Evaluating the criticaldeterminants for adoptinge-market in Australiansmall-and-medium sized

enterprisesXiaoxia Duan, Hepu Deng and Brian Corbitt

School of Business Information Technology and Logistics, RMIT University,Melbourne, Australia

Abstract

Purpose – The purpose of this paper is to present an empirical investigation of the criticaldeterminants for the adoption of e-market in Australian small-and-medium sized enterprises (SMEs)within the technology-organization-environment (TOE) framework.

Design/methodology/approach – A conceptual model is proposed for better understanding theadoption of e-market in Australian SMEs. Structural equation modelling is used for testing andvalidating the proposed conceptual model based on the survey data collected from Australian SMEs.A logistic regression analysis is conducted for identifying the relationship between the criticaldeterminants and the adoption of e-market in Australian SMEs.

Findings – A positive relationship is identified between the perceived direct benefit, top managementsupport, external pressure, trust and the adoption of e-market in Australian SMEs. The top managementsupport emerges as the most critical determinant. The perceived indirect benefit, size and organizationreadiness, however, do not show a significant influence on the adoption of e-market in Australian SMEs.

Research limitations/implications – This study focuses on the investigation of the criticaldeterminants in the adoption of e-market in Australian SMEs. In order to gain an holisticunderstanding of the e-market adoption in Australian SMEs, the impact of adopting e-market on theperformance of SMEs should be examined. Furthermore, this study does not distinguish the adoptionof e-market in SMEs between Australian metropolitan and rural areas. SMEs located in rural areas bythe nature and location own specific characteristics such as a limited access to resources and lessinfluenced by external pressure. The issues of their concern in the adoption of e-market might bedifferent from those located in metropolitan areas.

Originality/value – This study contributes to the e-market adoption research in proposing andvalidating a research model for the e-market adoption within the TOE framework, and by highlightingthe criticality of the determinants of adopting e-market in Australian SMEs.

Keywords Australia, Small to medium-sized enterprises, Electronic commerce, E-market,Technology adoption, Technology-organization-environment

Paper type Research paper

IntroductionAn electronic market (e-market) is a virtual marketplace in which buyers and sellers arebrought together in one central market for conducting electronic commerce activitiesincluding the exchange of goods, services or information online (Dou and Chou, 2002;Grieger, 2003). It has become increasingly popular due to its potential benefits tobusiness, especially to small and medium sized enterprises (SMEs),

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Management Research ReviewVol. 35 No. 3/4, 2012

pp. 289-308q Emerald Group Publishing Limited

2040-8269DOI 10.1108/01409171211210172

including strengthened customer relationships, ease of reaching targeted markets,improved efficiency, reduced costs, and greater competitive advantage (Daniel et al.,2004; Lai, 2007; Standing et al., 2010).

The potential of e-market for SMEs, however, has not been fully utilized. A majorityof SMEs have not made use of e-market. Those who have adopted e-market have notmoved beyond the entry-level adoption (Molla and Licker, 2005). This is due to variousobstacles that SMEs face in adopting e-market, particularly the lack of ability toeffectively address the significant technical, organizational and environmental issues(Zhu et al., 2003; Duan et al., 2010; Singh et al., 2010). Such issues, however, have notbeen well studied in the e-market adoption literature (Standing et al., 2010). Inparticular, what is missing from the existing literature is:

. a theoretical framework specific to the adoption of e-market;

. the validated measurement model for the e-market adoption; and

. an empirical assessment of the critical determinants for the adoption of e-marketin Australian SMEs.

This study seeks to address these issues by proposing a conceptual model for the adoptionof e-market in Australian SMEs within the technology-organization-environment (TOE)framework. Structural equation modelling (SEM) is used for testing and validating theconceptual model based on the survey data collected from Australian SMEs. A logisticregression analysis is conducted on the validated research model that reveals a positiverelationship between the perceived direct benefit, top management support, externalpressure, trust and the adoption of e-market, among which top management supportemerges as the most critical determinant. The perceived indirect benefit, size andorganization readiness, however, are less influential in the adoption of e-market inAustralian SMEs.

The remainder of the paper is organized as follows. The next section reviews therelevant literature on the TOE framework. Within this framework, a research modeland hypotheses are presented, followed by the research method, analysis anddiscussion. The last section draws the conclusion.

Theoretical backgroundSMEs are important contributors to the global economy accounting for approximately50 per cent of all national gross domestic product (GDP), with approximately99.5 per cent of all businesses having 100 or less employees (OECD, 2007). In Australia,SMEs are defined as the organizations with less than 200 employees (AustralianBureau of Statistics (ABS), 2007). They are essential to the Australian economy asabout 96 per cent of businesses are categorized as SMEs, employing approximately3.5 million people and contributing to an estimated 30 per cent of national GDP. In June2006 there were more than 1.9 million actively trading SMEs in Australia, and over1.8 million businesses had annual turnover of less than $2 million (ABS, 2007).

SMEs are a distinct group of organizations with specific characteristics intechnology adoption (Chwelos et al., 2001; Barry and Milner, 2002). Brigham and Smith(1967), for example, show that SMEs tend to be more prone to risk in technologyadoption. Cochran (1981) reveals that SMEs tend to be subjected to a higher failure ratein technology adoption. Tetteh and Burn (2001) demonstrate that SMEs have

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inadequate record keeping that made it hard to plan their business strategically.Overall, technology adoption in SMEs is characterized by:

. lack of technical expertise (Barry and Milner, 2002);

. inadequacy of capital and organizational planning (Raymond, 2001); and

. high dependence on business partners (Chwelos et al., 2001).

