Innovation and SMEs Financial Distress During the Crisis Period: The Greek Paradigm

Innovation and SMEs Financial Distress During the Crisis Period: The Greek Paradigm Christos Lemonakis, Alexandros Garefalakis, Grigoris Giannarakis, Efthalia Tabouratzi and Constantin Zopounidis 1 Introduction In modern economies characterized as “knowledge-based,” the main factor determining the performance of a firm in terms of survival and productivity is innovation (Markatou 2011). In literature, many writers are expressing similar contexts. According to Sum (2013), innovation is an important factor for firms’ success and competitiveness especially C. Lemonakis (*) Techological Educational Institute of Crete (TEI of Crete), Ag. Nikolaos, Greece e-mail: lemonakis@staff.teicrete.gr A. Garefalakis Department of Accounting and Finance, Technological Educational Institute of Crete, Heraklion, Greece e-mail: agarefalakis@staff.teicrete.gr G. Giannarakis Department of Business Administration (Grevena), Technological Educational Institution of Western Macedonia, Kila Kozanis, Greece e-mail: ggianaris@gmail.com © The Author(s) 2017 C. Floros and I. Chatziantoniou (eds.), The Greek Debt Crisis, DOI 10.1007/978-3-319-59102-5_10 cvfloros@gmail.com 285 286   C. Lemonakis et al. for those located in environments where changes in the market are continuing and, as reported by Kumral et al. (2006), in such environments, a firm’s ability to innovate and to become financially viable is closely connected with the formation of a comparative advantage. Tödtling and Trippl (2005) include innovation as an essential component of economic growth and firms’ financial effectiveness, and Dressler (2013) considers innovation as a “key factor” in order for businesses to grow, consolidate and to ensure durable profitability in a competitive environment. Moreover, Klomp and Van Leeuwen (2001) linked the innovation with firms’ revenues performance, productivity and employment growth, while Soriano and Huarng (2013) argued that innovation is “the only business related more closely than anything else to economic growth.” According to Eurostat (2004), innovation is “the introduction of a new or significantly improved product (good or service) or the application of a new or significantly improved process, organizational a market enterprise marketing process or method.” Still, according to Kumral et al. (2006), innovation is “a complex sequence of events that include all activities to develop or create new products, services or processes on the market.” Further, the Keupp et al. (2012) define innovation as “a new product, a new service, a new technology in the production process, a new structure or management system, or a new plan or program for organizational members.” According to Sengupta (2012), innovation involves changes in organizational and managerial competencies, developing new markets and new products. The Lee et al. (2010) describe innovation as a process divided E. Tabouratzi Department of Accounting and Finance, Technological Educational Institute of Crete, Estavromenos, 71004 Heraklion, Crete, Greece e-mail: tamthal@staff.teicrete.gr C. Zopounidis Financial Engineering Laboratory & Audencia Nantes School of Management, Technical University of Crete, Chania, Greece e-mail: kostas@dpem.tuc.gr cvfloros@gmail.com Innovation and SMEs Financial Distress …     287 into two parts: the “search technology” for technological opportunities and the “technology exploitation” of market opportunities, while also point out that the second part is mainly directed at SMEs. Another description given by Freeman and Soete (1997), where, according to which, the innovation consists of two parts: the recognition of a potential market for a new product or process and the technical knowledge that is either generally available or is new scientific and technological knowledge derived from research activity. Finally, Smits (2002) attempts to provide a simplistic definition of innovation, noting however that this is a complex process that takes place in terms of products, companies, industries, and at national and international communities. 2 Types of Innovation Guan and Zhao (2013) separated innovation in open and closed ones. In open innovation, a firm opens its “borders” in order to circulate knowledge and from the external environment in order to create opportunities for cooperation with various institutions and actors, such as universities, government, customers, or suppliers, aimed at introducing new innovations. According to Fontana et al. (2006), firms designated as “open” are more likely to consider the knowledge generated in universities as important for their innovation and, yet, for those who are willing to share their innovation are more likely to work with universities. They also argue that firms that opening their borders often voluntarily disclose important pieces of knowledge they possess, through scientific publications, conferences, and through patents and the Internet. This practice is followed, both to enable firms to gain feedback from the external environment and also to expand their reputation and their collaborative networks and, secondly, to ensure that others “know that you know.” On the other hand, the closed innovation, an enterprise remains self-sufficient as it is argued that “successful innovation requires control” and that “no one can be sure about the quality of the ideas of others.” Especially for SMEs, S. Lee et al. (2010) conclude that their participation in collaborative networks is an effective way to facilitate open innovation and, through it, to highlight their potential activity. cvfloros@gmail.com 288   C. Lemonakis et al. 2.1 Innovation and Knowledge In literature, it is