Personnel with backgrounds other than medicine and
administration are increasingly joining public health institutions, ushering in a transformative shift. This
influx of diverse professional backgrounds has the potential to bring fresh perspectives, interdisciplinary
insights, and a broader range of skills to public health institutions, leading to an environment that is
more adaptable and innovative in addressing the evolving healthcare landscape. Regarding "socio-
economic characteristics," we've identified the "emerging generational shift" as a factor driving
transformation in public health institutions, coded in six text segments. During the crisis, there has been
a notable influx of younger persons into these institutions. As one interviewee observed, this new wave
of professionals injects fresh perspectives and a heightened digital fluency into the field, acting as
catalysts for innovation within traditionally more conservative environments. In the category
communication behavior lack of communication,
mentioned in 18 text segments. An interviewee stated that a culture of communication is essential, but
this has not yet arrived in public administration..
Persuasion Stage
In ,
can provide a tendency for the subsequent decision (Rogers, 2003). These attitudes are typically
influenced by the perceived characteristics of the innovation
compatibility, observability, complexity, and trialability, summarized in Table 4.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 9
Persuasion stage
Factors in public health institutions
# coded text
segments
in #
interviews
Relative advantage
Workload reduction
23
15
Expenditure
15
9
Quality improvement
7
6
Compatibility
Compatibility with interconnected systems
40
21
Compatibility with existing workflows
11
10
Compatibility with daily tasks
4
3
Observability
Exchange with other public health institutions
33
18
Observability within institutions, through
colleagues and supervisors
8
5
Observability through Model Health Institutions
7
7
Complexity
Implementation and application complexity
11
10
Intuitiveness
11
10
Trialability
Trialability
7
7
Table 4. Results for the DOI Element Persuasion Stage.
Relative advantage. Relative advantage, in the context of innovation, pertains to how much better the
innovation is perceived to be compared to the idea it replaces. Within this category, workload
is the primary and most crucial influencing factor, with 23 codes. One interviewee
emphasized this point by stating, 'Digitalization is, for me, the key, especially for saving time at work.
An advantageous aspect is not only seen in the pure reduction in workload but also in the freedom
enabled by the reduction of workload, which in turn offers room for innovation, as one interviewee
emphasizes: "nd that also means you have more time for the creative aspects. In a health institution,
there are so many things you can do for public health, but often, you don't get to them.". In addition,
employees expect, above all, the reduction of mundane tasks. Beyond the quantitative lens of workload
reduction, which is currently in the foreground, q is a factor in public health
institutions. One interviewee put it in a nutshellNothing is worse than digitally mapping the existing
analog process one-to-one. That doesn't make the process any better. That means I really have to think
about how can I make the process better, make it different, change the process using digital means?".
The core consideration of the relative advantage in public health institutions depends crucially on the
weighing up of opportunities and risks. In this context, expenditure is discussed in 15 codes.
Concerns are expressed regarding the perceived "enormous effort involved in implementing
innovations. These concerns are partly rooted in the expectation of "additional work due to duplication"
or the fear that "something could get lost". In this regard, one interviewee emphasized: "The evaluation
is not purely technical, it is strongly influenced by the availability of time and resources.". Despite these
concerns, there is an awareness that although digitalization initially takes up a lot of time, it also holds
potential for the future. A reflective comment from one interviewee underlines this awareness: "On the
one hand, you have to keep the business running, but on the other hand, you also have to think long-
term. The biggest hurdle is that new software initially means additional work, not additional relief."
