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The Bottom Line
Revisiting technology-organization-environment (T-O-E) theory for enriched
applicability
Hart O. Awa, Ojiabo Ukoha, Sunny R. Igwe,
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Hart O. Awa, Ojiabo Ukoha, Sunny R. Igwe, (2017) "Revisiting technology-organization-environment
(T-O-E) theory for enriched applicability", The Bottom Line, Vol. 30 Issue: 01, pp.2-22, https://
doi.org/10.1108/BL-12-2016-0044
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Revisiting technology-
organization-environment (T-O-E)
theory for enriched applicability
Hart O. Awa
Department of Marketing, University of Port Harcourt, Choba, Nigeria
Ojiabo Ukoha
University of Maryland Eastern Shore, Princess Anne, Maryland, USA, and
Sunny R. Igwe
University of Port Harcourt, Choba, Nigeria
Abstract
Purpose –This paper aims to propose and test a ten-factor framework of four contexts from technology-
organization-environment (T-O-E) theory and unified theory of acceptance and use of technology (UTAUT) to
provide insight(s) that complements and extends extant inquiries on technology adoption.
Design/methodology/approach –Survey data were collected from small service enterprises with
strong operations in Port Harcourt, Nigeria, and the mode of sampling was purposive and snow ball, whereas
analysis involved structural equation modeling.
Findings –The results show that factors in the technological, organizational and environmental contexts
have direct statistically significant relationship with adoption; thus, adoption is more driven by T-O-E factors
than by individual factors. For individual context, social factor equally was statistically supported, whereas
hedonistic drive was not.
Research limitations/implications –The study is limited by its scope of data collection and phases;
therefore, extended data are needed to apply the findings to other sectors/industries/countries and to factor in
the implementation and post-adoption phases and business to business (B2B) adoption to forge a more holistic
framework.
Practical/implications –Implicit is that the findings encourage vendors and policy makers to recognize
the strength of interpersonal and group relationships in addition to T-O-E contexts in developing investment
decisions.
Originality/value –The paper contributes to the growing research on innovation adoption by using
factors within the T-O-E and UTAUT frameworks to explain SMEs’adoptionof technologies.
Keywords UTAUT, SMEs, Technology, Adoption, Interactions, T-O-E
Paper type Research paper
1. Introduction
Technology adoption information is critical and makes for informed investment decision in
competitive environment. For this, technology adoption is well-studied (Nkhoma and Dang,
2013;Chuang et al.,2014;Awa et al.,2011); this informs the conceptualization of theoretical
frameworks to provide information on the strength(s) and the interaction of specific factors
(Gangwar et al., 2014;Raymond and Uwizeyemungu, 2007;Gounaris and Koritos, 2008).
Whereas the rational choice frameworks espouse change resistance, technological
determinism and utility maximization (Eze et al.,2013;Awa et al.,2015); contemporary
theorists emphasize flexibility and the unpredictability of people and their creativity rather
BL
30,1
2
Received 23 December 2016
Revised 7 February 2017
9 February 2017
Accepted 9 February 2017
The Bottom Line
Vol. 30 No. 1, 2017
pp. 2-22
© Emerald Publishing Limited
0888-045X
DOI 10.1108/BL-12-2016-0044
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0888-045X.htm
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than processes (Chuang et al.,2014;Barrett et al.,2006;Rao et al., 2011). The celebrated
traditional theories as theory of reasoned action (TRA, Ajzen and Fishbein, 1980), theory of
planned behavior (TPB, Ajzen, 1991), technology acceptance model (TAM, Davis, 1989),
innovation diffusion theory (IDT, Rogers, 2003) and unified theory of acceptance and use of
technology (UTAUT) (Venkatesh et al.,2007) assume perfect information and are accused of
illusion of accumulated tradition that predicts individual-level adoption (Oliveira and
Martins, 2011). However, perfect information is rarely accessed regularly; thus, most
adoption decisions seldom follow the demands of traditional theories (House and Singh,
1987;Abrahamson, 1991).
Scholars call for more integrated and more holistic frameworks (Barrett et al.,2006;
Jacobsson and Linderoth, 2010;Al-Natour and Benbasat, 2009) that offer updated
information and meet the contemporary demands (Vijayasarathy and Turk, 2012;Leau
et al., 2012) for non-determinism and more social interactions (individuals rather than
technology determine adoption) and focus more on different types of innovation adoption at
the organization level (Oliveira and Martins, 2011). Amongst others, technology-
organization-environment (T-O-E, Tornatzky and Fleischer, 1990) and decision maker-
technology-organization-environment (D-T-O-E, Thong, 1999) provide the necessary
alternative lenses. Often, adoption is driven by attitudinal factors (TRA, Ajzen and Fishbein,
1980) and perceived usefulness (PU) and perceived ease of use (PEOU) (TAM, Davis, 1989);
these are purely utilitarian because they neglect the social and psychological parameters
(Venkatesh and Bala, 2008). IDT and TPB proposed an integrated model to make-up but
their frameworks rarely underpinned as much studies as T-O-E framework (Rogers, 2003;
Ajzen, 1991;Raymond and Uwizeyemungu, 2007;Al-Natour and Benbasat, 2009). However,
IDT’s constructs cross-cut T-O-E’s technology and organization; therefore, by integrating
environment, T-O-E framework provides a more superior theoretical information than IDT
in studying adoption behavior (Gangwar et al.,2014;Hossain and Quaddus, 2011;Oliveira
and Martins, 2011).
