5 
 

 

 

 

Cloud solution in Business Intelligence for SMEs –

vendor and customer perspectives 

 
Alessandro Agostino  , Klaus Solberg Søilen  , Bart Gerritsen 

 
  Department of Management and Technology, Halmstad University 

E-mail: klasol@hh.se 

Faculty of Industrial Design Engineering, TU Delft 

E-mail: B.H.M.Gerritsen@tudelft.nl 

 

Received November 12, accepted 20 December 2013 

 

ABSTRACT: The aim of this study was to identify key success factor for SME customers of cloud based 

Business Intelligence products. A deep interview was made with four producers and a questionnaire was carried 

out among 36 SMEs. The findings suggest that the most important CSFs were the level of software 

functionalities, the ubiquitous access to data, responsive answers to customer support requests, handling large 

amounts of data and implementation cost. Each of these factors addresses a specific area that customers pay 

close attention to during the adoption process of a cloud BI solution. Offering ubiquitous access to date and 

respsonsive answers to customer requests are particularly emphasized for SMEs. We also found that industry 

tailored software is preferred, monthly or quarterly billings, and contact by email or phone for service. The 

paper shows recommendations, implications of research and suggests further research on the topic. 

 

KEYWORDS: Business intelligence, SME, cloud computing, ubiquitous computing 

 

Introduction 

The amount of data available for analyses is 

growing considerably and IBM estimates 90% of 

the data in the world has been created in the last 

three years (IBM research, 2011; Negash, 2004). In 

the last thirty years, storage space has been 

increasing dramatically whereas its cost has 

followed the opposite trend (Storage Trend study). 

More businesses are realizing the massive potential  

 

that lies in their data. This is a potential that can be 

leveraged to make better decisions, offer more 

value to both customers and shareholders, and 

discover patterns that could be “disruptive” (Scholz 

et al., 2010; Sheikh, 2011; Nyblom et al., 2012). 

The discipline that specializes in turning data into 

useful information is sometimes called Business 

Intelligence (BI). Why it is important for the 

bottom line of a company to have better 

information, is a crucial question and has been 

Available for free online at https://ojs.hh.se/ 

 

Journal of Intelligence Studies in Business 3 (2013) 5-28 

mailto:klasol@hh.se
mailto:B.H.M.Gerritsen@tudelft.nl
https://ojs.hh.se/


7 
 

addressed by Nyblom et al. (2012) and Watson and 

Wixom (2007). 

Having access to the right information at the right 

time increases the likelihood of making better 

decisions (Yeoh and Koronios, 2010). These will 

bring tangible benefits to the organization, both in 

terms of “increased revenue” and/or “decreased 

costs”. The importance of any IT solution can be 

measured in terms of how it affects, directly or 

indirectly, the two aforementioned basic metrics 

(Poston and Grabski, 2001; Tata Consulting 

Service White paper, 2012; Rust, 2002). A 

competitive advantage can be achieved not only 

through innovation in markets or products. Storing, 

collecting and analysing information have become 

a new frontier of competitiveness, and scholars 

foresee that data will become a new “corporate 

asset” and main source of revenue (Brown et al., 

2011; Raisinghani, 2004). Many CIOs (Chief 

Information Officers) now consider Business 

Intelligence a top priority for their organizations 

(Gartner, 2013), after many data analytics best 

practices have proved to offer considerable benefits 

to enterprises and individuals (LaValle et al., 2011; 

May, 2009). Moreover, the benefits arising from 

the collection and analysis of data are not restricted 

to a specific industry, but may relate to the majority 

of organizations (Gangadharan and Swami, 2004; 

Raisinghani, 2004). Many sectors have already 

gained benefits from big data, but many 

organizations also still need to understand how to 

obtain value from it (LaValle et al., 2011).  

Historically, BI systems have been mainly adopted 

in large and multinational enterprises (Olszak and 

Ziemba, 2012; Wong, 2005) which could afford the 

considerable cost required in terms of money, 

expertise and capabilities. As remarked by scholars 

(Solberg Søilen and Hasslinger, 2012b; Hwang et 

al. 2004), the resources necessary to implement a 

traditional BI tool are not available in most SMEs. 

Bergeron reports similar findings and suggests that 

conventional BI systems would not meet the needs 

of SMEs (Bergeron, 2000; InsideInfo Whitepaper). 

Furthermore, despite the precautions taken, the 

failure rate that characterizes BI projects, over 

50%, (Beal, 2005; Meehan, 2011; Laskowski, 

2001; Legodi and Barry, 2010 found in Adamala 

and Cidrin, 2011) does not encourage SMEs to 

invest in what is seen as risky activities.  

Although major organisations have led the way in 

introducing and implementing Business 

Intelligence solutions, the recent speed of 

globalization, competition and the amount of data 

to be processed has forced SMEs to evaluate the 

purchase of BI tools (Olszak and Ziemba, 2012; 

Wong, 2005). These software applications help a 

small business compete with larger ones, increase 

market share or provide insights and patterns that 

otherwise cannot be seen (Grabova et al., 2010). 

Olszak and Ziemba (2012) surveyed business-

owners and managers of SMEs, who confirmed the 

importance of analyzing data even in a small 

company: 

Problem formulation  

In the last few years, uncertain and turbulent 

economic conditions have forced companies, small 

as well as bigger ones, to find ways of streamlining 

operations and cutting costs in many areas (Östling 

and Fredriksson, 2012; Sheikh, 2011). The increase 

in data volume calls for an efficient way to manage 

the information within an organization, especially 

of a SME where the use of Information Technology 

consistently lag behind (Rath et al., 2012). The 

advent of cloud computing could represent a 

breakthrough for the IT segment, since the 

advantages brought in by this technology are 

particularly appealing to SMEs (Benlian et al., 

2009; Rath et al.). The importance of this 

technology is also demonstrated by the growth 

achieved in this market in recent years. The Cloud 

Software-as-a-service (SaaS) market grew by over 

17% in 2012, reaching $14.5 billion in revenue and 

is expected to hit $22 Billion in 2015 (Bucur, 

2012). IDC forecasts that the amount of revenue 

generated by SaaS BI suppliers will expand three 

times as fast as the overall BI market for the year 

2013, logging a Compound Annual Growth Rate 

(CAGR) of 22.4% (Deng and Zhang, 2012).  

If SMEs can find ways to successfully deploy cloud 

BI systems, it is reasonable to assume that those 

solutions will boost their competitiveness and 

provide a means to manage the information more 

efficiently. However, despite the promising 

numbers and high expectations, the SaaS Business 

Intelligence market currently represents only 3% of 

the total BI turnover and the adoption rate among 

SMEs is still low (Figure 1; FSN, 2012; Rath et al.; 

Scholz et al., 2010).  



8 
 

 

Figure 1. SMEs Business Intelligence adoption 

A variety of factors might explain this poor result, 

such as the novelty of SaaS technology, which has 

still to spread, or the fact that applications 

characterized by a high strategic importance for a 

company, such as BI, experience lower adoption in 

a cloud setting.  

Volatile market conditions force Business 

Intelligence suppliers to adapt their offerings to 

current customers’ needs. The knowledge of which 

key factors affect the decision of a SME to adopt a 

cloud BI solution is mostly unexplored. The ability 

to investigate this problem will have practical as 

well as theoretical benefits: 

 Understanding the real needs of SMEs in 
terms of managing information, through 

the adoption of BI systems, in a more 

comprehensive way. The “use” of 

information has a dramatic influence on 

the performance of a SME (Lybaert 1998 

found in Scholz et al., 2010). In addition, 

BI systems appear to be adopted mostly in 

large and international companies, so 

previous studies have been focused on 

those organizations (Scholz et al., 2010; 

Olszak and Ziemba, 2012; Wong, 2005). 

