SMEs' adoption of enterprise applications

SMEs’ adoption of enterpriseapplications

A technology-organisation-environment model

Boumediene RamdaniBristol Business School, Bristol, UK, and

Delroy Chevers and Densil A. WilliamsUniversity of the West Indies at Mona, Kingston, Jamaica

Abstract

Purpose – This paper aims to empirically explore the TOE (technology-organisation-environment)factors influencing small to medium-sized enterprises’ (SMEs’) adoption of enterprise applications(EA).

Design/methodology/approach – Direct interviews were used to collect data from a randomsample of SMEs located in the northwest of England. Using partial least squares (PLS) technique, 102responses were analysed.

Findings – Results indicate that technology, organisation and environment contexts impact SMEs’adoption of EA. This suggests that the TOE model is indeed a robust tool to predict the adoption of EAby SMEs.

Research limitations/implications – Although this study focused on examining factors thatinfluence SMEs’ adoption of a set of systems such as CRM and e-procurement, it fails to differentiatebetween factors influencing each of these applications. The model used in this study can be used bysoftware vendors not only in developing marketing strategies that can target potential SMEs, but alsoto develop strategies to increase the adoption of EA among SMEs.

Practical implications – This model could be used by software vendors to determine which SMEsthey should target with their products. It can also be used by policy makers to develop strategies toincrease the rate of EA adoption among SMEs.

Originality/value – This paper provides a model that can predict SMEs’ adoption of EA. SMEs,adoption, enterprise applications, enterprise systems, ICT, PLS, technology-organisation-environmentframework, TOE

Keywords Enterprise applications, Enterprise systems, Small to medium-sized enterprises,Technology-organization-environment framework, Communication technologies

Paper type Research paper

IntroductionIt is beyond question that a key influence upon the competitiveness of firms of all typesis the ability to utilise Information and Communication Technologies (or ICT) (Storey,1994; Williams, 2007). Most small firms still under-utilise the potential value of ICT byonly restricting them to administrative tasks (Brock, 2000). E-commerce adoption bySmall and medium-sized enterprises (or SMEs), which is not just about buying andselling goods online, but also about conducting business transactions either within thefirm or with external stakeholders, has been thoroughly investigated(e.g. Alonso-Mendo et al., 2009; Stockdale and Standing, 2006; Zappala and Gray,2006; Daniel and Wilson, 2002). However, SMEs’ adoption of Enterprise Applications(EA), which provide SMEs with opportunities that are largely unexploited, did not

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SMEs’ adoptionof EAs

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Journal of Small Business andEnterprise Development

Vol. 20 No. 4, 2013pp. 735-753

q Emerald Group Publishing Limited1462-6004

DOI 10.1108/JSBED-12-2011-0035

have the same attention. Because SMEs adopting EA may improve their competitiveperformance, it is important to first understand the factors that influence SMEs’adoption of EA. This paper aims to achieve this through exploring the TOE(Technology-Organisation-Environment) factors influencing SMEs’ adoption of EA.

Business software packages had their roots in manufacturing resource planning(MRP) systems and started as a support for a variety of transaction-based back officefunctions at which time they were called enterprise resource planning (ERP) systems(Volkoff et al., 2005). Since then they have evolved to include support for front-officeand interorganisational activities including customer relationship management (CRM),and supply chain management (SCM) (Volkoff et al., 2005; Davenport, 2000; Markusand Tanis, 2000). EA are defined as: “commercial software packages that enable theintegration of transaction-oriented data and business processes throughout anorganization (and perhaps eventually throughout the entire interorganizational supplychain)” (Markus and Tanis, 2000, p. 176). In our definition, EA include ERP, CRM,SCM, and e-procurement systems (Shang and Seddon, 2002).