These unique characteristics of SMEs warrant the need of a comprehensive frameworkfor understanding the adoption of technology from the technological, organizationaland environmental aspects (Fink, 1998).

The TOE framework (Tornatzky and Fleischer, 1990) is a comprehensive tool forstudying the adoption of technology in an organization. It identifies three aspects of anorganization that influence the adoption of new technology including organization,technology, and environment. The technology aspect depicts the technologies that arerelevant to the organization in its pursuit of the business objectives. The organizationaspect is defined by several descriptive measures including firm size and scope,managerial structure and internal resources. The environment aspect describes the macroarea in which an organization conducts the business, with business partners, competitorsand the government. Various factors categorized in these three groups are deemed toaffect the decision of an organization towards their adoption of latest technologies.

The applicability of the TOE framework for investigating the determinants intechnology adoption in SMEs is exemplified in existing information systems (IS)literature (Iacovou et al., 1995; Kuan and Chau, 2001; Ramdani et al., 2009). Iacovou et al.(1995), for example, apply the TOE framework for exploring the determinants of theadoption of electronic data exchange (EDI) systems in seven small firms, leading to theidentification of the perceived benefit, organizational readiness, and external pressure ascritical in the adoption of this technology. This model is tested and validated on a largersample size of 286 Canadian SMEs (Chwelos et al., 2001). Kuan and Chau (2001) furtherconfirm the usefulness of the TOE framework in the study of EDI adoption in small firmsby proposing a perception-based model with the identification of six critical factors. Theimportance of considering the technological, organizational and environmental factorsin studying the technology adoption in small firms is demonstrated in the study.

There are numerous studies in the IS domain which further illustrates theapplicability of the TOE framework in studying the adoption of technology in SMEs.Thong (1999), for example, develops an integrated model for studying the adoption ofIS in 166 small firms that shows the criticality of the characteristics of the chiefexecutive officer of the small firm, the technological characteristics, and theorganizational characteristics in the IS adoption. Premkumar and Roberts (1999)investigate the adoption of information technologies (IT) and IS in 78 rural small firmsfrom the perspectives of innovation, organization and environment, result in theidentification of the relative competitive advantage, top management support,organizational size, external pressure and competitive pressure as the criticaldeterminants for adopting IT and IS systems. Mehrtens et al. (2001) adopt the TOEframework for investigating the adoption of internet in seven SMEs. Lertwongsatienand Wongpinunwatana (2003) show the suitability of the TOE framework for studyingthe e-commerce adoption study in Thailand SMEs. Ramdani et al. (2009) adopt theTOE framework for predicting the potential enterprise systems adopters in SMEs inEngland.

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The studies above show the applicability of the TOE framework for studying theadoption of technology in SMEs. There are, however, no empirical studies in theliterature on the adoption of e-market within the TOE framework. E-market evolvedfrom the EDI system is developed with the IT and IS support (Guilherme and Aisbett,2003) based on the needs of e-procurement of an organization (Angeles, 2000). TheTOE framework suitable for studying the adoption of EDI, e-procurement, IT and IS inmodern organizations are therefore applicable for the study of e-market adoption.

The suitability of the TOE framework for studying the adoption of e-market isdemonstrated in Swanson’s (1994) research where the adoption of technology innovationalong the line of the TOE framework is examined based on the three types of technologyinnovations. The result shows that the TOE framework is able to fully explain the typeof innovation embedded in the core of the business in the technology adoption process.E-market falls into this type of innovation, in the sense that e-market strengthenorganizational competitiveness and can streamline the integration the business withsuppliers and customers (Kaplan and Sawhney, 2000; Duan et al., 2010). This shows thatthe TOE framework is well suited for studying the adoption of e-market.

The solid theoretical basis and the consistent empirical support discussed as abovepromise that the TOE framework can be extended for studying the adoption ofe-market in Australian SMEs. This study will use this framework to develop a modelthat can be used to investigate the critical determinants for the adoption of e-market inAustralian SMEs giving the specific nature of Australian SMEs.

Research model and hypothesisThrough a comprehensive review of related literature on the adoption of e-market,a conceptual model is developed within the TOE framework for facilitating theinvestigation of the adoption of e-market in Australian SMEs. Figure 1 shows thisresearch model with four dimensions including technology, organization, environmentand trust, discussed in the following.

TechnologyThe technology dimension considers the perceived benefit of adopting e-market in anorganization. The perceived benefit positively affects the adoption of technology in anorganization (Rogers, 2003). Organizations adopt technology when there is a perceivedneed for using the technology to overcome a perceived performance gap or exploit abusiness opportunity. The greater the perceived benefit, the more likely an organizationwill adopt the technology. In the adoption of e-market, the perceived benefit is classifiedinto the direct benefit and the indirect benefit (Joo and Kim, 2004). The direct benefit isrelated to the reduction of operational savings and the tangible benefit in the organizationsuch as the access to a larger number of suppliers or customers, increased pricetransparency, and saved operation costs (Kuan and Chau, 2001; Joo and Kim, 2004). Theindirect benefit is associated with the impact of adopting e-market on the management ofbusiness process and customer relationships. It refers to improving the company’s image,increasing operational efficiency,and improvingtrading partner relationships (Daniel etal.,2004; Standing et al., 2010). The above argument leads to the following hypothesis:

H1. The perceived direct benefits positively influence the adoption of e-market.

