Organizacija, Volume 50 Number 3, August 2017Research Papers
255
DOI: 10.1515/orga-2017-0013
The Importance of Business Model 
Factors for Cloud Computing Adoption: 
Role of Previous Experiences
Kristina BOGATAJ HABJAN1, Andreja PUCIHAR2
1 SOLVERA LYNX nove tehnologije za energetiko d.d., Ljubljana, Slovenia
kristina.bogataj-habjan@solvera-lynx.com
2 University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia
andreja.pucihar@fov.uni-mb.si
Background and Purpose: Bringing several opportunities for more effective and efficient IT governance and ser-
vice exploitation, cloud computing is expected to impact the European and global economies significantly. Market 
data show that despite many advantages and promised benefits the adoption of cloud computing is not as fast and 
widespread as foreseen. This situation shows the need for further exploration of the potentials of cloud computing 
and its implementation on the market. The purpose of this research was to identify individual business model factors 
with the highest impact on cloud computing adoption. In addition, the aim was to identify the differences in opinion 
regarding the importance of business model factors on cloud computing adoption according to companies’ previous 
experiences with cloud computing services. 
Methodology: Based on literature review, prior research results, and interviews with cloud computing providers and 
users, a research model was developed. Statistical analysis focused on identification of factors’ importance on cloud 
computing adoption and differences in opinions according to respondents’ previous experiences with cloud comput-
ing services. The study was done among 80 companies and five major cloud computing providers in Slovenia. 
Results: The research results reveal statistically significant differences in opinions on the importance of cloud com-
puting business model factors according to respondents’ previous experiences with cloud computing services. The 
results can provide orientation for redesign or innovation of existing business models towards the creation of a 
customer-oriented business model for the more successful exploitation of cloud computing services and business 
opportunities. For potential users, the findings represent guidelines for the successful adoption of cloud computing 
services.
Conclusions: In our research, the investigated business model factors could be classified into so-called “business 
model organizational factors”, as they primarily need to be considered by cloud service providers when defining or 
innovating their business models. For future research, the model should also include the impact of environmental 
factors, such as Competition, Business Partners, Legislation, Economic Situation, in order to investigate their impact 
on cloud adoption.
Keywords: Cloud Computing adoption; SaaS; IaaS; Paas; Business model factors
1 
Received: December 5, 2016; revised: February 2, 2017; accepted: March 4, 2017
1 Introduction
More than five decades of research and development ac-
tivities in virtualization, distributed computing, networks, 
and software solutions and services enables the implemen-
tation of current cloud computing services and facilities. 
Nowadays, cloud computing is defined as the provisioning 
of ubiquitous, on-demand available, dynamically scalable, 
virtualized IT services and facilities to the customer based 
on the minimum intervention of the provider (Marston, Li, 
256
Organizacija, Volume 50 Number 3, August 2017Research Papers
Bandyopadhyay and Ghalsasi, 2011). With its character-
istics of service orientation, flexibility, resource sharing, 
elasticity, virtualization, and charging according to service 
usage, cloud computing offers significant changes in tra-
ditional IT implementation and governance and results in 
optimization and cost reduction (Chebrolu, 2011). 
According to IDC InfoBrief report (2015), approx-
imately four of ten organizations have already adopted 
public or private cloud services, with the primary goals of 
increased efficiency and decreased costs. In 2017, cloud 
adoption impact is also expected to result in the strategic 
allocation of IT budgets at the higher level and increased 
revenue. According to the same study (IDC InfoBrief, 
2015), over 50 per cent of organizations’ IT spending is ex-
pected to be for third platform technologies, solutions, and 
services, built on the technology pillars of mobile com-
puting, cloud services, big data and analytics, and social 
networking, rising to over 60 per cent by 2020. According 
to IDC Research (IDC Research, 2015), IaaS spending, 
in particular, is expected to grow at a compound annual 
growth rate (CAGR) of 15.1% and is expected to reach 
$53.1 billion by 2019. The total global cloud computing 
market is expected to grow from $40.7 billion in 2011 to 
$241 billion in 2020. According to Gartner (Gartner Inc, 
2016), by 2020, a corporate “no cloud” policy will be as 
rare as a “no internet” policy is today. 
Cloud computing is, therefore, expected to impact the 
European and global economies significantly, especially 
the ICT industry and other industries, in terms of the po-
tential benefits of cloud computing usage through re-en-
ergized productivity, efficiency, and competitiveness. Ac-
cording to Eurostat (2016), 19% of EU-28 companies used 
cloud computing in 2015, mostly for hosting their e-mail 
systems and storing files in electronic form. Almost half of 
those companies used advanced cloud services relating to 
financial and accounting software applications, customer 
relationship management or to the use of computing power 
to run business applications. According to the results of the 
same study, significant differences can be observed across 
countries. For example: in Finland, Iceland, Italy, Swe-
den, and Denmark, the cloud computing service adoption 
rate is over 30%. In contrast, the rate in Hungary, Bulgar-
ia, Greece, Poland, Latvia, and Romania was below 10% 
(Eurostat, 2016).
The data show that despite many advantages and prom-
ised benefits, resulting mainly in more effective and effi-
cient IT governance and service offerings in companies, 
the adoption of cloud computing is not as fast and wide-
spread as foreseen. This situation demonstrates the need 
for further exploration of cloud computing potentials and 
its implementation on the market. While companies adopt-
ing cloud computing services aim to achieve maximum 
business value from the services, providers face the chal-
lenge of providing efficient and attractive business models 
for achieving competitive advantages on the market. 
The situation calls for further action. Cloud computing 
providers need to re-evaluate and redesign their current 
business models to accelerate cloud computing adoption. 
They need to position themselves in the market, recognize 
potential networks, partnerships and understand adop-
tion factors that need to be taken into consideration when 
addressing current and potential users. It is important to 
understand what the customers’ needs are, what their re-
quirements are, what they prefer, refuse, and what their 
fears related to cloud computing adoption are. To assure 
success and long-term development in the cloud comput-
ing market, it is also important to consider interactions of 
the identified factors.
To address the above-presented challenges, we con-
ducted a study focused on the evaluation of business mod-
el factors of cloud computing providers. The study was 
done in Slovenia. The aim of the study was twofold. First, 
we identified cloud computing business model factors and 
developed a research model, consisting of 40 factors, clas-
sified into eight factor groups. Factors and factor groups 
were identified from prior research and interviews with 
cloud computing providers and users. Second, the defined 
business model factors were estimated to have an impact 
on cloud computing adoption in companies. In particu-
lar, we were interested in the factors’ importance and the 
differences in opinion according to respondents’ previous 
experiences with cloud computing services. It is expect-
ed that understanding of cloud computing business model 
factors and their importance for its adoption will contrib-
ute to the development of more efficient cloud computing 
business models tailored to the customers (users) expec-
tations. In addition, results of the study will contribute to 
higher levels of awareness of companies considering cloud 
computing adoption and faster adoption rate. The study 
was done among 80 companies and five major cloud com-
puting providers in Slovenia. 
The paper is organized as follows. After the introduc-
tion, a literature review is given, which is followed by 
introduction of methodology. The next chapter presents 
the research results. The paper ends with discussion and 
conclusions. 
2 Literature review
The American National Institute of Standards and Tech-
nology (Badger et al., 2011) classify cloud computing 
services according to their key features and implemen-
tation model as follows: software solutions as a service, 
platform as a service, and infrastructure as a service. For 
value maximization of cloud service delivery to end users, 
it is necessary to understand the business aspects of cloud 
computing (Marston et all, 2011). There have been sever-
al studies (Tweel, 2012; Chebrolu, 2011; Low, Chen, and 
Wu, 2011; Benlian, Hess, and Buxmann, 2009; Chen, Shi-
ue, and Shih, 2011, Watson, 2010) conducted related to the 
Organizacija, Volume 50 Number 3, August 2017Research Papers
257
impact of cloud computing adoption factors and research 
models based on different technology adoption theories, 
such as: Technology Acceptance Model (TAM), Unified 
Theory of Acceptance and Use of Technology (UTUAT), 
Task/Technology Fit Theory (TTF), Technology Organi-
zation Environment Model Factors (TOE), and others. 
Previous research models revealed some individual factors 
that were included in our study. However, according to our 
evidence, no comprehensive research model of cloud com-
puting business model factors providing a comprehensive 
overview of the importance and correlation of these factors 
to cloud computing adoption exists. 
In this chapter, we present a literature review of cloud 
computing adoption factors and business models. The pre-
sented research results have served as a basis for initial 
research model of business model factor design.
