Interrelation of Digitalization and Digital Transformation in a Maritime Company

Abstract

Many organizations must undergo digitalization and digital
transformation (DT) simultaneously; in itself, either is daunting. For 15
months, we followed the ongoing digitalization and DT activities at a
maritime company with over 3700 employees through a qualitative analysis of 20 interviews, a workshop, and several documents. We see how digitalization and DT are inherently interrelated; DT and digitalization have common enablers through technology such as AI, and common processes in continuous software development. They also share many challenges, including lack of resources and internal resistance against change. Through acquiring data in the digitalization of core services, companies can undergo DT by utilizing data in new and profound ways to build services with new value propositions. In conclusion, digitalization and DT are necessary for incumbent companies and require careful balancing of resources, competence, and technology.

Full text

Interrelation of Digitalization and Digital
Transformation in a Maritime Company
Rasmus Ulfsnes(B
), Nils Brede Moe , Geir Kjetil Hanssen ,
and Thor Aleksander Buan
SINTEF, Trondheim, Norway
{rasmus.ulfsnes,nils.b.moe,geir.k.hanssen,thor.buan}@sintef.no
http://www.sintef.no
Abstract. Many organizations must undergo digitalization and digital
transformation (DT) simultaneously; in itself, either is daunting. For 15
months, we followed the ongoing digitalization and DT activities at a
maritime company with over 3700 employees through a qualitative anal-
ysis of 20 interviews, a workshop, and several documents. We see how
digitalization and DT are inherently interrelated; DT and digitalization
have common enablers through technology such as AI, and common pro-
cesses in continuous software development. They also share many chal-
lenges, including lack of resources and internal resistance against change.
Through acquiring data in the digitalization of core services, companies
can undergo DT by utilizing data in new and profound ways to build ser-
vices with new value propositions. In conclusion, digitalization and DT
are necessary for incumbent companies and require careful balancing of
resources, competence, and technology.
Keywords: Digitalization ·Digital transformation ·Product
management
1 Introduction
Today’s companies are challenged by being ever more efficient in how they
develop and apply technology, not only for improving existing services but
also for innovation and development of new services and products. Through
software development or purchasing and implementation of off-the-shelf prod-
ucts [15], organizations aim to undergo the digitalization of services to achieve
these improvements. Digitalization can be defined as how technology is used
to change business processes through automation of tasks [7] or changing busi-
ness processes for improved communication and coordination between business
processes [19]. The maritime industry has notably been lagging behind other
sectors [13]. This lag has introduced a much larger technology gap where the
maritime industry not only has to consider the digitalization of internal pro-
cesses but also the effects on how: 1) sensors on ships provide exponentially
c
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N. Carroll et al. (Eds.): ICSOB 2022, LNBIP 463, pp. 51–66, 2022.
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52 R. Ulfsnes et al.
more data, 2) artificial intelligence can be applied to generate new insights from
sensors and other data sources, and 3) Big data and its ability to provide insights
across more extensive data sets spanning entire fleets of ships [13].
Digitalization is a challenging and daunting task; it involves changing how a
company operates by introducing technology to personnel that previously have
not been using software quite that way [16]. While digitalization and improve-
ments are well and good, this is not sufficient to compete in an ever-increasingly
disruptive domain where organizations and people expect increasingly effective
digital solutions.
Various research has aimed to clarify and define the concept of digital trans-
formation [14,19,20,22]. Vial [20] proclaims that digital transformation is “a
process that aims to improve an entity by triggering significant changes to its
properties through combinations of information, computing, communication, and
connectivity technologies.” However, if digital transformation is more advanced,
disruptive, or indeed a different concept than digitalization, it would need to be
built on top of digitalization in incumbent companies.
Motivated by the increasing digitalization of the maritime industry, the
potential and uncertainty of digital transformation, and its illusive connection
to digitalization, we ask the following research question:
How are digital transformation and digitalization interrelated?
We will first outline the interrelation by applying the notion that digitaliza-
tion and digital transformation are distinct in themselves and differ in how they
affect the companies’ value proposition [22] while observing how AI, business
models, data, and software developments affect both phenomena.
We present rich insight into a large software- and data-intensive organization
that has established an innovation framework as part of a transformation initia-
tive. Our findings provide insight into transformation strategies and challenges,
and even more so, insights into how digitalization and digital transformation
should be seen as a joint approach.
The remainder of the paper is organized as follows: In Sect. 2,wepresent
relevant literature on digitalization and digital transformation and a framework
we use for understanding both concepts. In Sect. 3, we describe our case company
and research method in detail. In Sect. 4, we present results from a case study
and discuss our findings in Sect. 5. Section 6concludes and presents key findings
from the study.
2 Background
In this chapter, we explain digitalization and digital transformation as distinct
concepts and how they relate to each other before looking into the capabilities
that are necessary for both to succeed.
2.1 Digitalization vs. Digital Transformation
In digital transformation, technology is central in redefining companies’ value
propositions, which causes the emergence of a new organizational identity. There-

