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Perceived Strategic Value and e-Commerce Adoption among SMEs inSlovakiaKojo Saffu a , John H. Walker a & Marica Mazurek ba Faculty of Business, Brock University, St. Catharines , Ontario ,Canadab Centre of Science and Research, Institute of Economic Sciences,University of Matej Bel , Banská Bystrica , SlovakiaPublished online: 27 Feb 2012.

To cite this article: Kojo Saffu , John H. Walker & Marica Mazurek (2012) Perceived Strategic Valueand e-Commerce Adoption among SMEs in Slovakia, Journal of Internet Commerce, 11:1, 1-23, DOI:10.1080/15332861.2012.650986

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Perceived Strategic Value and e-CommerceAdoption among SMEs in Slovakia

KOJO SAFFU and JOHN H. WALKERFaculty of Business, Brock University, St. Catharines, Ontario, Canada

MARICA MAZUREKCentre of Science and Research, Institute of Economic Sciences,

University of Matej Bel, Banska Bystrica, Slovakia

This article examines the link between the determinants of aPerceived Strategic Value construct and an Adoption constructfor e-commerce in Slovakian small and medium-sized enterprises.Results indicate that three factors—Organizational Support,Managerial Productivity, and Decision Aids—comprise thePerceived Strategic Value construct. For the Adoption construct,four factors—External Pressure, Ease of Use, Readiness and Com-patibility, and Perceived Usefulness—were revealed. Canonicalcorrelation analysis showed a positive link between these con-structs. Theoretical, practical, and policy implications are pre-sented. This study enhances the understanding of the PerceivedStrategic Value and of the adoption of e-commerce for Slovakiansmall and medium-sized enterprises.

KEYWORDS adoption, e-commerce, perceived strategic value,Slovakia, SMEs

INTRODUCTION

There are many different definitions for electronic commerce (e-commerce).Schneider and Perry (2000) defined e-commerce as business activities con-ducted using electronic data transmission via the Internet and World WideWeb (www). Gibbs, Kraemer, and Dedrick (2003) defined e-commerce asthe use of the Internet to buy, sell, or support products and services. Accord-ing to Turban and colleagues (2010), ‘‘e-commerce is the process of buying,

Address correspondence to John H. Walker, Faculty of Business, Brock University, 500Glenridge Ave., St. Catharines, Ontario, L2S 3A1, Canada. E-mail: jowalker@brocku.ca

Journal of Internet Commerce, 11:1–23, 2012Copyright # Taylor & Francis Group, LLCISSN: 1533-2861 print=1533-287X onlineDOI: 10.1080/15332861.2012.650986

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selling, transferring, or exchanging products, services and=or informationusing computer networks mostly the Internet and Intranets.’’ This studydefines e-commerce as the use of the Internet for conducting business.

A number of studies have looked at factors that Internet users considerimportant for the success of Internet commerce. These factors include thevalue and benefits of finding, ordering, and receiving goods (Keeney1999); online payment, shipping errors, and vendor trust (Torkzadeh andDhillon 2002); as well as perceived value of the vendor’s products and ser-vices and shopping convenience (Mukhopadhyay, Mahmood, and Joseph2008). Factors that have been identified as influencing the adoption ofe-commerce by companies include relative advantage, compatibility, organi-zational readiness, and managerial support (Alam, Ali, and Jani 2011; Ifinedo2011; MacGregor and Kartiwi 2010; Pham, Pham, and Nguyen 2011). Accessto international markets at minimal cost (Lal 2002), a reduction in transactioncosts (Dembla, Palvia, and Krishnan 2007), and the provision of new busi-ness opportunities (Grandon and Pearson 2004) have also been mentioned.Zhu and Thatcher (2010) have drawn attention to supportive governmentpolicies and socio-cultural infrastructure.

For small and medium-sized enterprises (SMEs), e-commerce poses athreat, because they do not have the same financial and human resourcesas the large multinational firms with whom they are competing (Elia,Lefebvre, and Lefebvre 2007). Top management enthusiasm and compati-bility between e-commerce and the firm’s current work are additional threatsto the adoption of e-commerce by SMEs (Mirchandani and Motwani 2001).

There is increasing recognition that the study of e-commerce diffusion isimportant for policy makers, academics, businesses, and governments (Wolcottand Goodman 2003). The adoption of e-commerce depends upon managerial,organization, environmental, and technological factors (see, for example, Lal2002; Kartiwi and MacGregor 2007). It was noted by Pool and colleagues(2006) that the adoption of information technology (IT) not only provides acompetitive advantage for SMEs but is also necessary for their survival.

Most of the technology adoption research has been carried out indeveloped countries, for example, Canada and the United States (Davis,Bagozzi, and Warshaw 1989; Rogers 2003). Research in developing countriesis limited (Al-Shaikh et al. 2010; Alrawi 2007; Bennett 2007; Gurau 2008). Thepresent study addresses this gap in the literature and makes theoretical andpractical contributions. First, the proposed model leads to a better under-standing of the determinants of adoption of technology in a transitioningeconomy. From a practical point of view, the study assists Slovakian SMEsto take action to ensure effectiveness in adopting e-commerce. Firm-levelInternet adoption strategies must be understood and implemented.

This study empirically investigates the link between the perceived stra-tegic value (PSV) of e-commerce and its adoption by SMEs. More specifically,its focuses on understanding and implementing firm level e-commerce

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adoption strategies by SMEs in a transitioning country, Slovakia. The goal isto ascertain if the determinants in Slovakia are the same as those identified inthe extant literature in developed countries.