2.1 Cloud computing adoption factors 
The literature review of factors impacting cloud comput-
ing adoption has revealed the following research models, 
suggesting some of the factors that could be classified as 
business model factors (Bogataj and Pucihar, 2012):
Technology Acceptance Model factors impacting cloud 
computing adoption
Watson (2010) investigated factors impacting the adoption 
of cloud computing by decision-making managers. The 
purpose of his research was to evaluate the factors im-
pacting the adoption of cloud computing as a part of their 
strategic information technology planning. The impact of 
the following factors to cloud computing adoption was in-
vestigated: Cost-effectiveness, the Need for Cloud Com-
puting Services, Perceived Cloud Computing Reliability, 
and Cloud Computing Service Security and Effectiveness. 
The study results show a strong positive correlation be-
tween each of these four independent variables to cloud 
computing adoption. These findings are also interesting 
for our research as the model comprises some factors (Re-
liability, Security) that can also be classified as business 
model factors.
Low et al. (2011) investigated important factors affect-
ing SaaS adoption. His explorative model integrates the 
Technology Acceptance Model (TAM) related theories 
with additional imperative constructs: Marketing Efforts, 
Security, Trust, Environmental Factors, Perceived Use-
fulness, and Attitude Towards Technological Innovations. 
These findings are also interesting for our research because 
this model comprises some of the factors that can also be 
classified as business model factors. The study results 
show that Marketing Efforts have a positive effect on So-
cial Influence as well as on Perceived Ease of Use, Per-
ceived Usefulness, Security, and Trust.
Factors impacting cloud computing adoption - combina-
tion of different adoption theories
Benlian et al. (2009) investigated the factors impacting 
SaaS adoption. The introduced research model is based 
on the combination of Transaction Cost Theory, the Re-
source-Based View, and the Theory of Planned Behaviour. 
The study results revealed that decision patterns about 
SaaS adoption differ across application types. Social In-
fluence, Attitude Toward SaaS Adoption, Adoption Uncer-
tainty, and Strategic Value turned out to be the strongest 
and most consistent drivers across all application types. 
Furthermore, the study results show that Firm Size does 
not matter in SaaS adoption, since large companies and 
small- and medium-sized (SMEs) companies had similar 
adoption rates.
Low et al. (2011) investigated the impact of techno-
logical, organizational and environmental factors to cloud 
computing adoption in the high-tech industry. Their ex-
plorative model investigates the impact of the following 
factors on cloud computing adoption: Relative Service 
Advantage, Service Complexity, Service Connectivity, 
Management Support, Company Size, Technology Readi-
ness, Competitive Pressure, Trading Partner Pressure. The 
results demonstrate the statistically significant impact of 
the following factors: Relative Service advantage, Man-
agement Support, Company Size, Competitive Pressure, 
and Trading Partner Pressure. In contrast, the results show 
no significant impact of Service Complexity and Service 
Connectivity to cloud computing adoption. Furthermore, 
Tweel (2012) introduced a research model by utilizing fac-
tors from Innovation Diffusion Theory, Institutional The-
ory, and Technology-Organization-Environment Frame-
works in order to examine the relationship of the factors 
in adopting cloud computing. The study demonstrates the 
statistically significant positive impact of Relative Ad-
vantage, Compatibility, and Top Management Support to 
cloud computing adoption. The results show statistically 
significant positive correlation between all investigated 
factors in the research model, except Company Size. 
Borgman et al. (2013) presented a research model con-
ceptualizing the link between the TOE Framework and the 
Decision of Organizations to Adopt Cloud Computing, as 
well as the moderating effect of IT Governance Structures 
and Processes on these relationships. The results show that 
a high Perceived Relative Advantage of cloud computing, 
a high level of Top Management Support and High Com-
petition Intensity are positively linked to the decision for 
cloud computing adoption. Moreover, according to the re-
search results of Lumsden and Gutierrez (2013), Compat-
ibility and Relative Advantage are the most essential com-
ponents impacting cloud computing adoption. According 
to the same research (Lumsden and Gutierrez, 2013), in 
the organizational group of factors, Top Management Sup-
port is most critical for the adoption of cloud computing. 
258
Organizacija, Volume 50 Number 3, August 2017Research Papers
Gangwar et al. (2015) presented a research model in-
tegrating TAM factors and TOE Model factors. The study 
identified that Relative Advantage, Compatibility, Com-
plexity, Organizational Readiness, Top Management Com-
mitment, and Training and Education as relevant variables 
for affecting cloud computing adoption, using Perceived 
Ease of Use and Perceived Usefulness as mediating varia-
bles. Furthermore, Competitive Pressure and Trading Part-
ner Support were found to directly affect cloud computing 
adoption intentions. The findings of this research are also 
interesting for our research as the model comprises some 
of the factors (Complexity, Compatibility) that can also be 
classified under the umbrella of business model factors. 
Task-Technology Fit and Information Systems Effective-
ness model factors impacting SaaS adoption
Chen et al. (2011) investigated SaaS adoption through 
TTF. Their explorative model investigates the impact of 
Technology Characteristics, Task Characteristics, Indi-
vidual Abilities to Adoption Intention, Relation Consid-
eration, and Benefit Consideration. The results show that 
Technology Characteristics. Task Characteristics and Indi-
vidual Abilities can influence the degree of TTF positively. 
The higher the degree of TTF is, the higher the intention 
to adopt SaaS is. 
Chebrolu (2011) was assessing the correlation of Stra-
tegic Alignment, Information Technology Effectiveness 
and Cloud Computing Adoption in IT companies. The 
study findings show a very strong statistically significant 
correlation between Cloud Computing Adoption and In-
formation Technology Effectiveness. In contrast, the cor-
relation between Cloud Computing Adoption and Strategic 
Alignment was not recognized as statistically significant.
Table 1 and Table 2 present summaries of previous 
research findings of related research studies, investigating 
the impact of strategic, economic, social factors, UTAUT, 
TTF, and TAM factors on cloud computing adoption. 
2.2 Business models and business 
model factors
Many authors (Timmers, 1998; Afuah and Tucci, 2001; Pet-
rovic, Kittl, and Teksten, 2001; Hedman and Kalling, 2003; 
Rappa, 2010; Osterwalder and Pigneur, 2005, 2009, 2010; 
Amit and Zott, 2001; Gordijn, Akkermans, and van Vliet, 
2000) provide definitions of the business models, business 
model frameworks and ontologies. For example, Timmers 
(1998) defines “business model” as an architecture and 
presentation of a) information and service/product flows, 
including a description of business actors and their roles, 
b) potential advantages for individual business actors, c) 
sources of revenue. Gordijn et al. (2000) define “business 
models” as descriptions of what the business is about and 
explanations of “who provides services or products of value 
to whom, and what he expects in return”. 
Lambert and Davison (2012) provide an overview of 
research on business models for the period of 1996-2010. 
Their findings contribute to the definition of business model 
elements and concepts. The following findings are relevant 
for our study: Rappa (2010) defines the business model as 
the method of doing business by which a company can gen-
erate revenue. Amit in Zott (2001) define “business mod-
els” as descriptions of value creation steps, aiming at final-
izing different transactions. In general, the business model 
can be defined as the logic of an organization that reflects 
its business strategy (Johansson, Malmstrom, Chroneer, 
Styven, Engstrom, and Kåreborn, 2012). In business mod-
el definition, it is also important to include the company’s 
ecosystem (Pucihar, Lenart, Kljajić, Marolt, and Maletič, 
2016).
Osterwalder and Pigneur (2009, 2010) define “business 
model” as follows: “a business model describes the ration-
ale of how an organization creates, delivers and captures 
value”. They introduce the business model canvas, which is 
nowadays a popular strategic tool that enables practitioners 
to design business models in a creative way. The canvas 
tool is a comprehensive conceptual model with various de-
sign variables in different domains. In this context, a busi-
ness model is defined as a presentation of a) Values offered 
by the organization to one or more customer segments, b) 
Organizational business framework and partner network 
aiming at producing, marketing, delivering created values 
and profit generation. The concept also considers the fol-
lowing elements: a) Customer Relationship Management, 
b) Partner Network, c) Revenue Generation, d) Price Mech-
anisms (Osterwalder and Pigneur, 2009).
The STOF Business Model Framework (Bouwman, 
De Vos, and Haakre, 2008) elaborates ways of dealing 
with design issues and success factors for business mod-
els. The method describes the interdependencies between 
the four core domains: Service, Technology, Organization, 
and Finance. It provides a detailed description of each do-
main and the interdependencies of critical design issues of 
each domain and between domains (Bouwman, De Reuver, 
Solaimani, and Daas, 2012).
