Organizaciia, volume 46, number 1 Contents 1/2013 RESEARCH PAPERS 3 VILJEM PSENICNY, RIKO NOVAK Organisational Factors of Rapid Growth of Slovenian Dynamic Enterprises 13 FRANC BRCAR, BORIS BUKOVEC Analysis of Increased Information Technology Outsourcing Factors 20 MAHDI MORADI, MAHDI SALEHI, MOHAMMAD EBRAHIM GHORGANI, HADI SADOGHI YAZDI Financial Distress Prediction of Iranian Companies Using Data Mining Techniques 29 ELENA MARULC, GABRIJEL DEVETAK The Impact of The Intellectual Charm of Physicians on the Healthcare Organizations Editorial office: University of Maribor, Faculty of Organizational Science, Založba Moderna Organizacija, Kidričeva 55a, 4000 Kranj, Slovenia, Telephone: +3864-2374226, E-mail: organizacija@fov.uni-mb.si, URL: http://organizacija.fov.uni-mb.si. Published bimonthly. Full text of articles are available at http://www.versita.com/o (international) and http://organizacija.fov.uni-mb.si. 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Articles are currently abstracted/indexed in: INSPEC, Ergonomic Abstracts, Cabells Directory of Publishing Opportunities, Directory of Open Access Journals, CSA Sociological Abstracts, Die Elektronische Zeitschriftenbibliothek, Research Papers in Economics, ECONIS EDITOR / UREDNIK Jože Zupančič, Univerza v Mariboru, Fakulteta za organizacijske vede CO-EDITORS / SOUREDNIKI Marko Ferjan, Univerza v Mariboru, Fakulteta za organizacijske vede Boštjan Gomišček, Univerza v Mariboru, Fakulteta za organizacijske vede Jurij Kovač Univerza v Mariboru, Fakulteta za organizacijske vede Marjan Senegačnik Univerza v Mariboru, Fakulteta za organizacijske vede EDITORIAL BOARD / UREDNIŠKI ODBOR REVIJE Rado Bohinc, Univerza na Primorskem, Slovenija Roger Blanpain, Catholic University of Leuven, Belgium Franc Čuš, Univerza v Mariboru, Slovenija Vlado Dimovski, Univerza v Ljubljani, Slovenija Daniel C. Ganster, University of Arkansas, USA Jože Gričar, Univerza v Mariboru, Slovenija Werner Jammernegg, Vienna University of Economics and Business Administration, Austria Marius Alexander Janson, University of Missouri, USA Stefan Klein, University of Muenster, Germany Miroljub Kljajič, Univerza v Mariboru, Slovenija Hermann Maurer, Technical University Graz, Austria Matjaž Mulej, Univerza v Mariboru, Slovenija Valentinas Navickas, Kaunas University of Technology, Lithuania Ota Novotny, University of Economics, Prague, Czech Republic Milan Pagon, Zayed University, Dubai, United Arab Emirated Björn Pappe, Technical University Aachen, Germany Dušan Petrač, NASA, USA Hans Puxbaum, Vienna University of Technology, Austria Gabor Rekettye, University of Pecs, Hungary Markku Sääksjärvi, Helsinki School of Economics, Finland Vladislav Rajkovič, Univerza v Mariboru, Slovenija Henk G. Sol, Technical University Delft, The Netherlands Velimir Sriča, University of Zagreb, Croatia Paula Swatman, University of South Australia, Australia Brian Timney, The University of Western Ontario, Canada Maurice Yolles, Liverpool John Moores University, United Kingdom Douglas Vogel, City University of Hong Kong, China Gerhard-Wilhelm Weber, Middle East Technical University, Turkey Stanislaw Wrycza, University of Gdansk, Poland DOI: 10.2478/orga-2013-0001 Organisational Factors of Rapid Growth of Slovenian Dynamic Enterprises Viljem Pšeničny, Riko Novak DOBA Fakulteta, Prešernova ulica 1, 2000 Maribor, Slovenia, viljem.psenicny@doba.si, riko.novak@doba.si The authors provide key findings on the internal and external environmental factors of growth that affect the rapid growth of dynamic enterprises in relation to individual key organisational factors or functions. The key organisational relationships in a growing enterprise are upgraded with previous research findings and identified key factors of rapid growth through qualitative and quantitative analysis based on the analysis of 4,511 dynamic Slovenian enterprises exhibiting growth potential. More than 250 descriptive attributes of a sample of firms from 2011 were also used for further qualitative analysis and verification of key growth factors. On the basis of the sample (the study was conducted with 131 Slovenian dynamic enterprises), the authors verify whether these factors are the same as the factors that were studied in previous researches. They also provide empirical findings on rapid growth factors in relation to individual organisational functions: administration - management - implementation (entrepreneur - manager - employees). Through factor analysis they look for the correlation strength between individual variables (attributes) that best describe each factor of rapid growth and that relate to the aforementioned organisational functions in dynamic enterprises. The research findings on rapid growth factors offer companies the opportunity to consider these factors during the planning and implementation phases of their business, to choose appropriate instruments for the transition from a small fast growing firm to a professionally managed growing company, to stimulate growth and to choose an appropriate growth strategy and organisational factors in order to remain, or become, dynamic enterprises that can further contribute to the preservation, growth and development of the Slovenian economy. Keywords: organisational factors of rapid growth, dynamic enterprises, enterprise's internal environment, enterprise's external environment 1 Introduction Every economy, including Slovenia's, has small, medium-sized and large enterprises that are growing and generating new jobs and garnering the largest share of economic growth. Such companies can even be found during times of crisis. Their growth is affected by individual internal and external factors of rapid growth that researchers recognised decades ago and that have only changed slightly over time. They include different organisational factors, as growth largely depends on the effects of key organisational relationships in a growing company, i.e. between administration, management and implementation. Andre (2008) states that individual relationships are characteristic of a specific organisation and can be described by using the specific characteristics or attributes by which they differ. In our case, these are individual descriptive attributes that best describe the seven recognised rapid growth key factors of dynamic enterprises that relate to the three key organisational functions. In this article, the authors provide key findings on the already recognised factors of rapid growth of dynamic enter- prises that were studied on the basis of different studies that were implemented both in Slovenia and abroad during different time periods (1990-1994, 1998-2002, 2006-2010). In a previous extensive study, the authors dealt with individual growth factors in detail and studied the effect of these factors that define the organisational characteristics of a growing company, especially a company transitioning from a small fast growing firm to a professionally managed growing (dynamic) company. The main goal of the research is to show the findings verifying the key growth factors of Slovenian dynamic enterprises in conjunction with some organisational factors that influence the (fast) growth, based on research findings from 2011, and compare them with the findings of dynamic enterprises and their key growth factors for the period of last twenty years (previous researches were made by Žižek and Liechtenstein, 1994; Mei-Pochtler, 1999; Psenicny, 2002; Psenicny et al., 2012). The research assumes that the key growth factors have not significantly changed in the last twenty years and that the factors depend on, or are related to, the effects of some key organisational relationships in a growing dynamic company (entrepreneur, manager, employees). Received: 26th September 2012; revised: 29th October 2012; accepted 12th November 2012 2 Purpose The purpose of this article is to use the extensive study, which was conducted in 2011 and 2012, to provide findings for the posed main research question of whether rapid growth factors are still the same (congruent) as those studied by past researchers (Žižek and Liechtenstein, 1994; Mei-Pochtler, 1999; Psenicny, 2002; Psenicny et al., 2012), which attributes describe them the best and to what extent these factors depend on or are related to the effects of key organisational relationship in a growing dynamic company. In this respect, the authors focus on the key factors of rapid growth according to Žižek and Liechtenstein (1994). The research in 1994 was conducted with a sample of 150 dynamic Slovenian enterprises. The empirical data were obtained with the same survey, same number of descriptive attributes and questions. The enterprises were chosen from the database of all economic subjects in Slovenia in that time period with some excluding activities. As mentioned in the Introduction, an extensive study was conducted in 2011 encompassing 4,511 dynamic Slovenian enterprises that were chosen from the AJPES database of Slovenian companies employing strict previously determined criteria for dynamic enterprises. A survey was sent to the companies in the form on an online survey and covered over 130 questions or more than 250 descriptive attributes of seven factors of rapid growth. The study was conducted on a sample of 131 dynamic Slovenian enterprises. 3 Theoretical background The importance of organisational factors that influence growth was already studied by Gamble and Blackwell (2002) who determined three basic sets: 1. the individual, 2. the organisation, 3. the environment. Maier (2007) states that in terms of factors affecting success of management, 10 % relate to technology management, 20 % to the organisational processes and 70 % to human resources. Kreitner and Kinicki (2004), on the other hand, stress the importance of technology, which represents only 20 percent of knowledge management with the remaining 80 percent being people. Dimovski et al. (2005:34) similarly stresses that every company, small or large, dynamic or static (Tajnikar, 2006:84 86) usually creates the three mentioned basic functions or roles in the company (administration - management - implementation). In a broader sense, organisational culture, management, communication, information technology and human resources management could be emphasised as key organisational factors of knowledge management (similar findings also indicate by O'Gorman, 2001; Fischer and Reuber, 2003; Prajogo and McDermott, 2005; Rebelo, and Gomes, 2011). In relation to dynamic enterprises and the importance of individual organisational factors, Rozman (2012:6 8) establishes that when a new company is set up, the entrepreneur starts by implementing all the activities himself (he is the owner of the company, administers it, freely uses it, plans, organises, controls and also implements the activity) - 1. administration (administrators). Chrusciel and Field (2006:505 506) understand administration as a function of owners. When an entrepreneur eventually wishes to remove himself from the numerous obligations and remain as only the owner (administrator) of the company, he hires a manager who assumes the remaining obligations. In doing so, the owner still manages and uses the company, i.e. determines its main directions and aims, its vision and success criteria. He also decides on the division of assets and the company's activities - 2. management (managers). Robbins (2001) believes that management is the main function of managers. Managers receive their tasks and authority to implement from administrators, i.e. owners. Their essence lies in harmonising technically dispersed work. With the company's growth, especially the growth of dynamic enterprises, the scope of work expands thus generating the need for additional employment. All gaps in production, development, marketing, finances and administration need to be filled with the best experts or other workers - 3. implementation (implementers). Rozman (2000:5 6) establishes that implementation is performed by implementers (specialists) who implement their part of the task (they cannot transfer it to others). 