clear that knowledge is one of the main components of the innovation (Davenport 2005; De Faria et al. 2010; Kim and Huarng 2011). As highlighted by Sarvan et al. (2011), the real strength of the business, in terms of competitiveness, depends on their ability to access information and create knowledge. The importance given by the successful firms in the systems’ knowledge management, through which the process of creation is achieved, organizing, diffusion, use and exploitation of knowledge is not accidental. According to Lai et al. (2014), the knowledge management system is the intermediary between the collaborative networks and corporate performance of the participants in innovation. Through such a system, firms face favorable conditions when taking strategic decisions, because the assessment and solution of a problem are based on knowledge (Sedziuviene and Vveinhardt 2010). A firm’s ability to recognize the importance of new information, to assimilate, and to exploit it for commercial purposes is broadly described by the term “absorption capacity” (Gebauer et al. 2012). It is therefore evident that the greater absorptive capacity has a business, the greater the ability to access and operate more knowledge and the less financial distress it faces. The De Faria et al. (2010) have linked this capacity for innovation, arguing that the ability of a company to exploit the knowledge not produced by the same, but from a research institute, has a positive effect on the probability that the company be a successful innovator and a viable organization. Similarly, Wei et al. (2009) described the absorptive capacity of an enterprise as a fundamental element for innovation, while Kang and Park (2012) pointed out that business innovation is affected by costs in R&D (Research and Development) and research personnel, through the absorption capacity they generate. However, as indicated by Gebauer et al. (2012), it is not enough not only to manage the accumulation of external knowledge for a successful innovation strategy, but also to adopt more operational capabilities, such as systematization, coordination, and socialization of knowledge are needed. Finally, according to López-Nicolás and Meroño-Cerdán (2011), knowledge management is an important mechanism to enhance the absorptive capacity, innovation, and business performance. cvfloros@gmail.com Innovation and SMEs Financial Distress …     289 2.2 Innovation and SMEs Small and medium-sized enterprises (SMEs) are considered by many authors as engines for economic development of a country (Lee et al. 2012; Sawers et al. 2008; Zeng et al. 2010) and cover a significant part of interest of the policy makers, as the majority of the economic structure and, compared with large enterprises, SMEs are the main employers in a state (Hoffman et al. 1998; Lee et al. 2010, 2012; Muscio and Nardone 2012; Solleiro and Gaona 2012; Lemonakis et al. 2013a). According to Villa and Antonelli (2009), the proportion of SMEs in any domestic industry is close to 90% of all enterprises, while the share of employees in these personnel is more than 60% of the working population. The fact that they represent the majority of businesses is an important reason chosen as sample in most surveys and studies, including the present book chapter. In general, SMEs are described as reflective, without plan or at best opportunistic (Hagen et al. 2012). Still, Sawers et al. (2008) characterize these businesses as flexible as they have the ability to react quickly to changing needs and environment and argue that their successful development strengthens a country’s competitiveness. However, although the flexibility of SMEs is an advantage for accelerating innovation, few of them have the ability to manage the entire innovation process by themselves in order to turn their inventions into products or services. They often lack resources and capacities at the stages of manufacturing, distribution, promotion, and research, and this leads to cooperate with other firms in order to reduce the risk, cost, time completion of the procedures required for an innovation process, as well as to gain access to sales and marketing networks (Lee et al. 2010). Still, because, according to Hall and Lerner (2010) and based on theories, research, and empirical calculations, smaller businesses face higher capital costs compared with larger ones, turning to financing through venture capital and partnerships. Also, as mentioned by Sawers et al. (2008), because of the limited ability of smaller firms to compete with their larger competitors, due to lack of knowledge, employees’ skills, lack of capital, low levels of human resources management, and external issues as well (presence of major players in the market), partnerships cvfloros@gmail.com 290   C. Lemonakis et al. aimed at innovation is a way for smaller firms to overcome these barriers. Moreover, Revilla and Fernández (2012) argue that the partnerships allow small businesses on the one hand to supplement the existing resources and to overcome the financial obstacles they face because of their small size and, secondly, to gain access to new knowledge. Still, according to Ozman (2009), the companies forming alliances because they are not self-sufficient and can cooperate in order to reduce uncertainty and gain access to resources. Zeng et al. (2010) note that SMEs have limited financial resources, which implies less investment in R&D and generally more uncertainty undertaken in terms of financial viability and barriers to innovation; they also require some additional resources, such as marketing knowledge and managerial skills. They conclude that cooperation networks are on these businesses a means to address those barriers and to reduce the uncertainty in innovation. Therefore, they argue that it is necessary for small businesses to connect to different firms, research facilities, suppliers, and customers in an innovation network that will allow them to share knowledge and benefit from the available skills provided within the network. These external skills and resources through partnerships are available for exploitation by SMEs, and they can give them the right boost and the ability to innovate, looking for ideas, knowledge, and resources, essential for creating successful product and services. 