A comprehensive overview of business model defini-
tions also results from the 5th Framework Programme re-
search project E-Factors: A Thematic Network and E-Busi-
ness Models. According to their definition, a business model 
concept is structured of following groups of elements: a) 
Technical and Technology, b) Organizational, c) Industrial 
d) Individual, e) Social (E-Factors consortium, 2003).
The previous overview of research on business mod-
el definitions is not exhaustive. Nevertheless, it offers a 
comprehensive outline of business model elements and 
business model design methods. Business models should 
evolve through time and vary regarding product or service 
life cycle and its level of adoption in the market (De Reu-
ver, 2007).
Organizacija, Volume 50 Number 3, August 2017Research Papers
259
Author Factor / Factor group Findings
Gangwar et al. • Technological (Relative Advantage, Compati-
bility, Complexity)
• Organizational (Readiness, Top management 
commitment, Training and Education)
• Environment (Competitive Pressure, Trading 
Partner Support)
• Others (Perceived Ease of Use, Perceived Use-
fulness)
• Statistically significant, positive impact of the 
following variables: Perceived Ease of Use 
and Perceived Usefulness as mediating vari-
ables to cloud computing adoption: Relative 
Advantage, Compatibility, Complexity, Orga-
nizational Readiness, Top management Com-
mitment, and Training and Education
• Competitive Pressure and Trading Partner 
Support were found to directly affect cloud 
computing adoption intentions.
Borgman et al. • Technological (Relative Advantage, Complex-
ity Compatibility)
• Organizational (Top Management Support, 
Firm Size, IT expertise of Business Users)
• Environmental context (competitive and regu-
latory environment)
• IT Governance Structures 
• IT Governance Process
• Positive impact of a high perceived Relative 
Advantage of cloud computing, a high level of 
Top Management Support and a high Compe-
tition Intensity are positively linked to the de-
cision for cloud computing adoption.
Lumsden and Gutierrez • Technology (Relative Advantage, Complexity, 
Compatibility)
• Organization (Top Management Support, Firm 
size, Technology Readiness)
• Environment (Competitive Pressure, Trading 
Partners Pressure)
• Statistically significant positive impact of the 
following variables to cloud computing adop-
tion: Compatibility and Relative Advantage, 
Top Management Support.
Tweel • Relative Advantage
• Compatibility
• Company Size
• Company Organizational Readiness
• Top Management Support
• Skimming
• Environmental Factors (Legislation, social 
Norms, Expectations, Skimming).
• Statistically significant, positive correlation of 
all investigated factors of introduced model, 
except with Company Size. 
• The most important factors impacting cloud 
computing adoption: Compatibility, Top Man-
agement Support, Relative Advantage. 
Chebrolu • Company Strategic Alignment
• Modularity, Connectivity, Compatibility of 
Services 
• Information Technology Effectiveness.
• Statistically significant, positive correlation 
of Information Technology Effectiveness and 
cloud computing.
Low et al. • Relative Service advantage
• Service Complexity
• Service Connectivity
• Management Support
• Company Size
• Technology Readiness
• Competitive Pressure
• Trading Partner Pressure.
• Statistically significant, positive correlation of 
all investigated factors
• Statistically significant, positive impact of al-
most all factors to cloud computing adoption, 
except Service complexity and Service Con-
nectivity.  
Benlian et al. • Business and Technological Uncertainty of 
Service Adoption
• Unique Service Value
• Strategic Service Value
• Ability for Service Replacement
• Individual attitude And Behaviour  
• Statistically significant, positive correlation of 
Individual Attitude and Adoption Rate
• Statistically significant, negative correlation of 
Strategic Service Value and Adoption Rate and 
Unique Service Value and Adoption rate.
Table 1: Summary of previous research results – impact of strategic, economic and social factors to cloud computing adoption
260
Organizacija, Volume 50 Number 3, August 2017Research Papers
Table 2: Summary of previous research results – impact of TAM, UTAU, and TTF factors on cloud computing adoption
Author Factor / Factor group Findings
Chen et al. • Technology Characteristics
• Task Characteristics
• Individual Abilities to Adoption Intention
• Relation Consideration
• Benefit Consideration
• Statistically significant, positive impact on Re-
lation Consideration and Technology Charac-
teristics to cloud computing adoption. 
Wu • Marketing Effort
• Environmental Factors
• Perceived Usefulness
• Attitude Towards Technological Innovations
• Security and Trust
• TAM theory factors
• Statistically significant, positive correlation of 
the following factor groups:
• Marketing Effort and Security and Trust
• Attitude towards Technological Innovations 
and TAM theory factors
• Perceived Usefulness and Attitude Towards 
Technological innovations.
Watson • Perceived cost effectiveness
• Perceived need for service adoption
• Perceived service security 
• Perceived service reliability
• Statistically significant, positive correlation of 
all investigated factors and cloud computing 
adoption.
Rapid development and ever-changing and compet-
itive environments demand continuous evaluations, ad-
justments, and development of business models in order 
to remain competitive over time and sustain future growth 
(Amit and Zott, 2012; Teece, 2010; Zott, 2009). Further-
more, business model innovation is becoming an essen-
tial and continuous activity for the companies to survive 
and thrive in today’s global competitive markets (Hanelt, 
Hildebrandt and Polier, 2015).
3 Methodology 
In this chapter, we present the research model, research 
questions and hypotheses, and data collection method. 
3.1 Research model
The research framework used in our research was derived 
from prior research results and is based on two holistic 
frameworks: Osterwalder’s (2004) business model frame-
work and the framework designed in the research project 
E-Factors: A Thematic Network and E-Business Models 
(E-Factors consortium, 2003). The preliminary research 
model consists of the following business model pillars: 
Provider’s Capability for Cloud Computing, Value Prop-
osition, Customer Relationship Management, Revenues, 
and Costs. Each of the four pillars in the research model 
consists of several groups, for which several business mod-
el factors have been identified. For the purpose of initial 
research model evaluation, we conducted interviews with 
five major cloud computing providers and five cloud com-
puting users in Slovenia. The detailed research model is 
presented in Figure 1. Further in this chapter, we describe 
the model in detail (Bogataj and Pucihar, 2012; Bogataj 
and Pucihar, 2013; Bogataj Habjan and Pucihar, 2017).
Provider’s Capability for Cloud Computing
The Provider’s Capability for Cloud Computing pillar con-
sists of two groups of factors: Collaboration with Partners 
and Provider’s Tangible Assets. Both groups consist of 
several factors related to provider’s capability for service 
and value delivery: 
Collaboration with Partners
• Co-branding or linked branding is considered to be 
a strategy of the joint presentation of two or more 
independent brands within one service (Erevelles, 
Stevenson, and Srinivasan, 2008).
• Collaboration among Partners is considered to be the 
level of arrangement to cooperate among partners in 
their network. 
• Dispute Resolution Mechanisms with partners are 
considered to be the definition of potential prob-
lem-solving means among partners in the cloud pro-
vider’s partner network.
• Partner Network Size is considered to be the number 
of cloud providers included in a partner network. 
Provider’s Tangible Assets
• The Financial Resources of the provider are consid-
ered to the availability of the provider’s financial re-
sources, proving the capability for execution of busi-
ness and development investments in order to ensure 
service quality.
Organizacija, Volume 50 Number 3, August 2017Research Papers
261
• Provider’s Technology and equipment are considered 
to be ICT availability (Hardware, Software Solutions, 
Telecommunication Equipment, and Services) for of-
fering cloud computing services.
Provider’s Intangible Assets 
• The Provider’s Reputation is considered to be its cor-
porative reputation. 
• References and Recommendations are considered 
to be the range of recommendations of satisfied cus-
tomers, expressing positive collaborative experiences 
with the cloud computing provider. They support po-
tential new users in selecting a specific service pro-
vider or selecting a specific cloud computing service.
• Knowledge and Experiences are considered to be 
a set of data and provider’s behaviour, acquired 
through education and work. 
Value Proposition
The Value Proposition pillar covers all aspects related to 
the product/services of the company: in our case, cloud 
computing services. This pillar is also related to the man-
ner in which providers differentiates their service offering 
from their competitors. For the purpose of our research, 
value proposition pillar consists of two factor groups: ser-
vice value for customers, and orientation of services to 
target customers.
