Cloud Adoption Readiness Assessment Framework for Small and

Medium Enterprises in Developing Economies

Evidential Reasoning Approach

Mesfin Workineh

, Nuno M. Garcia

and Dida Midekso

IT Doctorial Program Software Engineering track, Addis Ababa University, Addis Ababa, Ethiopia

Lusophone University of Humanities and Technologies, Lisbon, Portugal

Keywords: Cloud Computing, Adoption Readiness, Evidential Reasoning, Multiple Criteria Decision Analysis,

Uncertainty, Small and Medium Enterprises, Organizational Capabilities, Developing Economies.

Abstract: The aim of this paper is to develop Cloud computing (CC) adoption readiness assessment framework for

small and medium enterprises (SMEs) in developing economies. The benefits obtained from CC let the

SMEs in developing economies to consider CC as an alternate technological solution. These SMEs require

adoption readiness assessment framework in order to eliminate complexities during adoption. Most of the

existing frameworks involve technological characteristics to assess adoption readiness and also do not

handle uncertainties of decision makers. But, technological characteristics are not foremost indicates of

adoption readiness. Therefore, this study proposes Cloud adoption readiness assessment framework based

on organizational resources perspective using evidential reasoning (ER) approach. The finding of this study

contributes to the existing CC literature and helps the practitioner to make an informed adoption decision.

Lastly, the effectiveness of proposed framework is shown using case study.

1 INTRODUCTION

Cloud computing (CC) is a model which provides

computing resources as a utility over the Internet.

This technology shifts the trend of owning

computing resources as a product to getting as a

service.

The benefits obtained from CC (Armbrust et al.,

2009; Schubert et al., 2010; Zhang et al., 2010;

Espadanal and Oliveira, 2012) and the paradigm

shift let the small and medium enterprises (SMEs) to

adopt CC as an alternate technological solution.

Cloud computing is an ideal technological solution

for such SMEs with limited capital and human

resource (Surendro and Fardani, 2012).

The potential benefits obtained from adopting CC

outweigh the risks for SMEs (Low et al., 2011;

Madisha and Van Belle, 2011). But SMEs in

developing countries are lagging behind to adopt this

technology (Amponsah et al., 2016; Yeboah-

Boateng and Essandoh, 2014). This is because of the

obstacle they might face like uncoordinated adoption

and lack of inadequate business and technical insight

(Garrison et al., 2012). To coordinate adoption

process they must perform internal Cloud readiness

assessment before adoption (El-Gazzar, 2014). If an

organization is not attained the desired level of

readiness before adoption, the adoption of CC result

into failure (Akande and Belle, 2014). Therefore, it

is necessary to measure the degree of readiness of the

organization in advance (Surya and Surendro, 2014).

Since resources are a key driver and barrier of

technology adoption in developing countries (Molla

and Licker, 2005), it is important to consider the

adoption readiness and likelihood of adoption

success of an organization from a resources

perspective. But, there is a paucity of organizational

capabilities based Cloud adoption readiness

assessment models which guide SMEs. Also very

little is known about IT capabilities needed to

determine adoption readiness and successful

deployment of CC in SMEs (Carroll et al., 2014;

Rockmann et al., 2014). Therefore, for SMEs to

adopt and benefit from CC they need clear resource-

based assessment model (Loebbecke et al., 2012).

Most of the existing Cloud adoption readiness

assessment models are based on technological

characteristics like ease-of-use, perceived usefulness

and so on (Akande and Belle, 2014; Carcary et al.,

Workineh, M., Garcia, N. and Midekso, D.

Cloud Adoption Readiness Assessment Framework for Small and Medium Enterprises in Developing Economies - Evidential Reasoning Approach.

DOI: 10.5220/0006883907470754

In Proceedings of the 13th International Conference on Software Technologies (ICSOFT 2018), pages 747-754

ISBN: 978-989-758-320-9

747

2014; Idris et al., 2014; Kauffman et al., 2014).

Technological characteristics are not real indicators

of preparedness of an organization in the least

developing nations (Workineh et al., 2017). The

foremost indicators of adoption of innovation are

organizational capabilities (Azadegan and Teich,

2010; Iacovou et al., 1995).

The existing studies also do not handle decision

makers (DMs) uncertainty. Evidential reasoning

approach appears to be appropriate multi-criteria

decision-making approach for handling DMs

uncertainty (XU, 2012). That's why this study

proposed Cloud adoption readiness assessment

framework based on ER approach.