The attention of software vendors has moved recently to SMEs offering them a vastrange of EA, which were formerly only adopted by large firms (Ramdani et al., 2009).SMEs are considered to be a major economic player and a potent source of national,regional and local economic growth (Taylor and Murphy, 2004). SMEs differ from largecompanies in important ways affecting their information-seeking practices (Buonannoet al., 2005; Lang and Calantone, 1997). Thus, the adoption of ICT in SMEs cannot beseen as a miniaturised version of larger firms.

Without a better understanding of the complex processes and the differentiatingfactors that affect the level of ICT adoption, the drive to adopt and develop ICT will notsuccessfully contribute to SMEs’ competitiveness (Martin and Matlay, 2001). Thequestion of “why some SME choose to adopt EA, while seemingly similar others facingsimilar market conditions do not?” is still under-studied. This study intends to fill thisgap by exploring technological, organisational and environmental factors influencingEA adoption by SMEs.

Theoretical backgroundSeveral theories have been used to examine the adoption/diffusion of ICT in SMEs:technology acceptance model (TAM) (e.g. Grandon and Pearson, 2004); theory ofplanned behaviour (TPB) (e.g. Harrison et al., 1997); Combined TAM and TPB(e.g. Riemenschneider et al., 2003); TAM2 (e.g. Venkatesh, 2000); diffusion ofinnovations (DOI) (e.g. Premkumar, 2003); resource-based view (e.g. Mehrtens et al.,2001); stage theory (e.g. Poon and Swatman, 1999); ICT and relationshiptransformation model (Ritchie and Brindley, 2005); and unified theory of acceptanceand use of technology (UTAUT) (e.g. Anderson and Schwager, 2003). While thecontributions to this area of research are appreciated, it has been recognised that alarge bulk of the literature produced fragmented findings. Researchers seem toinvestigate the impact of only a limited number of variables, or to pick and choose froma list of factors ( Jeyaraj et al., 2006) that have been empirically tested and validated tohave influenced the adoption/ diffusion of ICT.

The TOE framework developed by Tornatzky and Fleischer (1990) is argued to bean integrative framework that provides a holistic and guiding theoretical basis sinceresearch in the adoption/diffusion of ICT typically evaluate various technological,

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organisational, and environmental factors that facilitate or inhibit adoption/diffusion.TOE framework has been used to explain the adoption of various innovations (Baker,2011) including: interorganizational systems (e.g. Mishra et al., 2007), e-business(e.g. Zhu et al., 2006), Electronic Data Interchange (EDI) (e.g. Kuan and Chau, 2001),open systems (e.g. Chau and Tam, 1997), and general applications (e.g. Thong, 1999).This framework has been claimed to be a “generic” theory of technologyadoption/diffusion (Zhu et al., 2003) that can be used to study EA adoption bySMEs (Figure 1).

TOE modelThis section describes the variables that have emerged from the major constructs inthe TOE model.

Technological contextThis context can be claimed to have a high impact on SMEs’ adoption of EA.Premkumar (2003) claims that there are very few studies that have examined theimpact of technological characteristics on SMEs’ adoption of ICT by SMEs. Relativeadvantage, compatibility, complexity, trialability and observability are considered tobe technological factors that influence EA adoption by SMEs.

Relative Advantage is defined as “the degree to which an innovation is perceived asbeing better than the idea it supersedes” (Rogers, 2003, p. 229). Previous studies foundthis variable to be positively related to ICT adoption (e.g. Grandon and Pearson, 2004;Kuan and Chau, 2001). When an ICT is perceived to offer relative advantage over thefirm’s current practices, it is more likely to be adopted (Lee et al., 2004). This view hassupport in the general innovation/ diffusion research (e.g. Moore and Benbasat, 1991;Tornatzky and Fleischer, 1990), and more specifically in SMEs’ studies (e.g. Thong,

Figure 1.TOE model of enterprise

systems adoption bySMEs


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1999; Cragg and King, 1993). EA provide many benefits to adopters in terms ofaccommodating business growth, improving business processes and reducing businessoperating and administrative costs (Markus and Tanis, 2000). In a highly competitivemarketplace, these benefits make significant motivations for adopting these technologies.

