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A model of adoption
determinants of ERP within
T-O-E framework
Hart O. Awa
Department of Marketing, University of Port Harcourt,
Port Harcourt, Nigeria, and
Ojiabo Ukoha Ojiabo
Department of Mathematics, University of Maryland,
College Park, Maryland, USA
Abstract
Purpose –The purpose of this paper is to attempts to provide further insight into IS adoption by
investigating how 12 factors within the technology-organization-environment framework explain
small- and medium-sized enterprises’(SMEs) adoption of enterprise resource planning (ERP) software.
Design/methodology/approach –The approach for data collection was questionnaire survey
involving executives of SMEs drawn from six fast service enterprises with strong operations in
Port Harcourt. The mode of sampling was purposive and snow ball and analysis involves logistic
regression test; the likelihood ratios, Hosmer and Lemeshow’s goodness of fit, and Nagelkerke’sR
2
provided the necessary lenses.
Findings –The 12 hypothesized relationships were supported with each factor differing in its
statistical coefficient and some bearing negative values. ICT infrastructures, technical know-how,
perceived compatibility, perceived values, security, and firm’s size were found statistically significant
adoption determinants. Although, scope of business operations, trading partners’readiness, demographic
composition, subjective norms, external supports, and competitive pressures were equally critical but
their negative coefficients suggest they pose less of an obstacle to adopters than to non-adopters.
Thus, adoption of ERP by SMEs is more driven by technological factors than by organizational and
environmental factors.
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 and to
factor in the implementation and post-adoption phases in order to forge a more integrated and holistic
adoption framework.
Practical implications –The model may be used by IS vendors to make investment decisions, to
meet customers’needs, and to craft informed marketing programs that would appeal to actual and
potential adopters and cause them to progress in the customer loyalty ladder.
Originality/value –The paper contributes to the growing research on IS innovations’adoption by
using factors within the T-O-E framework to explains SMEs’adoption of ERP.
Keywords Organizational change, Information management, Adoption,
Small and medium-sized enterprises (SMEs)
Paper type Research paper
1. Introduction
The contemporary global economy emphasizes the wise use of intellectual capital and
technology to build competitive advantage via integrating processes, supporting
corporate strategies, and optimizing resources (Metaxiotis, 2009; Pang and Jang, 2008).
Enterprise resource planning (ERP) software represents one of such state-of-the-art
information technologies that integrate managerial and operational processes within and
beyond the traditional boundaries (Ahituv et al., 2002; Hitt et al., 2002; Shiau et al.,2009).
Information Technology & People
Vol. 29 No. 4, 2016
pp. 901-930
© Emerald Group Publishing Limited
0959-3845
DOI 10.1108/ITP-03-2015-0068
Received 30 March 2015
Revised 20 July 2015
19 October 2015
Accepted 19 October 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0959-3845.htm
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Lall and Teyarachakul (2006) and Shiau et al. (2009) posit that ERP integrates and
supports production, procurement, human resources, finance and accounting, marketing
and distribution, and other functional and sub-functional systems. Scholars (Federici,
2009; Holsapple and Sena, 2005; Maguire et al., 2007) propose that process integration of
this sort provides enterprises with opportunities to reduce costs and improve efficiencies
through cycle time and lead-time compression, communications, optimal inventory
holding, improved product quality, network relationship, and search activities. Although
ERP makes operationally significant promises, van Everdingen et al. (2000) and Maguire
et al. (2007) believe that significant number of vendors principally targets large firms
when the agility of small enterprises and their aggressive quest to build competitiveness
make them have even better need for the software. Empirical evidence (Waarts et al.,
2002; Kumar and Hillegersberg, 2000; Kannabiran and Dharmalingam, 2012) confirms
that large firms are more predisposed to adopt digital applications than small firms.
Small- and medium-sized enterprises (SMEs) rarely have the cognate experience and
resources to effectively implement digital applications (Chuang et al., 2009; Shiau et al.,
2009; Kannabiran and Dharmalingam, 2012) though studies (Ramdani et al., 2009; Lall
and Teyarachakul, 2006; Alsene, 2007; Maguire et al., 2007) show that the need to
improve market positioning and to benefit from governments’support programs has
lately precipitated SMEs’adoption of ERP in some economies. However, adoption
models have been proposed by scholars to track down the pace of diffusion of any
innovation. Among such models are technology acceptance model (TAM) (Davis, 1989),
theory of reasoned action (TRA) (Ajzen and Fishbein, 1980), theory of planned behavior
(TPB) (Ajzen, 1991), innovation diffusion theory (Rogers, 2003), stage model (Poon and
Swatman, 1999), technology-organization-environment (T-O-E) (Tornatzky and
Fleischer, 1990), and resource-based view (Caldeira and Ward, 2003). Although some
of these models/theories evolve from the TRA and have their principal constructs
overlapping, each contributes to the underpinning adoption theory (Eze et al., 2013).
On assumption that their propositions are well-known, this paper proposes 12
constructs from the T-O-E framework and uses that to explain SMEs’adoption of ERP.
Significant number of ERP studies focuses on its adoption (van Everdingen et al., 2000;
Moller et al., 2004); implementation (Alsene, 2007; Okrent and Vokurka, 2004); financial
and economic benefits (Matolcsy et al., 2005; Nicolaou et al., 2003); success measurement
(Wu and Wang, 2006); critical success factors (Motwani and Subramanian, 2005;
Maguire et al., 2007) and extended ERP modules (Metaxiotis et al., 2003).
These notwithstanding, limited inquiries seek to move from the utilitarian, attitudinal,
techno-economic, and deterministic contexts of TAM, TRA, and TPB in determining
adoption behavior. Irrespective of the scholarly (Barrett et al., 2006; Jacobsson and
Linderoth, 2010) advocate for more social interactive systems as a panacea for the
challenges of deterministic system, many ERP studies (Maguire et al., 2007; Wu and Wang,
2006; Alsene, 2007) scarcely borrow the framework of T-O-E to underpin their work. TAM
focuses extensively on technology to the neglect of social and psychological parameters
and thus, offers limited explanation and prediction to the phenomena of interest
(Venkatesh and Bala, 2008). IDT (Rogers, 2003) and TPB (Ajzen, 1991) incorporate the
social and psychological parameters but their frameworks are yet to underpin as much
studies in the contemporary IS domain as TAM and T-O-E framework. T-O-E framework
is valid, robust enough, and most dominant in studying organizational-level adoption
(Gangwar et al., 2014) and has gained more theoretical and empirical support from
existing studies (Iacovou et al., 1995; Kuan and Chau, 2001; Zhu et al., 2003; Thong,
1999; Eze et al., 2013; Yoon and George, 2013; Zheng et al., 2011; Henriksen, 2006;
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Hong et al., 2006; Legris et al., 2003) than SM, TAM, TRA, TPB, and IDT. For instance,
T-O-E factors significantly determine the adoption of EDI (Iacovou et al., 1995; Kuan
and Chau, 2001) and provide barriers to ICT adoption (Thong, 1999) and support in ICT
field (Zhu et al., 2003).