STUDY CONTEXT

Slovakia has been successfully transitioning from a Soviet-style centrallyrun economy to a modern market economy. Significant economic reformshave been made by Slovakia since it separated from the Czech Republicin 1993. For instance, Slovakia’s economic growth for 2001 to 2008exceeded the European trend (Central Intelligence Agency [CIA] 2011).SMEs, categorized as businesses with less than 250 employees (EuropeanUnion 2003), are a significant source of jobs and economic growth withinthe European Union (Schmiemann 2009). Between 2004 and 2006,Slovakian SMEs created more jobs than large enterprises, outpacing theEuropean Union average for the same period. Similarly, labor productivityincreased by approximately 29% compared with 8.1% for the EuropeanUnion. For the rate of change of value added to the economy, Slovakia’sSME rate was greater than the European Union average (see Schmiemann2009). Bielik and Rajcaniova (2007) stated that the European Union is avery competitive environment, and hence, SME innovation is importantto effectively respond to challenges and opportunities within this marketas well as the international market.

The Slovakian government has formulated a competitiveness strategythat incorporates the information society. According to Miklo�ss (2004), theuse of IT is one of the best approaches for Slovakia to become a dynamic,knowledge-based economy. The promotion of a high-quality and affordableinformation and communication infrastructure is warranted. Nonetheless,according to the European Commission’s Digital Competitiveness Report(European Commission 2010), Slovakia’s information society is laggingbehind general developments in the European Union. e-Commerce indica-tors are lower than European Union averages. For example, the percentageof enterprises selling online and the percentage of enterprises purchasingonline in Slovakia are 5% and 9%, respectively. In contrast, the correspond-ing percentages for the European Union are 16% and 28%, respectively.Additionally, the slow development of fixed broadband penetration isproblematic, although commercial wireless broadband is increasing. ForSlovakian SMEs to remain competitive in the highly dynamic EuropeanUnion, they need to be innovative and embrace IT, including the use ofe-commerce. This will allow Slovakian SMEs to create jobs and contributeto the Slovakian government’s competitive strategy. This is imperative, sincethe number of Internet users globally has doubled between 2005 and 2010(International Telecommunications Union [ITU] 2011).

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RESEARCH FRAMEWORK

The research framework incorporates two research streams, namely percep-tion of the strategic value of IT and IT adoption. It is theorized that there willbe a relationship between the strategic value of IT and IT adoption amongSlovakian SMEs. Figure 1 presents this relationship, which is discussed belowin more detail.

Perception of Strategic Value of IT

Empirical research by Palvia and Palvia (1999) indicated that there was apositive relationship between profitability and computer utilization in smallbusinesses. However, lack of resources is a primary factor that inhibits theuse of IT by small businesses (Iacovou, Benbasat, and Dexter 1995). In thelight of resource constraints faced by small businesses, small business own-ers=managers demand even more strategic value from their use of resourcesas compared with larger firms. Consequently, there is the need for SMEs tomeasure how e-commerce value is perceived by owner=managers who areentrusted with allocating an SMEs’ scarce resources. A study by Tallon,Kraemer, and Gurbaxani (2000) showed that executives relied on their per-ceptions in determining the value creation of IT investment. These authorsalso found that personal experience and peer evaluations were importantdeterminants of IT value. An important value created by e-commerce is thereduction in transaction costs (Dembla et al. 2007; Saloner and Spence 2002).

FIGURE 1 Hypothesized model. Source: Subramanian and Nosek (2001); Grandon andPearson (2004).

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According to Subramanian and Nosek (2001), as well as Rainer andcolleagues (2011), funding approval for information systems (IS) is basedon business justification and the strategic value of the applications asviewed by top management. Management uses their own value system tointerpret the organization’s value system in judging costs, benefits, and risksin the course of resource allocation decisions. The preference of top-levelmanagement is an alignment of IT with business strategy (Burn and Szeto2000). Measuring the perceived value of IS planning is mentioned as animportant research area (Segars and Grover 1998). The influence of valuestructures on top management’s decision is least understood in decisionand management science, leading researchers to advocate more researchon CEOs’ and the upper-level management’s perceptions of IT value(Busch et al. 1991).

Subramanian and Nosek (2001) conceptualized the PSV of IS (PSVIS).For measuring PSVIS, the authors developed and validated an instrumentthrough confirmatory factor analysis. Their instrument had a good fit, conver-gent and discriminant validity between the constructs, as well asuni-dimensionality. Their first conceptualization was the perception of oper-ational support value to take advantage of operational efficiency benefits andaid operational strategy, cost reduction, and improved customer service. Thisis identical to automation, leading to savings, quality improvement, and amore effective organization (Segars and Grover 1988), and Weill’s (1992)transactional (operational efficiency) and strategic (operational expansion)IT objectives. Prior research (e.g., Mahmood and Soon 1991) supports thisconceptualization.

Managerial productivity (MP) enhancement was the second conceptua-lization of PSVIS. This implies better access to information, leading toimprovement in communication among managers by using databases thatare available internally and externally. The third conceptualization was theperception of IS as a strategic decision aid (DA) tool. This is akin to the visionto transform and effect change at the interface with customers and the indus-try (Segars and Grover 1998).

Utilizing Subramanian and Nosek’s (2001) framework, Grandon andPearson (2003, 2004) developed and validated a PSV model of SMEs inChile and in the United States. They suggested that the model may be use-ful to managers in other developing countries when making decisionsrelated to e-commerce adoption. Based on the foregoing discussion, themajor determinants of PSV of e-commerce were organizational support(OS), MP, and DAs. OS addresses how e-commerce improves customer ser-vices, provides an effective support role to operations, and reduces costs,among other things. MP focuses on access to information, improvescommunication in the company, and raises managers’ productivity. DAsrefer to managers’ decision-making, availability of information, and industrylinkages.