Compatibility of an innovation with a business is defined as “the degree to which aninnovation is perceived as consistent with the existing values, past experiences, andneeds of potential adopters” (Rogers, 2003, p. 240). Premkumar (2003) foundcompatibility to be an important determinant of ICT adoption by SMEs. The adoptionof new technologies can bring significant changes to the work practices of businessesand resistance to change is a normal organisational reaction (Premkumar and Roberts,1999). Therefore, it is important that the changes are compatible with its infrastructure,values and beliefs.

Complexity is defined as “the degree to which an innovation is perceived asrelatively difficult to understand and use” (Rogers, 2003, p. 257). The complexity of atechnology creates greater uncertainty for successful implementation and thereforeincreases the risk in the adoption decision (Premkumar and Roberts, 1999). Althoughthis factor has been found to be negatively associated with organisational adoption ofICT (e.g. Grover, 1993; Cooper and Zmud, 1990), it has also been found to be animportant determinant of ICT adoption by SMEs (e.g. Thong, 1999).

Trialability is defined as “the degree to which an innovation may be experimentedwith on limited basis” (Rogers, 2003, p. 258). In the context of small business, Kendallet al. (2001) found trialability to be positively related to e-commerce adoption. The ICTunder examination in this study are currently new to the SME market. Hence,trialability is expected to be relevant.

Observability is defined as “the degree to which the results of an innovation arevisible to others” (Rogers, 2003, p. 258). In the context of small business, observabilityis the only attribute out of the five technological characteristics that has not been foundto be positively related to organisational adoption of new technologies. ICT that havebeen seen to make impacts in the industry, in which an SME operates, are more likelyto be viewed in a favourable light.

Organisational contextThis context can be claimed to have a high impact on SMEs’ adoption of EA. Factors inthe organisational context seem to be the primary focus of many studies in SMEs(Premkumar, 2003). Top management support, organisational readiness, ICT experienceand size are considered to be organisational factors that influence EA adoption by SMEs.

Top management support has been found to be one of the best predictors oforganisational adoption of ICT (Jeyaraj et al., 2006). Top management can stimulatechange by communicating and reinforcing values through an articulated vision for theorganisation (Thong, 1999). Many studies found top management support to be criticalfor creating a supportive climate to adopt new technologies (e.g. Premkumar andRoberts, 1999; Grover and Goslar, 1993). In SMEs, the decision maker is very likely to bein the top management team and his/her support is vital for the adoption to take place.

Organisational readiness is defined as “the availability of the needed organisationalresources for adoption” (Iacovou et al., 1995, p. 467). Organisational readiness, as usedin previous research on EDI adoption, measures whether a firm has sufficient ICTsophistication and financial resources (Iacovou et al., 1995; Swatman and Swatman,

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1992). Indeed, economic costs and lack of technical knowledge are identified as two ofthe most important factors that hinder ICT growth in small firms (Cragg and King,1993). ICT sophistication assesses whether a firm is technologically ready, whilefinancial resources express an organisation’s capital available to invest in ICT(Chwelos et al., 2001).

ICT experience determines whether a firm is deterred from adopting a newtechnology because of its limited experience with ICT. Previous studies have foundthat prior ICT experience influences organisational adoption of new technologies(e.g. Kuan and Chau, 2001; Fink, 1998). Dholakia and Kshetri (2002) suggest thattechnologies already existing in an organisation influence the future adoption of a newtechnology. They argue that the incremental cost and knowledge required to adopt theInternet, for example, will be much smaller if a firm already owns a computer and atelephone line. This variable has been included in the research model to test whetherdifferent categories of firms: SMEs with low-end ICT use, medium-level ICT users, andhigh-end ICT users (Southern and Tilley, 2000) differ in their adoption of EA.

