DOI: 10.4018/JGIM.2020040108
Journal of Global Information Management
Volume 28 • Issue 2 • April-June 2020
This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License
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160
A Systematic Literature Review
on IT Outsourcing Decision and
Future Research Directions
Payam Hanafizadeh, Chalmers University of Technology, Gothenburg, Sweden
https://orcid.org/0000-0002-5233-987X
Ahad Zareravasan, Masaryk University, Brno, Czech Republic
https://orcid.org/0000-0002-9477-0676
ABSTRACT
During the recent decades, some academic research on the subject of information technology
outsourcing (ITO) decision has appeared in different outlets, which may impede the use of such
resources and as a result, repetition of research by various researchers is very likely. The purpose
of this paper is then to conduct a systematic literature review (SLR) pertaining to research on ITO
decision. Then, this review intends to 1) classify ITO decision literature, 2) provide a list of factors
affecting ITO decision, and 3) identify ITO strategies. To this end, 91 ITO articles published between
2000 and 2018 in 51 unique journals were reviewed. The results yielded three kinds of descriptive,
relational, and comparative ITO decision studies. The determinants of ITO decisions are classified
into technological, organizational, environmental and user adoption factors. Furthermore, the trend of
studied ITO strategies in the reviewed literature is analyzed, and future sourcing varietals are proposed.
Finally, some insights and future research directions are proposed based on the review results.
Keywords
IT Outsourcing Decision, IT Outsourcing Strategies, Systematic Literature Review, Technology Organization
and Environment (TOE) Model
INTRODUCTION
Information technology outsourcing (ITO) is defined as handing over to a third party, management
of IT assets, resources, and activities for a required result. Examples of outsourced IT activities
include Information systems (IS)/ Applications development, operations, and maintenance,
network and telecommunications management, help desk and end-user support, and systems
planning and management (Rajaeian, Cater-Steel, & Lane, 2017). In this paper, we use ITO as a
generic term that covers various ways to obtain IT resources/ services from external organizations
(through, e.g., offshore outsourcing, IS development outsourcing, Business Process Outsourcing
(BPO), and Cloud computing (CC)).
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One of the main recent ITO research streams relates to ITO decision subject. ITO decision can
be viewed from vendor and client perspectives (Moon, Yao, & Jiang, 2011). ITO decision from
vendor perspective includes but not limited to risks in provided services, contract conflicts, pricing
mechanisms, and so on (Palvia & Palvia, 2017) which is not of concern in this research. ITO research
on client aspect—the focus of this study— can be categorized into five distinct groups, each of which
tries to answer one of the questions: why, what, where, when, how to outsource. The category of “why”
tries to mainly answer “why” firms outsource IT or “what” their related motives and drawbacks are.
The question of “what” to outsource investigates to what degree firms can outsource their strategic or
core competence IT services to external vendors and which sourcing strategy best suits a firm. The main
issue concerned with making the location decision for outsourced activities is “where” to outsource.
Also, the correct timing of outsourcing decisions is categorized under “when” question. Finally, to
understand the outsourcing phenomenon and the strategic changes in the companies comprehensively,
one also has to be aware of “how” outsourcing is done, that is, the process of outsourcing (Hätönen
& Eriksson, 2009). To answer these questions, scholars have investigated different aspects of ITO
decision. However, to the best of authors’ knowledge, there is no classification scheme for ITO
decision extant literature. To address this gap, we ask the first research question:
RQ1: How extant ITO decision literature can be classified?
ITO decision making is a very complicated task with a wide range of factors impacting upon
decision outcome (Zare Ravasan, Hanafizadeh, Olfat, & Taghavifard, 2017). Studies implied that to
make the ITO decision adequately, the chief information officer (CIO) must spend approximately 80%
of his time, for three to six months (D.-H. Yang, Kim, Nam, & Min, 2007). Therefore, identifying
factors affecting proper ITO decisions could lead to more managed IT outsourced services and
processes. Scholars to address this practitioner concern, have investigated a varied set of factors
influencing ITO decisions considering different theoretical bases such as transaction cost economics
(TCE), resource-based view (RBV), technology acceptance model (TAM), and so on. By taking
so many theories into account, researchers have tested a large number of relationships between
independent and dependent variables. Due to its diversity, findings from the overall body of literature
on ITO decision have been difficult to summarize, analyze, and evaluate the contradicting results of
different research. For instance, Hanafizadeh and Zare Ravasan (2018a) reported a positive relationship
between asset specificity and ITO decision while Poppo and Zenger (2002) suggested a negative
relationship. To address this research problem, we proposed our second research question as follows:
RQ2: What are the factors affecting ITO decision?
Furthermore, scholars investigated determinants of different types of ITO like offshore/ nearshore
outsourcing, rural outsourcing, IS development outsourcing, Business Process Outsourcing (BPO),
Cloud computing (CC), Software as a Service (SaaS), platform as a Service (PaaS), and Infrastructure
as a Service (IaaS), and so on. However, there is no assessment of this body of literature to provide
a trend analysis of ITO strategies/ types covered in the prior research. Then, to fill this research gap,
we propose third research question as follows:
RQ3: What are the ITO strategies trend in extant ITO research?
Besides the first three questions, we also present additional findings in a distinct section that
are interesting and provides valuable insights with a focus to answer following research question.
RQ4: What are promising areas for future research?
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To address these research questions, a Systematic Literature Review (SLR) approach was adopted
in this study. SLR is one of the most effective ways of summarizing extant empirical literature
and thereby formulating conclusions by employing different theoretical bases, research methods,
sampling, etc. The need for and importance of literature reviews in the IT discipline has been recently
highlighted because such papers provide valuable insights on prior research and provide a direction
for future studies and can be used to raise academics as well as practitioners’ awareness of extant
research (Rajaeian et al., 2017). With this in mind, this study attempts to provide an SLR to answer
aforementioned research questions.
PRIOR ITO LITERATURE REVIEWS
In this section, a brief review of prior major ITO literature reviews is proposed to illustrate this
review contribution. Among the first literature review studies, we can refer to Dibbern et al. (2004)
in which 84 publications between 1992 and 2000 were reviewed. They examined the research
objectives, methods used and theoretical foundations of the papers in their review. Moreover,
they organized ITO papers based on ITO decision (why, what, which) and implementation (how,
outcome) phases. Fjermestad and Saitta (Fjermestad & Saitta, 2005) reviewed 29 articles on ITO
published between 1981 and 2004. They provided an eight-component critical factors framework
(strategic alignment, contracts, technology, culture, strategic partnership, management support,
governance committees, and economics).
Mahnke et al. (2005) by reviewing 19 ITO articles published between 1995 and 2002 that used
Transaction cost economics (TCE), Resource-based View (RBV) and the Relational View, concluded
that the independent variables from current theoretical explanations are too limited. Furthermore,
they proposed a process model of ITO based on these theories. Gonzalez et al. (2006) analyzed 131
articles on IS outsourcing published between 1988 and 2005. They examined the literature based
on research approach and research perspective (i.e., client, supplier, or both), and finally, 24 most
prolific ITO researchers are introduced. Lacity et al. (2009) focused a review of the ITO literature on
proposing practical implications for firms. They analyzed 191 ITO articles published between 1990
and 2008 around six topics: (1) determinants of IT, (2) ITO strategy, (3) ITO risks, (4) determinants
of IT, (5) client and supplier, and (6) sourcing varietals.
Alsudairi and Dwivedi (2010) categorized 315 outsourcing articles published from 1992 to
2008 by the frequency of articles published by subject area, journal, author, university affiliation,
and citation counts and provided insights based on analyzed data. Lacity et al. (2010) reviewed
164 published empirical papers between 1992 and 2010 and provided a model of ITO decisions
including independent variables associated with motives to outsource, transaction attributes, client
firm characteristics, and influence sources. A year later, by extracting 87 BPO articles published
between 1996 and 2011, Lacity et al. (2011) developed two models of BPO: one model addresses
BPO decisions and another for BPO outcomes. The model of BPO decisions includes independent
variables associated with motives to outsource, transaction attributes, and client firm characteristics.
The model of BPO outcomes includes independent variables associated with contractual and relational
governance, country characteristics, and client and supplier capabilities.
Liang et al. (2016) employed the main path analysis to map the entire field of ITO literature
published from 1992 to 2013 and identified 12 main research streams: motivations for ITO, ITO
decisions, ITO risks, transaction cost economics, client-vendor relationship, vendor’s perspective,
psychological and formal contracts, Application Service Provider (ASP), BPO, offshore outsourcing,
open-sourcing, and multi-sourcing. They provided series of future research paths. In a recent work,
Schneider and Sunyaev (2016) reviewed the CC and ITO literature published before April 2014 and
coded the determinant factors that influence sourcing decisions. They showed that most determinant
factors of sourcing decisions in the ITO context remain valid for the CC context. However, the
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findings for some factors (i.e., asset specificity, client firm IT capabilities, client firm size, institutional
influences, and uncertainty) are inconclusive for the CC and ITO contexts.
Lacity et al. (2016) extended the time frame of their previous review (i.e., Lacity et al. (2010),
Lacity et al. (2011)) by including more recent publications from 2010 to 2014, to investigate the most
current research findings on business services (i.e., ITO and BPO), and to compare the results with the
prior reviews. They reported more in-depth exploration of the direct effects of transaction attributes,
sourcing motivations, client and provider capabilities, and governance on sourcing decisions and
outcomes. They have also studied a broader variety of sourcing decisions, including shared services,
captive centers, rural sourcing, and back-sourcing. Most recently, Rajaeian et al. (2017) suggested
a range of Multiple Criteria Decision Making (MCDM), optimization and simulation methods to
support different ITO decisions. Also, they raised concerns about the limited use of reference design
theories, and the lack of validation and naturalistic evaluation of the decision support artifacts reported
in ITO decision support literature.
In this section, we compare our findings with prior ITO literature reviews to highlight our
contributions. We examined previous ITO literature reviews as above. Some of these reviews exhibit
substantial differences in research focus with the current review. For example, Lacity et al. (2011)
reviewed studies with a focus on the BPO field. We mainly focus on empirical findings from the
research, not on the authors or the publications of ITO research which was followed by Gonzalez et
al. (2006) and by Alsudairi and Dwivedi (2010). The target audience of this review is academics, not
practitioners, as contrasted with Lacity et al. (2009) and Fjermestad and Saitta (2005).
Furthermore, similar to Dibbern et al. (2004) and Mahnke et al. (2005), to answer the RQ1, this
review provides an analysis of theoretical foundations applied in previous research and categorizes
ITO literature in three descriptive, relational, and comparative groups. Identifying frequently applied
theoretical foundations in ITO research could help academics to find out which areas are matured
and where more explorations are required.
The focus of this review in RQ2 is on the findings at the level of dependent and independent
variables and the relationships between them in the specific aspect of the ITO process; i.e., ITO
decision from the client perspective which shares some similar findings with Lacity et al. (2016),
and Schneider and Sunyaev (2016) reviews. ITO benefits, risks, costs, security issues are among
the repeated factors in all models. However, we proposed our model based on TOE framework by
including a new category (i.e., user factors) to incorporate a set of factors influence ITO decisions
from the perspectives of user community (here, stakeholders involved or interested in ITO process
and outcomes such as top-level managers, middle level managers, IT managers, CIOs, and sourcing
managers) which can be considered the unique finding of our model. Providing the factors under TOE
categories adds value to academic community, since a significant part of recent publications were based
on TOE framework (e.g., Gu, Cao, & Duan, 2012; Hanafizadeh & Zare Ravasan, 2017). The proposed
model in this review (see Figure 3) can help academics to find out what major determinants of ITO
decisions are consistently reported in previous research and what factors need more investigations
to fill the research gap. Moreover, this review to answer RQ3, provides a trend analysis of studied
ITO strategies (see Figure 4). Besides, this review, to answer RQ4, proposes future research stream
in new sourcing varietal (e.g., open-sourcing, multi-sourcing, micro-sourcing, and crowdsourcing).
A summary of the ITO literature reviews is provided in Table 1.
SYSTEMATIC LITERATURE REVIEW PLANNING
This review aims to examine the ITO decision literature thoroughly. The study was carried out by
the two authors in student and supervisor role. In case of conflicts, the decision was taken by the
supervisor to resolve it. This research follows the SLR method proposed by Bano and Zowghi (2014)
which is composed of three steps of planning, execution and reporting results. During the planning
phase, we followed a formal protocol for conducting SLR. The protocol contained the details of our
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primary and secondary search strategy, papers inclusion/exclusion criteria in the review, quality
assessment criteria, data extraction strategy, and data synthesis. Then, the steps proposed in the
planning stage is conducted in the execution phase. Finally, findings are provided to answer four
proposed research questions.