3 SMEs and Financial Distress Beaver in 1966 first defined the financial distress as “the inability of a firm to pay its financial obligations as they mature.” Gestel et al. (2006) characterized financial distress more broadly as the result of chronic losses which cause a disproportionate increase in liabilities accompanied by shrinkage in the asset value (Gestel et al. 2006). According to Platt and Platt (2002), a firm is considered to be financially distressed if it experiences for a period of years negative net operating income or suspension of its dividend payments (Platt and Platt 2002). More generally, financial distress appears when a firm cannot pay off the debt to its creditors. This financial failure can produce either the firms’ default or cvfloros@gmail.com Innovation and SMEs Financial Distress …     291 even bankruptcy. Default is a firm’s failure to meet its legal obligations of a loan or other credit form undertaken. On the other hand, bankruptcy is a legal procedure involving a legal representative or a business that is unable to repay outstanding debts. The bankruptcy process begins a law process where all of the debtor’s assets are measured and evaluated in order to be used to repay a portion or the entire amount of outstanding debt. SMEs play a crucial role in European economy accounting for nearly 99% of all firms and contributing to more than half of the value-added created by businesses. SMEs remain largely unexplored by the academia, mainly due to the challenges they face in modeling their credit risk profile. Unlike large corporations, SMEs frequently have limited or even no access to the capital markets. As a result, widely used structural marketbased models for credit evaluation such as the distance-to-default measure inspired by Merton (1974) cannot be applied to them. Instead, empirical models such as credit-scoring approaches (i.e., Altman 1968) are the most commonly used. In the early credit-rating literature, academics mostly use accounting ratios to predict firm distress. Altman and Sabato (2007) developed a default prediction model for SMEs using only accounting information on a sample of nearly 2000 US firms over the period 1994–2002. They found that their model outperforms other commonly used corporate models such as Altman’s z’-score (Altman and Hotchkiss 2006). Lehmann (2003), Grunert et al. (2004), and Altman and Karlin (2010) examine key financial factors for SMEs. Glennon and Nigro (2005) and Altman and Karlin (2010) are the first to examine business cycle effects on SMEs defaults, while Glennon and Nigro (2005), using a dataset of US loans guaranteed by SMEs, find that success or failure of a loan is associated with both regional and industrial economic conditions. 4 Methodology The sample of this study consists of 158 small unlisted Greek firms from the manufacturing industry, which participated in field research, by completing an electronic questionnaire. The data analysis used the years 2009–2013. cvfloros@gmail.com 292   C. Lemonakis et al. In this study, we use the Altman’s Z-score, to formulate the distress factor for Greek SMEs. More specifically, this criterion is a linear combination of five sub-indicators, with different participation rates, which have been determined in advance by Professor Altman (Altman’s 1968 with amendments in Altman and Sabato 2007). More specifically, the sub-indices are: working capital/total assets (X 1), retained earnings/total assets (X 2), profit before interest and taxes/total assets (X 3), brokerage shares/total liabilities value (X 4), and sales/total assets (X 5). Thus, the index Z is as follows: Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5. According to Altman, the bigger the Z-score, the better for the firm in terms of financial distress. Firms with Z-score above 3.00 are considered healthy, while those with less than 1.80 are confronted with a significant probability of bankruptcy in the next two years (80–90%). Firms with Z-score within the range of 1.81–2.70 face the possibility in the next two years from the publication of the balance sheet to be in financial difficulty (distress). Finally, firms whose Z index range from 2.71 to 2.99 should take steps to avoid future financial problems. According to Altman’s index Z, taken the total of 158 sample firms, 101 of them are located in the safe zone (63.9%), while 25 of them are in the gray area (15.8%), and the remaining 32 are in the default zone (20.3%). In Table 1, sample firms’ Z-score descriptives are shown. Table 2 lists the average values of the Z-score per year for the sample of companies. From this table, it is evident that as the years go to 2013, scores reveal a lower level of risk undertaken by firms, in other words, lowering the risk factor during the crisis period. Table 3 shows the average values of the Z-score according to the size of companies, namely1: 1. Micro-enterprises—Firms’ type 0: fewer than 10 employees and an annual turnover (the amount of money taken in a particular period) or balance sheet (a statement of a firm’s assets and liabilities) below €2 million. 