H2. The perceived indirect benefits positively influence the adoption of e-market.

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OrganizationThe organization dimension includes the size of an organization, the organizationalreadiness and the top management support. The size of an organization directly affectsthe adoption of technologies in the organization (Rogers, 2003). Large organizationsusually have greater ability to adopt innovations. One possible explanation is thatlarger organizations have more financial and technical resources for taking risks withnew technologies than smaller organizations. Bakos (1991) indicates that the cost andexpertise required to build or adopt an e-market might favour big organizations. Evenwithin the small business category, a relatively larger organization is in a betterposition to engage in e-market. It then leads to the following hypothesis:

H3. Organization size is positively related to the adoption of e-market.

The organization readiness is determined by the financial readiness and the technologicalreadiness. The financial readiness of an organization refers to the financial resourcesavailable for e-market installation costs and for ongoing expenses. The technologicalreadiness is related to the level of sophistication of IT usage and IT management in anorganization. The extent by which an organization utilizes IT such as electronic fundstransfer, EDI and internet has a positive impact on the system integration with e-market(Barry and Milner, 2002). Based on the above argument, the following hypothesis isproposed:

H4. Organization readiness is positively related to the adoption of e-market.

Top management support is critical in SMEs for creating a supportive climate andproviding adequate resources in adopting technology. It ensures the limited resources

Figure 1.A conceptual model for the

adoption of e-market

Perceived Direct Benefit

Perceived Indirect Benefit

Organization Context

Organization Readiness

Top Management Support

Environment Context

External Pressure

Trust Context

E-market Trust

Trading Partner Trust

H3

H2

H1

E-marketAdoption

H4

H5

H6

H7a

H7b

Size

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and technical expertise to be allocated for supporting the essential needs of newtechnology (Ramdani et al., 2009). It helps overcome barriers and resistance to changein the organization. An SME that is likely to adopt e-market will most often have thesupport of top management who have a positive attitude towards the adoption oftechnology, who are innovative and who are knowledgeable about IT. This discussionleads to the following hypothesis:

H5. Top management support positively influence the adoption of e-market.

EnvironmentThe environment dimension concerns about external pressure. External pressurerefers to the pressure from competitor, trading partners and the government to theorganization (Chwelos et al., 2001). The presence of pressure from competitors oftenforces individual organization to adopt technology for being competitive in a dynamicenvironment. In the adoption of e-market, organizations are more prone to adopte-market as competitors become more e-market capable (Stockdale and Standing,2004). The existence of pressure from trading partners or government departmentsalso has great influence in the decision of an organization for its adoption oftechnology, especially SMEs in the sense that they are more likely to be economicallydependent on their larger partners for survival. Thus, the following hypothesis isproposed:

H6. External pressure is positively related to the adoption of e-market.

TrustThe trust dimension considers the degree of trust in e-market and trading partners bySMEs in the adoption of e-market. It is included to extend the TOE framework in thisstudy due to the nature of e-market considering that the main communication methodfor all parties is online. Both economists and sociologists agree that trust is a crucialenabling factor in relations to the adoption of online technology (Pavlou and Gefen,2004; Connolly and Bannister, 2008; Casalo et al., 2008). The trust in e-market refers tothe trustworthiness of the e-market in handling transactions, securing systems andmaintaining relationships when operating the system. The trust in trading partnersconcerns the trust in the trading partners to conform online transaction rules such asfor buyers to pay on time, or for suppliers to provide valid product or serviceinformation. The above argument therefore leads to the following hypothesis:

H7a. Trust in e-market is positively related to the adoption of e-market.

H7b. Trust in trading partners is positively related to the adoption of e-market.

Research methodData collectionSurvey is a technique for studying the cause of phenomenon as well as the attitudesand behaviours of certain group of people, providing with empirical evidence (Creswell,2003). The use of survey is considered appropriate for this study upon which it ispossible to test and validate the proposed research model empirically and to pinpointthe critical determinants for the adoption of e-market in Australian SMEs. For serving

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these purposes, a survey of top executives in Australian SMEs is set to collect dataregarding the adoption of e-market in Australian SMEs nationwide in three months.

The process is carried out in three steps. First, a sample of 900 SMEs targeted all eightAustralian industries was selected from a database rented from D&B Australia. Thename, e-mail address and mail address of the top executives in SMEs were derived.Then, an initial e-mail was sent out to explain the purpose of study and invitation toparticipate. 197 of these e-mails were undeliverable. Approximately, two weeks after theinitial e-mail, mails were sent out to follow up the rest of the potential respondents.71 were declined due to incorrect address or organization no longer in business. A total of229 responses were received in both rounds. 16 were unusable hence removed from theanalysis, leaving 212 usable responses with a 33.5 per cent response rate.

Construct operationalizationTo ensure a set of valid measurements is used for statistical analysis, this study adoptsthe instrument validation paradigm in management IS for validating the measurementmodel (Straub, 1989). It follows successive steps including the theoretical modelling ofthe constructs and statistical test and refinement of the measurement model. Tosuccessfully model the constructs as proposed in Figure 1, the measurement items werefirst developed through a comprehensive literature review, followed by the pre-test,pilot test and interviews of expert and top management for ensuring their contentvalidity. Specifically, the initial survey instrument derived from the literature reviewwas pre-tested with five faculty members with expertise in e-market and technologyadoption. The improved version was pilot-tested with the top management in SMEs forassessing the clarity, readability and for gaining an initial idea of time commitment.The response to the pilot-test survey was then followed up with interviews lasting 30 to45 minutes with each respondent for gaining a better insight into thecomprehensiveness of coverage of the instrument in capturing the importantconcepts. A minor revision was conducted for rephrasing some statements in thesurvey. The survey measurements and their origins are summarized in Table I.