IDT framework uses indicators within the constructs of organization and
technology to explain adoption behavior whereas T-O-E integrated the
environmental context (Gangwar et al., 2014). Scholars (Hossain and Quaddus,
2011) propose that T-O-E framework is one of the few adoption frameworks that
attempt moving toward the socio-economic features while recognizing the interplay
of technology development and organization’s conditions involving the necessary
business and organizational reconfiguration shaped by industry environment.
Further, TRA, IDT, TPB, and other grounded adoption theories are not specific for
technology adoption; IDT finds practical utility in many disciplines but it does not
specifically target acceptance of ICT platforms as much as TAM and T-O-E
frameworks (Moore and Benbasat, 1991). Aside T-O-E framework being free from
industry and firm size constraints (Wen and Chen, 2010), it provides a more holistic
picture about adoption factors, user adoption processes, and implementation; the
foreseeing challenges; the technology’s impact on value chain and post-adoption
diffusion; and the development of organizational capabilities using the technology
(Wang et al., 2010; Salwani et al., 2009). Therefore, the strength of this paper lies on
contributing on the sector-specific characteristics since there is a dearth of studies
that use the T-O-E framework to study SMEs’adoption determinants of ERP; and to
complement knowledge of other inquiries (e.g. Pang and Jang, 2008; Eze et al., 2013)
that reported industry-specific factors that determine the adoption of ERP within the
framework of T-O-E.
2. Theoretical contexts
2.1 SMEs and adoption of ERP
The definitions of SMEs vary across nations; predominantly, they propose employment
figures, annual turnover, and fixed assets (Rizk, 2004; Small and Medium Sized
Development Agency of Nigeria (SMEDAN), 2005) as common denominators.
In America and many European nations, for instance, SMEs employ less than
500 persons (OECD, 2000); in South Africa and Australia between 100 and 200 persons
(Scupola, 2009); and in Denmark 250 employees (OECD, 2002). The provisions of Small
and Medium Sized Development Agency of Nigeria (SMEDAN, 2005) categorized SMEs
into three based on the same parameters. Previous (Mutuala and Brakel, 2006; Jutla
et al., 2002; Scupola, 2009; Shiau et al., 2009; Ramdani et al., 2009; Metaxiotis, 2009;
Ongori, 2009; Federici, 2009; Ongori and Migrio, 2010) inquiries confirm that SMEs are
the potent drivers of the informal sector as well as important sources of flexibility, local
capital formation, innovations, improved living standards, and employment creation.
They provide approximately 80 percent of economic growth (Jutla et al., 2002), one-third
of GDP and 70 percent employment in Australia (Scupola, 2009), and account for
between 96 and 99 percent of enterprises in North America, Europe, and most OECD
countries (Scupola, 2009; Shiau et al., 2009; Ramdani et al., 2009). However, because of
these onerous socio-economic roles played by SMEs, adoption of modern technologies
supposedly improves their competitive advantage.
Khasawneh (2008) and Musawa and Wahab (2012) explain that adoption of
technologies defines individual and/or organization levels voluntary decision to first
accept and/or use an innovation. Most digital ICT technologies turn the world flat,
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remove the competitive disadvantages of small enterprises and geographic isolation
(Wymer and Regan, 2005), and offer the adopting enterprises the opportunity to build
and strengthen global competitive advantages. Supposedly, SMEs are comparatively
better positioned than large firms (based on their operating agility) to exploit the
potentials of new technologies. The government and its agencies in many economies
have regularly launched programs to support informal sector because they drive the
contemporary economy. Specifically, adoption of ERP systems by SMEs defines
working with configurable information systems packages that integrate operations
within and across departments/divisions/units that form the enterprise’s community.
It entails building inter-and intra-functional alignments of operations as well as
real-time and standardized flow of information within a community; thus, information
and information-based processing modules of a unit/department can be accessed
within and across boundaries of an enterprise for building competitive advantage
(Metaxiotis et al., 2005; Scupola, 2009). Pang and Jang (2008, p. 100) insist that “ERP
projects facilitate automation of many, if not all, basic processes in order to integrate
information across an enterprise and to eliminate complex, expensive interfaces
amongst computer systems.”Implicit is that the cornerstone of ERP software is that
people, processes, and the new technology should be aligned to ensure information
sharing as well as business flexibility and efficiency (Davenport, 1998).
Scholars propose that successful implementation of ERP systems improves operational
efficiency and consistency, product quality, customer service and customer friendliness,
and ultimately competitiveness through value-added information, transparency, and new
levels of innovation from network externality and knowledge sharing (de Burca et al., 2005;
Esteves, 2009; Zhang et al., 2005). It is proposed (Levy et al., 2005; Gengatharen and
Standing, 2005) that ERP software levels the playing field; it offers SMEs a considerable
opportunity to compete more effectively with their rivals, including large enterprises. SMEs
are more adaptable and responsive to changes than large firms and often benefit from the
speed and operational agility offered by the electronic environment (Metaxiotis, 2009;
Stockdale and Standing, 2004). For these, ERP’s developers and vendors had since 2004
began working hard to encourage SMEs to upgrade their legacy systems and to reposition
their operations more competitively. However, the lack of awareness of the benefits of an
end-to-end system as well as SMEs’limited resources often challenges the acquisition and
implementation of ERP software. Often small size explains the inability to commit
resources, to assign ERP tools to something different from short-run operating issues, and
to understand ERP’s benefits (Martin and Matlay, 2001; Metaxiotis, 2009).
Ahituv et al. (2002) and Huang et al. (2004) posit that many SMEs find it difficult to take
full potential benefits of ERP solution because its implementation is technically complex
and demands huge investment in time, money, and internal resources. Further, ICT
strategy and implementation are critically affected by the fact that only about 15 percent of
small businesses and 30 percent of medium-size businesses employ ICT experts or own
ICT department (Metaxiotis, 2009). Scholars (Pang and Jang, 2008; Motwani et al., 2002)
conclude that a cautious, evolutionary, and bureaucratic process supported by careful
change management, network relationships, and cultural readiness facilitate successful
adoption of ERP. Other organizational conditions that influence ERP adoption include top
management resistance to change (Child, 1974; Hambrick and Mason, 1984), adoption
without organizational readiness and proper change management (Motwani et al., 2002),
poor implementation process (Umble et al., 2003), and ineffective ERP systems (Lall and
Teyarachakul, 2006). Nevertheless, studies (Maguire et al., 2007; Moller et al., 2004) confirm
the growing use of ERP by SMEs to gain competitive advantage.