Compatibility. Compatibility is a crucial characteristic of innovation, denoting the degree to which a
new concept or solution is seen as harmonious with established values, prior experiences, and existing
requirements. The interviews reveal a technical and process-related perspective on compatibility. The
technical perspective, coded as "compatibility with interconnected systems", is predominant as it was a
topic in 40 text segments. The process-oriented lens with the factors "compatibility with existing
workflows" and "compatibility with daily tasks" are also critical facets, highlighted in 10 and 3
interviews, respectively. Regarding compatibility with interconnected systems, interviewees
emphasized the importance of having a central system. However, the reality is quite different, with
public health institutions currently relying on many separate systems that require interoperability. In
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 10
infection control, the public health institutions utilized a special software. Yet, integration has proven to
be a challenge in related areas, such as school entry examinations, where interactions with this software
may be necessary. An interviewee pointed out, It would be more elegant, effective, and significantly
simplify our work if such integration were fully realized through interfaces. Our interviews have
revealed that within public health institutions, compatibility holds particular significance concerning
existing workflows, daily tasks, and other interconnected systems. In the context of ompatibility with
existing workflows, it is essential to acknowledge the inherent diversity of processes within public
health institutions, stemming from their federal organizational structures and integration into communal
systems. This intricate web of workflows poses a challenge for software solutions, demanding a high
degree of adaptability on the part of the software, which often falls short of practical implementation.
An interviewee highlighted this issue, stating, The user interface is unbelievably complicated and
completely detached from our daily workflows, prompting people to continue storing files elsewhere.
Whenever something doesn't work as intended, it gets supplemented and replaced with personal tools.
This observation underscores the critical need for software solutions that can seamlessly integrate with
the workflows of public health institutions. When considering ompatibility with daily tasks it
becomes apparent that software systems must offer precise customization to align with the specific needs
of users effectively. One interviewee emphasizes that standardized software often falls short in this
regard with an example: People tend to envision using a tablet to check off tasks, but it simply doesn't
work that way because the operational environment one encounters isn't structured for mere ticking off.
Instead, it involves detailed descriptions of rooms and hygienic conditions. Considering this, software
solutions need to accommodate individualization to cater to the diverse and nuanced nature of tasks
within the public health sector. This adaptability ensures that software seamlessly integrates into daily
workflows, ultimately enhancing operational efficiency and user satisfaction.
Observability. Observability encompasses the degree to which the characteristics of an innovation are
discernible by others. In public health institutions, observability can pertain to innovations in other
public health institutions and within the institution itself. The observability within other public health
institutions involves the xchange with other public health institutions, with 33 codes. In this
context, one interviewee emphasizes the significance of "this exchange, the networking among public
health institutions, because it doesn't serve any purpose if everyone works in isolation, focusing solely
on their own tasks.". observability within institutions, through colleagues
and management . In this context, it proves invaluable when colleagues
actively experiment with digital solutions and demonstrate the benefits they bring to their peers.
Moreover, successful digitalization in public health institutions hinges on leadership that is open to the
idea. As one interviewee pointed out, "It's crucial that the leaders are not resistant to introducing new
programs that require employees to learn new things. They often prefer to stick to their routines and
workflows and may not immediately see the direct advantages of adopting a new program." Regarding
observability within other public health institutions, another concept that emerged during the interviews,
despite having fewer codes than "exchange with other public health institutions," is the idea of a "model
health institution." This concept represents exemplary institutions that are well-equipped with digital
resources and serve as compelling showcases for the benefits of employing digital tools in other
institutions. According to the interviews, it is crucial to create such role models and genuinely equip
these exemplary health institutions with the necessary IT infrastructure and digital tools.
Complexity. pertains to how challenging an innovation is perceived in terms
of comprehension and practical application. Within the public health sector, two factors were identified,
each found in eleven codes in eleven interviews. Firstly, the implementation and application
complexity of innovation, and secondly, the intuitiveness. In this context, interviewees have
observed that digital artifacts utilized within public health institutions often exhibit complexity in user
interfaces and the content they convey. One interviewee expressed his experience, stating, To be
completely honest, after about an hour and a half, I was getting a bit frustrated because I thought, my
goodness! This just seems never-ending. This highlights the importance of simplifying and
streamlining the design and functionality of digital tools to ensure user-friendliness and efficiency.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 11
Trialability. Another artifact characteristic is trialability, which measures the degree to which an
innovation can be tested on a limited scale. Interestingly, during our interviews, this characteristic was
rarely mentioned (in 7 text segments in 7 interviews), with one notable exception being the recognition
of willingness among some public health institution staff to experiment with new solutions when they
become available. This eagerness to explore innovations was underscored by their appreciation for
sit public health institutions and guide personnel in
trying new features or software. This emphasizes the value of guided experimentation and hands-on
experience in facilitating the acceptance of new technologies within the public health sector. However,
the codes for this artifact characteristic were so diverse that no distinct factors could be distinguished.