4 Dynamic enterprises Through time, changes in ownership result in changes in organisational relationships in a company, especially in a dynamic enterprise. The entrepreneur is being replaced by the hired management, the number of implementers increases as well as the complexity of the information, decision-making and implementation process. Organisational changes, which are the result of rapid growth, are substantial and many companies are unable to transition from a small firm to a professionally managed growing company. This transition requires knowledge and competencies that are held educated, qualified and highly motivated individuals. Many researchers, including Kingstone (1987:225 232) and others call these dynamic entrepreneurs, dynamos. Different researchers use the term dynamic enterprises for companies that grow swiftly or (develop) at an above-average rate (Širec and Rebernik, 2010:46), intensively employ labour and are always a step ahead of the competition (adapted from Birch, 1987; Žižek and Liechtenstein, 1994; Roure, 1999; Mei-Pochtler, 1999; Psenicny, 2002; Acs and Mueller, 2008; Bavdaž et al., 2009; Littunen and Virtanen, 2009; Hölzl, 2009; Haisu and Zhongxiu, 2010; Mateev and Anastasov, 2010; Rebernik et al., 2012). The aim of such companies is not merely to survive but, above all, to succeed. Psenicny (2002) states that dynamic enterprises, according to chosen criteria (e.g. growth of sales), in a time interval of at least five years record growth levels that place them among the top 5 or even 10 % of companies in the economy or sector. On the basis of these facts, the authors verified key (internal and external environmental) growth factors that affect the rapid growth of Slovenian dynamic enterprises and their congruence with the findings of Žižek and Liechtenstein (1994). In doing so, they were not only limited to the internal and external factors of establishing an organisation's structure, but were able to verify all relevant factors. 5 Organisational factors of rapid growth In relation to organisational sciences, external factors include values, institutional conditions, the market and the development of science and technology and internal factors encompassing everything from the business strategy, technology and the production programme to the organisation's employees, size, location, management and tradition. Both sets of factors are somehow connected by the company's organisational culture. From the viewpoint of this study, special emphasis was placed on establishing the effect of the main factor - the entrepreneur and holder of the administrative/governance (ownership) and management function - and searching for eventual congruence/differences among these three main organisational functions from the viewpoint of a dynamic enterprise. The main condition for the growth of each company is undoubtedly stemmed from the desire of the owner (the entrepreneur) to see his company to grow. In this respect, the entrepreneur, with all his characteristics, abilities, knowledge and motivation, is the fundamental and most important factor of rapid growth. Tajnikar (2006:78 80) establishes that there are also many other factors that affect growth intensity, such as an open economy, a developed financial system, connectivity between research and development of different institutes and entrepreneurs and the possibility of withdrawal, cashing in and collecting yields. In their operation, companies are also subject to different individual internal and external factors that affect their success and "speed" of growth. Different authors wrote about rapid growth factors (Penrose, 1959; Žižek and Liechtenstein, 1994; Charan and Tichy, 1998; Mei-Pochtler, 1999; Roure, 1999; Psenicny; 2002; Psenicny and Novak, 2012a). The main internal and external factors that affect rapid growth and that are mentioned by the majority of these authors are: 1. the enterprise's external environment, 2. the entrepreneur or the entrepreneurial team, 3. the business strategy, 4. the management system, 5. employees, 6. innovations and implementation of changes, 7. growth financing. These factors of dynamic entrepreneurship can be described by a number of characteristics (attributes) that affect them from either the external or the internal environment of the enterprise. Each attribute describes a certain area of operation, or characteristics of the entrepreneur and the company, and is included in the form of a statement or question in the extensive survey. Mei-Pochtler (1999:97 104) calls them "facilitators and inhibitors of fast growth of enterprises". In a previous study, the authors verified the congruence of these attributes or variables and their connectivity in common sets (factors) that best describe an individual factor of rapid growth (Psenicny and Novak, 2012a). 6 Methodology - verifying the congruence of factors of rapid growth of Slovenian dynamic enterprises In their previous study, the authors established or verified which factors affect rapid growth of enterprises and whether these factors change through time. The obtained and arranged results of the implemented study were processed in the SPSS statistical program and were used to facilitate the verification and relevance of the posed question. The obtained data allowed the authors to establish the congruence of factors and individual descriptive attributes of rapid growth and thus compare them to the previous findings of researchers. They searched for a connection between individual descriptive attributes and the key organisational factor - the entrepreneur. 6.1 Sample In 2011, Slovenian dynamic enterprises exhibiting growth potential during the time period of 2006 2010 were chosen from the database of all economic subjects (excluding activities such as banks, insurance companies, public institutions and similar) on the basis of criteria that were shaped pursuant to available findings on dynamic enterprises. From this list, all companies whose 2006 and 2010 data did not meet the criteria to be listed as dynamic enterprises were excluded. From the database covering the operation of 126,976 companies, a database of 4,511 companies (including 1,010 sole proprietors and 3,501 commercial companies) that cumulatively met all previously determined criteria was compiled. Among the 4,511 dynamic companies covered by the survey, the detailed structure of the companies was as follows (companies covered in the survey represent altogether 3,50 % of all commercial enterprises in Slovenia - excluding activities such as banks, insurance companies, public institutions and similar): ■ 1,004 micro and small sole proprietors (1,41 % of all sole proprietors); ■ 6 medium-sized sole proprietors (54,55 % of all medium-sized sole proprietors) and ■ 3,501 commercial companies (6,28 % of all commercial companies). An extensive survey with over 130 questions with more than 250 descriptive attributes of the seven factors of rapid growth was sent to all 4,511 companies. In its basic form, the survey was developed for researching European gazelles (Žižek and Liechtenstein, 1994). It was later completed and used to analyse the growth of dynamic enterprises in different studies (Psenicny, 2002) and was subsequently updated for the study conducted in 2012 (Psenicny et al., 2012). 6.2 Research question With the aim of forming a new and fresh economic policy in Slovenia, an empirical study was designed in 2011 whose purpose was to analyse the growth of Slovenian companies in the last five years (2006 2010). The analysis focused on the fastest growing Slovenian companies - dynamic enterprises that generated growth in the last years. The authors used these guidelines to verify the success of dynamic enterprises in Slovenia on the basis of rapid growth factors, i.e. whether these factors are the same as the ones studied in the past by Žižek and Liechtenstein (1994) and which individual attributes describe them the best. Žižek and Liechtenstein (1994) conducted their study employing a sample of 150 dynamic enterprises. The enterprises in the 1994 research were also chosen from the database of all economic subjects in Slovenia at that time. The empirical data were obtained with the same survey, same number of descriptive attributes and questions (excluding activities such as banks, insurance companies, public institutions and similar). In this article, the authors paid special attention to searching for a connection between individual descriptive attributes that affect the key organisational factor - the entrepreneur -from the governance, management and implementation viewpoint. 6.3 Data Analysis By employing factor analysis, which is used to cluster individual variables (attributes) and whose aim it is to determine a smaller number of linear combinations of the observed variables by using them to maximise the variance accounted for in the original data, 71 new factors were obtained explaining over 89 % of variance. We further named, described and verified all the newly obtained factors in terms of content. Using individual variables (attributes) that best describe and explain an individual factor and which were obtained via factor analysis we were able to, in the event that they would substantially differ from the ones we were verifying, form new nominal factors of rapid growth and compare them with the existing factors in terms of content. Based on the rotated matrix, we have examined all the strongest attributes (variables) for each new factor. To all 71 factors, we have added the corresponding strongest attribute (variable). We further presented the interpreted and newly named factors (all seventy-one) that included only attributes with eigenvalues of over 0.5 or 0.4 if these were questions from the same set (based on key questions that describe the seven growth factors). In this manner, only the attributes with the highest eigenvalues remained or attributes that were the strongest for an individual factor. We later combined individual factors whose attributes describe similar fields (question sets) and obtained actual factors that best describe an individual field. It was on this basis that content-based factors of rapid growth were formed. After excluding the "weakest" attributes, we were left with 59 "new" factors comprised of attributes with the highest eigenvalues. Therefore, below are the results of combining similar factors that describe more or less the same or very similar fields. 7 Shaping and verifying congruence of factors of rapid growth Congruence was verified for the seven main factors of rapid growth of dynamic enterprises that were primarily shaped according to Žižek and Liechtenstein (1994), summarised from Psenicny (2002) and later verified and updated according to Psenicny and Novak (2012a). The primary study has shown that the seven main factors of rapid growth are best described by 243 descriptive attributes. After verifying these factors and their individual descriptive attributes, the authors established that all seven main factors of rapid growth remained the same but they managed to describe them with a smaller number of (the strongest) descriptive attributes, i.e. 150 recognised descriptive attributes. They established that in the last twenty years, rapid growth factors did not change substantially and there were also no fundamental changes in individual descriptive attributes of a specific rapid growth factor. The main differences in the number of descriptive attributes were thus found in the first factor (the external environment), which they describe with 22 descriptive attributes compared to the primary study where 35 descriptive attributes were used to describe this factor. The second factor (the entrepreneur) was described with 16 attributes while in the primary study 39 descriptive attributes were used. The third factor (business strategy) is the most extensive factor that they described and interestingly enough used the same number of descriptive attributes, i.e. 68. The fourth factor (the management system) was initially described by 23 descriptive attributes; the authors described it with 7 descriptive attributes. The fifth rapid growth factor (the employees), which they later verified also from the organisational point of view, was described by 14 descriptive attributes, a difference from the initial 22. It was established that the sixth factor (innovations) is best described by 4 descriptive attributes; initially there were 11. For the last, seventh factor (financing), the authors established that it is best described by 19 descriptive attributes; initially there were 45. Below are the descriptions and depictions of congruence/ differences among individual descriptive attributes of rapid growth factors. A more detailed aspect is the link or connection between basic organisational factors of rapid growth, i.e. between the entrepreneur as the administrator (owner and manager in one person) of the company, the entrepreneur as the manager and the company's employees as implementers. 