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2.2 T-O-E framework
The T-O-E framework of Tornatzky and Fleischer (1990) is the first of its kind in adoption
literature that proposes a generic set of factors that explain and predict the likelihood of
innovation adoption. Its proposition suggests that adoption is influenced by technology
development (Kauffman and Walden, 2001); organizational conditions, business and
organizational reconfiguration (Chatterjee et al., 2002); and industry environment (Kowath
and Choon, 2001). Technology describes that adoption is influenced by the pool of
technologies internal and external to the firm as well as their perceived usefulness,
technical and organizational compatibility, complexity and learning curve, pilot test/
experimentation, and visibility/imagination (Awa et al., 2015a, b). Organization captures
descriptive measures such as firm’s business scope, top management support,
organizational culture, complexity of managerial structure measured by centralization,
formalization, and vertical differentiation, quality of human capital, and size and size-
related issues such as internal slack resources and specialization ( Jeyaraj et al.,2006;
Sabherwal et al., 2006; Tornatzky and Fleischer, 1990).
Environment relates to those operational facilitators and inhibitors; significant
among them are competitive pressure, trading partners’readiness, socio-cultural
issues, government encouragement, and technology support infrastructures such as
access to quality ICT consultants ( Jeyaraj et al., 2006; Zhu et al., 2003; Al-Qirim, 2006).
Thong (1999) espouses decision-maker from Tornatzky and Fleischer’s (1990)
organization to have D-T-O-E adoption framework on accounts that a firm’s
approach to strategic issues is shaped by the idiosyncrasies of the managers. Often
analysts believe that ICT adoption behavior is incomplete without such constructs as
owner’s enthusiasm and growth ambition (Fillis et al., 2004), top management support
and managerial productivity (Grandon and Pearson, 2004), managers’belief differences
(Riemenschneider and McKinney, 2002), and CEO’s knowledge and characteristics
(Shiau et al., 2009; Thong, 1999). T-O-E framework cross-cuts Rogers’(2003) three
proposed groups of adoption predicators –leader characteristics relating to change;
internal characteristics (centralization, complexity, formalization, interconnectedness,
organizational slack, and size), and external characteristics (system’s openness).
The major snag with T-O-E framework is that some of the constructs in the adoption
predictors are assumed to apply more to large organizations, where clients are sure of
continuity and less complaints, than to SMEs (Parker and Castlemen, 2009; Awa et al.,
2011). Nevertheless, T-O-E framework has gained empirical validity and underpinned
many ICT adoption inquiries, especially those that focus on EDI or inter-organizational
information systems (IOIS). Scholars (Eze et al., 2013; Chau and Tam, 1997) adopted T-O-E
frameworkintheirstudyandidentifiedtheinnovation’s characteristics, organization’s
technology, and external environment as quite useful in explaining and predicting the rate
of adoption. Zhu and Kraemer (2005) found technology competence, firm size, financial
commitment, competitive pressure, and regulatory support as critical adoption factors
within T-O-E framework. Similarly, Kuan and Chau (2001) confirm the usefulness of T-O-E
framework in small enterprises when they proposed a perception-based EDI adoption
model with six determinants –cost structure, technical competence, industry pressure,
government pressure, direct perceived usefulness, and indirect perceived usefulness.
Other studies found environmental and organizational factors (Henriksen, 2006)
more statistically significant determinants than technological factors even when Thong
(1999) had found that adoption has significant relationship with technology and
organization. Further, Zhu et al. (2004) concluded technology readiness as the strongest
adoption factor and added that financial resources, global scope, and regulatory
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environment significantly contribute to e-business value. Zhu et al. (2003) found higher
level of consumer readiness, trading partners’readiness, and competitive pressures as
critical environmental factors though relatively more technologically inclined firms
reflected by greater scope of business are more likely to develop robust e-business
operations. Although these studies differ in their contributions to knowledge, they
seem to pay scanty attention on how to explain and predict SMEs’adoption of ERP
within the framework of T-O-E; therefore, this study intends to bridge the void.
2.3 Study framework and hypotheses
High involvement decision as ERP adoption demands conscious search effort and using
the Bass model to reduce the perceived technical, financial, and social risks (Awa et al.,
2015a, b). Most traditional adoption theories are accused of illusion of accumulated
traditions (Benbasat and Barki, 2007), technological determinism, and techno-centric
predictions (Vankatesh et al., 2007); implying that technology, rather than individuals,
determines organization’s structure and behavior. Integrating the constructs of T-O-E
framework with some proposed new ones in the research model, somewhat social and
behavioral constructivism is enrolled to bring both human and non-human actors into
the network. The postulate of this model is similar to Actor Network Theory (ANT) since
it emphasizes dynamic capabilities and mutual interplay of technical and social systems.
The research framework of this study is captured in Figure 1. The figure reports on
H6
H7
H8
H3
Technology
Organization
Environment
ICT infrastructures
Technical know-how
Perceived
compatibility
Perceived values
Size of the firm
Demographic
composition
Scope of business
operations
External support
Competitive pressure
ERP adoption
H6
H1
H11
Security
Trading partners’
readiness
Subjective norms
H10
H4
H2
H5
H9
H12
Figure 1.
Proposed framework
explaining ERP
adoption within
T-O-E
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the three adoption drivers (see Tornatzky and Fleischer, 1990) and 12 constructs;
technology –ICT infrastructure, technical know-how, perceived compatibility, and
perceived value, and security; organization-demographic composition, size, scope of
business operations, and subjective norms; and environment- competitive pressure,
external support, and trading partners’readiness. The choice of these constructs was
based on their huge scholarship and their specific application to ERP solution.
Technology. Often technology describes a force for creative destruction (Kotler, 1984)
though its understanding here relates more to the theory of perceived behavioral control;
that is, users’agility shaped by cognate resources needed to exploit the potentials of the
proposed applications. Awa et al. (2015a, b) opine that the technology itself and the skills,
opportunities, and resources for operating it must be strategically analyzed before
adoption is finalized. Emphasizing the extended TRA, scholars (Kwon and Zmud, 1987;
Kuan and Chau, 2001; Zhu et al., 2002, 2003; Zhu and Kraemer, 2002; Tornatzky and
Fleischer, 1990; Khemthong and Roberts, 2006; Al-Qirim, 2006) confirm that the
availability of internal and external technology resources (e.g. ICT infrastructures,
internet skills, ICT technical know-how, user time, and developers) assist in the effective
adoption of facilities. Other technology factors proposed are relative advantage, security,
reliability, capability, cost, quality of software in the market, vendor supports, type of IS/
IT solution within the firm and compatibility, IS/IT objectives and assumptions, and
evaluation of IS/IT benefits (Al-Qirim, 2004; Caldeira and Ward, 2002).
Five technology factors were factored in our proposed framework; they are ICT
infrastructures (access to network services to support web and internet technologies),
technical know-how (availability of installation and maintenance facilities), perceived
compatibility (alignment with existing structure and procedures), security
(personalized information), and perceived values (the outcome in term of building
competitive advantage). Enterprises that exhibit strong and sophisticated technology
competence show more likelihood to adopt ERP. Zhu and Kraemer (2002) and Zhu et al.
(2004) found that enterprises that have sufficient financial resources to procure ERP
infrastructures are faster in implementing e-business than those that lack finances.
