Why Digital Maturity Models Fail: An Exploratory Interview Study

Within the Digital Transformation Steering Process

Maximilian Breitruck

Institute of Information Systems and Digital Business, University of St. Gallen, St. Gallen, Switzerland

Keywords: Digital Maturity Models, Digital Transformation, Transformation Steering, Explorative Interview Study.

Abstract: Digital Maturity Models (DMMs) are widely used tools to assess and guide organizational digital

transformation (DT). However, their practical contribution to the transformation process often fails due to

insufficient stakeholder involvement, inadequate adaptability, or unsuitable assessment tools. This study

explores these shortcomings through a socio-technical lens, analyzing why DMMs fail to deliver value in

transformation processes. Drawing on an exploratory interview study with experts from the industry, eight

key dimensions of failure, such e.g. as misalignment with organizational strategies, cultural resistance, and

inadequate iterative usage practices, were identified. These initial results reveal that beyond the design of

DMMs, systemic organizational and procedural barriers significantly hinder DMM utility. Building on that,

ultimately, a comprehensive framework of utility barriers and derived requirements for building and

integrating DMMs should be developed.

1 INTRODUCTION

With virtually every organization relying at least once

on (Digital) Maturity Models (DMMs) during a

transformation process the development of numerous

DMMs has lead to their widespread adoption

(Thordsen et al., 2020). However, their increasing use

has also exposed significant weaknesses. In addition

to fundamental shortcomings related to their

scientific foundation, development process, and

associated rigor, practitioners have frequently

reported that these models fail to deliver the

anticipated benefits, often falling short of effectively

supporting the transformation process as expected

(Thordsen & Bick, 2023a). Value creation in this

context is not always directly quantifiable and

depends heavily on the type of model used and its

intended purpose. Despite the high heterogeneity of

such models, existing research identifies capability

maturity models as the dominant form of DMMs

(Pöppelbuß & Röglinger, 2011). These models

establish various dimensions of a digitally mature

organization, each comprising capabilities defined

across multiple developmental stages. This structure

is designed to enable organizations to determine their

current level of digital maturity (DM), identify a

https://orcid.org/0009-0000-1741-2042

target state, and derive a model-supported pathway to

achieve that state (Pöppelbuß & Röglinger, 2011).

This in essence reflects a simplified outline of the

typical DMM usage process. The application

perspective of DMMs is equally heterogeneous, as

these models can fundamentally be applied to any

level within organizations. Over time however their

primary use has become focused in the context of

transformation processes at the organizational level,

specifically in transformation steering (Ifenthaler &

Egloffstein, 2020; Minh & Thanh, 2022a). In

practice, DMMs are often analogized as a compass

for the transformation process, helping organizations

navigate their journey (Minh & Thanh, 2022a).

Practitioners utilize these models to understand their

current state, determine the actions required to

achieve a desired maturity level and monitor their

progress along the way. Ultimately, users of DMMs

aim to actively guide and manage the transformation

process to ensure the organization successfully

reaches its target state (Rossmann, 2018).

Underperformance occurs in this process when the

DMM fails to adequately support the transformation

as expected. For various reasons, DMMs often fall

short on their promises, leading to their declining use

over the course of the transformation (e.g. Thordsen

Breitruck, M.

Why Digital Maturity Models Fail: An Exploratory Interview Study Within the Digital Transformation Steering Process.

DOI: 10.5220/0013427800003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th Inter national Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 883-890

ISBN: 978-989-758-749-8; ISSN: 2184-4992

883

& Bick, 2023b). This support in transformation

processes is however the exact justification for

existence and continuous development of these

models—to enable organizations to initiate and steer

their transformation processes effectively.

Nevertheless, there is currently little structured

knowledge from a practical perspective regarding the

specific reasons for this underperformance in the

application of DMMs. In particular, the question

regarding where this loss of value occurs remains

unaddressed (Thordsen & Bick, 2023b). DMMs can,

therefore, be perceived as socio-technical systems

embedded within the organizational digital

transformation. As such, challenges can arise from

multiple perspectives—technological, organizational,

procedural, or human—which ultimately contribute

to the failure of the model to deliver its intended

value. Therefore, this paper aims to systematically

address the following research question: What factors

hinder Digital Maturity Models from effectively

supporting value creation during the organizational

digital transformation? To analyze this question, the

following chapters will first establish a theoretical

foundation, upon which an exploratory interview

study with industry experts will be built upon. The

study aims to systematically identify the key

challenges in the practical application of DMMs.