Primary Search Strategy
The concept of outsourcing can be understood as a combination of three fields: 1) strategic
management, 2) international business, and 3) supply chain management (SCM) (Hätönen & Eriksson,
Table 1. ITO literature reviews
Citation
Review
Period
Sample
Size
Results
Dibbern et al. (2004)
1992 -
2000
84 ITO
papers
The authors examined the research objectives, methods used and
theoretical foundations of the papers
Fjermestad and
Saitta (Fjermestad &
Saitta, 2005)
1981 -
2004
29 ITO
papers
The authors provided an eight-component critical factors framework
(strategic alignment, contracts, technology, culture, strategic
partnership, management support, governance committees, and
economics).
Mahnke et al. (2005)
1995 -
2002
19 ITO
papers
The authors proposed a process model of ITO based on TCE, RBV
and Relational View theories
Gonzalez et al.
(2006)
1988 -
2005
131 ITO
papers
The authors analyzed the literature, and finally, 24 most prolific ITO
researchers are introduced.
Lacity et al. (2009)
1990 -
2008
191 ITO
papers
The authors organized the literature that answered six practitioner
questions: (1) determinants of IT, (2) ITO strategy, (3) ITO risks, (4)
determinants of IT, (5) client and supplier, and (6) sourcing varietals
Alsudairi and
Dwivedi (2010)
1992 -
2008
315 ITO
papers
The authors categorized the literature by the frequency of articles
published by subject area, journal, author, university affiliation, and
citation counts
Lacity et al. (2010)
1992 -
2010
164 ITO
papers
The authors coded the relationships between ITO decision and
related determinants.
Lacity et al. (2011)
1996 -
2011
87 BPO
papers
The authors developed two models of BPO: one model addresses
BPO decisions and another for BPO outcomes
Liang et al. (2016)
1992 -
2013
798 ITO
papers
The authors identified 12 main research streams: motivations for
ITO, ITO decisions, ITO risks, transaction cost economics, clientvendor
relationship, vendor’s perspective, psychological and formal
contracts, ASP, BPO, offshore outsourcing, open-sourcing, and
multi-sourcing.
Schneider and
Sunyaev (2016)
1992 -
2014
88 CC
and ITO
papers
The authors showed that most determinant factors of sourcing
decisions in the ITO context remain valid for the CC context
Lacity et al. (2016)
1992 -
2014
174 ITO
papers
The authors by extending the time frame of their previous review
(i.e., Lacity et al. (2010), Lacity et al. (2011)), investigated the most
current research findings on business services (i.e., ITO and BPO),
and compared the results with the prior reviews
Rajaeian et al.
(2017)
1995 -
2016
133 CC
and ITO
papers
The authors suggested a range of MCDM, optimization and
simulation methods to support different ITO decisions
This review
2000 -
2018
91 ITO
papers
This review intends to 1) classify ITO decision literature, 2)
provide a list of factors affecting ITO decision, and 3) identify ITO
strategies.
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2009). In the ITO context, the field of IT can also be included. Accordingly, this study reviewed the
literature on ITO decision in databases related to these four fields (i.e., 1) strategic management, 2)
international business, 3) SCM, and 4) IT), including: ScienceDirect, Emerald Fulltext, Springer,
Infor-Sci IGI Global, Taylor & Francis, EBSCOhost, and Ingenta Journals. The search engines Google
Scholar and Scopus were also used to ensure coverage of publications in other databases.
The keywords IT/IS outsourcing/ sourcing, Software as a Service (SaaS), Platform as a Service
(PaaS), Infrastructure as a Service (IaaS), Application Service Provider (ASP), Business Process
Outsourcing (BPO), offshore outsourcing, cloud computing (CC), grid computing, AND decision/
adoption/ acceptance, degree of outsourcing, were used to search the titles and abstracts of the papers.
The publications between 2000 and 2018 were included in the review for two reasons 1) considering
the most recent literature, and 2) publications before 2000 had already been taken into account in
previous review studies (Dibbern et al., 2004; Lacity, Willcocks, & Khan, 2011).
Study Selection Criteria
Different types of peer-reviewed journal publications, with available English full-text versions,
were all considered for the review purpose. Conference papers, master’s theses, doctoral
dissertations, textbooks, chapter books, and working papers were excluded because academics
and practitioners use journals to disseminate new findings. Finally, the papers yielded by the
step above were evaluated through considering their abstracts to exclude the studies that did not
belong to the domain of ITO decision.
Secondary Search Strategy
To ensure that we did not miss any of the relevant studies, we devised a secondary search strategy
by scanning and reviewing all the references of the included studies. All the eligible citations were
surveyed with the inclusion/exclusion criteria described in the previous step. To further ensure that
we did not miss any important and relevant papers, in the final step, we selected following literature
reviews which are highly cited and published in top-ranked journals to screen their citations for
discovering any missed publication:
1. 	 Lacity, M. C., Khan, S. a., & Willcocks, L. P. (2009). A review of the IT outsourcing literature:
Insights for practice. The Journal of Strategic Information Systems, 18(3), 130–146;
2. 	 Lacity, M. C., Khan, S., Yan, A., & Willcocks, L. P. (2010). A review of the IT outsourcing
empirical literature and future research directions. Journal of Information Technology, 25(4),
395–433;
3. 	 Lacity, M. C., Solomon, S., Yan, A., & Willcocks, L. P. (2011). Business process outsourcing
studies: a critical review and research directions. Journal of Information Technology, 26(4),
221–258;
4. 	 Lacity, M. C., Willcocks, L. P., & Khan, S. (2011). Beyond Transaction Cost Economics:
Towards an endogenous theory of Information Technology Outsourcing. The Journal of Strategic
Information Systems, 20(2), 139–157;
5. 	 Liang, H., Wang, J. J., Xue, Y., & Cui, X. (2016). IT outsourcing research from 1992 to 2013:
A literature review based on main path analysis. Information and Management, 53(2), 227–251;
6. 	 Schneider, S., & Sunyaev, A. (2016). Determinant factors of cloud-sourcing decisions: Reflecting
on the IT outsourcing literature in the era of cloud computing. Journal of Information Technology,
31(1), 1–31;
7. 	 Lacity, M., Khan, S. A., & Yan, A. (2016). Review of the empirical business services sourcing
literature: an update and future directions. Journal of Information Technology, 31 (3), 269-328;
8. 	 Rajaeian, M.M., Cater-Steel, A., & Lane, M. (2017). A systematic literature review and critical
assessment of model-driven decision support for IT outsourcing. Decision Support Systems.
102, 42–56.
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Data Extraction
We extracted three types of data: Publication details (e.g., publication date, journal name, country),
context description (research method, sample, ITO strategy, theoretical base), and findings (research
category, factors affecting ITO decision, outsourcing strategy).
Data Synthesis and Analysis
To respond to RQ1, identified ITO decision published papers were classified into three groups
including descriptive, relational, and comparative as illustrated in Figure 1 that will be discussed
more in section “ITO DECISION LITERATURE CLASSIFICATION”.
To answer RQ2, we needed to identify and categorize factors affecting ITO decision in the extant
literature. Since scholars employed different titles for the same concept or factor, we needed to code
factors. Codification process is conducted by two authors, and the results are displayed in Tables 5 to
8. Coding factors were not done on the basis of the variable titles mentioned in reviewed papers, but
they were coded by their inherent meaning and definition. We did our best to ensure that we coded what
the authors actually defined and measured. For example, uncertainty has been used in the literature
with two different concepts including behavioral and environmental uncertainty. Environmental
uncertainty refers to the ambiguity of technology, market needs, and customer requirements. While
behavioral uncertainty refers to the problems arising from vendor performance assessment difficulties
(Watjatrakul, 2005). Such terms appeared in published articles with different wording. For instance,
Alvarez-Suescun (2010) defines uncertainty as problems arising from information asymmetry
and ambiguity between client and its vendor. Considering the fact that it is similar in meaning to
“performance assessment and control issues”, we have organized it into this category. On the other
hand, Gerbl et al. (2014) defined uncertainty as a situation in which there is a dynamic customer need,
and both outsourcing parties are trying to adopt unpredictable changes over the time which we have
coded as “environmental uncertainty”. Similarly, Bush et al. (2008) referred to changes in customer
needs which we have coded as “environmental uncertainty” too. Furthermore, there were cases in
which a single phrase referred to more than one constituting factors. For instance, Gerbl et al. (2014)
referred to “client outsourcing capability” which was included and accordingly coded in our research
as “availability of required skills and competencies” and “client prior outsourcing experience”. The
coding process was conducted by two of the authors of the paper, and at the end of the process,
Figure 1. Positioning ITO decision within the ITO literature
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comparison on coded variables was made to explore dissimilarities and achieve consensuses on the
title of variables. The factors’ brief definition has appeared in Appendix I and the master codes are
illustrated in Appendix II.
Finally, to answer RQ3, an attempt has been made to identify investigated ITO strategies in prior
research which are conducted by counting strategies studied in the publications each year. Based on
research gaps identified in each step mentioned above, future research paths are proposed in section
“future paths of research” to answer RQ4.
SYSTEMATIC LITERATURE REVIEW EXECUTION
By surveying the selected resources, we came up with a total of 315 papers in our results for the
primary search. The papers that were totally irrelevant were filtered after the study selection process.
After screening the papers in this step, 75 studies from primary search results were left. We then
deployed secondary search strategy to ensure the adequacy of our results. We retrieved further 16
studies that were relevant but were missing in our primary search results. After this phase, the total
number of included papers raised up to 91 studies published between 2000 and 2018. Figure 2 presents
the whole SLR execution process.
ITO DECISION LITERATURE CLASSIFICATION
This section attempts to provide an answer to the RQ1. Identified ITO decision papers are categorized
into three main categories including descriptive, relational, and comparative (adapted from a review of
Hanafizadeh et al. (2014)). The descriptive group refers to studies that explore the characteristics and
attitudes of ITO clients, barriers to adoption, and the appealing features that lead to an ITO decision.
These studies rely on both primary and secondary evidence to describe the nature of ITO decision, but
they do not seek out to explain or theorize the relationships between the various factors influencing
the decision. The relational studies which have attracted increasing attention in recent years intended
Figure 2. SLR execution process
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to figure out how different factors that affect ITO decision interact with each other while they exert an
influence on ITO decision. The main distinguishing feature of these studies compared to descriptive
studies is that they strive to explain and predict the phenomena of ITO decision using models and
theories. The comparative group of studies which have been neglected in the literature investigates
ITO decision by concentrating on comparisons among key variables. These key variables can be
represented by three groups of studies: context, service/process type, and ITO level. The impetus
for comparative studies is that the ITO decision is likely to vary based on economic, cultural, social,
political, technological, and developmental variables and strategic nature of services and different
levels of client ITO level. The majority of the extant ITO decision literature were devoted to relational
studies (51%), followed by descriptive (33%) and comparative (16%) (See Appendix II). A total of
51 journals published 91 articles on ITO decision between 2000 and 2018. Table 2 presents the list
of our search output according to the journals (only journals with three or more articles reported).
Our findings (Appendix II) disclose that the 91 reviewed publications were conducted in 22
unique countries. Table 3 presents the list of our search output according to the contributed country
(only countries with three or more articles reported).
Furthermore, our collection consists of 54 surveys (59%), 30 case studies (33%), and seven
conceptual models (8%). Further interpretation of the categories and values are provided in the
following sections.
Descriptive Studies
The primary focus of these studies includes ITO motives, ITO risks, and ITO drawbacks. Following
is an overview of some exemplary studies of the descriptive type.
Table 2. ITO studies according to journal titles
Journal Title Count
International Journal of Information Management 12
Information & Management 5
Journal of Global Information Management 4
Information Systems Frontiers 4
Journal of Enterprise Information Management 4
Journal of International Management 4
The Journal of Strategic Information Systems 3
Journal of World Business 3
Table 3. ITO studies according to contributed countries
Country Count Country Count
USA 20 India 6
Germany 11 Japan 4
Multi-national 11 France 3
Taiwan 9 Iran 3
UK 7 Saudi Arabia 3
China 6 Spain 3
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By adopting the TOE framework as a theoretical base, in their qualitative exploratory study,
Alshamaila et al. (2013) used semi-structured interviews to collect data in 15 different small-medium
enterprises (SMEs) and service providers in the northeast of England. The main factors identified as
playing a significant role in SME adoption of cloud services were: technological (relative advantage,
uncertainty, compatibility, complexity, trialability), organizational (size, top management support,
innovativeness, prior IT experience), and environmental (industry, market scope, supplier efforts
and external computing support). Other studies merely focused on a narrower context to describe
ITO decision. For instance, Brender and Markov (2013) focused on risk and control analysis with a
sample of Swiss companies concerning their prospective adoption of public cloud services. In another
research surveying 329 Spanish companies, Gonzalez, Gasco, and Llopis (2009) proposed top 10
reasons and 11 main risks of ITO and compared the ranking results in 2001 and 2006. Based on their
findings, no dramatic changes seem to have taken place in the interval period. Owing to the fact these
studies focused on a specific context, in spite of providing an in-depth insight into that context, there
is a slim chance of generalizing the results to other industries, countries, etc. In descriptive studies
class, ITO motives or benefits such as scalability, cost savings, access to new technologies, enhanced
maintenance and upgrade, focus on core competencies, and flexibility (Kedia & Lahiri, 2007; Lee,
Chae, & Cho, 2013) are discussed more in comparison with ITO risks and enablers.
