JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 7, APRIL 2022

16-1

The Nature of Digital Transformation Project
Failures: Impeding Factors to Stakeholder
Collaborations
Naoshi Uchihira, JAIST and Tatsuya Eimura, JAIST
Abstract—For successfully executing digital transformation (DX) projects in a company, collaboration among various
internal and external stakeholders is intrinsically crucial and key to success. However, there are various factors that can
impede such collaborations, six of which were identified in this paper based on interviews with DX project stakeholders.
Six impeding factors include information gap, experience gap, incompatible evaluation criteria, conflict of interest,
perception gap of the future, and lack of mutual trust. Although previous studies have discussed superficial factors which
directly explain the success or failure of such projects, this paper isolates and explores them in greater depth than the
traditional ones. These essential impeding factors are helpful to find solutions to eliminate them and lead DX projects tosuccess.
Index Terms— digital transformation, stakeholder collaboration, project management, impediments.

W

I. INTRODUCTION

ITH rapidly evolving digital technologies such as
artificial intelligence (AI), the Internet of Things (IoT),
and cloud computing, the need for and potential of digitaltransformation (DX) projects in various companies andorganizations has been widely recognized. However, whilesome advanced companies have done well with DX, others
are left struggling. Although the details are unclear, it has
been reported that 84% of DX projects fail [1]. According to
the “DX Promotion Index Self-Diagnosis Results Analysis
Report (2020 Edition)” of the IPA (Information Technology
Promotion Agency, Japan), approximately 70% of companies
stay at the level where the company-wide strategy is unclear
and DX is only sporadically implemented [2]. In particular,
unlike KAIZEN (also called “defensive DX”) that uses digitaltechnology with a clear objective, DX that creates new andunconventional values (called “offensive DX”) faces various
difficulties in achieving success, which should be overcome
by collaborations among the various stakeholders involved.
However, various factors impede such collaborations, which
are major causes of unsuccessful DX projects. Since the DX
vision, which creates new and unconventional value, is notalways clear or shared among the stakeholders. Then, theunclear vision and future create various gaps among thestakeholders. Based on interviews with DX projectstakeholders, we have identified six factors that impede thesecollaborations and examined their characteristics whilesuggesting possible solutions to eliminate them.
II. LITERATURE REVIEWIn recent years, DX research has received wide recognition
with several studies identifying its challenges and the factorsthat contribute to its success or failure. Davenport andWesterman discussed why many high-profile companies,
such as General Electric, have struggled with DX, by pointing
out that transformation in the pioneering period is not easy, aswith past cases of e-commerce [3].
Naoshi Uchihira and Tatsuya Eimura are with School of Knowledge
Science, Japan Advanced Institute of Science and Technology (JAIST),
Nomi, Ishikawa, 923-1292 Japan, Corresponding author e-mail:uchihira@jaist.ac.jp.

As for the factors that make DX projects successful, manystudies from a macro perspective have been conducted.
Osmundsen et al. mentioned DX drivers, objectives, andsuccess factors based on a literature review by identifying thelatter as following: supportive and agile organizational
culture, well managed transformation activities, leveraging
knowledge, engaging managers and employees, growing
information system (IS) capabilities, developing dynamic
capabilities, developing a digital business strategy, and thealignment of business and IS [4]. The authors point out thatthe key success factors include trust and cooperation amongorganizations and the utilization of knowledge inside andoutside the organizations. Through a Delphi study of experts,
Hartl and Hess investigated the organizational culturenecessary for DX, and found that the clan, adhocracy, andcultural values of Cameron and Quinn's organizational
cultural typology are effective for DX success [5]. Mhlungu
et al. identified four success factors as customer centricity,
governance, innovation, and resource attainment, based on aquestionnaire survey, and showed that information
technology (IT) managers and non-IT managers that are in aposition to promote DX have a common understanding ofthese factors [6]. Based on a literature review, Saltz andShamshurin examined success factors of big data analytics
projects from six perspectives, data, governance, process,objectives, team, and tools, and mentioned a need to refine
the process methodology to carry out the project [7].
On the flip side, factors causing failures have been
extensively studied as well. Saldanha cited a lack of disciplinein defining and executing the right steps for DX projects totake off and stay ahead as a reason for failure, then proposed
five stages—foundation, siloed, partially synchronized, fully
synchronized, and living DNA—that lead to the final DX
stage, as well as the important disciplines and checklists foreach stage [8]. For example, digital reorganization andstaying up-dated are important disciplines in Stage 4, i.e.,fully synchronized. Stief et al. analyzed DX’s success andfailure factors by interviewing 23 DX experts, and classified
the opportunities and challenges of DX into the fivecategories of market-orientation, process-orientation,technology-orientation, company-orientation, and productorientation [9]. DX experts suggested counteracting the16-2