This paper is organized as follows: in section 2,

related work

is reviewed. A basic concept of

evidential reasoning approach is presented in section

3. The ER approach for Cloud adoption readiness

evaluation is elaborated in section 4. Section 5 shows

the case study. Section 6 discusses managerial and

theoretical implication and forward future research

directions. The last section gives concluding remarks.

2 RELATED WORK

The trend for the adoption of CC is increasing

significantly from time to time and gained enormous

interest in research (Loebbecke et al., 2012). As a

result of this several empirical studies proposed in

the literature to assess adoption readiness of an

organization (Akande and Belle, 2014; Carcary et

al., 2014; Kauffman et al., 2014; Surya and

Surendro, 2014). These studies assess qualitatively

Cloud adoption readiness of an organization taking

technological characteristics into consideration.

There are very few exceptional studies which

assess Cloud adoption readiness quantitatively only

from organizational capabilities perspective

(Surendro and Fardani, 2012; Workineh et al.,

2017). But these studies do not show the extent of

readiness of an organization quantitatively.

Loebbecke et al. (2012) proposed a method for

assessing the cloud readiness of an organization’s.

The method relies purely upon yes/no criteria and the

decision maker’s judgment. This results in

subjectivity and uncertainty of the DMs.

The above studies do not handle DM uncertainty

and also do not assess cloud adoption readiness

quantitatively from organizational capabilities

perspective only. Hence, Multi-criteria decision

making method which is considered as a better

approach to avoid biases of the DM is required to

assess adoption readiness of CC from organizational

resources perspective. This study is in a position to

explore the applicability of such an approach in the

context of Cloud readiness assessment.

3 CONCEPT OF ER APPROACH

The ER approach employs belief structures to elicit a

decision maker’s preferences and to handle

uncertainties involved during measurement (XU,

2012). The belief degree refers to the degree of

confidence that assessed object has anticipated

measurement grade on a particular criterion.

Suppose A is an object to be assessed, with L

criteria C= {C

, C

…, C

, ..., C

} and N evaluation

grade H = { H

, H

, Hn,..., H

}, with the weights

of criteria are given as ω = {ω

,...,ω

}

where ω

> 0 and ω

is normalized weight.

The assessments of the K alternatives on the L

criteria can be represented using belief decision

matrix (Table 1) with S(C

)) as its element at the

row and i

column, where S(C

)) is given as :

S(C

))={(H

, β

n,i

)), n=1, 2,…, N, i=1,2,…,

L, k=1,2,…, K}.

(1)

Where

0 ≤ β

n,i

)≤ 1 and



≤

i n,

1)(

n,i

) denote the belief degree of alternative A

when assessed to grade Hn for n=1, 2,…, N on

criterion C

Table 1: belief decision matrix.

…

S(C

))

…

S(C

))

…

S(C

))

…

S(C

))

The belief degree unassigned to any specific

evaluation grade (β

), can be represented as:



−=

i n,

ββ

(2)

4 ER APPROACH APPLICATION

The ER approach consists of five major steps (Xu

and Yang, 2005). In this section, these ER steps to

assess Cloud adoption readiness of an organization

were illustrated.

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4.1 Hierarchical Assessment Model

and Index Identification

The criteria identified from literature (Workineh et

al., 2017) are structured hierarchically in figure 1.

The relative weight of these criteria and sub-criteria

is computed using Analytic Hierarchy Process

(AHP) method (Saaty, 1990) and given in table 4.

Five evaluation grades were set by decision maker to

assess each qualitative criterion as shown in table 2.

Figure 1: Cloud adoption readiness Evaluation criteria.

Table 2: Linguistic variables and Evaluation grade value.

Evaluation Grade H1 H2 H3 H4 H5

Linguistic variable NR SR R MR CR

evaluation

grade value

% < 60 60-70 70 -80 80-90 90-100

average 30 65 75 85 95

Where NR=Not ready, SR=Slightly Ready, R=ready,

MR=more likely ready, CR=certainly ready

4.2 Apply Information Transformation

For ER algorithm to aggregate evaluation index, all

lower level criteria need to be transformed to

associated upper level criteria measurement grade.