Relational Studies
The relational studies intend to figure out how different factors that affect ITO decision interact with
each other while they exert an influence on the decision. The dominant theories in this category are
depicted in Table 4 which is further discussed in the following.
Transaction Cost Economics (TCE) has been the most frequently appropriated theoretical
framework for the study of ITO. TCE is a theory that addresses make-or-buy decisions explicitly,
so it has been viewed as a strong theoretical base for analyzing ITO decisions. TCE has enjoyed an
abundance of empirical and theoretical academic attention in other disciplines, which may have also
influenced its appeal to ITO researchers (Lacity, Willcocks, et al., 2011). Barthélemy and Geyer
(2005) were amongst the first scholars who used this theory to examine some of the determinants
of the outsourcing versus quasi-outsourcing decision. Utilizing 160 primary data collected in
France and Germany, they proposed that the decision is strongly influenced by both internal (i.e.,
asset-specificity, size, and internal organization of IT) and external (i.e., institutional environment)
determinants. It should be noted that the TCE-based ITO research has led to inconsistent and even
unexpected results within studies (Lacity et al., 2016). As an example, Aubert et al. (2004) found the
positive influence of asset specificity on the ITO decision while Poppo and Zenger (2002) exposed a
negative influence. Miranda and Kim (2006) hypothesized an adverse impact of uncertainty on the
proportion of the IT budget being outsourced, but they found a positive relationship contrary to their
hypothesis. Several explanations have been proposed for such mixed results such as research methods,
boundary conditions, TCE assumption violation explanations, and alternate theory explanations
(Lacity, Willcocks et al., 2011). Among the most cited TCE based factors affecting ITO decisions,
we can refer to asset specificity, uncertainty, measurement difficulty, transaction frequency, external
production cost advantage, and opportunism.
Given the limitations of the TCE model which covers just transaction-related economic and cost
parameters in ITO decisions, some scholars employed Resource-Based View (RBV) and Resource
dependence theory (RDT). The RBV is regarded as a valuable theoretical framework for analyzing
the influence of both firm- and process-level factors such as internal capabilities in ITO and the
strategic importance of the process on the ITO decision. The RBV views the firm as a bundle of
assets and resources that can bring about a competitive advantage if employed in distinctive ways.
The RBV fulfills a significant in the study of ITO as a superior performance that can be achieved in
organizational processes relative to competitors would explain why such processes are performed
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internally (Gerbl et al., 2014). While a RBV focuses on the firm completing an internal analysis of
capabilities, the RDT recognizes that all organizations are dependent upon the external environment.
TOE framework has been employed in seven of the reviewed studies. This model has been
proposed by Tornatzky et al. (1990) to explain the process of innovation in the context of an enterprise.
It takes into account three features of an enterprise that impact upon the adoption of innovation as
technology, organization, and environment. The technology context refers to the internal and external
technology relevant to the organization and the technologies that are available for possible adoption.
The organization context refers to the descriptive characteristics of the firm (i.e., organizational
structure, firm size, managerial structure, the degree of centralization), resources (human and
slack resources), and the process of communication (formal and informal) among employees. The
environment context is comprised of the market elements, competitors, and the regulatory environment.
The first application of TOE in ITO decision dates backs to Koong’s et al. (2007) work in which they
categorized factors affecting US organizations’ ITO to international vendors under the TOE model
categories. Afterwards, other scholars used TOE for investigating factors affecting new emerging
strategies such as CC in England (Alshamaila et al., 2013) and Taiwan (Hsu, Ray, & Li-Hsieh, 2014;
Lin & Chen, 2013; Low, Chen, & Wu, 2011) contexts. Some scholars tried to merge TOE with other
models such as Human-Organization-Technology fit (HOT-fit) to provide a more inclusive model
(e.g., Lian, Yen, & Wang, 2014).
Diffusion of Innovation (DoI) is a prominent adoption model in IS literature. It proposes five
attributes that explain the adoption of innovation in an organization. They are: 1) relative advantage,
the extent to which an innovation is superior to its previous generation; 2) compatibility, the degree
to which an innovation can be assimilated into the existing business processes, practices, and value
systems; 3) complexity, the level of difficulty to use the innovation; 4) observability, extent to which
the innovation is visible to others; and 5) trialability, the ease of experimenting with the innovation.
DoI is a popular model for investigating the adoption of innovation in organizations and especially
can explain the individual level adoption process in the pre-adoption and adoption-decision stages
of innovation adoption. However, it has received substantial criticism in its application at an
organizational level (Hameed, Counsell, & Swift, 2012b). One of the major limitations of DoI is
that it applies an individualist level and ignores the influence of organizational and environment
factors (M. K. O. Lee & Cheung, 2004). Furthermore, DoI does not address the full implementation
process of IT. The model provides no rationale for determining whether the innovation is put into
use by the adopter. However, there is only two previous research in the field of ITO decision using
this theory (Oliveira, Thomas, & Espadanal, 2014; Y. Wu, Cegielski, Hazen, & Hall, 2013) which
deserves more studies in the future.
Technology Acceptance Model (TAM) is an adaptation of Theory of Reasoned Action (TRA)
introduced by Davis (1989) and aims at predicting user acceptance of IT and explains the behavior
of individuals in IT acceptance. TAM hypothesizes that IT adoption has two perceived attributes that
influence user adoption, namely “perceived usefulness” and “perceived ease of use”. Afterward, the
original TAM has been evolved into TAM2 (Venkatesh, 2000), Unified Theory of Acceptance and
Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003), and TAM3 (Venkatesh
& Bala, 2008). The original TAM has been used in ITO decision literature by Wu (2011a) for SaaS
and by Benamati and Rajkumar (2008) for software development outsourcing decision. Although
evolved models of TAM have been used in IT innovation adoption, such studies are considered as
ITO decision. Among factors that can be investigated based on the TAM2 and TAM3, we can refer
to subjective norms, job relevance, output quality, result demonstrability, personnel image and
previous experience. Additionally, based on UTAUT, performance expectancy, effort expectancy,
social influence, facilitating conditions, and demographic factors such as age, gender, experience,
and voluntariness of use could be proposed.
Agency theory argues that, in a principal-agent relationship, the agent (the external
vendor) cannot entirely implement the goals set up by the principal (the client). Although
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agency theory was initially conceptualized at the individual level of analysis, it has previously
been applied to understand principal-agent conflicts in inter-firm relationships such as
outsourcing alliances. The concept of information asymmetry is central to principal-agent
models: the agent is assumed to possess private information that the principal is only able to
acquire with added cost and effort. Choudhury and Sabherwal (2003) have found that agency
problems are indeed more pronounced in outsourced software development projects relative
to internal projects, reinforcing the assertion of information asymmetry among clients and
vendors in ITO arrangements. Therefore, the extent to which a client can overcome such
information asymmetry at the project level should influence the degree to which ITO is
considered viable by client IT managers. Tiwana and Bush (2007) in their studies, using
agency theory investigated the effect of project outcome measurability and vendor behavior
observability on ITO decision.
In the relational studies category, some scholars have merely tried to study the factors affecting
ITO decisions by using a distinct theory rather than intending to propose a more comprehensive model
based on two or more supporting theories. For instance, Chou et al. (2006) using social capital theory,
investigated a high-tech company in Taiwan context and explored the effects of network ties, network
configuration, appropriable organization, trust, norms, obligation, identification, shared codes and
language, and shared narratives on ITO decision.
In other studies (e.g., Jayatilaka, Schwarz, & Hirschheim, 2003; Tiwana & Bush, 2007),
knowledge-based theories and related factors such as knowledge availability in client/vendor side,
requirements specifiability or requirements volatility were explored. However, it can be implied that
ITO could lead to knowledge loss in client side because the client might lose skills and knowledge
required to do the tasks internally throughout the ITO period. However, such arguments call for
empirical investigations on longitudinal datasets.
Based on the review results, it can also be claimed that 12 out of 45 relational studies (see Table
4) have employed no specific theory in their proposed conceptual model and suggested a set of factors
affecting ITO decisions without using any ground theories or frameworks.
Table 4. Distribution of papers by underlying theories
Theory Count Theory Count
Transaction cost economics (TCE) 12 Social capital theory (SCT) 1
Resource-based view theory (RBV) 7 Competitive forces 1
Technology-Organization-Environment (TOE) 7 Dunning’s eclectic theory 1
Technology Acceptance Model 6 Social cognitive theory 1
Risk -benefits framework (RBF) 4 Opportunity Risk framework 1
Institutional theory 3 Theory of Reasoned Action (TRA) 1
Innovation diffusion theory (DoI) 3 Organizational capability theory 1
Agency theory 3 Relational Exchange Theory 1
Resource dependence theory (RDT) 2 Two-factor theory (enablers/ inhibitors) 1
Strategic Management Theory 2 Production costs economics (PCE) 1
Knowledge-based perspective (KBP) 1 Theory of Planned Behavior (TPB) 1
Corporate Social Responsibility 1 Institutional Economics 1
Incomplete contracts theories 1 NOT specific theory 12
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Comparative Studies
The first research in this category during the period is the study of Barthélemy and Geyer (2005) in
which they conducted a cross-sectional survey for collecting primary data from a group of 160 large
French and German firms to examine some of the determinants of the outsourcing versus quasioutsourcing
decision. They showed that the decisions were strongly influenced by both internal (i.e.,
asset- specificity, size, and internal organization of IT) and external (i.e., institutional environment)
determinants. Moreover, they found that IT quasi-outsourcing more frequently occurred in Germany
than in France. Although they surveyed a good sample size with a variety of industries covered, the
generalizability of their results are limited due to the following reasons: 1) they solely focused on
the outsourcing versus quasi-outsourcing alternative, 2) they merely addressed the two developed
countries, and 3) their research was cross-sectional, and it is impossible to determine whether firms are
increasing, decreasing, or keeping their current ITO level. In another research, based on the innovation
characteristics from the DoI theory, and the TOE framework, Oliveira et al. (2014) investigated the
determinants of CC adoption in the manufacturing and services sector using a surveyed data from
369 firms in Portugal.
Another set of comparative studies probed the effect of service/process type. For instance,
Benlian et al. (2009) investigated SaaS adoption of three candidate ERP, CRM and Office
application in Germany. In the third set of studies in this category, the effect of independent
variables on the dependent variable (ITO decision) for different ITO levels or ITO strategies
has been explored. For instance, Gewald and Dibbern (2009) examined the effect of perceived
benefits and perceived risks on ITO decisions in three kinds of banks: 1) ‘‘Pro’’ The bank
has already outsourced the process. 2) ‘‘Neutral’’ The outsourcing option is currently under
investigation. 3) ‘‘Contra’’ The bank decided against outsourcing the process. The results implied
difference among three categories regarding perceived benefits and risks. In another study,
using questionnaires from 106 hospital CIOs in Taiwan, Lian et al. (2014) investigated the level
of human (Chief Executive Officer (CEO) innovativeness, perceived technical competencies),
technology (security, complexity, compatibility, costs), organizational (relative advantage, top
management support, adequate resource, benefits), and environment (Regulations and government
policies, perceived industry pressure) factors in four types of innovator, early adopters, majority,
and rejecter hospitals. Hospitals that have completed the development of CC technology are
considered as innovators. Hospitals that are currently developing their CC platform are regarded
as the early adopters. Most research subjects are still assessing the need to adopt this new platform
and hence, they will be grouped under the “majority” category. Finally, rejecters are those who
have decided not to adopt CC. This study indicates that there is a significant difference among
hospitals about different outsourcing level. For example, there is a significant difference between
innovators and rejecters regarding perceived industry pressure.