The needs of SMEs are quite different 

than for big companies (Scholz et al., 

2010; Olszak and Ziemba, 2012).  

 Preventing the development of initiatives 
or projects with poor market appeal or 

suggesting that BI suppliers should focus 

on critical issues that otherwise would 

have been overlooked – resulting in a 

more compelling offering for the 

customers. 

 Providing a solid ground for future 
research by validating and adding new 

perspectives to the current body of 

knowledge in the field of Business 

Intelligence, well aware that the value of 

these studies will decline rapidly with 

time, given the advancement of 

technological innovation (Yeoh and 

Koronios, 2010).  

 

Based on this we have defines the following 

Research Question (RQ) for this study: 

RQ. What are the key success factors for 

the adoption of a cloud BI solution in 

small and medium sized enterprises 

(SMEs)? 

  



38 
 

 

Table 1. Key adoption factors in Business Intelligence 

 

Previous research  

A unique definition of small and medium sized 

enterprise has not been put forward yet (Carter and 

Jones-Evans, 2006). For the purpose of this study, a 

company is considered a SME if it fulfills the 

following requirements: 

 Up to 500 employees and $25 M in annual 
revenue in the Unites States (Carter and 

Jones-Evans, 2006).  

 Less than 250 workers; a maximum 
annual turnover of €50 million or €43 

million in the balance-sheet, for European 

enterprises (Carter and Jones-Evans, 

2006). 

 For Asian companies there is not an 
official definition and it varies greatly 

from country to country. For instance, 

Chinese companies with 2000 employees 

can still be considered medium businesses, 

whereas in Lao, a company with more 

than 100 employees is considered a big 

company (Harvie, 2004; Xiangfeng, 

2007). 

 

According to Rockart (in Vodapalli, 2009), Critical 

Success Factors (CSFs) represent a number of areas 

where the achievement of great results will ensure a 

competitive position for the individual, department 

or organization (Anthony, Dearden and Vancil, 

1972 found in Olszak and Ziemba, 2012). It is 

worthwhile remembering that a mere list of key 

success factor does not automatically imply the 

success of the project (Adamala and Cidrin, 2011). 

As remarked by scholars (Yeoh and Koronios, 

2010), a list of key success factors identified for the 

development of Information Systems, such as BI, is 

only a part of the task necessary to ensure the 

project’s completion. The key factors represent the 

areas that, if successfully managed, can increase 

the likelihood of a successful adoption. Software 

evaluation criteria refers to making preference 

decisions over the available alternatives that are 

characterized by multiple attributes (Jadhav and 

Sonar, 2009). 

An initial research revealed that the key factors 

concerning the adoption of a Business Intelligence 

software have been considerably covered (Adamala 

and Cidrin, 2011; Yeoh and Koronios, 2010; 

Vodapalli, 2009), both in term of SMEs (Olszak 

and Ziemba, 2012; Wong, 2005) and Software-as-

a-service (Godse and Mulik, 2009). However, the 

key factors in adopting SaaS Business Intelligence 

solutions in SMEs have not been sufficiently 

treated. Little has been said regarding the 

connection between SMEs and cloud BI, therefore 

there is a lack of a proven framework that can be 

used for analyzing the domain. We developed these 

ideas following a similar approach adopted by 

other scholars (Scholz et al., 2010; Yeoh and 

Koronios, 2010), represented in table 1. 

Organization perspective Process perspective Technology perspective 

Adequate budget 

 

Support from senior 

management 

 

Competent BI project manager 

 

Sufficient skilled staff/team 

 

Clear business vision and plan 

 

Past experience and 

cooperation with a BI supplier 

 

Rolling out training initiatives 

 

Well defined business 

processes and issues 

 

Well defined users’ 

expectations 

 

Adjusting the BI solution to 

users’ business expectations 

 

Understanding how and when 

data will be delivered 

 

 

 

Integration between BI system 

and other systems (Desktop 

applications, software..) 

 

Data quality 

 

BI flexibility and 

responsiveness on users’ 

requirements 

 

Appropriate technology and 

tools 

 

User-friendly BI system 

 

Delivers actionable 

Information 



9 
 

Other scholars (Scholz et al., 2010) have faced a 

similar challenge during an investigation of 

traditional BI systems. They have developed a 

framework to link IT software adoption in SMEs, 

BI adoption and BI success factors. Based on this 

we present here two areas which will be taken into 

consideration in building the framework that helps 

answer the RQ: 

 Critical success factors: CSFs for 
IT/BI software implementation: focus 

on SMEs  

 Evaluation Criteria: IT packages and 
Software-as-a-Service evaluation 

criteria 

 

The factors belonging to these distinctive areas will 

be combined, resulting in a table that will be 

initially refined with the results from the qualitative 

interviews and then tested with a self-completion 

questionnaire.  

Critical Success Factors (CSFs) in BI have been 

treated by many authors (Eckerson, 2005; Wise, 

2007; Yeoh and Koronios, 2010; Olszak and 

Ziemba, 2012) and they could be considered a set 

of tasks and procedures that should be addressed in 

order to ensure BI systems accomplishment 

(Olszak and Ziemba, 2012). In this paragraph, these 

factors are reviewed and particular attention is paid 

to the ones related to SMEs. Table 2 summarizes 

the literature on the argument. 

 

 

 

 

 

 

 



10 
 

Table 2: Software-as-a-service evaluation factors (Godse and Mulik, 2009; Benlian et al., 2009; Sheikh, 2011; 

Sharma et al., 2010,  Saugatuck Technology report, 2009, Jadhav and Sonar, 2009) 

The bulk of studies on Critical Success Factors 

have focused on large companies and it is believed 

that not all the factors are applicable to the small 

and medium sized enterprise environment (Wong, 

2005; Bergeron, 2000; InsideInfo Whitepaper). 

These studies analysed traditional and expensive 

IT/BI projects, commonly characterized by long 

implementation periods; whereas the typology of 

BI systems we focus on in this study requires a 

minimal implementation effort (Sheikh, 2011). 

Given this premise, the use of past research on 

critical success factors seems inappropriate for the 

purpose of this study. Indeed, there are substantial 

differences between cloud and traditional BI 

implementation, in terms of resources, complexity, 

and architecture. This research offered valuable 

foundations applicable throughout the whole 

research. Not all factors presented in table 1 and 2 

will appear in the final framework and some of 

them have been adjusted to fit the context of this 

investigation. Elements such as clear business 

vision and plan, support from senior management, 

well defined business processes and issues, 

sufficient skilled staff are typical of long IT 

projects, which require multiple interactions 

between the client and the vendor, given the 

amount of resources required to roll-out the 

initiative. The overall process of adopting a cloud 

Functionality Architecture Usability Vendor’s 

reputation 

Cost Risk 

mitigation 

Customization 

of the 

interface 

 

Enhance 

capabilities 

 

Embed 

reports on 

multiple 

platform 

(blogs, web, 

email..) 

 

Ability to 

deliver ad-hoc 

business 

analyses 

 

Capability of 

the software 

package to 

run on wide 

variety of 

computer 

platforms 

 

Present and 

display data 

effectively 

 

Error 

reporting 

Integration 

(API, 

connectors..) 