7.1 Descriptive fields and congruence between individual attributes The study found that the external environment of a dynamic enterprise is the second most extensive factor of rapid growth. The key congruent attribute was the attribute describing the legislative field of dynamic entrepreneurship. This relates to the state's tax policy, which is very discouraging and which does not encourage companies to make new investments. For this reason, for companies to grow faster, extensive changes in their financial and business environment that would stimulate new investments are needed. The second factor is the entrepreneur. The authors established that he is defined by the following strongest attributes that describe his field and level of education, previous years of work experience, years and diversity of work experience, company ownership, training abroad and managerial experience. The business strategy factor was recognised as the most extensive factor. The business strategy of dynamic enterprises is usually directed towards professional management and attitude towards the employees and final buyers while first choosing the legal form. The employees and the management are involved in the company's operation or strategy. In order to grow, companies need to choose sales markets where they generate the largest share of profit assuming that in terms of competitiveness they are among the top 3 % in their industry. Competitive advantage is generated or maintained through low purchase costs, low labour costs, good organisation, good knowledge of market trends and needs, the orientation of the company's employees towards increasing efficiency of the production process, etc. The management system in dynamic enterprises is very employee-centred and employees are well remunerated and managed by highly qualified and equally well remunerated managers. Dynamic enterprises pay special attention to logistics and information support, technological development, the company's main advantages over its competition, choice of suppliers, etc. The entrepreneur performs the same tasks as in the past or in the previous job while has at the same improved his organisational skills. Dynamic enterprises predominantly see their advantage in the qualifications of their employees who are also difficult to find for a specific narrow work segment. They first need to train and qualify the employees for the work themselves, and these employees are later involved in continual training and education. Their central growth strategies include a good human resources policy and increased employment of new employees with the company's good results in business. The majority of dynamic enterprises are definitively innovative in different fields, as this is required for their fast growth and development. The strongest and congruent attributes relate to innovations and the quality of work (of services or products). Dynamic enterprises allocate substantial resources to investments in research and development. This is also their key strategy that they intend to follow in the future. In order to facilitate growth, companies also focus in the field of making products. The factor of financing is undoubtedly one of the most important factors in the growth of a dynamic enterprise. Companies need to finance their growth prudently while at the same time generate sufficient profit to be able to finance their future growth (mostly with own sources, savings and borrowing). The generated surplus is invested in development and their investments are financed with loans. More venture capital and changes in the financial environment would substantially facilitate growth of dynamic enterprises. 7.2 Verifying relationships between individual content-based descriptive attributes and the main organisational functions (entrepreneur, management, employees) The authors found that even after twenty years, there were no fundamental changes in basic factors of rapid growth in Slovenian dynamic enterprises. They also found that their content-based descriptive attributes also did not change substantially. On this basis, they further verified or searched for the main content-based differences and deviations in individual content-based attributes related to the organisational relationship between the entrepreneur, the manager and employees on the basis of empirical findings from 1994 and 2011 (Psenicny and Novak, 2012b). Entrepreneur* as owner and manager (governance functton) Manager (management functton) Employees (implementtng functton) Figure 1: Basic organisational functions in a dynamic enterprise Source: Own * Entrepreneur (governance and administrative function in one person - from the beginning of the growth to the managing of the roles) - for the transition from a small fast growing firm to a professionally managed growing company On the basis of empirical findings, the authors established that almost four fifths of dynamic enterprises are owned by the entrepreneur of the entrepreneurial management team. With respect to the 1994 findings, there were two thirds such companies. Over 90 % of all dynamic enterprises in both studies were set up by the entrepreneur. Another interesting aspect is that in the 1994 study, two thirds of entrepreneurs used to hold (in their previous job before setting up a dynamic enterprise) a management position, while in the 2011 study, such previous employment was found in "only" slightly less than two fourths. As the majority of dynamic enterprises were set up by the entrepreneur, the next in-depth question established that in 2011, slightly less than 40 % did so with the help of their family and friends. With respect to the 1994 findings, one third of companies were established in this manner. In 1994, entrepreneurs emphasised the owner's or the management team's organisational skills and the entrepreneur's/management team's experience as the main reason behind the success of a dynamic enterprise. An interesting aspect of the 2011 study is that entrepreneurs did not emphasise any specific reason for the company's success but have, on average, highlighted almost all four of the listed options. More than three fourths of entrepreneurs participating in the 2011 study manage their dynamic enterprises themselves or perform all the important activities themselves. In 1994, only one third of entrepreneurs managed their dynamic enterprise themselves, while slightly more than one fourth managed their company with a good team comprised of non-owners and owners. The established differences in the monthly salary of entrepreneurs, managers and employees show that in the 2011 study employees in over two fourths of cases earned an average monthly net salary between EUR 800.00 and EUR 1,000.00. The same was seen in the 1994 study. The average monthly earnings of the management team in 2011 amounted to between EUR 1,000.00 and EUR 1,500.00 in slightly more than one third of cases. In the 1994 study, manager salaries were substantially higher standing between EUR 2,000.00 and EUR 2,500.00. The average net monthly salary of one half of the entrepreneurs in the 2011 study amounted to between EUR 1,000.00 and EUR 2,000.00. Twenty years ago, entrepreneurs had substantially higher salaries with one fifth remitting between EUR 3,500.00 and EUR 4,000.00 to their account. One third of those entrepreneurs emphasised that they were not paying themselves more due to tax reasons or in order to prepare the company for a new investment. In both studies, one third of entrepreneurs had already thought about including their employees in the company's ownership structure but had not yet done so; however they planned to do so in the future. From the viewpoint of the entrepreneurial function, it was established that dynamic enterprises were on average set up by the entrepreneur or by the entrepreneurial management team. The average age of the entrepreneur at that point was between 40 and 49 years with at least 10 years of work experience. Twenty years ago, dynamic enterprises were also set up by the entrepreneur or the management team. It is interesting to note that the average age of the entrepreneur at that point was also between 40 and 49 years with at least 10 years of work experience. The entrepreneur's motivation for faster growth of the company is predominantly influenced by anticipated higher yield, risk premiums and social recognition or the recognition of the business environment. For entrepreneurs, this was their first and only established company and they did not own other companies. For fast growth, membership of the entrepreneur in one of the entrepreneurial organisations is important, as they exchange important information, make new acquaintances and conclude new business deals, socialise with other entrepreneurs and in general enjoy their time with other people. In order to facilitate growth, entrepreneurs also hire experienced consultants for specific fields. The emphasised fields for hiring a consultant were marketing, sales and purchase management. No substantial differences were found between individual descriptive attributes or their values. From the viewpoint of the management function, dynamic enterprises, in order to record faster growth, should consider the importance of the most important fields in creating their competitive advantage. This predominantly relates to lower labour costs, company organisation and marketing strategy. The companies should also consider the importance of individual fields for company growth, i.e. the orientation of employees towards meeting customer demands. The importance of the main elements of human resources management in order to improve operations is also of exceptional importance and companies should pay special attention to this field, including the personnel policy or the right choice of employees, financial remuneration of employees and establishing of teams. This rapid growth factor also does not show any substantial differences in the content-based descriptive attributes with regard to the previous study. The third and final organisational function is the implementing function, where the key factors are the employees of a dynamic enterprise. In order for the company to be successful, a high educational structure of employees is required, i.e. at least secondary school or higher education. Twenty years ago, secondary school was emphasised as the highest educational level, however the majority of employees were appropriately qualified to perform their jobs. They acquired the majority of Table 1: Individual emphasised and related organisational factors with regard to the entrepreneur - manager - empl^-yees relationship Field 1994 study (N = 150) 2011 study (N = 131) Company ownership Entrepreneur or entrepreneurial management team Founder of the dynamic enterprise The entrepreneur Previous job Management position "Help" in setting up the company Alone + family and friends Main reason for the success of the dynamic enterprise Organisational skills of the owner or the management team Company management (entrepreneur alone or with the help of qualified managers) The entrepreneur performs all the important activities himself Net monthly salary of employees Between EUR 800.00 and EUR 1,000.00 Net monthly salary of managers Between EUR 2,000.00 and EUR 2,500.00 Between EUR 1,000.00 and EUR 1,500.00 Net monthly salary of the entrepreneur Between EUR 3,500.00 and EUR 4,000.00 Between EUR 1,000.00 and EUR 2,000.00 Employee ownership in the company It might work, it is planned for the future It might work but it will not be implemented Source: Psenicny et al., 2012 Table 2: Values of descriptive attributes for the organisational factor of entrepreneur Content-based descriptive field Values Ownership source of the dynamic enterprise Entrepreneur or management team Age of the entrepreneur Between 40 and 49 years (44 mean) Level of education of the entrepreneur Secondary school or higher Years of work experience of the entrepreneur prior to setting up the now dynamic enterprise More than 10 years The entrepreneur's ownership share (main shareholder) in another company None or one Number of previously successful start-ups One or none Membership of the entrepreneur in an entrepreneurial organisation Important Socialising with people Yes Hiring consultants for specific fields Marketing, sales and purchase management Willingness to meet other dynamic entrepreneurs Yes The effect of main factors on the entrepreneur's motivation for faster growth of the dynamic enterprise Anticipated higher yield, risk premiums and social recognition or the recognition of the business environment Source: Pšeničny and Novak, 2012b Table 3: Values of descriptive attributes for the organisational factor of management Content-based descriptive field Values Main fields in generating competitive advantage Low labour costs, company organisation, the marketing strategy The most important areas for company growth The orientation of employees towards meeting customer needs Key elements of human resources management in order to improve operation The personnel policy or the right choice of employees, financial remuneration of employees and establishing of teams Source: Pšeničny and Novak, 2012b knowledge and skills while working for the dynamic enterprise where they were employed at the time of the study. In their growth, dynamic enterprises consider the appropriate number of employees from the start. In the first year of operation, this number should be from 1 to 4. The same number is recommended also for the third and fifth year of operation. This, however, generally does not correspond to the actual situation in dynamic enterprises that require successful employees for their growth and their number continually increases through a longer period of time until the company reaches a satisfactory growth level. A similar stipulation also applies to the hiring of consultants for specific fields in the company with 1 to 4 being the recommended number. For this organisational factor there were also no substantial content-based differences in the descriptive attributes. In the final part of the article, the authors provide their findings on verifying the relationships between individual content-based descriptive attributes (studies implemented in 2011 and in 1994) and the basic organisational functions (entrepreneur, management, employees). 