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48 Determinants of Open Source Software Adoption – An Application of TOE Framework Tomasz Przechlewski and Krystyna Strzała

Abstract Open source software (OSS) is currently one of the most debated phenomena in both academia and the software industry. Several OSS systems have achieved significant market success but they are rather server-side applications, such as the Apache Web server, MYSQL database server, or other components of IT infrastructure. On the other hand, penetration of OSS systems on the market of desktop applications is rather limited and it is virtually dominated by products of one software vendor, i.e., Microsoft. In this chapter, the benefits and barriers of OSS implementation in Poland are investigated. Based on the well-known technology–organization–environment model of IT technology adoption of a simple model was developed and evaluated empirically, based on the data from the survey of 178 enterprises and public institutions. Statistical analysis using partial least squares (PLS) was performed. Of the four factors considered to determine adoption decisions (benefits, costs, environment, and organization), it was found that only perceived benefits and environment are significant. Keywords Open source software



Software adoption

 Statistical survey

1. Introduction Open source software (OSS) refers to any IT system whose source code is freely accessible [1, 2]. There is a huge interest in OSS movement recently both from business and academia, cf. [3–5]. However, the main research interest in OSS so far has been to explain the incentives of individuals, so organizations get engaged in OSS projects [6–9]. Other contributions approach OSS phenomenon from a diversity of angles; among these are social organization of OSS projects [10, 8], economics of OSS and OSS business models [11], or OSS software development methods [12]. The literature focusing on the implementation issues, motivation, and benefits of organizational users is relatively scarce [13–15]. In this study a simple conceptual model of OSS adoption based on the technology–organization–environment (TOE) theoretical framework is verified using data from the survey of Polish public institutions and enterprises. The rest of the chapter is organized as follows. A brief overview of OSS is presented in the subsequent section. Conceptual models of users’ acceptance of IT, including technology–organization–environment model is discussed next. Then research method and survey design is described, followed by results of the data analysis. Discussion of the findings concludes the chapter.

Tomasz Przechlewski  Katedra Informatyki Ekonomicznej, Uniwersytet Gdan´ski, Sopot 81-864, ul, Piaskowa 9, Poland Krystyna Strzała  Katedra Ekonometrii, Uniwersytet Gdan´ski, Sopot 81-824, ul Armii Krajowej 119/121, Poland G.A. Papadopoulos et al. (eds.), Information Systems Development, DOI 10.1007/b137171_48, Ó Springer ScienceþBusiness Media, LLC 2009 461

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2. Research on IT Adoption and Implementation A great variety of IT systems are used in business and public institutions nowadays and thus IS innovations can be of various types – some are technical in nature and concerns organization’s IT department only while others may affect whole of the organization. From mere technical point of view, IT systems can be divided onto two broad categories: server systems, which are part of the infrastructure and are usually transparent for ordinary users and desktop applications. Swanson [16] divided IT systems into the following three groups: Type I innovations are confined to the technical tasks; Type II are concerned with business administration; and Type III innovations are embedded in the core of the business. Based on strategic importance of IT systems to organization, Kwan and West [17] classified IT systems into the following categories: strategic, mission critical, support, and laboratory. They argue that evaluation criteria used during procurement process, such as risk, system features, and costs depend heavily on the relative importance of the system for the organization. In particular, they claim minimizing risk not costs and maximizing features are of primarily concern for strategic systems. A popular model explaining IT adoption at the organizational level, developed by Tornatzky and Fleischer [18], identifies three aspects that influence the process by which technological innovations are implemented: technological context, organizational context, and environmental context. Technological context describes technologies relevant to the organization. Organizational context concerns firm size and scope, centralization, formalization, managerial issues, slack resources available, and the skills of organization’s staff. Prior studies indicate larger organizations on the average have more slack resources and are more likely to achieve economies of scale and thus are more innovative [19, 20]. On the other side, the association between adoption and formalization reported in most innovation studies is negative. Environment factors concern organizations surroundings, such as type of industry, legal settings, environmental uncertainty, external pressure. Both external pressure and uncertainty is consistently recognized as innovation facilitators. The TOE framework was employed in a number of empirical studies1 to explain adoption in various organizational contexts (SMEs, large organizations) of different technologies, such as Open Systems [22, 23], Internet technologies [20], EDI/IOS systems [24–27], or e-CRM implementation/ adoption [28].2 Particularly relevant to OSS adoption are studies concerned with Open Systems, or EDI to IOS migration. For example, perceived barriers and satisfaction with existing systems appeared to be significant to adoption of open systems while perceived benefits were not [22]. The difference between the beliefs concerning the benefits of open systems of adopters and non-adopters was insignificant. Explaining this phenomenon Chau and Tam [22, 23] claim that the most prominent obstacle of OSS migration is the lack of skilled personnel. They argued that adoption of complex technologies is (primary) a process of reducing knowledge barriers. This claim is supported by several other studies [31, 32]. Dedrick and West [29] developed a TOE-based model explaining adoption of OSS-server platforms and tested it empirically with series of semi-structured interviews. Technological factors include relative advantage perceived primarily in terms of lower costs and improved reliability, compatibility and complexity; organizational context consists of IT innovativeness, IT centrality, slack; and environmental factors contains external support and availability of skilled IT personnel and legitimacy. It is claimed that cost savings are most important driver of OSS adoption while such benefits as possibility to modify source code3 appeared to be negligible. 1