 

Scalability 

and system 

response time 

 

Reliability  

 

Security 

(Backup, 

recovery) 

 

 

Simple User – 

interface 

 

Offline 

support 

platform 

 

Support for 

Mobile/Tablet  

Devices 

 

Ubiquitous 

access 

 

Collaborative 

reporting and 

analytics 

 

Ability to 

support 

different 

combination 

of user types 

(beginners, 

intermediate, 

advanced)  

Number of 

clients/users 

 

Brand Value 

and 

popularity 

 

Certificates 

and standard 

requirements 

 

Effective 

Manuals and 

Training 

tools 

 

Level of 

Service 

offered 

 

 

Annual 

subscription 

 

One time 

implementation 

costs 

 

Maintenance 

cost 

Easy to buy 

 

Special 

contractual 

agreements 

 

Flexible 

subscription 



11 
 

Business Intelligence solution is less complex and 

these factors do not play a major role. Another 

example is Rolling out training initiatives, which 

represents a customer support activity. Generally, 

SaaS BI software is easy to use and the training 

support is mainly delivered through online 

libraries, tutorial videos, 24/7 call center, and email 

services.  

IT packages and software-as-a-service (SaaS) 

evaluation criteria 

In this section we discuss the different criteria that 

are evaluated before the purchase of a software. 

Focus is on the IT solutions evaluation criteria, 

with a specific consideration for SaaS cloud 

factors. Research on the key criteria for purchasing 

cloud SaaS software has been carried out by several 

authors (Godse and Mulik, 2009; Benlian et al., 

2009; Sheikh, 2011; Sharma et al., 2010; Xin and 

Levina, 2008; Jadhav and Sonar, 2009). Given the 

focus of this paper on cloud SaaS software, it’s 

necessary to understand precisely which are the 

fundamental factors that drive the purchase of these 

solutions. Table 2 was originally developed by 

Godse and Mulik (2009) and grouped together, in s 

simple and comprehensive way, the more important 

evaluation factors characterizing cloud SaaS 

software. However, during our research we found 

other factors not listed in the original version of the 

table, which help explain new discoveries: 

1. Functionality: It represents the sum or any 
aspect of what a product, such as a 

software application or computing device, 

can do for a user (SearchSoa, 2005). One 

of the main concerns that potential 

customers have, before buying any SaaS 

product, is to understand the real 

functionalities. In the previous paragraphs 

we highlighted some benefits of cloud 

products. However, by comparing cloud 

BI and traditional BI functionalities, the 

former comes off worse. The limited  

2. customization allowed by a SaaS products 
is far outdone by traditional solutions, 

which provide cutting-edge analyses 

created specifically for the needs of a 

different set of clients.  

3. Architecture: In this category we refer to 
the security, reliability, scalability and 

integration of the IT architecture. Security 

is a crucial aspect that every SaaS vendors 

should address, clarifying doubts that 

companies have in letting third-parties 

manage confidential and sensitive 

information (Godse and Mulik, 2009). 

Scalability refers to the ability of the 

product to maintain the same performance 

despite the increase in utilization. 

Reliability indicates the product’s ability 

to work and remain available to the users 

under specific environmental conditions 

for a given amount of time. Finally, a 

product is easily integrated if it can be 

combined with other applications. 

4. Usability: this section refers to the features 
that facilitates the interaction between the 

user and the software. Examples are the 

user-interface and all the tools that support 

the customers in troubleshooting (Godse 

and Mulik, 2009). Even though the SaaS 

products are generally easier to use than 

traditional software, there are different 

level of “usability”. 

5. Vendor reputation: This specific attribute 
is valid for all software purchases, 

irrespective of the product's features and 

architecture. Therefore, our initial 

assumption is that it should be taken into 

consideration for cloud solutions. 

6. Cost: The total cost of ownership is 
composed of two elements. The consulting 

and configuration services go under the 

name of implementation cost. The 

monthly or annual fee that is due to the 

supplier in exchange of the right to utilize 

the software is named subscription cost 

(Godse and Mulik, 2009). 

7. Risk mitigation: In this section we grouped 
all the activities that facilitate the 

transition to a new product. 

 

  



12 
 

Table 3: Research method 

Method 

Our research follows a two-stages approach, 

qualitative and quantitative, as summarized in the 

table below. 

Stage one: qualitative – Defining the previous 

theories, gain up-to-date market insights and 

categorization of key factors. 

An extensive literature review in two main domains 

was conducted. The subsequent step involved the 

analysis of the table, aimed at identifying possible 

weaknesses or improvements agreed by BI experts, 

daily BI users and BI vendors. Indeed, some 

previous theories explain the factors in generic 

terms, while this investigation is aimed at 

representing key adoption factors for SMEs in a 

more detailed manner. This process is done through 

four interviews. The research assumes that experts' 

judgments and experience could add important 

value in situations where theory is incomplete or 

obsolete (Yeoh and Koronios, 2010). Finally, 

factors coming from the literature review, 

improved with the interviews’ data, and were 

consolidated into a single table. 

Stage two: quantitative - Empirical assessment of 

the model. 

Due to the limited academic literature about our 

problem, stage one was used to provide a solid 

ground for the following analysis. Here, the 

preliminary table (table 4), resulting from the 

qualitative interviews was further assessed and 

validated with a quantitative method. Based on this 

table, all the content was carefully shaped into a 

survey. The research instrument was used to 

capture respondents’ perceptions and empirically 

classify the importance of the factors. A pool of 

candidates, who fulfilled the following 

requirements, were selected: “SMEs’ employees 

who use a cloud Business Intelligence solution”. 

None of the participants had any relationship with 

the authors. 

To deepen the understanding and assure a certain 

level of reliability, three different data sources were 

used: 

Secondary data. As this is a fast changing research 

area, papers older than five years could not offer 

much value. Therefore, throughout the paper not 

only did we use books or publications, but we also 

extensively relied on recent research papers and 

analyses made by trustworthy professional firms 

and as found at recent conferences (Gartner, 

Aberdeengroup and IDC) or information was found 

directly by Business Intelligence suppliers.  

Qualitative interviews. All interviews were held 

through Skype and notes were taken for future 

reference. Even though face-to-face interviews are 

preferred for in-depth studies aimed at grasping 

nuances in the interviewees’ behaviours, video-

calls through Skype can also represent an effective 

way, given money and distance constraints (Hay-

Gibson, 2009).  

The first BI expert is the co-founder of RJ Metrics 

(www.rjmetrics.com), a Philadelphia based 

company that sells cloud Business 

Intelligence/Analytics solutions, with a focus on e-

commerce organizations. The other knowledgeable 

person in the area of Business Intelligence is the 

Marketing Manager of Insightsquared 

(www.insightsquared.com), whose main offering is 

centred on sales analytics, optimization and 

forecasting. Both companies deal repeatedly with 

small and medium sized business owners. 

Regarding the BI users’ point of view, a first 

interview was conducted with the marketing 

Purpose of Research Research Methods Research Group 

1. Previous theories in light of 
cloud BI. Gaining new 

insights on the BI market. 