8 Findings 8.1 Results The comparative analysis of study results from the early nineties, late nineties and the beginning of this century has shown that with rapidly growing or dynamic enterprises we can easily point out seven key factors of rapid growth and 250 (Žižek and Liechtenstein, 1994) descriptive attributes that describe or determine these factors. The authors did not find any essential content-related differences but they did successfully reduce the number of the required attributes to define rapid growth factors from 250 to 150 key descriptive attributes, facilitating future recognition of rapidly growing dynamic enterprises in Slovenia. The main organisational factors of rapid growth (entrepreneur, management - manager and employees) in rapidly growing dynamic enterprises in Slovenia also did not substantially change in the last twenty years, i.e. pursuant to the content- Table 4: Values of descriptive attributes for the organisational factor of empl^-yees Content-based descriptive field Values The number of employees in the first year of operation (n) From 1 to 4 The number of employees in year (n + 1) From 1 to 4 The number of employees in year (n + 5) From 1 to 4 The number of employees in the year of the survey From 1 to 4 The number of outsourced employees in year (n + 3) From 1 to 4 The number of outsourced employees in year (n + 5) From 1 to 4 The number of outsourced employees in the year of the survey From 1 to 4 The educational structure of employees Primary, apprentice, secondary Appropriate qualifications of employees The majority are appropriately qualified The manner of acquiring qualifications of company employees for successful work At seminars outside the school system, at previous jobs, while working for the current company Source: Pšeničny and Novak, 2012b based descriptive attributes that were obtained for these factors on the basis of a study conducted in 2011. The fact is that the most important aspect of a company's sustainable growth is the successful transition from a small firm managed by the entrepreneur - manager. 8.2 Managerial implications The study has shown that the majority of small growing companies are managed by the entrepreneur himself and that this individuality even increased in fast growing companies with regard to the study conducted twenty years ago. In other words, today's entrepreneurs are more reluctant to work in an entrepreneurial management team than they were two decades ago. The same as two decades ago, dynamic enterprises are still not inclined towards including their employees in the ownership and management structure or in the participation of profit, which is one of the main characteristics of dynamic enterprises across the world (Psenicny, 1999, 18 21). Only in the case of a company with over 50 employees, the entrepreneur is required to strengthen the company with professional managers, hire consultants and, to a greater extent, include employees in the decision-making and management processes. It is also interesting to note that three quarters of entrepreneurs are planning on adding 1 to 9 new jobs in the future, which will additionally facilitate the need to introduce new consultants and managers. 8.3 Discussion of results The content analysis of differences in answers has thus shown that the main organisational relationships between the entrepreneur, the manager and the employees have not substantially changed. There are many signs that the rapidly growing companies, which were analysed, are not ready for the transition from a small growing company to a professionally managed rapidly growing dynamic enterprise. Deliberation on and the search for reasons for stagnation or the lagging behind of the "entrepreneurial spirit" in rapidly growing companies should be the focus of further research, while on the other hand actual opportunities for the development of organisation in rapidly growing companies should be established in order to facilitate and enable faster progress, development and growth of these companies in future years. 8.4 Conclusions It could also be emphasised that there are certain obstacles that hinder and hold entrepreneurs back, keeping them at a minimal growth level and impeding extensive organisational interventions, i.e. professionalisation of management and operation. These obstacles mainly relate to individual obstacles in the financial and business environment (poor possibilities of taking on long-term loans, high interest rates, poor economic possibilities, high tax burdens, limiting governmental decrees, etc). In all this, the continuation of the currently running business is still affected by the entrepreneur's motivation to set up a dynamic enterprise, i.e. to realise his idea and vision. This corresponds to the key growth strategy for the future, which is undoubtedly developing and offering existing and new products and services to new markets. 9 References Andre, R. (2008). Organizational behavior: an introduction to your life in organizations. Prentice Hall: Pearson Education International. Acs, J. Z. & Mueller, P. (2008). Employment Effects of Business Dynamics: Mice, Gazelles and Elephants. Small Business Economics, 30(1), 85-100, http://dx.doi.org/10.1007/s11187-007-9052-3 Bavdaž, M., Drnovšek, M. & Lotrič Dolinar, A. (2009). Achieving a Response from Fast-Growing Companies: The Case of Slovenian Gazelles. Economic and Business Review, 11(3), 187-203. Birch, L. D. (1987). Job Creation in America: How our smallest companies put the most people to work. New York: Free Press Macmillan. Charan, R. & Tichy, M. N. (1998). Every Business is a Growth Business. New York: Three Rivers Press. Chrusciel, D. & Field, W. D. (2006). Success Factors in Dealing with Significant Change in an Organization. Business Process Management Journal, 12(4), 503-516, http://dx.doi.org/10.110 8/14637150610678096 Dimovski, V., Penger, S. & Žnidaršič, J. (2005). [Sodobni management]. Modern Management. Ljubljana: Faculty of Economics. Fischer, E. & Reuber, R. (2003). Support for Rapid-Growth Firms: A Comparison of the Views of Founders, Government Policy Makers and Private Sector Resource Providers. Journal of Small Business Management, 41(4), 346-65, http://dx.doi.org/ 10.1111/1540-627X.00087 Gamble, P. & Blackwell, J. (2002). Knowledge Management: a State of Art Guide. London: Kogan Page. Haisu, W. & Zhongxiu, F. (2010). Empirical Study on Commonness of Fast Growing Private Enterprises in China: Study on Listed Companies on GEM in Shenzhen Stock Exchange. Journal of Chinese Entrepreneurship, 2(3), 282-291, http://dx.doi. org/10.1108/17561391011078767 Hölzl, W. (2009). Is the R&D Behaviour of Fast-Growing SMEs Different? Evidence from CIS III Data for 16 Countries. Small Business Economics, 33(1), 59-75, http://dx.doi.org/10.1007/ s11187-009-9182-x Kingstone, B. (1987). The Dynamos: Who are They Anyway. New York: John Wiley & Sons. Kreitner, R. & Kinicki, A. (2004). Organizational Behavior. New York: John Wiley & Sons Inc. Littunen, H. & Virtanen, M. (2009). Differentiating Factors of Venture Growth: From Statics to Dynamics. International Journal of Entrepreneurial Behaviour & Research, 15(6), 535-554, http://dx.doi.org/10.1108/13552550910995425 Maier, R. (2007). Knowledge Management Systems: Information and Communication Technologies for Knowledge Management. New York: Springer. Mei-Pochtler, A. (1999). Strategies for Growth. Edinburgh: Europe's 500. O'Gorman, C. (2001). The Sustainability of Growth in Small and Medium-Sized Enterprises. International Journal of Entrepreneurial Behaviour & Research, 7(2), 60-75, http:// dx.doi.org/10.1108/13552550110396095 Penrose, E. (1959). The Theory of the Growth of the Firm. New York: John Wiley & Sons. Prajogo, D. I. & McDermott, C. M. (2005). The relationship between total quality management practices and organizational culture. International Journal of Operations & Production Management, 25(11), 1101-1122, http://dx.doi. org/10.1108/01443570510626916 Pšeničny, V. (1999). [Kako ujeti evrogazele]. How to Catch the European Gazelles. Podjetnik, 15(6), 18-21. Pšeničny, V. (2002). Pogoji in možnosti za dinamično podjetništvo v Sloveniji. [Conditions and Opportunities for Dynamic Entrepreneurship in Slovenia]. Doctoral Thesis. Ljubljana: University of Ljubljana, Faculty of Economics. Pšeničny, V. & Novak, R. (2012a). Dejavniki hitre rasti dinamičnih podjetij 2012. Poročilo o preverjanju skladnosti posameznih atributov. [Rapid Growth Factors of Dynamic Enterprises. Report on Compliance Analysis of Individual Attributes]. Maribor: DOBA Faculty. Pšeničny, V. & Novak, R. (2012b). Vsebinsko preverjanje opisnih atributov hitre rasti oblikovanih po Pšeničny in Novak 2012 -Analiza podlage teoretičnega modela dinamičnega podjetništva v Sloveniji. [Content related analysis of descriptive attributes of rapid growth designed by Pšeničny and Novak 2012 -Theoretical Model of Dynamic Entrepreneurship in Slovenia Groundwork Analysis]. Maribor: DOBA Faculty. Pšeničny, V., Maček, A., Vidovič, D. & Novak, R. (2012). Podjetja z visokim potencialom rasti 2012. Poročilo o raziskavi, 1. in 2. Faza. [High Growth Potential Firms 2012. Research Report, 1st and 2nd Phase]. Maribor: DOBA Faculty. Rebelo, T. M. & Gomes, A. D. (2011). Conditioning factors of an organizational learning culture. Journal of Workplace Learning, 23(3), 173-194, http://dx.doi.org/10.1108/13665621111117215 Rebernik, M., Tominc, P. & Crnogaj, K. (2012). Usihanje podjetništva v Sloveniji: GEM Slovenija 2011. [Decline of entrepreneurship in Slovenia: GEM Slovenia 2011]. University of Maribor, Faculty of Economics and Business. Robbins, P. S. (2001). Organizational Behaviour. New Jersey: Prentice Hall. Roure, J. (1999). Europe's Most Dynamic Entrepreneurs; The 1998 Job Creators. Brussels: Europe's 500. Rozman, R. (2000). Analiza in oblikovanje organizacije [Analysis and Design of an Organization]. University of Ljubljana, Faculty of Economics. Rozman, R. (2012). Slovenian Organisation Theory and its Ties with Associated Theories and Sciences. Dynamic Relationships Management Journal, 1(1), 2-25. Širec, K. & Rebernik, M. (2010). Vrzeli slovenskega podjetniškega okolja: slovenski podjetniški observatorij 2009/10. [Gaps of Slovenian Business Environment: Slovenian Entrepreneurship Observatory 2009/10]. Maribor: Faculty of Economics and Business. Tajnikar, M. (2006). Tvegano poslovodenje: Knjiga o gazelah in rastočih poslih. [Risk Management: Book of Gazelles and Growing Business]. Portorož: GEA College of Entrepreneurship. Žižek, J. & Liechtenstein, H. (1994). Venture capital & entrepreneur-ship in central & east Europe, final report. Database survey on 750 central and east European dynamic entrepreneurs. Gent: EFER. Viljem Pšeničny is an entrepreneur, business consultant, assistant professor of entrepreneurship and Dean of DOBA Faculty. He was the State Secretary of Economy in 2010 and 2011 and the Secretary-General of the Chamber of Craft and Small Business of Slovenia from 2002 to 2010. He is the author and co-author of numerous articles, manuals, projects and studies related to entrepreneurship, especially within the growth of small enterprises. He is one of the co-founders of GEA College that he managed as the Director and entrepreneur since its setting up in 1990 until 2002. In 1986, he and his friends together set up one of the first new small enterprises in Yugoslavia - the joint stock company GRAD - and later a number of smaller firms across the former Yugoslavia. He also initiated the setting up of a number of business incubators, served as an adviser in the setting up of the Promotion Network for Small Business in Slovenia and Croatia and facilitated the development of a number of infrastructure mechanisms for facilitating entrepreneurship in Slovenia, the countries of former Yugoslavia and the EU. He is one of the co-founders of the UPI Foundation for