IT Application Expenditure and IT Services Expenditure. We believe our findings inform both user firms and the OSS community to understand the potential cost ...
Impact of Open Source Software Adoption on Firm IT Expenditure Sanjeev Kumar and M. S. Krishnan Ross School of Business, Univ of Michigan, Ann Arbor Abstract Open Source Software (“OSS”) has attracted a lot of research interest but focus has primarily been on the development process (“supply side”)and not on the “demand-side” of OSS. Further, there is limited empirical investigation of the impact of OSS adoption. In this study we have attempted to fill the gap and empirically analyzed impact of OSS adoption on IT expenditure in firms. We find that OSS adoption is positively associated with overall firm IT expenditure. OSS adoption was found to have a significant positive impact on IT Labor Expenditure while no significant impact was found on IT Application Expenditure and IT Services Expenditure. We believe our findings inform both user firms and the OSS community to understand the potential cost impact of OSS adoption. Our results give a real context to the current debate on benefits of OSS vis-a-vis commercial software and provide managers with insights needed for making informed OSS adoption decisions. Keywords:Open Source Software, Free Software, Business Value of IT, IT Investment
Introduction and motivation Open source software (“OSS”) is developed by Internet-based communities of software developers who voluntarily collaborate in order to develop software that they or their organization need. OSS has become an important economic and cultural phenomenon. Sourceforge.net, a leading infrastructure provider and repository for OSS projects, lists more than 100,000 such projects and more than 1,000,000 registered users. Use of OSS products in firms has reached significant levels for many products and is growing at a rapid rate for many others. For example: revenue from Linux operating system is expected to grow to $35 billion by 2008 while Apache web server is estimated to run on 68% of all web servers in 2004 compared to 21% for the nearest competitor Microsoft. The open source phenomenon has attracted significant research interest but the focus has primarily been on the “supply side” of OSS: What motivates developers to contribute to OSS projects without monetary compensation? How do developers organize, communicate and keep control of the projects? etc. Though the research on supply side of OSS has improved our understanding of the OSS development process and helped integrate it with conventional economic frameworks, the relative lack of attention on the “demand side” of OSS process is likely to skew our understanding of the OSS life cycle. Creation of OSS products is motivated not for its own sake but is mandated because of user needs. Hence, there is a need to explore the demand side of OSS life cycle and our research attempts to fill this gap. Further, as the OSS products and their unique distribution and support mechanisms gain volumes, debate on relative merit of OSS vs closed source software is gaining prominence. We aim to contribute to the debate and extend our understanding of relative cost-benefit of OSS adoption by empirically analyzing impact of OSS adoption on IT expenditure of a broad sample of large US based firms. The study contributes to both theory and practice. In terms of theory, the paper extends the literature on determinants of firm IT expenditure (Dewan, Michael and Min 1998) and also contributes to better understanding of impact of OSS adoption and the competition between OSS and commercial closed source software (Bonaccorsi and Rossi 2003). From a managerial perspective, results of this study will help managers make informed decision about adopting OSS. The results will also help the OSS development community to understand impact of OSS products on user firms and consequently direct future development efforts.