To get an aggregated evaluation index for the

decision criterion first, the evaluation result on these

main criteria need to be transformed to decision

criterion measurement grades based on a rule or

utility function depending on decision maker’s

preferences. A rule-based transformation is usually

used to transform verbal grades to a different

number of verbal grades.

The quantitative criterion also needs to be

combined with other qualitative criteria in the same

level so that a single aggregated evaluation index

generated for the decision criterion. For instance, the

operational expenditure of an organization needs to

be transformed into five verbal measurement grades

of decision criteria. To transform, a range of values

of criteria need to be defined by expert as shown in

table 3. The transformation process needs to be done

without any data loss (Yang, 2001).

Table 3: Transformation of Lowest Level Criteria

Assessments to Upper Levels.

Sub-criteria/ assessment

grades

R M

Operational expenditure

in million

1 2 3 4 5

implementation

expenditure in million

2 4 6 8 10

Let h

n+1

and h

be the values of upper and lower

evaluation grade respectively, then the distributed

degree of belief for certain quantitative input data

(h) (where h

≤ h ≤ h

n+1

) with respect of upper and

lower evaluation grade is given as:

−

;

−=

(3)

Where

1+n

and

are the degree of belief

associated with respect to upper and lower level

evaluation grade respectively.

4.3 Compute Basic Probability Mass

Decision makers directly assess a given alternative

against each lower level criterion and assign belief

degrees to each assessment grade to measure the

performance in table 4. Then basic probability mass

n,i

, which represents the degree to which the i

criterion is assessed to the n

evaluation grade Hn,

of each lower level criterion computed as:

ininiin

Hmm

)(

n=1, 2 … N (4)

Let m

H,i

be a remaining basic probability mass

unassigned to any individual grade for i

criterion.

Then m

H,i

can be

computed as (Xu et al., 2006; Xu

and Yang, 2005; Yang and Xu, 2002a, 2002b; Yang,

2001):



−=−==

ini

iniiH

mHmm

11)(

βω

(5)

Cloud Adoption Readiness Assessment Framework for Small and Medium Enterprises in Developing Economies - Evidential Reasoning

Approach

749

The unassigned basic probability mass may be

caused due to: weights of the i

criterion

incompleteness of evaluation on i

criterion

(6)

Where:

−1 and

)1(



−

ini

βω

For instance, government readiness has two sub-

criteria national infrastructure and regulation and

policy. Hence before computing probability mass of

government readiness first, the probability mass of

the two sub-criteria has to be computed.

Based on the belief degree assigned for national

infrastructure in table 4 (

1,1

=0,

1,2

=0.7,

1,3

=0.3,

1,4

=0,

1,5

=0) and the relative importance of

criterion (

=0.667), the probability mass computed

using equation 4 as:

1,111,1

1,2

0.4669,

1,3

=0.2001,

1,4

= 0, and

1,5

=0. The

unassigned probability mass is

1,H

= 0.33 where

−1 =1-0.667=0.33 and

=0.

Similarly, the probability mass for Regulation

and policy criterion need to be computed to

aggregate together.

4.4 Aggregating Assessment

The ER algorithm aggregating multiple criteria

based on belief decision matrix and the evidence

combination rule of the Dempster-Shafer theory

(Taroun and Yang, 2011; Yang and Xu, 2002a).

Each row of the decision matrix represents basic

probability mass related to a lower level criterion.

For instance, the basic probability mass assigned to

evaluation grade and remaining probability mass

unassigned to any individual grades for sub- criteria

of government readiness gives decision matrix (M1).













67.000013.020.0

33.00020.047.001

,5,4,3,2,1 Hiiiiii

mmmmmm

Let

1,)1(, nIn

mm = for n=1, 2,...,N,

1,)1(, HIH

mm =

1,)1(,

HIH

mm = , then the combined probability

mass of government readiness based on the values of

the two sub-criteria computed as follow:

)(

1,12,2,11,2,11,1)2()2(,1

mmmmmmKm

HHII

++=

(7)

)2(I

K (0x0.20 + 0.33x0.20 + 0.67x0)

)2(I

K (0.066)

Where

)2(I

K normalization factor which is used to

resolve the conflict and can be calculated as:

2,)1(,)2(

−

=≠=













−=



nntt

tInI

mmk

=1.19 (8)

Hence for government readiness,

0785.0066).19.1)66.0(

)2()2(,1

=== xkm

Similarly the remaining degree of belief can be

calculated as:

)2(,2 I

m = 0.498,

)2(,3 I

m = 0.134,

)2(,4 I

m =0, and

)2(,5 I

m =0

The unassigned belief degree due to the weights

of the criterion can be calculated as:

)(

2,1,)2()2(, HHIIH

mmKm = (9)

263.067.033.019.1

)2(,

== xxm

The unassigned belief degree due to

incompleteness of evaluation can be calculated as:

[

]

2,1,2,1,2,1,)2()2(,

HHHHHHIIH

mmmmmmkm ++=

(10)

Then the remaining belief degree that is not

assigned to any individual grade {H} can be

calculated as using equation 6 as:

=0.263 + 0=0.263

From the final combined basic probability mass

the combined degree of belief for a criterion

calculated as (Yang, 2001; Yang and Xu, 2002a; Xu

and Yang, 2005; Taroun and Yang, 2011):

()

)2(,

−

n=1,2,..., N (11a)

ICSOFT 2018 - 13th International Conference on Software Technologies

750

)2(,

−

(11b)

The distributed belief degree for government

readiness using eq. 11a obtained as:

=0.1065,

=0.6757,

= 0.1818,

=0 and

=0.

Then the final distributed assessment for

government readiness criteria can be represented as:

S(A)={(NR, 10.65%), (SR, 67.57%), (R, 18.18%),

(MR, 0), (CR, 0)}

Let’s say the high level criterion has L sub-

criteria which is assessed with five evaluation grade

H= {H

, H

}. Then the assessment of an

object on this criterion lead to an assessment matrix

M(2) taking basic probability mass

assigned to

evaluation grade and remaining probability mass

unassigned (

) to any grades. To find

aggregated single distributed belief degree, each row

of the matrix need to be aggregated recursively.













LHLLLLL

mmmmmm

,,5,4,3,2,1

2,2,52,42,32,22,1

1,1,51,41,31,21,1

..................

The aggregation carried out first by aggregating

the first row with the second row. Then this result

will be aggregated with the third row. This

aggregation continues iteratively until all rows of the

matrix are combined in this fashion.

The more generalized version of combined

probability represented using the following equation:













1,)(,

1,)(,1,)(,

)1()1(,

iIH

iIn

(12)

Where

1,1

1,)(,)1(

−

=≠=















−=

ntt

itiIniI

mmk

(13)

for i={1, 2,..., L-1}

The unassigned degree of belief can be also

computed as:

{H}:

)(,)(,)(,

iIHiIHiIH

mmm += (14)

Where

)(

1,)(,)1()1(, +++

iHiIHiIiIH

mmKm (15)













1,)(,1,)(,

1,)(,

)1()1(,

iHiIHiHiIH

iHiIH

iIiIH

mmmm

(16)

From the final combined basic probability mass

the combined degree of belief calculated as follow:

()

)(,

LIH

LIn

−

n=1,2,..., N (17a)

)(,

LIH

−

(17b)

4.5 Apply Utility Function

After obtaining aggregated distributed belief

structure, ranking or sorting alternatives based on

their performance may be required. But the

distributed belief degree is not suitable for such a

purpose. Hence, to precisely evaluate the objects

expected utilities of individual evaluation grades,

denoted by U(H

), need to be estimated first.

Utilities to each grade can be assigned as evenly

distributed among evaluation grade or taking the

preference of DMs to a certain evaluation grade, the

utility function assigns a number to an evaluation

grade. For an alternative A, suppose the utility of an

evaluation grade Hn is U(Hn), then the expected

utility of the aggregated assessment is defined :

)()())((

HUAASU



(18)

Note that βn denotes the lower bound of the

likelihood that the alternative A is assessed to Hn.

The upper bound of the likelihood is given by (βn

+β

) (Yang, 2001; Yang and Xu, 2002a, 2002b).

If the assessment is imprecise, a utility interval

can be established for distribution assessment based

on where the unassigned degree of belief goes either

to the least preferred grade or goes to the most

preferred grade (Yang, 2001). Suppose the highest

preferred grade having the highest utility is H

n+1

and

the least preferred grade having the lowest utility is

. Then the maximum, minimum, and average

expected utility of alternative A is given by:

Cloud Adoption Readiness Assessment Framework for Small and Medium Enterprises in Developing Economies - Evidential Reasoning

Approach

751

)())()((

)()()(

max

nHn

HUAA

HUAAU

ββ



−

(19)

)()(

)())()(()(

11min

HUA

HUAAAU



(20)

)()(

)(

minmax

AUAU

avg

= (21)

If all the original assessments S(A) in the belief

decision matrix are complete, then β

(A)=0

and the

evaluation value of an object A is a point value.