the Development of Entrepreneurship Education, a member of the Union of Economists, the Manager Association and of Rotary International. He was also one of the initiators in setting up the Association of Entrepreneurs of Slovenia. Riko Novak is a practicing expert for personal business consulting for micro enterprises, especially for the fields of marketing and the creativity and optimisation of business process (ideas) in a company. He obtained his professional knowledge from the fields of economic and legal affairs during his graduate and undergraduate studies. He acquired his theoretical and practical experience through research and academic and economic spheres, which has proven to be very useful in his professional career. In recent years, he has been involved in detailed studies of dynamic entrepreneurship in Slovenia, rapid growth factors of dynamic enterprises and the internationalisation of small and medium-sized (dynamic) enterprises. He is the author and co-author of professional articles, articles presented at national and international conferences, professional publications and research reports. Organizacijski dejavniki hitre rasti slovenskih dinamičnih podjetij Avtorja v članku podajata ugotovitve preverjanja posameznih notranje in zunanje okoljskih dejavnikov hitre rasti, ki vplivajo na hitro rast dinamičnih podjetij v povezavi z nekaterimi ključnimi organizacijskimi dejavniki oziroma funkcijami. Temeljna organizacijska razmerja v rastočem podjetju nadgrajujeta s spoznanji dosedanjih raziskovanj in prepoznanih odločilnih dejavnikov hitre rasti, in sicer s kvalitativno in kvantitativno analizo na osnovi analize 4.511 slovenskih dinamičnih podjetij s potencialom rasti ter vzorca teh podjetij iz leta 2011, ki je služil za nadaljnjo kvalitativno analizo in preverjanje ključnih dejavnikov rasti z 250 opisnimi atributi. Na osnovi obravnavanega vzorca preverjata (vzorec je 131 slovenskih dinamičnih podjetij), ali so ti dejavniki enaki, kot so jih ugotavljali raziskovalci v preteklosti. Prav tako podajata empirične ugotovitve o dejavnikih hitre rasti v povezavi s posameznimi organizacijskimi funkcijami: upravljanje - poslovodenje - izvajanje (podjetnik - menedžer - zaposleni). S pomočjo faktorske analize sta iskala moč povezanosti posameznih spremenljivk (atributov), ki najbolje opisujejo posamezni dejavnik hitre rasti in se navezujejo na omenjene organizacijske funkcije v dinamičnih podjetjih. Izsledki preveritve ključnih dejavnikov hitre rasti nudijo podjetjem možnost upoštevanja teh dejavnikov pri načrtovanju in izvajanju poslovanja in s tem izbor primernih instrumentov za prehod iz malega rastočega podjetja v profesionalno vodeno rastoče podjetje, za stimulacijo rasti, izbor primerne strategije rasti ter organizacijskih dejavnikov, vse z namenom, da ostanejo ali postanejo dinamična podjetja in tako dodatno prispevajo k ohranitvi in rasti ter razvoju slovenskega gospodarstva. Ključne besede: organizacijski dejavniki hitre rasti, dinamična podjetja, notranje okolje podjetja, zunanje okolje podjetja DOI: 10.2478/orga-2013-0002 Analysis of Increased Information Technology Outsourcing Factors Franc Brcar, Boris Bukovec Faculty of Organization Studies, Novi trg 5, 8000 Novo mesto, Slovenia, bukovec.boris@siol.net, franc.brcar@gmail.com The study explores the field of IT outsourcing. The narrow field of research is to build a model of IT outsourcing based on influential factors. The purpose of this research is to determine the influential factors on IT outsourcing expansion. A survey was conducted with 141 large-sized Slovenian companies. Data were statistically analyzed using binary logistic regression. The final model contains five factors: (1) management's support; (2) knowledge on IT outsourcing; (3) improvement of efficiency and effectiveness; (4) quality improvement of IT services; and (5) innovation improvement of IT. Managers immediately can use the results of this research in their decision-making. Increased performance of each individual organization is to the benefit of the entire society. The examination of IT outsourcing with the methods used is the first such research in Slovenia. Keywords: Informatics, Outsourcing, Information Technology Outsourcing, ITO, Model, Business Process Management 1 Introduction Organizations are faced every day with a more and more difficult market situation. Management should use all potential means for advancement. Business process management contains large reserves for this. One possibility by which an organization can improve performance is business process outsourcing (BPO) and information technology outsourcing (ITO or IT outsourcing). Business process outsourcing means that a business process or part of a process is transferred to an external supplier or multiple suppliers for implementation. Outsourcing means obtaining products or services from an external source. Often, the term provider or vendor is used instead of supplier, but in general this is our business partner. The model defines the presentation of reality that is too complex to be studied such as it is. The model must represent reality as closely as possible. The subject of this research study is outsourcing of informatics as an organizational unit or service, i.e. IT outsourcing; as a socio-technical system that contains technical and human components. Expressions IT and informatics are used interchangeably. The survey was conducted with large-sized Slovenian businesses. IT outsourcing is defined as a general research area and the design of the IT outsourcing model as a specific research question based on the level of outsourcing of IT activities. Arguments, for or against outsourcing, are of great importance in decision-making. Past experience is also important. A positive attitude increases the level of outsourcing and a negative view decreases the level of outsourcing. The purpose and goal of this research study is to produce a model of outsourcing information technology. 2 Theoretical background and literature review 2.1 IT outsourcing We are familiar with many different types of IT outsourcing. The level of outsourcing can be measured by the number of activities outsourced according to all activities or by the amount of costs according to total costs for informatics. An extreme example is full IT outsourcing by an organization. In most organizations informatics represents a significant part of business, but the importance varies in different organizations. Depending on this, we must make decisions on the future of informatics. IT leadership challenges are (Hoving, 2007, p. 147): (1) harnessing technology; (2) providing business value; (3) managing resources; and (4) executing work. Findings by Dibbern, Chin and Heinzl (2012, p. 488)' confirm, "that a sourcing arrangement chosen by an organization is a result of the consideration of multiple types of rational choice reasoning, including efficiency and effectiveness criteria as well as social and environmental influences". IT outsourcing must be treated as a process composed of two phases (Fink, 2010, p. 130): (1) decision phase - needed and available IT resources; and (2) implementation phase -outsourced IT resources. It has to provide four types of ability Received: 24th April 2012; revised: 24th August 2012; accepted 4th January 2013 (Sia, Koh, and Tan, 2008, p. 408): (1) robustness; (2) modifi-ability; (3) new capability; and (4) ease of exit. Cullen and Willcocks (2003, p. 3) stress's that ITO is (1) a strategy for managing the delivery of IT services, and (2) a transition path toward that vision, and defines eight stages of life-cycle: (1) discard myths; (2) prepare strategies; (3) choose target services; (4) design future; (5) select suppliers; (6) make transition; (7) manage the ITO; and (8) reconsider options. 2.2 Arguments for IT outsourcing The goals that an organization wants to achieve with IT outsourcing must be clearly defined in its business strategy. Objectives must be known to all employees, and in-return, they must agree with them. If we want success, employees must identify themselves with the objectives. The motives for deployment are different in different organizations; deployment is conditioned by many determinants. The five reasons why outsourcing has strategic meaning for CEOs are (Willcocks, 2010, pp. 63-65): (1) outsourcing impact on market value; (2) outsourcing is pervasive and growing - spending alone needs attention; (3) outsourcing can damage corporate health; (4) outsourcing can play a positive, strategic role; and (5) CEOs alone possess the crucial bargaining power. Lacity, Willcocks and Feeny (2004, p. 139) stress: (1) better employee management; (2) redesigned processes; (3) customer-centric servicing; (4) enabling technology; and (5) new facilities. Gonzalez, Gasco and Llopis (2009, pp. 184-186; 2010, pp. 290-295) quote ten arguments for deployment: (1) focus on strategic issues; (2) increased IS department flexibility; (3) improved IS quality; (4) elimination of troublesome, everyday problems; (5) increased access to technology; (6) decrease obsolescence risk; (7) staff cost savings; (8) providing alternatives to in-house IS; (9) technology cost savings; and (10) following the fashion. Similar reasons are stated by McLellan, Marcolin and Beamish (1995, pp. 312-317): (1) changing organizational boundaries; (2) restructuring the organization; (3) mitigating technological risk and uncertainty; (4) accessing new technology; (5) improving management of IS operations; and (6) link between IT and business strategy. There are many reasons for the deployment of IT outsourcing, but cost reduction is only one (Khan, Niazi, and Ahmad, 2011, p. 690). Organizations lose opportunities with other reasons if they are focused only on cost reduction, where cost reduction is the most important motivation (Fisher, Hirschheim, and Jacobs, 2008, p. 177; Lewin and Peeters, 2006, p. 22). Other organizations choose other strategic goals, such as (Quinn, 1999, p. 9; 2000, pp. 13-14): (1) improvement of the efficiency and effectiveness of informatics; and (2) improving the company's capacities to stay competitive. Johnston, Abader, Brey and Stander (2009, p. 37) conclude that cost is the most influential factor when deciding whether to outsource on not, irrespective of an organization's size and type. Organizations often decide for other reasons and not solely due to cost reduction, taking into account short and long-term consequences that are difficult to predict (Baldwin, Irani and Love, 2001, p. 23). 2.3 IT outsourcing expectations We have to define determinants that are necessary for the success of outsourcing and that could be compared with outsourcing expectations. The success is valued in two ways: (1) the project implementation efficiency and effectiveness; and (2) improvement of the efficiency and effectiveness of informatics. It is valued more widely by: (1) efficiency and effectiveness of the organization; (2) efficiency and effectiveness of informatics; (3) the relationship of efficiency and effectiveness between the vendor and the buyer; and (4) the efficiency and effectiveness of the outsourcing project implementation. The cost of informatics is a relatively small part of the total organization's costs; as such, if we implement total IT outsourcing, the influence on business, in many cases, is not significant. It is difficult to distinguish IT outsourcing savings from other causes, e.g. the global economic situation. Gorla and Lau (2010, p. 91) evaluate, that satisfaction with IS outsourcing is only 33% and that 78% of projects are discontinued either by switching vendors or by back-sourcing. In-house IT capabilities are associated with IT outsourcing success (Aubert, Houde, Patry, and Rivard, 2012, p. 20; Dutta, Gwebu, and Wang, 2011, p. 240). The seven characteristics of IT offshore outsourcing projects that differentiates success and failure (Rottman and Lacity, 2008, pp. 266-271) are: (1) projects that engage one large offshore supplier are rated higher than projects that engage one small offshore supplier or multiple suppliers; (2) projects with some offshore suppliers employed onsite are rated higher than projects with all suppliers employed offshore; (3) projects with greater-value contracts are rated higher than projects with lesser-value; (4) long-term projects are rated higher than short-term; (5) some organizational units' projects are rated differently than other organizational units; (6) development and maintenance/support projects are rated equally; and (7) recent projects are rated higher than older ones. Some authors indicate a positive, others a negative, and thirdly a neutral impact of outsourcing on the IT efficiency and effectiveness in organizations. Hirschheim and Lacity (2000, p. 105) note in their study that half of the organizations achieve