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 7, APRIL 2022

currently insufficient knowledge regarding the application ofdigital technologies to achieve growth and establish acompetitive advantage. They all agreed on the importance ofdigitization within companies, but pinpointed the lack ofcollaboration between IT and general management.
The reasons for such a lack of progress have beenpreviously charted across studies, but the impediments tostakeholder cooperation are seldom discussed. Ikegami and
Iijima interviewed managers and heads of DX promotion
divisions across eight Japanese companies and identified
seven challenges in DX promotion, which include the siloednature of DX and lack of an overall strategy, complexity ofexisting systems and difficulty of integrating them with new
systems, inability to invest in the development of the datanecessary for DX, and the lack of DX-savvy personnel [10].
Bilgeri et al. also mentioned the difficulty of collaboration
between business units in large manufacturing companies,
with internal pricing conflicts as a major factor [11].
According to the aforementioned studies, difficulty of
stakeholder collaborations is one of the main reasons for DX
failure. However, its root cause has not been comprehensively
analyzed. This paper focuses on and analyzes the essential
factors impeding stakeholder collaborations.
III. RESEARCH METHOD
In this study, we classified and analyzed the impedimentsof DX projects, particularly those related to stakeholder
collaboration difficulties, through interviews. These
stakeholders included executives, DX promotional divisions,
IS divisions, DX-related technology divisions (research anddevelopment (R&D) divisions), business operations divisions(factory, administrative divisions, etc.), and IT solution
vendors (software development companies).
We adopted semi-structured 1-2 hour online interviews for
six experts involved in DX projects at six Japanese companies(Table I), where the interviewees were asked about theirperception of DX, their current usage (especially the use of
big data and AI), and the difficulties that existed among
themselves. The audio was recorded and converted to text,
which was then coded and analyzed using the qualitative data
analysis tool, MAXQDA. The qualitative data analysis
consists of two steps (initial (open) coding andcategorization). Additional confirmation and supplementaryquestions about the interview content were collated via e-mail.
TABLE IPROFILES OF THE SIX INTERVIEWEES

A
B

C
D

Company attribute andsectorElectric appliancemanufacturer, DX
promotion division
Electric appliancemanufacturer, R&D
division
Materials manufacturer,
DX promotion division
E-commerce company,
In-house systems
development division

Responsibilities
Collaboration between DX
promotion division andbusiness operations division.
Research and development of
supply chain systems and theirdeployment inside and outsideof the company.
Collaboration between DX
promotion division andbusiness operations division.
Data scientist to solve internal
technical problems.

E

AI systems development
company, Executive

F

Medical outsourcing
company, IS division

Defining customer
requirements and managing
systems development projects.
Development and maintenanceof internal information
systems.