For the overview of various IT adoption surveys using TOE framework see [21]. It should be noted, however, that TOE framework still does not represent well-developed model [29, 30]. A vast span of variables was included and inconsistency of variable selection may be observed (For example, costs belong to ‘‘technology context’’ in [22, 29] or to ‘‘organizational context’’ in [27].) 3 The benefit often raised by OSS advocates and developers. 2

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3. Conceptual Model of Open Source Adoption Most successful OSS application belongs to IT infrastructure and desktop applications, thus they can be classified as Type I or Type II innovation according to Swanson taxonomy [16]. Desktop usage is dominated by proprietary applications, and OSS programs are introduced usually as replacements for them, such as OpenOffice for MS Office or Mozilla Firefox in place of MSIE browser. As concerns most valuable systems (Type III or strategic/mission critical), it is obvious there are no ready to download OSS applications that could provide strategic advantage to the organization; however, such systems based on OS components can be developed or bought from external integrator/developer. Most OSS implementations can be regarded as an organizational innovation that requires both technical and administrative innovation. Although open source software presents an interesting example of IT adoption by organizations, there are quite a few quantitative studies describing this phenomenon [29, 14]. Based on the TOE framework discussed above, we propose a conceptual model for OSS adoption, as illustrated in Fig. 48.1. Drawing upon empirical evidences combined with prior research on IT adoption [17, 14, 22], we believe that TOE framework is appropriate for explaining adoption of OSS systems. Variable selection to maximum extent possible is based on previous studies on adoption of similar technologies [22].

Environment environmental impact

OSS adoption

direct benefits

satisfaction

Technology organizational barriers

Size

indirect benefits

Organization

Figure 48.1. Proposed model of OSS adoption based on TOE framework.

In our model OSS adoption is determined by the following variables: perceived benefits, perceived barriers, organizational, and environmental factors. Perceived benefits refer both to more tangible direct or acquisition benefits as well as indirect benefits. The former includes OSS benefits, frequently mentioned by OSS advocates, namely low acquisition cost, source code availability, and more software/hardware choices. OSS can be acquired without costs as license fees are eliminated. Next, availability of source code allows – at least potentially – for better customization, thus allowing for more software/hardware choices. The anticipated advantages that OSS systems can provide to organization are cost savings and better software quality. Several reports indicate OSS adoption can result in significant cost savings [33]. The administrative/legal overhead of software ownership is significantly reduced (i.e., no need for troublesome software audit). Upgrading/maintenance costs are lower too. Excessive upgrade expenditures often force users to upgrade only a part of their IT infrastructure.4 Hardware costs may be reduced too as Linux is often used on inexpensive Intel computers (Wintel platform) replacing Unix systems running on expensive hardware.

4

In result the organization exploits several versions of the systems. Obviously, such heterogeneous IT infrastructure is more expensive to maintain. Alternatively, one could pay for unnecessarily upgrades. Frequently hardware replacement is needed for new software to work properly which enlarge upgrade expenditures even further.

