A Mapping Study about Digital Transformation of Organizational

Culture and Business Models

Eduardo C. Peixoto

, Hector Paulo

, César França

and Geber Ramalho

Cesar School, Avenida Cais do Apolo, 77, Recife, Brazil

Federal University of Pernambuco, UFPE, Av. Prof. Moraes Rego, 1235 - Cidade Universitária, Recife, Brazil

Keywords: Digital Transformation, Digital Companies, Organizational Culture, Business Models, Maturity Models,

Literature Mapping.

Abstract: According to some predictions, investments in Digital Transformation (DT) may reach US$ 6.8 trillion in

2023. Nevertheless, about 70% of DT initiatives struggle for success. In fact, few agree about what DT is,

how a company becomes digital and how to measure its progress towards it. Organizational Culture and

Business Models, however, are seen as key companies’ dimensions. Then, in this article, we report a literature

mapping study on the characteristics of Organizational Culture and Business Model in the context of DT. We

also investigate how these dimensions are assessed by Digital Maturity Models (DMM). Our data reveal that

the most frequent cited Organizational Culture characteristic is Organizational Learning, and that Data and

People are companies’ Business Models key resources in the context of DT. The selected studies did not

provide enough information about how the characteristics of these two dimensions are evaluated by DMM.

We concluded that companies’ dimensions characteristics have not been exhaustively explored and further

studies on how to evaluate them in the context of DT are needed.

1 INTRODUCTION

In August 2011, the founder of Netscape and

investment fund Andreessen Horowitz published in

The Wall Street Journal the article “Why Software Is

Eating the World” (Andreessen, 2011). In the article,

Andreessen pointed out the growing appreciation of

purely software companies and the increased use and

dependence on software in the operating model of

many companies in different sectors. Less than 10

years later, in 2018, Apple, Alphabet/Google and

Microsoft already figured as the main companies in

the US capital market, dethroning the then-

traditional, top of the ranking companies from a

decade ago. During this period, many other digital

companies caused a rapid migration of value from

analog to digital markets. Nowadays, to stay

competitive and defend against new entrants, being

digital has become an imperative (Boulton, 2018).

https://orcid.org/0000-0002-1396-7873

https://orcid.org/0000-0003-0946-8462

https://orcid.org/0000-0002-0863-3764

https://orcid.org/0000-0001-8306-4410

Nevertheless, going through a Digital

Transformation (DT) process has not been an easy

task for any company. According to Tabrizi et al.

(2019), 70% of all digital transformation initiatives

fail.

A possible reason why companies and managers

struggle in their DT initiatives is that going through a

DT process is complex and therefore it affects

simultaneously many company dimensions.

Teichert et al. (2019), for example, in a systematic

review of the literature about Digital Maturity Models

identified 15 company dimensions commonly used to

evaluate a company status regarding DT.

Organizational Culture (OC) and Business

Models (BM) were among the most relevant of these

dimensions. "Several surveys reported that company

culture is considered as the number one hurdle to

digital transformation. Hence, cultural change is a

prerequisite and can become a bottleneck for digital

transformation" (Teichert, 2019). BMs, on the other

hand, are strategic drive (Salviotti et al., 2019).

408

Peixoto, E., Paulo, H., França, C. and Ramalho, G.

A Mapping Study about Digital Transformation of Organizational Culture and Business Models.

DOI: 10.5220/0010991600003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 408-417

ISBN: 978-989-758-569-2; ISSN: 2184-4992

According to Rogers (2016), the transformation “is

about new growth strategies and business models

replacing old ones as established companies learn

new ways of operating”. Other researchers associate

DT with the transformation of BMs (Morakanyane et

al., 2017; Schallmo et al., 2020) as well.

However, few studies explore the characteristics

of these two dimensions of companies in DT. In this

study, we followed guidelines for conducting a

systematic mapping with aims to answer the

following research questions:

1. RQ1:

What are the characteristics of

organizational culture (OC) associated to

digital transformation?

2. RQ2:

What are the characteristics of

business model (BM) associated to digital

transformation?

3. RQ3: How are these characteristics evaluated

on Digital Maturity Models (DMM)?

By investigating what is in the literature about the

characteristics of OC and BMs of in the context of

DT, we aim to extend knowledge to help practitioners

enhance their chance to succeed on DT initiatives.

The results may also contribute to the elaboration of

more accurate assessment of these two dimensions on

digital maturity models.