Although the six interviews are not enough, we have beenable to extract typical DX promotion difficulties in largescale Japanese companies.
IV. RESULTS
A. Characteristic difficulty patterns
The number of Japanese characters in all text data was
47,372. As a result of the initial coding, 224 segments wereretracted from the interview text data. Then, 224 segments
could be grouped into 15 major categories (Figure 1) andsubsequently classified into organization-wide issues (n = 9),
stakeholder and inter-stakeholder issues (n = 5), and others (n= 1).
Code System

A

A2

B

Interviewees

C2

C1

D

E

F

DX (Data Infrastructure)
DX (Business)
Organization
Top Management
Customer/User
Corporate Culture
Knowledge/Education
DX Challenges
Executive vs DX Promotion
DX Promotion vs Field
Field
DX Promotion
IT Venders
Sense of Urgency
MissionFig. 1. Code matrix (MAXQDA). The size of the rectangle indicates thenumber of segments belonging to the category.

The following is a list of typical difficulty patterns based on
the second issue (stakeholder and inter-stakeholder issues):
1) Current business takes priority and there is no time forDX. (Business operations division and DX promotion
division)“If you’re an IT engineer, you’re probably aware of AI.

However, I have the impression that the section of theinformation system division I belong to has its hands fullwith the work, rather than thinking about how to utilize AI.
They have an awareness of AI, but the word AI is almost
never mentioned in their work routine.” (Interviewee F)

2) Return on investment cannot be explained. (Business
operations division and DX promotion division)“Another issue is that it is unclear to what extent we canrigorously estimate the cost-effectiveness of AI topersuade those in the field (business operation).”(Interviewee C)

16-3

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 7, APRIL 2022

3) The business operations division is not aware of theintentions of top management and the DX promotion division.
(Executives, DX promotion division, and business operations
division)“ Although the DX is being envisioned by the top

management and the DX promoters, it is not being
communicated well to the field (business operations
division). Also, there is an atmosphere in the field thatsays why don’t we just introduce it anyway?”(Interviewee A)“ Since the benefits of AI automation are difficult toconvey to people in the field (business operations
division) in the first place, there is a tendency for people
to perceive it as something troublesome. This also affects
support for AI projects in the field. This is not only aproblem for the field, but also a problem for the middle
managers. The middle managers need to communicate
well with the field.” (Interviewee E)4) Data scientists do not know what is going on in business
operations. (DX-related technology division and business
operations division)“The problem is that there is still a gap in awareness and

B. Impeding factors in stakeholder collaborations
By examining the difficulties recognized from theseinterviews, we were able to inductively isolate six essential
factors (see Table II) after the researcher's discussion. Thesesix essential factors are MECE (Mutually Exclusive andCollectively Exhaustive) in the scope of the interview data.
The difficulty pattern consists of several essential factors. The
relationships between these factors and the aforementioned
difficulty patterns are shown in Figure 2.
These six impeding factors can be classified into threegroups as shown below:
Group1: Knowledge Gap(1) information gap (explicit knowledge) and (2) experiencegap (tacit knowledge)
Group2: Profit-loss Gap(3) incompatible evaluation criteria and (4) conflict ofinterest
Group3: Perception Gap(5) perception of the future and (6) lack of mutual trust

Impeding Factors
Information Gap(Explicit Knowledge)knowledge between data scientists who do data analysis
and employees who are actually work in the field and haveknowledge about the business operation.” (Interviewee A)

Experience Gap(Tacit Knowledge)

5) Customers do not want to give their data to IT vendors.
(IT vendor and business operations division)

Incompatible
Evaluation Criteria“ It is the customer’s work to manage the data andoperate the system, so if they disclose it to a manufacturer
like us, the know-how may leak out and the customer’s
own work may be lost, so that is where there is businessinterference between the customer and the manufacturer.”(Interviewee A)

Conflict of Interest

6) The business operations division is satisfied with thecurrent process. (DX promotion division and business
operations division)“The people who are closer to the actual business don’t

think it’s crazy that there’s a lot of manual work, that
there’s no way to do it automatically, that they’re nottrying to find a way to do it other than manual process,much less using technology to solve it.” (Interviewee D)7) Unclear purpose of AI implementation triggered by the
AI boom. (IT vendor and business operations division)“In the midst of the AI boom, there are many cases where

people in the business operation division come to us forgetting advices after receiving a top-down order to dosomething using AI.” (Interviewee B)
In addition to the above, there are a variety of difficulty
patterns. However, we believe that they are superficial.
Classifications based on the stakeholder’s relationships are
possible, but it may not reveal essential factors.