5 Discussion
This paper focuses on factors responsible for resistance and acceptance of the digital transformation of
public health institutions. To achieve this goal, we adopted Rogers' DOI theory from 1962 as our guiding
theoretical framework and contextualized it for the public health sector. Leveraging the DOI theory, our
primary emphasis centered on the stages leading up to the decision-making process, including the
ts into the
factors that sway the choice in favor of or against digital transformation. The factors identified in the
three stages are summarized in Figure 2 and will be elaborated below.
Figure 2. Contributing Factors to Resistance and Acceptance in Digitally Transforming Public
Health Institutions (R – Resistance, A – Acceptance, factors are categorized based on their priority with
key elements highlighted in bold, if present in at least ten codes)
Prior Conditions. Current research frequently examines the importance of social influence on individual
acceptance or resistance, which seems the most comparable to our constructs in the prior conditions,
especially to the norms of the social system. However, there are contradictory results in the research on
the factor of social influence (e.g., Zhai et al., 2021), which makes it necessary to contextualize this
factor. In public health, the prior conditions are characterized by public health institutions operating
within a federal governance framework and being intricately intertwined with local community
structures. Our analysis demonstrates that these conditions were classified as contributing to resistance
primarily because of the absence of economic viability and corresponding incentives and unresolved
responsibilities across different levels of authority. Because of the federal organization, public health
institutions exhibit considerable diversity in their geographical locations (rural and urban settings) and
varying sizes, which can also hinder the widespread use of digital solutions due to differing
requirements. Furthermore, it is crucial to note that the digital transformation of these public health
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 12
institutions was set in motion during times of COVID-19 pandemic. This unique circumstance
introduced distinct stressors, such as having restricted time resources and the mandatory acceptance of
specific software for infection control. However, it also presented opportunities for increased funding
availability, leading us to categorize this item as promoting acceptance and resistance. The specific
condition of operating within a public authority sector, where public health institutions had been
underfunded for decades regarding infrastructure and staffing, led to a scenario marked by an aging
workforce with limited digital competencies who exhibited mixed sentiments regarding digital
transformation. Furthermore, IT solution providers primarily consist of medium-sized enterprises that
lack the necessary capacity for innovativeness in their IT solutions.
Knowledge Stage. As discussed in the context of the prevailing conditions, the public authority context
contributed to a chronic understaffing issue, resulting in a workforce characterized by a high average
age. This aging workforce tends to be less inclined towards digital innovations and often focuses on
preserving established non-digital routines. However, the crisis necessitated a substantial response,
leading to two significant developments: Firstly, a generational shift emerged within the workforce as
younger individuals joined the organization. This shift introduced a fresh perspective and a greater
affinity for digital approaches, marking a change in the workforce's mindset. Other studies offer
conflicting findings regarding the predictive power of personality variables, such as age, in innovation
diffusion. Dedehayir et al. (2017) found no significant impact of age across various innovator groups,
whereas a healthcare study by Haring et al. (2022) indicates that personality variables influence the
innovation processes. Secondly, there was a substantial influx of lateral entrants with backgrounds
distinct from those traditionally found in public health institutions. This lateral entry brought in diverse
skills and experiences, further challenging and reshaping the prevailing digital mindset among staff
members. In summary, the emerging generational shift and the frequent lateral entry of individuals with
different backgrounds have instigated a significant change in the digital mindset within the workforce.