In this paper, we report a literature mapping study

on the characteristics of Organizational Culture and

Business Model in the context of DT and investigate

how these dimensions are assessed by Digital

Maturity Models (DMM). In Section 2, we review

some important theoretical background. In Section 3,

we describe the method and procedures used for this

mapping study. We also discussed the threats of our

study. In Section 4, we present the results: an

overview of the selected articles and our answer to the

research questions. In Section 5, we present our

conclusion, final considerations and give suggestions

for future research. In Section 6, we list the articles

used as references in this study

2 THEORETICAL

BACKGROUND

2.1 Digital Transformation

A clear understanding of what Digital Transformation

(DT) is remains in considerable confusion and

misconceptions. The failure to distinguish between

digital transformation and its related terms

(digitization and digitalization) has created

difficulties for practitioners to claim authority and

responsibility for the strategy and implementation of

the digital transformation of their organizations

(Gong, & Ribiere, 2021; Morakanyane et al., 2017).

There are certain convergences though.

Morakanyane et al. (2017) conducted a systematic

literature review to reconcile several definitions on 1-

what DT is; 2- what characteristics DT has; 3- what

drives DT; 4- what DT impacts and 5- what are the

organization transformed areas. Henceforth, we refer

to DT with the semantics they have proposed:

“An evolutionary process that leverages digital

capabilities and technologies to enable business

models, operational processes and customer

experiences to create value.”

2.2 Organizational Culture (OC)

Schein, E. (2010) defined OC as a collection of

assumptions, norms, values, beliefs, and rules of

behavior shared by employees of an organization to

portray culture. It is how employees of an

organization obtain a sense of identity (Alvesson &

Sveningsson, 2015).

Other authors say that culture factor is how the

traditions, language, and laws (or rules of behavior)

held in common by a nation, a community, or other

defined group of people affect people on how they

behave regarding digital transformation. It is a topic

of organizational behavior, which commonly

includes employee attitudes, engagement,

identification, commitment, motivation, and climate

(Trenerry et al., 2021). Many authors say that the

culture of an organization is reflected in the behavior

of its employees (Al-Faihani et al., 2020).

Schein, E. identifies three different levels of OC

(Artifacts, Espoused Beliefs and Values, Underlying

Assumptions). Underlying assumptions are the

essence of culture and represent the belief system

towards behavior, relations, and reality are

manifested in values that become apparent in visible

artifacts such as behavior, language, or technology

(Hartl, & Hess, 2017).

Artifacts describes any organizational attributes

that can be observed, felt, and heard in an OC (Duerr,

2018). Exposed Beliefs and Values, in turn, refer to

goals, ideals, norms, standards, and moral principles.

In the context of digital transformation, Culture is

seen as the perspective of the human being in relation

to changes in the digital age, both in the role of leader

and author of transformations as in the role of an

instrument in the new configurations of societies and

businesses (CESAR, 2021).

A Mapping Study about Digital Transformation of Organizational Culture and Business Models

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Morakanyane et al. (2017) noticed that culture is

an essential driver of successful DT in organizations.

According to Schallmo et al. (2018), incompatible

culture is one of the main reasons why DT initiatives

fail. Some studies even suggest that the most

prominent barriers to digital transformation are

managerial and cultural (Jones, Hutcheson, &

Camba, 2021).

2.3 Business Models (BM)

Many authors produced definitions of what a BM is

(Dasko & Sheinberg, 2005), and despite the academia

effort to capture its core it has been frequently

confused with other popular terms in the management

literature (Dasilva & Trkman, 2014). Al-Debei, El-

Haddadeh and Avison (2008) reconciled several

definitions, from different basis (such as value

proposition, revenue sources, business logic, current

and future business reality simplification, among

others) to produce the following definition:

“The business model is an abstract representation

of an organization, be it conceptual, textual,

and/or graphical, of all core interrelated

architectural, co-operational, and financial

arrangements designed and developed by an

organization presently and in the future, as well

as all core products and/or services the

organization offers, or will offer, based on these

arrangements.”

In the 2010's, Osterwalder (2010) proposed a

graphical representation named Business Model

Canvas (BMC), which became later standard to

represent and explain the BMs.

The BMC remains a well-known and easy to

interpret representation of a BM. Therefore, for the

purposes of this mapping, we considered the nine

BMC elements to characterize BMs and seek for the

literature on how they are cited in the context of DT.