IDT finds practical utility in many disciplines but it is not as much specific as TAM and
T-O-E frameworks in technology acceptance (Gangwar et al., 2014;Moore and Benbasat,
1991). The T-O-E framework focuses more on social and psychological perspectives (Barrett
et al.,2006;Jacobsson and Linderoth, 2010;Al-Natour and Benbasat, 2009;Rao et al.,2011)
and has earned much more robust empirical and theoretical validations in the information
systems (IS) field than many other adoption frameworks (Yoon and George, 2013;Zheng
et al.,2011;Henriksen, 2006). Notwithstanding the growing scholarly accolades for the T-O-
E framework and given the flexibility of everything and the fact that updated information is
a competitive tool, scholars (Alatawi et al., 2012;Awa et al.,2015;Henderson et al.,2012;Ven
and Verelst, 2011) propose a further enriched framework for stronger academic utility. Ven
and Verelst (2011) accused T-O-E of not providing specific information rather used
taxonomies to categorize factors into contexts. In attempt to categorize factors into adoption
contexts and to establish the underlying relationships amongst them, the T-O-E framework
should integrate other adoption theories that suggest individual contexts (Alatawi et al.,
2012;Al-Natour and Benbasat, 2009;Awa et al., 2011;Henderson et al.,2012).
Individual characteristic was rarely espoused clearly in the T-O-E framework even
though scholars (Awa et al., 2011;Thong, 1999;Hambrick and Mason, 1984 Shiau et al.,
2009) posit that adopters’idiosyncrasies as well as their emotional feelings of the
technology’s usefulness and effortlessness drive firm’s strategies, motivation and attitude.
Therefore, integrating UTAUT2 (Venkatesh et al.,2007) and/or D-T-O-E (Thong, 1999)
theories to deal with decision makers’characteristics extend the T-O-E’s theoretical
prowess. The paper proposes an integrated T-O-E framework that benefits from other
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grounded frameworks and provides more comprehensive and insightful information into
explaining and predicting enterprise-context adoption of technologies.
2. Theoretical framework and hypotheses
In the knowledge economy, information is a tool for competitive advantage; enterprises
want technology applications that are effortless and save costs and time for the
stakeholders. Enterprises critically observe the environment and estimate the most
appropriate time to invest in a particular technology. Therefore, technology adoption is
based on information and defines individual and/or organizational voluntary decision to
first accept and/or operationally use it to generate information for solving problems
(Khasawneh, 2008;Musawa and Wahab, 2012). Inquiries (Rogers, 2003;Davis, 1989;Thong,
1999;Tornatzky and Fleischer, 1990;Ajzen, 1991;Ajzen and Fishbein, 1980) propose
frameworks that explain and predict this kind of behavior. Some of these frameworks focus
extensively on distinct roles (testers, developers and analysts) and some stable
characteristics of technology; thus, processes work well irrespective of the people involved
(Fowler, 2005;Barrett et al., 2006;Gerber, 2010), and by measuring and refining processes,
variations are assumed addressed (Nerur et al., 2005;Raymond and Uwizeyemungu, 2007).
Challenges traditional frameworks for incomplete requirements and specifications and for
lacking users’support and change requirements and specifications.
A framework has value-added information when it is evolutionary, responds to changes
and allows for interaction and collaboration amongst stakeholders (Al-Natour and Benbasat,
2009;Beck et al., 2001a;Vijayasarathy and Turk, 2012). For enterprise-context adoption, T-
O-E is one of the populous frameworks that recognize the non-determinism of technology
adoption; it places emphasis on people and not on roles and recognizes that people are not
replaceable component. The T-O-E framework consists of technology development;
organizational conditions, business and organizational reconfiguration; and industry
environment (Nkhoma and Dang, 2013;Angeles, 2014;Oliveira and Martins, 2011). The
factors within the T-O-E’s contexts have been continually worked upon by other scholars
(Musawa and Wahab, 2012;Hossain and Quaddus, 2011;Awa et al.,2015) to broadly
strengthen its theoretical base and have gained empirical and theoretical validity and
underpinned many IS inquiries (Zhu et al.,2003;Yoon and George, 2013;Alatawi et al.,2012;
Zheng et al.,2011;Oliveira and Martins, 2011;Eze et al.,2013;Zhu and Kraemer, 2005;
Henriksen, 2006). However, Tornatzky and Fleischer (1990) rarely bargained for a fixed
model and therefore, scholars (Alatawi et al., 2012;Henderson et al., 2012;Awa et al.,2015;
Premkumar, 2003;Nkhoma and Dang, 2013) propose integrating the T-O-E framework with
other frameworks to enrich the theoretical lenses.
Premkumar (2003) proposes that it is not theoretically sufficient to consider T-O-E
contexts; rather, the framework’s capabilities are enriched when individual and task
contexts as well as their cognate factors are integrated. Thus, the paper proposes a
framework that integrates T-O-E with UTAUT framework (see Figure 1); the UTAUT
theory captures the individual adopter’s characteristics (Venkatesh et al.,2007). The
proposed framework captures four adoption drivers with each having two or three factors to
make them specific. Although the framework recognizes the fact that the T-O-E framework
captures individual decision makers’interest as a factor under organization, it propagates
that treating decision makers as a driver/context rather than a factor has the propensity of
strengthening the T-O-E’s critical explanatory and predictive utility. Studies (Venkatesh
et al., 2007;Awa et al., 2015;Shiau et al., 2009;Grandon and Pearson, 2004)affirm that
knowledge of the peculiarity of minds of powerful coalition’s assumption about future,
alternatives and consequences to alternatives assists in analyzing organization’s behavior.
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The proposed framework conceptualizes individual context in terms of subjective norms
(Rogers, 2003;Ling and Yttri, 2002;Al-Natour and Benbasat, 2009) and hedonistic drives
(Venkatesh et al., 2012). Although hedonistic drives may not be populous in IS studies and
enterprise-context decision, it describes pleasure in adoption (Venkatesh et al., 2012) and
determines technology adoption (Musawa and Wahab, 2012;Angeles, 2014;Henderson
et al.,2012). For technology context, our framework factored perceived simplicity, perceived
compatibility and performance-expectancy out of the many factors from literature (Eze et al.,
2013;Awa et al.,2015;Nkhoma and Dang, 2013;Hossain and Quaddus, 2011) on the grounds
that empirical evidence (Khemthong and Roberts, 2006;Grandon and Pearson, 2004;Brown
and Lockett, 2004) confirms them critical adoption determinants. Perceived simplicity
emphasizes effortlessness (Davis, 1989;Awa et al., 2015); perceived compatibility aligns and
integrates technology with processes (Nkhoma and Dang, 2013;Khemthong and Roberts,
2006;Premkumar, 2003); and performance-expectancy compares incoming technology with
the incumbents (Davis, 1989;Venkatesh et al.,2007).