FACTORS AFFECTING ITO DECISION
This section attempts to provide an answer to the RQ2 regarding identifying factors that impact upon
ITO decision. ITO decision can be viewed as a multi-faceted concept (Hanafizadeh & Zare Ravasan,
2017). Tornatzky et al. (1990) introduced technology, organizational, environmental (TOE) model
to model factors that affect technology-based innovations. Organizational level adoption models like
ITO decision models might embrace user acceptance related factors which mainly arises from user
behavior-centric theories such as TAM, TRA, and TPB (Hameed et al., 2012b). Accordingly, based
on the original TOE model and by including new user category, factors affecting ITO decision are
classified into four categories of technology, organizational, environmental and user (TOEU). TOE
has been selected here, for the following reasons:
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1. 	 There are theoretical models which focus on specific aspects of ITO decisions (i.e., Transaction
Cost Economics (TCE), Resource-Based View (RBV), Diffusion of innovations (DoI), etc.).
TOE has more holistic and multifaceted view on ITO decisions which consider technological,
organizational and environmental factors simultaneously (Hanafizadeh & Zare Ravasan, 2017);
2. 	 Given the capability of the TOE model in covering a wider set of factors affecting adoption
decisions, its application in the ITO decision studies has been suggested in previous research
(e.g., Lian et al., 2014; Low et al., 2011);
3. 	 Although various factors affect ITO adoption, all these factors can be classified in technological,
organizational, or environmental contexts (Côrte-Real, Ruivo, Oliveira, & Popovič, 2019;
Hanafizadeh & Zare Ravasan, 2018b). Thus, it is feasible to apply the TOE model to categorized
ITO decision determinants.
The relationship coding scheme between these independent factors (i.e., TOEU factors)
and dependent factor (i.e., ITO decision) (see Tables 5 to 8) assigned four possible values to the
relationships: ‘+1,’‘-1,’ ‘0,’ and ‘NT’. We coded a ‘+1’ for positive relationships, ‘-1’ for negative
relationships, ‘0’ for relationships that were studied but not empirically significant. Positive
relationships signified when higher values of an independent variable were associated significantly
with higher values of a dependent variable. Negative relationships indicate a time when higher
values of an independent variable were significantly associated with lower values of a dependent
variable. If the study was quantitative, P<0.05 was used as the requirement for a significant positive
or negative relationship. If the study was qualitative, the authors’ strong argument for a significant
positive or negative relationship was not taken account. Also, an ‘NT’ was used for not empirically
tested relationships. Besides, studies which have just proposed a conceptual model or where there is
no consensus on the relationship between independent and dependent variables due to, for instance,
the type of service (i.e., strategic, propriety) or different countries and contexts are assigned to this
category. Furthermore, to extract concise, meaningful, and helpful findings across the literature,
we counted the number of times a relationship between an independent and dependent variable was
studied and the number of times it was found to be positively significant, negatively significant,
insignificant, or that a relationship not tested or not resulted in a unique result. We created a model
by extracting the most frequently examined variables that produced consistent results across studies,
using the rules that the variable had to be empirically examined at least five times and that at least
60% of evidence had to be consistent.
Technology Factors
Technology category refers to innovation or technology characteristics (ITO in here) which influence
the decision to adopt. These factors are summarized in Table 5. Among these factors, only two
factors of perceived benefits and perceived risks involve detailed items which are all embraced under
their associated factors. Perceived benefits include items such as scalability, cost saving, access to
new technologies and methods, enhanced maintenance and upgrade, focus on core competencies,
and flexibility (Kedia & Lahiri, 2007). Also, perceived risks incorporate financial, performance,
psychological, strategic, economic and political risks (Gewald & Dibbern, 2009; Graf & Mudambi,
2005). As can be seen from the Table 5, nine out of 18 identified variables in this category have been
empirically examined at least five times and for eight cases, we observed at least 60% of consistency.
Regarding our review, perceived benefits of ITO was the most studied factor in this category which
provided strong, consistent results. 34 out of 40 studies found a positive relationship between perceived
ITO benefits and ITO decision. Benefits can be divided into two main sections including tangible
and intangible. Tangible benefits are those directly related to gain dollar figure in organizations such
as time and cost savings. Intangible benefits are those difficult or sometimes impossible to quantify.
Intangible benefits include the focus on core competencies, access to specialized resources, quality
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improvements and so on (Lacity & Willcocks, 2000). Both tangible and intangible benefits affect the
decision to adopt ITO and can be utilized in this context.
Perceived risks are the second most widely studied factor in this category in which 22 out of 24
studies suggested negative effects of that on ITO decision. Perceived risks have been found to influence
adoption behavior in various ways. Previous IS research indicated that perceived risk influences the
adoption decision of new IT systems and innovations (Gewald & Dibbern, 2009). Taken together, the
influential role of perceived risks in adoption processes in general and in ITO decisions, in particular,
is widely supported with strong consistency at the organization level.
Complexity is another major determinant of ITO. Based on the TCE theory, complexity refers
to the degree to which an innovation is perceived as difficult to use and understand in the business
environment (Lin & Chen, 2013). Regarding ITO, complexity concerns the extent to which the
outsourcing task is sophisticated and difficult to standardize, requiring a great deal of specialized
knowledge to undertake the task. In simpler terms, complexity shows the difficulty in adopting an
innovation, which may be presumed as the inverse of perceived ease of use in technology adoption
research (Davis, 1989). The ITO complexity can arise from contract governance issues, pricing
mechanisms, adopting and using new outsourcing options like cloud computing and Software as a
Service (SaaS) (Hanafizadeh & Zare Ravasan, 2018b).
12 out of 15 studies proposed a positive relationship between perceived ITO security and ITO
decision. When the IT function is outsourced to an external service provider, the organization no
longer retains full control of information security whereas full control is retained when the IT function
Table 5. Relationships between technology variables and ITO decisions
Id Factor Title +1 -1 0 Sum* NT Total
T01 Perceived benefits 34 1 5 40 9 49
T02 Perceived risks 0 22 2 24 6 30
T03 Performance assessment and control issues 0 8 5 13 5 19
T04 Perceived complexity 0 10 4 14 1 15
T05 Perceived security 12 0 3 15 4 19
T06 Consistency 10 0 5 15 1 16
T07 Asset specificity 7 1 1 9 4 13
T08 Service strategic nature 0 2 5 7 2 9
T09 Perceived cost 0 11 1 12 3 15
T10 Data protection issues 1 3 0 4 0 4
T11 Perceived privacy 4 0 0 4 4 8
T12 Integration issues 0 2 2 4 1 5
T13 Trialability 2 0 2 4 3 7
T14 Lack of proper and in-place standards 0 1 2 3 0 3
T15 Transaction frequency 1 0 1 2 1 3
T16 Service/ Process complexity 1 1 1 3 0 3
T17 Business process IT detachability 1 0 0 1 0 1
T18 Business process modularity 1 0 0 1 0 1
* In the sum column, the bolded text refers to factors that have been empirically examined at least five times; the italicized text denotes at least 60% of
consistency.
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is provided in-house. With this regard, it is very critical that organizations make sure that vendors
have adequate security/privacy measures in place.
Concerning our review, consistency is another major determinant of ITO decision. Consistency is
the degree to which ITO is perceived as consistent with the existing values, strategies, legacy systems,
and needs of potential adopters (Alshamaila et al., 2013). When, low level of consistency perceived
in an organization aimed at ITO, negative resistances arise which can adversely affect ITO outcomes.
Asset specificity is another major determinant of ITO decision which suggested moderately
consistent level on the relationship between asset specificity and ITO decision. Asset specificity is the
degree to which assets needed for the transactional relationship are not transferable to other activities
or relationships. In term of outsourcing, low asset specificity indicates that assets required for the
process do not have to be tailored to a given transactional relationship and both vendors and clients
can easily find other partners. The results between the degree of asset specificity and outsourcing level
are not conclusive in previous research. For example, in the presence of specific assets, Barthelemy
and Geyer (2005) observed that firms used subsidiaries instead of relying on ITO. Loebbecke and
Huyskens (2006) and Aubert et al. (2012) could not find support for the role of asset specificity in the
ITO decision. On the contrary, Aubert et al. (2004) and Hanafizadeh and Zare Ravasan (2018a) found
asset specificity to be positively linked to ITO. Ellram et al., (2008) argued that the more specific
the assets required to support an activity, the less likely that firm is to outsource that activity. Such
activities are not good candidates for ITO because the firm could develop a high level of dependence
on the supplier, and the supplier could then show opportunistic behavior, raising prices, reducing
service levels, or the like. In cases where the supplier owns the specific assets, the supplier is subjected
to the potentially significant risk associated with accepting the activity.
With regard to our review, service strategic nature is not significantly related ITO decision.
According to Gilley and Rasheed (2000), there are two types of outsourcing as peripheral and core
process/service. Outsourcing peripheral activities allow firms to focus on those activities they do
best. This allows the firm to become more innovative and agile in its core domain as well as help
improve the quality of outsourced service and diminishing associated costs (2010). On the other hand,
outsourcing core or near-core strategic activities may lead to declining innovation and reduced firm
performance. One interpretation of this argument might be that the perceived strategic importance
of a process/service to the firm has simply a moderating rather than direct effect on ITO decisions
(Hanafizadeh & Zare Ravasan, 2018b).
Based on the results of our review, perceived cost demonstrated a consistent negative relationship
with ITO decision. From innovations costs perspective, the advantages of each innovation should
exceed the costs of adopting it. Therefore, the costs of innovation have a major bearing on the decision
to its adoption (Lu, Lin, & Tzeng, 2013). In this respect, ITO is no exception. This subject not only
approaches the details related to ITO costs, but also collects issues related to ‘‘hidden’’ costs such
as search cost, transition cost, and beyond-baseline service (Tafti, 2005). In some situations, such as
lock-in of ITO relationship, changes in contracts, unexpected events, and disputes and litigation, the
adoption cost of the outsourcing service becomes a critical concern (Koong et al., 2007). In general,
firms would like to maintain the balance between both benefits and costs associated with the adoption
of innovation (Teo, Lin, & Lai, 2009). Regarding ITO, costs include the total cost of ownership, cost
of excess capacity of resources, integration costs, future service innovation costs, labor costs related
to service maintenance, cost of potential future service provision changes and so on.
Organizational Factors
Organizational factors are related to organizational features and characteristics which a new technology
or innovation (ITO in here) adopting firm possesses. Table 6 summarizes the 25 identified factors
in the organizational category. In IT adoption literature, managerial characteristics with a focus
on managerial features and demographic information have been considered and studied under the
organizational category or even under a distinct category of managerial characteristics. Among
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these factors, we can refer to manager’s age, sex, education level, managerial tenure, and attitude
towards innovation (Chuang, Nakatani, & Zhou, 2009). Based on the results of Table 6, regarding
ITO decision, manager’s IT knowledge, tenure, age, sex, and education level have been overlooked
in the extant literature. As can be seen from the Table 6, eight out of 25 identified variables in this
category have been empirically examined at least five times. Among these, for seven variables, we
observed at least 60% of consistency while we have not found such consistency in one variable, i.e.,
technological readiness.
Availability of required skills and competencies is the major ITO determinant identified in this
review. Some previous indicate that a firm with a higher level of IT capabilities and competencies
is more likely to adopt new technology (Hameed, Counsell, & Swift, 2012a; Hsu et al., 2014).
Nevertheless, when discussing ITO, some believe that firms with lower IT capability may be more
likely to outsource, a view diametrically opposed to the findings of some past studies (Hofmann &
Woods, 2010; e.g., Lian et al., 2014; Sultan, 2011) which proposed that organizations with adequate
IT resources are more likely to adopt CC as an option to outsource IT services. However, there is
little evidence to generalize which kinds of firms regarding the availability of required skills and
Table 6. Relationships between organizational variables and ITO decisions
Id Factor Title +1 -1 0 Sum NT Total
O01 Availability of required skills and competencies 11 0 3 14 1 15
O02 Trust 9 0 1 10 2 12
O03 Size 4 1 8 13 0 13
O04 Client prior outsourcing experience 7 0 1 8 2 10
O05 Positive attitude towards ITO 6 0 0 6 1 7
O06 Top management support 11 0 2 13 0 13
O07 Organizational knowledge level 0 2 1 3 1 4
O08 IT department size 3 1 0 4 0 4
O09 Technological readiness (IT infrastructure) 4 1 4 9 4 13
O10 Requirements specifiability 6 0 1 7 0 7
O11 Top management/organizational innovativeness 4 0 0 4 1 5
O12 IT department budget 1 2 0 3 0 3
O13 Bounded rationality of organization 0 0 1 1 1 2
O14 Lack of qualified employees to do the tasks in-house 1 0 1 2 0 2
O15 IT budget development 0 0 1 1 0 1
O16 Internal resistance to ITO 0 2 0 2 0 2
O17 Obligations for ITO 0 0 2 2 0 2
O18 Poor firm-level financial performance 1 0 0 1 0 1
O19 CEO compensation 1 0 0 1 0 1
O20 CIO skills 1 0 0 1 0 1
O21 Knowledge loss 0 2 0 2 1 3
O22 Management style 2 0 0 2 0 2
O23 Vendor management capacity 2 0 0 2 0 2
O24 Business level strategy 0 0 1 1 0 1
O25 Industry type 1 0 1 2 0 2
* In the sum column, the bolded text refers to factors that have been empirically examined at least five times; the italicized text denotes at least 60% of
consistency.