Otherwise, the value of an object A is an interval.

5 CASE STUDY

In this section, the result from using the ER

framework to assess Cloud readiness of a public

University in Ethiopia, namely Ambo University, is

considered. Ambo University delivers its services in

four campuses. Currently, it is strengthening ICT

office to improve its service delivery for

academician, students and other stakeholders. All

campuses have dedicated broadband Internet

connection and mini data centre. According to the

ICT director the university has an interest to adopt

public Cloud services for some of the services

rendering to stakeholders to gain cost reduction and

bring agility to services’ it provides. Hence, it is

required to assess its extent of preparedness in

advance for successful adoption. The extent of

preparedness of the university computed and expert

opinion on the result was obtained.

To evaluate the adoption readiness of Ambo

University first, the DMs evaluate the University

against the basic criteria as shown in table 4. Then

distributed degrees of belief of assessments given by

the DMs are fed into a demonstration version of

intelligent decision support system (IDS),

implementing ER approaches (Xu et al., 2006; Xu

and Yang, 2005) and then aggregated results for

decision criteria are obtained as shown in figure 2.

A quantified form of overall distributed

assessment is given as expected utility. The expected

utility is computed based on belief degree of

evaluation grade and based on the evenly distributed

utility among evaluation grades ((U(H

)=0,

U(H

)=25, U(H

)=50, U(H

)=75, U(H

)=1)). Hence,

the expected utility of the assessment or the degree

of readiness of the University is computed as

0.3422. Since the utility for lower level assessment

grade is zero the minimum and expected utilities are

equal.

Figure 2: Distributed assessment of Cloud adoption

readiness for Ambo University.

The extent of readiness of the University in the

second level criteria is clearly shown in figure 3.

Based on the assessment given by DMs the

minimum, an average and maximum utility for

decision variable obtained from IDS as minimum

utility: 0.3422, maximum utility: 0.5206, and

average utility: 0.4314. The interval between

minimum and maximum utility can capture the

extent of adoption readiness of an organization. The

experts in the ICT office of the University also agree

with the validity of the result obtained.

Figure 3: Extent of readiness on main criteria.

6 DISCUSSION

Cloud adoption decision is a strategic decision in

which the decision made at the early time might

affect the organization at the later time. For adoption

decisions to go well at the later time the adoption

readiness must be assessed in advance. A detailed

understanding of Cloud readiness enables an

organization to adopt cloud solutions successfully.

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752

In order to ensure adoption readiness, SMEs in

developing nations must assess its readiness from an

organizational capabilities perspective.

The framework proposed in this study assesses

adoption readiness quantitatively from organization

resources perspective and can handle uncertainty in

DMs. It can also evaluate the extent of readiness of

an organization more precisely and helps the DMs to

make an informed decision before adoption.

6.1 Theoretical and Managerial

Implications

This study can extend the boundary of the Cloud

computing literature by (1) establishing Cloud

adoption readiness assessment framework based on

ER approaches, and (2) Identify organizational

capability based hierarchically assessment model

and the relative importance of each criterion. The

framework can also serve as a Practical guideline to

carry out cloud adoption readiness assessments and

to make an informed adoption decision.

6.2 Limitations and Future Work

The criteria for hierarchically assessment model

were identified only from literature. For

exhaustiveness of the criteria experts from the

industry have to be interviewed. The intention of

measuring adoption readiness is to avoid adoption

failure or to predict the likelihood of adoption

success. So, in the next step hierarchically

assessment model needs to be enhanced in order to

predict the likelihood of Cloud adoption success of

an organization.

7 CONCLUSIONS

For an organization to adopt successfully latest

technology like CC managers need to consider

adoption readiness of an organization in advance.

Adoption readiness assessment is a key step in

feasibility analysis of technology adoption. When

decision-maker is evaluating adoption readiness

against a pre-determined set of criteria, he/she may

face some uncertainty due to lack of decision data

and incomplete information. But none of the

methods proposed in the literature able to address

the issue of uncertainty. Hence, a framework which

can handle such kind of problems and helps DMs to

make an informed decision is needed. This study

proposed Cloud adoption readiness assessment

framework based on ER approach to fill this gap.