Service Value for Customers 
• Economic Value of the Service/cost savings is con-
sidered to be a recognized service value that can be 
expressed monetarily or from the aspect of cost re-
duction (i.e. investment, maintenance, better efficien-
cy in source exploitation, etc.). 
• Usability is defined as the level of service conformity 
to the needs, desires, and demands of customers. 
• Flexibility is defined as the level of service conformi-
ty in regard to customer’s needs. 
• Trademark is defined as the label or combination of 
labels, established for representing a cloud comput-
ing provider or its service. 
• Added Value is defined as a standard upgrade of 
cloud computing service. 
• Connectivity/Interoperability is defined as the ability 
to use the same service through different service pro-
viders of cloud computing services. 
• Customer Support is defined as the level of customer 
assistance for using cloud computing services. 
Orientation of Services to Target Customers 
The focus of cloud computing service by individual seg-
ments of target customers in the research is defined accord-
ing to the activity area and size of the target customer, as 
well as according to the complexity of their processes and 
their geographic activity. 
Customer Relationship Management
The Customer Relationship Management pillar is related 
to providers’ activities towards their customers. In our re-
Figure 1: Introduced research model – Provider’s business model pillars (Adapted from Osterwalder, E-Factors Consortium 
and interview results) (Bogataj and Pucihar, 2013)
262
Organizacija, Volume 50 Number 3, August 2017Research Papers
search, this pillar consists of two groups of factors: mar-
keting and trust-building mechanisms: 
Marketing
A group of factors, defined as the usage of different mar-
keting channels to offer cloud computing services: Internet 
and Social Media, Events (conferences, workshops, etc.), 
Direct Marketing, Use of Partners’ Marketing Channels, 
Publications.
Trust-Building Mechanisms
• User Authentication or Access Control is a basic 
starting element for secure service provisioning.
• System Security is considered to be the ability of 
cloud service providers to prevent access to network 
and data to unauthorized users, with suitable tools 
and technologies (i.e. firewall, virtual private net-
works, use of safer protocols, advanced encryption 
techniques, etc.). 
• Service Quality is not exclusively limited to perfor-
mance, but it can also be defined with other charac-
teristics, such as security, availability, upgrade pos-
sibility, etc.
• Service and System Availability are defined in close 
relation to their reliability. 
• Service Recovery Procedures are considered to be 
the definition of recovery/restoration process after 
service malfunction (due to hacking, loss of power, 
or an accident). 
Revenue Model and Costs
In our research, the business model pillar of Revenue Mod-
el and Costs defines the revenue model and costs structure 
for value creation. The logic of revenue generation is an 
indicator of business results and organizational success 
(Sainio and Marjakoski, 2009). For the purpose of our re-
search, we define the following factors in these groups:
Revenue Model
• Service Billing Pay Per Use is defined as a means of 
charging that is based on the number of transactions, 
used disk space, recorded use of other resources, etc. 
• Service Billing Pay Per Service is defined as the 
means of charging in regard to service use.
• Service Billing Based on Market Price is defined as 
the means of charging for the service based on supply 
and demand. 
• Service Billing Based on Target Customers is defined 
in the research as means of charging for the service 
according to customer type, their characteristics, 
abilities, and willingness to pay for the service. 
Costs
• Provider’s Hardware Costs are defined as the costs 
of hardware equipment necessary to provide cloud 
computing services: servers, computers, processors, 
hard disks, etc.
• Provider’s Software Costs are defined as service pro-
vider costs of application servers, operating systems, 
software solutions for virtualization and other soft-
ware solutions providing cloud computing infrastruc-
ture, platforms, and services (Li, Liu, Qui, and Wang, 
2009).
• Human Resources Costs of the Provider are defined 
as costs which occur due to service provisioning and 
maintenance, technical assistance, ensuring system 
security, etc. (Li, Liu, Qui, and Wang, 2009). The 
above-stated costs originate mostly from the salaries 
of employees. 
• Outsourcing Costs of the provider, defined in the 
research as costs that occur because of the need to 
involve external services, equipment, and infrastruc-
ture when providing cloud computing service. This 
includes costs for providing security, service resto-
ration, additional technical expertise, etc.
• Collaboration Costs With Other Cloud Computing 
Providers are defined as costs that occur in business 
cooperation among service providers. 
• Network Costs are defined as the costs of network 
devices and network equipment, as well as the direct 
costs of energy for network activity.
Figure 2 presents the initial research model in detail. 
3.2 Research questions and hypothesis
Based on the introduced research model, the aim of the 
research was to address the following research question: 
Q1: What are the differences in opinion about the im-
portance of business model factors on cloud computing 
adoption according to previous cloud computing experi-
ences?
Based on the research question, we developed the follow-
ing hypothesis: 
H01 There are no significant differences in opinion 
about the importance of business model factors on cloud 
computing adoption according to previous cloud comput-
ing experiences.
3.3 Data collection
The importance of the factors and their impact on cloud 
computing adoption was investigated by a survey conduct-
ed among 80 companies in Slovenia, which was conduct-
ed using the questionnaire, which was designed based on 
Organizacija, Volume 50 Number 3, August 2017Research Papers
263
Figure 2: Initial research model for investigation of business model factors for cloud computing adoption
264
Organizacija, Volume 50 Number 3, August 2017Research Papers
initial research model. The questionnaire was tested with 
10 participants (six representatives of cloud computing 
providers and four representatives of cloud computing end 
users). 
The questionnaire was structured in the following 
question groups:
• Data on respondents (field of work, experiences) and 
companies (role, size, main activity area, number of 
employees in ICT, previous investments in ICT, etc.)
• Opinion on cloud computing business model factors’ 
importance for cloud computing adoption (introduced 
40 factors) for each service type (SaaS, PaaS, IaaS). 
A five-point Likert scale was used from “1” meaning 
not important at all to “5” meaning very important. 
• Experiences with cloud computing adoption (SaaS, 
PaaS, IaaS).
Managers and IT managers of 900 companies (300 ran-
domly selected large companies, 300 medium-sized com-
panies, 300 small companies) were invited to participate 
in the survey. In total, 80 responses were valid for further 
statistical analysis. 
4 Research results
Most of the surveyed companies, 42% (n = 34) can be cat-
egorized as medium-sized companies; 35% (n = 28) of the 
sample were smaller (micro and small) companies. Large 
companies represent 23% (n = 18) of the sample. 
A total of 32.89% of companies have already had pre-
vious experiences with cloud computing services. Table 3 
(below) presents the proportion of responses according to 
previous cloud computing experiences.
Model Reliability and Validity
The reliability and validity of the structural model were 
tested with Cronbach alpha and the average variance 
(AVE). The model consists of nine combined variables. 
Table 6 (below) presents the AVE and Cronbach alpha 
values. Confirmation of the reliability and validity of the 
structural model allowed the continuation of the analysis.
Impact of business model factors on cloud computing 
adoption
Based on the statistical analysis results, it can be con-
Table 3: Respondents according to previous cloud computing experiences by service type
Experiences with cloud computing ser-
vices Frequency %
Experiences with SaaS 25 32.89%
Experiences with PaaS 25 32.89%
Experiences with IaaS 16 21.05%
Business model pillar Business model factor group AVE Cronbach alpha
Y – Cloud Computing 
Adoption 0.726 0.811
Value Proposition
D1.1 – Service Value for 
Customers 0.729 0.982
D1.2 – Service Orientation 
Towards Target Customers 0.896 0.989
Providers Capability for 
Cloud Computing
D2.1 – Collaboration with 
Partners 0.755 0.971
D2.2 – Assets 0.758 0.978
Customer Relationship 
Management
D3.1 – Marketing 0.690 0.960
D3.2 – Trust-Building Mech-
anisms 0.750 0.980
Revenue Model and Costs
D4.1 – Revenue Model 0.754 0.971
D4.2 – Costs 0.590 0.925
Table 4: AVE and Cronbach alpha values
Organizacija, Volume 50 Number 3, August 2017Research Papers
265
cluded that there is no statistically significant impact of 
the analysed business model factors on cloud computing 
adoption. 
Nevertheless, slope coefficient values and t-statistics 
also reveal some business model factors with moderate 
or strong impact on cloud computing adoption, by service 
type. Based on slope coefficient values and t-statistics, in 
the Value Proposition business model factor group, Ser-
vice Added Value and Service Usability can be recognized 
as having the highest (although not statistically significant 
at p=0.05) impact on all cloud computing service types. 