2.4 Digital Maturity Models (DMM)

Maturity Models are tools generally designed to assess

and evaluate the development of a company towards

high-level productive processes. The term “maturity”

refers to a state of being complete, perfect, or ready and

is the result of progress in the development of a system

(Morakanyane, Grace, & O’Reilly, 2017). They have

become a popular management tool because they help

to determine the baseline level of a company’s maturity

(Williams et al., 2019).

In the same sense, a Digital Maturity Model

(DMM) specifically reflects the status of a company’s

digital transformation (Morakanyane, Grace, &

O’Reilly, 2017). It compares the company state to the

ready or perfect state of a Digital Company and helps

to identify gaps and can be used by practitioners to

plan actions to overcome these company gaps to the

digital mature state.

A DMM can be prescriptive, descriptive, or

comparative. Prescriptive DMM needs to, by

definition, provide actionable plans. A Descriptive

DMM gives a good overview on the historical

development of a company but offers little guidance

on how to proceed (for planning, it is used in

combination with a roadmap). In addition, a

Comparative DMM allows for internal or external

benchmarking and provides the opportunity to

compare the maturity levels of similar business units

or organizations (Kretzschmar, 2021).

Usually, a DMM has levels, dimensions, and

attributes (or characteristics). Attributes are the

elements of the dimension used to gauge the state of

the company in relation to the perfect state of a

dimension. The combined results of the state in each

dimension point to the overall state, the level of the

company in relation to what is being measured.

In his literature review, Teichert et al. (2019)

describes 22 different DMMs, which shows that

DMMs are abundant in the literature. OC and BMs

appear in that work as two of the most common

digital maturity dimensions.

3 METHODS AND PROCEDURES

According to Templier and Paré (2015) and Paré, G.

et al. (2015), we can define literature reviews based

on the review’s purpose, which needs to be

determined before the work is done. These ones can

be to describe, test, extend, and critique.

For this article, our purpose is to examine the state

of the literature as it pertains to a specific research

question. Therefore, this kind of literature reviews –

better known as Mapping Studies – do not aim to

expand upon the literature, but rather provide an

account of the state of the literature at the time of the

review.

We followed the literature review guidelines

proposed by

Kitchenham, B., & Brereton, P. (2013) and

adopted a qualitative approach centered on a content

analysis of the literature (Schaller et al., Vatananan-

Thesenvitz, Pulsiri, & Schaller, 2019).

The review steps are detailed in the following

subsections.

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3.1 Search Strategy

The search process started with a manual search in

specific database chosen for their relevance to the

subject, such as IEEE Xplore Digital Library,

ScienceDirect, Emerald, Scopus. We basically

searched for “Digital Transformation”.

From this initial search, we selected a small set of

candidate articles, according to their potential to

answer our research questions. This initial step was

conducted by two researchers, and the selection was

made by consensus, based on title and abstract.

From this initial set of articles, we analyzed

frequency and relevancy of technical terms that could

help us to build a more comprehensive search string.

These words were grouped by meaning (e.g., firm,

organization, enterprise) – to assemble blocks of

terms. After that, we simulated different

combinations of search strings, checking if our initial

set of articles were reached in the resulting lists, as a

search quality control mechanism. We ended up with

the search strings as shown in Table 1: Search String.

Table 1: Search String.

("review" OR "survey" OR "roadmap" OR "mapping")

AND "digital" AND ("maturity" OR "levels" OR "phases"

OR "progress" OR "states" OR "transformation")

Finally, applying Publish-OR-Perish (POP)

, we

used the search string (Table 1: Search String.) to

perform an automatic search on “Title words” on

Google Scholar and found 553 candidate articles for

our research.

For this stage, we did not apply any temporal

coverage, nor did we apply snowballs. All searches

were automatic.

3.2 Selection Strategy

As selection criteria, we determined that the papers

included in the review had to (E1) be written in

English; (E2) published in peer reviewed conferences

or journals, or as book chapters; (E3) be focused on

OC and BM associated to digital transformation. For

viability purposes, we limited our research on the

period between 2017 and 2021 (the initial list with

553 studies contained 231 papers, 223 unique, that

were published before 2017).

After removing duplicates, 69 articles remained,

and were submitted to a full-text analysis. In this

phase 12 articles were excluded because they either

https://harzing.com/resources/publish-or-perish

(E4) do not contain answers to any of our research

questions; or (E5) full text was not available.

