Ganpaolo Iazzolno (Italy), Guseppe Mglano (Italy), Rosa Forgone (Italy), Marangela Grmonte (Italy) Captal effcency and market value n knowledge and captalntensve frms: an emprcal study Abstract The ncreasng gap between market and book value allows us to understand that frms value s based not only on physcal but also on ntangble assets. Intellectual captal resources are very mportant, especally n knowledgentensve but also n captal-ntensve ndustres. The man ams of ths work are: (1) to propose a methodology based on the value added components, startng from Pulc s pont of vew (Pulc, 1998; 2000; 2008), whch s able to dscrmnate between knowledge-ntensve and captal-ntensve ndustres; (2) to nvestgate the relatonshp between ntellectual captal effcency and market value (and between physcal captal effcency and market value) for frms belongng to both knowledge and captal-ntensve sectors. Keywords: knowledge-ntensve frms, captal-ntensve frms, ntellectual captal effcency, physcal captal effcency, market value, value added. JEL Classfcaton: C01, G32, M12, M41. Introducton Many scholars have studed the asymmetry between the market and the book value: one of the man elements that nfluence frms market value s the ntellectual captal (Edvnsson, 1997; Sveby, 1997; and Lynn, 1998). Therefore, t has become nterestng to study the relatonshp between ntellectual captal and market value. In ths age, ntellectual captal often replaces tradtonal resources such as land, captal and work (Sveby, 1997; Bonts, 1999; O Donnell et al., 2006). The actual accountng systems are able to show only physcal assets wthout consderng ntangbles. Another nterestng topc s the dfference between captal-ntensve Frms (CIFs) and knowledgentensve frms (KIFs). Even f the concept s rather clear and accepted, n the lterature there are few proposals of crtera able to dstngush whether a sector (or a frm) has to be consdered as knowledgentensve or captal-ntensve. The am of the paper s threefold: (1) startng from Pulc s pont of vew (Pulc, 1998; 2000; 2008), we propose a methodology based on the value added components that s able to dscrmnate between knowledge-ntensve and captal-ntensve ndustres; (2) we propose two new ndcators useful for measurng the ntellectual captal and the physcal captal effcency, extendng the concept of IC (value added ntellectual coeffcent) proposed by Pulc (1998; 2000; 2008); (3) we analyze the relatonshp between Intellectual captal effcency and market value (and between physcal captal effcency and market value) for frms belongng to both knowledge and captal ntensve sectors. Ganpaolo Iazzolno, Guseppe Mglano, Rosa Forgone, Marangela Grmonte, 2013. The paper s organzed as follows. Secton 1 descrbes the lterature revew and the hypothess development; secton 2 llustrates the methodology of research; secton 3 descrbes the analyss carred out; the dscusson s ncluded n secton 4. The fnal secton concludes the paper. 1. Lterature revew and hypothess development 1.1. Lterature revew. Many scholars have studed the gap exstng between frms market value and book value and observed that there s an undetectable value unmentoned n fnancal statements (Chen et al., 2005; Camps and Costa, 2008; Iazzolno and Fortno, 2012; Iazzolno and Petrantono, 2005). Market value s made up of a combnaton of tangble and ntangble value (Tseng and Goo, 2005; O Donnell et al., 2000). The topc of frm performance evaluaton has been studed from dfferent pont of vew (Iazzolno et al., 2012). Intellectual captal s becomng a fundamental part of correctly estmatng frms market value. Several studes were carred out that tred to evaluate the mpact of ntellectual captal on market value and frms performance (Rahman, 2012; Madtnos et al., 2011; Gan and Saleh, 2008; Chen et al., 2005; Tseng et al., 2005; Cabrta and Bonts, 2008; Frer and Wllams, 2003; Schuma et al., 2008; Iazzolno et al., 2013). In the new knowledge-based economy, many frms belongng to several ndustres emphasze knowledge ntensty and nnovaton as tools of competton (Hsung and Wang, 2012; Pantano et al., 2013; Corvello et al., 2013; Corvello and Iazzolno, 2013). Many scholars have attempted to classfy both captal ntensve (CIFs) and knowledge-ntensve frms (KIFs). Despte the wdespread use and apparent acceptance of the concept of KIF, there s no shared defnton n 147