Interrelation of Digitalization and Digital Transformation 53
fore, digital transformation hit barriers such as hierarchical leadership [2], poor
alignment of organizational units [14], conflicts between existing and new busi-
ness strategies [23], and resistance from employees [20,22]. Such barriers and
frictions cause interruptions, as development, operations, and business teams
constantly need to be involved in complex alignment activities to succeed in the
transformation. In contrast, digitalization involves using digital technology to
support an existing value proposition, implying that an organization’s existing
identity is reinforced [22], although not redefined. As the identity is not changed,
digitalization is less likely to experience the same internal barriers as found in
a digital transformation. However, little is known about barriers and enablers
when undertaking digitalization and digital transformation simultaneously as an
interrelated process.
Both digitalization and digital transformations are by themselves challenging
and hard to undertake, and companies are required to do both at the same
time [14]. Netflix shows a public example, where they started out by physically
delivering DVDs to the customers’ mailboxes. Then realizing the potential of
streaming, building up a streaming platform for delivering movies and tv-series
to the customer, piggybacking on the increasing internet capacity. Then when
challenged by other movie studios, Netflix understood the potential value of data
from their streaming service about what movies and shows the users watched,
how long they watched, and whether or not they liked it. Netflix could now
accurately predict what type of content customers wanted and then started
producing shows and movies based on the data from the streaming service. In
short, Netflix went from a digitalized DVD provider to a digitally transformed
tv and movie studio. However, they are still delivering all three types of services.
DVDs provided Netflix in 2021 with 200M $ of revenue [11], showing a need to
balance different types of services and strategies.
The challenge of supporting the existing value proposition while at the same
time creating new value propositions through innovation is challenging [10]. This
challenge is also known as the innovator’s dilemma [3] where existing companies
fail to innovate due to the success of their existing portfolio, which is further exac-
erbated by digital technology and digital transformation, increasing the speed
and ability of smaller companies to challenge the existing companies in new
ways. Nagji et al. [10] argue that a company’s innovation portfolio needs to be
tailored for the company to thrive, i.e. there is a need to balance digitalization
and digital transformation.
2.2 Capabilities for Digitalization and Digital Transformation
Building on the distinction between digitalization and digital transformation,
there is still a question of what is needed to undertake a digitalization or digital
transformation journey. Bosch and Olsson [1] outline four necessary dimensions
when transitioning from traditional to becoming a digital (transformed) com-
pany.This research based on incumbent companies, is very similar to the context
of MarComp as they both have existing physical services that need to be main-