Furthermore, in the business model factor group of Collab-
oration with Business Partners, Collaboration Agreement 
Among Partners can also be identified as having the high-
est (although not statistically significant at p=0.05) im-
pact on all cloud computing service types. In the business 
model factor group of Revenue Model, the factor Service 
Billing Based on Target Customers can be recognized as 
having the highest (although not statistically significant at 
p=0.05) impact on all cloud computing service types. 
Table 5 (below) presents business model factors with 
positive slope coefficients and their statistical importance 
to cloud adoption by service type (SaaS, PaaS, IaaS). 
Differences in opinion on importance of business mod-
el factors impacting cloud computing adoption according 
to previous experiences with cloud computing 
Table 6 (below), titled “Differences in opinion on im-
portance of business model factors impacting cloud com-
puting adoption according to previous experiences with 
SaaS”, presents statistically significant differences in opin-
ion on the importance of business model factors among 
companies with previous SaaS experiences (e.g. imple-
mented ERP, CRM as a Service, Document Management 
System as a Service, etc.) and companies without previous 
SaaS experiences.
We can claim that companies with previous SaaS ex-
periences estimate the following factors as more important 
in comparison to companies without previous experiences 
with SaaS: Service Usability, Customer Support Services, 
Co-Branding User Authentication, and Service Recovery 
Procedures in case of detected problems. In contrast, we 
can claim that companies without previous experiences 
with SaaS (specifically experiences with customer rela-
tionship management services) view the following factors 
as more important in comparison to companies with such 
experience: User Authenticity and Service Quality as more 
important compared to companies with such experience. 
Table 7 “Differences in opinion on the importance of 
business model factors impacting cloud computing adop-
tion according to previous experiences with PaaS” (below) 
presents statistically significant differences in opinion 
about business model factors’ importance on cloud com-
puting adoption. The differences are presented among 
companies with previous experiences with PaaS and the 
companies without previous such experiences. We can 
claim that companies without previous experiences with 
PaaS recognize in Table 7 (below) “presented business 
model factors to be more important for cloud computing 
adoption in comparison to companies with previous such 
experiences.
Table 8 “Differences in opinion on the importance of 
factors according to experience with the service type, In-
frastructure as a service” presents statistically significant 
differences in opinion about business model factors’ im-
portance on cloud computing adoption. The differences 
are presented among companies with previous experienc-
es with IaaS and those without such experiences. We can 
claim that companies without previous experiences with 
IaaS recognize almost all factors (except Service Billing 
Based on Service Usage) in Table 9 (below) as more im-
portant for cloud computing adoption in comparison to 
companies with previous experiences with IaaS. 
Based on the results, hypothesis H01, stating there are 
no significant differences in opinion about the importance 
of business model factors to cloud computing adoption 
among the companies with previous cloud experiences and 
the companies without previous cloud computing experi-
ences, has been rejected.
5 Discussion and conclusions
5.1 Discussion of research results
Our study was aimed at identifying business model factors 
with the highest impact on cloud computing adoption. In 
particular, we were interested in identifying the differenc-
es in opinion regarding factors’ importance according to 
respondents’ previous experiences with cloud computing 
services (SaaS, PaaS, IaaS). For those purposes, we devel-
oped a research model consisting of 40 cloud computing 
business model factors, placed into eight factor groups. 
The research model was built upon prior research and 
adapted from Osterwalder’s business model framework 
(Osterwalder, 2004) and the E-Factors research project 
(E-Factors consortium, 2003). The initial research model 
was evaluated and adapted based on interviews with five 
major cloud providers and five cloud computing users. 
Furthermore, a survey was conducted among 80 compa-
nies in Slovenia, which represented an 8.89 per cent re-
sponse rate. The initial sample size was 900 randomly se-
lected companies.
Business model factors’ importance 
First, based on the statistical analysis results, it can be con-
cluded that there is no statistically significant impact of 
the analysed business model factors on cloud computing 
adoption. Nevertheless, slope coefficient values and t-sta-
tistics reveal some business model factors with moderate 
or strong impact on cloud computing adoption, by service 
266
Organizacija, Volume 50 Number 3, August 2017Research Papers
Table 5: Business model factors with positive slope coefficients and their statistical importance to cloud adoption – by service 
type
Business model 
pillar
Business model factor 
group Business model factor
Slope coeffi-
cient t-statistics
Value Proposi-
tion
D1.1 Value Proposition
Customer Support Service → SaaS 5.5764 0.737
Customer Support Service → PaaS 4.5745 0.5218
Service Added Value → SaaS 2.0215 0.2754
Service Added Value → PaaS 0.6933 0.1058
Service Added Value → IaaS 3.4923 0.4442
Service Usability → SaaS 1.8263 0.2158
Service Usability → PaaS 1.4948 0.2041
Service Usability → IaaS 1.3551 0.1152
Service Connectivity → SaaS 0.1723 0.0193
Service Connectivity → IaaS 1.5868 0.1097
Service Economic Value → IaaS 3.3911 0.4897
D1.2 Service Orientation 
Towards Target Customers
Based on Industry → SaaS 1.688 0.2466
Based on Customer Size → SaaS 0.554 0.0434
Based on Customer Size → PaaS 6.3405 0.4501
Based on Customer Size → IaaS 16.6395 1.1293
Based on Geographical Focus → PaaS 2.5631 0.3609
Based on Geographical Focus → IaaS 4.7213 0.701
Provider’s Capa-
bility for Cloud 
Computing
D2.1 Collaboration with 
Business Partners
Collaboration Agreement Among Partners → 
SaaS 6.8348 1.1638
Collaboration Agreement Among Partners → 
PaaS 0.0801 0.0245
Collaboration Agreement Among Partners → 
IaaS 0.4 0.1051
Co-Branding → SaaS 2.3194 0.4075
Co-Branding → PaaS 4.8125 0.7936
Dispute Resolution Mechanisms → PaaS 0.3704 0.0553
Size of the Partners’ Network 6.5178 0.7411
D2.2 Assets
Provider’s Technology & Equipment → SaaS 6.0943 0.9282
Provider’s Reputation → SaaS 2.4508 0.3933
Provider’s Reputation → PaaS 3.1992 0.4155
Provider’s Financial Resources → SaaS 1.4277 0.2541
Provider’s Financial Resources → IaaS 1.4128 0.1187
Provider’s References and Recommendations 
→ PaaS 1.4149 0.2757
Provider’s References and Recommendations 
→ IaaS 0.5752 0.0567
Organizacija, Volume 50 Number 3, August 2017Research Papers
267
Customer 
Relationship 
Management
D3.1 Marketing
Partner’s Channels → SaaS 3.3262 0.478
Internet and Social Networks → IaaS 2.6258 0.4777
Publications → PaaS 15.6806 1.193
D3.2 Trust-Building Mech-
anisms
System Security → SaaS 2.8447 0.3455
User Authentication → SaaS 4.2828 0.7241
Service Quality → PaaS 4.544 0.3811
Service Quality → IaaS 1.6883 0.1477
Service and System Availability → SaaS 1.6711 0.2187
Service and System Availability → IaaS 1.0593 0.1046
Changing the Provider Trust-Building Mecha-
nism → SaaS 0.8035 0.1419
Changing the Provider Trust-Building Mecha-
nism → PaaS 3.6428 0.526
Service Recovery Procedures → IaaS 1.4396 0.075
Revenue model 
& Costs
D4.1 Revenue model
Service Billing Pay Per Use → SaaS 5.0161 0.6061
Service Billing Pay Per Use → PaaS 3.1423 0.4053
Service Billing Pay Per Use → IaaS 4.2671 0.475
Service Billing Based on Target Customers → 
SaaS 1.1348 0.1308
Service Billing Based on Target Customers → 
PaaS 2.5428 0.1746
Service Billing Based on Target Customers → 
IaaS 4.5038 0.3307
Service Billing Based on Market Value → 
PaaS 0.6273 0.0904
D4.3 Costs
HW Costs → SaaS 1.552 0.3636
HW Costs → IaaS 0.6253 0.1803
Human Resources’ Costs → IaaS 0.4338 0.1324
Outsourcing Costs → SaaS 0.7145 0.1919
Outsourcing Costs → PaaS 2.4583 0.4665
Outsourcing Costs → IaaS 2.2053 0.803
Collaboration Costs → SaaS 0.385 0.056
Network Costs → SaaS 0.1903 0.0273
Network Costs → PaaS 0.2483 0.0141
Network Costs → IaaS 1.3417 0.2266
R2 = 0,412 – SaaS, R2 = 0,4474 – PaaS, R2 = 0,439 - IaaS
Table 5: Business model factors with positive slope coefficients and their statistical importance to cloud adoption – by service 
type (continued)
268
Organizacija, Volume 50 Number 3, August 2017Research Papers
Table 6: Difference in opinion on importance of business model factors impacting cloud computing adoption according to pre-
vious experiences with SaaS
Business model factor 
group Business model factor