Size has been identified by Jeyaraj et al. (2006) as one of the best predictors oforganisational adoption of ICT. The typical argument is that larger firms have agreater need, resources, skills and experience and the ability to survive failures thansmaller firms (Levenburg et al., 2006; Yap, 1990). Thus, it can be argued that largerSMEs are more likely to adopt EA.

Environmental contextThis context can be claimed to have a high impact on SMEs’ adoption of EA. Industry,market scope, competitive pressure and external ICT support are considered to beenvironmental factors that influence EA adoption by SMEs.

Industry in which the firm operates has been argued to influence the adoption ofICT (Levenburg et al., 2006; Raymond, 2001). Service industries, which rely on theprocessing of information, depend on ICT (Goode and Stevens, 2000). Retail industries,which rely on the transfer of goods, may have a greater dependence on point-of-salesystems (Premkumar and King, 1994). Manufacturing industry rely more on ERPsystems. Fallon and Moran (2000) showed that ICT usage varies not only acrosssectors (i.e. across Standard Industrial Classification) but also within constituentsub-sectors.

Market scope refers to the market area that a firm chooses to operate in from local tointernational markets. Working on a wider market area introduces a high level ofcomplexity in dealing with legal and cultural issues (Davenport, 1998; Hamel andPrahalad, 1994). As companies become more global and develop international supplychains, the limitation of MRP have become more apparent (Buonanno et al., 2005).Firms tend to expand their ICT infrastructure beyond their organisational boundariesthrough the development of inter-organisational business systems.

Competitive pressure has been identified by Jeyaraj et al. (2006) as one of the bestpredictors of organisational adoption of ICT. Competition in the adopter’s industry isgenerally perceived to positively influence the adoption of ICT (Gatignon andRobertson, 1989). This is argued to be even more evident if the innovation directlyaffect the competition (Kuan and Chau, 2001; Premkumar and Roberts, 1999).Premkumar and Ramamurthy (1995) claim that it can become a strategic necessity toadopt the new technologies to compete in the marketplace.


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External ICT support refers to the availability of support for implementing andusing ICT (Premkumar and Roberts, 1999). This factor has not only been found to bean important determinant of ICT success (e.g. Delone, 1988; Raymond, 1985), but alsoto be positively related to organisational adoption of ICT (e.g. Premkumar and Roberts,1999; Fink, 1998). With the popularity of outsourcing and the growth of third-party’ssupport, firms are more willing to adopt new ICT if they feel there is adequatethird-party’s support (Premkumar and Roberts, 1999).

Research methodFAME (Financial Analysis Made Easy) database was used to retrieve details of SMEsoperating in manufacturing, retail and wholesale and service in the northwest ofEngland. It contains information on 3.4 million companies, 2.4 million of which are in adetailed format. A simple random sample of 300 SMEs was chosen and one in fourSMEs with complete details were selected.

Firms with fewer than 250 employees were considered to be SMEs (DTI, 2004; EC,2003). This study used direct interviews to collect information from owner/ manager orthe ICT manager. Although the interviewer-administered survey technique isexpensive and time consuming, it was preferred because it enabled us to gain a fairlygood response rate (40 percent). Compared to the response rate standard of 60 percentsuggested by Curran and Blackburn (2001), this study’s response rate is not high butfalls in line, and even better in some cases, than previous studies (e.g. Grandon andPearson, 2004, Thong et al., 1996, Premkumar and Potter, 1995).

Because the questions about EA are of contemporary nature, theinterviewer-administered survey technique was useful as it enabled the interviewerto correct ambiguities and unfold issues raised by respondents. In conducting theinterviews, letters were sent to all firms in our sample frame followed by calls to invitethem to participate in the study. Firms with positive responses were asked to provide adate for the researcher to visit the company site and conduct the interview. Therespondents were informed that their participation was voluntary and the informationthey provided was confidential. The characteristics of the sample are shown in Table I.The sample included firms from varying size and from several industry sectors.