the lterature. The concept of knowledge-ntensve frms does not have a unque and unversal meanng (Robertson and O Malley Hammersley, 2000). Wth the purpose of defnng and dstngushng knowledge-ntensve frms, researchers used two dfferent perspectves: (1) the nput-based perspectve; and (2) the output-based perspectve. Accordng to the nput perspectve, the term knowledge-ntensve mtates the economsts labellng of frms as captal-ntensve or laborntensve (Starbuck, 1992). These labels descrbe the relatve mportance of both/ether captal and/or labor as nputs to the producton process. In a captalntensve frm, captal (ntended as physcal captal such as plants, tools, etc.) s more mportant than labor (ntended as human effort); n a labor-ntensve frm, labor has the greater mportance. By analogy, labellng a frm as knowledge-ntensve mples that knowledge has more mportance than other nputs (.e. captal and labor); n fact, t can be consdered as the raw materal for developng these types of frm. On the other hand, the output-based perspectve emphaszes the role of knowledge n the output of the producton process, reflectng the tradton ndustral classfcaton schemes, where organzatons are grouped n ndustres by ther outputs. 1.2. Hypothess development. In order to evaluate the effcency of ntellectual and physcal captal, we rely on the concept of value added, as ntroduced by Pulc (1998; 2000; 2008). We propose an extenson of Pulc s methodology that consders also the other components of value added than the cost of employees (human captal). The brdge that Pulc created between the noton of value added and that of value creaton n a knowledge economy context consttutes the prncpal strong pont of hs proposal. For a detaled analyss of the concept of IC (value added ntellectual coeffcent) see Iazzolno and Lase (2013). The man strong pont of Pulc was to recover the noton of value added, as ntended n the Value Added Income Statement. The IC provdes a cumulatve measure of the changes n value added produced by effcency gans related to the use of both physcal/fnancal captal and ntellectual captal. Pulc defnes the IC as: IC HCE SCE CEE, where, HCE = Human captal effcency =, HC (HC = Human captal = Cost of employees); SCE = SC Structural captal effcency =, (SC = Structural captal = HC); CEE = Captal employed effcency =, (CE = Captal employed = Book CE value of nvested captal). The term CE refers to the value of physcal and fnancal (or tradtonal) assets whle the terms HC and SC respectvely refer to the components of the ntellectual captal or (.e.) human captal and structural captal. We propose a dfferent approach to measure the physcal captal effcency from Pulc. We measure the effcency of physcal and fnancal captal as the rato of to the change occurred n physcal and fnancal assets, rather than the rato of to a stock of assets. Furthermore, we propose to ntroduce one more ndcator to measure the ntellectual captal effcency. Besdes the human captal (cost of employees), we propose to extend the analyss to the other components of the Value Added, n order to consder the effcency of the dfferent nputs and the contrbuton to the value creaton of all the producton process nputs. In the followng secton a more detaled explanaton of our methodology s provded. 2. Methodology of research The am of the paper s threefold: (1) at frst, startng from Pulc s pont of vew (Pulc, 1998; 2000; 2008), we propose a methodology based on the value added components that s able to dscrmnate between KIFs and CIFs; (2) secondly, we propose two new ndcators useful for measurng the ntellectual captal effcency (ICE) and the physcal captal effcency (PCE), extendng the concept of IC proposed by Pulc (1998; 2000; 2008); (3) thrd, we analyze the relatonshp between ICE and market value (and between PCE and market value) for both KIFs and CIFs. 2.1. A crteron to dscrmnate between KIFs and CIFs. In order to make a dstncton between KIFs and CIFs (and then also between knowledge-ntensve ndustres and captal-ntensve ndustres) we refer to the concept of value added and ts components. By extendng Pulc s work, we propose to consder also the other components of value added rather than human captal (HC). Value added s made up of several sub-components that contrbute n a dfferent way to value creaton. Accordng to the formulaton based on factors of producton, value added could be wrtten as: Cost of employees Deprecaton and amortzat on Interests expenses Taxaton Net Income, where = Value Added. Furthermore, n ths formula Deprecaton and amortzaton s consdered as a whole component. We 148