Help in the categorization 

process 

Semi-structured interviews  Case. Four interviews with BI 

experts,  vendors and 

customers of Business 

intelligence software solutions  

2. Ranking the importance of 
key adoption factors  

Questionnaire 36 SMEs who have 

implemented a cloud BI 

solution 



13 
 

director of a Mexican company with less than 150 

employees, which provides solutions for Human 

Resource departments. The second was an 

interview with the head of the analytics department 

of Soliditet, a 100+ years old Stockholm based 

company with 250 employees, market leader in 

providing credit and business information for 

companies based in the Nordic regions. Anonymity 

has been preserved according to wishes 

The web-based questionnaire. Another source of 

primary data comes from the self-completion 

questionnaire. The purpose of the questionnaire 

was to rank the importance of the factors listed in 

table 4. The limitations posed by the RQ restricted 

the available representative sample. Being aware of 

this difficulty, we made sure to have a pool of 

respondents large enough to draw insightful 

conclusions. The structure of the questionnaire's 

questions, the language used in formulating them, 

and the recommendations made by Bryman and 

Bell’s (2011) have been taken into consideration. 

Nine questions were asked, both open-ended and 

with multiple choices, with a preference for the 

latter. Indeed, Bryman and Bell (2011) remark that 

closed-questions are more suitable for comparison 

among variables, which also represent the nature of 

our research here. 

Sample and limitations 

Regarding sampling, the RQ and the framework 

represent the most important delimitation criteria 

for the sampling choice (Miles and Huberman, 

1994). The cloud BI subject, accounting for only a 

small portion of the total BI market, restricted from 

the beginning the sampling procedure. In addition, 

the available sample was further restricted by 

considering other criteria such as the size of the 

companies (SMEs) and the actual utilization of a 

cloud BI software. Out of the total population 

composed of 388 BI customers, 342 “good” 

addresses were selected, which constitute the total 

sample. The rest of the contacts were either info@-

addresses, phone numbers or e-form compilations 

which have been discarded. After completing the e-

mail collection, a web-survey was created and 

published online. An email, including the link to 

the questionnaire, was sent to all 342 addresses and 

a time limit was set to 60 days. 19 emails were 

automatically received with the notification of 

maternity leave, job change, not availability or 

wrong address. 36 full responses were received, 

generating a  10% response rate. According to 

Braun Hamilton (2003, found in Solberg Søilen and 

Sabanovic, 2012), the average response rate for 

web survey is roughly 13 percent, but he affirms 

that this number could vary. 

This result could be seen in two ways. From one 

side, Bryman and Bell (2011) affirm that the 

absolute size of the sample carries the most weight. 

The two authors also claim that there is not a 

standard procedure for evaluating sample size. It 

depends on a number of considerations and there is 

not a definitive answer (Bryman and Bell, 2011). 

Returning to the results achieved by this study and 

considering the year 2012, the cloud BI segment 

represents 3% of total market (Figure 4; FSN, 

2012; Rath et al. 2012; Scholz et al., 2010), hence 

the available representative sample was lower than 

other research conducted on traditional Business 

Intelligence (adjusted for the revenue difference).  

Results and analysis 

By combining the previously mentioned factors, we 

created a table that has been assessed and refined 

with empirical data. The development of this 

framework has been necessary to reach the 

objective stated earlier. The process of 

categorization showed to be a difficult one. On the 

one hand, authors who have previously studied BI 

suggest dividing critical success factors in four 

categories: Technological, Organizational, Process 

and Environmental (Yeoh and Koronios, 2010). 

Scholars who have studied cloud computing 

software have used a different approach, as shown 

in table 2 (Godse and Mulik, 2009). Since this 

research combines multiple aspects, previous 

suggestions are not fully applicable to this 

investigation. Others have recognized the limitation 

of pre-defined frameworks (Vodapalli, 2009). We 

decided to categorize and label the key factors 

according to both the author’s previous experience 

and the results from the interviews. The decision to 

not apply other authors’ categorization is not a 

critic of these studies, but has been necessary due 

to the nature of this research. The result of the 

process is shown in table 4. The concepts will be 

used as a guide for the questionnaire development. 

 



14 
 

Table 4: Categorization of key adoption factors for SaaS BI 

In creating the table, we prioritized the factors that 

have been discussed across all the interviews. Other 

factors, that represent a specific view of one or 

more interviewees (not all), have been included in 

the table only after careful evaluation, trying to 

separate subjective and objective views. The 

insights generated from the discussions reveal some 

differences with the information found in the 

theories.  

Support: All four interviewees pointed out that 

supporting activities are becoming more crucial in 

establishing a good client-vendor relationship. This 

area has been emphasized both by customers and 

suppliers of BI software.  

In table 2 there is mention of the level of service 

offered, which is a quite broad statement that might 

also include the support activities. However, after 

the interviews we considered appropriate to create 

the category Support (table 4). This provides a 

more detailed representation of different elements.  

Flexibility: flexibility has been mentioned by 

scholars as a critical factor for the BI adoption and 

implementation (Olszak and Ziemba, 2012; Sheikh, 

2011). The interviews revealed that flexibility 

spans over multiple areas and customers pay 

attention to most of them before purchasing the 

solution. In addition to that, a discussion around the 

social network and web-data analyses came up 

multiple times. The increasing influence of social 

networks on the customers’ opinion has attracted 

Performance-

Functionality 

Ability to share reports through the software web interface 

The level of functionalities and capabilities offered by the product 

The speed of the product in performing analyses 

The ability to handle data in real time 

The ability to manage different amount of data 

Ability to offer actionable insights 

The effort required to deploy the product on a large scale basis 

 

Integration Tablet and mobile integration 

Ability to handle multiple sources of data (Excel, google documents, etc.) 

Level of integration with other BI applications or databases 

 

Flexibility The level of flexibility in terms of contract agreements and conditions 

The simplicity of the interface  

The level of skills needed to perform meaningful analyses   

Web-data analysis 

The level of customization and personalization 

Ubiquitous access to data 

Offline access to data 

The payment method 

Functional or Industry needs 

 

Reliability Provider’s brand reputation (including partners, suppliers and testimonials) 

The level of security guaranteed by the vendor ( Backup, recovery and 

privacy) 

Support Vendor’s clarity to customer support requests  

Responsiveness to general support requests 

The level of support offered by the vendor (Chat, 24hour) 

Cost of ownership The amount of Implementation cost (Training, setup..) 

The amount Subscription cost (Monthly or yearly fee) 



15 
 

the attention of companies, who monitor closely 

what happens throughout the web. The flexibility 

of a BI software to analyze not only common data 

sources (E.g. Csv or Excel), but also unstructured 

data (Text and social media content) is in high 

demand. 

Integration: According to previous theories (table 

3), the integration between the BI software and 

other applications already situated in the 

customer’s organization represents a critical area. 

During one of the discussions, the interviewee 

linked to a market study carried out in 2012 by an 

independent advisory firm and a well-known 

authority in the area of Business Intelligence 

(Dresner Advisory Services, 2012). It revealed that 

over 66% of companies taking part in the study, 

rely on two or more BI tools at the same time. 

Therefore, integration is also referred to among 

different BI applications. However, given the focus 

of this investigation on small and medium sized 

enterprises, the integration among BI tools is more 

related to big and multinational companies with 

various business units.  

Key factors from the qualitative interviews 

RJ Metrics’ co-founder 

During the interview many factors were discussed 

and we will categorize them in four areas. The 

functionalities took a substantial part of the 

discussion, and the interviewee emphasized their 

importance for SMEs. In particular, he explained 

that the majority of customers are not interested in 

having a vast number of features across different 

domains, but prefer a software able to perform a 

few analyses, but of high quality. For example, RJ 

Metrics provides a software mainly to e-commerce 

companies, where cohort analyses and trends 

spotting represent two essential functionalities.  