2 RESEARCH CONTEXT

2.1 Digital Transformation of

Organizations

Although existing research does not provide a clear

and homogeneous definition of the term, digital

transformation (DT) in the context of organizations

can be broadly defined as: "A fundamental change

process, enabled by the innovative use of digital

technologies, accompanied by the strategic leverage

of key resources and capabilities, aiming to radically

improve an entity and redefine its value proposition

for its stakeholders" (Gong & Ribiere, 2021, p. 12).

This definition explicitly links five distinct elements:

the change process, the entity undergoing the change,

the means by which the change is achieved, the

expected outcome, and the associated impact on the

respective entity.

The change process, which is essentially

fundamental in nature, must be distinguished from

non-fundamental changes, a distinction closely tied to

differentiating DT from related concepts such as

digitization and digitalization. In addition to its

fundamental nature, the scope of improvement and

the distinct end results serve as further differentiation

factors between the aforementioned related concepts

and DT (Gong & Ribiere, 2021). Fundamental

change, as exemplified by DT, is inherently tied to

radical improvement, as opposed to the incremental

improvements typically associated with less

fundamental change initiatives (Bekkhus, 2016).

Radical improvement entails a holistic disruption of

existing paradigms and structures, driving

fundamental change. In contrast, incremental

improvement is characterized by small, continuous

steps, primarily oriented towards process

optimization. In terms of impact the distinguishing

factor between digitalization and DT lies in the

achievement of non-quantifiable, long-term effects

that generate fundamentally new value for

organizations and their stakeholders rather than short-

term efficiency improvements (Chanias, 2017; Gong

& Ribiere, 2021). To facilitate this fundamental

change, specific measures are necessary. First, the

innovative application of digital technologies, such as

artificial intelligence, blockchain, and IoT, plays a

crucial role (Gong & Ribiere, 2021). The strategic

leverage of organizational resources and capabilities

is equally important, enabling the broader scope and

radical changes that distinguish digital transformation

from digitalization initiatives (Gong & Ribiere, 2021;

Heubeck, 2023). Furthermore, human resources are

essential for implementing changes within the

organization and supporting the development of

knowledge resources (Alvarenga et al., 2020;

Smirnova et al., 2019). Financial capital acts as a key

enabler for successful transformations, particularly

given that the long-term, non-quantifiable benefits of

digital transformation often require significant

investments. Organizations with sufficient reserves

can prioritize holistic transformation efforts over

short-term projects with immediately measurable

benefits, creating space for comprehensive change

without compromising on other organizational

priorities (Gong & Ribiere, 2021; Hess et al., 2016;

Liu & He, 2024). Capabilities, both dynamic and

digital, are also critical to navigate the complexity of

digital transformation. These capabilities enable goal-

oriented and agile actions, ensuring that human and

technological components are effectively

operationalized (Ellström et al., 2021; Gong &

Ribiere, 2021). Combined, these three means—

technologies, resources, and capabilities—allow

organizations to achieve both economic and

capability-driven outcomes. In the context of digital

transformation, capability-driven outcomes are

particularly significant, as they encompass long-term,

non-quantifiable benefits such as business model

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innovation, transformative leadership, and the

establishment of competitive advantages. These

outcomes serve as the primary differentiator from

economic benefits, such as process optimization or

cost reduction, which, while important, are already

achievable within digitalization projects. (Gong &

Ribiere, 2021; Leão & da Silva, 2021; Nwankpa &

Roumani, 2016).

2.2 Digital Maturity Models as Socio

Technical Systems

As the distinction between DT and digitalization

highlights, DT is not a trivial initiative that can be

implemented through simple project structures within

short timeframes. Instead, DT represents a

multifaceted and complex construct that must be

broken down into numerous sub-projects and

workstreams involving a wide range of stakeholders

to enable a gradual and systematic implementation of

the transformation process (Correani et al., 2020; Furr

et al., 2022; Jöhnk et al., 2020). As with all

organizational initiatives, the initial and most critical

steps in digital transformation involves defining

objectives, determining the current state, and

establishing a pathway to achieve the desired target

state. In the context of digital transformation, the

concept of maturity models has been adapted to the

digital domain. These digital maturity models

(DMMs) are designed to measure how digitally

mature organizations are, to define a target maturity

state, and—depending on the model type—even

support organizations in achieving that state (Gill &

VanBoskirk, 2016; Minh & Thanh, 2022b;

Pöppelbuß & Röglinger, 2011; Thordsen & Bick,

2023a). The variable being measured, referred to as

maturity, can be defined as a "measure to evaluate the

capabilities of an organization in regard to a certain

discipline" (Poeppelbuss et al., 2011).