have parttoned deprecaton and amortzaton nto three components: Tangble, Intangble and Others. Tangble amortzatons regard physcal captal (physcal assets) such as plants, machneres, etc.; ntangble amortzaton are lnked to ntellectual captal; other amortzaton regard other funds/ provsons (Fgure 1). Fg. 1. Composton of deprecaton and amortzaton The overall decomposton of value added s llustrated n Table 1. Table 1. Value added and ts components Components Types of captal % Cost of employees (human captal) Intellectual captal (human captal) % Deprecaton/Amortzaton Intangble amortzatons Intellectual captal % Tangble amortzatons Physcal captal % Other amortzatons (Not ncluded n ntellectual or phscal captal) Interest expenses Fnancal captal % Taxaton External captal (Government) % Net ncome Fnancal captal % Value added () 100% In the lterature there are no methodologes for classfyng KIFs and CIFs. We propose a classfcaton of frms based on the weght of value added components (on the whole value added), emphaszng: (1) cost of employees and ntangble amortzaton for KIFs; and (2) Tangble amortzaton for CIFs. In partcular, a KIF s characterzed by a hgh weght of human captal (cost of employees) and Intangble amortzaton on value added; whereas, a CIF s dentfed by a hgh weght of Tangble amortzaton on value added. We elaborated a formal rule for defnng f a sector (and then a frm) can be consdered as a KIF or a CIF: 1. Consderng a specfc sector : Sector Intangble amortzatons KIF where = 1,.., n HC and ff HC Tangble amortzatons Deprecaton/ amortzaton HC Medan TA Other amortzatons all sectors all sectors % 2. Consderng a specfc sector : TA TA all sectors Sector CIF Medan ff all sectors where = 1,.., n 2.2. Two new ndcators for measurng IC effcency and physcal captal effcency. After the classfcaton, we proposed two new effcency ndcators for ntellectual and physcal captal. Pulc (1998, 2000, 2008) proposed /HC as effcency ndcator of ntellectual captal; n fact, he used t to measure the knowledge workers productvty. HC s the amount of nvestment n human resources, thus /HC s an effcency ndcator of human captal. As a matter of fact, a hgh value of /HC means that the frm s makng the best use of ts employees. Ths ndcator shows how new value s created for each monetary unt nvested n human captal. We ntroduce two new ndcators: /Intangble amortzatons for measurng the ntellectual captal effcency and /Tangble amortzatons for measurng the physcal captal effcency. Therefore, the effcency ndcators we used n our analyss are: 1. : effcency of ntellectual Cost of employees captal (human captal) (already proposed by Pulc); 2. : effcency of ntellectual captal (ntangble assets) (new); Intangble amortzatons 3. : effcency of physcal Tangble amortzatons captal (new). 2.3. Analyss of the relatonshp between IC effcency and market value (and between physcal captal effcency and market value). The thrd am of our research s to nvestgate the relatonshp between IC effcency and market value (and between physcal captal effcency and market value) n: (1) KIFs and (2) CIFs. In order to acheve ths goal the followng hypotheses were tested: H1: In knowledge-ntensve ndustres, ntellectual captal effcency (average value n the tme range 2005-2009) postvely nfluences market value (average value n 2009-2011). H2: In captal-ntensve ndustres, physcal captal effcency (average value n the tme range 2005-2009) postvely nfluences market value (average value n 2009-2011). 149
H3: In knowledge-ntensve ndustres, ntellectual captal effcency (average value n the tme range 2002-2009) postvely nfluences market value (average value n 2009-2011). H4: In captal-ntensve ndustres, physcal captal effcency (average value n the tme range 2002-2009) postvely nfluences market value (average value n 2009-2011). Fg. 2. Hypotheses 1 and 3 These hypotheses are based on the followng consderatons: nvestments n Intangbles provde benefts n the long term. Investments regardng ntellectual captal consder expendtures on patents and brands but also n human resources and organzaton development. Thus, t s reasonable to assume that n order to obtan a return from these expendtures a long tme perod wll be necessary. We consdered two wde tme ranges: the frst from 2005 to 2009 and the second from 2002 to 2009. Fg. 3. Hypotheses 2 and 4 Smlarly we consdered the physcal captal effcency and then hypotheses 2 and 4. 3. Analyss 3.1. Dataset. The data were extracted from the AMADEUS Bureau Van Djk database. In partcular, we selected sx ndustres: Computer programmng and consultancy. Manufacture of chemcals. Manufacture of basc metals. 150