cost savings, whereas half do not. Similar conclusions are found by Bengtsson and Dabhilkar (2009, pp. 252-254) who conclude that some authors show significant positive effects, while others do not and that investments into technology and organization contribute more towards efficiency and effectiveness than business process outsourcing. Gilley and Rasheed (2000, p. 788) conclude that there is no connection between business process outsourcing and an organization's performances. Downing, Field and Ritzman (2003, p. 88) have a positive opinion about IT outsourcing and they conclude that outsourcing information systems can create lower overall process costs and may lead to superior overall process performance. In sharp contrast to common belief, Broedner, Kinkel and Lay (2009, p. 127) state that outsourcing has a strong negative impact on an organization's labor productivity. Aron and Singh (2005, p. 135) summarize in their study three reasons for the success or failure of business process outsourcing (n.b.: half of the attempts do not reach financial expectations): (1) choosing the right processes; (2) control both the operational and structural risks; and (3) match organizational forms to needs. Shi (2007) lists client-side problems, which are: (1) cost-saving mirage; (2) lack of process model maturity; and (3) lack of understanding or consensus of target business model. On the other side, we have vendor-side problems, which are: (1) competence gap; (2) heavy turnover of key personnel; and (3) weak security practices or requirements. And finally, reasons for failures or difficulties are often inside client-vendor relationships, which are: (1) lack of precise and detailed project specification; (2) language and culture misalignment; (3) knowledge transfer difficulties; (4) process calibration difficulties; (5) incompatible pace of technology change; (6) incompatible architectural style; and (7) loss of continuity due to employee shuffles. (p. 29) 2.4 IT outsourcing models The model is a simplified description of the real situation; it is important, that the model is most similar as possible to reality. Yang, Kim, Nam and Min (2007, p. 3771) define a business process outsourcing decision model based on three determinants: (1) expectation - cost savings, focus on core competence, and flexibility; (2) risk - information security, loss of management control, and morale problems; and (3) the environment - vendor's service quality, market maturity, and other firms' outsourcing decisions. The outsourcing model from a client's perspective is composed of three factors (Khan and Fitzgerald, 2004, p. 44): (1) organizational factors - decision makers/initiators, SWOT issues, implementations, and re-engineering potential; (2) technological factors - internal organizational capabilities, key requirements and usage, and support and maintenance; (3) process factors - in depth specifications/capture full requirements, project management - by meeting strict deadlines, contract, trust & security, communication, and standard quality; and (4) geographical/environmental factors - domestic/ overseas, resource/expertise, standard quality, infrastructure capability - ability to network, trade law, political stability, culture adaptability, and market entry advantage. Barthelemy and Geyer (2005, p. 535) highlight important internal determinants for decision making on IT outsourcing as: (1) IT activity specificity; (2) IT department size; and (3) IT internal organization (profit center); and as external determinants, which are: (4) institutional environment; and (5) sector IT intensity. Configuration can be described using seven parameters (Cullen, Seddon, and Willcocks, 2005, p. 362): (1) scope grouping; (2) supplier grouping; (3) financial scale; (4) pricing framework; (5) contract duration; (6) resource ownership; and (7) commercial relationship. Han, Lee and Seo (2008, p. 32) propose a relationship based model: (1) relationship effecting resources - technical and managerial IT capability, organizational relationship capability, and vendor management capability; (2) relationship formation processes -information sharing, communication quality, and collaborative participation; (3) relationship outcome - trust and commitment; and (4) performance - outsourcing success. Different authors have used different models for studying IT outsourcing. Alvarez-Suescun (2007, p. 767) used three variables for model design: (1) IS technical skills; (2) IS implementation capability; and (3) strategic contribution of IS. Lee (2001, p. 326) has tested the effectiveness of outsourcing based on: (1) organizational capability; (2) knowledge sharing; and (3) partnership quality. Gonzalez, Gasco and Llopis (2010, pp. 291-296) have created a model based on: (1) outsourcing reasons; and (2) outsourcing risks. Gorla and Lau (2010, p. 96) have made a model based on: (1) risks; and (2) past negative experiences. 3 Methodology 3.1 Data collection and analysis The questionnaire was created based on relevant literature. The validity of the questionnaire was checked at two levels: (1) the questionnaire was reviewed and evaluated by two experts from the field of management of information systems; and (2) after receiving 80 questionnaires, a pilot survey was conducted, with which the validity and intelligibility of the questions were determined. The questionnaire was sent to 484 organizations by standard postal mail and was addressed to the head of IT. The envelope contained the questionnaire, a cover letter, and a prepaid self-addressed envelope. The cover letter emphasized that the questionnaire was anonymous to the respondent and the organization. The final questionnaire results also were offered to the respondents. At the first stage, 92 responses were received. To obtain a higher response rate, another cover letter with the questionnaire was resent to all the organizations that did not respond. After the second call, a total of 141 questionnaires or a 29% response rate was achieved and served as the sample for this research study. The population was organizations that had more than 150 employees. Large-size companies were chosen using the criteria of the average number of employees in the financial year, the amount of net turnover, and asset values. Statistical data analysis was conducted with binary logistic regression. Statistical significance is usually defined at 0.05 i.e. at 5%, and in certain circumstances at 0.01 (1%) or 0.001 (0.1%) and denoted by p, p means a two-tailed test i.e. p (2-tailed). 3.2 Measuring instrument The first question examined whether the organization would expand IT outsourcing or keep it at the same level in the future. This question is used as a dependent variable in logistics regression analysis. This is a binary variable (0 - same level, 1 - increased level). The second question was with regard management's support for IT outsourcing. Respondents responded with a binary response: 0 (low support) and 1 (high support). The third question was used to determine the level of employees' knowledge on IT outsourcing in their organization. Two responses were possible: 0 - insufficient and 1 - sufficient (binary response). In addition, respondents were asked five questions regarding the experience of deploy- ment of IT outsourcing: (1) performance - improvement of efficiency and effectiveness; (2) costs - IT cost reduction; (3) delay - shortening time of IT services; (4) quality - quality improvement of IT services; and (5) innovation - innovation improvement of IT. All variables are binary variables, where 0 means low and 1 is equal to high. All seven variables were used in binary logistic regression as independent variables, i.e. predictors. The demographic data of respondents is summarized in Table 1. Table 1: Demographic Data of Respondents Question Response Function Head of IT 58.6% Other function or position 16.4% Head of department in IT 12.1% Director of business function 10.7% CEO 2.1% Education II. Bologna Cycle 49.6% I. Bologna Cycle 25.2% III. Bologna Cycle 14.4% Secondary Education 10.8% Gender Male 85.6% Female 14.4% Age (average) 41.8 years Seniority (average) 18.2 years 4 Results and discussion Descriptive statistics was used to determine the correlation among independent variables. Nonparametric Spearman's correlation coefficient (rs) was determined to be adequate as there were only binary variables. As can be seen in Table 2 all cor- relations were positive; most correlations have approximately a medium effect (+0.3). Stronger correlations were among Quality, Performance, and Delay. Perfect collinearity did not exist among predictors. Variance inflation factor (VIF) is less than 2.38 for all variables. This test confirms that multicol-linearity is not problematic among variables. IT function can be executed in an organization, can be partially executed by the supplier, can be fully executed by the supplier, or the organization has no such activities. For the purposes of this research study, it is important to examine whether IT outsourcing level will be increased or not. This is the dependent variable. The first factor represents top managers' support of IT outsourcing, which is crucial to success. The second factor represents level of knowledge on outsourcing. This is not important only among managers, but also among all employees. The third factor describes how outsourcing increase overall performance (i.e. effectiveness and efficiency through deployment). To achieve this goal, we must utilize all available resources. The fourth factor includes the importance of cost-cutting. Many organizations estimate that costs are too high for informatics and that they need to be lowered to maintain competitiveness. The fifth factor is delay. Many organizations expect that delay of services decrease with more competent suppliers' staff. The sixth factor represent quality of IT services and should increase with supplier cooperation. The last factor represents the innovation improvement of IT. IT staff can be more focused on their core business. Table 3 shows only five factors that have a statistically significant impact on the model. These are: (1) management's support; (2) knowledge on IT outsourcing; (3) improvement of efficiency and effectiveness; (4) quality improvement of IT services; and (5) innovation improvement of IT. The remaining two factors: (1) IT cost reduction; and (2) shortening time of IT services, have a lesser impact and were not statistically significant; as such are not shown in Table 3. However, being aware on the significance of these two factors is also very important. Quality improvement of IT services is most important for the decision that the organization will increase IT outsourcing (odds ratio i.e. exp(ß) = 19.10). The basic model is improved by adding five independent variables i.e. five predictors, value of -2LL is reduced from 115.64 to 70.90. The Table 2: Spearman's Correlation Coefficients Among Independent Variables (1) (2) (3) (4) (5) (6) (1) Management (2) Knowledge 0.32** (3) Performance 0.29** 0.34** (4) Costs 0.20* 0.16 0.44** (5) Delay 0.40** 0.30** 0.59** 0.54** (6) Quality 0.26** 0.35** 0.67** 0.39** 0.65** (7) Innovation 0.22** 0.22** 0.46** 0.37** 0.43** 0.48** Notes: * p < 0.05 (2-tailed); ** p < 0.01 (2-tailed) Table 3: Regression Model B (SE) 95% CI for Odds Ratio Lower Odds Ratio Upper Constant -3.41 (0.62)*** Management 2.17 (0.71)** 2.19 8.73 34.76 Knowledge 1.34 (0.65)* 1.12 4.03 14.45 Performance 2.43 (0.86)** 2.12 11.34 60.53 Quality 2.95 (0.92)** 3.18 19.10 114.82 Innovation 2.41 (0.84)** 2.17 11.13 57.20 Notes: R2 = 0.57 (Cox and Snell), 0.76 (Nagelkerke); Model x2(5) = 111.87, p < 0.001; *: p < 0.05; **: p < 0.01; ***: p < 0.001 total percentage of correct predictions of the model is 87.3%. Nagelkerke R2 = 0.76 imitates the coefficient of determination R2 multiple regression and can be interpreted as the percentage of explained variance. Only five factors in the final model were retained. This model can explain 76% of variance. Organizations often decide for deployment or expansion of outsourcing based on factors beyond IT outsourcing. This is especially present in times of economic crisis. Management, for example, decides on outsourcing to reduce the number of employees. In this case, the arguments for and against outsourcing lose their importance. Other management may decide otherwise in exactly the same situation. Due to the crisis in which we want to preserve as many jobs as possible, we decided for job transfer from an external supplier back to the organization, thus reducing the volume of outsourcing. IT outsourcing is sometimes politically motivated and is not based on real needs. The points of view of business executives and IT executives are often different and sometimes detrimental for outsourcing activity (Chakrabarty and Whitten, 2011, p. 812). Interestingly, respondents did not select cost-reduction as the most important factor. Many authors say that sole cost reduction (cheaper informatics) is not sufficient, but it is necessary through innovation (better informatics) to achieve higher added value for customers, better performance of informatics and the organization (Weeks and Feeny, 2008, pp. 132-135). Also Linder (2004, p. 52) stresses that the organization needs to use IT outsourcing to achieve strategic goals and not for cost reduction only, which is only one benefit. This conclusion also is confirmed in this research study. The outsourcing of information technology must be efficient and effective that can be evaluated through costs, delay, quality, human resources and environment. We expect that it will reduce informatics costs, will increase IT responsiveness, will improve the quality of IT services, will increase employee satisfaction, and that employees will have better working conditions. Many authors draw attention to the phenomenon of failed ITO deployment (Espino-Rodr^guez and Padron-Robaina, 2006, p. 64). This means, that there are many opportunities for improvement. 