In the case of DMMs, this specifically pertains to

digital capabilities, which, as previously established,

are an outcome of digital transformation.

Consequently, existing research often uses the term

Digital Transformation Maturity synonymously with

digital maturity. The Capability Maturity Model, the

most frequently used type of maturity model in the

literature, serves as the foundation for this approach.

It deconstructs the concept of DM into various

dimensions, each associated with corresponding

capabilities that represent specific maturity levels

(Aguiar et al., 2019; Paulk et al., 1993).

An organization can exhibit a certain capability

level within each dimension, which is then interpreted

as its maturity level in that area. Descriptive models

focus solely on reporting the current maturity level to

the user, enabling an initial As-Is assessment.

Prescriptive models take this a step further by

allowing users to define a target state and provide

guidance on how to progress from the current

maturity level to the desired state. Building on these,

comparative models add the functionality of

benchmarking, allowing organizations to compare

their performance internally (tracking progress over

time) or externally (evaluating their standing relative

to competitors). DMMs can thus play a central role in

the transformation process by first conducting an As-

Is assessment, providing a starting point for planning

digital transformation (descriptive). Building on this,

they offer guidelines to support detailed planning and

target setting (prescriptive). Furthermore, DMMs can

serve as a steering tool by tracking progress through

intermediate steps, measuring their success, and

benchmarking the organization’s advancing digital

maturity both internally and externally (prescriptive

and comparative) (Pöppelbuß & Röglinger, 2011).

DMMs often exist in traditional analog formats,

such as assessment documents and questionnaires.

However, opportunities have emerged for applying

these models in digital formats, enabling assessments,

target states, and selected steps toward these targets

to be stored and continuously accessed, for example,

on web-based platforms. As a result, DMMs have

increasingly evolved into technical systems that can

be integrated as decision-support tools within

transformation processes. Due to their growing

technical nature and integration into organizational

processes, the application of DMMs can increasingly

be perceived as a socio-technical construct

(Warnecke et al., 2019). Within this construct

outlined by Bostrom & Heinen (1977), the DMM

serves as the technology, the transformation acts as

the task, the transforming organization represents the

structure, and individuals involved in digital

transformation—such as steering committees, project

managers, and other key stakeholders—constitute the

people component. The socio-technical systems

(STS) perspective can be applied here to understand

how the surrounding organization reacts to the

introduction of IT artifacts like DMMs, how their

integration functions, and what potential barriers arise

in their use, along with possible solutions. This

perspective emphasizes the relationship between the

social system, comprising structure and people, and

the technical system, consisting of tasks and the

technology itself (Bostrom & Heinen, 1977). The

interplay between these components represents the

central variable to be optimized for the efficient

integration of technical systems into organizations. In

Why Digital Maturity Models Fail: An Exploratory Interview Study Within the Digital Transformation Steering Process

885

this case, it involves achieving the best possible

integration of DMMs into the transformation process

to generate value-add from these decision-support

artifacts, particularly in navigating this complex

process effectively.

3 METHODOLOGY

3.1 Qualitative Interview Study Design

To understand which factors, hinder DMMs from

delivering value as decision-support tools in digital

transformations, it is necessary to systematically

identify the barriers. This involves in particular

examining the integration of DMMs as socio-

technical systems (STS), focusing on how the various

system components interact and identifying where

usage-inhibiting issues arise within or between

system components. To address this, we employ a

qualitative, exploratory research approach (Strauss &

Corbin, 1998). While theory-driven hypotheses

regarding potential issues have been put forward,

there is little empirical evidence from the user

perspective. As a result, the field of study remains in

an early stage of maturity, where neither quantitative

nor more focused research methods are feasible, as

these would require the specific problem factors to

already be well-defined (Strauss & Corbin, 1998).