Measurement and data analysisMeasures used in the study are presented in Table II. Most of the measures wereobtained from previous research whose validity and reliability have beendemonstrated. All indicators variables are modelled as reflective because they are

Characteristics Number of firms Percent

Number of employees 34.3.10 35 34.310-49 34 33.350-249 33 32.4IndustryManufacturing 30 29.4Retail and wholesale 35 34.3Real estate services 37 36.3

Table I.Sample characteristics(n ¼ 102)

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seen as functions of their associated latent variables. Changes in the latent variable arereflected in changes in the observed indicators (Diamantopoulos and Siguaw, 2006).

Partial least squares (PLS) is a popular Structural Equation Modelling (SEM)technique to conduct data analysis (Goodhue et al., 2006). PLS-Graph was chosenbecause it is more suitable to handle relatively small sample sizes (Chin, 1998), incomparison to co-variance based SEM techniques like AMOS and LISTREL. Oursample of 102 respondents can be viewed as a small sample size. Also, researchers inthis field rely greatly on PLS for testing path models (Marcoulides et al., 2009) andtheory confirmation (Chin, 1998).

Validity and reliabilityPLS-Graph was used to assess the measurement model in terms of reliability andvalidity. Validity refers to how accurately the construct reflects what it intends tomeasure, and reliability refers to the consistency of the results obtained. Severalcriteria can be used to judge construct validity: face validity, usage principle,convergent and discriminant validity. Face validity was ensured by consulting experts

Constructs Operational measure Sources

Dependent variable

SMEs adoption of EA

Dummy variable1 ¼ Decision to adopt EA0 ¼ Decision to reject EA Thong and Yap (1995); Grover (1993)

Independent variablesTechnological constructRelative advantageCompatibilityComplexityTrialabilityObservability

Multi-itemsMulti-itemsMulti-itemsMulti-itemsMulti-items

Moore and Benbasat (1991)Moore and Benbasat (1991)Moore and Benbasat (1991)Moore and Benbasat (1991)Moore and Benbasat (1991)

Organisational constructTop management support Multi-items Yap et al. (1994)Organisational readiness Multi-items Grandon and Pearson (2004)ICT experience 1 ¼ Low IS users

2 ¼ Medium IS users3 ¼ High IS users

Southern and Tilley (2000)

Size Number of employees1 ¼ 0-92 ¼ 10-493 ¼ 50-249

DTI (2004)EC (2003)

Environmental constructIndustry 1 ¼ Manufacturing

2 ¼ Retail/Wholesale3 ¼ Services

Goode and Stevens (2000)

Market scope 1 ¼ Local2 ¼ Regional3 ¼ National4 ¼ International

Buonanno et al. (2005)

Competitive pressure Multi-items Premkumar and Roberts (1999)External ICT support Multi-items Yap et al. (1994)

Table II.Operationalisation of key

variables


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in the field and pilot testing of the questionnaires with SMEs’ owners/managers beforecarrying out the main study. This ensured correcting any ambiguities inmeasurements as well as capturing correctly the concepts used in this study.Moreover, construct validity is ensured by taking into account the usage principle(Babbie, 2007). The operational measures used in this study were taken from previouswork in the field that was published in reputable academic journals. Fornell andLarcker (1981) consider a construct to display convergent validity if the AVE is at least0.5. This means that the variance explained by the construct is greater than themeasurement error. All AVE readings in Table III were above 0.5 with the lowestreading of 0.553. This suggested that there was adequate convergent validity in allmeasures. Discriminant validity was also established as the indicator variables loadedbetter on their associated constructs than other constructs as indicated in Table III.

Reliability was also ensured by using face-to-face interviews where the interviewerensures that each respondent is answering the same questions. Moreover, aquantitative analysis of the measurement model in terms of reliability was performedusing PLS-Graph. The acceptable threshold for reliability to exist is loadings at 0.7 andabove (Chin, 1998; Gefen et al., 2000). Exceeding this threshold means that there is moreshared variance between the construct and its measures than error variance (Barclayet al., 1995). As shown in Table IV, all scales were above 0.7, with complexity being thelowest at 0.829. This indicated that reliability was established in all variables.