54 R. Ulfsnes et al.
tained simultaneously as they undergo digitalization and digital transformation.
They identify four orthogonal dimensions in which companies need to evolve:
– Product upgrade
– Data exploitation
–AI/ML/DL
– Business Model
These dimensions are necessary vectors for change that companies have used
to be able to both digitalize and digitally transform.
Product upgrade outlines how software allows the product to be upgraded.
Bosch and Olsson [1] describe a development going from a traditional physical
product, or in the case of software products where the product is 1) “sold as-is,”
developing towards 2) more frequent deployments due to quality improvements,
followed by 3) data from the use of the products are used to improve the features
of the product. In 4) continuous software updates, the system is continuously
improved, and finally, 5) all the previous steps are combined into a fully digital
product delivery.
Data exploitation is a prerequisite for digitalization and digital transforma-
tion. Data can be exploited across five key areas: 1) Quality assurance and diag-
nostics using system behavior data. 2) Product performance and feature usage,
where features and performance data are collected and used for product and
software development. 3) Customer KPIs, where specific data for the customer
are streamed, analyzed, and served back to the customer to provide operational
insight. These can be understood as Data Products. 4) Data-as-an-asset, which
is data captured from multiple customers, analyzed across the customer base,
and served as insights, showing not only operational data compared to one cus-
tomer, but for all customers. And 5) Secondary customer base, where data from
the existing customer base is used to develop and monetize customers outside
the traditional customer base.
Artificial intelligence (AI), machine learning (ML), and deep learn-
ing (DL) are technologies driving the potential of digital technology forward.
1) Data analytics utilize ML for automation and optimization of processes. 2)
A data set centric way of working where data permeates the applications, and
machine learning models are trained on static data and used directly by the
applications. 3) Dynamic data stream, where models are dynamically retrained
based on data changes or system behavior changes. Data and changes across
the customer base are used to retrain and expand the models. 4) Federated
local training and customization, where models and data are deployed and tar-
get specific customers with their own set of customization and local models,
with dynamic retraining. 5) Fully autonomous usage where the system itself
has authority over decisions, mostly associated with autonomous vehicles. One
notable challenge with using AI/ML is the risk aversion of top management [8],
which limits the potential success of AI initiatives.

Interrelation of Digitalization and Digital Transformation 55
The business model shifts when a company is transforming. One example is
going from a transactional model of selling services and products to subscription-
based models where insight and data are an additional value add for the cus-
tomers. The shift in business models is also found in other research on digital
transformation; Tkalich et al. [17] outline how digital transformation and dig-
italization simultaneously change the four interrelated elements of a business
model: product strategy, revenue logic, distribution model, and the service and
implementation model. These changes introduce tensions, which require organi-
zational changes to be overcome.
2.3 Continuous Software Development
The digital dimensions of AI, data, and products require that the company has
software development practices to deliver software with higher frequency, qual-
ity, and security. Further, a fundamental principle in digitalization and digital
transformation is to provide working software to users at regular short intervals
to ensure an increase in customer value through feedback.
In practice, there needs to be a close and continuous linkage between business,
software development, and operations, described as continuous software engi-
neering [6]. A case study by Mikalsen et al. [9] illustrates how cross-functional
teams - consisting of business representatives from business development, IT
developers, testers, and user experience (UX) designers achieve a continuous
business planning process, development, and maintenance. The need for agile
software development teams to interact with other units in the organization
dynamically and responsively is why companies today aim to scale agile meth-
ods beyond software development. Mixing AI/ML/DL, data, and digital business
offerings into these activities have made software development more complex.
The reason is that agile software teams must cooperate with non-agile units.
Agile teams work highly iterative in a sense and response manner while other
organizational functions may operate at a steady pace, avoiding change [5].
3Method
We report findings from a company that was focusing on both digitalization and
digital transformation. Their product development area is our unit of study. It
allows us to understand how multiple disciplines from multiple organizational
units interact when improving existing and creating new software-based prod-
ucts. Some of the new products supported the existing value proposition while
others were redefining it. Our study is a holistic case study [24]. According to
Yin, case studies are the preferred research strategy when a “question is being
asked about a contemporary set of events over which the investigator has little
or no control” (ibid, p. 9). We followed the five-step process proposed by Yin:
1) Case study design. 2) Preparation for data collection. 3) Collecting evidence:
execution of data collection on the studied case. 4) Analysis of collected data,
and 5) Reporting.