Independent 
variable t p M1 M2 
Service Value for Customers Service Usability Document manage-ment -2.15 0.045 4.27 4.89
Service Value for Customers Customer Support Services ERP -2.58 0.018 4.20 4.89
Collaboration with Partners Co-Branding ERP -2.09 0.049 2.87 3.78
Assets Knowledge and Experi-ences
Customer relation-
ship management 2.886 0.011 4.82 3.92
Marketing Internet and Social Media Group work 2.243 0.035 4.45 3.23
Trust-Building Mechanisms User Authentication Customer relation-ship management 3.000 0.007 4.75 4.00
Trust-Building Mechanisms User Authentication Document manage-ment -2.35 0.028 4.13 4.78
Trust-Building Mechanisms Service Quality Customer relation-ship management 2.165 0.049 4.92 4.33
Trust-Building Mechanisms Service Recovery Pro-cedures
Document manage-
ment -2.22 0.038 4.40 4.89
Legend: M1 – average on factor importance / companies without previous SaaS experiences, M2 – average on factor impor-
tance / companies with previous SaaS experiences, t- value, p – statistical significance
Table 7: Differences in opinion on importance of business model factors according to previous experiences with PaaS
Business model factor 
group Business model factor
Independent 
variable t p M1 M2
Service Value for Cus-
tomers Service Added Value
Software Devel-
opment 2.515 0.029 4.00 2.25
Service Value for Cus-
tomers Customer Support Services Data Warehousing 2.206 0.052 5.00 4.45
Service Orientation to 
Target Customers
Based on Customer Process 
Complexity
Software Devel-
opment 3.245 0.008 4.11 2.00
Collaboration with 
Partners
Defined Collaboration with 
Partners Data Warehousing 4.977 0.000 5.00 3.69
Collaboration with 
Partners
Dispute Resolution Mecha-
nisms Data Warehousing 4.168 0.002 5.00 3.92
Assets Provider’s References & Rec-ommendations Memory Capacity 3.237 0.008 4.83 3.14
Assets Provider’s Knowledge and Experiences
Software Devel-
opment 2.892 0.014 4.78 3.40
Migration of Soft-
ware Solutions 2.854 0.015 4.70 3.25
Marketing Partners’ Marketing Channels Software Devel-opment 2.286 0.052 3.89 3.00
Revenue Model Service Billing Based on Service Usage Data Warehousing 2.93 0.014 5.00 4.08
Legend: M1 – average on factor importance / companies without previous PaaS experiences, M2 – average on factor impor-
tance / companies with previous PaaS experiences, t- value, p – statistical 
significance companies with previous SaaS experiences, t- value, p – statistical significance
Organizacija, Volume 50 Number 3, August 2017Research Papers
269
Table 8: Differences in opinion on importance of business model factors according to experience with service type, IaaS
Business model factor 
group
Business model 
factor Independent variable t p M1 M2
Service Value for Cus-
tomers Service Flexibility
IaaS - Server Capacity 2.689 0.03 5.00 4.30
IaaS - Network Equip-
ment 2.505 0.03 4.75 3.75
Service Value for Cus-
tomers
Customer Support 
Services
IaaS - Memory Ca-
pacity 2.535 0.030 4.67 3.67
Service Orientation to 
Target Customers
Based on customers˙ 
company size
IaaS - Network Equip-
ment 2.449 0.03 4.13 2.25
Service Orientation to 
Target Customers
Based on customer 
process complexity  
IaaS - Network Equip-
ment 2.736 0.02 4.25 2.25
Service Orientation to 
Target Customers
Based on customers˙ 
geographical location
IaaS - Network Equip-
ment 2.736 0.02 4.25 2.25
Collaboration with 
Partners
Collaboration with 
partners
IaaS - Network Equip-
ment 2.616 0.02 4.11 2.80
Assets Provider’s Technolo-gy & Equipment IaaS - Server Capacity 2.283 0.05 5.00 4.36
Assets
Provider’s
References & Rec-
ommendations
IaaS - Server Capacity 2.39 0.04 5.00 4.27
Revenue Model Service billing based on service usage
IaaS – Network Equip-
ment -2.26 0.05 4.00 4.83
Legend: M1 – average on factor importance / companies without previous IaaS experiences, M2 – average on factor impor-
tance / companies with previous IaaS experiences, t- value, p – statistical significance
type. Based on slope coefficient values and t-statistics, in 
the Value Proposition business model factor group, Ser-
vice Added Value and Service Usability can be recognized 
as having the highest (although not statistically significant 
at p=0.05) impact on all cloud computing service types. 
Furthermore, in the business model factor group of Collab-
oration with Business Partners, Collaboration Agreement 
Among Partners can also be identified as having the high-
est (although not statistically significant at p=0.05) im-
pact on all cloud computing service types. In the business 
model factor group of Revenue Model, the factor Service 
Billing Based on Target Customers can be recognized as 
having the highest (although not statistically significant at 
p=0.05) impact on all cloud computing service types.
Differences in opinions
The research results reveal statistically significant dif-
ferences in opinions. We can assert that companies with 
previous SaaS experiences estimate the following factors 
as more important compared to companies without previ-
ous experiences with SaaS: Service Usability, Customer 
Support Services, Co-branding, User Authentication, and 
Service Recovery Procedures in the case of detected prob-
lems. In contrast, we can assert that companies without 
previous experiences with SaaS (specifically experiences 
with Customer Relationship Management Services) con-
sider the following factors to be more important in com-
parison to companies with such experience: User Authen-
ticity and Service Quality. 
Furthermore, companies without previous PaaS expe-
riences declare the following factors to be more important 
to cloud computing adoption in comparison to companies 
with previous PaaS experiences: Service Added Value, 
Customer Support Services. Service Orientation to Target 
Customers Based on Customers’ Process Complexity, Dis-
pute Resolution Mechanisms, References & Recommen-
dations, Knowledge & Experiences, Partners˙ Marketing 
Channels, and Service Billing Based on Service Usage. 
Companies without previous experiences with IaaS 
recognize the following business model factors as more 
important for cloud computing adoption in comparison to 
the companies with previous experiences with IaaS: Ser-
270
Organizacija, Volume 50 Number 3, August 2017Research Papers
vice Flexibility, Customer Support Services, Service Ori-
entation to Target Customers Based On Customers’ Size, 
Service Orientation To Target Customers Based on Cus-
tomers’ Process Complexity, Service Orientation To Target 
Customers Based on Customers’ Geographical Location, 
Collaboration With Partners, Technology & Equipment, 
and References & Recommendations.
5.2 Implications for practice
The results of our research also have implications for prac-
titioners. They can provide orientation for the innovation 
of existing business models towards the creation of a cus-
tomer-oriented business model for the more successful 
exploitation of cloud computing services and business op-
portunities. For potential users, the findings may represent 
guidelines for the successful introduction and adoption of 
cloud computing services.
The research model presents a comprehensive consid-
eration of the impact of business model factors on cloud 
computing adoption. Besides identifying business model 
factor groups and individual factors having the highest im-
pact on cloud computing adoption, it also addresses the 
differences in opinion resulting from previous experienc-
es with cloud computing services. Furthermore, end users 
should consider these factors with special consideration 
when defining service level agreements with providers and 
deciding about cloud computing service adoption.
It is understandable that cloud computing business 
models will be changing and evolving over time, adapting 
to market requirements, technological development, envi-
ronment/social needs, and legislation. However, the results 
of this study show which factors of business models are 
considered to be more sensitive and important when com-
panies consider cloud computing adoption. This can help 
providers to rethink, redesign, or re-market their current 
business models and tailor them according to the needs of 
different customer segments. 
5.3 Limitations and future research 
recommendations
Findings of this study should also be interpreted consider-
ing its limitations. 
The analysis shows a low proportion of explained var-
iance in the model (it can be argued that cloud computing 
adoption is impacted by many other factors. not included 
in our structural model). Due to this result, future research 
should further investigate individual business model fac-
tors or their grouping into new factor groups. The research 
should thus focus on the definition of research models with 
a higher proportion of explained variance. 
The response rate in our study was 8.88%. For further 
research, an increase in the rate of participating companies 
is recommended. As the study has been done in Slovenia, 
research should be expanded to other geographical areas. 