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IT Adoption

Prior research has established the factors that influence different ITadoptions. These factors can be categorized as organizational readiness(OR; Iacovou et al. 1995; Chwelos, Benbasat, and Dexter 2001; Grandonand Pearson 2003, 2004), compatibility (C; Grandon and Pearson 2003,2004), external pressure (EP; Davis et al. 1989), perceived ease of use(EU), and perceived usefulness (PU; Davis et al. 1989). Organizational readi-ness was conceptualized as the financial and technological resources atthe disposal of the firm to adopt. Organizational readiness includes the topmanagement’s enthusiasm to adopt IT, existing technology infrastructure,compatibility of the firm’s e-commerce, and culture and values.

Compatibility has been found to be a significant factor that impacts onadoption of IT (Beatty, Shim, and Jones 2001) and e-commerce (Grandonand Pearson 2003). EP includes competition, the government, industry,and reliance on firms that are using e-commerce (Gibbs et al. 2003). Per-ceived EU addressed the extent to which a firm’s perceived adoption ofe-commerce would be effortless (Davis et al. 1989). Finally, PU implies theextent to which a firm using e-commerce perceived it to be useful in termsof an improvement in corporate job performance.

The connection between PSV and adoption is captured by the theory ofplanned behavior (TPB), which argues that perceptions influence intentions,and they, in turn, influence human behavior (Ajzen 1991). Researchers (e.g.,Agarwal 2000) have used the TPB to predict and explain behavior such as theuse of IT. A strong association between the perceptions and attitudes of man-agers toward IT types and use has also been established by prior research(e.g., Sanders and Courtney 1985).

To confirm the hypothesized model presented in figure 1 and to obtaina better understanding of the relationship between the strategic value andadoption of e-commerce by SMEs, two major questions are investigated:what are the determinants of the PSV of e-commerce in Slovak SMEs, andwhat are the determinants of e-commerce adoption among Slovak SMEs?

METHODOLOGY

Sample and Data Collection

The data used in this study were collected from 230 SMEs on their premisesusing questionnaire surveys between June and August 2009. The SMEs werelocated in various Slovakian cities, including Banska Bystrica, Ko�ssice,Poprad, Stara L’ubovna, and Zilina. The questionnaire used in the data collec-tion was developed and validated by Grandon and Pearson (2003, 2004).There were three sections in the questionnaire: Section 1 gathered generalinformation about the company, Section 2 focused on perceptions of

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strategic value of e-commerce, and Section 3 addressed the perceptions ofadopting e-commerce. Section 1 focused on demographic characteristics ofthe respondent (gender, age, years in present position, and years with firm)as well as questions on his=her company (number of employees, industrysector, and use of Internet-based technologies, such as e-mail communi-cation with customers and suppliers and a Web site). To measure PSV andadoption of e-commerce, in Sections 2 and 3 respectively, a 7-point Likertscale was used ranging from 1 (strongly disagree) to 7 (strongly agree).

Following the advice of Craig and Douglas (2000) for conducting inter-national consumer research, the survey instruments were modified for across-cultural adaptation of the scale items. The questionnaire was first trans-lated into Slovak by a Slovakian–English translator in Banska Bystrica,Slovakia. The translated questionnaire was then back-translated into Englishto ensure its precise meaning and the cross-cultural equivalence of thelanguage (see Berry 1980). A Brock University professor, originally fromSlovakia, provided an independent review of the translation. After pilot testswere carried out and further corrections were made, the questionnaire wasthen completed by the SME’s owners=managers while the research assistantwaited.

Analysis

Because factor analysis was used in some of the analyses, the sample sizewas based on the general rule of thumb suggested by Hatcher (1994) andHair and colleagues (2010) for conducting factor analysis. According to thesescholars, the sample size should be at least 100 observations or a ratio of 5:1observations per variable to be analyzed. Hair and colleagues further statedthat a 10:1 ratio was more acceptable. Consequently, with 15 and 23 variablesto be analyzed with factor analysis in Sections 2 and 3, respectively, question-naires from 230 companies were originally sought. However, usable datawere collected from 211 firms, a response rate of 84.4%, which is more thanthe minimum of 115. In order to obtain a satisfactory factor analysis, theguidelines of Hair and colleagues were followed. These guidelines state thata substantial number of the correlations among the variables exceed 0.3; anypartial correlations exceeding 0.7 are problematic; Bartlett’s test of sphericityshould be statistically significant; the overall measure of sampling adequacy(MSA), denoted as the Kaiser-Meyer-Olkin (KMO) MSA, must be at least 0.5;and the individual MSAs should also be at least 0.5.

Since the dataset satisfied these guidelines, principal component analy-sis was used to maximize the extracted variance for identifying and estimat-ing the proposed constructs, namely PSV and Adoption, defined by Grandonand Pearson (2003, 2004). Varimax rotation was used to ensure that the fac-tors were uncorrelated and that the factor loadings for each variable weremaximized on one factor (see Hair et al. [2010] and Hatcher [1994]). The

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‘‘eigenvalue greater than 1.0 rule’’ was used for determining how manyfactors to retain. Hair and colleagues (2010) provided details on determiningsignificant factor loadings. For a sample of 211, with a power of 80% and a0.05 type I error, significant factor loadings have to exceed 0.4.

The convergent and discriminant validity of the derived factors was thenexplored. Convergent validity occurs if the items load strongly (>0.5) ontheir respective factors, and discriminant validity is confirmed if each ofthe variables loads stronger on its associated factor than on any other factor(see Hair et al. [2010]). In order to derive a summative scale to replace theindividual variable scores, the construct reliability was evaluated throughCronbach’s alpha. The minimum acceptable Cronbach’s alpha value is 0.7(Nunnally 1978; Robinson, Shaver, and Wrightman 1991).