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competencies are more or less likely to outsource. To sum up, it can be suggested that there might be
a U-shaped relationship between the availability of required resources and ITO decision. Meanwhile,
organizations with more or fewer skills and competencies are more likely to outsource rather than
organizations with the moderate level of skills and competencies. Such arguments could be empirically
investigated in the future research.
Results of our review illustrated that trust was another major determinant of ITO, positively affects
ITO decisions. When ITO contracts are initiated, relationship strength may cover the implicit part
of the exchange that leads to setting up patterns of activities in dealing with issues of ITO exchange.
Relationships based on trust between the parties strengthen the bond between clients and their service
providers (Becerra, Lunnan, & Huemer, 2008). Establishing strong, trustworthy relationship results
in effective governance by facilitating mutual understanding between parties (Vivek, Richey Jr, &
Dalela, 2009). There is also evidence showing that outsourcing projects in the 1990s shifted from
contractual to trust-based relationships (Kim, Lee, Koo, & Nam, 2013).
Client prior ITO experience serves as another major determinant of ITO decision. Many ITO
decisions are not independent decisions; instead, they rest on prior ITO experiences (Benamati &
Rajkumar, 2008). There exists a relationship between a firm’s prior knowledge and their understanding
of a new context or situation (Alshamaila et al., 2013). Past marketing research found that customer
satisfaction with prior working experiences with a provider affected their loyalty to that provider. The
strength of the relationship increased with the length of prior experience in such cases (Benamati &
Rajkumar, 2008). In the case of ITO, being familiar with ITO strategies and past positive experiences
can have a direct influence on managers’ ITO perceptions. Several studies have found prior experience
to be important in technology adoption decisions (Hanafizadeh & Zare Ravasan, 2018a), especially
in ITO where, it has been estimated that almost 50% of all ongoing ITO contracts are discontinued in
favor of either using a different vendor or bringing the work back in-house (back-sourcing) (Whitten,
Chakrabarty, & Wakefield, 2010). Consequently, it sounds sensible to claim that organizations’ prior
experience plays a facilitative role in the ITO decision.
Positive attitude towards ITO is another major determinant of ITO decision. In the TPB, the
attitude toward the behavior is proposed to influence the actual behavior, as it integrates different
relevant beliefs about the outcome expectations of the decision options (Benlian et al., 2009). A
decision-maker on ITO thus shapes an attitude based on beliefs about factors such as the cost-benefit
evaluations, complexity, risks, and so forth.
Another major determinant of ITO decision is top management support. According to some
recent studies, top management commitment/support is by far the most important success factor
in ITO (Lacity, 2018). Top management commitment/support provides both encouragement and
resources (e.g., human, time, skills). Regarding ITO, such support is crucial since it is unsettling for
the individual employee’s point of view as their situation is unclear. Their minds might be obsessed
with questions like: Will they be transferred to the outsourcing vendor or even worse? Will they be
made redundant? Such situations consequently stimulate human resources to start looking for jobs
elsewhere. Then, practitioners should seek for engaging top management in ITO decisions and gain
his/her full support (Hanafizadeh & Zare Ravasan, 2018a).
Requirements specifiability is defined as the ease with which project requirements can be
wholly and accurately conveyed to a vendor at the beginning of the project through a formal project
specifications document (Tiwana & Bush, 2007). Low levels of requirements specifiability or in
another term, uncertainty in requirements implies that the organization really does not know what
it wants from the process. Firms, like people, will set resources aside to cope with unplanned
contingencies. In economic terms, this is bounded rationality—there are too many issues for a
human to comprehend and effectively address. The managers’ in-depth understanding of the firm’s
business needs and requirements can encourage a supplier to engage in information sharing and
communication activities. This will also increase the level of collaborative participation between the
client and the supplier. If a firm has a process with uncertain requirements, managers may choose
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to keep that process in house because doing so retains control of unanticipated benefits and costs
(Hanafizadeh & Zare Ravasan, 2017). Meanwhile, by outsourcing such processes, the firm may be
limiting its options or flexibility.
Environmental Factors
Environmental context is the arena in which a firm conducts its business—its industry, competitors,
and dealings with the government (Tornatzky et al., 1990). Environmental factors are believed to
have significant effects on ITO decisions, however, in the context of offshore ITO, more concerns
arise such as political, legal, social, and economic issues. Recently, some scholars have investigated
issues like tax rate, investment motives, market maturity, geo-distance between source and destination
company, and cultural distances between vendor and client as factors affecting offshore ITO initiatives
(e.g., Chen & Lin, 2018; Graf & Mudambi, 2005). Table 7 summarizes relationships between
environmental variables and ITO decisions. As can be seen from the table, six out of 16 identified
variables in this category have been empirically examined at least five times. Among these, for four
variables, we observed at least 60% of consistency while we have not found such consistency in case
of two variables: Partners’ pressure, and environmental uncertainty.
Competitive pressure is the most frequently studied factor in this category which refers to
perceived pressure from business competitors that encourage a firm to adopt new technology for the
sake of maintaining competitiveness. Koong, Liu, and Wang (2007) discussed that businesses with
higher competitive environment are more likely to turn to new technology to achieve a competitive
advantage. Low et al. (2011) for instance, in the context of ITO, asserted that competitors pressure
exerts significant influence on the adoption of cloud computing.
Regulations and government policies is another frequently studied factor in this category. Eight
out of 13 reviewed studies suggested a consistent positive relationship between this factor and ITO
decision. Regulations and government policies mean that governmental support requires a firm
or motivate it to adopt ITO. Regulations and government policies exert an important influence on
location attractiveness (especially in offshore/near-shore outsourcing) including factors such as tax
rates, employment legislation, and government investment in infrastructure, education, and general
skills development. Employment and social regulation have often been used as important levers used
by governments to enhance their attractiveness (Gerbl et al., 2014).
Table 7. Relationships between environmental variables and ITO decisions
* In the sum column, the bolded text refers to factors that have been empirically examined at least five times; the boxed text denotes at least 60% of
consistency.
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Social effects factor is another major determinant of ITO decision with more than 80 percent
consistent results. Social influence can be defined as the degree to which an individual perceives it
is important that others believe they should use a new system. Social influence regarding ITO refers
to employees’ and managers’ intention to adopt ITO based on the influence of people or even firms
around them, which have a positive relationship with ITO decisions.
Cultural distance among partners is another major determinant of ITO decision. The effective
management of an outsourcing contract hinges on the judgment of the partner’s culture (Hanafizadeh
& Zare Ravasan, 2018b). Since the influence of culture is often bidirectional, mutual understanding
of each other’s cultural issues is conducive to better management of the outsourcing relationship as
well. Cultural differences manifest in the form of diverse employee’s values and norms, attitudes
towards technology, customers, interpersonal contact and interaction, and overall role perception
(Graf & Mudambi, 2005). Norms for customer service differ across countries and even firms. For
example, if the organizational culture is hierarchical, then customer service representatives may be
less empowered to take the initiative to solve customer problems. Extant literature on cross-cultural
variations has pointed out how cultural distance may negatively influence the relationship between
outsourcing partners, cross-border knowledge and technology transfer, collaborative learning, and
alliance performance (Kedia & Lahiri, 2007).
User Factors
ITO decision can also be considered from the organizational and user view (Hameed et al., 2012b).
Factors reviewed in the three previous sections (TOE factors) formulate the organizational side.
While the innovation-decision can generally be studied from the user perspective in two ways: 1) user
innovation adoption for personal use (e.g., mobile, e-banking, social networks, online tools) and 2)
user innovation adoption for organizational use (e.g., Enterprise Resource Planning (ERP) adoption).
Concerning the purpose of this research, the first theme is beyond the scope of this paper because
ITO is an organizational phenomenon rather than personal issue or use concern. User in this category
refers to a person or group of them that are supposed to involve in ITO decision as well as stakeholders
involved or interested in ITO process and outcomes (e.g., top-level managers, middle-level managers,
IT managers, CIOs, sourcing managers, etc.). Theories such as TRA, TAM, and TPB have been
used in user adoption studies. Based on the results of this review, user factors in the context of ITO
decision have been neglected in the extant literature. Among these factors, we can refer to perceived
ease of use and perceived usefulness based on the TAM (Benamati & Rajkumar, 2008; Ratten, 2014;
W.-W. Wu, 2011b). However, other factors such as user/employees attitudes towards outsourcing,
past positive/negative experiences, training, employees’ involvement, financial motives, and technical
support could be of great value for research. Table 8 summarizes the list of user factors in the context
of organization ITO decisions. As can be seen from the table, just one (perceived ease of use) out of
three identified factors has been counted more than five times, with a consistency rate of 100 percent.
According to the TAM, perceived ease of use helps determine an individual’s perceptions about the
ease of using a technological innovation (Venkatesh, 2000). Perceived ease of use is a driver of the
adoption of technological innovations (Ratten, 2014). Some technological innovations are hard to
Table 8. Relationships between environmental variables and ITO decisions
Id Factor Title +1 -1 0 Sum NT Total
U01 Perceived ease of use 6 0 0 6 1 7
U02 Perceived usefulness 4 0 0 4 1 5
U03 User innovation 2 0 0 2 0 2
* The italicized text denotes at least 60% of consistency.
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use because of the knowledge required to learn how to use them. In case of ITO, when users have
the competence to use outsourced services easily, they will positively accept that.
Concluding Factors Affecting ITO Decisions
Based on the results of the review, factors affecting ITO decisions yielded 62 factors categorized
into four categories of technology, organizational, environmental, and user (see Figure 3). Also, 375
positive, negative and non-significant relationships between dependent and independent variables were
identified. As explained before, variables that empirically examined at least five times, with at least
60% of consistency have been considered as significant factors in ITO decisions. Positive/negative
evidence of 60 to 80 percent of consistency is denoted with “+/” and evidence of 80+ percent with
“++/--” in Figure 3. For instance, 34 out of 40 cases empirically studied positive relationships between
perceived benefits and ITO decision implied 80+ consistency rate which is depicted in Figure 3 with
“++”. More discussion on findings is provided in the following.
ITO STRATEGIES
The RQ3 is related to ITO strategies in the recent literature. Vast and varied ITO strategies such as
cloud computing (CC), IS development outsourcing, BPO, ASP, and offshore outsourcing are proposed
and applied throughout organizations in the recent years. A brief definition of each outsourcing option
included in the reviewed research is provided below:
1. 	 Cloud computing is a shared computing technology where software and hardware are delivered
as a service through the real-time network, including Software as a Service (SaaS), platform as
a Service (PaaS), and Infrastructure as a Service (IaaS) (Abdel-Basset, Mohamed, & Chang,
2018; Y. C. Lee, 2019);
2. 	 IT operations and maintenance outsourcing as a general term is defined as the significant
contribution made by external vendors in the physical and/or human resources associated
with the entire or specific components of the IT infrastructure (i.e., designing,
implementing and maintaining hardware, software, and network components) in the
client organization (Chakrabarty, 2011);
Figure 3. Conceptual model of the major determinants of ITO decision
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3. 	 IS development outsourcing is outsourcing software development tasks including but not limited
to designing, analyzing, developing, testing, implementing and maintaining the software product
(Chou et al., 2006; Watjatrakul, 2005);
4. 	 BPO refers to an outsourcing relationship where a third party provider is responsible for
performing an entire business function for the client organization (Chakrabarty, 2011);
5. 	 Offshore outsourcing refers to the decision to transfer the provision and management of services
to external service providers outside of the client organization’s home country and also on cruise
ships off territorial waters (Chakrabarty, 2011);
6. 	 ASP is a form of outsourcing where an organization rents generally available packaged software
applications and related services (Chakrabarty, 2011);
7. 	 Micro-sourcing is an emerging outsourcing practice which hands over small, discrete business
functions or applications to many small service providers (B. Lu, Hirschheim, & Schwarz, 2015).
Based on the results depicted in Figure 4, the number of ITO decision publications in each year
has increased from 2001 to 2011, but the trend is diminished after that till the end of 2018. During
the period, cloud computing (with 39) was the most studied outsourcing strategies.
FUTURE PATHS OF RESEARCH
The RQ4 is related to ITO decision promising areas for future research. This section addresses
this research question which provides valuable academic insights by clearly figuring out future
research directions.