5 Conclusions The level of IT outsourcing will increase in Slovenia in the future. Five factors were confirmed in this study that significantly influence future decisions: (1) IT outsourcing management's support; (2) employees' knowledge about IT outsourcing; (3) improvement of efficiency and effectiveness as a result of IT outsourcing; (4) quality improvement of IT services caused by IT outsourcing; and (5) innovation improvement of IT because of IT outsourcing. An interesting finding from this study was that (1) IT cost reduction and (2) shortening time of IT services, were not significant factors. Organizations decide on outsourcing based on several reasons and the most important is not cost reduction in informatics. The Slovenian economy is lagging behind the rest of the European Union by over 10 years, maybe even 20 years. In the developed world the outsourcing of information technology has reached a maximum and it is to be expected that this trend of expansion would be reversed. In Slovenia, research concerning this area is still relevant namely because of this delay. The volume of outsourcing will continue to increase. This study is the first such to be carried out in the Slovenian territory and represents a contribution to science in this area. Managers can immediately use the results of this research study in their work. The contribution of this study to society is also important. From the national-economic point of view it is important that all or most organizations that choose IT outsourcing are successful. In the model proposed, we included the importance of arguments for and against outsourcing, positive and negative past experiences, and other factors. These are some suggestions for further research. The limitations of the study are that the study was limited to the Slovenian area, only large-sized organizations were included in the sample, and only companies were included. References Alvarez-Suescun, E. (2007). Testing resource-based propositions about IS sourcing decisions. Industrial Management & Data Systems, 107(6), 762-779, http://dx.doi,o rg/10.1108/02635570710758716 Aron, R. & Singh, J. V. (2005). Getting Offshoring Right. Harvard Business Review, 83(12), 135-143. Aubert, B. A., Houde, J.-F., Patry, M. & Rivard, S. (2012). A multi-level investigation of information technology outsourcing. Journal of Strategic Information Systems, 21(3), 233-244, http://dx.doi,org/10.1016/j.jsis.2012.04.004 Baldwin, L. P., Irani, Z. & Love, P. E. D. (2001). Outsourcing information systems: drawing lessons from a banking case study. European Journal of Information Systems, 10(1), 15-24, http:// dx.doi,org/10.1057/palgrave.ejis.3000372 Barthelemy, J. & Geyer, D. (2005). An empirical investigation of IT outsourcing versus quasi-outsourcing in France and Germany. Information & Management, 42(4), 533-542, http:// dx.doi,org/10.1016/j.im.2004.02.005 Bengtsson, L. & Dabhilkar, M. (2009). Manufacturing outsourcing and its effect on plant performance-lessons for KIBS. Journal of Evolutionary Economics, 19(2), 231-257, http:// dx.doi,org/10.1007/s00191-008-0129-1 Broedner, P., Kinkel, S. & Lay, G. (2009). Productivity effects of outsourcing: New evidence on the strategic importance of vertical integration decisions. International Journal of Operations & Production Management, 29(1/2), 127-150, http://dx.doi,o rg/10.1108/01443570910932020 Chakrabarty, S. & Whitten, D. (2011). The Sidelining of Top IT Executives in the Governance of Outsourcing: Antecedents, Power Struggles, and Consequences. IEEE Transactions on Engineering Management, 58(4), 799-814, http:// dx.doi,org/10.1109/TEM.2010.2090884 Cullen, S., Seddon, P. B. & Willcocks, L. P. (2005). IT outsourcing configuration: Research into defining and designing outsourcing arrangements. Journal of Strategic Information Systems, 14(4), 357-387, http://dx.doi,org/10.1016/j.jsis.2005.07.001 Cullen, S. & Willcocks, L. (2003). Intelligent IT outsourcing: eight building blocks to success. Oxford: Elsevier ButterworthHeinemann. Dibbern, J., Chin, W. W. & Heinzl, A. (2012). Systemic Determinants of the Information Systems Outsourcing Decision: A Comparative Study of German and United States Firms. Journal of the Association for Information Systems, 13(6), 466-497. Downing, C. E., Field, J. M. & Ritzman, L. P. (2003). The value of outsourcing: A field study. Information Systems Management, 20(1), 86-91, http://dx.doi.org/10.1201/1078/43203.20.1.2003 1201/40088.11 Dutta, D. K., Gwebu, K. L. & Wang, J. (2011). Strategy and Vendor Selection in IT Outsourcing: Is there a Method in the Madness?. Journal of Global Information Technology Management, 14(2), 6-26. Espino-Rodr^guez, T. F. & Padron-Robaina, V. (2006). A review of outsourcing from the resource-based view of the firm. International Journal of Management Reviews, 8(1), 49-70, http://dx.doi.org/10.1111/j.1468-2370.2006.00120.x Fink, L. (2010). Information technology outsourcing through a configurational lens. Journal of Strategic Information Systems, 19(2), 124-141, http://dx.doi.org/10.1016/j.jsis.2010.05.004 Fisher, J., Hirschheim, R. & Jacobs, R. (2008). Understanding the outsourcing learning curve: A longitudinal analysis of a large Australian company. Information Systems Frontiers, 10(2), 165-178, http://dx.doi.org/10.1007/s10796-008-9070-y Gilley, K. M. & Rasheed, A. (2000). Making More by Doing Less: An Analysis of Outsourcing and its Effects on Firm Performance. Journal of Management, 26(4), 763-790, http:// dx.doi.org/10.1016/S0149-2063(00)00055-6 Gonzalez, R., Gasco, J. & Llopis, J. (2009). Information Systems Outsourcing Reasons and Risks: An Empirical Study. International Journal of Human and Social Sciences, 4(3), 181-192. Gonzalez, R., Gasco, J & Llopis, J. (2010). Information systems outsourcing reasons and risks: a new assessment. Industrial Management & Data Systems, 110(1/2), 284-303, http://dx.doi. org/10.1108/02635571011020359 Gorla, N. & Lau, M. B. (2010). Will Negative Experiences Impact Future IT Outsourcing. Journal of Computer Information Systems, 50(3), 91-101. Han, H.-S., Lee, J.-N. & Seo, Y.-W. (2008). Analyzing the impact of a firm's capability on outsourcing success: A process perspective. Information & Management, 45(1), 31-42, http://dx.doi. org/10.1016/j.im.2007.09.004 Hirschheim, R. & Lacity, M. (2000). The Myths and Realities of Information Technology Insourcing. Communications of the ACM, 43(2), 99-107. http://dx.doi.org/10.1145/328236.328112 Hoving, R. (2007). Information Technology Leadership Challenges - Past, Present, and Future. Information Systems Management, 24(2), 147-153, http://dx.doi.org/10.1080/10580530701221049 Johnston, K. A., Abader, T., Brey, S. & Stander, A. (2009). Understanding the outsourcing decision in South Africa with regard to ICT. South African Journal of Business Management, 40(4), 37-49. Khan, N. & Fitzgerald, G. (2004). Dimensions of Offshore Outsourcing Business Models. Journal of Information Technology Cases and Applications, 6(3), 35-50. Khan, S. U., Niazi, M. & Ahmad, R. (2011). Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review. Journal of Systems and Software, 84(4), 686-699, http://dx.doi. org/10.1016/j.jss.2010.12.010 Lacity, M., Willcocks, L. & Feeny, D. (2004). Commercializing the Back Office at Lloyds of London: Outsourcing and Strategic Partnerships Revisited. European Management Journal, 22(2), 127-140, http://dx.doi.org/10.1016/j.emj.2004.01.016 Lee, J.-N. (2001). The Impact of Knowledge Sharing, Organizational Capability and Partnership Quality on IS Outsourcing Success. Information & Management, 38(5), 323-335, http://dx.doi. org/10.1016/S0378-7206(00)00074-4 Lewin, A. Y. & Peeters, C. (2006). The Top-Line Allure of Offshoring. Harvard Business Review, 84(3), 22-24. Linder, J. C. (2004). Transformational Outsourcing. MIT Sloan Management Review, 45(2), 52-58. McLellan, K., Marcolin, B. L. & Beamish, P. W. (1995). Financial and strategic motivations behind IS outsourcing. Journal of Information Technology, 10(4), 299-321. Quinn, J. B. (1999). Strategic Outsourcing: Leveraging Knowledge Capabilities. Sloan Management Review, 40(4), 9-21. Quinn, J. B. (2000). Outsourcing Innovation: The New Engine of Growth. Sloan Management Review, 41(4), 13-28. Rottman, J. W. & Lacity, M. C. (2008). A US Client's learning from outsourcing IT work offshore. Information Systems Frontiers, 10(2), 259-275, http://dx.doi.org/10.1007/s10796-007-9061-4 Sia, S. K., Koh, C. & Tan, C. X. (2008). Strategic Maneuvers for Outsourcing Flexibility: An Empirical Assessment. Decision Sciences, 39(3), 407-443, http://dx.doi.org/10.1111/j.1540-5915.2008.00198.x Shi, Y. (2007). Today's Solution in Tomorrow's Problem: The Business Process Outsourcing Risk Management Puzzle. California Management Review, 49(3), 27-44. Weeks, M. R. & Feeny, D. (2008). Outsourcing: From Cost Management to Innovation and Business Value. California Management Review, 50(4), 127-146. Willcocks, L. (2010). The next step for the CEO: Moving IT-enabled services outsourcing to the strategic agenda. Strategic Outsourcing: An International Journal, 3(1), 62-66, http:// dx.doi.org/10.1108/17538291011023089 Yang, D.-H., Kim, S., Nam, C. & Min, J.-W. (2007). Developing a decision model for business process outsourcing. Computers & Operations Research, 34(12), 3769-3778, http://dx.doi. org/10.1016/j.cor.2006.01.012 Franc Brcar is a university graduate in engineering (B.Sc. in Engineering) and received his Master's of Science in Informational and Management sciences (M.Sc.). He has had extensive experience working for a major automobile company. He has worked as a specialist in the field of operational systems and databases as well as worked in the introduction and maintenance of systems for computer construction and complete ERP solutions. Recently he has been examining general management, management of IT systems, management of business processes, and management of innovations and quality. He is a lecturer and a postgraduate / Ph.D. candidate at the Faculty of Organisation Studies Novo mesto. Boris Bukovec is an Associate Professor at the Faculty of Organisation Studies Novo mesto. He received a Master's and Doctor's Degree in Quality Management at the Faculty of Organizational Sciences in Kranj. His area of research is contemporary paradigms, approaches, models and tools of quality management and organizational changes. In his research he combines current theoretical findings with his twenty years' experience in various positions of leadership he has held as a member of executive teams in the automobile industry. He also has published numerous articles from the area of quality and excellence. Analiza faktorjev povečanja zunanjega izvajanja informatike Študija raziskuje področje zunanjega izvajanja informatike. Kot ožje raziskovalno področje definiramo izgradnjo modela na osnovi vplivnih faktorjev. Namen raziskave je ugotoviti, kateri faktorji vplivajo na razmah zunanjega izvajanja informatike. Naredili smo anketo med 141 največjimi slovenskimi organizacijami. Podatke smo statistično obdelali z binarno logistično regresijo. Končni model vsebuje pet faktorjev: (1) podpora najvišjega managementa; (2) znanje o zunanjem izvajanju informatike; (3) povečanje uspešnosti in učinkovitosti; (4) povečanje kakovosti storitev informatike in (5) povečanje inovativnosti informatike. Izsledke raziskave bo menedžment v organizacijah lahko takoj uporabil pri svojih odločitvah. Večja uspešnost vsake posamezne organizacije pomeni pridobitev za celotno družbo. Ta raziskava zunanjega izvajanja informatike je z uporabljenimi metodami prva tovrstna študija v slovenskem prostoru. Ključne besede: informatika, zunanje