IS literature (Kannabiran and Dharmalingam, 2012;Eze et al., 2013;Al-Natour and
Benbasat, 2009;Awa et al.,2015) proposed many organizational factors, but our framework
captures top management support, enterprise size and scope of business operations. Studies
confirm these as critical adoption factors (Chuang et al., 2014;Awa et al.,2015;Yoon and
George, 2013;Alatawi et al.,2012); top management creates enterprise-level supportive
climate for adoption (Thong, 1999;Hambrick and Mason, 1984), enterprise size explains that
adoption varies with enterprises’slack variables and level of investments (Zhu and
Kraemer, 2005;Nkhoma and Dang, 2013;Leau et al.,2012) and scope of business operations
suggest building efficiency using novel technologies when the business scope is large (Zhu
et al.,2003;Yoon and George, 2013). Finally for the environment, scholars (Salwani et al.,
2009;Awa et al.,2015) propose many factors, but this paper adopts the institutional theory
of DiMaggio and Powell (1983) because the normative and mimetic pressures summarize
almost all the external factors proposed by other scholars.
2.1 Technological context
Technological context comprises of internal and external variables that influence
individual, organization and industry adoption of innovations (Huang et al., 2008). The
Figure 1.
The proposed
conceptual
framework
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organization-
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variables include the five IDT’s innovation attributes that influence adoption
likelihoods (Dedrick and West, 2003;Rogers, 2003) as well as other attributes proposed
by other scholars (Musawa and Wahab, 2012;Hossain and Quaddus, 2011;Ramdani
et al., 2009;Awa et al., 2015;Raymond and Uwizeyemungu, 2007). However, the
proposed framework captures only perceived simplicity, perceived compatibility and
performance-expectancy. First, making technology simple is a stride in adoption;
scholars (Wang et al., 2012;Nkhoma and Dang, 2013;Rao et al., 2011) recognize the
pervasiveness and the complexity of technology as well as complex organizational
processes as adoption factors. Perceived simplicity reduces adoption uncertainties and
risks (Premkumar and Roberts, 1999) and represents a critical adoption predictor
(Khemthong and Roberts, 2006;Riemenscheider et al., 2003;Brown and Lockett, 2004);
thus, sophisticated technology systems inhibit intentions (Chwelos et al., 2001)
and conversely, perceived simplicity has a negative association with adoption
(Grover, 1993).
Second, perceived compatibility measures the alignment of new technology with
existing structure, infrastructures and procedures, values and norms, experiences and
the information sharing needs within the social systems (Khemthong and Roberts, 2006;
Premkumar, 2003). Scholars (Caudle et al., 1991;Fuller et al., 2007) reported that the fit
and integration between the incumbent and in-coming technologies is a major adoption
driver. Compatibility is a significant adoption factor in radio-frequency identification
(RFID) and knowledge management, whereas insignificant in electronic data
interchange (EDI) and enterprise resource planning (ERP) because these technologies
are internet-based and internet is so omnipotent that compatibility is not considered as a
critical adoption factor (Hossain and Quaddus, 2011;Wang et al., 2012;Raymond and
Uwizeyemungu, 2007). Landsbergen and Wolken (2001) report that incompatibility of
hardware, software and telecommunication networks resulting from lack of experience
and institutional memory negatively interact with inter-and-intra-firm information
sharing.
Third, performance expectancy defines the degree to which a new technology is
perceived superior to the incumbent(s) in building competitive advantage. However,
some studies found performance expectancy a critical adoption predictor (Mehrtens
et al., 2001;Grandon and Pearson, 2004); others underpinned by the T-O-E framework
found it one of the most significant adoption drivers (Yoon and George, 2013;Shiau
et al., 2009;Ramdani et al., 2009). Yet others (Rogers, 2003;Lee et al., 2001) concluded
that IS innovations perceived to offer relative advantage over the existing practices are
more likely to be adopted. In another study, Zhu et al. (2004) concluded that enterprises
that efficiently exploit the complexities of internet technologies and exhibit technology
readinessaremorelikelytocreatevalueswith innovative technologies faster than
others, who lack such readiness. We hypothesize three relationships below:
H1. Perceived effortlessness in manipulating innovative technology significantly aids
adoption; the simplified manipulation of a system makes adoption faster.
H2. Perceived compatibility between new and incumbent technologies significantly
impacts adoption; adoption is faster when new technologies provide compatibility
and integration.
H3. Performance expectancy or perceived benefits of technology in facilitating
operations impact adoption; new technologies perceived to offer relative advantage
over the existing ones are more likely to be adopted.
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2.2 Organizational context
Organizational context in IS studies focuses on descriptive measures, which amongst others
include availability and proficiency in using resources, firm scope, firm size and slack
resources, social influences, culture and structural configurations, information sources and
communication channels, degree of centralization and managerial beliefs (Zhu et al.,2003;
Eze et al.,2013;Kannabiran and Dharmalingam, 2012;Pan and Jang, 2008). Our framework
factors include top management support, enterprise size and scope of business operations.
Top management support explains management’s favorable disposition and encouragement
that foster innovation adoption. Studies (Raymond and Uwizeyemungu, 2007;Zhu et al.,
2003; Shiau et al., 2009) report that organization’s direction is shaped by top managers’
idiosyncratic givens. Evidence shows that top management provides supportive climate,
communicates and reinforces corporate values through articulated vision, and thus, serves
as one of the most critical adoption determinants of new technologies (Jeyaraj et al.,2006;
Balaid et al.,2014;Premkumar, 2003).