2. Small enterprises—Firms’ type 1: fewer than 50 employees and an annual turnover or balance sheet below €10 million. cvfloros@gmail.com Innovation and SMEs Financial Distress …     293 Table 1 Sample firms Z-score descriptives Elements Z-score Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability 5.008907 3.528773 165.7912 −4.148076 8.528052 10.19623 170.4646 936,816.8 0.000000 Table 2 Z-score average values per year for sample firms Years Average scores 2009 2010 2011 2012 2013 4.106124551 4.234974537 4.721238542 5.546187816 6.436011904 Table 3 Z-score average values per year for each sample firms’ size Firms’ type Average Altman’s Z-score per firms’ size (sample firms) Firms’ type 0 Firms’ type 1 Firms’ type 2 6.909649379 4.916812615 4.382580504 3. Medium-sized enterprises—Firms’ type 2: fewer than 250 employees and annual turnover below €50 million or balance sheet below €43 million. Most firms in the sample are located in the Attica region (39.87% of the sample) and Thessaloniki (13.29% of the sample), as shown in Table 4. In Table 5, the percentage of sample firms categorized by their ownership status is shown. The table shows that the majority of our sample firms concerns family run businesses. cvfloros@gmail.com 294   C. Lemonakis et al. Table 4 Number of firms per prefecture Prefecture Number of firms per prefecture Percentage (%) Argolis Arta Attica Achaia Boeotia Drama Dodecanese Evros Euboea Ilia Imathia Heraklion Thessalonica Janina astride Karditsa Kastoria Kilkis Kozani Corinthia Cyclades Laconia Larissa Lasithi Lefkada Magnesia Blonde Pieria Rethimno Rodopi Serres Sindos Trikala Fthiotida Florina Chalcidice Chania Grand total 1 2 63 2 5 1 4 3 5 1 1 4 21 1 2 1 6 2 1 2 1 1 3 1 1 2 2 2 1 1 3 3 1 2 1 4 1 158 0.63 1.27 39.87 1.27 3.16 0.63 2.53 1.90 3.16 0.63 0.63 2.53 13.29 0.63 1.27 0.63 3.80 1.27 0.63 1.27 0.63 0.63 1.90 0.63 0.63 1.27 1.27 1.27 0.63 0.63 1.90 1.90 0.63 1.27 0.63 2.53 0.63 cvfloros@gmail.com Innovation and SMEs Financial Distress …     295 Table 5 Sample firms’ category by ownership type Property type Number of firms Percentage (%) Family run Stock company unlisted Other type Listed firm Total 96 44 10 8 158 60.76 27.85 6.33 5.06 100.00 The average age of the sample firms by geographic prefecture is listed in Table 6. Newer firms are located in Larissa and Trikala, while older ones in Magnesia and Chania prefectures. 4.1 Proposed Model The initial form of the model equation is given by the form: Υi,t = β0 + β1 X1i,t + β2 X2i,t + . . . + ei,t Altman’s Z-score is used as the dependent variable. The model used for our analysis is described subsequently for t = 2009, 2010, 2011, 2012, and 2013 (5 consecutive years), for the i-th firm: Z − Scorei = a0 + a1 AGEi + a3 LN(Intangibles)i + a4 LN(EQUITY )i + a5 SHORT _TERM_DEBTi + a6 ERPi + a7 COOPERATION WITH UNIVERSITIES AND RESEARCHi + a8 PATENDSi + a9 NUMBER OF WOMEN IN THE BOARDi + εi wherein the variables used are shown in Table 7, and εi represents the error term. Below, in Table 8, descriptive statistics of the variables used are shown while in Table 6 the Correlation Matrix for the variables used is also presented (Table 9). The estimation of a regression in panel is important to determine the way they will treat the data. There are two options: fixed effects and random effects, often with significant differences in the results. cvfloros@gmail.com 296   C. Lemonakis et al. Table 6 Sample forms average age per prefecture Prefecture Average age of sample companies per prefecture Argolis Arta Attica Achaia Boeotia Drama Dodecanese Evros Euboea Ilia Imathia Heraklion Thessalonica Janina astride Karditsa Kastoria Kilkis Kozani Corinthia Cyclades Laconia Larissa Lasithi Lefkada Magnesia Blonde Pieria Rethimno Rodopi Serres Sindos Trikala Fthiotida Florina Chalcidice Chania Average 19 16 25 20 21 27 26 18 23 32 15 29 27 16 17 33 18 18 17 42 14 34 15 34 15 58 28 33 33 42 24 19 11 19 30 19 63 24.92993631 We run the Hausman test to take the choice of using between Fixed and Random effects. In the event that probability <0.05, we reject the null hypothesis and choose the Fixed Effects model. The resulting Prob equals to 0.0051 < 0.05 (see Table 10); thereby we select the Fixed cvfloros@gmail.com Innovation and SMEs Financial Distress …     297 Table 7 Variables used Variables Meaning Z_score Dependent variable Independent variables Altman’s Z-score for each firm Age Age of the firm, calculated as year 2014—Year of firm’s establishment An intangible asset is an asset such as firm’s intellectual property (i.e., trademarks, copyrights, goodwill, brand recognition) This is a proxy for Innovation in this study. Equity = Assets—Liabilities as taken from firms’ financial statements Any firm’s debt that is due within a period of one year, such as short-term bank loans or other kinds of debt. ERPs use (0 = No, 1 = Yes) Intangibles Equity Short-term debt Use of Enterprise Resource Planning (ERP) systems Cooperation with universities and research centers Patents use Women in board Partnerships with Universities and Research Institutes (1 = Poor… 5 = High) Patents use (0 = No, 1 = Yes) Number of women in board (Representation of women in board as directors or even members in the board) Effects model for the analysis. This model will be used for the analysis and interpretation of the regression results. Furthermore, based on the control (Breusch-Pagan-Godfrey), the existence of heteroskedasticity shown in the above model made us use of the consistent estimators White (White cross-section standard errors and covariance, df corrected), for typical error rates, in order to reduce heteroskedasticity. In Table 11, the results of the model are shown. The fitting of the model is good (R2 = 0.47) and based on the Akaike criterion equals to 5.06 < 