Persuasion Stage. Characteristics of innovations that promote or inhibit persuasion have often been
investigated in research (e.g., Buck et al., 2021; Gagnon et al., 2016; Garavand et al., 2016). The relative
advantage, in particular, is considered an important determinant of the acceptance rate of an innovation
(Rogers, 2003). Regarding the relevant advantage, our analysis underscores that employees are more
likely to embrace innovations when they expect a reduced workload. The reduced workload not only
engenders flexibility for additional tasks but also fosters the potential for further initiatives to advance
digital transformation. Noteworthy is that although this expected flexibility to increase digital
transformation is also considered relevant, it is subordinate to workload reduction. This prioritization
may be related to the level of knowledge in the public health sector. The reduction of workload as a
driving factor and the perceived effort for introducing and using innovation are also relevant in other
studies in the healthcare context (Buck et al., 2021; Gagnon et al., 2016). However, it is noteworthy that
employees in public health institutions seem to think less about potential quality improvements through
digital technologies than other healthcare professionals. Buck et al. (2021) showed, for example, that
radiologists, who are considered relatively digital-savvy, already have concrete ideas about what
opportunities may arise regarding improved quality. Notably, the lack of how-to-knowledge and the
deficiency in principles-knowledge could indicate that public health employees currently have a limited
understanding of these innovations' operational mechanisms and potential benefits. These basic
knowledge deficits can lead to individuals having difficulty developing a positive attitude toward
innovation because they simply lack the necessary knowledge (Rogers, 2003). Furthermore, the current
shortage of how-to-knowledge may contribute to the perception that introducing and utilizing
innovations demands substantial effort. Considering the prior conditions that highlight a low level of
digital skills in the public health sector, the knowledge level of public health employees should be
addressed. Since this lack of digital skills is sometimes fundamental and promotes resistance to digital
transformation, the focus should not only be on building specific how-to-knowledge but also on
principles knowledge to create a basic understanding of the benefits and risks of digital transformation.
Noteworthy, the compatibility factor influences individuals' persuasion in public health, whereas this
factor is deemed less relevant than other factors, according to Rogers (2003). Particularly, the seamless
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 13
fit into interconnected systems (i.e., interoperability) plays a pivotal role for individuals in the public
health sector. This facet is scarcely mentioned in other studies (e.g., Buck et al., 2021). Additionally,
while not as pressing, compatibility with existing workflows and alignment with their daily tasks are
also considered important factors, albeit to a somewhat lesser degree. The observability of a digital
innovation plays a pivotal role in its acceptance. In this context, staff within public health institutions
deem it essential to become acquainted with digital innovations through two primary channels: a)
interactions and knowledge exchange with other public health institutions, fostering a broader
understanding and acceptance of these innovations, and b) internal exchanges within the organization,
facilitated by supervisors or colleagues, which provide valuable insights and guidance on the acceptance
of digital solutions. While somewhat less critical but still noteworthy, the potential to experience digital
innovations through model health institutions that offer opportunities to witness a digitally mature state
also contributes to their acceptance. Regarding the influence of complexity, diverse findings emerge
from prior research. Some studies indicate that ease of use is not particularly relevant for healthcare
personnel (Prakash and Das, 2021; Zha et al., 2022), while others suggest its significance (Ji et al., 2021;
Lin et al., 2021). Our study has demonstrated that complexity, especially concerning implementation
and intuitive usage, constitutes crucial features that innovations must possess to gain acceptance among
healthcare professionals. The factors "complexity" and "compatibility" cannot be exclusively addressed
by institutions within the public health sector but rely on their corresponding IT solution providers.
Some software manufacturers sporadically demonstrate efforts to assist on-site institutions in the public
health sector. Nevertheless, it is imperative to develop interoperable user-preference-based applications
that have both technical solutions and existing processes to advance digital transformation. Specifically,
technical interoperability assumes a critical role in a connected sector where numerous institutions
collaborate daily, contributing significantly to the successful progression of digital transformation.
Our research makes contributions to theory and practice. Situated within the interdisciplinary research
tradition of healthcare, our study considers the acceptance and resistance of individuals in different
contexts (Buck et al., 2021; Gagnon et al., 2016; Garavand et al., 2016). Earlier studies have already
shown that considering the user perspective in different contexts in healthcare plays a decisive role in
the success of digitization efforts (e.g., Aceto et al., 2018; Kraus et al., 2021). For theory, this study
contributes to the body of literature on digitalization in public health and to the body of literature on the
DOI theory. First and foremost, this study contributes to the body of literature on digitalization in the
healthcare sector, a field that has thus far lacked comprehensive research into the factors driving
acceptance and resistance (Joukhadar et al., 2023). Yet, with the recent crisis situations highlighting the
inadequate digital preparedness of public health institutions (Kauffmann et al., 2023), it becomes
imperative to uncover the factors that can facilitate digitalization in this critical sector. This study
identifies 33 factors associated with acceptance and resistance, providing valuable insights that can be
leveraged to promote digitalization in healthcare. Secondly, this study contributes to the body of
literature on the DOI theory. While the DOI theory has proven instrumental in elucidating process
aspects and identifying factors that promote digitalization among individuals who are already receptive
to it, it lacks the necessary contextualization to address digital transformation in specific settings. This
study advances the DOI literature and follows Burton-Jones and Volkoff (2017) call for context-specific
rather than general perspectives. This contextualization for public health institutions can serve as a
blueprint for healthcare institutions operating within federal systems and public institutions in other
sectors, such as education or public employment (Doctor et al., 2023).