Often with mix results, perceived compatibility (Tornatzky and Fleischer, 1990;
Khemthong and Roberts, 2006; Premkumar, 2003), perceived simplicity (Khemthong
and Roberts, 2006; Riemenscheider et al., 2003; Brown and Lockett, 2004), perceived
observability (Musawa and Wahab, 2012; Wang et al., 2010), and perceived values
(Iacovou et al., 1995; Mehrtens et al., 2001; Lee et al., 2004; Grandon and Pearson, 2004)
were other confirmed critical adoption predictors. The adoption of new technologies
brings about significant changes to the work practices and resistance to change is a
normal organizational reaction (Premkumar and Roberts, 1999).
Lee et al. (2004) report that innovations perceived to have more relative advantage over
the incumbent practices is more likely to be adopted. Whereas, Grover (1993) found
negative association between complexity and adoption of IS innovations, Thong (1999)
confirmed it a critical determinant in the context of small businesses. Zhu et al. (2004)
conclude that the technology-driven nature of e-business precipitates that enterprises that
efficiently exploit the complexities of internet technologies and exhibit technology
readiness are more likely to create values with ERP faster than others, who do not show
such readiness. ICT infrastructures provide the platforms upon which real-time
interaction exists among community members, internet skills offer the technical know-
how, and ICT know-how provides the business and managerial skills needed to effectively
develop and operate the applications (Eze et al., 2013; Zhu et al., 2003). Scholars
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(Metaxiotis, 2009; Scupola, 2009; Porter, 1996) suggest that technology competence goes
beyond physical assets; it includes intangible resources, which perhaps generate
competitive advantages for innovators since skills and know-how complement physical
assets and are more difficult to imitate by rivals. The proficient know-how understands
the usefulness of the technology and uses his experiences to turn the complex part of the
technology into mental effortlessness (Davis, 1993; Lu et al., 2003).
Privacy, safety, and security are essential in digital interactions especially when
transactions move beyond the confines of simple concept. Security defines the ability of
the company’s website to protect consumer information and their financial transaction
data from being stolen during transmission (Hua, 2009). It is much more critical
determinant of intention to purchase online than the perceived ease of use and perceived
usefulness (Hua, 2009; Salisbury et al., 2001). Studies (Lu et al., 2003; Cho et al., 2007; Luarn
and Lin, 2005) suggest positive relationship between security trust in web transactions
and customer attitude, intention to buy, and purchase behavior. Others (Limthongchai and
Speece, 2003; Hua, 2009; Yang and Jun, 2002; Then and DeLong, 1999; Miyazaki and
Fernandez, 2000; Belkhamza and Wafa, 2009) found security threat as the most critical
barrier that prevents online browsers from becoming online buyers. Shafi (2002) found
that people use internet facilities mostly for conservative tasks such as communications
and information gathering, and due to security concerns and access issues they are less
likely to use the internet in a more advanced forms. Benassi (1999) and Green (1997)
confirm that people leave websites when their personal information is requested for. We
propose the following hypothesized relationships on technology factors:
H1. The availability of ICT infrastructures significantly determines SMEs’adoption
of ERP.
H2. The availability of technical know-how significantly determines SMEs’
adoption of ERP.
H3. The perceived compatibility between ERP software and existing platforms
makes adoption possible among SMEs.
H4. The perceived values and/or benefits of ERP software in facilitating operations
make adoption possible among SMEs.
H5. There is a significant relationship between perceived security and adoption of ERP.
Organization. Organization factors are anchored directly on the availability and use of
internal resources (Wymer and Regan, 2005). Some IS studies perceive organization in
the contexts of social influences (Ajzen, 1991; Taylor and Todd, 1995; Rogers, 2003;
Venkatesh and Davis, 2000), individual difference factors (Awa et al., 2011, 2015a, b;
Hambrick and Mason, 1984; Chuang et al., 2009), organizations’mission (Awa et al.,
2010), and facilitating conditions (Triandis, 1980; Thompson et al., 1994). Other studies
included cultural and structural configurations (Sheridan, 1994; Ongori, 2009), quality
of human resource, type of products, complexity of managerial structure (Glover and
Goslar, 1993; Scupola, 2009), and information sources and communication channels
(Rai, 1995; Kannabiran and Dharmalingam, 2012). Yet others emphasize firm’s scope of
business operations, enterprise size and size-related issues such as slack resources and
specialization (Hitt, 1999; Zhu et al., 2003; Damanpour, 1991; Eze et al., 2013; Pang and
Jang, 2008). Organizational factors studied here were subjective norms, scope of
business operations, enterprise size and demographic composition.
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Size represents an environmental and organizational issue (Kamal, 2006) though it
measures the size of the community served and the number of services provided
(Akbulut, 2002). It is a critical adoption factor in central and local governments
(Cho et al., 2007; Tornatzky and Fleischer, 1990); firms in larger cities adopt more
sophisticated innovations than those in smaller cities (Norris, 1999). Other studies
(Hwang et al., 2004; Zhu et al., 2003; Zhu and Kraemer, 2005) emphasize that adoption is
assumed slower among smaller enterprises often because they rarely possess the
cognate economy of scale advantage and facilitating slacks as well as the resilience to
bear the associated risks and to encourage trading partners and other stakeholders to
adopt technology with network externalities. Densmore’s (1998) study shows that the
proportion of EDI adoption among larger firms is about 95 percent and only about
2 percent in small firms. The 1999 statistics from OECD countries reported that the
diffusion of ICT among large firms was 80-86 percent; for firms with 20 employees or
more, 61-95 percent; and for very small firms, 19-57 percent (OECD, 1999). On the basis
of the level of competitive advantage built, other studies (Wang et al., 2010; Hossain and
Quaddus, 2011; Ramdani et al., 2009) confirm size a critical factor in RFID, e-commerce,
and ERP adoption though non-critical in EDI adoption.
Further, inquiries (Awa et al., 2015a, b; Hambrick and Mason, 1984; Thong, 1999; Zhu
et al., 2003; Chuang et al., 2009) support that top management’s demographic differences
and knowledge about the innovation may be thornier barriers to adoption than size since
organization’s strategies are often shaped by the managers’functional and emotional
peculiarities about the future, alternatives, and consequences. Awa et al. (2011) submit
that innovation adoption is influenced by group heterogeneity and cohesiveness as well
as group members’functional tracks, education, age, gender, and experience. Favorable
experiences influence the adoption of similar technologies on accounts of stimulus
generalization and technology cluster (Awa et al., 2010); education influences personal
innovativeness, belief/value systems, cognitive preferences, and receptivity of an
innovation (Becker, 1970); the German market for mobile phone is 60 percent male and
40 percent female (Lu et al., 2003); and young executives are much more associated with
corporate growth and entrepreneurial behaviors (Child, 1974).
Further, sociologists believe that often members of a group exhibit cohesiveness
even against their own feelings in order to show commitment to the group norms.