With the advent of social networks, collaboration 

and sharing have become pillars of many 

applications since they encourage users to 

communicate and work together. The interviewee 

mentioned that customers are not very keen on 

using multiple software tools at the same time. 

They prefer to have everything in one place and 

this is one of the main factor they look for before 

buying the software (E.g. “Can we share reports 

within the software?”). 

Cost and deployment time have been mentioned 

together and they do influence the final decision. 

RJ Metrics takes seven days to deploy the overall 

solution and customers appreciate this short 

installation time. Not only does it reduce the 

overall cost, but it also minimizes the number of 

problems typical of the implementation phase. 

Supporting activities are not only restricted to the 

after sales customer support, but represent the 

overall ability to assist users in using the software 

and provide a detailed explanation to doubts or 

questions. This includes the area of security, where 

prospects perform a detailed due diligence before 

letting external parties manage sensitive data. 

Marketing manager of a Mexican company 

The marketing department of the company uses 

BIRST software. The conversation lasted almost 

one hour and was detailed. After a brief 

introduction of his company’s operations and 

analytics activities, we discussed his perspective on 

the key adoption factors. The ability to produce fast 

analyses was the first area in the dialogue. As 

previously mentioned, his company provides 

solutions for Human Resource (HR) departments, 

and payroll management is one of the most 

important service. Payroll activities are 

characterized by remarkable seasonal trends, since 

the bulk of the work is done at the beginning and at 

the end of each year. In these two periods, the 

interviewee explained, the company runs a lot of 

promotional campaigns, mainly delivered through 

the website. He personally has a six weeks time 

window to tweak the advertising material according 

to real time data response, delivered on a daily 

basis. 

The ease of implementation was another concern he 

had before purchasing the solution, which has been 

deployed in three weeks. A long implementation 

time could reduce the overall ROI generated by the 

BI investment, and generally prospects pay close 

attention to this aspect and evaluate the track record 

of BI suppliers in previous projects. 

Flexibility has been debated. In his department, the 

users analyse data for different purposes, including 

tracking campaigns’ results, evaluating new 

opportunities and measuring customers’ 

satisfaction. The employees in charge of each 

analysis, examine data and present results in 

different ways. The BI software should be able to 



16 
 

accommodate all the users’ needs. In addition, he 

mentioned the importance of offering insights to 

the customers. Often BI solutions not only provide 

an answer to many questions, but also leave users 

with new doubts.  

Head of analytics department at Soliditet 

Soliditet’s analytics department makes use of two 

BI tools. One is SPSS software for statistical 

calculations, while for the cloud part it uses a 

Microsoft product. The conversation started with a 

brief overview of the Business Intelligence market 

and how Soliditet is trying to exploit some 

opportunities through analyses of the company's 

spreadsheets. The discussion became particularly 

interesting since the beginning, when the 

interviewee mentioned that Soliditet is looking for 

another BI solution and it is in the process of 

evaluating different options. One of the main 

requirement was "We want a solution well-

integrated with tablets", which is one of the main 

tools used by the company to interact with the 

customers. Therefore, this type of integration was 

important not only for the company itself, but also 

for fostering the relationship with customers and 

prospects. The integration aspect came out again 

when he made clear that SPSS software will remain 

the main tool used by the business analysts and 

both solutions should work together. 

Then the discussion moved towards the cost of 

ownership, which was an important point for the 

company. The interviewee was well aware of the 

costs for different BI solutions and he explained 

that SPSS software was chosen as a compromise to 

the expensive, although powerful, traditional BI 

applications. Another area that got considerable 

attention was the reliability of the software. The 

company is looking for a solution that fits the 

budgetary requirements, but it does not want that 

the financial limitations would lead to the purchase 

of a solution with limited value for the company. 

Marketing manager of InsightSquared 

The Simplicity criteria was the central point of the 

discussion, since interviewee claims 

InsightSquared acquired many customers primarily 

by luring them with an easy-to-use software. 

Indeed, the software sold by the company takes 

only 48 hours to get installed and it is intuitive. 

According to him, it is difficult to create a general 

list of the most important functionalities for a cloud 

BI software, because it is highly dependent on the 

industry segment. However, he did think that a few 

of them should represent a cornerstone of every BI 

software: Ease-of-use has already been mentioned. 

Configurability is another one, since companies 

have their own way of using data, which is often 

unique. Hence, the level of customization for the 

cloud software is fundamental. Nonetheless, 

provided the limitations of cloud technology, 

customization cannot reach the same levels of the 

traditional BI implementations. However, the 

software has to be able to accommodate different 

users’ needs, not only in more superficial subtleties 

such as colour or font preferences. 

Moving to a different area of discussion, the 

marketing manager claims that a very simple 

software, such as Facebook or Twitter, simplifies 

the activities related to the customer service. By 

creating a self-explanatory product, there is a little 

need for online tutorials or pop-up guides. 

However, he agrees that technical problems do 

occur and a customer support team is essential to 

promptly solve some targeted questions. For 

instance, each screen of InsightSquared software is 

equipped with an "about this report" section to 

guide users when they need a little more 

information on how certain calculations are made. 

Integration also came up as an interesting part of 

the dialogue and the interviewee provided 

insightful information on this point. He states that 

his company often receives questions related to 

integration with third party data source solutions. 

For this reason, InsightSquared is currently 

dedicating a good amount of resources to 

improving the offering in this area. Nonetheless, he 

points out very clearly that it is complex to find a 

trade-off between the financial investment 

necessary to develop a new integration and the total 

number of integrations available. Indeed, the 

development of new connectors is important, but 

scaling up the product is also a crucial aspect. It is 

not possible to satisfy all the customers and 

therefore it’s fundamental to prioritize and integrate 

the most popular systems such as CRM and ERP 

solutions. Lastly, flexibility was the last argument 

of the discussion. Surprisingly, not only has 

InsightSquared not built the software around 

flexibility, but it has also put some limits on the 

level of flexibility. 

Stage two: findings and discussion 



17 
 

In this section we will present and discuss the 

findings of the quantitative analysis. Further, we 

provide an analysis regarding the implications these 

results might have, both practically and in future 

research. The section begins with the analysis of  

Figure 2. Key adoption factors - Mean Values 

the overall results and then it focuses on the 

comparison between the categories mentioned in 

table 4. Due to the inability to draw significant 

Figure 3. Key adoption factors – descending order 

statistical conclusions from the data gathered, the 

analysis will be centred on polar results or, to be 

more precise, on results that scored extremely well 

or poor in the questionnaire. Figure 2 represents the 

overall results and it can be understood that the 

higher the value in the bar chart, the more 

important is the specific key adoption factor, 

according to the survey’s respondents. Hence, in 

figure 3, we represent the key adoption factors in 

descending order. 

 

 

 

 



18 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5: Key adoption factors - descending order 

Overall results 

From the analysis of the questionnaire’s results, the 

single most important factors are represented by the 

software functionalities. Other authors discuss this 

element in their works (Sheikh, 2011; Jadhav and 

Sonar, 2009), but without paying particular 

attention to its importance. From these results, it is 

clear that cloud BI’s customers care about the 

software’s functionalities. Even though it is quite 

normal that a prospect pays attention to the 

functionalities of an application, the highest score 

achieved could be explained in the following way: 

As previously mentioned, in the past years the 

number of Business Intelligence vendors have 

increased greatly. Some of them specialize in a 

particular niche of the market; for instance RJ 

metrics provides solutions for e-commerce 

businesses. Therefore, the customers expect a 

software that effectively addresses most of the 

problems in a specific domain.  