Construct Operational measure Sources

Dependent variableAdoption Dichotomy 1 ¼ e-market adopter, 0 ¼ e-market non-adopterIndependent variablePerceived direct benefit Multi-items, Likert

scaleTan et al. (2003) and Joo and Kim (2004)

Perceived indirectbenefit

Multi-items, Likertscale

Kuan and Chau (2001) and Joo and Kim (2004)

Size Number of employees ABS (2007)Organization readiness Multi-items Kuan and Chau (2001) and Teo et al. (2009)Top managementsupport

Multi-items, Likertscale

Ramdani et al. (2009)

External pressure Multi-items, Likertscale

Chwelos et al. (2001), Kuan and Chau (2001) andJoo and Kim (2004)

E-market trust Multi-items, Likertscale

Pavlou and Gefen (2004) and Verhagen et al.(2006)

Trading partner trust Multi-items, Likertscale

Pavlou and Gefen (2004) and Verhagen et al.(2006)

Table I.E-market adoption

constructs, operationalmeasure and sources

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The constructs are then to be tested and refined using confirmatory factor analysis(CFA) in SEM discussed in the measurement model validation section.

The decision of adopting an e-market is measured as a dichotomy, represented by 1or 0 binary number. A SME is classified as an adopter if it has adopted an e-market andnon-adopter if it has not. The size of SMEs is measured by the number of employeesranged from 1 to 200. Other variables are operationalized as multi-item constructs,either measured using a seven-point Likert-type scale ranging from strongly disagree(1) to strongly agree (7), or measured by multiple choices of the preference of therespondent to the survey.

General profile and summary statisticsMost respondents are from manufacturing (25.1 per cent), construction (18.1 per cent)and services (16.3 per cent) industry. Figure 2 shows the distribution of industry andthe size of the responding SMEs. Among the respondents, 74.4 per cent respondents areManaging Director or CEO in SMEs, suggesting the quality of the data source. About86.5 per cent of the responding SMEs have been in business for more than ten years.About 84.7 per cent SMEs own their web sites. The main functions of the web site arefor information demonstration and product listing. About 37.7 per cent ofthe responding SMEs have adopted one or more e-markets, 91.2 per cent of theSMEs are still using the traditional face-to-face method. In those SMEs that haveadopted the e-market, the preferred type of e-market is the seller dominated e-market(79.0 per cent). The third party dominated e-market which is believed to be suitable forSMEs (Molla and Licker, 2005) attracts only 11.1 per cent of the respondents.

Measurement model validationThe SEM technique is adopted for assessing the validity and reliability of themeasurement model proposed in Figure 1 before the model can be used for furtherhypothesis testing. The use of SEM in this study is mainly due to its ability to includelatent variables to represent unobserved concepts while accounting for themeasurement error in each (Hair et al., 2010) and its capability of simultaneouslyassessing the multiple correlations and covariance among variables when performingthe model validity check.

A CFA on the measurement model is conducted by using AMOS 8.0 based on theresults of survey data from 212 samples. The process involves in three steps. The firststep is the model specification process, in which the adequate sample sizes of 212 and themultivariate normality distribution in the data set guarantee the use of the maximum

Figure 2.Distribution of size andindustry in respondingAustralian SMEs

10.8%20.1%

4.2%16.3%

3.3% 9.3%3.7%

13.5%

6.5%

25.1%

18.1%

15.4%

23.8%29.9%

Industry

Agriculture, Foresty andFishingMining

Construction

ManufacturingTransportation, Electronic,Gas and Sanitary Services

Trading

Finance, Insurance andReal EstateServices

1-56-20

21-5051-100

101-200

Employees

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likelihood method in the estimation. The second step is an iterative model modificationprocess for developing a best set of items to represent a construct through refinementand retesting. This results in dropping the items that do not meet the validity andreliability test. The last step is to estimate the goodness of fit (GOF) parameters of theoverall model to test the extent to which the data support the research model. The mostcommonly used parameters are the likelihood ration chi-square (x 2), the ratio of x 2 todegrees of freedom (x 2/df ), the root mean square error of approximation (RMSEA), andcomparative fit index (CFI).

Table II shows the results of CFA analysis on the measurement model. Theconvergent validity and discriminant validity check are performed in the iterativemodel modification process. The convergent validity assesses the extent to which theitems measuring a construct converge together and measure a single construct. Thediscriminant validity measures the degree to which the items of theoretically distinctconcepts are unique from each other (Hair et al., 2010).

The convergent validity assessment includes three steps. The first step is tocalculate the x 2 values for each of the constructs. If any x 2 rejects a factor at p , 0.05,modification indices are used to identify the common factors among items. The laststep is to drop those items that do not fit into any factor from the subsequent analysis.The factor loading (FL) value is also computed during the convergent validity checkprocess. A rule of thumb is that the FL should be at least 0.50, and ideally 0.70 or higherwith all FLs statistically significant. Following the rules described as above, 11 itemsare dropped from 37 items in the original conceptual model. Other items with FLsranged from 0.69 to 0.98 shown in Table II indicate high convergent validity.