Table 1: Interview partners.

IP. Job Title Industr

1 CIO Medical

2 Senior VP Manufacturin

3 Head of Data & AI Telecommunication

4 Directo

Consulting

5 Manage

Consulting

6 Head of Di

italization Industrials

In line with the research question, we have chosen to

conduct a qualitative interview study. The theoretical

development is guided by Strauss and Corbin’s

(1998) methodology, where insights are "grounded"

in the data obtained from experienced experts. Our

research adopts a neopositivist approach, classifying

digital leaders as "competent truth-tellers" who serve

as carriers of knowledge (Schultze & Avital, 2011).

The experts interviewed are distinguished by their

direct involvement in the context of digital

transformation (DT), either as members or advisory

actors of the steering committee responsible for

planning and overseeing DT initiatives. These experts

have utilized DMMs in their work, granting them

access to explicit knowledge about how these models

are employed in DT, how they are integrated into

processes, and why issues arise during their

application—potentially leading to reduced utility or

even complete abandonment of their use.

3.2 Data Collection

Utilizing interviews as a data collection method is a

cornerstone of qualitative research, enabling the

collection of "authentic accounts of participants" who

have directly confronted or been involved with the

phenomenon under investigation (Schultze & Avital,

2011). To ensure an adequate degree of rigor, we

follow the established approach by Levina, widely

recognized in IS literature (Levina, 2021). As of now,

six interviews have been conducted between October

and December 2024 with experienced digital leaders

who have been actively involved in digital

transformation initiatives. An additional 5–10

interviews are planned. Participants were directly

approached by us, with particular attention given to

ensuring sufficient diversity in terms of

organizational size and industry sector, making

theoretical sampling feasible (Strauss & Corbin,

1998). An increasing degree of similarity in responses

is already becoming apparent, indicating an emerging

saturation point. The in-depth interviews were

conducted one-on-one using a semi-structured format

(Myers, 2019). The discussions were divided into two

parts. The first part focused on the participants' roles

in digital transformation initiatives and the general

structure of such initiatives within their organizations.

The second part delved deeper into the subject of

DMMs, with a particular emphasis on the socio-

technical systems (STS) components and subsystems

in the context of DMM usage in DT. Participants

were asked to identify points in the process where

ssues in DMM usage occurred, if at all, and to

elaborate on how these issues influenced the long-

term utilization of the models.

3.3 Data Analysis

The interviews conducted so far were recorded and

transcribed, to provide a solid foundation for

subsequent coding analysis. A qualitative analysis

software was used to structure the coding process

(Strauss & Corbin, 1998). During the open coding

phase, 123 codes were documented. Similarities and

differences among these codes were then

consolidated during axial coding, resulting in 70 first-

order concepts.

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Figure 1: STS-Findings Overview.

In the second-order analysis, the findings were re-

evaluated to ensure that the combined concepts

adequately explained the phenomenon under

investigation. This process ultimately led to the

formation of 21 second-order themes, which were

organized into 8 dimensions aligned with the STS

framework. These dimensions and their underlying

themes are described in the following sections and are

outlined within table 2. As the majority of interviews

were conducted in German, the relevant quotes were

translated into English to enhance the accessibility

and comprehensibility of the results.

4 PRELIMINARY RESULTS

To refine the coding scheme, the resulting

dimensions, to which the second-order themes were

assigned, were mapped to the four components of

STS theory. In the following section, the issues

reported by the experts regarding the application of

DMMs in the transformation process are

systematically analysed in relation to the respective

components.

The analysis was carried out starting with the

social subsystem, which comprises the two

components People and Structure. As previously

mentioned, in the research context the People

component refers to individuals involved in the

digital transformation, such as the steering committee

(SteerCo), employees reporting to the SteerCo, top

management. Other organizational participants

indirectly involved in the transformation, referred to

hereafter as external stakeholders, are also included.

The issues within this component were found to relate

to two dimensions: stakeholder inclusion beyond the

steering committee and organizational barriers.