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Except cost savings many OSS systems are attributed with superior quality (superior reliability, functionality, and security). Raymond [34] argues obscurity of the source code and typical vendor’s policy toward releasing (infrequently) patches/upgrades hinders both reliability and security of the software. Bugs are reported to vendors but cannot be fixed properly (as source code is not accessible), so frequently some quick-and-dirty way arounds are devised to cope the problems. Such practices degrade the quality of the source code, in result may cause reliability and security problems. In the case of OSS patches and security, upgrades are fixed much quicker. With the public accessibility to the source code there is little room for back doors. Following Attewell [35] many authors emphasize the role of know-how and organizational learning in the adoption process and divide perceived barriers of IT adoption into the following dimensions: overall financial migration costs, knowledge of the IT personnel, and compatibility problems [22]. Costs and technical knowledge5 have been reported as important factors that significantly hinder IT implementation in a number of studies [30, 22, 23]. We posited the above mentioned three cost dimensions (financial costs, compatibility, and knowledge) in our model. Environment context embraces external services and external support, and peer adoption. It is reported in many previous studies that these factors are positively related to IS adoption [37, 13, 24]. Both dimensions are related to the economic concept of network effect. Even there is frequently no direct network effect, one can list several positive indirect ones. The larger is the net of users of particular (OSS) system, the greater are incentives: (1) for the developers to improve it, (2) for organizations to adapt it as hiring skilled workforce is cheaper, and (3) for external providers of support services. Two additional variables included in the model are satisfaction with existing systems and IT human resources availability. Satisfaction provides the impetus to improve performance [38] so low level of satisfaction should result in higher motivation to change. A number of previous studies confirm this assumption [23, 22]. Previous studies indicate that there is a positive relationship between IT human resources availability and the adoption of IT. The rationale is that larger IT staff lowers knowledge barriers and could absorb more risk involved in managing the implementation of new systems [20]. Consistently with previous studies [23, 22, 26], we posit the following set of hypotheses: Higher perceived direct benefits are related positively with the extent of OSS adoption (H1a), Higher perceived indirect benefits are related positively with the extent of OSS adoption (H1b), Higher perceived organizational barriers are related negatively with the extent of OSS adoption (H2), IT human resources availability is related positively with the extent of OSS adoption (H3a), Higher satisfaction with proprietary systems results in lower extent of OSS adoption (H3b), and Environmental impact is positively related with the extent of OSS adoption (H4). A statistical survey was performed to verify the research hypotheses.

4. Research Method, Survey Design, and the Sample Description The targets of the survey were twofold: provide accurate figures concerning OSS use in Poland in general, and provide data to verify our TOE-based model of OSS adoption. The target population consisted of all the enterprises which manage non-trivial IT systems. This definition excluded enterprises which outsourced IT systems or those who used only very simple applications (i.e., PC for word-processing or Web browsing). Practically speaking, the sampling unit was an enterprise which had some sort of IT department and hired IT professional(s). Eligible respondents were IT managers best qualified to speak about organization’s IT infrastructure. As vast majority of small firms (with employment less than 50) had no IT staff they were excluded from the target population. The sample frame for the study was taken from the BJS register which contains all establishments operating in Poland and is maintained and used by Polish Central Statistical Office (GUS). Using BJS a random sample was taken from the population, stratified for NACE groups and firm size. The robust 5

Following Iacovou et al. these factors are defined as technological readiness in a number of studies [36].