Results and discussionTo better predict, explain, and increase the adoption of EA among SMEs, we need tobetter understand why some SME choose to adopt EA, while seemingly similar othersfacing similar market conditions do not.

Measures such as path coefficients, which indicated the strengths of therelationships between dependent and independent variables, as well as the R 2 value,which represented the amount of variance that was explained by the independentvariable, were used to assess the proposed relationships. In essence, the R 2 value was ameasure of the predictive power of the dependent variable in the model (Hair et al.,2006; Vogt, 2007). Together the R 2 and the path coefficients indicated how well thedata supported the adopted model.

It was discovered that technological context, organisational context andenvironmental context had positive impacts on SMEs’ adoption of EA (as shown inFigure 2). Both technological context and organisational context were significant at 1percent with environmental context being significant at 10 percent. The varianceexplained by the three independent variables was 0.33.

All three hypotheses were supported at either the 0.01 or 0.10 levels as shown inTable V. Significantly what we need to find out is which specific variables in each ofthe three constructs have the most important influence on the decision to adopt EA.The results from this analysis are shown in Table VI.

Results from our statistical analysis indicate that technological, organisational andenvironmental contexts impact the decision to adopt EA by SMEs. Except for ICTexperience and external ICT support, all of the other factors were significant at 0.01apart from size which was significant at 0.05. There are inconclusive results in theliterature when it comes to which factors (internal or external) are most influential inSMEs’ adoption of ICT. On the one hand, SMEs take technology adoption decisions

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Table III.Convergent/discriminant

validities


743

Composite reliability

Relative advantage 0.968Compatibility 0.954Complexity 0.829Trialability 0.892Observability 0.913Top management support 0.912Organisational readiness 0.926ICT experience 1.000Size 1.000Industry 1.000Market scope 1.000Competitive pressure 0.880External ICT support 0.921

Table IV.Reliability analysis

Figure 2.Resulting path coefficientwith loadings, significanceand R 2

Variable HypothesisPath

coefficient t-stats Result

Technologicalcontext

H1 Technological context has a high impacton SMEs’ adoption of EA 0.236 2.534 * * Supported

Organisationalcontext

H2 Organisational context has a high impacton SMEs’ adoption of EA 0.346 4.339 * * Supported

Environmentalcontext

H3 Environmental context has a high impacton SMEs’ adoption of EA 0.139 1.347 * Supported

Notes: *Significant at p # 0.10; * *significant at p # 0.01; R 2 for adoption of EA ¼ 0.333

Table V.Results of hypothesistesting

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based on internal factors (Lee, 2004). On the other hand, Buonanno et al. (2005) arguesthat the decision regarding the adoption of ERP systems in SMEs is more affected byexogenous reasons than business-related factors.

Technological context has a positive impact on SMEs’ adoption of EA. Relativeadvantage, compatibility, complexity, trialability and observability have all beenfound to be significant technological factors in determining EA adoption by SMEs.Consistent with results from previous studies looking at the adoption of other types ofICT (e.g. Kuan and Chau, 2001; Premkumar and Roberts, 1999; Iacovou et al., 1995),relative advantage has been found to be a significant factor in the decision to adopt EAin SMEs. According to Premkumar and Roberts (1999), firms adopt technology only ifthey perceive a need for the technology to overcome a perceived performance gap orexploit a business opportunity. Some of the benefits of EA mentioned by potentialadopters include: accommodating business growth, reducing business operating andadministrative costs, and integrating applications cross-functionally. Unlike othertypes of ICT adoption by SMEs, EA adoption has been found to be influenced bycompatibility, complexity, trialability and observability. Compatibility has beensignificant because incompatibilities in terms of business processes can be a potentialdeterrent to ICT adoption (Premkumar, 2003). Complexity has also been found to besignificant. The lack of in-house ICT expertise may make the adoption of EA seemcomplex and difficult to implement. Trialability has been found to be anothersignificant technological factor influencing EA adoption by SMEs. Unlike other typesof ICT, EA are perceived by SMEs’ managers as a considerable investment and theywould like to experiment with these systems before adoption. The availability of EA on