Similarly, other studies (Chuang et al.,2014;Awa et al., 2015;Yoon and George, 2013;
Alatawi et al., 2012) found that top management’s knowledge of technology’sbenefits and
the enterprise’s expectations as well as the concomitant support are critical adoption
predictors. Conventionally, every enterprise uses new technologies to improve competitive
advantage (Raymond and Uwizeyemungu, 2007;Wang et al., 2012); as enterprise’s size
increases, task complexity and coordination turn more complicated and dependence on
modern technologies increases (Stair and Reynolds, 1998). Larger enterprises have greater
slack variables and exploit economy of scale advantage, and resilience to bear the risks/
failures associated with allocating greater resources to novel ventures that involve network
externalities (Hwang et al.,2004;Zhu and Kraemer, 2005). Studies (Spinellis and Giannikas,
2012;Lin and Lin, 2011) show that enterprise size critically influences the adoption of new
technologies. Specific studies (Wang et al., 2012;Hossain and Quaddus, 2011; Ramdani et al.,
2009) found size to be a critical factor in RFID, e-commerce and ERP adoption and non-
critical in EDI adoption. An earlier study found that the proportion of EDI adoption amongst
larger firms is about 95 per cent and only about 2 per cent for small firms (Densmore, 1998).
Greater scope of business attracts more enterprise likelihoods to invest in novel
technologies (Hitt, 1999;Zhu et al.,2003). The scope of business operation as an adoption
predicator is perceived to play three roles (Bakos, 1998;Zhu et al., 2003;Yoon and George,
2013); first, compressed costs of internal co-ordination, administrative complexities and
communications and information processing (Gurbaxani and Whang, 1991). Second,
strategically compressing external co-ordination costs (search for buyers and sellers,
demand aggregation and inventory management, physical stores location, etc.), which,
though difficult to ascertain, increase with business scope (Chopra and Meindl, 2001;
Shapiro and Varian, 1999;Zhu et al.,2003). Third, achieving synergy and overlap between
the new technologies and existing ways of doing things. We propose the following
hypotheses based on the evidence:
H4. Organizations with good support from top management are more likely to adopt
new technologies faster than those without such support.
H5. Large enterprises are more likely to adopt new technologies faster than those that
are small.
H6. The scope of business operations significantly determines the possibility of
adopting new technologies; firms with large scope of operations adopt technologies
faster than those with smaller scope.
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2.3 Environmental context
There are many environmental factors proposed by scholars (Salwani et al., 2009;Tornatzky
and Fleischer, 1990;DePietro et al.,1990;Awa et al., 2011), but this paper adopts the
normative and mimetic pressures of the institutional theory. Amongst others, the demands
of trading partners and customers, professional associations, legal framework, government
and her agencies and congress shape technology adoption (Deephouse, 1996); federal laws
and policies as well as economic and budgetary mechanisms influence adoption of
innovations (Landsbergen and Wolken, 2001). Bingham (1976) studied the inter-
organizational influence on adoption, including grants, transfers and technical assistance
and found statistically significant interaction. Neglecting the dynamics of these factors may
attract penalty and missing of encouragements and worthy opportunities (Akbulut, 2002).
Further, environment is dynamic; thus, industry players curiously monitor other players
and analogously mimic actions of other players to remain competitive.
Often enterprises tailor their programs to suit contemporary practices as may have been
instituted and/or shaped by player(s) (DiMaggio and Powell, 1983) in so much as to force
retaliatory and endless vicious circle (Awa et al.,2015;DiMaggio and Powell, 1983). Studies
(Oliveira and Martins, 2011;Pan and Jang, 2008)confirm that enterprises borrow
mindfulness and other traits of successful competitors, especially those that relate to
innovation adoption:
H7. The existence of normative pressures positively affects technology adoption; when
pressures are high from customers, legal institution, governments, trading partners
and others, adoption will be faster.
H8. The existence of mimetic pressures amongst rivals positively affects the likelihood
of technology adoption;when such pressures are high, adoption is assumed faster.
2.4 Individual context
Afirm’s strategic and tactical focus and/or enterprise-level innovation adoption is largely
dependent upon the idiosyncratic givens of decision actors, which often reflect their
attitudes, perceptions, psychographics, motivation and other individual difference factors
(Hambrick and Mason, 1984;Awa et al., 2015;Awa et al., 2011). To unveil such functional
and/or emotional feelings of decision makers, our framework factors subjective norms and
hedonistic drives. Subjective norms express one’s social status and group cohesiveness
(Venkatesh and Davis, 2000;Rogers, 2003) and predominantly represent psychological
motives of behavior(s) that define other peoples’opinions, superior influences and peer
group opinions (Taylor and Todd, 1995). Moreover, 73 per cent of the executive class in big
cities of China owned mobile phones not purely for functional reasons but to convey social
status (Samson and Hornby, 1988), and users of communication interfaces were more driven
by social influences, especially at the social development and learning stage of life (Ling and
Yttri, 2002).
The economic man theory suggests assessing the outcome of alternative actions and
making choices that are more gratifying. Venkatesh et al. (2012) factored hedonistic drives
into their UTAUT2 framework and perceived it as the pleasure and fun derivable from
using particular technologies. Hedonistic drive is a critical innovation adoption determinant
(Brown and Venkatesh, 2005), especially in choice relating to mobile technologies and social
networking tools. We hypothesize our argument below:
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H9. The existence of social influence positively affects technology adoption; when
group members show cohesiveness to the common norms and values relating to
technology, they tend to adopt faster.
H10. The hedonistic motivation of the decision-making group positively affects
technology adoption.