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procedure used enable for estimation of OSS usage in Poland. The pre-test and pilot survey was conducted in September 2005 and the main study was conducted in October 2005. In two-step approach the establishments were approached by phone with the question whether they manage ITC on their own, and if so, whether they are using or planning to use OSS within a year. In total, 994 respondents were asked. As expected, many establishments do not have IT department – thus do not manage their IT systems on their own, rely on service outsourcing, or use only very simple applications, like Office ones (391 establishments or 39.3%). Usage ratios were computed based on answers of 553 enterprises declaring to manage ITC systems on their own. In total, 336 respondents (60.8% of establishments managing ITC systems) declared that they use OSS systems, while 217 (39.2%) use only commercial software. Further 50 respondents refuse to take part in the survey (approximately 5%). The details of the survey – one of a few of such an extent and statistical soundness – can be found in [39]. Overall, the sample represented a wide range of establishments, increasing the generalizability of the results. The second part of the survey was designed as a self-administered, Web-based one. Of those 336 respondents declaring to use OSS systems, 216 completed the questionnaire (64.3%). The study revealed a number of interesting features. Number of IT stuff is surprisingly low with 80% of the enterprises have three or less persons employed in ITC department (the mode within mid-sized and large establishments is 1.0 and 2.0, respectively). The survey shows not only that OSS usage is high (60.8% establishments declares to use OSS) but also the usage of OSS in the public sector is higher than the average (77.0%).6 Finally OSS usage ratios by application type are the highest for server applications (45% of those using OSS claims that). Again the figures are consistent with other surveys [14]. Subsequent analysis and the estimation of the model are based on the sample of 178 OSS users. Table 48.1 presents selected descriptive statistics of the final sample. Table 48.1. Sample description (employment, number of IT Staff, number of computers used). Statistic

Employment

No. of IT Staff

No. of Computers

Mean Standard deviation Mode

365.29 584.37 162.50

5.16 17.89 2.00

162.40 428.69 50.00

Measurement items for the model presented in Section 3 were developed from prior studies on OSS, Open Source, EDI/IOS migration/adoption, as well as expert opinions and from OSS-oriented magazines, advocates of this software, and practitioners. Perceived benefits were measured using five items and the respondents were asked to give their level of agreement or disagreement with the following potential benefits of adopting OSS: available source code; higher number of applications; no license fee (direct benefits); and cost savings regarding maintenance, support, and administration, higher performance, stability and security, better quality to TCO ratio (indirect benefits). Perceived organizational barriers were operationalized with the following three items: high migration costs, compatibility problems within organization’s IT infrastructure, personnel are only familiar with commercial applications, and satisfaction with proprietary systems is high. Finally, environmental factors include the following four items: availability of external support, integration in another acquired product, recommendation of integrator/IT provider, and adoption of OSS systems in peer organizations. All but two construct (satisfaction, IT staff) are operationalized by multiple items. 6

These figures are consistent with the results of the FLOSS survey which shows that public-sector organizations in Germany, Sweden, and the United Kingdom have above-average use and planned use rates compared to commercial firms (37% versus 31%, cf. [14]).

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A five-point Likert scale was used in most items. Server systems (operating systems, database management systems, Web-based systems) and desktop applications were evaluated separately by informants. To estimate the extent to which OSS is used in the organization-dependent variable in the model is a multi-item construct measuring perceived overall importance of OSS as well as OSS adoption in a few key application areas, such as servers, database management systems, Web-based systems, and desktop applications (respondents were asked to select between using/planning to use OSS in important applications, using/planning to use OSS in auxiliary applications, and not using and do not plan to use OSS).

5. Estimation of the TOE Model Partial least squares path modeling (PLS), as implemented in SMARTPLS [40], was used to empirically evaluate the model of OSS acceptance. PLS is a structural equation modeling technique that allows for formative as well as reflective indicators, small sample sizes and does not imply assumptions of multivariate normal distribution [41]. In this study all measurement relationships between indicators and constructs are specified as formative.7 Formative indicators are not expected to be unidimensional and correlated with each other. Therefore, ‘‘traditional’’ measures of validity and reliability are not applied to them. Formative constructs are evaluated in terms of significance of weights only [24, 42]. Moreover, PLS is more appropriate when the research model is in an early stage of development and has not been tested extensively [24]. The review of the literature presented in Section 2 clearly indicates that TOE-based models are still not well-established theory. Hence, PLS seems the appropriate estimation method for our model. The relevant statistics for the multi-item constructs of the measurement model are presented in Table 48.2. Separate models for server systems and desktop applications were estimated. Most measurement items have significant loadings. The structural model in PLS is assessed by examining the path coefficients and its significance with t-statistics. Standard R2 coefficient is used as an indicator of the overall goodness-of-fit measure of the model. The path coefficients and t-values for server applications are shown in Fig. 48.2a. As concern’s server systems coefficient values associated with direct benefits, indirect benefits, environmental impact, and the availability of IT staff were significant (thus H1a, H1b, H4, and H3a hypotheses are supported) while impact of organizational barriers and satisfaction with proprietary systems were not (H2 and H3b). Model shows acceptable fit to data with R2 = 33.1%. In case of desktop applications all coefficients were insignificant except environmental impact and indirect benefits so only H1b and H4 are supported. Fit to data is lower than in the previous model with R2 = 28.8% (cf. Fig. 48.2b). It should be noted that (1) organizational barriers were consistently insignificant in both estimated models and (2) environmental impact and indirect benefits were shown to be the most significant factors as indicated by their path coefficients magnitude. The results obtained are exactly opposite to those reported in [22] where the perceived barriers (of open systems adoption) were significant while perceived benefits were not. The authors explain this phenomenon by lack of knowledge concerning new technologies such as Unix or TCP/IP. It seems that nowadays both proprietary and OSS software are based on many common open standards, so migration between them often do not require radically new knowledge or skills. On the other hand, in case of most popular OSS desktop applications there is a application-level interface standard (Gnome/KDE is similar to MS Windows desktop, Firefox to MS Internet Explorer, and OpenOffice to MS Office). Both proprietary and OSS systems employ the same protocols, formats and interfaces which facilitate migration. The other significant factor consistently shown both for server and desktop applications was environmental impact, namely external support and peer adoption. Respondent’s positive attitude 7