To examine the relation between the summative scales used to representthe factors comprising these two constructs, canonical correlation as well ascanonical variates, described by Hotelling (1935, 1936), were used. Canonicalcorrelation analysis can be used to describe the relationships between twosets of data using correlation coefficients, as well as ascertaining the magni-tude and statistical independence of the relationship. Thus, canonical analysisis a multivariate statistical model that looks at the relationship between a setof multiple dependent variables and a set of multiple independent variables(Hair et al. 2010; Green, Halbert, and Robinson 1966). With this technique,it is also possible to control for suppressor or moderator effects that mayexist among the various dependent variables (Green et al. 1966).

The maximum number of canonical (correlations) functions that can bedetermined is equal to the minimum of (p, q), where p is the number of vari-ables in the dependent set and q is the number of variables in the inde-pendent set (Hair et al. 2010; Johnson and Wichern 2007). While the firstcanonical function will extract the maximum variance in the sets of variables,successive canonical functions will extract the residual variance notexplained by earlier canonical functions. For each canonical function, a lin-ear set of canonical variates for the dependent and independent variableswill be determined that have as much intercorrelation among the variatesas possible. Note that successive sets of canonical variates must be orthog-onal to all previous sets. For testing the significance of the canonical correla-tions, Khattree and Naik (2000) advised that an initial hypothesis of all thecanonical correlations equalling zero is performed, and if this hypothesis isrejected, successive tests are performed on the remaining canonical correla-tions, excluding the first, then the first two, etc., continuing until the lasthypothesis is not rejected. These successive grouping of tests are based onthe F-distribution approximation of Wilk’s K, denoted Rao’s F. Other multi-variate tests are Wilk’s K, Pillai’s Trace, Hotelling-Lawley Trace, and Roy’sGreatest Root that are used to test the hypothesis that all the canonicalcorrelations are zero. In this study, p values �.05 were considered to be stat-istically significant.

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For those canonical correlations that are found to be significant, Hair andcolleagues (2010) stated that besides testing the canonical correlations, themagnitude of the canonical correlations and the redundancy measure for thepercentage of variance accounted for within the two datasets should be exam-ined. For examining the magnitude of the canonical correlations, 0.3 is sug-gested as a cut-off value. However, at the same time, a redundancy analysisshould be performed. A redundancy analysis is a three-step process, whichdetermines the amount of variance in one set of variables that is explainedby the variables in the other set (Hair et al. 2010). The three-step processwas summarized by Hair and colleagues, as well as by Khattree and Naik(2000). A table is prepared that provides the percentage of shared variancebetween the variables of one set with its variate. Then multiplying this percent-age by the squared canonical correlation coefficient, which is the proportion ofvariance shared by the canonical variates, the measure of redundancy is thendetermined. The measure of redundancy, the percentage of variance of one setof variables explained by the other set’s canonical variate can then be summedup over all the canonical functions to derive a single index of redundancy(Stewart and Love 1968). The authors state that knowing the proportion ofredundant variance associated with a canonical function may be helpful whenexamining the magnitude of the canonical correlation.

The significant canonical functions, the canonical correlation magni-tude, and the redundancy analysis can then be interpreted. Three methodsare suggested by Hair and colleagues (2010), including the examination ofcanonical weights, canonical loadings, and canonical cross-loadings. Of thethree methods, the canonical cross-loading method is preferred, since itincreases the external validity of the findings. The canonical cross-loadingscorrelate each of the variables in one set with the canonical variate in theother set. For determining the significance of a cross-loading, Hair and col-leagues indicated that the cut-off value used should be the same as that forfactor loadings. Because of this study’s sample size, a cut-off value of 0.4was used. However, other researchers (Grandon and Pearson 2003, 2004)used 0.3. After an examination of the cross-loadings, a validation of theanalyses should be performed. Due to the small sample size, the originalsample was not split into an estimation sample and a validation sample.Rather, sensitivity analyses on the PSV variables were performed, as sug-gested by Hair and colleagues. In each sensitivity analysis, one of the vari-ables is removed from the canonical analysis, and the resulting canonicalloadings, shared variance, and redundancy are examined for stability.

RESULTS

The paper-based questionnaire responses were entered into an Excel1 work-sheet by one of the study investigators. Twenty percent of the questionnaires

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were then randomly selected by another investigator, and the worksheetentries were compared with the paper entries for data verification. No differ-ences between the electronic and paper entries were detected. The Excel1

worksheet was then imported into IBM SPSS1 Statistics 18 for Windows(Chicago, Illinois, USA) and SAS 9.11 (Cary, North Carolina, USA). The formersoftware package was used for the respondent and firm characteristics as wellas the factor analyses, and the latter software package was used for thecanonical correlation analyses.

Table 1 presents a summary of general information about the respon-dents and their firms. For categorical data, frequencies and percentages areprovided, whereas for continuous data, the mean and standard deviationare given. The respondents were mostly female, and approximately 43%had more than a high-school education. The majority of the firms had Inter-net service providers as well as Web sites. Most of the responding firms com-municated with customers and suppliers through e-mail and also utilizede-commerce.

The dataset for the ‘‘strategic value’’ constructs was suitable for conduct-ing factor analysis. Of the 105 correlations, 93 were greater than 0.3. Therewere no partial correlations greater than 0.7, with the largest being 0.506.Bartlett’s test of sphericity was significant. The KMO MSA was 0.941, con-sidered meritorious, and the individual MSAs ranged from 0.882 to 0.941.The initial factor analyses (see table 2) indicated that three of the OS variables(OS4, OS5, and OS7) had cross-loadings on the three factors with eigenva-lues greater than 1. These three variables were deleted from a further factoranalysis. A variable has a cross-loading when there are significant loadingson more than one factor. The new dataset still met all the requirements forconducting a factor analysis, although the KMO MSA dropped to 0.870, stillconsidered meritorious. The results of this final factor analysis are presentedin table 3.