Two researchers carried out the selection process,

both of whom independently analyzed each paper.

The exclusion criteria (E1 … E5) were carefully

noted, and meetings were held to resolve any

disagreements. Figure 1: Overview of the Selection

Method depicts the search process, indicating

numbers of papers at each step, for this review.

Figure 1: Overview of the Selection Method.

3.3 Validity Threats

There is a risk that we might have missed some good

material because, in this specific subject, we would

expected that the industry has advances as relevant as

those documented in specific literature. Industry

focused databases such as DUP, MIT SMR and HBR

were not included in our research. To explore further

and exhaust the possibilities, there are even other

scientific databases not yet explored such as Emerald,

Springer, JSTOR, SCOPUS, PsycINFO and ACM

Digital Library that may bring different findings. Due

to the novelty of the subject and the lack of

confidence about the best scientific bases to search

such a transversal subject, we decided on Google

Scholar, because its ability to index several databases.

We cannot guarantee that this is the best set of articles

to look at, nor that the search will be exactly

reproducible, but it returned a sensible number of

articles (553).

The initial manual selection was quite arbitrary

and represents another validity threat to our Method

and Procedure of articles selection.

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4 RESULTS

4.1 Selected Studies Overview (N=57)

At the end of the search and selection process, 57

studies remained for analysis. They originated from

institutions in 31 different countries, distributed in

five continents. Figure 2 shows the number of articles

per countries, with two or more articles. The country

with the most publications was Germany (n=10),

followed by Croatia (n=5) and the United Kingdom,

Portugal, and Russia (n=3).

Figure 3 shows the distribution of studies per year

of publication. It shows a steady increase in

publications in recent years. We consider 2017 a

tipping point year, because 39% more articles were

published after 2017 (2017 included) than in all years

prior to 2017.

Figure 4 shows the number of articles per those

scientific databases, only two or more articles. We

can see that the most important databases were

ResearchGate, IEEE Xplore, Springer, Elsevier and

Frontiers which indicates that this subject spreads in

several relevant research forums.

Finally, Figure 5 shows the number of articles per

publication type. Most selected papers have been

published in Journals (n=27) and Conferences

(n=22). A few of them were published like books

(n=4), dissertations (n=2) and thesis (n=2).

The complete list of selected articles can be

downloaded from https://doi.org/10.5281/zenodo.57

33316.

Figure 2: Number of Articles per Country.

Figure 3: Distribution of Papers Included in the Review

across Years.

Figure 4: Studies Selected in per Database.

Figure 5: Articles per Type.

4.2 RQ1: What Are the Characteristics

of Organizational Culture (OC)

Associated to Digital

Transformation?

Our analysis found several characteristics of OC

associated to digital transformation, which means that

companies’ digital transformation efforts are driven

to design environments that value these cultural

aspects. Nevertheless, with the data we collect it is

not possible to infer which factors have the greatest

impact (see the complete list of OC characteristics

associated to digital transformation in Table 2:

Characteristics of OC associated to digital

transformation.). Seventeen studies provided answers

to RQ1 on a perspective of OC characteristics. We

adopted the framework of Edgar Schein to organize

these characteristics data according to their cultural

nature.

In terms of artifacts, OC is described based on the

use of new digital technologies (social media, mobile,

analytics or embedded devices) to enable major

business improvements, like enhancing customer

experience, streamlining operations, or creating new

BMs and ICT competencies of employees.

Employees must know how to extract the best from

technologies at work as we can see in the following

excerpts: “Use of new digital technologies, such as

social media, mobile, analytics or embedded devices,

in order to enable major business improvements like

enhancing customer experience, streamlining

operations or creating new business models” [S54,

p7] and “employees need to know how technologies

can be used to work, as well as how they can be used

to improve the work” [S34, p8].

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The use of data science and analytics to guide

business decisions (Schallmo et al. 2020) and sharing

knowledge and assets (Bumann & Peter, 2019) are

other examples of cultural artifacts associated to

digital transformation.

Regarding culture values and beliefs, the

research identified that about 80% of the examined

studies include mention goals, ideals, norms,

standards, and moral principles. For these studies,

attributes like organizational learning, innovation,

collaboration, customer centricity, agility and

flexibility, empowerment and openness towards

change are the most important characteristics.