56 R. Ulfsnes et al.
3.1 Case Company
MarComp (name suppressed for anonymity) is a multinational provider of ser-
vices for the energy, process, and maritime industries with over 3700 employees.
They were chosen because they participated in a research program on digital
transformation. The company recognized a critical issue of missing interaction
between software development, sales, marketing, and operations, which led to a
transformation initiative. In 2019 the company established an innovation frame-
work based on the Corporate Startup [21]. The framework consists of a six-part
stage-gate process in which a committee controls which initiatives to move to
the next stage or are to be stopped. MarComp has set out a dual transformation
agenda, renewing and growing their existing services through digitalization and
establishing new digitally transformed services on top of existing ones.
Table 1. Data sources
Data source Location Time Participants Data gathered
Innovation
framework, lean
startup
Virtual Sep. 2020–May.
2021
15 Interviews
with 5 product
managers
Interviews on the
startups,
innovation
process, work
processes,
software
development
process, context,
stakeholders
Digitalization
program
Virtual &
physical
May. 2021–Jun.
2021
5 interviews with
discipline leaders
Interviews on
transformation of
discipline,
road-maps,
transformation
process, context
Workshop and
meetings
Physical Oct. 2021 1 Digital
transformation
workshop (1
manager, 1 data
science lead, 2
leaders of
software
development,
head of AI and
data analytics, 2
program
managers
Written notes,
written material
from participants
Strategy
documents
Apr. 2021–Dec.
2021
Strategy
documents,
project
descriptions,
road-maps

Interrelation of Digitalization and Digital Transformation 57
3.2 Data Collection and Analysis
Our data collection (Table 1) started in 2020 when the company needed to
rethink the product development process to reach the estimated earnings of
several digital solutions. The ideas and initiatives in the innovation framework
can be categorized as digitalization efforts or digital transformation efforts. The
researchers participated in internal meetings, customer meetings, and workshops
initiated by the innovation framework. All activities were documented by taking
notes, meeting minutes, and pictures of materials produced in the workshops.
Also, we got access to product documentation, contracts, and data on user activ-
ity on some of the digital products. We ended the data collection in December
2021. The results were presented back to the practitioners in feedback meetings.
We used a variety of strategies to analyze the material [12]. Through several
iterations, we utilized a combination of descriptive and holistic coding to build an
understanding of the data. Firstly, building up a set of descriptive codes before
consolidating them into groups of themes based on the dimensions (AI, data
exploitation, business model, product upgrades) as outlined by Bosch and Ols-
son [1]. After grouping into themes, we applied Strauss and Corbin’s [4]coding
paradigm that involves context, causal conditions, intervening conditions, strate-
gies, and consequences. This method was then used to structure the strategies,
intervening conditions, and consequences for the phenomenon of digitalization
and digital transformation based on the distinction by Wessel et al. [22]. The
qualitative coding was done using NVIVO 1.6.2 and performed on both docu-
ments and transcribed interviews. This combination of top-down and bottom-up
coding ensures that the codes stay true to the data and that relevant literature
is considered.
4 Results
We present the challenges and enablers for software development in digitalization
and digital transformation, respectively, and demonstrate how they interrelate.
Table 2contains the complete list of identified intervening conditions and strate-
gies uncovered in the analysis. In the following sections, we will detail some of
the findings.
4.1 Strategies for Digitalization
Machine Learning is a Crucial Enabler for Digitalized Services. This
is exemplified by a service where ML is used to assist customers in answers to
questions with the combination of domain experts providing the learning input to
the machine learning algorithms. Another example is a service used to predict the
wear and tear on various parts of a ship (predictive maintenance). The strategy
highlights AI’s role in current and future services, which rely on vast amounts of
data. Moreover, the roadmap for the digitalization program highlights the need
for a common data platform.