Innovative individuals have positive attitudes, ability to communicate with others and
a high level of social participation and social mobility (Rogers, 2003; Marchionni and
Ritchie, 2007; Choudrie and Dwivedi, 2005). Lu et al. (2003) found subjective norm to be
an important determinant of intention and practically epitomizes the perception of
others about adoption behavior(s). In the findings of Taylor and Todd (1995), social
influences are more of moving from functional to psychological motives of behavior(s)
perhaps because they define other peoples’opinions, superior influences, and peer
group opinions. Samson and Hornby (1988) report that in China, 73 percent of the
executive class in big cities owned mobile phones early 1998 not solely for
communications but also for social status. Similarly, Ling and Yttri (2002) suggest that
younger users of communication interfaces are more subject to social influences
because they are at social development and learning stage of life. The work also
suggests that young users’social networks are more dynamic and thus exposed to
more influences than other users.
However, the greater the scope of business, the more likelihood a firm invests in IS
innovations (Hitt, 1999). Zhu et al. (2003) describe the role of scope of business operation
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as an adoption predicator. Digitalization of operations reduces internal co-ordination
costs, administrative complexities and information processing. Also, firms with larger
scope of business glamour for e-business to reduce search costs for both buyers and
sellers (Bakos, 1998) and to achieve demand aggregation and improved inventory
management (Chopra and Meindl, 2001) and have more latitude of benefiting from
synergy of e-commerce and traditional business. Zhu et al. (2003) observe that web
connectivity and knowledge sharing may help consumers to locate physical stores:
H6. The size of SMEs significantly determines the possibility of adopting ERP facilities.
H7. The demographic composition of the decision making team significantly
determines SMEs’adoption of ERP facilities.
H8. The scope of business operation is more likely to positively determine ERP
adoption.
H9. Subjective norms significantly determine the possibility of adopting ERP facilities.
Environment. Organization’s propensity to innovate and/or to engage in strategic and/
or tactical issues is often shaped by the opportunities and threats as well as the
strengths and threats (SWOT) imposed by its environment (Raymond, 2001).
Managing change involves anticipating (eliminating surprises) and responding to
environmental trends (Abell, 1978) and the deployment of actions that result from
strategic and/or proactive decisions. Awa and Kalu (2010) opine that the environmental
changes must be anticipated, monitored, assessed, and incorporated into the
organization’s decision process because they often suggest radical changes in
resource requirements though firm’s resources and key competencies are rarely easy
to adjust. The technical and strategic aspects of ERP systems are almost of equal
importance (Yen and Sheu, 2004) in creating competitive advantages amidst environment
characterized by stiff competition and dynamic changes (Pang and Jang, 2008). Porter
(1996) emphasizes that operational effectiveness, strategic positioning, and proactive
decisions are the internal and external goals of any businesses.
Firms’decision to use ERP is influenced by such industry factors as peer influences,
rate of technical change, external supports, government rules and regulations,
competitive pressure, trading partners’(e.g. vendors, dealers, and suppliers) readiness,
market volatility, coercive influences from customers (Raymond and Blili, 1997;
Tornatzky and Fleischer, 1990), and perceived trust (Awa et al., 2010). The factors
captured in our framework were external support, competitive pressure, and trading
partners’readiness. On the basis of retaliatory moves and endless vicious circle, studies
(Zhu and Kraemer, 2005; Zhu et al., 2003; Iacovou et al., 1995; Jeyaraj et al., 2006) recognize
competitive pressure as a strategic necessity and a critical innovation adoption driver.
Porter and Millar (1985) analyze the significance of competitive pressure on adoption and
suggest that modern technologies alter the rules of competitive games, restructure the
industry make-ups, and unravel novelty in outperforming rivals. ICT platforms induce
change in industry structure such as disintermediation and intermediation (Bailey and
Bakos, 1997), offer new means for competing and alter competition rules via lock-in
(Shapiro and Varian, 1999), electronic integration (Venkatraman and Zaheer, 1990), and
brick-and-click synergy (Steinfield et al., 2002).
Further, external support (perhaps from government agencies and NGOs) was
confirmed not just a significant determinant ICT success (Delone, 1988) but also
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correlates positively with ICT adoption (Premkumar and Roberts, 1999).
Scholars (Pflughoeft et al., 2003; Sinkkonen, 2001; Windrum and de Berranger, 2004;
Purao and Campbell, 1998) captured in their frameworks network externalities with
trading partners to ensure electronic interactions and transactions along the value
chain. Against the findings of these other studies, Windrum and de Berranger (2004)
found that pressure from suppliers and allied firms was not statistically significant in
determining the adoption of intranets or extranets. Awa et al. (2010, 2015a, b)
propose that most ICT platforms transcend the digitalization of business domain of
individual enterprises; there is need for integrated and electronically compatible
trading systems that link the enterprises and their trading partners to provide internet
enabled services for one another:
H10. Competitive pressure significantly determines SMEs’adoption of ERP solution.
H11. External support significantly influences the adoption of ERP software by
SMEs.
H12. Trading partners’readiness significantly determines SMEs’adoption of ERP
solution.
3. Population and methods
The survey data were drawn from SMEs operating in six fast growing service
enterprises with strong operations in the city of Port Harcourt, Nigeria. The SMEs were
those in ICT maintenance, legal services, healthcare services, laundry and dry cleaning,
make-ups (e.g. barbing, hair dressing, and manicure and pedicure), and management
consultancy. We relied on the provisions of Small and Medium Sized Development
Agency of Nigeria (SMEDAN, 2005) to capture those with at least ten employees, huge
investment in ICT, and annual turnover of five Million Naira or less. The criterion of ten
employees was necessary because of the peculiarity of Nigeria and to avert the
conditions spanning between 100 and 500 employees as stipulated by developed and
emerging nations. The US Small Business Administration and many European nations
measure SMEs in terms of employing 500 or less persons (OECD, 2000); South Africa
and Australia between 100 and 200 persons (Scupola, 2009); and Denmark 250
employees (OECD, 2002). Another criterion used here to define the population was that
the SMEs must use ERP to integrate operations within the enterprise community and
be duly registered with Corporate Affairs Commission (CAC) and other relevant
government approved bodies. From the initial pilot study, a list of 373 SMEs was
drawn and the cluster of federal and state ministries and parastatals as well as huge oil
deposit and commercial activities in the city of Port Harcourt makes it to play host to
expatriates and major and minor Nigeria’s tribes.
Besides, Bingham (1976) and Bouchard (1993) relied on critical mass theory and
warn that cities with higher socio-economic status (and are in close proximity) are more
prone to amenity-based values than low socio-economic cities, who often emphasize
necessity-based (e.g. innovations designed to correct some specific deficiencies).
The sampling frame was purely owners and executives and the modes of sampling
were purposive and snow ball; we use our experiential knowledge and judgment to
choose the first few cases whose opinions best represented that of the community and
then relied on referrals for further guide. In order 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).
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4. Data analysis and results
The logistic regression and Wald’s statistics were used for analysis; the first uses the
likelihood ratios, Hosmer and Lemeshow’s goodness of fit, and Nagelkerke’sR
2
to
estimate the explanatory strength of the latent variables, and the Wald’s statistics test
the significance of the regression coefficients of the 12 hypothesized relationships.