Variable Loadings Weights t-statistics

Technological contextComposite reliability ¼ 0.927AVE ¼ 0.717Relative advantage 0.893 0.327 7.265 * *

Compatibility 0.863 0.233 7.331 * *

Complexity 0.781 0.237 5.157 * *

Trialability 0.834 0.147 3.116 * *

Observability 0.860 0.232 7.505 * *

Organisational contextComposite reliability ¼ 0.671AVE ¼ 0.396Top management support 0.730 0.494 4.778 * *

Organisational readiness 0.845 0.601 6.069 * *

ICT experience 0.065 20.016 0.116Size 0.577 0.229 1.786 *

Environmental contextComposite reliability ¼ 0.696AVE ¼ 0.396Industry 0.744 0.402 3.478 * *

Market scope 0.781 0.532 4.347 * *

Competitive pressure 0.613 0.419 3.066 * *

External ICT experience 0.212 0.133 0.852

Notes: *Significant at p # 0.05, * *significant at p # 0.01

Table VI.Weights, loadings,

reliabilities and t-values


745

a trial basis can assist SMEs in their decision to adopt these systems. This means thatSMEs will be able to assess the performance of EA and would be able to resolve anyproblems before committing to fully implementing these systems (Ramdani andKawalek, 2007). Although results of its influence have been inconsistent (Karahannaet al., 1999; Moore and Benbasat, 1991), observability has been found to be significantfactor in EA adoption by SMEs.

Organisational context has a positive impact on SMEs’ adoption of EA. Size, topmanagement support and organisational readiness have been found to be significantorganisational factors in determining EA adoption by SMEs. Results indicate that sizestill plays an important role and larger firms in small business category have a greaterpropensity to adopt EA. This result is consistent with previous studies that have foundsize to be a critical factor of ICT adoption by SMEs (e.g. Premkumar and Roberts, 1999,Thong, 1999). While larger firms have the resources to invest in new EA, somemicro-firms were found to be able to manage their business operations without theneed to adopt these technologies. Top management support, which is constantly foundto be critical in organisational adoption of ICT, has been found to be a significant factorof EA adoption by SMEs. This is consistent with prior studies in SMEs(e.g. Premkumar, 2003; Premkumar and Roberts, 1999).

The primary decision maker is the owner/manager of an SME, and his or her visiondetermines the level of support for ICT adoption. SMEs intending to adopt EA havemore management commitment that is characterised by inviting software vendorsonsite to demonstrate what EA can do for their business (Ramdani and Kawalek, 2007).Top management support becomes more important for EA since it involves interactionwith trading partners and establishing business agreements for using thesetechnologies. The use of these technologies could significantly change the waybusiness is done within the organisation as well as externally with its trading partners(Premkumar and Roberts, 1999). Another significant organisational factor isorganisational readiness suggesting that without sufficient technological andfinancial resources, SMEs are unable to adopt EA. This is consistent with thefindings from previous studies in SMEs (e.g. Kuan and Chau, 2001; Fink, 1998; Iacovouet al., 1995). The influence of organisational readiness variable may have paled theinfluence of ICT experience, which has been found to be insignificant. These results arein line with previous findings (e.g. Kuan and Chau, 2001; Fink, 1998; Iacovou et al.,1995).