The first set of future research propositions is based on of ITO decision literature classification
outcomes in this research (in response to RQ1). A set of diverse theories (e.g., TCE, RBV, TOE)
have been employed to study the ITO decision. These models do not consider user acceptance issues
after outsourcing services, tasks, or processes to an external vendor. However, one of the most
important issues in adopting any sort of innovations such as ITO is the user acceptance (Hameed
et al., 2012b). Therefore, it is required to incorporate and employ more user-centric theories in ITO
studies. Generally, TAM and TRA theories have been deployed in different contexts to study user
behavior in IT innovation. However, TAM and TRA are not good enough to investigate user behavior
when there are coercive forces to use and accept such as ITO decision. In such situations, Brown et al.
(2002) proposed using TPB which makes it feasible to study the factors affecting ITO decision with
a more focused view on user adoption and acceptance. Based on the analysis results on the reviewed
Figure 4. ITO strategies studied during the period
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literature with a focus on applied theories, it can be observed that 34 out of 91 reviewed papers did
not follow well-defined theoretical foundation (see Appendix II). Out of 57 remaining papers, there
is an almost equal split between the number of papers using a single theoretical approach (33) and the
number of papers using multiple theoretical perspectives (24). Early research mainly was confined
to one theoretical perspective. Over time, the use of two or more theoretical lenses in a single paper
became more common (Gerbl, McIvor, & Humphreys, 2016; Gerbl et al., 2014; Messerschmidt &
Hinz, 2013; Safari, Safari, & Hasanzadeh, 2015) which might be interpreted as a response to the
complex and continually evolving nature of ITO. Additionally, regardless of the theories employed,
all the reviewed studies encountered a real limitation concerning using cross-sectional rather than
longitudinal datasets. Meanwhile, it is not clear nor widely investigated in the extant literature whether
outsourcing level is decreasing, increasing or just somehow fixed during the time or facing different
situations. Besides, the effect of ITO strategy, as well as public/private type of the client company,
could be of research value. Finally, almost all studies devoted to developed countries (USA, Germany,
Taiwan, and England) and there is a research gap in: 1) factors affecting ITO different strategies (e.g.,
ASP, SaaS, CC) in less developed countries; and 2) comparing the factors affecting ITO decisions
in more or less developed contexts.
The second set of future research propositions (in response to RQ2) is based on factors affecting
ITO decisions. Regarding the results of the review, user-related factors affecting ITO decisions
are mainly viewed from the CIO or the person responsible for outsourcing viewpoint while other
stakeholders such as service/process owners, company owners, CEOs, and people whose careers are
impacted and even threatened by outsourcing initiatives, all have considerable impact on ITO decisions.
For instance, service/process owners’ perceived risks of losing their job position might lead to reduced
productivity, or deliberate sabotages. Then, investigating factors affecting on user behavior in ITO
decisions with a focus on service/process owners could be future research interests. Furthermore,
human resources change management to enhance outsourcing process and reduce deterrent resistance
could be followed in future research. Also, a list of factors affecting ITO decisions in four categories
is provided in this review, without considering factors’ inter-relationships as our review limitation
which could be followed in future research. Another future research avenue is considering the concept
of IT portfolio outsourcing rather than outsourcing IT services/processes in isolation. Moreover,
exploring the reason behind observed inconsistencies in previous research outcomes could be of
value for research and practice. Some of the inconsistencies might be influenced by context, client
size, or even the selected ITO strategy which should be examined in future research.
The third set of future research propositions (in response to RQ3) is based on ITO strategies. A
set of brand-new ITO strategies have been proposed and adopted in practice. Factors affecting such
ITO decisions are not well discussed and investigated in the academic literature. For instance, ITIL
v3 proposes ITO strategies as in-source, outsource, co-sourcing, multi-sourcing, business process
outsourcing, application service provider, knowledge process outsourcing. Also, Chakrabarty (2011)
proposed approximately 50 ITO strategies. However, as it is clear from Figure 4, a small set of ITO
strategies have been discussed in the extant literature. Meanwhile, investigating factors affecting
such new and emerging ITO strategies are proposed in future research. For instance, regarding the
social, cultural, legal and political challenges that arise in offshore outsourcing cases (Deng, Mao,
& Wang, 2013; Lacity et al., 2009), investigating and understanding factors affecting such decision
would be of great value. To overcome these difficulties and barriers in offshore outsourcing, the
concept of near-shore has emerged recently, which considers outsourcing to neighboring countries
with similar social and cultural context with cost-saving advantages (Carmel & Abbott, 2007). As
another research stream, there is a paucity of studies that explore the factors affecting knowledge
process outsourcing strategy. Such studies would provide considerable business value since knowledge
processes such as business analytics are among the main managerial concerns (Lacity, Solomon, et
al., 2011). Other strategies such as co-sourcing, multi-vendor (one client, multiple vendors), complex
sourcing (many vendors, many clients), spin-offs, and value-added outsourcing based on Chakrabarty
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(2011) terminology and some other sourcing options, including hybrid and plural sourcing (Medina
Serrano, González Ramírez, & Gascó Gascó, 2018), open-sourcing, crowd-sourcing, micro sourcing,
and rural sourcing could be of future research interest. Furthermore, following comparative studies
about factors affecting different ITO strategies such as ASP, BPO, and KPO could add value to
academics as well as practice.
IMPLICATIONS FOR PRACTICE
For the practitioners, this research provides a deeper understanding of the determinants of ITO decision
which somehow refers to the attributes of client firms that should be considered when pursuing ITO
initiatives. Especially, the efforts to respond RQ2 and the proposed conceptual model in Figure 3
provide insights for ITO practitioners. The figure depicts the most robust ITO determinants in four
technological, organizational, environmental and user categories. Both ITO clients and vendors should
focus on these consistent findings for either making ITO decisions or entering ITO relationships. For
instance, clients should focus on increasing their ITO benefits while reducing the ITO complexity, cost,
and risks. Simultaneously, vendors should provide their services with a focus on long-term mutual
benefits with minimum risks for both sides. As another practitioner point, contradicting some previous
research (e.g., Benlian et al., 2009; Tiwana & Bush, 2007) which suggested firms should keep their
core or strategic services, in-house, our review implied that there is no robust and consistent support
for this argument and firms can outsource their peripheral as well as core services to market vendors.
Among other factors should be considered by practitioners is the technological readiness of the client
firm which exhibited no influence on ITO decision. Meanwhile, firms are not required to establish
technological platforms before ITO, but instead, they should focus on locating and empowering
skilled human resources for writing more clear contracts and for managing and monitoring ITO
relations. This could finally decrease the potential of vendors’ opportunistic behaviors due to the
ambiguities in the contracts (Hanafizadeh & Zare Ravasan, 2017). Also, given the strong influence
of mutual trust in ITO relationships, service providers need to pay sufficient attention to make strong
and trustworthy relationships with client firms. Furthermore, client firms should attend user interests
which include the concerns of top-level managers, middle-level managers, IT managers, CIOs, and
sourcing managers in ITO initiatives which can help diminishing negative resistance to ITO efforts.
CONCLUSION AND LIMITATIONS
This paper reviews 91 ITO articles published between 2000 and 2018 in 51 unique journals. Through
categorizing reviewed literature in three descriptive, relational, and comparative studies, it implied
that the majority of the reviewed literature devoted to relational studies while comparative studies
are overlooked in the extant literature. Based on the quantitative results, conducting comparative
studies with a more focus on context, service/process type or strategic nature, ITO level, public/
private type of the client company, and ITO strategy are proposed as future research directions.
Besides, a set of theories such as TCE, RBV, TOE, Institutional theory, and risk-benefits framework
at the organizational level and TAM, TRA, and TPB at the user level were proposed and empirically
examined in identified literature and affecting factors based on each theory were extracted.
Furthermore, to identify a set of factors affecting ITO decision, four categories are proposed as
technological, organizational, environmental, and user factors. Moreover, extracted literature was
reviewed based on the ITO strategies and more or less focused ITO strategies were discovered.
According to the results, CC as a brand-new concept followed by ITO as a general term was the
most studied ITO strategies during the period.
All research suffers from limitations, and we, therefore, recognize the following limitations of this
review. First, it could not be guaranteed that every ITO article published in a refereed journal was found.
Second, regarding the scope of our work, the researchers only included ITO decision literature with
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clients’ viewpoint. Investigating factors affecting ITO from vendors’ standpoints could be suggested
as a future research direction. Third, the relationships in the proposed model only captured direct
effects, not interactive effects or dynamic interrelationships which could serve as an extension of our
work; we suggest additional research in these areas. Fourth, as a threat to the validity of the results,
we might have made errors in coding variables or merging some variables based on the similarity of
the intrinsic concept and meaning. Also, we have tried to mitigate the risks by parallel codification
process with the help of two of the authors and to check the similarities as well as dissimilarities;
however, there is no guarantee that the codes or classifications are perfect.
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APPENDIX I
T01 - Perceived benefits: Perceived benefits can be divided into two main sections: tangible and
intangible. ITO Tangible benefits are those directly related to gain dollar figure in organizations
such as time savings, cost savings, ROI, and so forth. Intangible benefits are difficult or sometimes
impossible to quantify which include a focus on core competencies, access to specialized
resources, quality improvements and so on (Guo & Liang, 2016; Lacity & Willcocks, 2014).
T02 - Perceived risks: The extent to which ITO exposes a client to a chance of loss or damage
(Kivijärvi & Toikkanen, 2015; Liang et al., 2016).
T03 - Performance assessment and control issues: The degree of difficulty in measuring
performance of exchange partners in circumstances of joint effort, soft outcomes, and ambiguous
links between effort and performance (Alvarez-Suescun, 2010; Diana, 2009).
T04 - Perceived complexity: The extent to which the ITO is sophisticated and difficult to standardize,
requiring a great deal of specialized knowledge to undertake the task (Lin & Chen, 2013).
T05 - Perceived security: The degree to which information security encompasses three main
security aspects (Integrity: gathering and retaining accurate information and avoiding malicious
modification; Availability: providing access to the information when and where desired; and
Confidentiality: avoiding disclosure to unauthorized or unwanted persons) is preserved by a
client (Khalfan, 2004).
T06 - Consistency: The degree to which ITO is perceived as consistent with the existing values,
strategies, legacy systems, and needs of potential adopters (Alshamaila et al., 2013).
T07 - Asset specificity: The degree to which an asset can be redeployed to alternative uses and by
alternative users without sacrifice of productive value (Ellram et al., 2008).
T08 - Service strategic nature: The degree to which a service represents the client’s primary
competency in creating value for the customer (Gilley & Rasheed, 2000).
T09 - Perceived cost: The perceived costs of searching, creating, negotiating, monitoring, and
administrating an ITO contract between the client and its service providers (Teo et al., 2009).
T10 - Data protection issues: Data protection requires the information of where personal data is
located, by whom it is processed and who is responsible for data processing (Karunagaran,
Mathew, & Lehner, 2017).
T11 - Perceived privacy: The client’s perception of information being improperly obtained and used
by service providers (Ratten, 2014).
T12 - Integration issues: Technological issues surrounding the integration of outsourced service/
system and legacy systems (S.-G. Lee et al., 2013).
T13 - Trialability: The degree to which ITO may be experimented with on a limited basis (Alshamaila
et al., 2013).
T14 - Lack of proper and in place standards: Lack of appropriate SLA standards, legal standards
regarding compensation and responsibility and so on (S.-G. Lee et al., 2013).
T15 - Transaction frequency: The number of times a client organization initiates a transaction,
typically categorized as either occasional or frequent (Lacity et al., 2016).
T16 - Service/ Process complexity: The degree to which a process is difficult to understand or carry
out (Y. Wu et al., 2013).
T17 - Business process IT detachability: The extent to which the process and its underlying IT
infrastructure are loosely coupled to allow separation, independent execution of the process on
another IT infrastructure, and recombination without loss of functionality (Tanriverdi, Konana,
& Ge, 2007).
T18 - Business process modularity: The extent to which a business process is loosely coupled,
mature, and standardized enough to be separated from a firm’s other business processes, executed
independently, and recombined without loss of functionality (Tanriverdi et al., 2007).
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O01 - Availability of required skills and competencies: The extent to which required skills and
competencies (money, managerial, technical, contractual) be available in the client organization
to outsource IT services (Gerbl et al., 2014; Koong et al., 2007; Lian et al., 2014).
O02 - Trust: The client’s expectation that the service provider will perform as expected and treat
the client firm fairly and reasonably (Garrison, Kim, & Wakefield, 2012).
O03 - Size: The size of a business is the organization’s resources, transaction volumes, or workforce
size (G. Lee & Xia, 2006).