For this factor analysis, three factors were extracted that explained68.2% of the total variance. None of the variables have a cross-loading;hence, they load cleanly and significantly on only one factor. The factor load-ings in the table are shown in descending order of factor loading magnitudefor each factor. Convergent and discriminant validity are seen from the factoranalysis. The hypothesized factors for OS, MP, and DAs are therefore por-trayed by factors 3, 2, and 1, respectively.

Examining the dataset for the ‘‘adoption’’ construct variables, it wasdetermined that this dataset was suitable for factor analysis. Of the 253 cor-relations among the 23 variables, 166 were greater than 0.3. The KMO MSAwas 0.899, meritorious, and Bartlett’s test of sphericity was significant.Additionally, the largest partial correlation coefficient was 0.573, and theindividual MSA values ranged from 0.666 to 0.973. The initial factor analysisidentified four factors with eigenvalues greater than one. However, twovariables—one of the ‘‘C’’ variables (C4) and one of the ‘‘EP’’ variables

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TABLE 1 Demographic and Descriptive Statistics of Respondents

Variable n (%)

GenderMale 79 (37.4%)Female 132 (62.6%)

Age (years) Mean 33.0 (SD 8.6)Education

High school 122 (57.8%)2-Year college 20 (9.5%)4-Year university 26 (12.3%)Master’s=MBA 29 (13.7%)Doctorate 2 (0.9%)Other 12 (5.7%)

IndustryManufacturing 56 (26.5%)Education 10 (4.7%)Government 6 (2.8%)Finance 14 (6.6%)Wholesale 21 (10.0%)Retail 26 (12.3%)Health care 4 (1.9%)Construction 17 (8.1%)Transportation 8 (3.8%)Insurance 3 (1.4%)Other 46 (21.8%)

PCs networkedYes 173 (82.0%)No 38 (18.0%)

Internet service providerYes 197 (93.4%)No 14 (6.6%)

Firm Web siteYes 152 (72.0%)No 59 (28.0%)

Internal e-mail systemYes 166 (78.7%)No 45 (21.3%)

Communicate with suppliers by e-mailYes 177 (83.9%)No 34 (16.1%)

Communicate with customers by e-mailYes 186 (88.2%)No 25 (11.8%)

Firm hires part-time=full-time IT staffYes 75 (35.5%)No 136 (64.5%)

Firm consults for IT servicesYes 147 (69.7%)No 64 (30.3%)

Firm utilizes e-commerceYes 58 (54.2%)No 49 (45.8%)

Note. n and percent of total (N¼ 211) are provided; SD¼ standard deviation.

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(EP5)—had cross-loadings on two of the factors. These two variables wereremoved from the dataset, and a new factor analysis was conducted. Thedataset still met all the criteria for conducting a factor analysis. Of note, theKMO MSA was 0.901, and the individual MSA’s ranged from 0.821 to 0.971.The new factor analysis extracted four factors, and the total explained vari-ance was 70.6%. The results of this final factor analysis are presented intable 4.

TABLE 3 Rotated Component Matrix—Final Perceived Strategic Value

Component

Item Description 1 2 3

OS2 Improve customer service 0.196 0.142 0.776OS3 Improve distribution channels 0.183 0.214 0.749OS6 Support linkages with suppliers 0.340 0.074 0.656OS1 Reduce costs of business operations 0.050 0.317 0.629MP4 Improve productivity of managers 0.373 0.761 0.108MP2 Provide managers access to methods and models in making

functional area decisions0.253 0.730 0.338

MP1 Provide managers better access to information 0.221 0.707 0.377MP3 Improve communication in the organization 0.324 0.688 0.190DA3 Support cooperative partnerships in the industry 0.794 0.193 0.303DA1 Support strategic decisions of managers 0.781 0.298 0.176DA2 Help make decisions for managers 0.764 0.370 �0.008DA4 Provide information for strategic decisions 0.762 0.369 0.081

Note. MP¼Managerial Productivity; OS¼Organizational Support; DA¼ Strategic Decision Aids.

TABLE 2 Rotated Component Matrix—Initial Perceived Strategic Value

Component

Item Description 1 2 3

OS1 Reduce costs of business operations 0.125 0.260 0.735OS2 Improve customer service 0.144 0.248 0.702OS3 Improve distribution channels 0.134 0.313 0.677OS4 Reap operational benefits 0.622 �0.019 0.427OS5 Provide effective support role to operations 0.523 0.013 0.576OS6 Support linkages with suppliers 0.502 0.372 0.260OS7 Increase ability to compete 0.406 0.085 0.494MP1 Provide managers better access to information 0.168 0.727 0.375MP2 Provide managers access to methods and models in making

functional area decisions0.203 0.744 0.334

MP3 Improve communication in the organization 0.307 0.684 0.200MP4 Improve productivity of managers 0.314 0.791 0.095DA1 Support strategic decisions of managers 0.800 0.290 0.196DA2 Help make decisions for managers 0.766 0.354 0.018DA3 Support cooperative partnerships in the industry 0.694 0.389 0.192DA4 Provide information for strategic decisions 0.732 0.324 0.059

Note. MP¼Managerial Productivity; OS¼Organizational Support; DA¼ Strategic Decision Aids.