The discriminant validity of the constructs is checked by comparing the averagevariance extracted (AVE) for each construct with the squared correlation of this constructto any other constructs (Hair et al., 2010). The AVE should be greater than any of the

Construct Item FL IR x 2/df p CFI RMSEA a

Perceived direct benefit (PDB) PDB3 0.92 * * * 0.85 0.48 0.62 1.00 0.000 0.91PDB5 0.92 * * * 0.85

Perceived indirect benefit (PIB) PIB3 0.75 * * * 0.56 0.12 0.89 1.00 0.000 0.85PIB5 0.81 * * * 0.66PIB6 0.86 * * * 0.74

Organization readiness (OR) OR4 0.98 * * * 0.96 0.28 0.59 1.00 0.000 0.98OR6 0.98 * * * 0.96

Top management support (TMS) TMS1 0.87 * * * 0.75 1.66 0.20 1.00 0.050 0.86TMS2 0.87 * * * 0.75

External pressure (EP) EP1 0.86 * * * 0.74 0.00 1.00 1.00 0.000 0.85EP3 0.86 * * * 0.74

Trust (T) T1 0.87 * * * 0.76 0.24 0.78 1.00 0.000 0.85T2 0.90 * * * 0.81T3 0.69 * * * 0.48

Full measurement model 1.24 0.10 0.99 0.034Recommended value $0.7 $0.5 #3.0 $0.05 $0.9 # .08 $0.7

Note: Significant at: * * *p ¼ 0.001

Table II.The measurement model

validation statistics

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squared correlation for that construct to show adequate the discriminant validity.In the process of the assessment, two constructs namely e-market trust and tradingpartner trust are highly correlated to each other with less discriminant power. They aretherefore suggested to be combined as a new construct, given the name trust. Nine itemsare dropped in the discriminant validity process, resulting in 14 items in six constructs forthe final conceptual model. Table III shows the correlation matrix between the constructs.All six constructs demonstrate high discriminant validity with AVE ranged from 0.65to 0.96.

The reliability check of the constructs includes the assessments of the itemreliability (IR) and the construct reliability. The IR indicates the amount of variance inan item due to underlying construct rather than error (Chau, 1997). It is assessed usingthe squared multiple correlation value or the square of the standardized FL. An item isconsidered to be reliable if IR is greater than 0.50 (Hair et al., 2010). IR values in Table IIfor all the items ranging from 0.56 to 0.96 are higher than the threshold which aretherefore deemed to be sufficient in measuring the constructs. The construct reliabilityassesses the degree of consistency between multiple items of a construct. It is tested bycalculating Cronbach’s a coefficients with an acceptable value of 0.70. All sixconstructs show high Cronbach’s a coefficient values ranged above 0.85. The constructreliability is thus considered to be strong.

The GOF of the final measurement model is assessed after various validity andreliability tests discussed above. The insignificance of parameters x 2 73.39 and x 2/df1.24 within the acceptable value x 2/df 3.00 indicate that the final research model is notsignificantly different from the survey data. The RMSEA value 0.034 less than therecommended value 0.08 as well as the CFI value 0.99 greater than the threshold 0.90also exemplify a good match between the final measurement model and the surveydata. The final measurement model consisting 14 items in six factors is thereforesuitable to proceed for further hypothesis testing.

Results and discussionLogistic regressionLogistic regression analysis is applied to test the research hypotheses empirically. Thismultivariate statistical technique is chosen over other analysis methods because thedependent variable in this study is dichotomous (Hair et al., 2010). By maximizingthe likelihood of the adoption decision represented by binary number 0 or 1,the significance and contribution of each independent variable is estimated. Based on thevalidated model for the adoption of e-market in the previous section, the logisticregression model can be defined as follows:

PDB PIB OR TMS EP T

PDB 0.85PIB 0.55 0.65OR 0.15 0.17 0.96TMS 0.32 0.39 0.30 0.75EP 0.19 0.53 0.09 0.60 0.74T 0.52 0.57 0.09 0.54 0.53 0.68

Table III.An AVE and squaredcorrelation matrix

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PðAdoption ¼ 1Þ ¼ Lðb0 þ b1 ·PDBþ b2 ·PIBþ b3 · S þ b4 ·OR þ b5 ·TMS

þ b6 ·EP þ b7 ·T þ eÞ

Where L ( · ) denotes the probability density function of the logistic distribution. b1 to b7

represent the estimated coefficients between the independent variables and dependentvariable. The independent variables are measured by the summated scales ofcorresponding items shown in Table II. 1 is the measurement error involved in theparameter estimation process. Testing the hypotheses is equivalent to testing whethercoefficients b1 to b7 are non-zero. Significant and positive coefficients imply strongsupport of the research hypothesis. The higher value of coefficient, the more influence ofcorresponding independent variables to the adoption decision.

There are four statistical tests involved in this stage for providing a reliablehypothesis testing result, including:

. a x 2 test for the change in 22 times the log of the likelihood (22LL) value fromthe base model;

. Hosmer and Lemeshow test of model fit and the explanation power;

. wald statistic estimation; and

. classification ability test.

The test results are summarized in Table IV.