Theory and hypotheses The current research on demand side of OSS has focused on two principal dimensions: relative quality of open vs. closed software products, and the dynamic of technological competition between open and closed software. Kuan (2001) argued for the superiority of the quality of OSS products based on the 1
idea of consumer integration into production. Johnson (2002) compared open and closed systems to a constrained social optimum and showed that OSS development will in general entail both an inefficient level and an inefficient distribution of development effort, due mainly to the possibility of free riding. Though the current research has provided important insights, the extant literature on the demand side of OSS has not taken the user experience and user benefit into account. The early academic literature on IT spending has largely focused on explaining the time pattern of IT adoption and growth (Gurbaxani and Mendelson 1990). More recently, researchers have examined the determinants of allocation among hardware and software budgets (Gurbaxani and Mendelson 1987). However, except few exceptions like (Dewan et al. 1998), there is limited work that elucidates firm characteristics that drive IT investments. Impact of OSS adoption on firm IT expenditure There is broad agreement that OSS has lower initial price compared to proprietary software. The initial cost for OSS may even be close to zero as most OSS products can be downloaded from the Internet without any monetary payment. Even in case when OSS products are purchased from large vendors like RedHat, the cost is significantly less than competing closed source products. Similarly, upgrade costs are lower for OSS compared to closed source competitors. Thus, we can argue that the IT Application Expenditure (defined as expenditure for acquiring and maintaining IT applications) is expected to be lower with higher OSS adoption. Hence, we hypothesize that OSS adoption is negatively associated with IT application expenditure (Hypothesis 1). As using OSS implies dealing with a different distribution and support system, OSS adoption is likely to affect IT Services Expenditure of the firm. Although the direction of the impact is not clear, we can expect OSS adoption to lead to an increase in IT services expenditure as a transition to OSS would mean forming new relationships with different service providers with the added cost of negotiation, consulting services etc. Transitioning to OSS would also imply that all users will need to be trained in the new technology, necessitating higher levels of support, therby increasing the IT services expenditure. Hence, we can posit that OSS adoption is positively associated with IT services expenditure (Hypothesis 2). OSS adoption affects IT labor expenditure in two opposing ways. In the initial phase of OSS use, OSS adoption requires firms to retrain their IT staff (or hire new staff with corresponding hiring cost) leading to an increase in IT labor costs. Productivity of IT staff trained in closed source technologies is bound to be lower before they are fully trained and experienced in OSS. In an independent study by the Yankee group, Linux was reported to require from 25% to 40% more full time equivalent support specialists than Windows (Yankee 2004). On the other hand OSS community has argued that OSS in fact required less IT labor since it is more secure and more stable and hence needs less administration. Software inspection firm Reasoning conducted a comparison of the OSS database MySQL with comparable commercial software and found that MySQL had 0.09 defects per thousand lines of source code while commercial software had higher incidence of defects at 0.57 defects per thousand lines of source code (Reasoning 2003). Further, OSS products are considered more secure and less of a target for security attacks than commercial counterparts, hence needing less intervention from system administrators. Combining the two viewpoints, we believe that once the initial transition and training expenses are covered, OSS is expected to require less IT labor expenditure. Hence, we hypothesize that OSS adoption is negatively associated with IT labor expenditure (Hypothesis 3). IT Labor is the biggest component of firm IT expenditure and is hypothesized to be negatively associated with OSS adoption. Further, even though we expect IT Services Expenditure to be positively associated with OSS adoption, IT Application Expenditure is expected to be negatively associated with firm IT expenditure. Combining the above hypotheses, we can posit for firm’s overall IT expenditure that OSS adoption is negatively associated with firm’s overall IT expenditure (Hypothesis 4). Other components of firm IT expenditure like Hardware and R&D expenses may also get indirectly affected by OSS adoption. For example: it is suggested that OSS facilitates use of low cost commodity hardware thereby reducing hardware cost. Google’s success in using OSS with low cost machines is well documented. However, the link between OSS adoption and hardware (and other components of IT
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expenditure) is tenuous and hence we are not including them in this study. We are controlling for firm size, IT function size and industry as they are expected to impact level of firm IT expenditure.