Pang and Jang (2008) propose that such multivariate test statistic is often preferred to
multiple regression tests when the dependent variable is dichotomous (adopters vs
non-adopters). Logistic regression assesses the impact of a number of factors on the
likelihood that the respondents report adoption or non-adoption of ERP software.
4.1 Measurement of instruments
Content and construct validities were assessed. The former explains the subjective and
judgmental opinions that support the adequacy with which a specific domain of content
has been sampled or the extent to which an instrument is truly a comprehensive measure
of the area under study (Alam et al., 2011; Nunnally, 1978). And then the latter deals with
the extent to which the statement items in a scale measure the same construct.
The constructs of this study are well-researched and have well-developed measures in
literature; thus, their scales have some measures of content validity. We drew the
measures for the constructs from the relevant literature (see Table II) and asked the
respondents to rate their level of agreement to the batteries of statements on a five-point
scale (from 5 ¼strongly agree through 1 ¼strongly disagree). Based on these, a
confirmatory factor analysis of the multi-item indicators was performed to test
dimensionality and reliability of variables and/or to identify the underlying constructs and
investigate the relationships among key survey interval-scaled questions. Specifically, the
factor extraction technique involved the principal components analysis (PCA) with
varimax rotation to analyze variances, confirm orthogonal or oblique relationships, and
ultimately guide decisions on the indicators to retain or drop. Kaiser’s rule suggests the
retention of components whose eigenvalues are greater than 1 (Kaiser, 1974).
The table reported only the items whose loading surpassed Stevens’(1992)
benchmark of 0.60; thus, it shows a well-explained factor structure since the observed
items were reasonable indicators of each of the latent variables. Kaiser-Meyer-Olkin
(KMO) measures of sampling adequacy supports the appropriateness of PCA (Kaiser,
1974) since the item-to-response ratio was considered acceptable (see Hinkin, 1995).
Bartlett’s tests of sphericity ( χ
2
) were significant at po0.001, further indicating
sufficient inter-correlation. The measures met the conditions proposed by Fornell and
Larcker (1981); the condition supports discriminant validity when the average variance
extracted is greater for each factor than the common variance of the two factors
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
Table I.
Sample description
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Factor dimension and items measured
Factor
loading Eigenvalue
Cronbach’s
α
Percentage of
variance
explained KMO
Bartlett’s
test of
sphericity
ICT infrastructures (see Pang and Jang, 2008) 7.24 0.881 28.02 0.810 71.23
Number of employees connected to the internet 0.77
Number of computers connected online 0.69
Number of computers to employees 0.63
Technical know-how (see Brown and Lockett, 2004; Riemenscheider et al., 2003) 6.02 0.802 23.10 0.821 73.16
Availability of technical/maintenance unit(s) 0.87
The number of technical officers employed 0.79
Regularity of staff training on ICT 0.73
Existence of ICT consultants 0.69
Availability of service providers and spare parts 0.63
Perceived compatibility (see Khemthong and Roberts, 2006; Grandon and Pearson,
2004; Tornatzky and Fleischer, 1990) 4.40 0.711 21.04 0.804 76.31
Fit between the new and existing technologies 0.82
Fit between the new systems and existing work procedures 0.78
Fit between the new systems and corporate culture 0.70
Fit between the new systems and corporate philosophies, norms, and values 0.64
Perceived values (see Al-Qirim, 2006) 2.94 0.784 18.00 0.871 78.26
Reduced operating costs 0.81
Improved operational efficiency 0.76
Improved customer service 0.72
Improved customer relationship 0.69
Reaching new customers 0.65
Security (see Alam et al., 2011) 2.54 0.768 16.01 0.817 79.11
Lack of confidentiality of transaction details 0.85
Web transaction information is not private 0.82
No confidence in web payment system 0.79
Current laws and regulations are insufficient to protect user’s interest 0.73
Scope of business operations (see Chopra and Meindl, 2001; Gurbaxani and Whang,
1991; Shapiro and Varian, 1999) 2.34 0.771 13.10 0.820 81.10
(continued )
Table II.
Factor, validity, and
reliability analyses
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Model of
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Factor dimension and items measured
Factor
loading Eigenvalue
Cronbach’s
α
Percentage of
variance
explained KMO
Bartlett’s
test of
sphericity
Reducing costs associated with operational expansion 0.88
Reduction of external costs of operations 0.82
Operations and lead-time compression 0.73
Integration of units and independent partners at a reduced cost 0.67
Demographic composition (see Awa et al. 2011, 2015a, b; Chuang et al., 2009;
Hambrick and Mason, 1984) 1.94 0.708 11.02 0.812 84.13
Heterogeneity of decision-makers 0.86
Homogeneity of decision-makers 0.83
Occupational mobility 0.75
Functional tracks 0.71
Size of the firm (see Jeyaraj et al., 2006; Lertwongsatien and Wongpinunwattana,
2003; Grandon and Pearson, 2004; Tornatzky and Fleischer, 1990) 1.64 0.801 9.11 0.832 85.32
Resources 0.76
Skills and experience 0.74
Level of resilience 0.68
Operational agility 0.62
Subjective norms 1.45 0.764 7.12 0.827 86.03
Influence by others 0.74
Group cohesiveness 0.71
Strong belief in group norms 0.68
Fear of group penalty 0.64
External support (see Akbulut, 2002; Bingham, 1976). Governments, NGOs and
inter-governmental influences may generate 1.22 0.790 6.16 0.911 74.67
Grants/donations 0.90
Transfer of technical assistance 0.83
Soft-loans 0.78
Loan guarantee and loan insurance 0.76
Subsidies and tax relieve operations 0.68
(continued )
Table II.
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Factor dimension and items measured
Factor
loading Eigenvalue
Cronbach’s
α
Percentage of
variance
explained KMO
Bartlett’s
test of
sphericity
Competitive pressure (see Jeyaraj et al., 2006; Lertwongsatien and
Wongpinunwattana, 2003) 1.10 0.809 5.21 0.902 78.43
Operational necessity 0.89
Strategic necessity 0.79
Vendor or third party support 0.73
Opponents adopt it 0.67
Trading partners readiness 1.06 0.829 4.76 0.881 75.28
Partners want integration 0.77
Partners are buoyant 0.74
Partners belief in the innovation’s values 0.70
Partners have the technical resources 0.66
Adoption (see NSSBF) 7.23 0.752 30.04 0.929 84.04
The use of ERP to improve customer service 0.88
The use of ERP for inventory management 0.79
The use of ERP for operational efficiencies and cost reduction 0.76
The use of ERP for inter-firm funds transfer 0.69
The use of ERP to update contents and integrate operations 0.70
Table II.
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Model of
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of ERP
together. Implicit is that the indicators of the latent variables loaded onto separate
factors in the expected manner. The internal consistency reliability is the most
commonly used psychometric assessment of survey instruments and scales (Zhang
et al., 2005; Kim and Cha, 2002); therefore, this was achieved by using the inter-item
consistency measure of Cronbach’sαcoefficient. The Cronbach’sαvalues range from
0.708 to 0.881, suggesting that the multi-item observed scale were considered
satisfactory in describing the relevant latent variables.