Travel agency and tour operator reservaton Table 2. The sample servce. Sectors No. of frms Advertsng and market research. Computer programmng and consultancy 58 Manufacture of paper. Manufacture of chemcals 25 Intally, we analyzed 2280 Italan SMEs belongng to Manufacture of basc metals 20 the above-mentoned sectors; subsequently, the sample Travel agency and tour operator reservaton servce 105 was reduced, by consderng the data avalablty from Advertsng and market research 170 2002 to 2011. A wde number of frms were removed, Manufacture of paper 156 owng to the presence of null value or unavalable Total 534 data. Furthermore, abnormal observatons or outlers 3.2. Knowledge- and captal-ntensve ndustres. In were removed, n order to mprove both ndexes of order to dstngush knowledge and captal-ntensve symmetry and kurtoss and then to guarantee a greater sectors we carred out the analyss of the ndustres; the effectveness of multple lnear regressons. percentage of the value added components (on the Therefore, the fnal sample was made up of 534 frms, as shown n Table 2. whole value added) for each frm was calculated. Average values for every sector are shown n Table 3. Table 3. Percentage of value added components for each ndustry Computer programmng and consultancy Travel agency Advertsng and market research Manufacture of chemcals Manufacture of basc metals Manufacture of papers Cost of employees 70% 58% 46% 41% 40% 41% Tangble amortzatons 3% 2% 1% 11% 10% 12% Intangble amortzatons 9% 8% 4% 8% 5% 7% Other amortzatons 2% 3% 1% 3% 2% 3% Total deprecaton and amortzaton 14% 13% 6% 22% 17% 22% Interest expenses 2% 4% 4% 6% 9% 6% Taxatons 7% 13% 1% 5% 2% 5% Net ncome 6% 12% 34% 26% 31% 26% Value added 100% 100% 100% 100% 100% 100% Observng the percentage of each component of value added a classfcaton can be obtaned for dstngushng between knowledge- and captalntensve ndustres. Therefore, we adopted the rules Computer programmng and consultancy Table 4. Classfcaton of ndustres Travel agency cted beforehand n the methodology for establshng whch frms could be classfed as KIFs and whch as CIFs. The results are shown n the Table 4 below. Advertsng and market research Manufacture of chemcals Manufacture of basc metals Manufacture of papers Cost of employees 70% 58% 46% 41% 40% 41% Tangble amortzatons (TA) 3% 2% 1% 11% 10% 12% Intangble amortzatons (IA) 9% 8% 4% 8% 5% 7% Medan HC/ 44% 44% 44% 44% 44% 44% Medan TA/ 7% 7% 7% 7% 7% 7% Rules calculus Knd of sector 70% > 44% and 9% > 3% Knowledge-ntensve 58% > 44% and 7% > 2% 46% > 44% and 4% > 1% Knowledgentensve Knowledgentensve 11% > 8% 10% > 5% 12 % > 7% Captal-ntensve Captal-ntensve Captal-ntensve In Table 5 the resultng classfcaton s reported. Table 5. Knowledge- and captal-ntensve sectors Knowledge-ntensve Computer programmng and consultancy Advertsng and market research Travel agency Captal-ntensve Manufacture of chemcals Manufacture of basc metals Manufacture of paper 3.3. The mpact of ntellectual and physcal captal on market value. A multple lnear regresson analyss was carred out that allowed us to evaluate the mpact of Intellectual and physcal captal on frm s market value. To buld sold regresson models we consdered not only the Cost of employees and the Deprecaton/Amortzaton, but all the components of value added. Before proceedng wth the multple lnear regressons, a correlaton analyss was carred out (by SPSS Statstcs 19), for each sector, among the ndependent varables used n the models. We developed four multple regresson models. 151
Model 1 (Hypoteses H1 and H2) FCFO 20092011 20052009 3 Tangble amortzatons 20052009 6 Interest expenses FCFO / Sales 20052009 0 1 Cost of employees 20092011 20052009 6 Interest expenses 20052009 20052009 3 Tangble amortzatons 20052009 Model 2 (Hypoteses H3 and H4) FCFO 20092011 20022009 3 Tangble amortzatons 20022009 6 Interest expenses FCFO / Sales 20052009 20052009 20052009 4 Others amortzatons 7 Net ncome 20052009 20052009 20052009 0 1 Cost of employees 20022009 7 Net ncome 20022009 0 1 Cost of employees 20092011 20022009 3 Tangble amortzatons 20022009 6 Interest expenses 20022009 20022009 20052009 20052009 2 Intangble amortzatons 20052009 20052009 20052009 ; 20052009 4 Others amortzatons 20022009 7 Net ncome 20022009 20022009 20022009 0 1 Cost of employees 20022009. 20022009 4 Others amortzatons 7 Net ncome 20022009 20052009 20052009 20052009 5 Taxatons 5 Taxatons 20052009 20052009 2 Intangble amortzatons 20022009 2 Intangble amortzatons 20022009 20022009 ; 20022009 4 Others amortzatons. 20022009 20022009 20052009 20022009 5 Taxatons 5 Taxatons 20052009 20052009 20052009 20022009 20022009 2 Intangble amortzatons 20022009 20022009 20022009 20022009 (1.1) (1.2) ; (2.1) (2.2) The regresson models were developed usng the average of dependent and ndependent varables n order to take nto account the value of varables for the years consdered. Frms ncluded n the sample are not lsted on a stock market; thus we used a proxy of market value (because ths s not avalable for non-lsted frms): FCFO (free cash flow from operatons). It could be used as a proxy of frm s market value as FCFO s the basc tem for calculatng the market value 1. The other dependent varable consdered n our analyss s FCFO/Sales. It allowed us to take nto account the dmensonal factors of frms. The ndependent varables of models concern the effcency of the value added components, as wrtten prevously. In partcular we consdered /Cost of 1 Accordng to the fnancal method for evaluatng frm market value. FCFO s calculated as: FCFO = EBITDA Workng captal Gross fxed assets Operatng taxaton, where: FCFO = Free cash flow from operatons; EBITDA = Earnngs before nterest, Taxes, Deprecaton and amortzaton. employees (or /HC) and /Intangble Amortzatons as ndcators of ntellectual captal effcency; we consdered /Tangble amortzatons as an ndcator of physcal captal effcency. 