58 R. Ulfsnes et al.
Table 2. Strategies and challenges for digitalization and digital transformation
Strategies Intervening conditions
Digitalization Machine learning and AI capabilities
Data scientists understanding the business
domain and context
Digital transformative services (new value
proposition) are used to coerce customers
into sharing data
Data standards as an opportunity
Customer self-service through digital services
Digitalization of services through
cross-department alignment
Lack of resources
Key performance indicator setup
Charging extra for digitalized services
Aligning data science with software
development
Slow uptake of data standards
Top management sees lean startup as
a risky approach
Risk aversion to AI in management
and explainable AI
Mixing legacy and digital solutions
Digital transfor-
mation
Data from digitalized services enables digital
transformation
Machine learning is a key enabler for digital
transformed services
Organizational changes
Customer contact to validate problem and
solution
Aligning data science with software
development
Selling digital services with different
value proposition is challenging
Order2Cash process and ERP system
do not support new services
Legal basis for using data for new ser-
vices
Lack of resources
New service might change how Mar-
comp is viewed
Top management sees lean startup as
a risky approach
Risk aversion to AI in management
and explainable AI
Data Scientists Understanding the Business Domain and Context. This
is presented as an essential aspect from the data scientist perspective and the
business side. The program managers and the head of AI emphasize the impor-
tance of data scientist knowledge of the business domain and the project man-
ager’s understanding of the possibilities and implications of using AI. This is
shown when the data scientists and business development people have joint work-
shops to educate each other about the business and machine learning domains.
The head of AI explains: “We did not have to bring the data scientist into the
workshops, but we invited a lot of them, it is important that they get to know
each other. Get to learn what is important for the business and what projects are
coming”.
Digital Transformative Services (New Value Propositions) are Used
to Coerce Customers Into Sharing Data. One key challenge with digitaliza-
tion is the change from the customers reporting information manually through
documents and forms to direct data connections to sensors. However, the incen-
tive for the customer/partner to participate in data sharing is elusive, asking the
question “What’s in it for me?” One explained “It is not sufficient to deliver
the same services as today, you have to a provide a new kind of value. The value
lies within the new opportunities that arise. You can make it more efficient and
easier for the customer, but the customer is interested in new services.” This
requires collaboration with the customer vendors producing the various com-
ponents that go into a product. One project manager explained: “The vendor

Interrelation of Digitalization and Digital Transformation 59
could see themselves wanting new functionality building on the sensor data. They
lacked machine learning expertise, so we proposed to develop the module jointly
when we had the data in our systems.”
Cross Department Workshops. Digitalization is a cross-organizational effort
where groups with various responsibilities, tasks, and competencies share
knowledge and coordinate their action. This can be achieved through cross-
organizational workshops. Digitalization represents an opportunity to under-
stand how work processes go across departments. In addition, the digitaliza-
tion effort will directly affect how the different departments’ daily work will be
affected, and the personnel must have ownership of the new digital solutions.
A project manager explained: “It is not just about us as a project coming in
and telling them what to do, we want them to have ownership, we need to have
participants from different departments in order to capture challenges across the
departments.”
Customer Self-service Through Digital Services. When the customer is
able to assist themselves without involving MarComp personnel, the workload
on both customers and internal resources is reduced. Multiple initiatives at Mar-
Comp aim to automate jobs usually performed manually by either the customers
or the employees of the company. In addition, the self-service systems also pro-
vide additional capture of data about the users, both internally and externally,
providing the possibility to develop services further. “The digital channels give
us direct contact with the technical personnel at the company, thus providing us
with the data and insight to develop new services” (Project manager).
Data Standards as an Opportunity. The establishment of public data stan-
dards through ISO was seen as a strategic move to enable other vendors and tech-
nical solutions to use the standard the company used internally. This enables the
company to much easier integrate different data sources into their data ingestion
platform as the standards match.
4.2 Intervening Conditions for Digitalization
Aligning Data Science with Software Development. Data science as a new
practice (including roles, competency, tools, etc.) extends the organization with
new capabilities. This must be aligned with established software development
for the two functions to coordinate and collaborate. A data analytics team can-
not contribute without a coordinated effort with, e.g., software development or
business development. Hence, further, development needs to clarify which parts
of the organization need to align so that they together drive digitalization as a
joint effort without hampering each other.
Lack of Resources. Digitalization requires new competence and resources, e.g.,
data scientists and/or changes in the technological platform. The simultaneous
software initiatives further exacerbate the issue by adding additional parallel
projects. In addition, the involvement of employees in understanding the current
work process is essential. One project manager complained “Even though NN