667.36 (Akaike info criterion calculated by AIC = 2 * k−2ln (L ) = 2 * 9−(−649.36) = 667.36), where L: the maximum value of the maximum likelihood function of the model and k the number of parameters in the model (Table 12). Important findings taken from the econometric model show significant and positive correlation (+) at 1% significance level of Z-score with SMEs key variable factors such as “Age”, “Equity”, “ERP”, “Cooperation cvfloros@gmail.com 0.141281 0.186888 1.000000 0.269573 0.043213 0.110825 0.313086 −0.003566 0.160224 1.000000 0.186888 0.311555 0.137790 0.172135 0.214066 −0.125288 1.000000 0.160224 0.141281 0.190038 0.143872 0.174256 −0.081384 0.127009 Equity Age Intangibles Equity Short-term debt ERP systems Cooperation with universities and research centers Patents use Women in board Intangibles 14.2 14.27936 16.62712 8.013399 1.231980 259 Equity Age Correlation matrix 10.1 10.31679 14.17460 −4.605170 2.560328 259 Intangibles Variables Table 9 22.1 19.00000 53.00000 10.00000 8.830687 259 Age Descriptive statistics Mean Median Maximum Minimum Std. Dev. Observations Table 8 3.69 4.00000 5.00000 1.00000 1.34813 259 ERP systems cvfloros@gmail.com 0.245427 0.023727 0.190038 0.311555 0.269573 1.000000 0.250824 0.190877 0.309827 0.048005 0.143872 0.137790 0.043213 0.250824 1.000000 0.042629 Short-term debt ERP systems 2136169 1436678. 12057807 92693.25 2025426 259 Short-term debt 0.034705 −0.100065 0.174256 0.172135 0.110825 0.190877 0.042629 1.000000 Cooperation with universities and research centers 2.17 2.00000 5.00000 1.00000 1.28071 259 Cooperation with universities and research centers 1.000000 −0.198882 −0.081384 0.214066 0.313086 0.245427 0.309827 0.034705 Patents use 0.48 0.00000 1.00000 0.00000 0.50078 259 Patents use −0.198882 1.000000 0.127009 −0.125288 −0.003566 0.023727 0.048005 −0.100065 Women in board 1.22 1.00000 4.00000 0.00000 1.001959 259 Women in board 298   C. Lemonakis et al. Innovation and SMEs Financial Distress …     299 Table 10 Hausman test Test summary Chi-Sq. Statistic Prob. Cross-section random 14.820569 0.0051 Table 11 Regression results Variable Coefficient Std. Error t-Statistic Prob. C Age Intangibles Equity Short_term_debt ERP Cooperation with universities and research Patends Number of women in the board −18.11081 0.039157 −0.154751 1.591816 −1.05E-06 0.191813 0.433730 3.233132 0.005378 0.033506 0.245471 1.27E-07 0.045821 0.092807 −5.601629 7.280598 −4.618655 6.484744 −8.262750 4.186159 4.673470 0.0000 (**) 0.0000 (**) 0.0000 (**) 0.0000 (**) 0.0000 (**) 0.0000 (**) 0.0000 (**) 0.724167 0.232625 0.231380 0.075048 3.129776 3.099672 0.0020 (**) 0.0022 (**) R-squared = 0.476729, (**): Significance at 1% Table 12 Aggregate results Variable Polarity Significance Age Intangibles Equity Short_term_debt ERP Cooperation with universities and research Patends Number of women in the board (+) (−) (+) (−) (+) (+) (+) (+) 0.0000(**) 0.0000(**) 0.0000(**) 0.0000(**) 0.0000(**) 0.0000(**) 0.0020(**) 0.0022(**) with Universities and research centers”, “Patents” and “Number of women in the board”, while significant negative correlation (−) of Z-score with the model factors, at 1% significance level, is with variables “Intangibles” (i.e. a proxy of Firms’ Innovation) and “Short_Term_Debt”. 5 Analysis of the Results The methodology described above is used in order to predict the financial distress factors in Greek SMEs firms. The implementation of the proposed models is done in order to analyze firms’ core characteristics cvfloros@gmail.com 300   C. Lemonakis et al. in case of bankruptcy. We used financial data obtained from the largest Greek business information services, database and a questionnaire for taking feedback for other factors taking into account for SMEs financial viability, such as cooperation with Universities and Research Centers, ERP and Patents use, firms’ short-term debt, Equity level. In depth, econometric analysis applied for taking out core characteristics for firms’ financial distress in the Greek SMEs. The economic crisis in the recent years has brought significant failures in almost any kind of business. It has shown that exogenous factors, such as political instability and country risk acting as catalysts in the markets. A consequence of such an economic phenomenon was to highlight the importance of creating models for detecting potential future financial problems. Our research is focused on describing correlation of Z-score (a dummy for financial distress or firms’ potential bankruptcy) and other explanatory variables. Bearing the above issues in mind, we see that robust evidence is reported insinuating a negative relationship between financial distress and innovation implemented in sample firms, revealing contrary to what was initially expected that innovative characteristics increase potential firms’ distress levels, due to the fact that Greek SMEs are mainly “importing clients” of innovation rather than producers. This happens because, also, either because of the sample that based upon mainly on micro-firms that deal with middle technology-level products and services, or because their inability to produce primarily innovative products and services due to lack of adequate funding to support their R&D schemes. Also, a strongly negative effect of Short-Term Debt is shown, as expected, with financial distress, where firms with less debt are in a healthier position than other with high volumes of short-term debt, especially in crisis periods, where credit lines for SMEs are shortening; in that sense, firms tend to reshape their operation with better use of their cash cycle. This is why firms’ Equity becomes highly important factor in crisis period and therefore has a positive correlation to Z-score. In