3.3.1. Model 1 (Data from 2005 to 2009). Ths model was developed to test hypotheses H1 and H2; hence, ndependent varables were drawn by the average of effcency ndcators mentoned beforehand consderng the perod of 2005-2009, whle the average frm s market value from 2009 to 2011 was measured through FCFO and FCFO/Sales (dependent varables). The results of the applcaton of Model 1 are shown n Table 6. In order to test the hypotheses, we have observed the t-tests (shown n Table 8 and Table 9 the Appendx). Through the coeffcent and ts sgnfcance t s possble to dentfy whch sectors satsfy H1 and H2. 3.3.2. Model 2 (2.1 and 2.2). Model 2 was developed to test H3 and H4; thus, ndependent varables were drawn by the average of effcency ndcators as done 152
n Model 1 consderng the perod from 2002 to 2009 as tme horzon, whle the average frm s Market Value from 2009 to 2011 was measured through Investment Management and Fnancal Innovatons, Volume 10, Issue 2, 2013 Table 6. Applcaton of Model 1 (1.1 and 1.2) FCFO and FCFO/Sales (dependent varables). The followng Table 9 shows the results of the applcaton of ths model. Model 1 Independent varables: Average of ratos 2005-2009 Dependent varable: FCFO/Sales (average) Dependent varable: FCFO (average) Sectors R 2 F R 2 F Computer programmng and consultancy 0.3 2.01* 0.27 1.72 Travel agency and tour operator reservaton servces 0.97 97.93*** 0.68 5.31** Advertsng and market research 0.71 4.35** 0.58 2.41* Manufacture of paper 0.17 2.74** 0.28 5.24*** Manufacture of chemcals 0.16 4.46* 0.53 25.48*** Manufacture of basc metals 0.18 4.51*** 0.06 1.45 Notes: *Sgnfcance level of 10%; **sgnfcance level of 5%; ***sgnfcance level of 1%. Model 2 Independent varables: average of ratos from 2002-2009 Table 7. Applcaton of Model 2 Dependent varable: FCFO/Sales (average) Dependent varable: FCFO (average) Sectors R 2 F R 2 F Computer programng and consultancy 0.17 1.53 0.01 0.07 Travel agency and tour operator reservaton servces 0.24 0.79 0.65 4.45** Advertsng and market research 0.77 5.97** 0.64 3.17** Manufacture of paper 0.17 2.74** 0.26 4.71*** Manufacture of chemcals 0.19 0.44 0.11 2.35** Manufacture of basc metals 0.08 1.89 0.10 2.35** Notes: *Sgnfcance level of 10%; **sgnfcance level of 5%; ***sgnfcance level of 1%. As done for the prevous model, t-tests have been observed n order to test both hypotheses H3 and H4. The results are shown n Table 10 and 11 (see Appendx). 4. Dscusson 4.1. KIFs and CIFs. The results demonstrate that, n general, the HC% value (percentage on ) s the hghest n all sectors wth respect to the other components. However, consderng the dfferent sectors, knowledgentensve frms (KIFs) have a hgher value of HC% than the captal-ntensve frms (CIFs). Furthermore, KIFs have a value of Intangble amortzaton (%) hgher than Tangble amortzaton (%). In fact, n knowledge-ntensve ndustres there s a greater use of ntellectual resources and consequently there are not many tangble assets. Through the results, t can be noted that, n the computer programmng and consultancy sector, there are the greatest values of HC% and Intangble amortzatons %. In fact, n ths ndustry there are companes that base ther busness on an ntensve use of ther staff competences. In CIFs there are hgher values of tangble than Intangble amortzaton, owng to the use of expensve producton plants. As can be notced, there are ntangble assets also n captal-ntensve sectors but they are lower than tangble assets. The valdty of these crtera s guaranteed by the objectvty of the assessment, snce fnancal data were used. 4.2. Model 1 (1.1 and 1.2). In Model 1, the analyses consderng the FCFO as dependent varable pont out that H2 s never satsfed n any captal-ntensve ndustres. The results prove that there are no sgnfcant relatonshps between FCFO and the effcency of physcal captal. Owng to the fact that t-value s lower than the crtcal threshold, t can be sad that the rato /Tangble amortzatons s not sgnfcant for determnng frm s market value (measured by FCFO). Hypothess H1 (consderng the same varables) s satsfed n the Travel agency sector (Table 7) n whch the ntellectual captal effcency ratos are sgnfcant for determnng frm s market value (FCFO). In contrast, measurng frm s market value by FCFO/Sales, the prevously mentoned hypotheses fnd a more emprcal confrmaton. H1 s satsfed for two knowledge-ntensve sectors: Computer programmng consultancy and Travel agency (Table 8). In captal-ntensve sectors, H2 s satsfed only n Manufacture of paper. Some observatons could be made by comparng a captal-ntensve wth a knowledge-ntensve sector (Table 8). 153