Regarding stakeholder engagement, it is evident

that including all project stakeholders who will

interact with the DMM during the selection process

or in evaluating its outcomes is critical. Resistance to

adoption often arises when the model fails to reflect

the perspectives and needs of its intended users

(stakeholder inclusion,) (IP5). Furthermore, the

involvement of top management is particularly

essential. The DMM must appear credible and

coherent to leadership to secure their trust and support

for its implementation (leadership involvement)

(IP2). Additionally, external stakeholders, even those

not directly engaging with the DMM, should be

informed about its application where possible. This

alignment ensures consistency with internal project

stakeholders and their discussions. Failure to

coordinate externally can result in internal challenges

during the use of the DMM (external coordination)

(IP2). In relation to organizational barriers, cultural

challenges were identified that hinder the integration

of DMMs into the steering processes of digital

transformations. These models often carry a highly

academic character, which can dampen commitment

in more pragmatically oriented organizations

(Cultural Challenges) (IP1, IP2). Additionally, the

overall commitment or lack thereof to the

transformation process itself may lead to the

abandonment of the DMM as a steering tool. This

occurs particularly when the perceived effort required

to utilize the model is deemed too high, causing its

application to be discontinued at an early stage

(Transformation Commitment) (IP1, IP2).

The Structure component, which in this context

refers to the organization and its inherently embedded

rules, hierarchies, processes, and existing technical

infrastructure, is susceptible to challenges across

three dimensions: strategic alignment and

governance. Organizations typically approach

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strategic topics, such as new value propositions, by

first defining their strategy and subsequently

identifying the capabilities needed to achieve their

goals. In contrast, DMMs often reverse this approach

by prescribing the capabilities required to meet a

predefined target, which the organization cannot

easily adjust. This was highlighted as a barrier in the

strategy formulation aspect of digital transformation

(strategy linkage) (IP3, IP1). Closely related to this is

the observation that organizations often measure their

success by achieving their specific goals rather than

by comparing themselves to a peer group. DMMs,

however, frequently rely on peer group good

practices, making it more challenging to apply the

standard performance assessment approach and to set

and achieve meaningful goals (objective

clarification) (IP3, IP1, IP2). This challenge also ties

into expectation setting. It is essential to clearly

establish the purpose of the model, how it will be

integrated into existing processes, and to develop a

shared understanding of its capabilities and

limitations. Divergent perceptions of what DMMs

are, what they can achieve, and how they can be

applied create additional barriers (expectation

setting) (IP3, IP5, IP7). Regarding governance, it is

essential to define in greater detail how the outputs of

the DMMs should be utilized within the

transformation process and how underlying process

structures can be built and leveraged for digital

transformation. Without such procedural foundation,

the model may be used, but its results would not be

effectively integrated into the transformation efforts,

thereby failing to generate added value from its

application (governance structuring) (IP5, IP4). This

requirement is closely tied to the need for anchoring

the DMM as a central reference point within the

organization. As noted earlier, all relevant

stakeholders must recognize the model as a pivotal

steering tool. This requires the initiators of the

model's adoption to consistently highlight its value

and utility, ensuring it becomes deeply embedded

within the organization and its processes (Anchoring)

(IP4, IP5).

The task component, regarded here as the core of

the transformation process, encompasses the work

invested, including objectives, underlying processes,

and how these are defined and executed to ultimately

achieve transformation goals. Beginning with

workflow integration, it is essential not only to

consider processes at a high level but also to ensure

integration into more operational aspects of the

transformation. This involves managing and tracking

granular progress, such as the status of individual

steps and identifying what actions are required to

complete them (workflow integration, IP4, IP5, IP2).

Additionally, it is crucial to provide support during

the use of the DMM and its accompanying materials.

Simply making the model available without adequate

guidance often results in it being perceived as

unsuitable or irrelevant, leading to abandonment (IP2,

IP6, IP7). This also highlights the importance of

standardization in application—establishing clear

guidelines on where and how the DMM should be

used within operational steering and planning. Such

standardization reduces the risk of underutilization

due to insufficient integration into organizational

workflows (standardization) (IP4, IP5). In addition to

proper integration into the task, iteration was

repeatedly identified as a critical challenge.

Specifically, the cycle iteration in which the DMM is

updated is crucial for it to function as an effective

steering artifact. Regular updates are necessary to

evaluate whether the chosen transformation roadmap

is working and to enable adjustments, thereby

deriving value from the DMM’s application (cycle

duration) (IP6, IP5). To support iterative use,

appropriate approaches for employing the DMM

must be developed. This requires a pragmatic

approach to the model's use, ensuring that it is

practical and conducive to frequent reapplication

(practicality, IP1, IP6).