other words, better-capitalized firms face less financial distress events. Another important finding is the notably less-risky performance of firms with more women in their board. This result demonstrates the very strong correlation between corporate financial viability and cvfloros@gmail.com Innovation and SMEs Financial Distress …     301 gender diversity. Smart firms appreciate that diversifying their boards with women can lead firms to more financial stability, with less distress effects undertaken. On the other hand, firms that cooperate with universities and research centers include governmental and other institutions can provide specialized training, education, information, research, and technical support to SMEs, minimizing their distress effect. Also, this cooperation facilitates overall the business process and creates externalities and potential cooperation with other firms, as the direct observation of them is also facilitated. The isolated firms, by contrast, face higher costs and a greater risk in doing business with an effective and productive way. Also, the positive relationship of the dependent variable with the age of the firm means that the oldest firms are more conservative and established in the marketplace; therefore, they face not so difficult issues related to financial risk and though they appear less distress behavior than their younger counterparts. The fact that the age of the firm has a positive effect on the firms’ financial viability is because older firms may not be able to change their operation as quickly as their younger counterparts do after entering in a distress event. Moreover, in that direction, Enterprise Resource Planning (ERP) systems are being used as significant strategic tools that provide competitive advantages in SMEs and lead them to operational excellence. This is—by default—an asset for their operation that ends up to firms’ less distress behavior, even though that, ERP implementation projects are complicated, costly, and include high failure risks. On the other hand, the propensity of small- and medium-sized enterprises (SMEs) to place their patents at first sight shows that small firms use their patents as a source of innovation, improving their efficiency and positioning in the marketplace, which is the reason for the positive correlation of Patents to Z-score, emphasizing the less distress effect their encounter after all. Finally, younger SMEs in our sample, even though they are more flexible than the older ones, they record higher probability in facing a distress event, during their operation. The cost of financial distress is likely to be particularly severe for small-sized firms in terms of revenues, cvfloros@gmail.com 302   C. Lemonakis et al. due to the fact that they are undercapitalized that tends to deteriorate the effects in case of financial distress. In order to avoid size consequences, smaller firms should gain easier access to funding, preserving the ability of smaller firms to be growing faster in an extremely hostile for business environment. 6 Policy Implications Nowadays, financial viability is inextricably linked with innovation and collaboration. The aim of this work was to identify the main factors associated with the financial viability of Greek SMEs. To achieve this objective, we gathered and processed quantitative and qualitative information through databases and fieldwork. The results of this work show that the size, age, and business cooperation with universities and research centers are determinants of firms’ financial viability, in agreement with the literature (see: Lemonakis et al. 2013b, Belderbos et al. 2004; Cai and Fan 2011; De Faria et al. 2010; Zeng et al. 2010, etc.). The success obtained through business partnerships and universities or research institutes is in most cases given. Therefore, policies that promote and enhance such cooperation is particularly important (Tödtling et al. 2009). As it is commonly accepted that government policies strongly influence the effectiveness of universities and research institutions, regarding innovation processes (Zeng et al. 2010), the policy makers should develop policies that will strengthen the ties between universities and the private sector (Solleiro and Gaona 2012), to provide a sound basis for cooperation, through which there will be exchanges of information between businesses and universities. Such an example is the science parks (Guan and Zhao 2013). In addition, the state, especially during the crisis period, should ensure the development of existing universities and research centers, and the establishment of new high-quality research institutions. Particularly for Greece, academics should be motivated to remain in the country and for reforms to strengthen the education system (Herrmann and Kritikos 2013). Also, in order to have in place, a restructured national innovation system should be established and new structures that will cvfloros@gmail.com Innovation and SMEs Financial Distress …     303 allow private and public organizations to participate in voluntary knowledge-sharing communities (Papadopoulos et al. 2013). Moreover, governments should promote innovation targeting policies to facilitate international links in order to establish cooperation and across borders (Kang and Park 2012) and to promote the innovation capacity of cluster composed of SMEs promoting open innovation in universities and research centers (Cai and Fan 2011). Ultimately, policy makers face a serious dilemma. On the one should facilitate the development of innovation to provide firms financial viability and minimizing distress events, and the economy, on the other, should introduce policies without large costs for the country (Papadopoulos et al. 2013). Note 1. 