Concerning Organizational Learning,

companies in the context of DT may translates this

value to practice through educational activities and

investing in continuous learning to enhance digital

skills and capabilities of employees: “Plan and

pursue massive educational activities/programs for

both clients and employees” [S06.2] and

“empowerment of employees, working in teams, and

enhancing digital skills and capabilities” [S27].

In addition, these companies pursue stimulating

environments that encourages employees to

experiment and learning through small, incremental,

and iterative changes.

As innovation value, we can find the prevalence

of behaviors that support risk-taking, disruptive

thinking, and the exploration of new ideas: “The

culture in digitally mature firms is collaborative and

more risk taking” [S37]. These companies often fund

innovation, and innovation is carried out regularly

through open innovation programs extent of

partnerships with external networks such as third-

party vendors, startups, or customers: “... using agile

methods, involving customer into innovation process,

funding innovation, innovation conducted regularly.”

[S11].

Collaboration appears associated to teamwork,

cross-functional collaboration, concentrates on

eliminating a sense of hierarchy to focus more on

horizontal collaboration, and readiness for

cooperation with external partners: “eliminates a

sense of hierarchy to focus more on horizontal

collaboration that cuts across organizational

boundaries.” [S05, p9].

A client-centric approach refers to the

orientation to focus on, and continuously adapt to,

customer needs. It may imply practices such as

personalized products/services, customer experience

analysis, co-creation for new products, and others:

“focus on customer choice, flexibility and agility

required to adjust as the customer preferences” [S37]

and “tracking of customers' experiences, prediction

of their needs.” [S34].

Table 2: Characteristics of OC associated to digital

transformation.

Schein

Level

Organizational

Cultural

Characteristics

References

Artifacts

Digital

Technologies

[S02], [S06], [S11], [S28],

[S34], [S37], [S42], [S49],

[S54]

Data-driven

decision

making

[S01], [S18], [S30], [S66]

Knowledge

sharin

[S01], [S11], [S26]

Sharing assets [S39], [S64]

Values

and

Beliefs

Organizational

learning

[S01], [S06], [S11], [S14],

[S19], [S23], [S27], [S28],

[S34], [S39], [S49], [S64]

Innovation

[S11], [S19], [S23], [S26],

[S27], [S28], [S30], [S31],

[S34], [S37], [S39], [S49],

[S64]

Collaboration

[S05], [S11], [S19], [S23],

[S26], [S27], [S28], [S31],

[S37]

Customer

Centricit

[S02], [S06], [S11], [S27],

[S31], [S34], [S37], [S64]

Empowerment

[S01], [S05], [S11], [S19],

[S27], [S28], [S39]

Agility and

Flexibility

[S05], [S11], [S19], [S23],

[S27], [S31], [S34], [S37],

[S49], [S64]

Openness

towards

chan

[S01], [S05], [S11], [S27],

[S28], [S37]

Moreover, it is possible to identify

empowerment elements, in decision-making process

that are taken together, allowing employees to pursue

ideas and developing their capabilities: “culture

should be transformed into a culture of involvement,

in which decisions are taken together ...” [S19] and

“allowing employees to pursue ideas in an

interdisciplinary and decentralized way ...” [S39].

As a value of OC, agility and flexibility can be

viewed as the organization's acceptance to change,

and efforts applied to act and re-structure, being

flexible and adaptable. Companies in the context of

DT have a quickly sensing/responding to changes as

strategy to gain market share and sustain their

competitive advantage: “Agility and Open Innovation

are considered an essential factor for maintaining

competitiveness and ultimately for the survival of a

company” [S19].

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Openness towards new ideas refers to readiness

to accept, implement and promote change. This kind

of company maintain supportive environments for

changing ways of work [S28].

Concerning values and beliefs the research

identified leadership, failure culture, creativity,

digital-first mindset, startup mentality and problem-

solving culture.

4.3 RQ2: What Are the Characteristics

of Business Model (BM) Associated

to Digital Transformation?

The BM characteristics associated to digital

transformation have several distinct characteristics,

but one appears in intensity above all the others: it is

how data appears in most elements of the BM.

Moreover, the study revealed that data and people are

indisputable key resources. BM characteristics were

extracted from 33 papers, (see the complete list in

Table 3: Characteristics of business model associated

to digital transformation.).

Other characteristics of the BMs in the context of

DT are also worth mentioning and are depicted in the

paragraphs that follows.