60 R. Ulfsnes et al.
does not have the capacity, he is the person we need. But he works in a small unit,
so there are not many people to help out if he is not working operationally.” This
also serves as an example of core personnel needing to balance the innovation
of new services and the operation of existing ones. For data science, this was a
particular problem as the requirement for understanding the business domain
was critical. To the level that new data scientists could not understand existing
code and had to rewrite it.
Key Performance Indicator setup. Existing KPIs are set up as a quantita-
tive representation of the production of, for example documents. In one initiative,
we found that the goal of the digitalized solution is to reduce the number of man-
ual document checks and instead provide a digital solution. However, the current
KPIs measure the number of documents handled per week per employee. Thus,
there is no incentive for the employee to help with digitalization. On a depart-
mental level, this is also found where the departments are measured through the
cost center. Still, the digital solution does not have the possibility of granulating
the cost per department.
Charging Extra for Digitalized Services. Investing in digitalization efforts
and expecting the customer to pay extra for the new digital service is challenging.
As described by a product manager, “What should it cost for the customer, if
they respond ’why should I pay more for this?’. But it costs money to develop
the service, how should you respond to that?” One approach was to add “buy”
buttons that the users could click to measure their willingness to pay.
Slow Uptake of Data Standards. Digitalization driven by increased produc-
tion, sharing, and use of data increases the need for data with the right quality
and transferability. One challenge is getting access to the data in a timely fash-
ion, another critical aspect is the format and structure of the data. Each vendor
with a different format and structure requires the data scientist and the man-
agers to collaborate and discuss with the vendor to understand and contextualize
the data.
4.3 Strategies for Digital Transformation
Data from Digitalization Enables Digital Transformation. Identifying
new ideas is possible due to the data gathered through previous digitalization
efforts. Using existing data to build new solutions has benefits, such as making
it easier to explain and get user feedback due to using actual customer data.
Building on this, the company can further scale the new solutions easier as the
data is already captured and understood. One project manager reflected: “It is
important that we take advantage of the opportunity we get from the availability
of the new data. It is important not only to capture the data but to build on it.”
Machine Learning is a Key Enabler for Digitally Transformed Ser-
vices. However, for the digitally transformed services, the ML capabilities are
not mainly used to optimize current processes but to create new solutions based
on the digitalized data. Examples are utilizing ML and AI methodologies on
historical data to build predictions on how ships and components will behave.

Interrelation of Digitalization and Digital Transformation 61
Organizational Changes. The digital transformed services are different in
value proposition than existing services. This leads to higher uncertainty, and
several iterations and pivots are needed before a working version is discovered.
Further, product managers are moved out of the production line to have decision
authority and the ability to work on the development of the new product. One
product manager elaborated “This program gives us the possibility to take deci-
sions on our own product, compared to previously this has dramatically reduced
the amount of discussion and coordination between other initiatives.”
Customer Contact to Validate Solutions. Contact customers or users to
validate a service with a new value proposition. Through contact, the product
managers are able to pivot the solution and try new approaches. This was made
easy when the service was using existing customer data as a basis for the solution.
One product manager said “We got lots of good feed back beca use i t was ba sed
on real data. They could click around and try stuff. And we got great feedback
from just four-five customers. Based on that, we were able to build a new version
pretty fast.”
4.4 Intervening Conditions for Digital Transformation
Selling Digital Services with Different Value Propositions. Sales of dig-
ital services with a new value proposition are fundamentally different from sales
of traditional services. The new services target different parts of the customer
organization than what the current sales network knows. It also changes from a
more transactional contract-based model to a more subscription or one-time buy
model. One project manager explained “There is no good plan for how to take
the products to market. We need someone to develop products. Previous projects
have shown that the current setup does not work.
Order2Cash Process and ERP System Do Not Support New Ser-
vices. To support new payment models and subscription-based services, the
Order2Cash process needs to accommodate new ways of ordering and invoicing
services matching the new business model. Developing the necessary changes to
the ERP system has high costs and is challenging to execute. A product man-
ager told us “There was a lot of resistance. We cannot change the systems just
because of one serv ice; w e need a bi gger discussion around this. We have been
waiting for over a year.”
New Services Might Change how MarComp is Viewed as a Brand.
Some concerns have been raised about the focus on new services beyond the
current service offering MarComp is delivering and the effect this can have on
the commercial side.
Top-Management Sees Lean Startup as a Risky Approach. Through
the use of lean startup concepts, there is an emphasis on getting feedback from
customers as soon as possible to validate the problem and eventual solution.
The idea of bringing unfinished systems and presenting them to customers is