The proposed framework helps DMs in identifying

the extent of readiness of an organization in each

criterion and in addressing areas that need to be

improved before adoption more precisely. As result

organization can avoid unsuccessful adoption.

Unlike others readiness assessment framework,

which judging an organization simply as ready or

not ready, the one proposed in this research clearly

shows the extent of readiness of an organization in

each criterion quantitatively. Therefore, it is found

out as an appropriate methodology for the decision

makers to make an informed decision.

Table 4: The index system of Cloud Adoption readiness.

Top layer Dimension Weight Cloud capabilities/ Factors Weight Belief Degree AU

Readiness

IT-

Infrastructure

30.7 % network technologies 88.9% {(H3 , 0.5), (H4 , 0.5)}

enterprise systems 11.1% {(H2 , 0.3), (H3 , 0.5)}

organizational

culture and

strategy

8.8% learning capabilities 20% {(H4 , 0.6), (H5 , 0.4)}

Top management commitments 67% {(H1 , 0.6), (H2 , 0.4)}

vendor management 4.1% {(H3 , 1)}

strategies 8.7% {(H2 , 0.5), (H3 , 0.5)}

Human

4.3% Awareness about CC 13.7% {(H2 , 0.5), (H3 , 0.4)}

Knowledge and skill 62.5% {(H1 , 0.6), (H2 , 0.4)}

Attitude 23.8% {(H2 , 0.25), (H3, 0.75)}

Finance

/Economic

23.4% payment for adopting 50% 5000000

payment for operational 50% 3000000

External

Environment

32.7%

Government readiness

• National infrastructure

• Regulation & Policy

80.0%

66.7%

33.3%

{(H2 , 0.7), (H3 , 0.3)}

{(H1, 0.6), (H2 , 0.4)}

support industries 20.0% {(H4 , 0.8)}

Cloud Adoption Readiness Assessment Framework for Small and Medium Enterprises in Developing Economies - Evidential Reasoning

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REFERENCES

Akande, A. O., Belle, J. P. V., 2014. A Proposed

Framework to Assess and Increase the Cloud

Computing Readiness of Financial Institutions in

South Africa, in: Confluence The Next Generation IT

Summit. Presented at IEEE, pp. 46–52.

Amponsah, R. A., Panford, J. K., Hayfron-Acquah, J. B.,

2016. Factors Affecting Cloud Computing Adoption in

a Developing Country-Ghana: Using UTAUT2 Model.

IRJET 3.

Armbrust, M., Fox, A., Grifﬁth, R., Joseph, A. D., Katz,

R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A.,

Stoica, I., Zaharia, M., 2009. Above the clouds: A

Berkeley view of cloud computing (Technical Report

UCB/EECS-2009-28 No. 4). EECS Department,

University of California, Berkeley.

Azadegan, A., Teich, J., 2010. Effective benchmarking of

innovation adoptions: A theoretical framework for e-

procurement technologies. Benchmarking: An

International Journal 17, 472–490.

Carcary, Marian, Eileen Doherty, Gerard Conway, and

Stephen McLaughlin. 2014. “Cloud Computing

Adoption Readiness and Benefit Realization in Irish

SMEs—An Exploratory Study.” Information Systems

Management 31 (4): 313–27.

Carroll, N., Helfert, M., Lynn, T., 2014. Towards the

Development of a Cloud Service Capability

Assessment Framework, in: Continued Rise of the

Cloud: Advances and Trends in Cloud Computing.

Springer, London, pp. 289–336.

El-Gazzar, R.F., 2014. A Literature Review on Cloud

Computing Adoption Issues in Enterprises, in:

Creating Value for All Through IT. Springer, Berlin

Heidelberg, pp. 214–242.

Espadanal, M., Oliveira, T., 2012. Cloud Computing

Adoption by firms, in: MCIS 2012.

Garrison, G., Kim, S., Wakefield, R.., 2012. Success

Factors for Deploying Cloud Computing.

Communications of the ACM 55, 62–68.

Iacovou, C. L., Benbasat, I., Dexter, A.S., 1995. Electronic

data interchange and small organizations: Adoption

and impact of technology. MIS Quarterly 19, 465-485.