3. Research philosophy and methods
Positivism and anti-positivism are the two opposing intellectual traditions adopted in social
science research to explain orthogonal or oblique relationships. Whereas positivist
orthodoxy assumes that social phenomena and their meanings have existence independent
of the social actors or confront us as external facts that we rarely reach or influence
(Saunders and Tosey, 2013;Edmonds and Kennedy, 2012); the anti-positivism recognizes
the differences between people and objects of the natural sciences (deductivism) and thus
emphasizes subjective meaning of social actions (inductivism) (Giddens, 2009;Bryman and
Bell, 2011). Although some studies may take triangulation choice, this paper follows a mono-
method; it uses ontological realism backed up by positivist epistemology and nomothetic
methodologies because it emphasizes observation-based objectivity and cause and effect.
This choice lies on Orlikowski and Baroudi (1991), who classify IS research as positivist if
there is evidence of quantifiable measures of variables, formal propositions, hypotheses
testing and the drawing of inferences. Similarly, Mukherji and Albon (2009) posit that a
positive philosophy leads to a systematic and scientific approach to research and therefore
lends itself to the use of quantitative methods.
3.1 Population and sampling frame
This study targets Port Harcourt-based small- and medium-sized service enterprises drawn
from information and computer technology (ICT) maintenance, legal services, healthcare
services, laundry and dry cleaning, make-ups (e.g. barbing, hair dressing and manicure and
pedicure) and management consultancy. Port Harcourt was chosen because she plays host
to expatriates and all Nigeria’s tribes based on the cluster of federal and state ministries and
parastatals as well as the huge oil deposit and commercial activities. However, enterprises
qualified for the study were those with at least ten employees, huge investment in ICT and
annual turnover of five million Naira (N 5,000,000) or less and we relied on the provisions of
Small and Medium Sized Development Agency of Nigeria (SMEDAN, 2005) to capture the
right ones. The criterion of ten employees was informed by the peculiarity of Nigeria and the
need to avert the conditions spanning between 100 and 500 employees as stipulated by most
developed and emerging nations (OECD,2000, 2002;Ramdani et al.,2009).
Further, another criterion used to define the population was that the SMEs must
integrate operations within the enterprise community using ICT platforms and be duly
registered with Corporate Affairs Commission (CAC) and other relevant government
approved agencies. The sampling frame was 373 owners and executives; they were chosen
because their strategic position makes it possible for them to address context issues related
to their enterprise’s adoption.
3.2 Questionnaire and sampling procedure
A questionnaire was designed to elicit the right responses. It was made up of structured
disguised questions (opinions were deduced from responses to indirect and close-ended
questions), structured-undisguised questions (opinions were deduced from responses to
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direct and close-ended questions) and unstructured questions (opinions were deduced from
responses to open-ended questions). Principally, questions on respondents’age and income
were disguised, whereas we estimated age by the information on the year of graduation
from high school and university/college; we estimated income by the strength of the
enterprise. Questions on the extent of technology adoption were direct and some of them
open-ended because the existing operations are the evidences. The questionnaire was hand-
administered within the months of April and September, 2015, by trained graduate students
of University of Port Harcourt with the help of contact addresses from CAC. However, the
non-probability sampling technique was adopted; through purposive and snowball, we use
experiential knowledge and judgment to choose the first few cases whose opinions best
represented that of the community and then relied on their referrals for other likely
participants. To minimize the fear of bias associated with non-probability samples, we rely
on Chein’s (1981) view to restrict and to precisely define the population (Table I).
3.3 Operational measures
We assess content and construct validities. The former explains the extent to which
the instrument comprehensively measures the subject matter, and the latter deals with the
extent to which the statement items in a scale measure the construct (Nunnally, 1978). The
constructs of this study are well-researched and have well-developed measures in IS
literature; thus, content validity was established based on theoretical reviews and extensive
process of item selection and refinement in the development of questionnaire and scales. ICT
adoption was measured using the capability indicators developed by Molla and Licker
(2005) because it is relevant to developing countries. Each enterprise was asked to specify
the specific ICT platforms they use within their communities. An enterprise is defined to
adopt innovation if it uses computer hardware and software applications to support
operations, intra-firm and inter-firm interactions, management and decision-making
processes (Awa et al.,2015). We adopted some scale measures proposed by Awa et al. (2015)
to ascertain the extent of adoption.
The measures ascertain if the enterprises use ICT platforms to enhance:
customer service and inventory management;
cost reduction and book-keeping;
e-trading, e-messaging and inter-firm alignment; and
e-mails, FAX, e-catalogue and e-news;
e-payroll and e-forms;
ordering and managing stocks; and
processing of loan credits and banking details.
Table I.
Sample description
No. SMEs Administration Managers/owners Senior executives Returns
1. ICT maintenance 65 14 51 38
2. Legal services 70 28 42 41
3. Healthcare services 80 18 62 52
4. Laundry and dry cleaning 52 40 12 42
5. Make-ups 56 12 44 34
6. Management consultancy 50 14 36 37
Total 373 126 247 244
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Top management support and normative pressure were measured using Soliman and Janz’s
(2004) five-point scale and perceived value by another multi-item scale developed by Moore
and Benbasat (1991). Enterprise size was conceptualized as an environmental and
organizational issue (Kamal, 2006) related to communities served and thenumber of services
provided (Akbulut, 2002) and operationalized by measures proposed by institutions
(SMEDAN, 2005;Jeyaraj et al.,2006;Lertwongsatien and Wongpinunwattana, 2003;
Grandon and Pearson, 2004). The multi-item scale was adapted to measure performance
expectancy, perceived compatibility and perceived simplicity (Al-Qirim, 2006).
For scope of business operations, we collapse the scales of scholars (Chopra and Meindl,
2001;Gurbaxani and Whang, 1991;Shapiro and Varian, 1999) to have a four-point scale,
where 1 = local, 2 = regional, 3 = national and 4 = international. Normative pressure
describes governments, NGOs and inter-governmental influences, and we operationally
measure it with scales developed by Akbulut (2002) and Bingham (1976). Finally, mimetic
pressure was measured using a collapsed version of the multi-item scale developed by
scholars (Premkumar and Roberts, 1999;Jeyaraj et al.,2006;Lertwongsatien and
Wongpinunwattana, 2003); social influences by the scale of BarNir and Smith (2002); and
hedonistic drives were measured using the scale similar to that proposed by Venkatesh et al.