We contribute to practice by delineating factors that can serve as the basis for formulating strategies to
enhance digitalization efforts. One strategy could involve prioritizing digitalization initiatives that align
with artifacts having attributes that are more likely to be accepted (e.g., workload reduction, seamless
compatibility with existing workflows and interconnected systems). Another strategy could entail efforts
to address awareness-knowledge, which seems to be underrepresented in public health institutions. This
approach would focus on the characteristics of artifacts that may currently face resistance but
nonetheless hold significant potential for advancing digital transformation.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 14
Future research about public health digitalization may focus on developing and validating strategies to
enhance digital transformation efforts, with the identified factors serving as a foundational framework.
Furthermore, future research could investigate the applicability of these factors to public health sectors
operating outside federally managed systems, shedding light on their adaptability across diverse
organizational contexts within the public health sector. Expanding the scope to the broader context of
the DOI theory, future research may delve into the contextualization of DOI elements for various settings
and explore the intricate interplay between these different contexts. Future research may also work on
the limitations of this work. These limitations comprise the validity of the factors, their capacity to
influence acceptance or resistance, and their generalizability. Firstly, the factors identified are based on
QCA, and further validation through expert consultations in the field of digitalization for public health
institutions is necessary. Secondly, while the interviews conducted determined whether a factor
contributed to acceptance, resistance, or both, it is essential to note that the primary focus of these
interviews was on building and evaluating the maturity model rather than delving deeply into the effects
of factors. Nevertheless, these interviews were foundational in identifying factors that warrant further
exploration regarding their impact. Thirdly, this study has been conducted in the context of German
public health institutions, and while it is postulated that the findings may have broader applicability to
other federally managed public health institutions, this assumption requires further research.
6 Conclusion
This study addresses the challenges of digital transformation in public health institutions, emphasizing
its critical importance in the face of recent crises. It employs Rogers' DOI theory to identify 33 factors
influencing acceptance and resistance within the public health sector. The study contributes to theory
by enriching the literature on the digital transformation process in healthcare and extending the DOI
theory to public health institutions, offering a blueprint for similar efforts in different sectors. For
practice, it discusses first strategies for promoting digitalization, focusing on aligning initiatives with
factors that promote acceptance, like workload reduction and compatibility with existing workflows. In
the future, research can build on these findings by developing strategies to enhance digital
transformation in public health institutions, exploring adaptability to different contexts, and
contextualizing DOI elements for diverse settings. These efforts can bridge the gap between contexts
and facilitate innovation diffusion across sectors, advancing digital transformation.
7 Acknowledgements
This paper has been developed within the EvalDiGe-Project, which the government financially supports
ion funding for the digitization of the public
health system is part of the German Reconstruction and Resilience Plan (Deutscher Aufbau- und
Resilienzplan; DARP), which is in turn part of the EU's NextGenEU.
References
Aceto, G., V. Persico and A. Pescapé (2018).
Journal of Network and
Computer Applications 107, 125154.
AlQudah, A. A., M. Al-
Applied Sciences 11 (22), 10537.
Studies in health technology and informatics 263, 6471.
organizations
Arthatama 4 (1), 1732.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 15
Journal of the Association for Information Systems
8 (4), 211218.
-Analysis of the Unified Theory
of Acceptance and Use of Technology (UTAUT): Challenging its Validity and Charting a Research
Journal of the Association for Information Systems 23 (1), 1395.
Buck, C., J. Hennrich and A. Kauffmann (2021). A Qualitative
International Conference on Information Systems
(Austin, USA).