Difficulty Patterns
1. Current business takes priority
and there is no time for DX.
2. Return on investment cannot be
explained.
3. The business operation division is
not aware of the intentions of top
management and the DX promotion
division.
4. Data scientists don't know what's
going on in business operations.
5. Customers don't want to give
their data to IT vendors.

Perception of the
Future

6. The business operation division issatisfied with the current process.

Lack of Mutual Trust

7. Unclear purpose of AI
implementation triggered by the AI
boom.

Fig. 2. Relationship between the impeding factors and difficulty patterns.

V. DISCUSSION
In this section, we relate the six essential impeding factorsthat were identified and the existing theories to discuss
solutions for their elimination.
1) Information (Explicit Knowledge) Gap
Explicit knowledge can be obtained to some extent
through documents (textbooks, manuals, et al.), but inreality, it is difficult to completely grasp it. For example,
it is unrealistic for people in the business operations
division to keep abreast of the latest trends in machinelearning technologies. The problem of asymmetric
information about state-of-the-art technical knowledge is
a common case. The poor absorptive capacity of theinformation receiver is one of the causes of this gap [12].
To bridge it, it is necessary to translate the information
appropriately, taking into account the absorptive capacity.

16-4

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 7, APRIL 2022

Improving the information literacy of people in thebusiness operations division is also an important measure.
2) Experience (Tacit Knowledge) Gap
The empirical knowledge cultivated in the business
operations division or in the DX promotion division is notnecessarily formalized in documents or other forms ofknowledge that have a high degree of information
stickiness [13], and it is necessary to bridge this gap
through close interaction and mutual empathy.
Specifically, it is effective to have veterans from thebusiness operations division serve concurrently in the DX
promotion division. Business ethnography in the field (ex.
manufacturing shop floors) could also be an effective
method [14].
3) Incompatible Evaluation Criteria
If an organization’s existing evaluation criteria are notcompatible with DX promotion, the organization cannot
actively cooperate in it based on rational judgment. For
example, keeping QCD (Quality, Cost, and Delivery) is
the most important, and supporting DX is a low priority inthe business operations division. In this case, theevaluation criteria should be changed so that they arecompatible with DX promotion. This can be incentivizedin bonus evaluations by computing the degree ofcontribution made to DX.
4) Conflict of Interest
If there are conflicting interests between stakeholders,collaborations between them will not be possible. It is
important to have a mechanism or contract that adjusts theinterests and makes the promotion of DX beneficial forboth parties. Theoretically, cooperation games andnegotiation games can be used to break out of theprisoner’s dilemma of non-cooperation games in gametheory. Specifically, DX can be promoted by obtaining afirm contractual agreement on data and model handlingcreated by AI, i.e., machine learning. A guideline
authorized by the government about a contract about data
and AI model is helpful.
5) Perception Gap of the Future
DX will not progress if the business operations division
thinks that the status quo is acceptable. It is criticallyimportant to share opportunities and risks for the companyof the future, and to develop and share a digital vision [15].
Digital leadership is required for its steady
implementation. Roadmapping is also an effective dialog
tool for filling recognition gaps among the stakeholders[16].
6) Lack of Mutual Trust
One of the major challenges in promoting DX is that its
effects are not known until it is actually implemented. For
such an uncertain task, it is important to have arelationship of trust between the business operations
division and the key persons of the DX promotion division,
based on past achievements and relationships. To achievethis, it is effective to assign people who can be trusted as
leaders and key personnel. It is also important toimplement “NEMAWASHI” [17] and stealth storm [18]to gain trust of stakeholders.