We will not analyse minutely all the other key 

factors in this short paper, but it is interesting to 

provide a more detailed picture of the most and 

least important ones; Ubiquitous access to data 

(using any device, in any location, and in any 

format), responsive answers to customer support 

requests, handling big amount of data  and 

implementation cost earned their position in the 

highest end of the table. All of these factors belong 

to different categories, resulting in four different 

categories for the first five elements. This is a 

remarkable finding and stresses the significance of 

excelling in multiple areas and not focusing on a 

single one. Ubiquitous access to data is an 

Order Key adoption factors 

1 The level of functionalities and capabilities offered by the product 

2 Ubiquitous access to data 

3 Responsiveness to general support requests 

4 The ability to manage different amount of data 

5 The amount of Implementation cost (Training, setup..) 

6 Ability to share reports through the software web interface 

7 The speed of the product in performing analyses 

8 The effort required to deploy the product on a large scale basis 

9 The level of customization and personalization 

10 The amount Subscription cost (Monthly or Yearly fee) 

11 Level of integration with other BI applications or databases 

12 Ability to offer actionable insights 

13 The level of security guaranteed by the vendor (Backup, Recovery and 

privacy) 

14 Vendor’s clarity to customer support requests 

15 Provider’s brand reputation (Including partners, suppliers and 

testimonials) 

16 The simplicity of the interface 

17 The level of skills needed to perform meaningful analyses 

18 The level of flexibility in terms of contract agreements and conditions 

19 The ability to handle data in real time 

20 Ability to handle multiple sources of data (Excel, Google documents, 

etc.) 

21 Web-data analysis 

22 Offline access to data 

23 Tablet and mobile integration 



19 
 

important factor that confirms the results of 

previous studies (table 2). In addition, with 

increasing access to the Internet and the 

internationalization of many companies, BI 

customers know that their data reside in multiple 

locations and thus expect the BI software to 

connect all the sources together. In this way, data 

accessibility from anywhere and at any time 

(Sheikh, 2011) becomes not only feasible, but also 

necessary to have a better understanding of the 

overall company’s performance. Cloud computing 

and its technological architecture foster data 

accessibility whenever a connection is available. 

The users do not have to worry about different data 

formats or computer platforms (table 2), since the 

files containing the data are stored in a separate 

location (“the cloud”). In order to offer the 

customers real ubiquitous access to data, we 

suggest cloud BI vendors to focus on two aspects: 

web browsers and multiple devices integration. It is 

true that cloud technology works whenever a 

connection is available, but users have different 

preferences regarding web browsers (Oh and Lee, 

2011). As a consequence, it is the vendors’ duty to 

make sure that data is represented in the correct 

way, irrespective of the web browser used by the 

customer.  

Moving to devices integration, Gartner research 

(2013) forecasts a shift from desktop PCs to mobile 

devices in the following years. This escalation of 

smartphones and tablets’ sales undoubtedly 

promotes ubiquitous data accessibility, provided 

that cloud BI sellers are able to show the same 

results on multiple devices. 

Responsive answers to customer support requests 

achieved the third highest position. This factor does 

not represent a specific software functionality but is 

more related to the perceived experience that 

customers have during and after the purchase. This 

result gives evidence to the importance of serving 

customers in a professional way, in addition to 

offering a valuable software. If we look at the other 

key adoption factor belonging to the category 

“Support”, it scored slightly above average and 

strengthened the importance of providing a good 

customers service in the decision process of 

choosing a cloud BI software.  

Why is the customer service so important? It might 

be that small and medium sized enterprises can 

only rely upon a limited number of resources in 

comparison with big enterprises. The latter often 

have an appropriate department responsible for 

solving IT-related problems while SMEs might not 

have the technical or financial capabilities to deal 

with complex problems of this sort. This could 

explain why SMEs rated so highly the importance 

of the customer service. 

Further, a software capable of handling big amount 

of data is a fundamental requisite for the 

customers. Given the rapid growth of data available 

(IBM research, 2011), a cloud BI software should 

be able to combine millions or even billions of data 

points and detect valuable trends or patterns. Doing 

this operation within an acceptable time span 

represents a challenge from a technological point of 

view and BI suppliers should dedicate resources to 

address this important matter. 

Finally, the total implementation cost is the last 

factor that scored 4 or above. Cost is definitely one 

of the main benefits offered by SaaS products and 

customers still pay attention to this aspect (table 2). 

However, what is interesting to highlight is that the 

highest ranking was achieved by the 

implementation cost rather than the subscription 

cost. By looking at traditional BI implementation 

projects, the implementation phase is the most 

critical one and it may last for years (Watson and 

Wixom, 2001), demanding important resources. 

Despite this cloud BI implementation is not a 

process as critical as in traditional BI, customers 

may still be worried and this can explain why the 

one-time implementation cost scored higher then 

subscription costs. 

Moving to the opposite side of the table, three 

factors stand out for their low scores: tablet and 

mobile integration, offline access to data and web-

data analysis. 

In light of the precedent analysis pertaining 

ubiquitous access to data, the position reached by 

tablet and mobile integration seems 

counterintuitive. Given the previous considerations 

and the Gartner research (2013), analysing data 

through multiple devices should have been an 

obvious necessary functionality. Nonetheless, our 

research reveals that this factor bears the lowest 

value. On the one hand, the representative sample 

of 36 respondents is not sufficient to draw a 

reliable conclusion and, as aforementioned, the 

results of this study can only provide a direction for 



20 
 

more in-depth research. On the other hand, it is 

possible that SMEs are not fully interested in 

displaying data on multiple devices. If they have a 

business restricted to a limited region or if they 

dispose of only a single office, the utility of having 

data everywhere loses some importance since there 

is no need to bring data around and display it on 

multiple devices. Alternatively, the purchase of a 

cloud BI software might be the first data analysis 

solution adopted by some SMEs. Hence, they 

would be more interested in basic functionalities 

and overlook others of minor importance. In any 

case, these are only tentative explanations and only 

represent the authors’ perspectives.  

Offline access to data is the second least important 

key adoption factor. We have not found this 

element mentioned in the previous literature, but it 

came out during the qualitative interviews. The 

interviewees mentioned that one of the drawback of 

cloud technology is its dependence from the 

availability of the Internet connection. It does not 

represent a problem in most western countries, but 

in the developing world it might be. This is the 

reason why offline access to data has been included 

in the study. However, the result speaks for itself. 

Customers are not interested in accessing data in 

offline mode and this could have been foreseen in 

advance to some extent. Indeed, when the decision 

of buying a cloud BI solution is made, the customer 

is well aware that most of the interactions with the 

software require an Internet connection.  

The last element that scored poorly is the ability to 

analyse data coming from web-sources. Despite 

social networks' popularity having risen and fallen 

in the early 2000s (Ellison, 2007), in the past years 

it has gained considerable attention all over the 

world. Initially these social platforms were used 

only as a means to communicate with friends, but 

later many organizations understood the enormous 

potential behind them. In fact, spontaneous 

customer feedback quickly spreads throughout the 

social networks, blogs, newsgroups and it 

represents a potential source of information for 

Business Intelligence tools (Gamon et al., 2005). 