Research Model, Data and Methodology The data used for the study was collected by the reputed industry publication InformationWeek for its 2004 ranking of IT industry. Preliminary and practice oriented analysis of the overall data was published by InformationWeek (InformationWeek 2004). The survey was answered by 382 firms. We discarded 77 responses because of missing values and data errors. The survey data was used to construct the following variables: IT Expenditure (ITEXP): Total IT Expenditure is defined as annual amount spent on IT including capital and operating expenses for IT infrastructure. Overall IT expenditure is then broken down in its three relevant categories: IT Application Expenditure (APPEXP): Includes expenditure for application development, maintenance and purchase of packaged applications. IT Services Expenditure (SEREXP): Includes expenditure for IT services consumed including user support, any outsourcing expense and other IT services. IT Labor Expenditure (LABEXP): Salaries, benefits and other costs related to full time IT staff. OSS Adoption (OSSADP): The extent of OSS adoption is measured using a categorical variable ranging from 1 to 7. Control: Firm Size (SIZE): We have used annual revenue as a measure of firm size. Control: IT Function Size (STFF): We have used the number of full time employee in the IT function of the firm as a measure of IT function size. Control: Industry (IND): Binary variable with value 0 for manufacturing and 1 for services sector. In our empirical model, the dependent variables are IT Expenditure and its three components IT Application Expenditure, IT Services Expenditure and IT Labor Expenditure. The explanatory variables are OSS Adoption, Firm Size, IT Function Size and Industry control. The model is derived by modifying the empirical model used by Dewan et al. (1998), which was in turn derived by extending the CobbDouglas production function. In particular, our estimation models are specified as follows ln (EXP VARIABLES) = β0 + β1 OSSADP + β2 ln SIZE + β3 ln STFF + β4 IND + ²
(1)
Where EXP VARIABLES is ITEXP, APPEXP, SEREXP or LABEXP. Thus there are four individual models to estimate. Ordinary Least Squares (“OLS”) is not appropriate for the model above as the error terms of the individual equations may be co-related because these equations pertain to the same firm. We allowed for these potentially correlated errors to obtain consistent and efficient estimates of parameters through Seemingly Unrelated Regression Estimation (SURE) technique. The Breusch-Pagan test for independence of error terms across equations was rejected providing support for the appropriateness of SURE technique. We tested for the standard assumptions of linear regression for all the individual models and found them to be well satisfied. The Breusch-Pagan/Cook-Weisberg test for heteroskedasticity failed to reject the null hypothesis of no heteroskedasticity. We checked for multi-collinearity through variance inflation factors and found them to be in acceptable range. We checked the plots of residuals and did not detect any significant patterns. We further checked for normality of all the dependent variables and found that the skewness-kurtosis test for normality did not reject the null hypotheses of normal distribution.
Results and Analysis Results of the SURE regression are shown in figure below. All the models are significant and possess strong explanatory power as reflected by high R-Square values. 3
The results show that, contrary to expectations, OSS adoption has significant positive relationship with overall IT expenditure. This implies that firms that use more OSS are in fact spending more on IT than firms that use less OSS in their organization. The reason for the increase is made clear by the models of components of overall IT expenditure. The results show that while OSS Adoption does not have a statistically significant impact on IT application expenditure and IT services expenditure, it has a strong and significant positive relationship with IT labor expenditure. Thus, although OSS adoption is not causing firms to spend more on IT services expenditure, the expected savings in IT application expenditure are not materializing as well. Combined with significant positive relationship between OSS adoption and IT labor expenditure, the overall impact on firm IT expenditure is positive. The results do not support our initial hypotheses, but the significant results provide interesting insights into the impact of OSS adoption and the corresponding implications for both user firms and the OSS community, as discussed in the next section. Other results are in conformance with previous studies. Both Firm Size and IT Function Size have significant and positive relationship with overall IT Expenditure as well as the three components. The coefficients of Firm Size and IT Function Size, because of log transformation, reflect elasticities of corresponding relationships. As the coefficients of Firm Size and IT Function Size are all positive, it implies that overall IT Expenditure and its three components increase with an increase in Firm Size and IT Function Size. However, the increase is less than proportional as coefficients are less than 1. Thus, we can see evidence of strong scale economies in IT Expenditure, consistent with previous research (Dewan et al. 1998). The coefficient for Industry binary variable is positive and significant for the overall model, indicating that firms in the services sector spend more on IT than those in the manufacturing sector.