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The variables have been sorted on factor loading magnitudes for eachfactor. Both convergent and discriminant validity are seen from the results.However, the hypothesized construct had five components; whereas theresults of this study indicate that there are four components. Three of the fac-tors are ‘‘EP,’’ ‘‘EU,’’ and ‘‘PU.’’ The last factor comprises variables from the

TABLE 4 Rotated Component Matrix—Adoption Construct

Component

Item Description 1 2 3 4

OR2 Technological resources to adopte-commerce

0.261 0.177 0.781 0.032

C2 With values 0.029 0.196 0.736 0.279C1 With culture 0.014 0.111 0.722 0.231OR1 Financial resources to adopt

e-commerce0.214 0.077 0.721 0.032

C3 With preferred work practices 0.159 0.260 0.675 0.245C5 Top management is enthusiastic

about the adoption of e-commerce0.316 0.297 0.407 0.337

EP2 Social factors are important in ourdecision to adopt e-commerce

0.125 0.198 0.177 0.773

EP3 We depend on other firms that arealready using e-commerce

0.220 –0.190 0.028 0.693

EP1 Competition is a factor in ourdecision to adopt e-commerce

0.271 0.322 0.203 0.678

EP4 Our industry is pressuring us to adopte-commerce

0.287 �0.008 0.321 0.654

EU4 It would be easy for me to becomeskilful at using e-commerce

0.176 0.887 0.149 0.063

EU5 I would find e-commerce easy to use 0.148 0.854 0.130 0.055EU1 Learning to operate e-commerce

would be easy for me0.116 0.781 0.225 –0.065

EU2 I would find e-commerce to beflexible to interact with

0.340 0.773 0.161 0.155

EU3 My interaction with e-commercewould be clear and understandable

0.288 0.756 0.183 0.116

PU4 Using e-commerce would enhancemy effectiveness on the job

0.905 0.150 0.112 0.222

PU3 Using e-commerce in my job wouldincrease my productivity

0.895 0.143 0.140 0.181

PU2 Using e-commerce would improvemy job performance

0.886 0.130 0.149 0.152

PU5 Using e-commerce would make iteasier to do my job

0.844 0.259 0.102 0.156

PU1 Using e-commerce would enable mycompany to accomplish specifictasks more quickly

0.754 0.314 0.239 0.196

PU6 I would find e-commerce useful inmy job

0.721 0.305 0.197 0.250

Note. C¼Compatibility; EU¼Ease of Use; EP¼External Pressure; OR¼Organizational Readiness;

PU¼ Perceived Usefulness.

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hypothesized factors of ‘‘OR’’ and ‘‘C.’’ As a result, this factor is called ‘‘readi-ness and compatibility’’ (RC).

Construct reliability was regarded as being sufficient for all the factors ofthe ‘‘strategic value’’ construct as well as all the factors of the ‘‘adoption’’ con-struct. The alpha values ranged from 0.724 to 0.953.

The overall model fit of the canonical variates is provided in table 5. Themultivariate tests of significance, which are displayed in table 6, indicate thatcollectively, the three canonical functions are significant. However, success-ive tests presented in table 5 indicate that only the first two canonical func-tions are significant. Looking at the canonical R2 values from the same table,it can be seen that the first canonical function, the PSV canonical variate,explains approximately 40% of the variance in the variance of the adoptioncanonical variate. The second canonical function explains approximately 8%,while the third canonical function explains only 1%.

The remaining two canonical correlations were retained for redundancyanalysis since they were both greater or equal to 0.3, after rounding, which isthe value suggested by Hair and colleagues (2010). The redundancy analysisis summarized in table 7. For the first canonical function, the redundancyvalues for the strategic value variate as well as for the adoption variate arenot very high, being 0.289 and 0.233, respectively. This can be attributedto the low, but significant, canonical R2. The shared variance for both ofthe variates is quite high. The redundancy values for the second canonicalfunction are much smaller. The sum of the total redundancies for the twocanonical functions is 0.543, and the proportion of total redundancyexplained by the first canonical function is 96.1%. Thus, the proportion of

TABLE 5 Measures of Overall Model Fit for Canonical Correlation Analysis

Canonicalfunction

Canonicalcorrelation

CanonicalR2

ApproximateF value

Numeratordf

Denominatordf

pValue

1 .629 .396 11.44 12 540.02 <.0012 .286 .081 3.32 6 410 <.0043 .100 .010 1.05 2 206 .352

Note. df¼ degrees of freedom.

TABLE 6 Multivariate Tests of Significance

Statistic ValueApproximate

F value Numerator df Denominator df p Value

Wilk’s Lambda 0.549 11.44 12 540.02 <.001Pillai’s Trace 0.487 9.99 12 618 <.001Hotelling-Lawley Trace 0.754 12.77 12 352.71 <.001Roy’s Greatest Root 0.656 33.76 4 206 <.001

Note. df¼ degrees of freedom.

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total redundancy explained by the second canonical function is 3.9%. Inaddition, the redundancy values for the second canonical function are about1=20th the magnitude of the redundancy values of the first canonical func-tion. Hence, only the first canonical function is considered for interpretation.