There is a tendency to use reflective constructs in majority of MIS research. For example, perceived benefits/barriers were defined as reflective in [36, 27, 30], although it is in our opinion disputable to classify such multifaceted construct in that way.

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Table 48.2. Summary of the measurement models of server and desktop applications. Servers

Desktop

Construct/measure

Adoption extent: OSS importance to organization OSS server or desktop usage Indirect benefits: Higher performance, stability Better quality to TCO ratio Cost savings regarding maintenance Organizational barriers: Personnel familiar with commercial... High migration costs Compatibility problems Environment impact: Peer adoption Integration in another product Availability of external support Acquisition (direct) benefits: Higher number of applications No license fee Source code available

Loading

t

Loading

t

0.8823 0.7370

11.2198 7.3087

0.7839 0.7688

5.8083 6.0324

0.8231 0.8952 0.7014

11.6999 7.6990 5.3467

0.6103 0.8031 0.8649

4.1677 6.7566 6.8828

0.5766 0.8571 0.6361

2.6927 5.0982 2.6913

0.3671 0.8943 0.6353

1.2563* 4.1267 2.2296

0.7566 0.0813 0.6918

5.5844 0.3708* 5.1039

0.8869 0.0645 0.4763

9.7348 0.3122* 2.9750

0.6469 0.0671 0.9318

4.7551 0.4325* 10.0504

0.8743 0.3839 0.6654

5.5867 1.6254* 3.3260

* Insignificant at a=0.1 level

a)

b) Direct Benefits

IT Staff 0.202 t=2.4339*

Indirect Benefits

0.134 t=2.2059*

0.093 t=1.2635

Satisfaction

0.061 t=0.8365

Adoption 0.331

–0.090 t=1.052

Organizational barriers 0.161 t=2.041*

Environmental impact

0.067 t=0.8323 0.298 t=3.9769*

Indirect Benefits

0.235 t=3.0873* Organizational barriers

IT Staff

Direct Benefits

–0.047 t=0.7647

Satisfaction

0.029 t=0.3928

Adoption 0.288

0.323 t=4.9020* Environmental impact

* significant at alpha = 0.05

Figure 48.2. Coefficients of the structural model for server systems (a) and desktop applications (b).

manifested by relative importance of perceived advantages and external support, together with insignificance of perceived organizational barriers is an important hint both for IT developers and integrators as well as IT managers.

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6. Summary In this chapter, a conceptual model of open source software adoption was developed based on technology–organization–environment. The model was subsequently empirically verified using data coming from statistical survey performed in Polish public institutions and enterprises. Separate models were estimated for server and desktop applications. For both models significant relation was shown between perceived indirect benefits and organizational factors (external support, peer adoption pressure) and the extent of OSS adoption. The impact of organizational barriers was insignificant for both categories of software. Our study raises important implications for managers and practitioners. The relative unimportance of perceived barriers seems encouraging for open source implementation perspectives within governmental institutions. As the study is subject to number of limitations, including small sample size, the findings should be interpreted with caution when generalizing the results.

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