(2012). Factor analysis was used to test construct validity and to examine the factor
structure of antecedent measures.
4. Data analysis and results
The conceptual framework and the hypothesized relationships were estimated using the
partial least square (PLS). PLS is developed as a second generation of structural equation
modeling (SEM) (Wold, 1985) to handle situations where the latent variables and a series of
cause-and-effect relationships exist (Gustafsson and Johnson, 2004;Hair et al., 1998). The
proposed framework suggests ten latent variables with interrelated dependence or causal
path amongst themselves and thus meets the conditions of PLS. Further performing the
psychometric evaluation of items using confirmatory factor analysis (CFA) meets one of the
critical conditions of path analysis (Hair et al.,1998), and with a sample of 244 respondents,
SEM analysis is good for this study given that the sample benchmark for SEM is 100 or 150
to 200 and above (Anderson and Gerbing, 1982;Boomsma, 1982). Barclay et al. (1995)
observe that PLS path model is analyzed and interpreted by assessing reliability and
validity as well as structural model.
First, the Cronbach coefficient alpha of ≥0.7 (Nunnally, 1978) confirms that the
instruments show fair level of internal consistency in response. Second, the constructs
loaded together in CFA to determine discriminant validity. Discriminant validity describes
the extent to which a given construct is different from other latent variables (Sanchez and
Roldan, 2005). The meeting of the conditional range of 0.670 to 0.936 (see Table II)as
proposed by scholars (Fornell and Larcker, 1981;Bagozzi and Yi, 1988) demonstrates
validity; the average variance extracted (AVE) is greater for each factor than the common
variance of the two factors together. The constructs show high oblique and non-orthogonal
relationships amongst themselves and confirm previous studies (Bagozzi and Warshaw,
1990;Anderson and Gerbing, 1982); thus, discriminant validity is confirmed. The results of
the fit statistics surpassed the thresholds, showing that the measurement model displays
good fit with the data gathered; the statement items surpass Steven’s (1992) benchmark of
0.60, and Kaiser–Meyer–Olin (KMO = 0.884) measure of sampling adequacy supports the
appropriateness of factor analysis (Mertler and Vannatta, 2002) because the item-to-
response ratio was considered acceptable (Hinkin, 1995) and the latent variables are less
than Kaiser rule of less than 30 (Kaiser, 1974).
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Table II.
Latent and observed
variables in factor
analysis
Context Factor dimension and items measured Mean EVA Alpha
Technology Perceived simplicity 0.818
Flexibility in interaction 5.71 0.678
Less specialized skills and training in using it 4.66 0.788
Learning with ease 5.31 0.910
Clarity and understandability 5.75 0.695
Compatibility 0.782
Fit between the new and existing technologies 4.72 0.675
Fit between the new systems and existing work procedures 4.93 0.713
Fit between the new systems and corporate culture 4.34 0.745
Fit between the new systems and corporate philosophies, norms and values 5.61 0.753
Performance expectancy 0.761
Reduced operating costs 4.21 0.677
Improved operational efficiency 4.82 0.810
Improved customer service 5.01 0.922
Improved customer relationship 4.40 0.751
Reaching new customers
Organization Top management support 0.784
Training and development on innovation 4.10 0.719
Good disposition to innovations 5.63 0.820
Policies and procedures of encouragement 5.21 0.870
Staff incentives on innovation 4.60 0.771
Updating enterprise’s technologies 4.15 0.851
Size of the enterprise 0.768
Resource strengths 4.62 0.709
Skills and experiences 4.74 0.840
Level of resilience 5.40 0.890
Operational agility 5.22 0.725
Number of employees 5.16 0.679
Scope of business operations 0.701
Local 4.81 0.792
Regional 4.23 0.804
National 5.31 0.850
International 5.74 0.669
Environment Normative pressure 0.788
Regulations on technical assistance and partnership 4.37 0.797
Safety provision and staff insurance 4.27 0.844
Environmental impact factor compliance 4.64 0.891
Policies and regulations 5.82 0.690
Mimetic pressure 0.791
Operational necessity 5.28 0.709
Strategic necessity 5.01 0.840
Vendor or third party pressure 4.68 0.870
Opponents adopt it 4.54 0.679
Individual Subjective norms 0.770
Influence by others 5.41 0.711
Group cohesiveness 4.19 0.740
Strong belief in group norms 4.83 0.821
Fear of group penalty 5.44 0.698
Hedonistic drives 0.721
Personal interest on innovation 4.12 0.709
Egoistic mindedness 4.15 0.840
Pleasure seeking 5.84 0.890
Meet social demands 4.45 0.687
Notes: Factor extraction: principal component analysis (PCA); rotation method: Varimax; KMO: 0.884
Bartlett’s test of sphericity, significance: 0.000
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Further, the values for goodness of fit index (GFI) of the latent variables based on Chau and
Tam’s(1997)benchmarks (0.8-0.89 –reasonable fit; 0.9 and above –good fit) showed
unidimensionality. Moreover, the adjusted goodness of fit index (AGFI) = 0.840;
comparative fit index (CFI) = 0.87;
x
2(d.f) = 3.44; normed fit index (NFI) = 0.93; Tucker–
Lewis index (TLI) = 0.944/0.912; root mean square error of approximation (RMSEA) = 0.072
and root mean residual (RMR) = 0.035/0.039 were good.