O04 - Client prior outsourcing experience: The extent to which a client is familiar with outsourcing
strategies and has past positive experiences (Benamati & Rajkumar, 2008).
O05 - Positive attitude towards ITO: The overall evaluative appraisal of a decision maker toward
ITO (Benlian et al., 2009).
O06 - Top management support: The extent to which the executives understand the nature of ITO
and therefore fully support it (Lian et al., 2014).
O07 - Organizational knowledge level: Technical skills and knowledge of the client’s technical staff
in the outsourced project’s domain (Tiwana & Bush, 2007).
O08 - IT department size: Number of IT employees (Hsu et al., 2014).
O09 - Technological readiness (IT infrastructure): Refers to the technological infrastructure and
IT human resources (Low et al., 2011).
010 - Requirements specifiability: Ease with which project requirements can be wholly and accurately
conveyed to a vendor at the beginning of the project through a formal project specifications
document (Tiwana & Bush, 2007).
O11 - Top management/organizational innovativeness: The extent to which the top management/
organization readily accept and conform to an innovative technology (Lian et al., 2014).
O12 - IT department budget: Annual budget for IT department as a percent of a firm’s total budget
(Hsu et al., 2014).
O13 - Bounded rationality of organization: The extent to which the internal organizational
actors are boundedly rational which limits their ability to ultimately anticipate eventualities,
thereby constraining decision makers’ ability to thoroughly stipulate contract terms
(Miranda & Kim, 2006).
O14 - Lack of qualified employees to do the tasks in-house: The extent to which a firm has qualified
employees to do the tasks in-house instead of outsourcing them (Messerschmidt & Hinz, 2013).
O15 - IT budget development: The rate of IT-budget development in the next two years
(Messerschmidt & Hinz, 2013).
O16 - Internal resistance to ITO: The extent to which a firm is resistant to ITO (S.-G. Lee
et al., 2013).
O17 - Obligations for ITO: commitment or duty for ITO in the future (Chou et al., 2006).
O18 - Poor firm-level financial performance: The financial performance of a firm regarding indexes
such as operating expenses, short-term cash needs, financial leverages and so on (Hall, 2005).
O19 - CEO compensation: CEO incentives such as annual bonuses, stock options, stock grants, and
long-term incentive payouts (Hall, 2005).
O20 - CIO skills: Relates to CIO’s IT and ITO skills (Hall, 2005).
O21 - Knowledge loss: The potential for losing the Know-How knowledge and doing services inhouse
in the future (Gerbl, McIvor, Loane, & Humphreys, 2015).
O22 - Management style: It is a perception which narrates to the overview of new management
practices, processes, or structures projected to further organizational goals (Raut, Priyadarshinee,
Gardas, & Jha, 2018).
O23 - Vendor management capacity: Risk with managing cloud services relationships that require
contractual governance (Kauffman, Ma, & Yu, 2018).
O24 - Business level strategy: Using the differentiation strategy vs cost-leadership strategy
(Hanafizadeh & Zare Ravasan, 2018a).
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O25 - Industry type: The sector to which the business belongs (Alshamaila et al., 2013).
E01 - Competitive pressures: The extent to which a firm perceives pressure from business competitors
that encourage the firm to outsource IT services for the sake of maintaining competitiveness
(Irvine, Zhu, Kraemer, Gurbaxani, & Xu, 2006).
E02 - Regulations and government policies: The extent to which regulations and government
policies motivate or limit outsourcing IT services to external vendors (S.-G. Lee et al., 2013;
Lian et al., 2014).
E03 - Environmental uncertainty: The extent to which the business requirements of the project are
expected to change during development (Bush et al., 2008).
E04 - Social effects: Normative influence of key referent groups, such as peers or co-workers, on
ones’ behavioral choice (Park & Ryoo, 2013).
E05 - Cultural distance among partners: The extent to which there is cultural distance among
partners concerning employee’s values and norms, attitudes towards technology, customers,
interpersonal contact and interaction, and overall role perception (Graf & Mudambi, 2005).
E06 - Partners’ pressure: The extent to which a firm perceives pressure from business partners that
encourage the firm to outsource IT services (Low et al., 2011).
E07 - Market maturity: The extent of which there are a high number of service providers in
the market, providing the firm’s required technical and business resources/ skills (Graf
& Mudambi, 2005).
E08 - Social norms: The ITO degree of compatibility with the values and norms (e.g., job creation,
protection of the natural environment) (Koong et al., 2007).
E09 - Opportunistic behavior of vendors: The extent to which vendors might show opportunistic
behavior, raise prices, reduce service levels, and the like (Miranda & Kim, 2006).
E10 - Geo-distance between client and vendor: The extent to which the time zone of the ITO
location to major markets and company headquarters are close (Graf & Mudambi, 2005).
E11 - Client-side infrastructures: Required telecommunication infrastructure to support outsourced
services in the client side region (Gonzalez, Gasco, & Llopis, 2010).
E12 - Vendor support: The quality of supplier support activities that can significantly influence the
probability that an operation will be outsourced to a vendor (Alshamaila et al., 2013).
E13 - Taxation issues: The extent to which the tax policy of the client country is attractive for the
off-shore service provider (Graf & Mudambi, 2005).
E14 - Investment motives: The extent to which the client country is attractive for the off-shore service
provider from the investment motives points of view (Graf & Mudambi, 2005).
E15 - Vendor power: The extent to which there is a sufficient number of reputable and trustworthy
external IT vendors who can potentially provide IT facilities and services to us for the outsourced
operation (Diana, 2009).
E16 - Human capital: The availability of required skilled human resources in the destination country
or region (Gerbl et al., 2015).
U01 - Perceived ease of use: The extent to which individuals in the client firm perceive ITO easy
to understand and adopt (Ratten, 2014).
U02 - Perceived usefulness: The extent to which individuals in the client firm perceive ITO useful
in the organization (W.-W. Wu, 2011a).
U03 - User innovation: The degree which individuals in the client firm are open to new information,
technology, ITO new approaches (e.g., ASP, SaaS, CC) (Ratten, 2014).
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APPENDIX II
Table 9. Master code
Id Citation Context Journal
Base
Theory*
Outsourcing
Strategy**
Research
Method
Technology
Factors
Organizational
Factors
Environmental
Factors
User
Factors
Class***
1
(Baldwin, Irani,
& Love, 2001)
UK
European
Journal of
Information
Systems
- ISDO Case study T01 O14 E02 D
2
(Jayatilaka et
al., 2003)
USA
European
Journal of
Information
Systems
TCE, RBV,
RDT, KBP
ASP Survey
T01, T02,
T05, T06,
T12
O01, O07 E01 D
3
(Currie, Desai,
& Khan, 2004)
UK
Journal of
Information
Technology
- ASP Survey
T01, T02,
T05
D
4
(Lorence &
Spink, 2004)
USA
International
Journal of
Information
Management
- ISDO Survey T01 E02 D
5
(Aubert et al.,
2004)
Canada
Information &
Management
TCE,
incomplete
contracts
theories
ITOMO Survey T03, T07 O01 R
6
(Barthélemy &
Geyer, 2004)
Germany/
France
Journal of
Computer
Information
Systems
- ITOMO Survey T01 O08 C
7
(Schniederjans
& Zuckweiler,
2004)
USA
Management
Decision
-
Offshore
outsourcing
Case study T01, T02 E05 R
8 (Hall, 2005) USA
Journal of
Management
Information
Systems
- ITOMO Survey O18, O19 R
9
(Barthélemy &
Geyer, 2005)
Germany
/France
Information &
Management
TCE ITOMO Survey T07 O08 E04 C
10
(Graf &
Mudambi,
2005)
USA
Journal of
International
Management
Dunning’s
eclectic
theory
BPO
Conceptual
Model
T01, T02,
T03, T07,
T08
O01, O09
E05, E07, E10,
E13, E14
R
11
(Watjatrakul,
2005)
Thailand
The Journal
of Strategic
Information
Systems
TCE, RBV ISDO Case study T03, T07 E03 R
12
(Miranda &
Kim, 2006)
USA MIS Quarterly
TCE,
Institutional
theory
ISDO Survey
T03, T07,
T15
O13 E03, E09 R
13
(Chou et al.,
2006)
Taiwan
International
Journal of
Information
Management
Social
capital
theory
ISDO Case study O02, O17 R
14
(Dhar &
Balakrishnan,
2006)
Multi-
national
Journal
of Global
Information
Management
RBF, TCE ITO Case study T01, T02 D
15
(Tanriverdi et
al., 2007)
USA
Information
Systems
Research
- BPO Survey
T01, T07,
T08, T17,
T18
O04 R
16
(Kedia &
Lahiri, 2007)
USA
Journal of
International
Management
TCE, RBV,
RDT
Offshore
outsourcing
Conceptual
Model
T01 O01, O02 E01, E10 R
17
(Koong et al.,
2007)
USA
Industrial
Management &
Data Systems
TCE,
Agency
theory,
RDT,
Competitive
forces,
TOE
Offshore
outsourcing
Conceptual
Model
T01, T02,
T09
O01
E01, E03, E08,
E15
R
18 (Kshetri, 2007) USA
Journal of
International
Management
institutional
theory
Offshore
outsourcing
Conceptual
Model
E02, E08 R
continued on following page
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Id Citation Context Journal
Base
Theory*
Outsourcing
Strategy**
Research
Method
Technology
Factors
Organizational
Factors
Environmental
Factors
User
Factors
Class***
19
(Tiwana &
Bush, 2007)
Japan/
USA
Journal of
Management
Information
Systems
TCE,
Agency
theory,
KBP
ITOMO Survey
T01, T03,
T08, T16
O07, O10 E03, E09 R
20
(Greenberg,
Greenberg,
& Antonucci,
2008)
USA
Business Process
Management
Journal
TCE BPO
Conceptual
Model
T03, T07,
T15
O13 E03, E09 R
21
(Bush et al.,
2008)
Japan
Information
and Software
Technology
- ISDO Survey
T01, T03,
T04, T08
O01, O02, O10 E03 R
22
(Benamati &
Rajkumar,
2008)
USA
Information
Resources
Management
Journal
TAM ISDO Survey T02 O04, O05 E01
U01,
U02
R
23
(Benlian et al.,
2009)
Germany
Business &
Information
Systems
Engineering
RBV, TCE,
TPB
CC Survey T08 O05 E04 C
24 (Diana, 2009) USA
Health care
management
science
TCE, SMT ISDO Survey T03, T07 E15 R
25
(Gonzalez et al.,
2009)
Spain
International
Journal of Social
Sciences
RBF ISDO Survey T01, T02 D
26
(Dias Ferreira
& Barbin
Laurindo,
2009)
Brazil
International
Journal of
Production
Economics
- ITOMO Survey T02, T09 R
27
(Gewald &
Dibbern, 2009)
Germany
Information &
Management
RBF BPO Survey T01, T02 C
28
(Matsuno, Ito,
& Xia, 2009)
Japan
Artificial Life
and Robotics
- ISDO Survey T07 O03 R
29
(Gonzalez et al.,
2010)
Spain
Information
Systems
Management
TCE, RBV,
Innovation
Theories,
RET,
Agency
Theory,
SMT, CSR
Offshore
outsourcing
Survey
T01, T02,
T09
E04, E05, E11 R
30
(AlvarezSuescun,
2010)
Spain
Information
Systems
Frontiers
TCE, RBV ITOMO Survey
T03, T07,
T08
O04 R
31
(Dey, Fan, &
Zhang, 2010)
Multi-
national
Information
Systems
Research
- ISDO Case study T03 O07, O10 D
32
(Armbrust et
al., 2010)
USA
Communications
of the ACM
- CC
Conceptual
Model
T01, T02 D
33
(Qu &
Pinsonneault,
2011)
Multi-
national
Journal
of Global
Information
Management
Institutional
Economics
ITO Survey O02, O09 E07 R
34
(Huong,
Katsuhiro, &
Chi, 2011)
Japan/
Vietnam
Journal
of Global