62 R. Ulfsnes et al.
not received well by top management; one product manager said “They hold
back, they are not willing to let it out for us to test, they were not negative to
the product, but letting it out.”
Risk Aversion to AI in Management and Explainable AI. By utilizing
AI and machine learning, new predictive solutions are developed, limiting the
number of manual inspections of ships that the company has to do. Implementing
the system in production can be challenging due to risk aversion in management,
not trusting the algorithms, and how accurate they are as compared to trusted
manual services. Further, it is difficult to explain how machine learning works
and how the outputs are calculated. One project manager said “We have a
fantastic system, but it is very difficult to tell the users so they know how to use
it properly.”
5 Discussion
Motivated by the need to understand the interrelation between digital transfor-
mation and digitalization, we conducted a longitudinal case study in MarComp,
a multinational provider of services for the energy, process, and maritime indus-
tries. We have reported on strategies and intervening conditions for digitalization
and digital transformation and how these factors are related. We now discuss
the case in light of our research question.
How are digital transformation and digitalization interrelated?
Fig. 1. Digitalization provides data

Interrelation of Digitalization and Digital Transformation 63
Firstly we note that data from digitalization is a crucial enabler for digitally
transformed services, providing the underlying data for innovation which may
redefine and/or create new value propositions in the company. So to succeed with
digital transformation which is a data-driven process, digitalization is important
as it gives access to the needed data. We observe this relationship in our results,
where data is first gathered through the digitalization of some product or ser-
vices; this can be seen in Fig. 1. There are multiple intervening conditions for
developing digital services, one being the ability to obtain, capture, and con-
sume data from third parties. This corroborates well with Vial [20], where data
availability is seen as a key building block for digital transformation.
Fig. 2. Interrelation between digitalization and digital transformation
Further, we observe that MarComp, after gaining experience with digitaliza-
tion and novel new products with a different value proposition from previous
products, defines a dual digitalization and digital transformation. In Fig. 2,we
observe how the relationship between digitalization and digital transformation
unfolds.
We also see shared strategies and intervening conditions; AI/ML integrated
into the teams is a strategy for both phenomena, just as lack of resources hin-
ders both. In addition, we find a need for a mediating role between the different
aspects of the dual transformation, AI, Data, agile software engineering, and
changes in work practice (internally or externally). In our case, we see that
product management takes this mediating role, which is in line with other stud-
ies [18].

64 R. Ulfsnes et al.
Most notably, we see that not only is data from digitalization crucial for
kick-starting digital transformation, but the promise of digital transformations,
through its significantly different value proposition, is a way for the company to
acquire more data for digitalization. The customers and vendors are interested
in new services and the added benefit it can provide them beyond just giving
data away. Especially co-creation of value through shared data and insights was
perceived as valuable. The concept of delivering more value to the customers
corroborates well with Bosch and Olsson [1], where the customer’s KPIs were
used to provide more value to the companies, although in our case, this also
involved vendors.
Thus, the relationship between digitalization and digital transformation is
more complex than simply that the one is enabling the other. This does not
necessarily mean that an organization must fully complete digitalization before
transforming, but that parts of the business model can be addressed individually.
Over time, this can become a system of careful experimentation, where confi-
dence in the ability to transform grows over time, similar to how the innovation
portfolio needs to be balanced as reported by Nagji and Tuff [10], and where
companies need to further engage in innovative activities on top of digitalized
products.
From our case study, we have found that digitalization and digital transfor-
mation share many of the same intervening conditions and strategies and are
related. Wessel et al. [22] described two cases showing two distinct paths where
company A chose the path of digitalization. In contrast, case B showed a big
bang transition towards a new organizational identity i.e., digital transformation.
Our data shows that there is no clear distinction between these two phenomena.
6 Conclusion and Future Work
We have conducted a 15-month study of professionals in a maritime company.
We found that digitalization and digital transformation are interrelated and that
it is more complex than simply that the one is enabling the other, i.e., it is not
necessarily that an organization needs to complete digitalization before it can
transform. We even found that digital transformation enables continuous digi-
talization. We found several intervening conditions and strategies for digitaliza-
tion and digital transformation; some where shared, and some where unique to
the phenomenon. In addition, we noticed that balancing digitalization and dig-
ital transformation is crucial to both concepts due to the interrelation between
them. Future work should dive deeper into the strategic element of balance
and what roles and processes are needed to undertake digitalization and digital
transformation.
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