izvajanje, zunanje izvajanje informatike, ITO, model zunanjega izvajanja informatike, menedžment poslovnih procesov DOI: 10.2478/orga-2013-0003 Financial Distress Prediction of Iranian Companies Using Data Mining Techniques Mahdi Moradi1, Mahdi Salehi1*, Mohammad Ebrahim Ghorgani2, Hadi Sadoghi Yazdi1 1Ferdowsi University of Mashhad, Iran 2East Oil and Gas Company, NIOC, Iran Decision-making problems in the area of financial status evaluation are considered very important. Making incorrect decisions in firms is very likely to cause financial crises and distress. Predicting financial distress of factories and manufacturing companies is the desire of managers and investors, auditors, financial analysts, governmental officials, employees. Therefore, the current study aims to predict financial distress of Iranian Companies. The current study applies support vector data description (SVDD) to the financial distress prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 3-fold cross-validation to find out the optimal parameter values of kernel function of SVDD. To evaluate the prediction accuracy of SVDD, we compare its performance with fuzzy c-means (FCM).The experiment results show that SVDD outperforms the other method in years before financial distress occurrence. The data used in this research were obtained from Iran Stock Market and Accounting Research Database. According to the data between 2000 and 2009, 70 pairs of companies listed in Tehran Stock Exchange are selected as initial data set. Keywords: financial distress prediction; Support vector data description; Fuzzy c-mean. 1 Introduction The empirical literature of financial distress prediction has gained considerable attention in the post 2007-2009 global financial crises. Policymakers (Dodd-Frank Act of 2010) and regulators (SEC, Basel III) emphasize about failure of many banks in the aftermath of the global financial crisis and are seeking the best way to predict business failures. Prior studies have addressed two major research trends in financial distress prediction. One is investigating the situation of failure to find the symptoms (Dambolena & Khoury, 1980; Gombola & Ketz, 1983; Jo & Han, 1997; Scott, 1981). The other is comparing the prediction accuracy of the diverse classification methods (Tam & Kiang, 1992; Jo & Han, 1997). This study belongs to the second group of research. The primary purpose of this study is to apply support vector data description (SVDD) to the financial distress prediction problem in an attempt to suggest a new model with better explanatory power and stability. We use a grid-search technique using 3-fold cross-validation to examine the optimal parameter values of kernel function of SVDD. In addition, to evaluate the prediction accuracy of SVDD, we compare its performance with fuzzy c-means (FCM).Using the data from Iran Stock Market and Accounting Research Database for 70 couples of companies listed in Tehran Stock Exchange during 2000 and 2009; we find that SVDD outperforms the other method. 2 Literature review The empirical literature of financial distress prediction has recently gained further momentum and attention from financial institutions. Academicians and practitioners realize that the problem of asymmetric information between banks and firms lies at the heart of important market crashes such as credit rationing and that improvement in monitoring techniques represents a valuable alternative to any incomplete contractual arrangement aimed at reducing the borrowers' moral hazard (Becchetti & Sierra, 2003; Stiglitz & Weiss, 1981; Xu, 2000). Among financial distress forecasting methods, discriminant analysis was the dominant method for predicting corporate failure from 1966 until the early part of the 1980s (Altman, 1968, 1983; Back et al., 1996b). It gained wide popularity due to its ease of use and interpretation. However, * Corresponding author: Ferdowsi University of Mashhad, Faculty of Economics and Business Administration, Azadi Square, Vakilabad Bolvard, Mashhad City, Khorasan Razavi Province, Iran, E-mail: mahdi.salehi@um.ac.ir Received: 22nd October 2012; revised: 14th December 2012; accepted 5th January 2013 both linear and quadratic discriminant analyses are sensitive to deviation from multivariate normality (Karels & Prakash, 1987; Laitinen & Laitinen, 2000). During the 1980s, the probit (Zmijewski, 1984) and, especially, the logit methods (logistic regression model) (Back et al., 1996b; Ohlson, 1980) used the discriminant method. These two models do give a crisp relationship between explanatory and response variables of the given data from a statistical viewpoint and do not assume multivariate normality, but the probit model assumed that the cumulative probability distribution must be standardized normal distribution, while the logit model assumed that the cumulative probability distribution must be logistic distribution. Since the 1990s, neural networks have been the most widely used techniques in developing quantitative financial distress prediction (Back et al., 1996b; Tam & Kiang, 1992; Wilson & Sharda, 1994), in particular, the approximation or classification powers of the MLP trained by the backpropagation algorithm (Hassoun, 1995; Hertz, Krogh, & Palmer, 1991). Many studies compared the neural networks backpropagation algorithm with the statistical methods and found neural networks backpropagation outperforms the other statistic methods, such as multivariate discriminant analysis (MDA), probit and logit methods (Back et al., 1996a; Shin, Lee & Kim, 2005; Wilson & Sharda, 1994). Neural networks have recently been employed to extract rules for solving fuzzy classification problems (Kim et al., 2003). A number of fields use the radial basis function network (i.e., RBFN), for classification problems (Jang, Sun, & Mizutani, 1997; Surendra & Krishnamurthy, 1997), function approximations (Chuang, Jeng, & Lin, 2004; Hertz et al., 1991; Jang, 1993; Jang et al., 1997; Nam & Thanh, 2003) and management sciences (Stam, Sun, & Haines, 1996; Vythoulkas & Koutsopoulos, 2003). The approximation or classification powers of the MLP trained by the back propagation algorithm (Hassoun, 1995; Hertz et al., 1991) and RBFN are determined by the number of hidden nodes. In fact, the number of hidden layers influences the performance of back propagation MLP. Additionally, an RBFN is functionally equivalent to a zero-order Sugeno fuzzy inference system under some conditions (Jang et al., 1997). In addition, it was proven that the zero-order Sugeno fuzzy inference system could approximate any nonlinear function on a compact set to an arbitrary degree of accuracy under certain conditions (Jang, 1993). However, if a phenomenon under consideration does not have stochastic variability but is also uncertain in some sense, it is more natural to seek a fuzzy functional relationship for the given data, which may be either fuzzy or crisp. Sun and Li (2008) use weighted majority voting combination of multiple classifiers for FDP, Chen and Du (2009) introduced an integration strategy with subject weight based on neural network for financial distress prediction. They all generated diverse classifiers by applying different learning algorithms (with heterogeneous model representations) to a single data set, and concluded that, to some degree, FDP based on combination of multiple classifiers was superior to single classifiers according to accuracy rate or stability. The most used machine learning technique is the neural network model (Haykin, 1999), trained by the back-propagation learning algorithm (Wong et al., 1997; Wong and Selvi, 1998) whose prediction accuracy outperforms statistical models including logistic regression (LR), linear discriminant analysis (LDA), multiple discriminant analysis (MDA) and other machine learning models, such as k-nearest neighbor (k-NN) and decision trees. In addition, the back-propagation neural network (BPN) model can be used as the benchmark for financial decision support models. Chen and Du (2009) found that prediction performance for the clustering approach is more aggressively influenced than the BPN model and the BPN approach obtains better prediction accuracy than the data mining (DM) clustering approach in developing a financial distress prediction model. classifiers which were diversified by using neural networks on different data sets for financial distress prediction, and their experimental results showed that multiple neural network classifiers did not outperform a single best neural network classifier, based on which they considered that the proposed multiple classifiers system may be not suitable for the binary classification problem as financial distress prediction. Song et al, (2010) presented genetic algorithm (GA) based approach and statistical filter approaches are applied to identify the best features for the support vector machine (SVM). The proposed GA-based approach is carefully designed in order to have the capability of simultaneously optimizing the features and parameters of the SVM. Experimental results on the data from Chinese companies showed that the GA-based approach could extract fewer features with a higher accuracy compared with statistical filter approaches. Recent studies in Artificial Intelligence (AI) approach, such as ANN (Ravisankar & Ravi 2010), SVM (Lin et al. 2011; Min & Lee 2005; Bao et al., 2012) have also been successfully applied to financial distress prediction. The purpose of this paper is to apply fuzzy clustering means and support vector data description (SVDD) in financial distress prediction model. Fuzzy c-means (FCM) clustering is one of well-known unsupervised clustering techniques, which allows one piece of data were two or more clusters. SVDD is known as the algorithm that finds a special kind of linear model with the maximum margin hyperplane. The maximum margin hyperplane gives the maximum separation between decision classes. The training examples that are closest to the maximum margin hyperplane are called support vectors. The SVDD classifier will be trained by different kernel functions in order to compare it with the benchmark of the neural network model. In SVDD, Using different kernel functions and the determination of optimal values of the parameters to train SVMs will lead to different results. Therefore, the current study aims to compare the accuracy of these two forecasting techniques in predicting financial distress of companies. The original classification accuracy indicates that SVDD outperforms the FCM model. 3 Technical background 3.1 Support vector data description SVDD, inspired by the idea of support vector machine by Vapnik (1995), is a method of one-class classification, for which not the optimal separating hyperplane but the sphere with minimal volume containing all or most objects has to be found; its sketch in two-dimensional spaces is shown in Figure 1. It is often used as a method of novelty detection. Novelty detection based on boundary essentially is to find a sphere with minimum volume containing all (or most of) the normal data objects. For a data set containing N normal data objects, when one or a few very remote objects are in it, a very large sphere is obtained, which will not represent the data very well. Therefore, some data points outside the sphere are allowed with introducing slack variable , then, the sphere can be described by centre a and radius R as follows min L( R) = R 2 + C ^^t 1=1 s.t.(xt - a)T (xt - a) < R^ + ^t ^ > 0 (i = 1,2,....N) (1) Where the variable C gives the trade-off between simplicity (or volume of the sphere) and the number of errors (number of target objects rejected). We construct the Lagrange L(R, a,at,^t) = R' + C'^^t-^at[R2 + -(xt^ -2axt + a2 t t (a, >o,r>0) (2) outlier hypersphere (boundary) Figure.1 The sketch of SVDD in two-dimensional space Setting the partial derivatives to 0, new constraints are obtained = 1> a = ^-= ^atxt Z L.a. ' c -at-yt = 0 yt Then, new optimal equation can be obtained N NN max L = (x,, x,) (, xf ) !=1 ,=1 f=1 (3) (4) s.t. 0 xf) = exp - (x, - Xf )2 (5) where a is the width parameter, also called extension constant. This function can suppress the growing distances for large feature spaces. 3.2 Fuzzy c-means FCM theory is the most perfect one among many fuzzy clustering analysis methods that are effective for pattern recognition; details can be seen in reference. Considering a sample set X = {x1, X2, . . . , xN }, xi, Rs, which is required to be divided into C categories; the aim of FCM is to obtain each category's clustering centre vc = {vc1, vc2, . . . , vcS} (1 _ c _ C) by minimizing the weighed square sum of inner-cluster error. Therefore its objective function is as follows Jm(U,V) = jr (Mc„)m(dcnm e[1,«) (6) c=1 n=1 With constraints s.t. 0 < < 1, 0(jt ^cn < N, n=l Men = 1, l < c < C, 1 < n < N 1 < c < C, l