Factor Coefficient(b) Wald p-valueSupport formodel

EstimatesPerceived directbenefit 0.20 * 5.13 0.023 H1: YesPerceived indirectbenefit 0.01 1.59 0.208 H2: NoSize 0.24 0.08 0.784 H3: NoOrganizationreadiness 0.04 2.07 0.150 H4: No

Top managementsupport 0.68 * * * 22.85 0.000 H5: YesExternal pressure 0.25 * 4.06 0.044 H6: YesTrust 0.09 * * 8.55 0.003 H7: YesGoodness-of-fitFinal (22LL) ¼ 185.52 D (22LL) ¼ 68.98 * * *

Hosmer-Lemeshow x 2 ¼ 12.03 p ¼ 0.15Pseudo R 2 ¼ 0.27 Cox and Snell R 2 ¼ 0.26 Nagelkerke R 2 ¼ 0.35Classification ability

Predicted % CorrectNon-adopter Adopter

ObservedNon-adopter 117 15 88.6Adopter 39 41 51.3Overall 74.5

Note: Significant at: *p # 0.05, * *p # 0.01 and * * *p # 0.001

Table IV.Statistical results of

logistic regression model

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The x 2 test for the deduction of 22LL value is conducted to assess if the set of theindependent variables in the research model is significant in improving the modelfit (Hair et al., 2010). A null base model is first created to act as the baseline for makingcomparisons of the improvement in model fit with the research model. Theindependent variables with the greatest reduction of 22LL value are then selected inthe forward stepwise model selection procedure. The x 2 and its significance for thededuction of 22LL value from the base model to the research model can be computedin the process to show the improvement of the model estimation fit. The significantdeduction of 22LL shown as D (22LL) with p # 0.001 in Table IV reflects a greatimprovement from the null base model to the research model, therefore demonstratesan adequate model fit.

The Hosmer-Lemeshow test is another x 2 test for comparing the research model withthe perfect base model that can classify respondents into their respective groupscorrectly (Zhu et al., 2003; Hair et al., 2010). An insignificant Hosmer-Leshow x 2

(x 2 ¼ 12.03, p ¼ 0.15) implies that the research model is not much difference from theperfect base model. In addition to the x 2 value, threeR 2 measures including Pseudo R 2,Cox and Snell R 2 and Nagelkerke R 2 are calculated for measuring the explanatorypower of the model based on the reduction in the 2 2LL value. Nagelkerke R 2 of 0.35,for example, representing 35 per cent of the change in the dependent variable due to thereduction of 22LL value performed in the previous step can be explained by theproposed research model. The insignificant Hosmer-Leshow x 2 and satisfactory R 2

suggest a good fit of the logistic regression model in this study with a satisfactoryexplanation capability.

The Wald statistic test is performed for measuring the level of significance ofindividual coefficients in the model (Ramdani et al., 2009; Hair et al., 2010). The resultsfrom Table IV indicate that four of the seven variables namely the perceived directbenefit, top management support, external pressure and trust emerge as significantdeterminants of the adoption of e-market, among which top management support is themost critical determinant with the strongest significant p-value 0.000 and the highestcoefficient 0.68. Whereas the perceived indirect benefit, size and organization readinesshave less effect, even though the correlation of 0.24 between the size and the adoption ishigher than most of the other determinants. The results thus provide support for theH1, H5, H6 and H7.

The classification ability test is used for assessing the predictive accuracy of themodel in classifying the respondents into the correct groups (Hair et al., 2010). In ourstudy, the classification ability test examines the capability of the research model forcorrectly classifying the adopters and non-adopters into their respective groups. Basedon the observation-prediction section in Table IV, the rate of the correct predications bythe logistic model and that by random guess is computed. The logistic model has anoverall prediction accuracy of 74.5 per cent. As there are 80 adopters and 132non-adopters, the classification accuracy by random guess would be(80/212)2 þ (132/212)2 ¼ 53.09 per cent. Thus, the logistic model is claimed to havemuch higher classification ability.

DiscussionThe perceived direct benefit, top management support, external pressure and trusthave been found to be significant determinants of the adoption of e-market

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in Australian SMEs from the logistic regression analysis, among which topmanagement support is the most critical. On the other hand, it is somewhatsurprisingly that organization readiness and size are found to be insignificantassociated with the adoption of e-market in this study. The perceived indirect benefit isalso less influential to the decision of adopting an e-market in SMEs.

The significant impact of the perceived direct benefit on the adoption of e-market inAustralian SMEs is in line with previous findings in the IS innovation literature (Kuanand Chau, 2001; Ramdani et al., 2009). The positive relationship between the directbenefit and the adoption of e-market suggest that e-market is considered by SMEs as atool to gain direct and immediate benefits such as increasing the day-to-dayoperational efficiency, reaching larger number of customers or suppliers and savingcost. Compared with the insignificance of the indirect benefit to the adoption ofe-market, a conclusion can be drawn that SMEs is more concerned with the immediateshort-term benefits that e-market can bring instead of the long-term indirect benefits.

The perceived indirect benefit is not significant in affecting SMEs’ decision inadopting e-market in this study. One possible reason is the lack of awareness of thoseindirect benefits by SMEs in general. This result is consistent with the findings byIacovou et al. (1995) and Kuan and Chau (2001), suggesting that the indirect benefits ofnew technology are not recognized as a competitive advantage in small businessesbecause small businesses are less informed compared to large organizations. Morepromotional effort would therefore be needed to increase the awareness of thesebenefits of e-market for attracting SMEs in adopting e-market. Another explanation forthe insignificance of the indirect benefits might be that even an e-market is able toprovide indirect benefits to SMEs, it falls behind SMEs’ expectations and therefore theperceived indirect benefit does not result in a positive adoption decision. If this is thecase, not only is it important for e-market service providers to provide information onpositive indirect benefits to SMEs, but it is also important that such benefits match oreven exceed the expectation of SMEs.