These suggest proceeding with the assessment of the structural model. The coefficients
of the model reported in Table III show significant paths between the context factors and
adoption as well as the estimates and the t-values for the path coefficients for the base and
revised model, thus leading to supporting all hypothesized relationships except one. The
result of path analysis shows that
b
= 0.234 and t-values = 2.66; p<0.05 confirmed that
perceived simplicity was a critical adoption factor amongst SMEs, thus lending support to
H1.At
b
=0.308, p<0.01, the path analysis shows a negative and significant
relationship between perceived compatibility and technology adoption. Although this
finding lends support to H2, the inverse relationship signifies that when the fear of
compatibility with existing processes and structures decreases, adoption increases or vice
versa. The
b
= 0.299, p<0.05 in the path analysis shows a statistically significant
relationship between performance expectancy and technology adoption, this supports H3.
In the context of organization, the study shows significant relationship between TMS
(
b
= 0.261, p<0.01), size of the enterprise (
b
= 0.310, p<0.01) and scope of business
operations (
b
= 0.285, p<0.01) and technology adoption, thus supporting H4-H6. The
normative variable has statistically significant relationship with technology adoption and
supports H7 at
b
= 0.165, p<0.05. At
b
= 0.224, p<0.01, path analysis shows that
mimetic pressure was a significant predictor of technology adoption and thus lends support
to H8. The result shows that subjective norms (
b
=0.214, p<0.05) had statistically
significant negative coefficient and lend support to H9. Finally, the relationship between
hedonistic drive (
b
=0.034, p= 0.330) and technology adoption was not statistically
significant.
Table III.
Path coefficient and
t-values
Context
Total
effect
(
b
)t-statistic Significance Decision
Technology Perceived simplicity–––––––––––adoption 0.234 2.660 0.011* H1 supported
Compatibility–––––––––––adoption 0.308 3.441 0.000** H2 supported
Performance expectancy –––––––––– –
adoption
0.299 3.001 0.014* H3 supported
Organization Top management support–––––––––––
adoption
0.261 2.870 0.000** H4 supported
Size of the enterprise–––––– –––––adoption 0.310 3.550 0.000** H5 supported
Scope-of-business operations–––––––––––
adoption
0.285 2.921 0.000** H6 supported
Environment Normative pressure–––––––––––adoption 0.165 2.102 0.015* H7 supported
Mimetic pressure–––––––––––adoption 0.224 4.011 0.000** H8 supported
Individual Subjective norms–––––––––––adoption 0.214 3.028 0.012* H9 supported
Hedonistic drives–––––––––––adoption 0.034 0.388 0.330 H10 Not
supported
Notes: *p≤0.05; **p≤0.01
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5. Discussion
This paper integrates other critical factors into the T-O-E framework to propose an
improved framework that provides information about understanding and predicting
adoption behavior. Specifically, it proposed a ten-factor framework from four drivers,
confirmed their reliability and validity as well as their goodness of fit and tested the
framework using coefficients generated from SEM. Except for one, the coefficients of the
hypothesized relationships were supported at either p<0.01 or 0.05. Perceived simplicity,
perceived compatibility and performance expectancy were captured under technology, and
their coefficients were statistically significant. In the context of perceived simplicity,
sophisticated technology systems inhibit adoption. This finding is consistent with those of
similar findings (Khemthong and Roberts, 2006;Riemenscheider et al.,2003;Brown and
Lockett, 2004;Akbulut, 2002;Chwelos et al.,2001) and contradicts Grover (1993).Grover
(1993) found negative correlations between perceived simplicity and adoption. The
explanation for the differences relates to technology category as some simplified
technologies that do show status of the user(s) may be rejected. For perceived compatibility,
previous studies (Hossain and Quaddus, 2011;Wang et al., 2012;Fuller et al.,2007) report fit
between existing and new technologies as a major adoption driver in RFID and knowledge
management.
Conversely, the finding contrasts yet other studies (Hossain and Quaddus, 2011;Wang
et al., 2012;Ramdani et al., 2009;Huang et al., 2008) that found compatibility non-critical in
EDI and ERP adoption. And our finding on perceived values confirmed previous studies
(Mehrtens et al., 2001;Lee et al., 2001;Grandon and Pearson, 2004;Yoon and George, 2013;
Shiau et al.,2009;Ramdani et al.,2009) that found perceived value one of the most critical
adoption factors. The latent variables –top management support (TMS), size of the
enterprise and scope of business operations –were found to statistically relate with
technology adoption. Managers with knowledge and experience about the uniqueness of
modern technologies in building competitive advantage are better disposed to offer the
necessary adoption encouragement. Previous studies (Chuang et al., 2014;Awa et al.,2015;
Yoon and George, 2013;Alatawi et al.,2012) report that organizational directions are shaped
by the peculiarities of top management. Similarly, studies (Hossain and Quaddus, 2011;
Ramandi et al., 2009; Spinellis and Giannikas, 2012) revealed statistically significant
relationship between enterprise size and adoption of RFID, e-commerce and ERP
technologies; others (Wang et al.,2012;Hossain and Quaddus, 2011; Ramandi et al., 2009)
contrast our finding when they found size of the enterprise non critical in EDI adoption.
With regards to scope of business operations, the finding is consistent with extant
studies (Hitt, 1999;Zhu et al.,2003;Gurbaxani and Whang, 1991;Shapiro and Varian, 1999;
Chopra and Meindl, 2001;Yoon and George, 2013) that show strong relationship between
large scope of business and high adoption of modern technologies for the sake of cost
reduction expressed in administrative efficiency and co-ordination. Normative and mimetic
pressures were captured in the framework, and their relationship with adoption was critical.
Extant studies (Deephouse, 1996;Bingham, 1976;Landsbergen and Wolken, 2001;Akbulut,
2002) confirm that neglect of the normative norms may be operationally counter-productive.
On mimetic pressure, our finding is in line with others (Oliveira and Martins, 2011;Pan and
Jang, 2008;Bakos, 1998;Gibbs and Kraemer, 2004;DiMaggio and Powell, 1983) that
reported that enterprises borrow traits of successful competitors, especially those relating to
innovation adoption. The result in the path analysis shows that subjective norms had
statistically significant negative coefficient though it lends support to H9. The existence of
superior information amongst peers on investment in technology leads to adoption or vice
versa and explains the negative value. Often, most new technologies to adopt require huge
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capital outlay and because enterprises would want to reduce perceived risks of untried
options, managers go all out to seek for superior opinions from informed persons.