Further investigation is also recommended to address 
public institutions, as well as investigating potential mod-
els of cloud computing adoption (public cloud, private 
cloud, hybrid cloud, etc.).
With the aim of in-depth understanding of the impacts 
of business model factors, deepening the investigation of 
each group of respondents (users/providers) is suggested. 
In this direction, it would be interesting to investigate and 
compare the characteristics of users and providers. Poten-
tial differences could also be investigated from the per-
spective of organizational structure, strategic alignment, 
the role of ICT, etc. 
In our research investigated business model factors 
could be classified into so-called “business model organi-
zational factors”, as they primarily need to be considered 
by cloud service providers when defining or innovating 
business models. For future research, the model should 
also include the impact of environmental factors, such 
as Competition, Business partners, Legislation, Econom-
ic Situation, in order to investigate their impact on cloud 
adoption. 
Acknowledgment
This research is a part of a doctoral dissertation entitled 
“Impact of Providers’ Business Model on Cloud Comput-
ing adoption”. The author Kristina Bogataj Habjan and su-
pervisor Associate Professor Andreja Pucihar would like to 
take this opportunity and express our gratitude to Professor 
Emeritus Miroljub Kljajić, serving as president of the eval-
uation commission. With his wisdom, reach knowledge 
and expertise, he has provided valuable directions, guide-
lines, and encouragements and significantly contributed to 
the thesis’ completion, quality, and scientific contribution.
The research was supported by the Ministry of High-
er Education, Science and Technology of the Republic of 
Slovenia, (Contract No.: BI-RU/14-15-047, Contract No. 
P5-0018) and Russian Federation Presidential Scholarship 
No. 16-in-689.
Literature
Afuah, A., & Tucci, C. (2001). Internet Business Models 
and Strategies. Boston: McGraw Hill.
Amit, R., & Zott, C. (2001). Value creation in e-business. 
Strategic Management Journal, 22(6-7), 493−520, 
http://dx.doi.org/10.1002/smj.187 
Amit, R., & Zott, C. (2012). Creating Value Through Busi-
ness Model Innovation. MIT Sloan Management Re-
view, 53(3), 41-49.
Badger, L., Bohn, R., Chu, S., Hogan, M., & Liu, F. 
(2011). US Government Cloud Computing Technolo-
Organizacija, Volume 50 Number 3, August 2017Research Papers
271
gy Roadmap. II(1 (Draft)).
Benlian, A., Hess, T., & Buxmann, P. (2009). Drivers of 
SaaS Adoption – An Empirical Study of Different Ap-
plication Types. Business & Information Systems En-
gineering, 1(5), 357−369, http://dx.doi.org/10.1007/
s12599-009-0068-x 
Bogataj, K. Pucihar, A. (2012). Business Model FActors 
Influencing Cloud Computing Adoption. 25th Bled 
eConference, Doctoral Consortium, June 21, 2012. 
Bled, Slovenia.
Bogataj, K. Pucihar, A. (2013). Business Model Factors 
Influencing Cloud Computing Adoption: Differenc-
es in Opinion. 26th Bled eConference. eInnovations: 
Challenges and Impacts for Individuals, Organizations 
and Society. Bled, Slovenia.
Bogataj Habjan, K. Pucihar, A. (2017). Cloud Computing 
Adoption Business Model Factors: Does Enterprise 
Size Matter? Engineering Economics, 28(3), 253 – 
261, http://dx.doi.org/10.5755/j01.ee.28.3.17422 
Borgman, H., Bahli, B., Heier, H., & Schewski, F. (2013). 
Cloud rise: Exploring Cloud Computing Adoption and 
Governance with TOE Framework. Proceedings of 
the 46th Hawaii International Conference on System 
Sciences (HICC). Wailea, Maui, HI.
Bouwman, H., De Reuver, M., Solaimani, S., & Daas, 
D. (2012). Business Models Tooling and a Research
Agenda. Proceedings of the 25th Bled eConference,
eMergence: Merging and Emerging Technologies,
Processes, and Institutions. Special issue. Bled, Slo-
venia.
Bouwman, H., De Vos, H., & Haakre, T. (2008). Mobile 
service innovation and business models. Springer.
Bouwman, H., Faber, E., & Van der Spek, J. (2005). Con-
necting future scenario’s to business models of in-
surance intermediaries. Proceedings of the 18th Bled 
eCommerce Conference: eIntegrity, (pp. 1-14). Bled, 
Slovenia.
Chebrolu, B. (2011). Assessing the relationships among 
cloud adoption, strategic alignment and IT effective-
ness. Journal of Information Technology Management, 
XXII(2), 13−29.
Chen, C., Shiue, Y., & Shih, P. (2011). Why firms do not 
adopt SaaS. African Journal of Business Management, 
5(15).
De Reuver, M. (2007). Business model dynamics: a lon-
gitudinal, cross-sectional case survey. Proceedings of 
the 20th Bled eConference, eMergence: Merging and 
Emerging Technologies, Processes, and Institutions. 
Bled, Slovenia.
E-Factors consortium. (2003). E-Business Model Roadm-
ap Deliverable 3.1. (Revised): E-Factors Report Part
1: Overview and Current Trends and E-Business Mod-
els. Retrieved on November 15, 2016, from https://
www.wi1.uni-muenster.de/wi/studies/archive/izi/ss05/
E-FACTORS_D3_1.pdf
Erevelles, S., Stevenson, T., & Srinivasan, S. (2008). An 
analysis of B2B ingredient co-branding relationships. 
Industrial Marketing Management, 37(8), 940-952, 
http://dx.doi.org/10.1016/j.indmarman.2007.07.002 
Eurostat. (2016). Cloud computing - statistics on the use 
by enterprises. (Eurostat) Retrieved on November 16, 
2016, from http://ec.europa.eu/eurostat/statistics-ex-
plained/index.php/Cloud_computing_-_statistics_on_
the_use_by_enterprises
Ganwar, H., Date, H., & Ramaswamy, R. (2015). Under-
standing determinants of cloud computing adoption 
using an integrated TAM-TOE model. Journal of En-
terprise Information Management, 28(1), 107-130.
Gartner Inc. (2016). Gartner Says By 2020 a Corporate 
“No-Cloud” Policy Will Be as Rare as a “No-Inter-
net” Policit Is Today. Retrieved on June 15, 2017, 
from http://www.gartner.com/newsroom/id/3354117
Gordijn, J., Akkermans, H., & van Vliet, H. (2000). Busi-
ness Modelling Is Not Process Modelling for E-Busi-
ness and the Web, Volume 1921 of the series, Lecture 
Notes in Computer science. In Business Modelling Is 
Not Process Modelling (pp. 40-51). Springer-Verlag.
Hanelt, A., Hildebrandt , B., & Polier, J. (2015). Uncov-
ering the Role of Is in Business Model Innovation - a 
Taxonomy - Driven Approach To Structure the Field. 
23rd European Conference on Information Systems 
(ECIS). Münster, Germany.
Hedman, J., & Kalling, T. (2003). The business mod-
el concept: theoretical underpinnings and empirical 
illustrations. European Journal of Information Sys-
tems, 12(1), 46-59, http://dx.doi.org/10.1057/palgrave.
ejis.3000446 
IDC InfoBrief. (2015). Don`t Get Left Behind. The Busi-
ness Benefits of Achieving Greater Cloud Adotion. Re-
trieved on November 16, 2016, from http://www.cisco.
com/c/dam/en/us/solutions/collateral/trends/cloud/cis-
co-bca-infobrief.pdf
IDC Research (2015). Worldwide Cloud IT Infrastruc-
ture Spending Forecast to Grow 26% Year Over Year 
in 2015, Driven by Public Cloud Datacenter Expan-
sion. Retrieved on May 16, 2016 from http://www.
businesswire.com/news/home/20150706005147/en/
Worldwide-Cloud-Infrastructure-Spending-Fore-
cast-Grow-26. 
Johansson, J., Malmstrom, M., Chroneer, D., Styven, E., 
Engstrom, A., & Kåreborn, B. (2012). Business Mod-
els at Work in the Mobile Service Sector. iBusiness, 
4(1), 84-92, http://dx.doi.org/10.4236/ib.2012.41010 
Lambert, S., & Davidson, A. (2012). Applications of the 
business model in studies of enterprise success, in-
novation and classification: An analysis of empirical 
research from 1996 to 2010. European Management 
Journal, 31(6), 668–681, http://dx.doi.org/10.1016/j.
emj.2012.07.007 
Li, X., Liu, T., Qui, L., & Wang, F. (2009). The Method 
272
Organizacija, Volume 50 Number 3, August 2017Research Papers
and Tool of Cost Analysis for Cloud Computing. Pro-
ceedings of the 2009 IEEE International Conference 
on Cloud Computing. Los Angeles, CA, USA.