The key to promoting DX is to understand the essential
impeding factors that exist among the stakeholders, rather
than the superficial difficulty patterns, and take appropriate
solutions to eliminate them. Figure 3 shows a structural
relation among difficulty patterns, impeding factors, andsolutions. For example, there are many cases in which theproof of concept (PoC) does not proceed to the next phase,because it is conducted blindly without identifying andeliminating the target impeding factors.
VI. CONCLUSION AND FUTURE WORKS
Based on interviews with DX project stakeholders, thispaper identifies six essential factors that impede collaboration,clarifies their characteristics, and discusses possible solutions
to eliminate them. Although many previous studies havediscussed the superficial factors directly explaining the
success or failure of DX, this paper is unique in giving a newperspective on impeding factors to stakeholder collaboration,
which lay the groundwork for future research on DX project
management.
A limitation of this study is that the impediments are notnecessarily comprehensive because there were only six
interviewees in Japanese large companies. For example, thedifference among the stakeholders’ execution pace may be an
additional impediment that was not considered in this paper.
In the future, we hope to increase the number of interviewsand expand the target companies (small and medium-sizedenterprises, companies in Europe, the U.S. and Asia) to aid
comprehensiveness.
In addition, the solutions to eliminate essential impeding
factors need to be systematized and made concrete in Figure
3, while this paper mentioned only partial solutions and therelated theories.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number 19K12234. We would like to thank DX experts who
participated in this interview.
REFERENCES
[1]

[2]
[3]

[4]
[5]
[6]
[7]

[8]

Rogers, B.: Why 84% Of Companies Fail at Digital Transformation
https://www.forbes.com/sites/brucerogers/2016/01/07/why-84-ofcompanies-fail-at-digital-transformation/?sh=194d06ae397b (accessedat Aug. 10, 2021)
IPA (Information-technology Promotion Agency, Japan): DX
Promotion Index Self-diagnosis Results Analysis Report (2020 edition)(in Japanese) (2021).
Davenport, T. H., and Westerman, G.: Why so many high-profile
digital transformations fail. Harvard Business Review (2018).
https://hbr.org/2018/03/why-so-many-high-profile-digitaltransformations-fail (accessed at Aug. 10, 2021)
Osmundsen, K., Iden, J., and Bygstad, B.: Digital Transformation:
Drivers, Success Factors, and Implications. 12th Mediterranean
Conference on Information Systems, 1-15 (2018).
Hartl, E., and Hess, T.: The role of cultural values for digitaltransformation: Insights from a Delphi study. 23rd Americas
Conference on Information Systems, 1-10 (2017)
Mhlungu, N. S., Chen, J. Y., and Alkema, P.: The underlying factors ofa successful organisational digital transformation. South African
Journal of Information Management, 21(1), 1-10 (2019).
Saltz, J. S., and Shamshurin, I.: Big data team process methodologies:
A literature review and the identification of key factors for a project's
success. 2016 IEEE International Conference on Big Data, 2872-2879(2016).
Saldanha, T.: Why digital transformations fail: The surprising
disciplines of how to take off and stay ahead. Berrett-Koehler
Publishers (2019).

16-5

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 7, APRIL 2022

[9]

Stief, S. E., Eidhoff, A. T., and Voeth, M.: Transform to succeed: An
empirical analysis of digital transformation in firms. International
Journal of Economics and Management Engineering 10 (6) 1833-1842(2016).
[10] Ikegami, H., and Iijima, J.: Unwrapping Efforts and Difficulties ofEnterprises for Digital Transformation. Digital BusinessTransformation, 237-250 (2020).
[11] Bilgeri, D., Wortmann, F., and Fleisch, E.: How digital transformation
affects large manufacturing companies’ organization. 38th
International Conference on Information Systems, Thirty EighthInternational Conference on Information Systems, 1-9 (2017).
[12] Cohen, W. M., and Levinthal, D. A.: Absorptive capacity: A new
perspective on learning and innovation. Administrative science
quarterly, 128-152 (1990).
[13] Von Hippel, E.: “Sticky information” and the locus of problem solving:
implications for innovation. Management science, 40(4), 429-439(1994).
[14] Hoang, L. A., Uchihira, N.: Requirement engineering in knowledgeintensive manufacturing shop floors: A knowledge-based approach.
The 14th International Conference on Knowledge, Information andCreativity Support Systems (KICSS2019), HO CHI MINH CITY, 2019.
[15] Westerman, G., Bonnet, D., and McAfee, A.: Leading digital: Turningtechnology into business transformation. Harvard Business Press(2014).
[16] Uchihira, N.: Dialogue Tool for Value Creation in Digital
Transformation: Roadmapping for Machine Learning Applications.
Lecture Notes in Networks and Systems, 266, 518-526 (2021).
[17] Sagi, D.: 'Nemawashi' a Technique to Gain Consensus in JapaneseManagement Systems: An Overview. International Journal of Arts,
Humanities and Management Studies, 1(4) (2015).
[18] Wedell-Wedellsborg, T., and Miller, P.: The Case for StealthInnovation. Harvard Business Review, 91(3) (2013).