Recently, the techniques developed to analyze this 

type of unstructured data have made great progress 

and we had expected a different score for this 

factor. Most of the unstructured data belongs to 

web-content and a recent report released by MYOB 

Business Monitor reveals that the overall online 

social presence for small and medium sized 

companies is rather low (Stafford, 2012), even 

though it is on an upward trend. This result can 

partially explain why the analysis of web-data 

sources scored poorly in the questionnaire. 

However, given the benefits achieved by those who 

extensively use social media channels and the 

expected growth in this domain (Milman, 2013), 

the rank achieved by this key adoption factor may 

change substantially in the near future. 

Three factors have been represented separately 

(Figure 4), given their different nature: 

 

 



21 
 

Figure 4. Payment method, customer service and type of preferred solutions 

By looking at the figure 4, the first thing that 

immediately stands out is the staggering preference 

for solutions built towards the industry’s needs. 

Therefore, from the result it’s clear that customers 

prefer to buy applications that deliver analyses only 

relevant to a specific market sector, and not 

industry-wide. Indeed, buying a fit-all product 

offers minimal value. For instance, the type of 

analyses required in the supply chain industry 

differs from the ones needed by insurance 



22 
 

companies. In the former, customer will be more 

interested in spotting opportunities for cost 

reduction throughout the chain, by analysing 

performance level or by adjusting manufacturing 

production according to the different requests 

(Baars et al., 2008). In the latter, insurers can gain 

significant value by detecting fraud through the 

cross-analysis of multiple sources of data, such as 

fraud patterns, accidents, social networks, and 

medical and criminal records (Brat et al., 2013).  

However, given the focus of our study on small and 

medium sized organizations, where the boundaries 

between departments may not be well-defined or 

even exist, the result makes more sense. SMEs 

want a solution that offers benefits for the whole 

company, not merely for a single business 

department. This conclusion could differ for big 

organizations, where the hierarchical structure is 

usually more rigid. 

A second element appears in this study: customers 

prefer to pay the subscription costs either on a 

monthly or a quarterly basis. At first sight this 

factor seems not very important and we have not 

found any theory that discusses this in detail. 

However, it came out two times during the 

explorative interviews and it was included in the 

questionnaire. Common sense would suggest that 

paying a software on a monthly basis is a daunting 

process for a company. Nevertheless, SMEs prefer 

not to lock in with a single product for a long time 

and they reserve the right to cancel the contract 

anytime if the solution does not deliver the 

expected value. It is worthwhile remembering that 

for some SMEs the purchase of a cloud BI solution 

has never been done before and therefore there is 

an element of uncertainty and skepticism. By 

paying on a monthly or quarterly basis, having the 

freedom to cancel the contract without losing 

money becomes an important element and it could 

explain the outcome of the survey.  

Lastly, it does not come without any surprise that 

phone and email are the most preferred methods for 

interacting with customer support. We included this 

question in the survey because various vendors 

extensively promote the availability of the live chat 

and the 24/7 support in their offering. Even though 

our data set is not representative for the population 

of SMEs, there is an initial indication that 

customers still rely on traditional communication 

media. 

In the following paragraph we analyse the results 

for each category, referring to figure 4. As 

aforementioned, we can only draw tentative but 

still interesting conclusions by looking at the 

opposite results. There are three categories that got 

a similar score: Support, Cost of Ownership and 

Functionality-Performance. 

Customer support is an essential part that has to be 

incorporated into the product offering. During 

traditional BI implementations, support is given 

through training initiatives, consulting services and 

other activities (table 3). Hence, there are often 

face-to-face interactions between the supplier and 

the customer. With cloud BI solutions, it’s likely 

that most of the services will be delivered online, 

including the customer support. For this reason, it’s 

crucial to adhere to certain quality standards and 

make sure the customer receive a good service. 

As expected, the cost of ownership revealed to be 

an important area that SMEs pay attention to. The 

previous literature recognizes its importance, as 

shown both in table 1 and 2. In fact, a minor 

financial risk is one of the main benefits offered by 

cloud technology (Finch, 2007; Olszak and 

Ziemba, 2012). This is appealing for small and 

medium sized enterprises (Benlian et al., 2009). 

Hence, to some extent this result confirms previous 

theories. Moreover, by going into the details, it can 

be seen that both the subscription cost and the 

implementation cost achieved similar results. This 

is important because it implies that customers are 

not lured by cheap offerings that address only one 

part of the total cost of ownership, but they pay 

equal attention to all the parts of the financial 

investment. Therefore, cloud BI vendors should 

carefully balance their price regarding the total cost 

of ownership. 

The last area with a relevant score is functionality. 

This result also strengthens the previous analysis, 

where a key adoption factor that belongs to this 

category reached the first position in the survey. 

Functionality is a big area that encompasses items 

quite different from each other. Despite this 

difference, all the elements scored similarly in the 

questionnaire, except handling data in real time 

which is well below the average. Generally, real 

time data comes from web-sources which are 

characterized by a quick spread of information: an 

article, an opinion or a statement. Therefore, 

handling data in real time and the ability to analyse 



23 
 

data coming from web-sources are closely related 

and can explain the poor results achieved by both 

of these factors. In all cases, functionality plays a 

big role for SMEs, and this may not come as a 

surprise because every company is in principal 

interested in this category. However, common 

knowledge about cloud BI would suggest that often 

the functionalities and performance do not reach 

the high standards of traditional BI. This is due to 

two reasons. On average, cloud solutions costs less 

than traditional ones and the overall quality might 

be affected as a consequence. In addition, 

traditional BI solutions are built and customized 

specifically around the needs of the clients and the 

functionalities will be more accurate than a 

“universal” tool. Despite these premises, the results 

of the study indicates that SMEs have high 

expectations in terms of performance and are not 

willing to pay for a cheap solution which does not 

add any specific value to the organization.  

On the opposite spectrum, a category that did not 

score as well as expected is Integration. As 

remarked by the interviewees, one of the very first 

question asked by a potential customer is “How 

well does the solution integrate with my 

applications?”. Thus, we expected a much higher 

consideration here. Nevertheless, by looking only 

at the average result, the conclusions we would 

draw may be misleading. If we pay attention to 

figure 4, the score of each element belonging to the 

integration category differs substantially. The 

general level of integration is positioned well and it 

partially contradicts the previous conclusion about 

Integration. The two key adoption factors that 

lowers the average result are mobile devices 

integration and the ability to handle multiple 

sources of data. We talked about the former in the 

previous section while it is necessary to think about 

the result achieved by the latter. As remarked by 

Gamon (Gamon et al., 2005), the potential sources 

of information for Business Intelligence are 

growing exponentially. Valuable data are found not 

only in traditional spreadsheets, but also in blogs, 

social networks, activity logs and many places. 

Therefore, if an organization is willing to have an 

overview of its customer base, it’s necessary to 

analyze multiple sources of data. There is a 

possible explanation of why this is not the case for 

SMEs. Small and medium sized enterprises still 

rely heavily on data stored in traditional 

spreadsheet (E.g. Excel) and more than 80% of 

them use desktop spreadsheet as the only analytical 

tool in the company (Maguire and Magrys, 2007; 

Ashrafi and Murtaza, 2008). This might partially 

explain why they are not interested in analyzing 

different data formats, but prefer to have a product 

for spreadsheet analysis such as excel. 