Table IV shows that at LR ¼99.400, the logistic likelihood regression reports strong
interactions between the dimensions of T-O-E framework and ERP adoption. The
goodness of fit test using the Hosmer and Lemeshow’s model shows a value of
χ
2
¼5.670 and the p-value ( po0.817) confirms that the proposed model does not
critically differ from a perfect one that correctly classifies respondents into their
respective groupings. The table further shows that 46 percent variance was explained
by Nagelkerke’sR
2
. The significance of the regression coefficients as reported by
Wald’s statistics showed mixed results; some adoption predictors have significant
negative coefficients (demographic composition, competitive pressure, external
support, subjective norms, trading partners’readiness, and scope of business
operations) and others have significant positive coefficients (firm’s size, safety and
security, perceived values, perceived compatibility, technical know-how, and ICT
infrastructure). These results fully lend support to H1-H2 and explain that although
those factors that have significant negative coefficients are significant adoption
predictors; they do not currently contribute to the explanation of adoption behavior.
We measure adoption as the voluntary decision to use ERP software as a part of
business strategy within and across the enterprise community. The overall
discriminating power reported in Table III shows a prediction accuracy of
78.70 percent based on the logistic regression equation. The table reports 178
adopters and 66 non-adopters; thus, guessing adoption by random choice would result
in (178/244)
2
+(66/244)
2
¼50.48 percent. Further, we conclude that the logistic
regression model has higher discriminating power than the random choice model since
the former has very stronger predictive power than the latter (Table IV).
At po0.01 the demographic composition has a significant negative coefficient and
substantially affects adoption more than the other five factors (subjective norms,
trading partners’readiness, competitive pressure, external support, and scope of
business operations) with negative coefficients. Further at po0.01, the coefficient of
perceived compatibility moderately supports H3, whereas at po0.05, the coefficients
of ICT infrastructures, technical know-how, perceived values, safety and security, and
size of the firm strongly support H1,H2,H4,H5, and H6.
5. Discussion
This paper attempts to provide insight into the critical factors within the framework of
T-O-E that influence SMEs’adoption of ERP. On accounts that the measures of the
Predicted
Observed total Adopters Non-adopters Percentage correct
Adopters 178 155 23 87.08
Non-adopters 66 22 44 66.67
Overall 244 199 45 78.70
Table III.
Classification
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12-factor dimensions were reliable and valid (see Table II) and the goodness of fit
criteria of the basic model meet the proposed thresholds, the hypothesized relationships
were tested to confirm the extent to which they support the proposed T-O-E
framework. The nine hypotheses were supported at either po0.01 or 0.05 with each
factor differing in its statistical coefficient.
5.1 Technology
Five hypotheses were captured here to reflect ICT infrastructures, technical
know-how, perceived compatibility, perceived values, and safety and security.
Availability of ICT infrastructures was found a critical factor in SMEs’adoption of
ERP; thereby supporting H1. Similarly and in support of H2, technical know-how
was reportedly found a critical ERP adoption factor among SMEs. A possible
explanation to these findings is that when compared to other economies (including
South Africa, India, and even Ghana), modern technologies are yet to attain a
relatively high level of adoption in Nigeria because very few employees/owners of
SMEs have computers and integrate operations online. Whereas some previous
studies (Zhu et al., 2002, 2003; Zhu and Kraemer, 2002; Khemthong and Roberts, 2006;
Al-Qirim, 2006) are consistent with this finding when they emphasize that the
adoption of facilities is dependent upon the availability of internal and external
technology resources; others (Premkumar and Ramamurthy, 1995; Thong, 1999)
contrasted the finding; they found that adoption of IS does not depend on existing
ICT infrastructures. Further, H3 is supported at po0.01 and accounts that perceived
compatibility has significant direct interactions with adoption. This confirms
previous studies (Tornatzky and Fleischer, 1990; Khemthong and Roberts, 2006;
Lertwongsatien and Wongpinunwattana, 2003; Premkumar, 2003) that perceived
compatibility is a critical adoption predictor.
With a significant positive coefficient at po0.05, the interaction between perceived
values and adoption is very critical and supports H4. Previous studies (Iacovou et al.,
1995; Mehrtens et al., 2001; Lee et al., 2004; Grandon and Pearson, 2004) suggest that
innovation adoption is largely dependent upon its relative advantage over current
practices. The result of security significantly supports H5 at po0.05 and confirms that
security issues are critical adoption factors, especially from customers’perspective.
Dimension factor Coefficient (SD) Wald’s statistic Sig.
ICT infrastructures 0.653 (0.314) 7.450 0.085*
Technical know-how 0.167 (0.331) 2.541 0.094*
Perceived compatibility 0.576 (0.302) 7.331 0.083**
Perceived values 0.459 (0.299) 2.404 0.049*
Security 0.686 (0.349) 3.425 0.065*
Size of the firm 0.103 (0.710) 0.255 0.011**
Demographic composition −0.483 (0.279) 2.330 0.090**
Scope of business operations −0.589 (0.374) 6.377 0.019**
Subjective norms −0.634 (324) 3.211 0.062*
Competitive pressure −0.495 (0.263) 2.219 0.090*
External support −0.480 (0.201) 2.106 0.041*
Trading partners’readiness −0.661 (0.347) 3.370 0.059*
Notes: −2 Logistic likelihood ¼99.400, Nagelkerke’sR
2
¼0.456, Hosmer and Lemeshow’sχ
2
¼5.670,
significance ¼0.817. *po0.01; **po0.05
Table IV.
Logistic
regression test
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Model of
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of ERP
Previous studies (Lu et al., 2003; Cho et al., 2007; Benassi, 1999; Green, 1997; Luarn and
Lin, 2005) support this finding when they found that security significantly influences
online purchases.
5.2 Organization
The demographic composition, scope of business operations, subjective norms, and size
of the SMEs were captured in the organizational dimension. First, size of the firm has a
significant positive coefficient and substantially supports H6 at po0.05. Firm’s size is
a critical adoption factor in RFID, e-commerce, and ERP though non-critical in EDI
adoption (Wang et al., 2010; Hossain and Quaddus, 2011; Ramdani et al., 2009).
Other studies (Hwang et al., 2004; Zhu et al., 2003; Zhu and Kraemer, 2005) emphasize
that smaller enterprises often lack the requisite resources to be entrepreneurial. Second,
the demographic composition has a significant negative coefficient and at po0.01; it
moderately affects adoption of ERP and supports H7. Inquiries (Hambrick and Mason,
1984; Thong, 1999; Zhu et al., 2003; Chuang et al., 2009) support that management’s
demographic differences and knowledge about an innovation influence organization’s
strategies. Group heterogeneity and cohesiveness as well as group members’functional
tracks, education, age, gender, and experience influence innovation adoption (see Awa
et al., 2010, 2011; Becker, 1970; Child, 1974).