The size of SMEs is always considered as a critical determinant of technologyadoption in the literature (Teo and Pian, 2003; Ramdani et al., 2009). Largerorganizations have a greater propensity to adopt new technology than the smaller onesdue to the financial resources and technical expertise they have in taking risks withnew technology. However, this study shows it is not the case in the adoption ofe-market. This finding is consistent with the study of internet adoption in SMEs byMehrtens et al. (2001). The low adoption cost and the low maintenance need comparedto other technologies decrease the importance of size to the adoption decision. This istrue in the adoption of e-market, as the cost and the technical requirements of adoptinge-market is much lower than other high-end technologies such as EDI or enterpriseresource planning. Most SMEs are able to afford to the adoption of an e-market. Theincreasing penetration of internet in Australian SMEs (Stockdale and Standing, 2004)further reduces the barrier to the adoption of e-market in the sense that internet is theplatform for conducting e-market activities. Size may therefore be less of a keydeterminant in the adoption of e-market.

The top management support is the most critical determinant of the adoption ofe-market in this study. This is consistent with nearly all the previous technologyadoption studies (Premkumar and Roberts, 1999; Ramdani et al., 2009). In SMEs, theprimary decision maker is the owner or manager of the business, their understanding

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and support guarantee the limited resources to be allocated for the adoption oftechnology, as well as creating a supportive climate in overcoming the barriersand resistance to adoption. The strong relationship between the top managementsupport and the adoption of e-market indicates that in order to promote the adoption ofe-market in SMEs, it is essential to communicate with the top management thepotential benefits of e-market.

The organization readiness is another insignificant factor in the decision ofadopting an e-market in Australian SMEs. In this study, the organization readiness isdetermined by the financial readiness and the technological readiness. The possiblereason of the insignificance effect of the organization readiness is due to the lessfinancial and technical requirements involved in the adoption of e-market compared toother high-end technologies discussed above. This finding is consistent with thefinding of previous studies in online technology adoption in which the financialreadiness is not considered as an issue because most of the internet adoption isaccomplished in house with no appreciable expenses incurred (Mehrtens et al., 2001).

The external pressure shows a significant relationship with the adoption ofe-market in Australian SMEs. The importance of external pressure on the adoption ofe-market in Australian SMEs is consistent with the previous studies such as Kuan andChau’s (2004) study of the EDI adoption in small businesses and Teo et al.’s (2009)study in e-procurement services. In this study, the positive relationship between theexternal pressure and the adoption of e-market reveals that Australian SMEs are moreprone to adopt e-market in order to maintain their competitive position and to developtheir relationships with trading partners. The adoption of e-market by the influentialtrading partners or competitors would accelerate their decisions in adopting e-market.This sheds the light for the e-market service provider that in order to promote theadoption of e-market in SMEs, some free adoption offers and incentives could be givento the influential parties first. After realizing the benefits of e-market, the influentialparties are able to encourage or force smaller businesses to adopt e-market. In addition,the assistance from government through the development of a series of polices andprograms for improving the economic environment and the growth prospect for SMEsalso facilitates the adoption of e-market in Australia.

Trust correlates significantly with the adoption of e-market. This finding isconsistent with the previous research in online technology adoption such as electronicfile adoption (Schaupp and Carter, 2008) and mobile commerce adoption (Wei et al.,2009). In the adoption of e-market, trust involves in the trustworthy of e-market itselfas well as of other trading parties. In order to increase the adoption rate of e-marketamong SMEs, the e-market service provider should consider not only to promote thetrust of SMEs in e-market by enhancing the online transaction security, such asincluding escrow services to control the payment process and credit card guaranteeservices to safeguard the transaction, but also to boost the trustworthy betweentrading parties by providing accurate and reliable information to each other.

ImplicationsThe research findings of this study have several important theoretical and practicalimplications. The theoretical contributions of this research to the existing literature inthe field of e-market and technology adoption in SMEs are three folds. First, this researchextends the usefulness of the TOE framework to the study of e-market adoption.

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Second, this research provides a validated measurement model for the e-marketadoption. Third, the research findings enrich the understanding of the criticaldeterminants of adopting e-market in SMEs in an Australian context.

The research findings of this study have practical implications to the e-marketservice providers in their effort for promoting the development of e-market in SMEs.Specific strategies and policies can be developed with a more focused aim on:

. broadcasting the immediate direct benefits of an e-market for e-business toSMEs;

. informing the top management in SMEs the potential benefits of adopting ane-market;

. promoting the adoption of e-market in the influential or big trading parties firstby giving free offers or incentives; and

. building effective mechanisms for enhancing the online transaction securitycontrol and trustworthy between trading parties for boosting SME’s trust in thee-market.

The research findings of this study also offer SMEs useful information and guidelinesfor assisting the development of doable strategies and policies for the adoption ofe-market. More specifically, the validated framework proposed for the adoption ofe-market for Australian SMEs can be applied by SMEs to assess their readiness for theadoption of e-market. SMEs can better consider the technological, organizational andenvironmental factors in the adoption of e-market prior to making the adoptiondecision. In addition, the critical determinants identified in the adoption of e-market inthis research can be used for SMEs to pinpoint their strength and weakness in theadoption of e-market. The importance of trust and perceived direct benefit to theadoption of e-market shows that SMEs are ready for adopting an e-market when:

. they have full trust in the e-market and their trading partners online; and

. they perceive a much higher benefit of e-market than the existing technology forconducting their businesses.