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Authors’ Biography Christos Lemonakis is a COOP bank’s Internal Auditor and an Adjunct Lecturer at the Dept. of Business Administration located in Agios Nikolaos, School of Management and Economics. He has published a series of papers in well-established international journals and participated in national and international conferences. His areas of interest are in Small Business Enterprises (SMEs), Financial Management, Accounting and Auditing. Alexandros Garefalakis is an Accountant and Business Consultant in numerous of companies and organizations. He is an Adjunct Lecturer at the Dept. of Accounting and Finance Department, Technological Education Institute of Crete (TEI of Crete), in Heraklion Crete. He holds a degree in Financial Applications from the Technological Education Institute of Western Macedonia, a Master’s degree in Accounting and Finance from University of Southampton, UK, and is a PhD Candidate at the Hellenic Open University. He has co-authored two books on Cost Accounting and Management cvfloros@gmail.com 308   C. Lemonakis et al. Accounting issues. He has more than 20 publications in peer reviewed journals and participation in more than 21 national and international conferences. His areas of research interest include the quality of narrative information of Financial Reports and business performance assessment as well as management efficiency and productivity. Grigoris Giannarakis holds a PhD in Corporate Social Responsibility and teaches in Technological Educational Institute of Western Macedonia various topics, such as investment analysis, Principles of Management, Principles of Marketing, Accounting Research Project Methodology. He has published a series of papers in well-established international journals. Efthalia Tabouratzi teaches Accounting at the University of Applied Sciences Crete (T.E.I. of Crete), Greece and is adjunct faculty at the Hellenic Open University. She earned a B.Sc. in Accounting from the University of Applied Sciences Crete (T.E.I. of Crete), a Postgraduate Diploma and M.Sc. in Applied Economics and Finance, with a concentration in Applied Accounting and Auditing from the National and Kapodistrian University of Athens, and PhD. in Accounting from the University of Aegean—Doctoral Thesis Title: “Comparative study of management of mergers in terms of Greek and international law”. Her research interests are in Accounting, Auditing and Corporate Finance. Dr. Tabouratzi has been involved, over the years, in various national and international research and educational projects. Constantin Zopounidis is the Director of the Financial Engineering Laboratory. Prof. Zopounidis received his Doctorat D’Etat (1986) in management science, a D.E.A. (1982) in financial management from the University of Paris-IX Dauphine and a B.A. in Business Administration from the University of Macedonia. Prof. Zopounidis has been with the School of Production Engineering and Management of the Technical University of Crete, since 1987 and served as Chairman of the Department during the period 2001–2005. Prof. Zopounidis is elected member of the Royal Academy of Economic and Finance Sciences of Spain (a video from the ceremony is available here) and Distinguished Research Professor at Audencia Group, Nantes School of Management. His research interests include financial engineering, financial risk management and multiple criteria decision making. He has published over 300 papers in premier international journals, edited volumes and conference proceedings. cvfloros@gmail.com Index A Absorption capacity 288 Accounting 3, 4, 11, 21, 22, 31, 53, 109–113, 113, 116, 122, 124, 131, 291 Altman’s Z-score 292, 295, 297 Anti-cyclical monetary policy 105 Anxiety 40, 62–64, 70, 71 Athens Stock Exchange (ASE) 155, 159, 168–170, 195, 196, 198, 199, 201, 202, 213, 214 Asymmetric shocks 56, 59 Austerity 26, 39, 40, 42, 46, 48, 52–54, 63, 65–71, 158, 168–170, 183 Average abnormal returns 202, 207, 208–212 B Bad banks 184, 219 Bailout loans 42 Bail-out and austerity programs 168 Banking crisis 53, 219 Banking policy 177–185 Bank of Greece 28, 29, 170, 182– 184, 225 Bankruptcy 177, 178, 291, 292, 300 Banks’ creditworthiness 110, 113, 115, 121, 124 C Capital controls 169, 273 Capital gains 190–194, 196, 207, 209, 214 Cash distributions 190, 192, 196–198 Causality tests 169 CDS 156, 159, 162, 163, 166, 169, 220, 221, 223–226, 234, 235, 238, 239, 242, 243, 246, 247, 250, 251, 254, 255, 258, 259, 262, 263, 266, 267, 270, 271 © The Editor(s) (if applicable) and The Author(s) 2017 C. Floros and I. Chatziantoniou (eds.), The Greek Debt Crisis, DOI 10.1007/978-3-319-59102-5 cvfloros@gmail.com 309 310   Index CDS spreads 115–118, 121–123, 125, 156, 159, 160, 168–170, 220, 221 Cointegration 161, 164, 165 Common Correlated Effects 165–167 Competitiveness 3, 9, 17, 39, 42, 44, 46, 52, 53, 55, 59, 60–62, 77, 88, 103, 105, 106, 157, 285, 288, 289 Competitiveness problem 39, 59, 62 Contagion 1, 52, 220, 234 Cost of equity 110 Credit rating 44, 53, 59, 101, 109, 111–114, 116, 119–121, 124, 153, 221, 291 Credit risk 110–117, 119, 121, 122, 124, 156, 159, 291 Cumulative abnormal returns 202, 208–211 Cyclical fluctuations 87, 98 D Debt overhang 66, 86, 87, 92, 104 Debt supercycle 85, 86, 91, 92, 101, 104, 106 Debt-to-GDP ratio 2, 5, 6, 9, 23, 26, 27, 30, 45, 46 Default 40, 45, 47, 58, 68, 69, 70, 76, 111, 113, 115, 125, 153–156, 158, 159, 168–170, 177, 178, 180, 185, 290, 291, 292, 301 Deferred tax assets 110–117, 119, 121–126 Deferred taxation 124 Deficit 3, 9–13, 17, 21–23, 26, 27, 30, 31, 38, 41, 43–45, 48, 49, 52–55, 57, 60, 72, 75, 130, 157, 158, 183, 225 Defined Benefit (DB) 130–133 Defined