The central artifact component in the STS is the

technology, which in this case is the DMM itself. Key

issues identified in this area include model design

and the associated user integration. One recurring

concern is that the models are often perceived as

overly complex, discouraging usage from the outset.

To ensure adoption, the DMM must be designed in a

way that avoids excessive dimensions or maturity

levels, which could render it opaque and difficult to

use (complexity) (IP4, IP5). Similarly, the model

must allow for some degree of adaptation to

individual needs without compromising its usability

(flexibility) (IP4, IP6). While complete customization

may not always be feasible, adapting the model to the

specific context is essential, as applying it generically

across different contexts often leads to mismatches

that undermine both its utility and user motivation

(context adaptation) (IP5, IP6). In addition, concrete

performance metrics are crucial to enable effective

project steering. While these metrics do not always

have to be quantitative, they should provide a means

to assess whether progress is being made toward

achieving the transformation objectives or whether

adjustments are necessary (performance integration,

IP3). Related to the model design is also user

integration, which is often tied to the supporting

materials of the model, such as assessment

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instruments, questionnaires, etc. It is crucial to ensure

that users have the easiest possible access to the

model, and by leveraging digital technologies,

assessments can be automated or made viewable for

later reference (user-friendliness) (IP6, IP1).

Additionally, it is important to ensure that

comprehensive documentation is available. This

documentation can serve as the foundation for

introducing the DMM into the transformation process

and also provide guidance for any support measures.

Without such documentation, incorrect usage or

improper implementation can quickly lead to a loss of

motivation and a decline in usage/utility shortly after

introduction (explanation) (IP5).

5 EXPECTED CONTRIBUTION

AND FUTURE WORK

Based on the exploratory insights gathered so far, an

initial version of a coding scheme/framework has

been developed. This scheme, structured along eight

dimensions aligned with the four components of

socio-technical systems (STS) theory, provides an

initial exploratory understanding of the root causes of

issues and the derived requirements to solve them

potentially. These insights shed light on why DMMs

have failed to deliver value or function effectively in

the transformation process and identify what is

needed to ensure their successful application. This

confirms the issues regarding model design already

highlighted in the literature: that DMMs are often too

complex, inflexible, or insufficiently context-specific

to be effectively applied in the operational execution

of a transformation. However, with regard to the STS

components beyond the DMM itself, i.e., the

technology, it has become evident that, contrary to

claims in existing research (e.g., Barry et al., 2023),

there are significant issues outside the design of the

DMM that contribute to the insufficient value these

models generate in digital transformation efforts. As

outlined in the dimensions related to the remaining

STS components, factors such as the anchoring of the

DMM within the organization and its associated

processes, cultural barriers, and the lack of leadership

support are also significant reasons why DMMs fail

to deliver the anticipated value. For the continued

progression of this research, the remaining interviews

will be conducted, and the coding framework will be

refined based on the additional insights gained. Given

that a certain repetition of problem areas has already

been observed after six interviews, with only a few

new insights emerging, it can be assumed that the

coding scheme and the derived findings will only

change marginally. Nevertheless, increasing the

sample size remains crucial to enhance the overall

validity of the results and ensure broader coverage

across different industries and organizational sizes,

thereby improving the generalizability of the

findings, which is the biggest limitation of the present

study. In its finalized form, the resulting framework,

based on the coding scheme, will identify the core

issues that prevent DMMs from delivering the

expected value in the transformation process. These

shortcomings often manifest as declining usage and a

lack of valuable additional insights, undermining the

DMM's function as a decision-support artifact. The

framework will also outline fundamental

requirements for the effective use of DMMs in digital

transformation processes aimed at addressing and

mitigating these issues. The findings of this paper are

intended not only for researchers but also, especially,

for practitioners attempting to integrate DMMs into

their transformation processes. Future research

should not only address the design-related issues of

DMMs and develop principles for their construction

but also focus more extensively on the challenges

outside the model’s design. This includes developing

processes and frameworks for effectively embedding

DMMs into transformation initiatives, identifying the

underlying contingency factors that enable successful

implementation, and defining what success

concretely entails. This emphasis is particularly

relevant as the findings of this study predominantly

report on negative cases of implementation,

highlighting the need for a deeper understanding of

how to achieve positive outcomes.

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