Low, C., Chen, Y., & Wu, M. (2011). Understanding the 
determinants of cloud computing adoption. Industri-
al Management & Data Systems, 111(7), 1006-1023, 
http://dx.doi.org/10.1108/02635571111161262 
Lumsden, R., & Gutierrez, A. (2013). Understanding the 
Determinants of Cloud Computing Adoption within 
the UK. Proceedings of the European, Mediterranean 
& Middle Eastern Conference on Information Systems. 
Windsor.
Marston, S., Li, Z., Bandyopadhyay, S., & Ghalsasi, A. 
(2011). Cloud computing - The Business Perspective. 
Proceedings of the 44th Hawaii International Confer-
ence on System Sciences, (pp. 1-11). Koloa, HI, USA.
Osterwalder, A. (2004). The Business Model Ontology - A 
Proposition In A Design Science Approach. Doctoral 
disertation. L’Université de Lausanne, Switzerland.
Osterwalder, A. P. (2010). Business Model Generation: A 
Handbook for Visionaries, Game Changers, and Chal-
lengers. Wiley.
Osterwalder, A., & Pigneur, Y. (2009). Business model 
generation. Retrieved on September 12, 2016, from 
Strategyzer: http://www.businessmodelgeneration.
com/book
Osterwalder, A., Pigneur , Y., & Tucci, C. (2005). Clari-
fying business models: Origins, present and future of 
the concept. Communications of the Association for 
Information Systems, 15, 1-40, http://aisel.aisnet.org/
cais/vol16/iss1/1?utm_source=aisel.aisnet.org%2F-
cais%2Fvol16%2Fiss1%2F1&utm_medium=PD-
F&utm_campaign=PDFCoverPages 
Petrovic, O., Kittl, C., & Teksten, R. (2001). Developing 
Business Models for eBusiness. Proceedings of the 
International Conference on Electronic Commerce. 
Vienna.
Pucihar, A., Lenart, G., Kljajić, B., Marolt, M., & Maletič, 
D. (2016). Role of ICT in Bussiness Model Innovation 
in SMEs - Case Of Slovenia. Proceedings of the 24th 
Interdisciplinary Information Management Talks: In-
formation Technology, Society and Economy Strategic 
Cross-Influences (pp. 231-241). Poděbrady, Czech Re-
public: Trauner Verlag.
Rappa, M. (2010). Business Models on the Web. Retrieved 
on November 12, 20016, from Managing the digital 
enterprise, http://digitalenterprise.org/models/models.
html.
Sainio, L., & Marjakoski, E. (2009). The logic of revenue 
logic? Strategic and operatioinal levels of pricing in 
the context of software business. Technovation, 29(5), 
368-378.
Teece, D. (2010). Business Models, Business Strategy and 
Innovation. Long Range Planning, 43, 172-194, http://
dx.doi.org/10.1016/j.lrp.2009.07.003 
Timmers, P. (1998). Business Models for Electronic Mar-
kets. Journal on Electronic Markets, 8(2), 3-8, http://
dx.doi.org/10.1080/10196789800000016 
Tweel, A. (2012). Examining the relationship between 
Technological, Organizational, and Environmental 
Factors and Cloud Computing Adoption. Doctoral dis-
sertation, Northcentral University, Arizona.
Watson, R. V. (2010). Factors influencing the adoption of 
cloud computing by decision making managers. Doc-
toral dissertation, Capella University, USA.
Wu, W. W. (2011). Developing an explorative model for 
SaaS adoption. Expert Systems with Applications, 
38(12), 15057-15064, http://dx.doi.org/10.1016/j.
eswa.2011.05.039 
Zott, C. A. (2009). Business Model Innovation: Creating 
Value In Times Of Change. Universia Business Re-
view, 3 (Special Issue on Business Models), 108-121.
Kristina Bogataj Habjan received her MSc in eBusi-
ness Management in 2005. She has been working in 
ICT industry as a product manager and is finalizing 
her PhD study in eBusiness Management focusing on 
cloud computing adoption business model factors at 
the Faculty of Organizational Sciences, University of 
Maribor. She has over 15 years of experiences in man-
aging IT R&D projects.
Andreja Pucihar is an Associate Professor at the Fac-
ulty of Organizational Sciences, University of Maribor 
in Slovenia. She received her PhD in Management of 
Information Systems in 2002. Her recent research is 
mainly focused to IS innovation, in particular in busi-
ness models, e-business, social media, innovation and 
digital transformation. She is a member of eCenter and 
Head of eMarkets Laboratory. She has published over 
150 papers in journals and conference proceedings. 
She has been involved into several national, interna-
tional (EU, CE, cross-border, bilateral) and industry 
projects. Since 2009 she is a conference chair of annu-
al international Bled eConference. She is an assistant 
editor of »Electronic Markets - The International Journal 
on Networked Business« journal and a member of the 
board of the following journals: »Journal of Theoretical 
and Applied Electronic Commerce Research«, and »In-
ternational Journal of Organisation Design«.
Organizacija, Volume 50 Number 3, August 2017Research Papers
273
Pomen dejavnikov poslovnega modela za  uvedbo računalništva v oblaku: vloga predhodnih izkušenj 
Izhodišča: Računalništvo v oblaku prinaša številne priložnosti za učinkovitejše upravljanje IT in izrabo IT storitev, 
zato v prihodnjih letih pričakujemo njegov pomemben vpliv na tržišču. Podatki o trenutni uporabi računalništva v 
oblaku na tržišču kažejo, da kljub prednostim in priložnostim računalništvo v oblaku še ni razširjeno kot je bilo 
pričakovano. Omenjena situacija nakazuje potrebo po nadaljnjem raziskovanju potenciala za uvedbo računalništva 
v oblaku. Namen raziskave je bilo identificirati posamezne dejavnike poslovnega modela računalništva v oblaku, ki 
imajo največji vpliv na njegovo uvedbo v organizacijah. Poleg tega, smo v raziskavi želeli identificirati razlike v mnen-
jih o pomembnosti posameznih dejavnikov poslovnega modela računalništva v oblaku glede na predhodne izkušnje 
organizacij. 
Metodologija: Na podlagi pregleda literature, predhodnih raziskav in intervjujev s ponudniki in uporabniki računal-
ništva v oblaku smo oblikovali raziskovalni model. Statistične analize so bile usmerjene na identificiranje pomemb-
nosti dejavnikov za uvedbo računalništva v oblaku in razlike v mnenjih glede na predhodne izkušnje uporabnikov. V 
raziskavi je sodelovalo 80 organizacij in 5 največjih ponudnikov računalništva v oblaku v Sloveniji. 
Rezultati: Rezultati raziskave so pokazali statistično pomembne razlike v mnenjih o pomembnostih dejavnikov 
poslovnega modela računalništva v oblaku glede na predhodne izkušnje organizacij. Rezultati raziskave predstavl-
jajo usmeritve za preoblikovanje ali inoviranje obstoječih poslovnih modelov ponudnikov računalništva v oblaku. Na 
podlagi tega lahko ponudniki oblikujejo poslovne modele računalništva v oblaku, ki bodo prilagojeni potrebam strank 
ter s tem omogočili učinkovitejšo izrabo storitev računalništva v oblaku in odpiranje novih poslovnih priložnosti. Za 
potencialne uporabnike, rezultati raziskave predstavljajo usmeritve za učinkovitejšo uvedbo storitev računalništva v 
oblaku. 
Zaključek: Dejavniki poslovnega modela, obravnavani v tej raziskavi, spadajo v tako imenovano skupino organizaci-
jskih dejavnikov. Ponudniki računalništva v oblaku jih upoštevajo pri preoblikovanju ali inoviranju poslovnih modelov. 
V prihodnjih raziskavah bi bilo smiselno vključiti tudi dejavnike, ki se nanašajo na poslovno okolje ponudnikov kot na 
primer konkurenca, poslovni partnerji, pravni vidiki in tržne razmere ter proučiti tudi njihov vpliv na uvedbo računal-
ništva v oblaku. 
Ključne besede: računalništvo v oblaku; uvedba; SaaS; IaaS; Paas; poslovni model; dejavniki