Idris, A. S., Mokhsin, M., Fauzi, F. H. M., 2014. The

Readiness of Cloud Computing: A Case Study In

Politeknik Sultan Salahuddin Abdul Aziz Shah, Shah

Alam, in: International Conference on Computational

Science and Technology – 2014. pp. 1–5.

Kauffman, R. J., Ma, D., Yu, M., 2014. A Metrics Suite

for Firm-Level Cloud Computing Adoption Readiness,

in: Economics of Grids, Clouds, Systems, and Servi-

ces. Springer International Publishing, pp. 19–35.

C. Loebbecke, B. Thomas, and T. Ullrich. 2012.

“Assessing Cloud Readiness at Continental AG” MIS

Quartely Executive, 11 (1): 11–23.

Low, C., Y. Chen, M. Wu, 2011. Understanding the

determinants of cloud computing adoption. Industrial

Management & Data Systems 111, 1006–1023.

Madisha, M., Van Belle, J. P., 2011. Factors Influencing

SaaS Adoption by Small South African Organisations,

in: 11th Annual Conference on World Wide Web

Applications. Durban, South Africa

Molla, A., Licker, P.S., 2005. eCommerce adoption in

developing countries: a model and instrument.

Information & Management 42, 877–899.

Rockmann, Robert, Andy Weeger, and Heiko Gewald.

2014. “Identifying Organizational Capabilities for the

Enterprise-Wide Usage of CC.” In PACIS 2014.

Saaty, T., 1990. How to make a decision: The Analytic

Hierarchy Process. Eur. J. Oper. Res 48, 9–26.

Schubert, L., Jeffery, K., Neidecker-Lutz, B., 2010. The

future of cloud computing: Opportunities for European

cloud computing beyond 2010 .

Surendro, Kridanto, and Adiska Fardani. 2012.

“Identification of SME Readiness to Implement Cloud

Computing.” In International Conference on Cloud

Computing and Social Networking, 1–4. IEEE.

Surya, G. S.F., Surendro, K., 2014. E-readiness framework

for cloud computing adoption in higher education, in:

International Conference of Advanced Informatics:

Concept, Theory and Application. IEEE, pp. 278–282.

Taroun, A., Yang, J.-B., 2011. Dempster-Shafer theory of

evidence: potential usage for decision making and

risk analysis in construction project management.

Journal of Built and Human Environment 4, 155–166.

Workineh, Mesfin, Nuno M. Garcia, and Dida Midekso.

2017. “Cloud Computing as Technological Solutions

for Higher Education Institutions: Adoption Readiness

Assessment Model: Research in-Progress.” In ICCNI,

1–7. Lagos, Nigeria: IEEE.

XU, D.-L., 2012. An introduction and survey of the

evidential reasoning approach for multiple criteria

decision analysis. Ann. Oper. Res. 195, 163–187.

Xu, D.-L., McCarthy, G., Yang, J.-B., 2006. Intelligent

decision system and its application in business

innovation self-assessment. DSSs 42, 664–673.

Xu, D.-L., Yang, J.-B., 2005. Intelligent decision system

based on the evidential reasoning approach and its

applications. Journal of telecom. & IT 3, 73–80.

Yang, J.-B., 2001. Rule and utility based evidential

reasoning approach for multiple attribute decision

analysis under uncertainty. EJO R 131, 31–61.

J.-B. Yang and D.-L. Xu,. 2002b. “On Evidential

Reasoning Algorithm for Multiple Attribute Decision

Analysis under Unceratinity.” IEEE Transaction on

Systems, Man, and Cybernetics-Part A: System and

Humans 32 (3): 289–304.

Yang, J.-B., Xu, D.-L., 2002b. Nonlinear information

aggregation via evidential reasoning in multiattribute

decision analysis under uncertainty. IEEE

Transactions on Systems, Man and Cybernetics, Part

A: Systems and Humans, 32, 376–393.

Yeboah-Boateng, E. O., Essandoh, K. A., 2014. Factors

Influencing the Adoption of Cloud Computing by

Small and Medium Enterprises in Developing

Economies. IJESE 2, 13–20.

Zhang, Q., Cheng, L., Boutaba, R., 2010. Cloud

computing: state-of-the-art and research challenges.

Journal of internet services and applications 1, 7–18.

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