Discussion and Conclusion The results provide the first empirical analysis of cost implications of OSS adoption and hence provides direction to the debate on relative merit of OSS vs. closed source software. The results, although contrary to expectations of OSS community, should not be construed as an argument against OSS adoption as this research may not capture all the aspects of OSS adoption impact. The results hold important implications for both the OSS community as well as the user organizations. The popular press is replete with reports of current euphoria about potential impact of OSS and is reminiscent of the dot-com bubble. This research provides the counter-point. From a manager’s perspective, the results underline the difficulty in successfully extracting value from a transition to OSS. This should temper the euphoria over OSS and bring the expectations to a more realistic level. We consider such re-setting of expectations to be an important step in ensuring long term viability of OSS and avoiding the kind of collapse seen by the dot-com era due to irrational expectations. Although the results are disheartening for the OSS community, this provides a launching pad for
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the community to identify and correct the causes of higher IT spending associated with OSS adoption. In particular, OSS community need to focus on reducing the high IT labor costs associated with OSS adoption. This can be achieved by initiatives such as easy to administer training modules, reducing complexities in user interface, automated transition management from closed source software to OSS etc. We hope the results of this study will channel the energies of the OSS community in these areas. OSS adoption was not found to have a significant positive impact on IT Services expenditure, indicating that OSS distribution and support mechanisms have reached maturity and do not cost more than their commercial counterparts. This is encouraging for OSS community as it emphasizes the success of alternate business models adopted for distributing OSS products and providing support to organizations using OSS. As we have taken an overall view of OSS adoption and have not differentiated between different types of OSS, the negative results of this study do not rule out OSS adoption providing significantly cost savings in some segments (for example: infrastructure applications). We are analyzing the argument as an extension of this study and will have detailed results available by the time of the conference. Further, since the current analysis only covers one year data, we can not distinguish whether the positive cost impact of OSS is because of initial transition cost or whether it is an inherent feature of OSS adoption. One year data also prevents us from categorically establishing causality between OSS adoption of IT spending increase as the positive association between IT spending and OSS adoption may also be a result of firms with higher IT spending adopting OSS in an effort to reduce IT spending levels. We are currently in process of extending the current model to a multi year dataset and would have corresponding results available by the conference. Although the results of this study are negative for OSS, we remain hopeful about potential impact of OSS products. In particular, we believe that OSS would result in significant cost savings in specific market segments, and once the initial cost of riding up the learning curve are accounted for. We in the process of including these aspects in our research and hope to present corresponding results for the same in conference1 .
References Bonaccorsi, A. and Rossi, C., 2003, “Why Open Source Software can Succeed,” Research Policy, 32, pp. 1243– 1258. Dewan, S., Michael, S. C. and Min, C.-k., 1998, “Firm Characteristics and Investments in Information Technology: Scale and Scope Effects,” Information Systems Research, 9 (3). Gurbaxani, V. and Mendelson, H., 1987, “Software and hardware in data processing budgets,” IEEE Transactions on Software Engineering, SE-13 (9), pp. 1010–1017. Gurbaxani, V. and Mendelson, H., 1990, “An integrative model of information systems spending growth,” Information Systems Research, 1 (1), pp. 23–46. InformationWeek, 2004, “Year 2004 InformationWeek 500 Ranking,” InformationWeek, Sep 20. Johnson, J. P., 2002, “Open Source Software: Private Provision of a Public Good,” Journal of Economics & Management Strategy, 11 (4), pp. 637–662. Kuan, J., 2001, “F/OSS Software as Consumer Integration into Production,” Working paper available at: www.papers.ssrn.com/ paper.taf?abstract id=259648. Reasoning, 2003, “Reasoning Study Reveals Code Quality of MySQL Open Source Database Ranks Higher than Commercial Equivalents,” http://www.reasoning.com/newsevents/pr/12 15 03.html, accessed on May 20, 2005. Yankee, 2004, “Yankee Independently Pits Windows TCO vs. Linux TCO,” http://www.microsoftwatch.com/article2/0,4248,1553620,00.asp?kc=MWRSS02129TX1K0000535, accessed on May 20, 2005.
1 Many references and details have been omitted from the current draft because of the page limit. Authors will be happy to provide full version of the paper on request
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