Burton-
Use? A Demonstration in the Context of Community-Information
Systems Research 28 (3), 468489.
Communications of the Association for Information Systems 53, 508531.
Information & Management 39 (4), 297311.
-Making Model for
Journal of
Biomedical Practitioners 6 (2).
Management Science 35 (8), 9821003.
International Journal of Innovation Management 21
(08), 1740010.
Doctor, E., T. Eymann, D. Fürstenau, M. Gersch, K. Hall, A. L. Kauffmann, M. Schulte-Althoff, H.
Schlieter, J. Stark and K. Wyrtki (2023).
Business & Information Systems Engineering 65 (5), 539554.
Gagnon, M.-P., P. Ngangue, J. Payne- -Health adoption by
Journal of the American Medical Informatics
Association : JAMIA 23 (1), 212220.
Garavand, A., M. Mohseni, H. Asadi, M. Etemadi, M. Moradi-
Electronic
physician 8 (8), 27132718.
Annual Review of Statistics and Its Application 10 (1), 451473.
Haring, M., F. Freigang, V. Amelung and M. Gersch (2022).
BMC Health Services
Research 22 (1), 1299.
Heinsch, M., J. Wyllie, J. Carlson, H. Wells, C. Tickner and F. Kay-
Journal of
Medical Internet Research 23 (5).
healthcare sector. What is impeding adoption and continued usage of technology-driven innovations
by end-Journal of Business Research 153, 150161.
-government: an opinion about possible effects of
Federalism and the separation of powers on E- Government Information
Quarterly 19 (4), 357368.
-
MIS
Quarterly 29 (3), 525557.
Artificial Intelligence-Journal
of Medical Internet Research 23 (6), e25929.
Digital Transformation of Public Health
Thirty-Second European Conference on Information Systems (ECIS 2024), Paphos, Cyprus 16
Communications of the Association for Information Systems 53
(1), 240277.
Kauffmann, A. L., M. Neubauer, F. Reinsch, M. Reuther, D. Schick, J. Stark, K. Bley, N. Heidmann
and T. Eymann (2023).
Wirtschaftsinformatik 2023,
Paderborn, Germany.
American Journal of Information Systems 3 (2), 4044.
Kraus, S., F. Schiavone, A. Pluzhnikova and A. C. Invernizzi (2021).
healthcare: Analyzing the current state-of-Journal of Business Research 123, 557567.
Lin, H.-C., Y.-F. Tu, G.-
Medicine: Factors Affecting Medical Staff's
Educational Technology & Society 24 (1), 123137.
Redesign to Reconfigure the Level of e- Frontiers in Public
Health 9, 728287.
Qualitative Social Research, Vol 1, No 2 (2000): Qualitative Methods in Various Disciplines I:
Psychology 1 A companion to qualitative research (2).
Mergel, I., N. Edelmann and N. Haug (2019).
Government Information Quarterly 36 (4), 101385.
Information and Organization 17 (1), 226.
Management Information Systems Quarterly 43 (1), 185205.
decision support systems: A mixed-Information & Management 58 (7), 103524.
Rogers, E. M. (1962). Diffusion of Innovations. 1st Edition. New York: Free Press.
Rogers, E. M. (2003). Diffusion of innovations. Fifth edition. New York: Free Press.
Information and Organization 21 (1), 116.
---
of the EU Medical System Generated by the COVID-19 Pandemic in the Field of Health Systems'
International Journal of Environmental Research and Public Health 19 (9), 4950.
MIS Quarterly 27 (3), 425.
Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital
International Journal of Environmental Research and Public Health 18 (11), 6053.
Information & Management 43
(3), 350363.
Zha, H., K. Liu, T. Tang, Y.-H. Yin, B. Dou, L. Jiang, H. Yan, X. Tian, R. Wang and W. Xie (2022).
BMC Medical Informatics and Decision Making 22 (1), 221.
Zhai, H., X. Yang, J. Xue, C. Lavender, T. Ye, J.-B. Li, L. Xu, L. Lin, W. Cao and Y. Sun (2021).
-Assisted Contouring
Journal of Medical Internet Research
23 (9), e27122.