As descrbed n Table 8, the effcency of ntangble captal measured by /Intangble amortzaton s a sgnfcant predctor of the market value for Travel agency, whereas the same rato s not a good predctor for Manufacture of paper. Furthermore, the effcency of ntangble captal has greater nfluence on Market Value n knowledge-ntensve than n captal-ntensve sectors; whereas the effcency of physcal captal nfluences the market value only n captal-ntensve sectors. Furthermore, n the Advertsng and market research sector, there s a great nfluence of the effcency of human captal on the market value (represented by both FCFO/Sales and FCFO), as shown n Table 7 and Table 8. 4.3. Model 2 (2.1 and 2.2). Frst of all, consderng FCFO/Sales as a dependent varable, hypothess H3 s never satsfed; thus, there s no emprcal evdence that ntangble captal effcency nfluences frm s market value n knowledge-ntensve sectors. Usng the same dependent varable, hypothess H4 s verfed only n Manufacture of paper, as /Tangble amortzatons s sgnfcant n ths sector as shown n Table 11. Secondly, usng FCFO as dependent varable, H3 s verfed only n the Travel agency sector; whereas H4 s confrmed n the Manufacture of basc metals. Therefore, comparng these two sectors some aspects can be notced (Table 10). In the Travel agency sector the effcency of ntangble captal s hgher than n the Manufacture of basc metals; n knowledge-ntensve sectors (such as the Travel agency) the effcency of ntangble captal has a sgnfcant nfluence on market value; whereas n the Manufacture of basc metals, the effcency of physcal captal s more sgnfcant than n knowledge-ntensve sectors (Travel agency) and t has a meanngful nfluence on market value. Conclusons The ncpt of ths study s that ntellectual captal effcency postvely nfluences frms market value. However, the mpact of ntellectual captal can be dfferent n relaton to the sectors n whch t s References taken nto account. Thus, ntellectual captal may be essental n knowledge-ntensve ndustres, as KIFs (knowledge-ntensve frms) base ther busness on ntellectual captal rather than physcal captal resources. Despte ntellectual captal also beng present n captal-ntensve sectors, CIFs use more physcal than ntellectual captal resources. In the lterature there are few applcatons that try to classfy KIFs and CIFs but they are confused. Hence, we have proposed a classfcaton based on the effcences mentoned above. Startng from Pulc s pont of vew (Pulc, 1998; 2000; 2008), we decomposed the accordng to the formulaton based on factors of producton; afterwards, startng from whole value of deprecatons and amortzatons we placed the effcency of Human Captal sde by sde to effcency of ntangble assets (/Intangble amortzatons) for ntellectual captal measurement; whereas we defned the effcency of tangble assets for measurng physcal captal. Subsequently, we tested the hypotheses descrbed n the methodology of research n order to nvestgate the exstng relatonshps between IC and market value wthn knowledge- and captal-ntensve sectors. In ths study, we consdered FCFO as a proxy of market value, because there are no lsted frms n our sample; although FCFO takes nto account only what happens wthn a company, not consderng exogenous factors, whch can affect frm s value. In concluson, the man ams of ths study are: 1. Provdng an objectve knowledge- and captalntensve sectors classfcaton based on components. 2. Investgatng the relatonshps between ntellectual captal (IC) and market value (MV) wthn knowledge- and captal-ntensve ndustres. Further researches could regard analyses that take nto account a wder sample of frms especally for knowledge-ntensve sectors. Furthermore, a larger set of sectors (both knowledge- and captal-ntensve) could be consdered. 1. Bonts, N. (1999). Managng Organzatonal Knowledge by Dagnosng Intellectual Captal: Framng and Advancng the State of the Feld, Internatonal Journal of Technology Management, 18 (5-8), pp. 433-462. 2. Cabrta, M. and Bonts, N. (2008). Intellectual captal and busness performance n the Portuguese bankng ndustry, Internatonal Journal of Technology Management, 43 (1-3), pp. 212-37. 3. Camps, D. and Costa, R. (2008). A DEA-based method to enhance Intellectual Captal management, Knowledge and Process Management, 15 (3), pp. 170-183. 4. Chen, M.C., Cheng, S.J. and Hwang, Y. (2005). An emprcal nvestgaton of the relatonshp between Intellectual Captal and frms Market Value and fnancal performance, Journal of Intellectual Captal, 6 (2), pp. 159-176. 5. Corvello, V., Iazzolno, G. and Rtrovato, V. (2013). Evaluatng Technologcal Innovatons: A Method Based on Comparable Transactons, European Journal of Economcs, Fnance and Admnstratve Scences, 56, January, pp. 37-50. 154