Information
Management
-
Offshore
outsourcing
Case study E05, E11 D
35
(Wu, Lan, &
Lee, 2011)
Taiwan
International
Journal of
Information
Management
RBF CC
Survey
Case
study
T01, T02 R
36
(Blaskovich
& Mintchik,
2011)
USA
Journal of
Information
Technology
- ITOMO Survey O20 E01 C
37
(Jain &
Natarajan,
2011)
India
Strategic
Outsourcing:
An International
Journal
RBF ITOMO Survey T01, T02 R
Table 9. Continued
continued on following page
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Id Citation Context Journal
Base
Theory*
Outsourcing
Strategy**
Research
Method
Technology
Factors
Organizational
Factors
Environmental
Factors
User
Factors
Class***
38
(Mohammed,
2011)
USA
Information
Security Journal:
A Global
Perspective
- CC
Conceptual
Model
T01, T05,
T11
O02 D
39 (Stafford, 2011)
China/
India
Journal
of Global
Information
Technology
Management
- ISDO Case study E05 D
40
(Benlian &
Hess, 2011)
Germany
Decision
Support Systems
ORF, TRA CC Survey T01, T02 R
41 (Wu, 2011a) Taiwan
Expert
Systems with
Applications
TAM, DoI CC Survey T01, T05 O05 E04
U01,
U02
R
42
(Low et al.,
2011)
Taiwan
Industrial
Management &
Data Systems
TOE CC Survey
T01, T04,
T06
O03, O06, O09 E01, E06 R
43 (Sultan, 2011) UK
International
Journal of
Information
Management
- CC Case study T01, T02 D
44 (Wu, 2011b) Taiwan
The Journal
of Systems &
Software
TAM CC Survey T01, T05 O05 E04
U01,
U02
R
45
(Aubert et al.,
2012)
Canada
The Journal
of Strategic
Information
Systems
PCE, TCE ITOMO Survey
T03, T04,
T07, T14
O01, O07 E03 C
46
(Garrison et al.,
2012)
Multi-
national
Communications
of the ACM
RBV CC Survey T06 O02 R
47
(Martens &
Teuteberg,
2012)
Germany
Information
Systems
Frontiers
- CC Case study T02, T09 R
48
(Tajdini &
Nazari, 2012)
Iran
International
Journal of
Business and
Management
RBF ISDO Survey T01, T02 R
49
(Messerschmidt
& Hinz, 2013)
Germany
The Journal
of Strategic
Information
Systems
Institutional
theory,
OCT
CC Survey
O02, O03, O05,
O08, O12, O15,
O11
E01, E06, E08 R
50
(Luo, Wang,
Jayaraman, &
Zheng, 2013)
China/
India
Journal of World
Business
TCE
Offshore
outsourcing
Survey
T05, T12,
T16
O03, O10 C
51
(Alshamaila et
al., 2013)
UK
Journal of
Enterprise
Information
Management
TOE CC Case study
T01, T03,
T04, T06,
T13
O03, O04, O06,
O11, O25
E01, E07, E12 D
52
(Y. Wu et al.,
2013)
USA
Journal of
Supply Chain
Management
DoI CC Survey T06, T16 R
53
(Lin & Chen,
2013)
Taiwan
International
Journal of
Information
Management
TOE CC Survey
T01, T04,
T13
D
54
(Brender &
Markov, 2013)
Sweden
International
Journal of
Information
Management
CC Case study
T02, T05,
T09, T10
O03 E02 D
55
(Lee et al.,
2013)
South
Korea
International
Journal of
Information
Management
Herzberg’s
two-factor
theory
CC Survey
T01, T04,
T05, T10,
T12, T14
O01, O16 E02, E07 D
56
(Gupta,
Seetharaman, &
Raj, 2013)
Multi-
national
International
Journal of
Information
Management
- CC Survey
T01, T05,
T11
U01 R
Table 9. Continued
continued on following page
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Id Citation Context Journal
Base
Theory*
Outsourcing
Strategy**
Research
Method
Technology
Factors
Organizational
Factors
Environmental
Factors
User
Factors
Class***
57
(Park & Ryoo,
2013)
South
Korea
Computers in
Human Behavior
Two-factor
theory
(enablers/
inhibitors)
CC Survey T01, T09 E04 U03 R
58
(Gooris &
Peeters, 2014)
Multi-
national
Journal of
International
Management
TCE
Offshore
outsourcing
Survey E05, E10 R
59
(Wang & He,
2014)
Taiwan
International
Journal of
Information
Management
- CC Case study T02 D
60
(Lian et al.,
2014)
Taiwan
International
Journal of
Information
Management
TOE +
HOT-fit
CC Survey
T01, T04,
T05, T06,
T09
O01, O06, O11 E01, E02 C
61
(Hsu et al.,
2014)
Taiwan
International
Journal of
Information
Management
TOE CC Survey T01, T02 O01, O03, O12 R
62
(Gerbl et al.,
2014)
Germany
Journal of World
Business
TCE, RBV BPO Case study
T02, T03,
T04, T08
O01 E02, E10 D
63 (Ratten, 2014)
USA/
China
Journal of
Entrepreneurship
in Emerging
Economies
TAM, SCT CC Survey T01, T11 E04
U01,
U02,
U03
C
64 (Sultan, 2014) UK
International
Journal of
Information
Management
- CC Survey T01 D
65
(Oliveira et al.,
2014)
Portugal
Information &
Management
DoI, TOE CC Survey
T01, T04,
T05, T06
O03, O06, O09 E01, E02 C
66
(Yang, Sun,
Zhang, &
Wang, 2015)
China
Computers in
Human Behavior
TOE CC Survey
T01, T06,
T13
O06, O09 E01, E06 R
67
(Rekik,
Boukadi, &
Abdallah,
2015)
France
International
Journal of Cloud
Computing
- CC Case study T01, T09 O07, O09 D
68 (Lu et al., 2015)
Multi-
national
Information
Systems
Frontiers
TCE, RBV
Micro
sourcing
Survey
T01, T02,
T03
O02 R
69
(Gerbl et al.,
2015)
Germany
Journal of World
Business
- BPO Case study
T03, T04,
T08
O01, O10, O21 E02, E10, E16 D
70
(Atkinson,
Bayazit, &
Karpak, 2015)
USA
International
Journal of
Information
Systems and
Supply Chain
Management
- ITOMO Case study
T02, T05,
T09, T11
D
71
(Safari et al.,
2015)
Iran
Journal of
Enterprise
Information
Management
TOE, DoI CC Survey
T01, T03,
T04, T05,
T06, T11,
T13
O09 E01, E04 D
72
(Martins,
Oliveira, &
Thomas, 2015)
Multi-
national
Journal of
Organizational
Computing
and Electronic
Commerce
TOE ISDO Survey T01, T04 O03, O06, O09 E01, E02 C
73
(Gutierrez,
Boukrami,
& Lumsden,
2015)
UK
Journal of
Enterprise
Information
Management
TOE CC Survey
T01, T04,
T06
O03, O06, O09 E01, E06 R
74
(Gerbl et al.,
2016)
Germany
International
Journal of
Operations
& Production
Management
TCE, RBV BPO Case study
T03, T07,
T09
D
Table 9. Continued
continued on following page
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Id Citation Context Journal
Base
Theory*
Outsourcing
Strategy**
Research
Method
Technology
Factors
Organizational
Factors
Environmental
Factors
User
Factors
Class***
75
(Bhatiasevi &
Naglis, 2016)
Thailand
Education and
Information
Technologies
TAM CC Survey
U01,
U02
R
76
(Faisal & Raza,
2016)
Multi-
national
Journal of
Enterprise
Information
Management
- ITOMO Survey T01 D
77
(Westphal &
Sohal, 2016)
Australia
Production
Planning and
Control
- ITOMO Case study
O04, O06, O10,
O14
D
78
(Wu, Ding,
Xu, Mo, & Jin,
2016)
China
Journal
of Global
Information
Management
- CC Survey
T02, T05,
T06, T09
R
79
(Alharbi,
Atkins, &
Stanier, 2016)
Saudi
Arabia
Complex &
Intelligent
Systems
TOE CC Survey T01, T06
O01, O04, O05,
O06, O09, O17
E06 C
80
(van de Weerd,
Mangula, &
Brinkkemper,
2016)
Indonesia
Information &
Management
- CC Case study
O01, O03, O06,
O09
D
81
(Karunagaran et
al., 2017)
Germany
Information
Systems
Frontiers
TOE CC Case study
T01, T02,
T03, T04,
T05, T09,
T10, T12,
T13, T14
O16, O22 E01, E03, E13 C
82
(Alassafi,
Alharthi,
Walters, &
Wills, 2017)
Saudi
Arabia
Telematics and
Informatics
- CC Case study
T02, T05,
T06, T10,
T12
O02 D
83
(Loukis,
Arvanitis, &
Kyriakou,
2017)
Multi-
national
Information
Systems and
e-Business
Management
TOE CC Survey
O01, O03, O04,
O05, O11
E01 R
84
(Raut, Gardas,
Jha, &
Priyadarshinee,
2017)
India
Journal of High
Technology
Management
Research
- CC Survey
T01, T05,
T06, T11,
T13
O01, O03, O04,
O06, O11, O12
E01, E02, E03 U01 R
85
(Williams &
Durst, 2018)
UK
Journal of
Business
Research
-
Offshore
outsourcing
Case study O21 D
86
(Mukherjee,
2018)
India
Journal of
Industry,
Competition and
Trade
-
Offshore
outsourcing
Case study T09 C
87
(Raut et al.,
2018)
India
Technological
Forecasting and
Social Change
- CC Survey T02 O02, O22 R
88
(Kauffman et
al., 2018)
Multi-
national
Electronic
Markets
TOE CC Case study
T01, T05,
T06 T09
O11, O23
E01, E02, E03,
E06
D
89
(Zhang, Liu,
Tan, Jiang, &
Zhu, 2018)
China
International
Journal
of Project
Management
- BPO Survey T02 R
90
(Alkhater,
Walters, &
Wills, 2018)
Saudi
Arabia
Telematics and
Informatics
TOE CC Survey
T01, T05,
T06, T11,
T13
O02, O06, O09,
O25
E01, E02, E04,
E10
C
91
(Hanafizadeh &
Zare Ravasan,
2018a)
Iran
Journal
of Global
Information
Technology
Management
TOE ITOMO Survey
T01, T02,
T03, T04,
T07, T08,
T09, T15
O02, O03, O04,
O06, O09, O10,
O21, O23, O24
E01, E03, E05,
E07, E15
R
* TCE: Transaction Cost Economics, RBV: Resource-Based View, RDT: Resource dependence theory, KBP: Knowledge-based perspective: TOE:
Technology-Organization-Environment; TAM: Technology Acceptance Model; TPB: Theory of Planned Behavior; TRA: Theory of Reasoned Action; SMT:
Strategic Management Theory; DoI: Diffusion of Innovation theory; HOT-fit: Human-Organization-Technology fit; RBF: Risk -benefits framework; PCE:
Production costs economics; RET: Relational Exchange Theory; CSR: Corporate Social Responsibility; ORF: Opportunity Risk framework; SCT: Social
Cognitive Theory; OCT: Organizational Capability Theory
** ISDO: IS development outsourcing; ITOMO: IT operations and maintenance outsourcing; CC: Cloud Computing; BPO: Business Process Outsourcing
*** D: Descriptive, R: Relational, C: Comparative
Table 9. Continued
Journal of Global Information Management
Volume 28 • Issue 2 • April-June 2020
201
Payam Hanafizadeh has held visiting positions at the University of Waterloo, University of Canberra, Chalmers
University of Tech., Masaryk University and Universidad Autónoma de Madrid. He received his MSc and PhD
in Industrial Engineering at Tehran Polytechnic University and pursues his research in the areas of e-business
and decision making under uncertainty. He was the former Editor-in-Chief of International Journal of E-Business
Research. He is the inventor of Robust Net Present Value appeared in Mathematical and Computer Modelling. He
has also published over 60 articles for leading journals such as: Internet Research, The Information Society, Journal
of Global Information Management, Telecommunications Policy, Expert Systems with Applications, International
Journal of Information Management, Systemic Practice and Action Research, Electronic Commerce Research,
Mathematical and Computer Modelling, Energy Policy, Management Decisions and Telematics and Informatics to
name only a few. Dr. Hanafizadeh is the co-author of Online Advertising and Promotion published by IGI Global.
He has (co-)authored several other successful books in Persian such as Electronic Commerce, Definitions and
Barriers (2nd edition), Holding Companies: Definitions, Concepts, and Structures, and Multi-Dimensional Construct
Research Method. He has also been serving on the Editorial Review Board for JITR, JECO, IJITSA, IJEIS,
and IJDSST. Dr. Hanafizadeh was named as the best researcher of Tehran Province in 2006. In addition to his
academic position, he has also consulted for several major public and private organizations including the Ministry
of Information, Telecommunication and Technology, the Ministry of Interior, National Petrochemical Company,
pharmaceutical companies, automotive companies, and banking industries.
Ahad Zareravasan is a Post-Doctoral researcher at Masaryk University, Brno, Czech Republic. He has published
over 30 papers in acclaimed journals, such as the International Journal of Information Management, Expert
Systems with Applications, Information Systems, International Journal of Production Research, Journal of Enterprise
Information Management, Production Planning & Control, and Telecommunication Systems. His research interests
include Big Data Analytics, business process outsourcing and Blockchain.