The scope of business operation has a significant negative coefficient and support
H8 at po0.05. In other words, the scope of business operation is a critical adoption
factor though it does not contribute in the explaining the adoption of ERP.
The explanation to this finding stems from the fact that adoption is not yet robust and
training and maintenance opportunities are not fully blown; thereby making it almost
difficult to exploit the full potentials of integration in the event of multiple business
interests. The role of the scope of business operation as an adoption predicator has
been variously confirmed (see Tornatzky and Fleischer, 1990; Thong, 1999; Hitt, 1999;
Zhu et al., 2003). Studies (Bakos, 1998; Chopra and Meindl, 2001; Shapiro and Varian,
1999; Gurbaxani and Whang, 1991) show that larger scope of business demands
e-business to reduce costs, to integrate demand and improve inventory management
and to benefit from synergy of modern applications and traditional business. The result
of subjective norms shows that it has a significant negative coefficient and supports H9
at po0.05. This finding lends support to previous studies (Lu et al., 2003; Taylor and
Todd, 1995; Samson and Hornby, 1988; Ling and Yttri, 2002) that emphasize group and
other people’s influence on behavior.
5.3 Environment
In the context of environment, external support and competitive pressure were
considered; both factors had significant negative coefficients and support H10 and
H11 at po0.05. Thus, the two factors are critical adoption factors but they do not
provide part of the explanation for ERP adoption. The explanation to these findings
rests on two platforms; first, adoption is still at infancy and yet to be used extensively
for building competitive advantage; and second, the support programs are rarely
transparent and hitch-freely delivered. Previous studies found mixed result on the
influence of competitive pressure on adoption (Premkumar and Ramamurthy, 1995;
Zhu et al., 2003); some scholars suggest direct (Zhu and Kraemer, 2005; Jeyaraj et al.,
2006) and others indirect (Thong, 1999; Lee et al., 2004) effects on adoption decision
with the proposition that organization’s willingness to adopt an innovation is largely
918
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dependent upon her internal necessity for it (Premkumar and Ramamurthy, 1995).
Chau and Tam (1997) found that the external environment has little influence on
adoption decision of ERP.
Many studies recognize the strategic necessity of competitive pressure (Zhu and
Kraemer, 2005; Zhu et al., 2003; Iacovou et al., 1995; Jeyaraj et al., 2006) in altering the
rules of the games, restructuring the industry make-ups, and unraveling novelty in
outperforming rivals (Porter and Millar, 1985). Others studies (Premkumar and
Roberts, 1999; Delone, 1988) confirm external support as a critical adoption
determinant. The result of the trading partners’readiness reads r¼−0.661 at
po0.05, indicating significant negative coefficient and support for H12. As usual the
factor is a critical one though it does not provide part of the explanation for ERP
adoption. In some nations like Nigeria, where the diffusion of ICT platforms is very
sluggish, the issue of network externalities is much more associated with enterprises
with large investments. The finding supports studies that propose that ERP and other
related technologies demand integrated and electronically compatible trading systems
(Pflughoeft et al., 2003; Purao and Campbell, 1998; Sinkkonen, 2001) and disagrees with
Windrum and de Berranger (2004), which found that pressure from allied firms, was
not a significant adoption determinant.
6. Conclusions and implications
ERP systems are complex, capital intensive, and integrated software that reposition
enterprises’competitive advantage provided the critical success factors are well known
and carefully managed. Using ERP systems to build competitive advantage spans
fundamental platforms for transaction processing (the primary focus of ERP studies),
supply chain management, customer relationship management, knowledge
management, decision support systems, and strategic management. SMEs, by their
traditional nature, lack the cognate resources to adopt such large and complex software
in order to exploit the primary and extended functions of ERP. Therefore, this paper
contributes to and/or extends adoption knowledge by proposing a 12-factor model of
ERP adoption from the T-O-E framework and examining the strength of each factor on
the SMEs’adoption process using logistic regression. The result was insightful as to
the critical nature of the indicators of the three adoption drivers in the context of ERP
adoption among SMEs. The coefficients of the 12 factors show that they are critical
adoption factors at po0.01 or 0.05 though some had negative values. Thus, 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 appear favorable, must have been carefully considered.
The indicators with significant negative coefficients (scope of business operations,
trading partners’readiness, demographic composition, subjective norms, competitive
pressure, and external support) were critical adoption determinants though they pose
less of an obstacle to adopters than to non-adopters. ICT infrastructures, technical
know-how, perceived compatibility, perceived values, security, and size of the firm are
significant determinants of adoption. Therefore, adoption of ERP by SMEs is more
driven by technological factors than by organizational and environmental factors.
The implication of these conclusions is of twofold –theoretical and practical. Beside the
proposed research framework, the main theoretical strength of this paper lies on
the statistical validation of the T-O-E framework on SMEs’adoption of ERP software.
The prediction is that SMEs’adoption of ERP platforms is strongly dependent upon the
platform’s display of relative value and compatibility, the existence of technical
919
Model of
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of ERP
know-how and infrastructural facilities, and security and top management predisposition
explained by size. Principally, these have significant practical implications to vendors in
terms of providing support for investment decisions, meeting the needs of the audiences,
and crafting marketing programs that would appeal to actual and potential adopters and
cause them to progress in the loyalty ladder.
Normally, every research endeavor must have some limitations in terms of the
application of its findings to real world and providing lenses for future studies.
Therefore, the key limitations of this study are as follow. First, cross-sectional data
often imply that the causal relationships identified may vary across sectors, industries,
regions, and countries or may even lose meaning overtime. Therefore, extended
measures and/or longitudinal studies may be required to strengthen of the direction of
the causality. Second, some errors seemed unavoidable in the SPSS conversion of data
just as all the measures of the constructs represented subjective perceptions and prone
to common error biases. Third, the study focuses on pre-adoption phase of ERP and so
other investigators may take up the implementation and post-adoption phases in order
to forge a more integrated and holistic adoption lenses. Finally, other factors which will
most likely affect ERP adoption and may not have been factored into the T-O-E
framework to understand the adoption processes of SMEs pose another strong areas of
future inquiries.
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Further reading
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Pyrczak Publishing, Los Angeles, CA.
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implementation for Chinese manufacturing companies”,International Journal of Quality
and Reliability Management, Vol. 17 No. 7, pp. 730-755.
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 Management Sciences, at the University
of Port Harcourt, Nigeria. He is at present the Faculty Co-ordinator of post-graduate programs
as well as the Quality Assurance Officer. Hart serves in the University-wide Examination
Committee and co-ordinates 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
in Nigeria, UK, USA, Canada, Australia, Ireland, and Eastern Europe. Specifically, Hart is in the
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top league of the very few Nigeria-based teachers who champions scholarly writing on technology
adoption and application of ICT platforms to business. Over 20 of his ICT-based articles had been
published by 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
Ojiabo, 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 nine of his papers in different but
indexed emerald journals. Among others, Hart’s interest spans co-creation, consumer psychology,
ICT application to business, and innovation adoption.
Ojiabo Ukoha Ojiabo is a Professor in Mathematics and Computer Science, at the 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.
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