Consistent with this finding are studies (Samson and Hornby, 1988;Ling and Yttri, 2002;
Taylor and Todd, 1995;Venkatesh and Davis, 2000;Rogers, 2003) that confirm social status,
peer group opinions, other peoples’opinions and superior influences as critical adoption
factors. Although previous studies (Brown and Venkatesh, 2005;Venkatesh et al.,2012)
found pleasure and fun derivable from using particular technologies as critical adoption
drivers, the present study reported otherwise; the relationship is not statistically significant
because of socio-economic differences and extent of adoption.
6. Conclusion and implications
IS innovations are highly differentiated technologies for which no single proposed adoption
model is all-encompassing; adoption takes place after many factors, including those that are
conventionally appear favorable, must have been carefully assessed. Therefore, this paper
boosts the theoretical strengths of the T-O-E framework by integrating its contexts with
other frameworks. The paper develops a ten-factor theoretical framework from four
adoption contexts and examined the strength of each on adoption using SEM. The results
were insightful and showed that the factors in the technological, organizational and
environmental contexts have direct statistically significant relationship with adoption; thus,
adoption is more driven by T-O-E factors than by individual factors. Aside hedonistic
drives, other factors studied were significant elements of technology adoption. In technology
adoption, cost and actual result in terms of building competitive advantage are more
important adoption factors than mere fun and pleasure of the decision maker. The
implications of these findings are theoretical and practical and suggest further research.
By conceptualizing the research framework that integrates T-O-E taxonomies with
individual-related factors of UTAUT2 and testing the framework statistically, the paper
contributes to the theoretical and methodological discourse in the IS domain and differs
sufficiently from other studies (Awa et al., 2015;Tornatzky and Fleischer, 1990;Venkatesh
et al.,2007). The T-O-E framework treats individual issues as factors under organization;
however, by treating them as contexts/drivers, this study offers opportunity to fletch out
many factors and to provide extended theoretical strength. Further, the study has
significant practical implications; vendors and policy makers are encouraged to recognize
the strength of interpersonal and group relationships in addition to T-O-E contexts in
developing investment decisions without losing sight of crafting programs and policies that
make for making competitive advantage.
6.1 Suggestions for further research
This study came across various limitations and areas that would attract further
investigation opportunities. The study focuses on few small service enterprises in Nigeria,
and therefore, it will be interesting to extend the enterprises and the scope and to further
compare small enterprises within manufacturing and trading sectors. The data collected
relate to contexts specific to ICT adoption, which is one type of technology; additional
research may involve extending our measures to strengthen the direction of the causality
because cross-sectional data often imply that the causality identified may vary across
sectors and environments or may even lose weight and meaning overtime. Measures of the
constructs represent subjective perceptions and prone to common error biases; so, further
studies can address that. Further, the study relates to pre-adoption phase and so future
investigators may take up the implementation and post-adoption phases and business-to-
business adoption to forge a more integrated and holistic adoption lenses. Some T-O-E
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factors proposed by Tornatzky and Fleischer (1990) and later scholars (Thong, 1999;Jeyaraj
et al., 2006;Chuang et al.,2014;Awa et al., 2015;Yoon and George, 2013) were not captured
in this study; therefore, future studies should address them and perhaps integrate them with
UTAUT2 or other grounded theories.
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Further reading
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About the authors
Hart O. Awa holds a PhD in Marketing with specialization in Consumer Behavior and is currently a
Senior Academic and a Faculty Member of the Management Sciences, University of Port Harcourt,
Nigeria. He is at present the Faculty Co-ordinator of post-graduate programs and formerly Quality
Assurance Officer and a Member of the University-wide Examinations Committee. Hart co-ordinates, and
serves as a point-man in, the faculty teaching of Scientific Research and ICT at the masters and PhD
programs. Aside being a member of many professional bodies, he serves in the editorial and review
boards of many reputable journals and has presented leading papers in conferences organized in Nigeria,
UK, US, Canada, Australia, Ireland and Eastern Europe. Hart is in the top league of the very few Nigeria-
based teachers who champions scholarly writing on technology adoption and application of ICT
platforms to business. He reviews and writes for some journals ranked A by the Australian Business
Deans Council; amongst them are Journal of Information Technology and People by emerald, Journal of
Small Business Management by Wiley, and International Journal of Human Computer Interaction by
Taylor and Francis. Hart wrote three topics in the three volumes in one Encyclopedia of Corporate Social
Responsibility published by London Metropolitan University through Springer, UK. On the average, over
20 of Hart’s articles appeared in journals indexed and abstracted in Thomson Reuters, Association of
Business Schools (ABS), Web of science emerging sources citation index (ESCI), Scopus and other top
databases. One of his papers, co-authored with Professors Ogwo E. Ogwo and Ojiabo Ukoha, won the
2015 best paper award at the Imperial College conference organized by WBI, Australia, and LARAP, UK.
Hart is a Member of the Emerald Literati network in recognition of his scholarly role in the Emerald
family and having published ten of his papers in different but indexed emerald journals. Among others,
Hart’s research interest spans co-creation and customer recovery, consumer psychology, ICT application
to business and innovation adoption.
Ojiabo Ukoha is a Professor in Mathematics and Computer Science, University of Maryland, USA.
His interests include the infusion of technology into mathematics instruction, factor analysis in
applied research and recruiting and retaining low-income students in computer science and other
STEM disciplines.
Sunny R. Igwe holds a PhD with specialization in Marketing Management. He is a Senior Lecturer
in the Department of Marketing, where he co-ordinates, and plays significant role in, post-graduate
programs. Sunny served in the University Housing Committee and as at now, he is the Faculty of
Management Sciences Co-ordinator of GES courses.
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