Customers are either digital or non-digital, the

company must deal with a much broader customer

spectrum than non-digital companies. Customers,

nevertheless, are embedding digital technology

within their daily routine to become more informed,

independent, and demanding. They can shape opinion

for good of for bad and the decision-making process

of others: “An important implication of these changes

is that customers no longer see themselves as captives

of the firms with which they transact” [S30].

Companies in the context of DT have a Value

Proposition not only for customers, but for itself as

well. At first, the customer in the center of the

business: customer experience is a core value, and

availability and convenience are sought all times:

“markets have evolved from organisational

centricity, in which manufacturers and service

providers largely define what to produce and market

to customers; to individual centricity, in which

consumers demand insight driven, customised

experience” [S62]. It delivers value through

personalization and customization. Value is obtained

with co-creation, through explicit actions or implicit,

through data capturing and intelligently data

processing. For the company itself, it uses technology

and data to reduce costs for the customer.

Channels are hybrid, a mixture of physical and

digital, but must be perceived as an omni channel:

“boundaries between physical and online industry

structures, including the convergence of physical

products and digital services, merging the physical

world with online content, and creating an

omnichannel environment for the customer” [S02].

Companies builds Customer Relationship in the

context of DT for direct and personalized

communication to customers. Customers on their side

demand transparency and need to be in the center of

the companies’ attention: “business transparency has

become an essential requirement, as nowadays”

[S11].

Table 3: Characteristics of business model associated to

digital transformation.

Business Model

Characteristics

References

Customer

ments

[S01], [S02], [S6.1], [S27], [S30],

[S35], [S37], [S39]

Value Proposition

[S02], [S03], [S05], [S6.1],

[S6.3], [S08], [S11], [S12], [S14],

[S20], [S28], [S30], [S35], [S39],

[S62], [S69]

Channels [S01], [S02], [S6.1], [S35], [S39]

Customer

Relationshi

[S01], [S03], [S6.1], [S6.3],

[S11], [S34], [S37], [S69]

Revenue Streams

[S01], [S03], [S6.1], [S6.3],

[S20], [S21], [S37]

Key Resources

[S01], [S02], [S03], [S05], [S6.1],

[S6.2], [S6.3], [S11], [S23],

[S29], [S30], [S32], [S33], [S34],

[S37], [S39], [S42], [S49]

Key Activities

[S01], [S02], [S03], [S05], [S6.1],

[S6.2], [S6.3], [S08], [S11],

[S12], [S14], [S18], [S27], [S28],

[S30], [S33], [S35], [S37], [S39],

[S64], [S66], [S69]

Key Partnerships

[S01], [S02], [S03], [S11], [S30],

[S33], [S37]

Cost Structure [S02], [S05], [S69]

Revenue Streams are based on a service logic,

which also implies lower tickets and continuous

transactions. The dematerialization of physical goods

into a digital remotely monitored asset enables a

company to charge customers for what products

deliver, instead of charging for the product itself:

“transform from a pure product business to a service

business and ultimately to data-driven, outcome-

based service models” [S06.3].

Companies’ Key Resources in the DT context are

1- digital technology, 2- data and 3- people (with

digital skills leadership with soft skills). Data

collected through channels, products or services, or

digitized assets is used to perform micro

segmentation (sometimes to the level of an

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individual), or to constantly reimagine products or

services to create (new) value for customers or for the

company optimization: “for instance, firms adopting

digital technologies consider data streams to be of

paramount importance and assign to them a central

role in supporting their digital transformation

strategies” [S03].

Data is a source for better decision making and

allows for value co-creation with customers or

partners. Educating and training employees for digital

is another Key Activity: “organisations should

harness the benefits of digital technologies by

collecting customer data and using customer insights,

for instance to predict customer behaviour and to

provide tailored and personalised products and

services with a better customer experience” [S39].

Knowledge and resources sharing among

companies and Key Partners and value co-creating

with customers are good indicators of a more intense

open innovation activities in DT context. The digital

platforms also changed the sequential organization of

a production value chain to a more dynamic and

flexible network of partners: “the value chain has

become far more distributed in times of digital

transformation —particularly value creation and

value capture.” [S01].

Digital assets are easy to carry or replicate, and

data captured allows a better coordination of physical

assets and process optimization, leading to a better

Cost Structure: “with regard to economics, DT may

result in improved firm performance and new forms

of value, caused by an improved service quality or

cost reductions” [S02].