Conclusions and implications 

Based on the research findings, there are five key 

adoption factors that scored   4 and therefore are 

classified as the most important: 

 The level of software functionalities (all) 

 The ubiquitous access to data (SME) 

 Responsive answers to customer support 
requests (SME) 

 Handling large amounts of data (all) 

 Implementation cost (all) 
 

Each of these factors addresses a specific area that 

customer pay close attention to during the adoption 

process of a cloud BI solution. The importance of 

handling different amounts of data, the software 

functionalities and the implementation cost confirm 

what has been found in the previous research, both 

for traditional and cloud BI. On the other side, the 

score reached by the other two factors can be 

tightly connected to cloud technology. Providing an 

excellent customer service becomes important 

where the face-to-face interactions are kept at 

minimum. Finally, the increasing spread of data 

and the process of globalization calls for an ability 

to access data everywhere. 

In terms of the categories, the results do not show 

any important dominance in a specific area, but 

rather, there are 3 categories that reached a similar 

level: Support, Cost of Ownership and 

Functionality. This outcome strengthens the 

statement that SMEs look for a software that is 

complete on multiple areas and do not stick to one 

area in particular. 

Practical implications 

This study has several important managerial 

implications, for providing more information and 

knowledge about the key factors for successful 

adoption of cloud BI software in SMEs. Managers 

and head of departments can leverage the findings 

in order to craft better value propositions or 

prioritize areas of development according to what 

customers value the most. We suggest to BI 

suppliers the following areas of discussion:  



24 
 

 It is commonly agreed that cloud software 
will become a cornerstone of almost every 

business (Sheikh, 2011). This revolution 

has already started: from email services to 

traditional systems like CRM, they are 

now being adopted in an On-Demand 

fashion (Sheikh, 2011). The classic 

concept of delivering goods to customers 

is being overcome by the idea of providing 

an ongoing service in exchange of a 

monthly fee (Östling and Fredriksson, 

2012). Therefore, even the sales process 

has to change from the “one-time” selling 

to the development of long-lasting 

relationships, with the attitude of offering 

value to clients on an ongoing basis. One 

of the most effective and successful ways 

to address this issue is to meet the 

customers’ expectations related to the 

specific software. From the 

questionnaire’s analysis it’s clear that 

successful cloud BI products excel in 

multiple areas, from the functionality to 

the reliability, going through customer 

support and a fair price-quality ratio. 

Hence, balancing the resources in the 

appropriate way becomes an important 

matter, avoiding overlooking some areas 

or focusing too heavily on others. 

Moreover, it is critical to shift the 

mentality from selling goods to delivering 

value to customers by building solid 

relationships (Östling and Fredriksson, 

2012). Whenever the customer perceives 

that there is no cooperation, the 

relationship will likely interrupt. This 

means the cancellation of contract and a 

loss of income for the BI provider.  

 BI Vendors’ marketing managers should 
create material that reflects what 

customers really want. In this particular 

case, it’s fundamental to promote a BI 

solution as a comprehensive package that 

delivers high value at a fair price point, 

supported by an excellent customer 

support service. Alternatively, it is also 

effective to mention the five most 

important key adoption factors and stress 

their importance. 

 One of the main reasons that motivate 
small and medium sized enterprises to 

embrace the cloud solutions is the 

perspective of lowering IT costs (Östling 

and Fredriksson, 2012). This common 

perception is supported by real data and 

real companies who did experience a 

decrease of hardware and maintenance 

costs by adopting cloud technology (Perry 

et al., 2009). The importance of costs is 

also reflected by the results of this study, 

but there are other areas of equal 

significance from the customers’ 

perspective. One of them is definitely 

customer support. In the past years, 

outsourcing has been grown steadily and 

became a global phenomenon (Rao, 2004). 

With the improvements of 

telecommunications infrastructure, IT 

operations can be managed efficiently in 

countries where labor cost is lower (Rao, 

2004). Even though the financial benefits 

are immediately clear, the quality of the 

service can damage the reputation of the 

company, if it does not reach high 

standards. Given the previous premise of a 

model shifting towards a customer-

relationship focus, we suggest to think 

carefully before outsourcing the customer 

support activity for cloud BI software. It is 

fundamental that requests are handled by 

experienced people, who knows the 

software well and can make specific 

recommendations to solve any issue. This 

will help building trust in the relationship. 

 

The results of this paper can be leveraged by small 

and medium sized enterprises. They can benefit 

from a more comprehensive understanding of the 

factors that are critical for a successful adoption of 

a cloud Business Intelligence solution. On the 

opposite side, the factors that are not important can 

pose a serious threat to the achievement of 

adequate satisfactory levels. Unlike the majority of 

previous studies, this investigation proposes five 

factors which are specifically tied to the needs of 

small and medium sized enterprises, providing a 

ground for future empirical research in this domain. 

The proposed set of key adoption factors is itself 

important, because it can act as a list of items to be 

checked during the evaluation process (Wong, 

2005). This helps to ensure that essential issues and 

factors are covered when an organizational plan to 

adopt a cloud BI solution for managing information 

(Wong, 2005). Moreover, it is of primary 

importance for Business Intelligence newcomers, 

who are evaluating the purchase of these solutions 

for the first time and may be confused by the 

amount of different alternatives available in the 

market. Therefore, the results of this study can 

provide a guidance and a basis for evaluating and 

comparing different solutions. 

Theoretical implications 



25 
 

This study has also interesting implications for 

future academic research. The literature highlights 

the necessity to study more thoroughly Business 

Intelligence in small and medium sized enterprises 

(Benlian et al., 2009), since the majority of studies 

were focused on big organizations and the same 

results may not be applicable to the SME’s 

landscape. We found two areas where the 

contribution is substantial. 

First and foremost, the results expand the body of 

knowledge related to small and medium sized 

companies. They confirm that the needs of SMEs, 

in terms of BI software, differ from large 

organizations, at least on certain areas (InsideInfo 

Whitepaper): Multiple sources of data, tablet and 

mobile integration and real-time data analysis. 

Moreover, our findings provide new information of 

how SMEs evaluate a certain typology of IT 

software. 

In addition, this investigation adds knowledge in 

connection to an up and coming technology: cloud 

computing. In particular, it focuses on a specific 

context in which this technology is used, for SMEs, 

which has been poorly explored in the past. As 

mentioned by Yeoh and Koronios (2010), in the 

market sectors highly influenced by technological 

innovations, such as cloud services, the value of 

previous discoveries declines over time. By using 

recent primary data, future research on Critical 

Success Factors will be more reliable. 

Suggestions for further research 

The commercial availability of cloud solutions 

dates back only few years. This is the case 

especially for cloud BI, which is still in the early 

phase of the growth curve (Bucur, 2012). The 

implications of adopting a novel technology may 

not be fully understood yet. Therefore, the 

respondents’ answers reflect this particular 

industrial situation. 

Further research on the same topic in three or four 

years’ time is suggested, if not before, when the 

adoption rates of cloud BI software will likely be 

higher and the sample of suitable candidates will be 

larger. It would be interesting to see if, in the 

future, the key factors for adopting a cloud solution 

will differ and help understand the underlying 

motivations. A research focused on the explanation 

of why some factors scored exceptionally well or 

worse could provide further insightful information. 

Nonetheless, the statistical power of this study is 

limited, and it would not be appropriate to dismiss 

all the other factors yet.  

Secondly, the survey represents only the companies 

that have BI software applications already in place, 

for the reasons already provided. Yet, according to 

Gartner research (Gartner, 2013), 55% of 

companies are currently evaluating an adoption of a 

BI system. It could be interesting to find out which 

are the main factors relative to companies who 

have not purchased any cloud BI software yet. 

Therefore the focus could be more on evaluation 

criteria. 

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