6. Corvello, V. and Iazzolno, G. (2013). Factors Affectng the Practces of External Problem Solvers n Broadcast Search, Journal of Technology Management & Innovaton, 8 (2), pp. 166-177. 7. Edvnsson, L. (1997). Developng Intellectual Captal at Skanda, Long Range Plannng, 30 (3), pp. 366-73. 8. Frer, S. and Wllams, M. (2003). Intellectual captal and tradtonal measures of corporate Performance, Journal of Intellectual Captal, 4 (3), pp. 348-360. 9. Gan, K. and Saleh, Z. (2008). Intellectual Captal and Corporate Performance of Technology-Intensve Companes, Asan. J. Bus. Account, 1 (1), pp. 113-130. 10. Hsung, H.-H. and Wang, J.-L. (2012). Value creaton potental of ntellectual captal n the dgtal content ndustry, Investment Management and Fnancal Innovatons, 9 (2), pp. 81-90. 11. Iazzolno, G. and Fortno, A. (2012). Credt rsk analyss and the KMV-Black and Scholes model: a proposal of correcton and an emprcal analyss, Investment Management and Fnancal Innovatons, 9 (2), pp. 54-68. 12. Iazzolno, G. and Lase, D. (2013). Value Added Intellectual Coeffcent (IC): a methodologcal and crtcal revew, Journal of Intellectual Captal, 14 (4), forthcomng. 13. Iazzolno, G. and Petrantono, R. (2005). A developng Knowledge-Audt Methodology: evdences from the Publc Admnstraton n Southern Italy, n Hamza, M.H. (eds.), Communcatons, Internet, and Informaton Technology (CIIT 2005), Proceedngs of the 4 th IASTED Internatonal Conference, Cambrdge, MA, USA, pp. 86-90. 14. Iazzolno, G., Lase, D. and Marraro, L. (2012). Busness multcrtera performance analyss: A tutoral, Benchmarkng: an Internatonal Journal, 19 (3), pp. 395-411. 15. Iazzolno, G., Mglano, G. and Gregorace, E. (2013). Evaluatng Intellectual Captal for supportng Credt Rsk Assessment: an Emprcal Study, Investment Management and Fnancal Innovatons, 10 (2), pp. 44-54. 16. Lynn, B.E. (1998). Performance evaluaton n the new economy: brngng the measurement and evaluaton of Intellectual Captal nto the management plannng and control system, Internatonal Journal of Technology Management, 16 (1-3). 17. Madtnos, D., Chatzoudes, D., Tsards, C. and Therou, G. (2011). The mpact of Intellectual Captal on frms Market Value and fnancal performance, Journal of Intellectual Captal, 12 (1), pp. 132-151. 18. O Donnell, D., Tracey, M., Henrksen, L.B., Bonts, N., Cleary, P., Kennedy, T. and O Regan, P. (2006). On the essental condton of ntellectual captal: labour! Journal of Intellectual Captal, 7 (1), pp. 111-28. 19. O Donnell, O Regan, P. and Coates, B. (2000). Intellectual Captal: a habermasan ntroducton, Journal of Intellectual Captal, 1 (2), pp. 187-200. 20. Pantano, E., Iazzolno, G. and Mglano, G. (2013). Obsolescence rsk n advanced technologes for retalng: a management perspectve, Journal of Retalng and Consumer Servces, 20 (2), pp. 225-233. 21. Pulc, A. (1998). Measurng the performance of ntellectual potental n the knowledge economy, The 2nd World Congress on the Management of Intellectual Captal, Hamlton, Ontaro, Canada. 22. Pulc, A. (2000). IC an accountng tool for IC management, Internatonal Journal of Technology Management, 20 (5-8), pp. 702-714. 23. Pulc, A. (2008). The Prncples of Intellectual Captal Effcency. A bref Descrpton, Croatan Intellectual Captal Center, Zagreb. 24. Rahman, S. and Ahmed, J.U. (2012). Intellectual Captal Effcency: Evdence from Bangladesh, Advances n Management & Appled Economcs, 2 (2), 109-146. 25. Robertson, M. and O Malley Hammersley, G. (2000). Knowledge management practces wthn a knowledgentensve frm: the sgnfcance of the people management dmenson, Journal of European Industral Tranng, 24 (2-4), pp. 241-253. 26. Schuma, G., Lerro, A. and Santate, D. (2008). The Intellectual Captal dmensons of Ducat s turnaround: explorng knowledge assets groundng a change management program, Internatonal Journal of Innovaton Management, 12 (2), pp. 161-193. 27. Starbuck, W.H. (1992). Learnng by knowledge-ntensve frms, Journal of Management Studes, 3 (4), pp. 262-275. 28. Sveby, K.E. (1997). The New Organzatonal Wealth: Managng and Measurng Knowledge-based Assets, Berrett-Koehler, New York. 29. Tseng, C.-Y. and Goo, Y.-J.J. (2005). Intellectual Captal and corporate value n an emergng economy: emprcal study of Tawanese manufacturers, R&D Management, 35 (2), pp. 187-221. 155