Contribution (DC) 130–133 Deleverage 91, 100 Distressed economic environment 129 Distress effects 301 Dividend income 190, 191, 193, 194, 197–199, 213 E ECB 25, 28, 38, 46, 47, 51, 58, 59, 101, 103–105, 157, 158, 181, 220, 221, 225 Economic and Monetary Union 38, 58 Economic cycle 15, 40 Economic growth 2, 6, 9, 51, 61, 130, 133–135, 182, 286 Economic recovery 2, 154 Economic shocks 56 Enterprise Resource Planning 297–301 Eurogroup 24 European Banking Authority 184 European Financial Stability Facility 24, 44 European Stability Mechanism 24, 25, 38, 77, 184 Excessive Deficit Procedure 4, 57 Exchange rate policy 38, 39, 58, 60 Exchange Traded Funds (ETFs) 135–137, 144–150, 159, 162, 163, 166 Ex-dividend date 189, 191, 192, 194, 195, 207, 214 cvfloros@gmail.com Index   311 F Financial crisis 45, 52, 56, 85, 95, 98, 100, 105, 109–111, 113–115, 124, 156, 157, 180, 182, 219 Financial data 300 Financial instability 182 Financial performance 110 Financial resources 290 Financial turmoil 86, 101, 112, 207 Firms’ success 285 Fiscal adjustment 20, 183 Fiscal asymmetry 38 Fiscal consolidation 12, 18, 20, 21, 30, 46, 53, 91, 98, 100, 104, 105, 183 Fiscal cost 52, 129 Fiscal deficit 11, 12, 23, 41, 54, 55 Fiscal expansion 57, 94, 103–105 Fiscal policy 10, 11, 13, 31, 38, 43, 55, 59, 69, 91, 98, 157 Funded pension schemes 130, 131, 133, 135–137, 140, 143, 149 G GDP 2–20, 22–26, 30–32, 38, 41, 43–50, 53, 54, 57, 61, 72, 73, 75, 77, 78, 92, 94, 96, 98, 101, 105, 106, 129, 130, 135–149, 154, 179, 183, 213, 225 Global financial crisis 30, 38, 180, 273 GNP 134 Government bonds 2, 17, 23, 28, 29, 92, 95, 100, 220, 221, 223– 226, 231, 232, 234–237, 240, 241, 244, 245, 247–249, 252, 253, 256, 257, 259–261, 264, 265, 268, 269, 271–274 Government bond yield 23, 50, 51, 76 Government spending 15, 17, 20, 88 Greece 1–8, 10–14, 18, 20–26, 28, 29–32, 37, 39, 41–59, 61, 63, 68–73, 77, 86–92, 100, 101, 103–105, 109, 114, 125, 130, 154, 157–159, 170, 177–184, 190–192, 196, 198, 199, 202, 204, 205, 207, 209–211, 213, 214, 219, 223–225, 231, 234, 235, 245, 273, 274, 302 Greek banking system 47, 181, 182 Greek debt-to-GDP ratio 6, 9, 30 Greek economic news 220, 221, 225, 234, 235, 245, 257, 269, 272–274 Greek economy 1, 6, 7, 9, 14, 17, 21, 29, 35, 41–43, 45, 86, 92, 101, 104, 158, 168, 170, 180, 182, 199, 220, 234, 235, 245, 272, 273, 275 Greek government debt 2, 5, 6, 9, 28, 30, 45, 54, 70, 220 Greek Loan Facility 23, 24 Greek sovereign debt crisis 57, 156, 225 Greek tax-collecting mechanism 115, 157 Growth rate 4, 6, 7, 32, 33, 60, 70, 86, 92, 96–99, 106, 135 H Hausman test 161, 296, 299 Highly leveraged banks 111, 113, 114 Hysteresis effects 86, 87, 92, 93, 95, 96, 98, 99, 101, 104, 106 I Idiosyncratic risk 169 IMF 6, 23–25, 31, 38, 42–44, 46, 77, 78, 158, 181–183, 219, 220, 225, 273 cvfloros@gmail.com 312   Index Impact of announcements 221, 222, 230–231 Inflation rate 4, 7, 8, 68, 94, 95 Innovation 42, 163, 224, 285–290, 297, 299–303 Interest payments 10, 11, 20, 22, 23, 25, 33 Interest rates 2, 3, 9, 10, 22–26, 27, 30, 32, 33, 53, 58, 59, 61, 86, 88–95, 98–100, 105, 131, 158, 221 Investment 21, 42, 46, 52, 53, 58, 59, 66, 69, 70, 77, 88, 90, 93, 94, 98, 101, 104, 105, 130, 133–146, 148, 149, 170, 180, 191, 290 K Keynesian model 86, 87, 104 L Less-capitalised banks 124 Linear regression 136 Liquidity 105, 180, 182, 183, 207, 220, 221, 275 Liquidity trap 85, 89, 90, 93, 94 Logistic regression 114 M Macroeconomic Imbalances Procedure 38 Macroeconomic news 223–225 Market-adjusted price drop ratio 201, 205 Market capitalization 159, 163, 196, 214 Market equilibrium 86, 87 Market microstructure 190, 191, 193, 198 Market reaction 190, 191, 202, 207, 209, 212 Monetary policy 38, 39, 43, 58–61, 71, 86–88, 90, 92, 93, 98, 100, 102, 104, 105, 220, 221, 225 O OECD data 91, 106 Optimum Currency Area 56 P Pay-as-you go pension schemes 130 Pension fund investment asset 142 Phillips curve 86–88, 90 Political instability 300 Primary expenditure 13–16 Private sector debt 45, 56 Private Sector Involvement 28, 44, 54, 70, 183 Public Sector Asset Purchase Program 103 Public spending 14, 15, 18 Q QE program 101, 103 Quantitative easing 58, 101, 103, 105 R Raw price drop ratio 201, 205 Realized volatility 222, 223, 226, 227, 230, 231, 233 cvfloros@gmail.com Index   313 Recovery 1, 7, 40–42, 45, 62, 63, 69, 71, 77, 85, 103, 104, 154, 183, 275 Red loans 184 Regression results 209–213, 297, 299 Repeated default 155 Return of capital 190, 191, 196–201, 203–205, 209, 212, 213 Return of capital yield 201–205, 209, 211, 212 Taxes 8, 18–21, 38, 43, 44, 189, 190, 194, 197–200, 214, 292 Taxes on return of capital 213 Tax reductions 112, 121 Transaction costs 190, 193, 195, 200, 202, 203, 208, 212, 214 U Unemployment 1, 39, 40, 41, 43, 45, 46, 50, 55, 57, 59, 62, 65–69, 76, 94, 98, 130 S Secular stagnation 85–87, 90, 93–97, 100–106 Short-Term Debt 297, 298, 300 Small-Medium sized Enterprises (SMEs) 134, 135, 186–291, 292, 297, 299–303 SMEs management 289 Soaring debt 104, 105 Social security contributions 18, 20 Stability and Growth Pact 5, 12, 38, 57, 78 Stock markets 43, 156, 193, 194, 200, 201, 220, 223, 225, 234, 235, 238, 239, 242, 243, 245–247, 250, 251, 254, 255, 257–259, 262, 263, 266, 267, 259, 270–274 Structural reforms 12, 30, 44, 46, 53, 92, 170, 213 V Venture capital (VC) 134–137, 144, 146, 148–150, 289 Venture Capital Amount 137, 145, 147, 148 VIX 159, 162, 163, 166, 168 Volatility 67, 132, 220–223, 226, 227, 230, 231, 232–233, 234, 245, 248–259, 264–271, 273–275 Volatility jump 220, 222, 223, 226, 228–230, 233–235, 245, 269, 272–275 Z Zero lower bound 86, 89, 90, 93–95, 98, 99, 101–103, 105 T Taxation 52, 124, 197 Tax-effect hypothesis 190, 193, 196, 207 cvfloros@gmail.com View publication stats