4.4 RQ3: How Are These

Characteristics Evaluated on

DMM?

Many of the studies investigate DMM dimensions,

and sometimes, the dimension characteristics (or

attributes), but very few describe how these

characteristics are evaluated. From our 57 selected

studies, only 6 ([S31], [37], [S38], [S39], [S42] and

[S43]) contained some description of the

characteristics of the OC in a digital maturity model,

but none describes how those characteristics are used

to evaluate a company maturity regarding digital

transformation.

The same happened to BMs. Few studies ([S06.2],

[S31], [S39], [S42], [S60] and [S64]) contained some

description of the characteristics of the BM in a

DMM, but none described how they are evaluated to

gauge the maturity of a company regarding digital

transformation.

We randomly dive into the cited DMM (snowball

on some articles that reviewed DMM) to further

investigate the issue, but without success. So, we

suspect that even the articles describing the DMM do

not document how the dimension and its

characteristics are evaluated.

5 CONCLUSIONS AND FINAL

CONSIDERATIONS

DT is a relatively new topic that can be studied in

different aspects. In this study, we collected evidence

that OC and BM are company dimensions that seem

to be key to DT and are frequently used on DMM to

gauge DT progress on companies. OC can be a barrier

or a driver to the DT. To many authors, transforming

the company BM is the end goal of DT. To help

practitioners set and follow better transformation

goals and increase DT initiatives chances to succeed,

this mapping study reviewed the literature to find out

how these company dimensions are characterized in

the context of digital transformation. We also

investigate how these dimensions are assessed in

Digital Maturity Models.

This study was carried by systematically selecting

and examining 57 articles. The research results

indicate that several OC characteristics are associated

with artifacts and values. The artifacts are associated

with a use of new digital technologies to enable major

business improvements like enhancing customer

experience, streamlining operations, or creating new

BM and ICT competencies of employees. In addition,

regarding culture values and beliefs, to these studies,

organizational learning, innovation, collaboration,

customer centricity, agility and flexibility,

empowerment and openness towards change are the

most important characteristics.

Furthermore, our study reveals that, in the context

of digital transformation, a successful company is a

true engine that uses digital technology to

continuously capture customer data, directly through

their products or services, or through third-party

products and services or through their customer

channels, to increase customer value. In this sense, it

implements a digital positive feedback loop, fed by

customer data, to continually increase or create value

for their consumers, which in turn allows more data

to be captured. The data captured from the company's

physical assets are also feedback, so that the company

can improve resource usage efficiency. In other

words, data is key as a resource and working with data

(innovating or optimizing through it) is a key activity.

A Mapping Study about Digital Transformation of Organizational Culture and Business Models

415

To master the use of data, companies constantly train

its employees on digital technologies.

How the characteristics of these two dimensions

are evaluated on DMM could not be investigated, as

the selected articles for the study did not contain

sufficient data to answer RQ3. Then, there is an

indication that the characteristics of company’s

dimensions in the context of DT are not sufficiently

documented in scientific papers. Nevertheless,

knowing how these characteristics are evaluated is an

important source to understand the state of plenitude

regarding companies’ DT. It is also mandatory to

validate or use a digital maturity model.

We collected data from a fair number of studies

from several countries (see Figure 2) and several

industries. We did not selected articles based on any

company characteristics, like revenue amount,

market size etc. Therefore, our finds are, in principle,

applicable, but not specific, to any kind of company.

Researches more fine-tuned to a specific type of

company (like industry or SME) may bring different

results.

Although many selected articles for this study are

literature reviews (and may have used articles

published before 2017), more than 70% of the

selected studies were published after 2018, and then

are updated with the state of the knowledge.

There are some limitations we faced that threaten

the validity of the results found: 1- the use of freely

accessible web search engine such as Google Scholar

with no guarantee that it would provide the best set of

articles, nor that the search will be exactly

reproducible; 2- not exploring gray databases such as

DUP, MIT SMR and HBR and some scientific

databases such as Emerald, JSTOR, SCOPUS,

PsycINFO and ACM Digital Library.

To understand the state of completeness regarding

companies’ DT, future studies should deepen the

analysis on how OC and BM characteristics are

evaluated in DMM. An analysis on companies' other

characteristics in the DT context could also contribute

to the development of a clear and grounded

understanding of what the companies’ characteristics

associated to Digital Transformation are.

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