Table 8. Model 1.1. Dependent varable: FCFO 2009-2011; ndependent varables 2005-2009 Model 1 Dependent varable: FCFO (Average) 2009-2011 TRAVEL PAPER ADVERTISING COMPUTER METAL CHEMICAL Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. (Constant) -2.414.018 -.144.885 2.511.027-2.695.011-3.092.002 -.144.885 /Taxaton -.183.102 1.036.303-11.976.000 -.110 -.551.592.196 1.209.236.136 1.614.109 -.718-11.976.000 /Net ncome 0.28*.153 1.598.114.163.871 -.133 -.558.587.050.322.750.058.723.471.009.163.871 /HC 0.30* 0.18* 1.752.083 -.701.484 -.790*** -3.775.003 0.592** 2.950.006 0.18** 2.182.031.043 -.701.484 /Interest expenses -0.39**.072.657.513 2.719.007 -.017 -.090.930 -.159-1.035.309.048.575.566.151 2.719.007 /Intangble amortzatons 0.28*.127 1.117.267.087.931 -.033 -.164.873 -.351-1.695.100.023.288.774.005.087.931 /Tangble amortzatons.080 0.244** 2.540.013.754.452 -.076 -.296.772.137.843.405.120 1.448.150.042.754.452 /Other amortzatons (AVERAGE) -.005.104 1.096.276.208.835 -.031 -.155.879 0.29* 1.701.099.005.067.947.011.208.835 Notes: * Sgnfcance level of 10%; ** sgnfcance level of 5%; *** sgnfcance level of 1%. Table 9. Model 1.2. Dependent varable: FCFO/Sales 2009-2011; ndependent varables 2005-2009 Model 1 Dependent varable: FCFO/ Sales (Average) 2009-2011 TRAVEL PAPER ADVERTISING COMPUTER METAL CHEMICAL Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. (Constant) -1.539.142-2.414.018 3.238.007-2.776.009-2.947.004 -.122.903 /Taxaton -.022 -.495.627.102 1.036.303 -.125 -.742.472.207 1.305.201.284 3.589.000 -.411-5.141.000 /Net ncome 0.98*** 24.398.000.153 1.598.114 -.068 -.337.742 -.001 -.006.995.060.795.428.013.182.856 /HC 0.11** 2.607.018 0.18* 1.752.083 -.818*** -4.609.001.518 2.643.013.204 2.568.011 -.048 -.598.551 /Interest expenses -.054-1.292.214.072.657.513 -.067 -.407.691 -.267-1.775.085.022.281.779.031.425.671 /Intangble amortzatons 0.18*** 4.685.000.127 1.117.267 -.234-1.351.202 -.197 -.970.339 -.035 -.460.647.009.123.902 /Tangble amortzatons -.046-1.159.262 0.24** 2.540.013 -.108 -.500.626.119.744.462.017.223.824.045.603.547 /Other amortzatons -.014 -.353.729.104 1.096.276 -.012 -.070.946.307 1.795.082 -.242-3.172.002.014.192.848 Notes: * Sgnfcance level of 10%; ** sgnfcance level of 5%; *** sgnfcance level of 1%. 156
Table 10. Model 2.1. Dependent varable: FCFO 2009-2011; ndependent varables 2002-2009 Model 2 Dependent varable: FCFO (Average) 2009-2011 PAPER ADVERTISING TRAVEL COMPUTER METAL CHEMICAL Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. (Constant) -1.629.107 3.280.007-0.56 0.583.919.363-2.896 0.004 3.628 0 /Taxaton -.093-1.027.307 -.138 -.766.458-0.13-0.655 0.522.043.295.769 0.16** 1.991 0.048-0.101-1.087 0.279 /Net ncome.007.069.945 -.091 -.502.625 0.025 0.137 0.893.010.067.947 0.17** 2.233 0.027-0.034-0.41 0.682 /HC 0.235** 2.352.021 -.864-4.447.001 0.267 1.556 0.138 -.093 -.624.536 0.14* 1.81 0.072-0.286-3.258 0.001 /Interest expenses -.053 -.519.605.031.169.868-0.44** -2.273 0.036.046.315.754 0.012 0.147 0.883-0.046-0.532 0.595 /Intangble amortzatons.369 3.476.001.049.262.798 0.32* 2.039 0.057.028.102.919 0.023 0.287 0.775 0.008 0.09 0.928 /Tangble amortzatons.001.015.988 -.093 -.495.630 0.229 1.353 0.194 -.042 -.151.881 0.16** 2.018 0.045-0.177-2.055 0.042 /Other amortzatons.016.181.857.027.154.880-0.06-0.383 0.707.000 -.003.998 0.017 0.212 0.832-0.129-1.518 0.131 Notes: * Sgnfcance level of 10%; ** sgnfcance level of 5%; *** sgnfcance level of 1%. Table 11. Model 2.2. Dependent varable: FCFO/Sales 2009-2011; ndependent varables 2002-2009 Model 2 Dependent varable: FCFO/Sales (Average) 2009-2011 PAPER ADVERTISING TRAVEL COMPUTER METAL CHEMICAL Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. Beta t Sg. (Constant) -1.344.182 4.330.001 -.875.394.561.578-1.476.142-1.905.059 /Taxaton -.126-1.314.192 -.138 -.963.354.500 1.715.105 0.24* 1.769.083.107 1.348.180.075.948.344 /Net ncome.038.374.709 -.026 -.183.858.214.793.439 -.031 -.240.811.039.493.623 -.005 -.060.952 /HC.114 1.070.288 -.859-5.542.000.117.467.647 -.167-1.225.226.111 1.378.170.089 1.088.278 /Interest expenses.022.206.837 -.017 -.116.910 -.471-1.674.113.201 1.523.134 -.027 -.333.740.026.324.746 /Intangble amortzatons.166 1.472.144 -.180-1.218.247.228.983.340.386 1.519.135 -.210-2.618.010 -.001 -.013.990 /Tangble amortzatons 0.25** 2.570.012 -.090 -.602.558.207.833.416 -.374-1.458.151.110 1.371.172.067.859.392 /Other amortzatons.096 1.008.316.015.106.918 -.106 -.456.654.101.776.441 -.041 -.525.600.010.130.897 Notes: * Sgnfcance level of 10%; ** sgnfcance level of 5%; *** sgnfcance level of 1%. 157