Naoshi Uchihira is Vice-President andProfessor at School of Knowledge
Science of Japan Advanced Institute of
Science and Technology (JAIST). He
received Dr. Eng. in Information Scienceand Engineering from Tokyo Institute ofTechnology, and Ph.D. (Knowledge
Science) from JAIST in 1997 and 2010,
respectively. He worked at the corporate
R&D center of Toshiba Corporation from 1982 to 2013. His
current research interests include project management anddigital innovation design.
Tatsuya Eimura was a master student ofGraduate School of Advanced Scienceand Technology, Japan Advanced
Institute of Science and Technology(JAIST). He is now working for
Accenture after receiving a master's
degree in knowledge science of JAIST in
2021.

TABLE I I
ESSENTIAL IMPEDING FACTORS TO STAKEHOLDER COLLABORATION

Impediments
Information Gap(Explicit
Knowledge)
Experience Gap(Tacit Knowledge)
Incompatible
Evaluation Criteria

Explanation
Gap due to information
asymmetry, where information
is defined as communicableformal knowledge.
Gaps in knowledge based on thepast experiences of the people
involved. Tacit knowledge thatis difficult to transfer.
Differences in the evaluation
criteria and priorities of eachdivision.

Conflict of Interest

Conflicts of interest between
stakeholders.

Perception Gap of
the Future

Differences in the perceptionsof the purpose, direction, andpath of DX (what DX willachieve in the future).

Lack of Mutual
Trust

Lack of interorganizational trustand negative thinking about an
uncertain future.

ExampleThe business operations division is
not aware of the latest information
on AI technology held by the DX
promotion division.
DX promotion division and IT
vendors try to promote DX with
idealism without knowing theactual business operation.
The DX promotion division
wants to promote DX, but thefront line prioritizes the steady
execution of the current work.
IT vendors want all of theircustomers’ data, but thecustomers do not want to provide
their know-how to IT vendors.
The top management thinks thatthe future of the company is injeopardy if DX is not promoted,
but the front line thinks that thecurrent extension is sufficient.
Members may wonder if DX isreally going to work, but they cango along with it if they trust aleader in the DX project.

16-6

JOURNAL OF INTELLIGENT INFORMATICS AND SMART TECHNOLOGY, VOL. 7, APRIL 2022

Superficial Difficulty Patterns of Stakeholder Collaboration
Difficulty
Pattern
Difficulty
Pattern

Difficulty
Pattern
Difficulty
Pattern

Difficulty
Pattern

Difficulty
Pattern
Difficulty
Pattern

Difficulty
Pattern

Difficulty
Pattern

Difficulty
Pattern

Essential Impeding Factors
Information Gap(Explicit Knowledge)

Experience Gap(Tacit Knowledge)

Perception of the Future

Incompatible Evaluation
Criteria

Conflict of Interest

Lack of Mutual Trust

Measures and tools to remove impediments
Measuresand Tools

Measuresand Tools

Measuresand Tools

Measuresand Tools

Fig. 3. Identification of the essential impeding factors for resolving superficial difficulties.