The criticality of top management support also suggests that the attitude of the topmanagement in SMEs is the key factor that leads to the adoption of e-market. Theorganization readiness and perceived indirect benefit, however, are not the main issuesto be considered in the adoption of e-market.

ConclusionThis study investigates the critical determinants for the adoption of e-market inAustralian SMEs within the TOE framework. It identifies the critical determinants foradopting e-market in Australian SMEs including the top management support, trust,external pressure and perceived direct benefit. Furthermore, this study shows that thesize and the organization readiness of SEMs are less influential in adopting e-market.The results of this study provide SMEs with a profound insight into the issuesinvolved in the adoption of e-market.

There are several limitations in this study. First, this study only investigates thecritical determinants involved in the e-market adoption. In order to gain an holisticunderstanding of the e-market to SMEs, the impact of e-market adoption on

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the performance of SMEs should be examined. Second, this study does not distinguishthe adoption of e-market between Australian metropolitan and rural areas. SMEslocated in rural areas by the nature and location owns specific characteristics comparedto the metropolitan SMEs such as limited access to resources and less influenced byexternal pressure (MacGregor and Vrazalic, 2005). The issues of their concern in theadoption of e-market might be different from those located in metropolitan areas. Third,the potential interaction between the industry type and the adoption of e-market are notexplicitly explored in this study. It is interesting to investigate if the diversity ofindustries influences the determinants for the adoption of e-market in SMEs.

The future research can address the above limitations by:. investigating the post adoption stage of e-market in SMEs;. conducting a comparison study of the adoption of e-market between

metropolitan area and regional area; and. exploring the relationship between the industry type and the adoption of

e-market.

Additionally, size and organization readiness are critical determinants in most of thetechnology adoption studies, whereas in this study they are found to be insignificant.To triangulate these findings and to explore the insights of their contribution to theadoption of e-market, qualitative research such as interviews or focus group can beconducted.

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Appendix

About the authorsXiaoxia Duan is a PhD student in the School of Business Information Technology and Logisticsat RMIT University, Australia. Her research interests are in the areas of electronic business,quantitative data analysis, performance evaluation, decision support systems and theirapplications in business. She has published in various reputed national and international

Variables Items Source of items

Perceiveddirect benefit

PDB1 Expand customer base Tan et al. (2003) and Joo and Kim(2004)PDB2 Reduce product cost

PDB3 Reduce information dissemination costPDB4 Increase price transparencyPDB5 Reduce operation costPDB6 Save time

Perceivedindirectbenefit

PIB1 Improve the overall performance ofpurchasing/selling process

Kuan and Chau (2001) and Joo andKim (2004)

PIB2 Improve organization imagePIB3 Improve the competitive advantagePIB4 Improve customer servicePIB5 Improve relationship with tradingpartnersPIB6 Increase operational efficiency

Organizationreadiness

OR1 Sufficient financial resource to adopt e-market

Kuan and Chau (2001) and Teo et al.(2009)

OR2 Sufficient financial resource to maintaine-marketOR3 Sufficient technological resource toadopt e-marketOR4 Sufficient technological resource tomaintain e-market

Topmanagementsupport

TMS1 Top management aware of the benefits Ramdani et al. (2009)TMS2 Top management highly interestedTMS3 Top management allocates adequateresources

Externalpressure

EP1 Recommended by important businesspartners

Chwelos et al. (2001), Kuan and Chau(2001) and Joo and Kim (2004)

EP2 Recommended by majority of businesspartnersEP3 Adopted by the majority of competitorsEP4 Adopted by the majority of competitorsEP5 Recommended by the government

E-markettrust

ET1 E-market has high integrity Pavlou and Gefen (2004) andVerhagen et al. (2006)ET2 E-market can be trusted at all times

ET3 E-market guarantees transactionsecurity

Tradingpartner trust

TPT1 Trading partners are in general reliable Pavlou and Gefen (2004) andVerhagen et al. (2006)TPT2 Trading partners are in general honest

TPT3 Trading partners are in generaltrustworthy

Table AI.Measurement instrument

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conferences and journals. Xiaoxia Duan is the corresponding author and can be contacted at:Xiaoxia.duan@rmit.edu.au

Dr Hepu Deng is a Professor in Information Systems at the School of Business InformationTechnology and Logistics at RMIT University, Australia. His research interests are in the areasof decision analysis, intelligent systems, digital business, knowledge management, and theirapplications in business. The multi-disciplinary nature of his research and the emphasis on boththeoretical and applied research lead to more than 100 refereed publications in top refereedinternational journals listed in the Science Citation Index, the Social Science Citation Index, andat major refereed international conferences.

Professor Brian Corbitt is the Deputy PVC, Business – Research and Innovation at RMITUniversity, Australia. He specializes in information technology strategy, health informationsystems, analysis and implementation in business modelling and electronic commerce traderelationships, and knowledge management. He has published ten books including six oneBusiness, eCommerce and eGovernment. He has also published over 150 refereed scholarlypapers, numerous reports to the governments of Thailand and New Zealand, and some 20 invitedpapers as a keynote speaker on information technology policy in Malaysia, Singapore, Thailand,New Zealand, Japan, Hong Kong, and Australia.

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