Examining the canonical weights, loadings, and cross-loadings in table 8for the first two canonical functions, it can be seen that the results for canoni-cal function 2 provide confirmation for its exclusion from interpretation,since the loadings and cross-loadings are generally less than 0.4. In fact,the largest cross-loading is 0.145. An examination of the factor loadings for

TABLE 7 Redundancy Analysis of Perceived Strategic Value and Adoption Variates

Standardized variance of the PSV variables explained by

Their own canonical variate(shared variance)

The opposite canonicalvariate (redundancy)

Canonical function PercentageCumulativepercentage Canonical R2 Percentage

Cumulativepercentage

1 0.730 0.730 0.396 0.289 0.2892 0.133 0.863 0.081 0.011 0.300

Standardized variance of the adoption variables explained by

Their own canonical variate(shared variance)

The opposite canonicalvariate (redundancy)

Canonical function PercentageCumulativepercentage Canonical R2 Percentage

Cumulativepercentage

1 0.587 0.587 0.396 0.233 0.2332 0.100 0.687 0.081 0.010 0.243

TABLE 8 Standardized Canonical Coefficients and Canonical Loadings

Construct VariableWeight

1Weight

2Loading

1Loading

2Cross-loading

1Cross-loading

2

PerceivedStrategicValue

OS 0.549 –0.552 0.906 –0.282 0.570 –0.081MP 0.404 1.300 0.846 0.507 0.532 0.145DA 0.199 –0.740 0.810 –0.250 0.510 –0.071

AdoptionRC 0.298 –0.578 0.775 –0.194 0.488 –0.055EP 0.225 1.072 0.670 0.486 0.422 0.139EU 0.415 0.566 0.795 0.150 0.500 0.043PU 0.353 –0.882 0.817 –0.320 0.514 –0.092

Note. 1¼Canonical function 1; 2¼ canonical function 2; EU¼Ease of Use; EP¼External Pressure;

MP¼Managerial Productivity; OS¼Organizational Support; PU¼Perceived Usefulness; DA¼ Strategic

Decision Aids; RC¼Readiness and Compatibility.

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the first canonical function shows that all exceed 0.6. The cross-loadings forthe first canonical factor range from 0.488 to 0.570 and are all significant(cut-off> 0.4). As can be seen, all the variables for the PSV construct exhibithigh correlations with the first adoption canonical variate. Similarly, all vari-ables of the Adoption construct have high correlations with the first PSV vari-ate. The rank order of importance, which is determined by the absolute valueof the canonical cross-loadings, for the PSV construct are DAs, OS, and MP.For the Adoption construct, the rank order of importance are PU, EU, RC,and EP.

Sensitivity analysis results on the PSV variables are given in table 9.From these results, it can be seen that the cross-loadings for the PSV variablesdid not change very much when each variable was removed. However, forthe Adoption variables, while the majority of the cross-loadings were stable,the cross-loadings for the EP variable dropped below 0.4 when the OS andDA variables were removed. Although, in a relative sense, the differencewas less than 14%, it was an exception to the general trend seen. The sharedvariance and redundancy values are stable when the different variables areremoved from the analysis. Thus, the sensitivity analysis supports the validityof the first canonical function.

DISCUSSION

There was a high proportion of female respondents in this study. This may beattributable to changes in the workplace roles of women in Slovakia as it

TABLE 9 Sensitivity Analysis of the Canonical Correlation Results

Results after deletion

Complete variate OS MP DA

Canonical correlation (R) 0.630 0.579 0.623 0.607Canonical root (R2) 0.396 0.335 0.389 0.368Perceived Strategic Value variates (canonical cross-loadings)

OS 0.570 Omitted 0.567 0.576MP 0.532 0.548 Omitted 0.515DA 0.510 0.501 0.537 Omitted

Shared variance 0.731 0.819 0.777 0.798Redundancy 0.289 0.274 0.302 0.294Adoption variates (canonical cross-loadings)

RC 0.488 0.433 0.479 0.482EP 0.422 0.397 0.435 0.362EU 0.500 0.485 0.493 0.465PU 0.514 0.438 0.505 0.518

Shared variance 0.587 0.576 0.607 0.623Redundancy 0.233 0.193 0.236 0.229

Note. EU¼Ease of Use; EP¼ External Pressure; MP¼Managerial Productivity; OS¼Organizational Sup-

port; PU¼ Perceived Usefulness; DA¼ Strategic Decision Aids; RC¼Readiness and Compatibility.

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transitions to a modern market economy (Wolchik 1994). It is also noted thatthe average age of respondents was lower than in previous studies (Grandonand Pearson 2003, 2004; Saffu, Walker, and Hinson 2008). Perhaps this showsthat younger people are taking on more important IT positions within Slova-kian SMEs, which may be ascribed to the pervasive computing society inwhich we live (Rainer et al. 2008).

Findings for the 211 Slovakian SMEs show that PSV and e-commerceadoption are significantly and positively related. Examining the PSV con-struct, there were three factors, as in the hypothesized model. OS was thestrongest predictor for the PSV construct, followed by MP, and DAs. Thesefindings support prior empirical studies both in developed (Grandon andPearson 2004) and developing countries (Grandon and Pearson, 2003; Saffuet al. 2008).

With respect to the Adoption construct, four factors were found. In rankorder of importance, these factors were PU, EU, RC, and EP. This is in con-trast with the hypothesized five-factor model. Reflecting on previous researchby Grandon and Pearson (2003, 2004) and Saffu and colleagues (2008), it isfound that PU, EU, and EP are present in all the models. On the other hand,while these researchers found individual factors for OR and C, we found asingle factor for both. This difference may be attributed to the inclusion=exclusion methods used in the factor analysis, the sample sizes used, andthe country-specific business environment. It is noted that the strongest pre-dictor of Adoption, PU in this study, was also the strongest predictor in stu-dies from the United States (Grandon and Pearson 2004) and from Ghana(Saffu et al. 2008). This finding confirms work by Davis et al. (1989), whofound that PU was also the most influential factor in the decision to adopt IT.

EU is the second most important factor in e-commerce adoption bySlovakian SMEs. Therefore, e-commerce must be easy to use for adoptionby Slovakian SMEs. While these findings are concurrent with Grandon andPearson’s (2004) findings of SMEs in the United States, as well as Saffu andcolleagues’ (2008) findings of SMEs in Ghana, they contradict the findingsof Grandon and Pearson’s (2003) findings of Chilean SMEs.