Environmental context has a positive impact on SMEs’ adoption of EA. Industry,market scope and competitive pressure have been found to be significantorganisational factors in determining EA adoption by SMEs. Industry and marketscope have rarely been studied in SMEs’ adoption of ICT and rarely included inprevious TOE models. Industry has been demonstrated to be a significant factor in EAadoption. Firms with a broader market scope view EA adoption as a way to serve theirdispersed markets more efficiently. Serving a broader market area, firms facecompetitive pressure that characterises international markets (Bartlett and Ghoshal,1989). Competitive pressure has also been found to be significant. External ICT supporthas been found to be insignificant. Evidence from prior studies has been inconclusive.On the one hand, Fink (1998) found it to be an important factor, since SMEs appear tovalue the information provided by software vendors. On the other hand, Premkumarand Roberts (1999) did not find it to be a critical factor in the adoption of

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communication technologies. They argued that there could be many reasons for thelack of significance of this variable. One reason could be that SMEs perceive ICTsupport to be an ongoing expense that they would not like to incur.

This study confirms the findings of previous TOE studies. In the technologicalcontext, relative advantage (Lee and Shim, 2007), compatibility, and complexity(Grover, 1993) have been confirmed to be significant. In the organisational context, size(Zhu and Kraemer, 2005), top management support (Grover, 1993), and organisationalreadiness (Zhu et al., 2004) have also been confirmed to be significant. In theenvironmental context, competitive pressure (Zhu et al., 2006) has been confirmed to besignificant. In addition to these factors, this study found trialability, observability,industry and market scope to be significant factors influencing SMEs’ adoption of ICT.

ConclusionThe major contribution of this study is to empirically explore the TOE factorsinfluencing SMEs’ adoption of EA. The results from the PLS analysis showed that theTOE model is indeed a robust tool for predicting EA adoption in SMEs. This modelprovides us with the factors that influence the adoption of EA by SMEs. In addition tothe factors that were tested in previous TOE studies, this study found trialability,observability, industry and market scope to be significant factors of ICT adoption bySMEs. These factors can be used by EA vendors to determine which SMEs they shouldtarget with their products. If these factors exist, then SMEs will be more willing toadopt EA. As there is no “one-size fits all” ICT policy across industries and differentsectors use ICT differently (Kotelnikov, 2007; Tan et al., 2010), the findings of thisstudy can be used to develop strategies to increase the rate of EA adoption amongSMEs.

The key limitations of this study are as follows. First, although this study focusedon the factors that influence the adoption of a set of systems (i.e. ERP, CRM, SCM ande-procurement), it fails to differentiate between factors that influence each of thesesystems. Second, the study focused on a limited geographical area, which makes itdifficult to generalise the results to other UK regions. It would be interesting to look atEA adoption from a cross-country perspective. Third, this study focused on thepre-adoption phase of EA innovation/ diffusion process. To gain a more holisticunderstanding, post-adoption phases should also be examined. Fourth, this studyfocused on three industries only. It would be interesting to see whether firms in otherindustry sectors are influenced by the same factors. With the recent popularity ofhosted software applications (Lockett et al., 2006), future studies could empiricallyexamine the factors influencing the adoption of these technologies and how they differfrom the findings of this study.

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About the authorsDr Boumediene Ramdani is Senior Lecturer in Strategy and Operations Management at BristolBusiness School. His research deals with innovation, competition and firm performance. Hiswork has been published in leading journals such as California Management Review andInformation & Management. He received his PhD from Manchester Business School and hisMBA and BSc from Leeds University Business School. He has taught at universities in the UK,France and Spain. His consulting activities focus on advising firms on strategy-related matters.Boumediene Ramdani is the corresponding author and can be contacted at: [email protected]

Delroy Chevers is a Lecturer of Operations Management at the University of the West Indies,Mona Campus. His research interests are information systems quality and project management.

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He is currently working on a simplified process assessment framework for developing countriesand the adoption of software process improvement initiatives in the English-speaking Caribbean.

Densil A. Williams is a Lecturer of International Business in the Department of ManagementStudies, at UWI, Mona Campus. His research interests are in the areas of international business,with special focus on the international activities of small firms; strategy and, internationaldevelopment. His works have appeared in major international journals in North-America andEurope. He has also presented his works at major international conferences in Europe and North-America.


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