The RC factor was slightly less important than EU in this study of Slova-kian SMEs’ e-commerce adoption. That is, factors assessing how compatibleand consistent e-commerce is with the SME’s culture, values, and work prac-tices are as important as those factors assessing the financial and technicalreadiness of the SME (see Grandon and Pearson 2003). Gibbs and colleagues(2003) noted the role that organizational culture has on e-commerce adop-tion, and this suggests that there needs to be congruence among the organi-zational culture, the SME’s infrastructure, and e-commerce adoption.

All previous studies found compatibility to be a strong factor ine-commerce adoption. Although the ranking of compatibility changed frombeing the most important in the Chilean SME study (Grandon and Pearson2003) to being the third most important in the SME studies from the United

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States (Grandon and Pearson 2004) and from Ghana (Saffu et al. 2008). Fur-thermore, OR was the least important factor for e-commerce adoption in theaforementioned U.S. and Ghanaian empirical studies; in the aforementionedChilean study, however, OR was significant, and it was not the least impor-tant factor vis-a-vis e-commerce adoption.

Even though EP was the smallest significant factor that influencese-commerce adoption by Slovakian SMEs, the finding confirms earlier studiesthat have found that external pressures from government, competitors, orindustry are determinants of e-commerce adoption (see, for example,Iacovou et al. [1995] and Chwelos et al. [2001]). It is also noteworthy that pre-vious SME empirical work from Chile, the United States, and Ghana did notfind consistency in the ranking of EP.

CONCLUSIONS AND IMPLICATIONS

To obtain a better understanding of the relationship between the PSVs ande-commerce adoption by Slovakian SMEs, the determinants of the two con-structs were investigated. From the factor and canonical correlation analysesof 211 Slovakian SMEs, three factors were identified, in order of importance,for the PSV construct: OS, MP, and DAs. Similarly, for the e-commerce Adoptionconstruct, four factors were found, in order of importance: ‘‘RC,’’ EP, EU, and PU.

The final model provides a better understanding of the relationshipbetween PSV and e-commerce adoption by SMEs in a transitioning Europeaneconomy. Owners=managers of SMEs in other transitioning countries mayfind the results from this study to be useful in making decisions relating toe-commerce adoption.

SMEs are noted to lack resources (Storey 1994). In light of the resource con-straints faced by SMEs, owners=managers demand more strategic value fromthose resources than do large firms. Owners=managers should support trainingand workshops for their employees. They should also facilitate access to infor-mation and communication among employees within the organization, such asproviding an intranet. Finally, owners=managers must assist their employees inmaking decisions as well as forming strategic partnerships with other companieswithin their industries as well as small business associations in Slovakia.

To facilitate e-commerce adoption, owners=managers need to empha-size the benefits of implementing e-commerce to their employees. This couldbe tied in with the aforementioned training and workshops. Training coursesneed to be provided to develop and enhance the e-commerce skills of theSME employees. At the same time, the training courses should demonstratemore efficient business processes and improve job productivity. Financialand technical resources must be provided.

It is essential for the government to facilitate e-commerce initiativesbecause of the significance of SMEs in the Slovakian economy (Schmiemann

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2009). For instance, the government could form partnerships with educationalinstitutions to provide SME-specific e-commerce training courses and work-shops. It may be advisable for different Slovak government agencies to pro-vide tax incentives to SMEs to support their e-commerce endeavors.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

This study had a number of limitations. First, SMEs from a variety of sectorswere included in the study. It is reasoned that results focusing on a single busi-ness sector may have given a better understanding of the e-commerce adop-tion practices. Second, there was no differentiation between SMEs that hadadopted e-commerce and those SMEs that had not adopted e-commerce. Thislimitation provides another avenue for future research in which adopters andnon-adopters could be compared using discriminant analysis, for example.Third, it is also noted that the effects of other factors, such as age, gender, num-ber of employees, size of the IT department, and IT budget, were not utilized.Cluster sampling may be an appropriate approach to incorporate this infor-mation. Furthermore, these factors could be examined in combination withadopters and non-adopters of e-commerce. Fourth, this study is cross-sectionaland not longitudinal in nature. As the Internet infrastructure in Slovakiachanges, a longitudinal study using the same SMEs would be appropriate.Fifth, this study focused on SMEs, neglecting large companies. It is furthernoted that these findings may not be generalizable to SMEs in other locations.In fact, additional business environmental factors would need to be measuredto permit a quantitative comparison of SMEs in various locations.

In the future, research should be conducted comparing=contrasting SMEsand large companies. The current study is of SMEs in a transitioning country,Slovakia. Comparative studies should be conducted in transitioning anddeveloped countries. The value of conducting these studies over time is recog-nized; however, as technology and economic conditions within a countrychange, it would be difficult to observe and to measure these changes over time(Zhu and Thatcher 2010). As pointed out by Pham and colleagues (2011), PSVsand e-commerce adoption may be influenced by internet security (see Levy,Powell, and Worrall 2005) and trust, (see Chong, Shafaghi, and Tan 2011;Mukhopadhyay et al. 2008). Therefore, it would be appropriate to incorporatethese variables into the model to measure their impact on the two modelconstructs.

ACKNOWLEDGMENTS

The authors would like to thank Benjamin Vahle, a graduate student onexchange from Germany, for his assistance with questionnaire data entryand preliminary data analyses. In addition, the authors would like to thank

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Professor (retired) Julia Frankel, Department of Modern Languages, Litera-tures, and Culture, Brock University, Faculty of Humanities, for her assistancewith the translation and interpretation of the survey. Also, the reviewers arerecognized for their insightful feedback that led to improvements to thearticle.

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