Proceedings of the 8th International Conference on Intellectual ...

More documents

Recommendations

Info

Maria Cristina Morariu H2a. VAHU is positively associated with market valuation. H2b. VAHU is positively associated with pr<strong>of</strong>itability. H2c. VAHU is positively associated with productivity. H3a. SCVA is positively associated with market valuation. H3b. SCVA is positively associated with pr<strong>of</strong>itability. H3c. SCVA is positively associated with productivity. H4a. VACA is positively associated with market valuation. H4b. VACA is positively associated with pr<strong>of</strong>itability. H4c. VACA is positively associated with productivity. 3. Methodology 3.1 Sample selection Our initial sample consisted <strong>of</strong> 21 Romanian firms that are listed on BSE selected because <strong>of</strong> <strong>the</strong>ir market representativeness and <strong>the</strong> practicality in data collection. A preliminary testing <strong>of</strong> <strong>the</strong> sample companies showed that variables are not normally distributed. Consequently we applied to <strong>the</strong>m <strong>the</strong> natural logarithm after which some companies were taken out due to null/negative values recorded by some variables. In conclusion, our final sample consists <strong>of</strong> 14 companies that are classified into sectors <strong>of</strong> activity and industries. For constructing a comparable framework with o<strong>the</strong>r studies, we have used for classification <strong>the</strong> definitions provided by <strong>the</strong> Global Industry Classification Standard (GICS) at June 30, 2010. Fur<strong>the</strong>rmore, for <strong>the</strong> purposes <strong>of</strong> testing our hypo<strong>the</strong>sis, <strong>the</strong> industries are classified into traditional industries (TI) and knowledge intensive business services (KIBS). Table 2 provides a picture <strong>of</strong> classification results. Table 2: Companies classification KIBS Criteria TI KI KI TI TI TI GICS Criteria Materials H ealthCare Financial Utilities Industrials Energy Prelim ina ry Sam ple 3 2 10 2 2 2 Fina l S amp le* 2 2 7 2 0 1 ALRO S.A.* ANTIBIOTICE S.A.* BANCA COMERCIALA C.N.T.E.E. CARPATICA S.A. TRANSELECTRICA* SOCEP S.A. OIL TERM INAL S.A. AZOMURES S.A.* BIOFARM S.A.* OLTCHIM S.A. RM. 3.2 Definition <strong>of</strong> variables BANCA TRANSILVANIA* BRD - GROUPE SOCIETE GENERALE* IMPACT DEVELOPER & CONTRACTOR S.A. S.S.I.F. BROKER S.A. S.A.* SIF M OLDOVA S.A.* SIF MUN TENIA S.A.* SIF OLTENIA S.A.* S.A.* S.N.T.G.N. TRANSGAZ S.A.* TURBOMECA NICA OMV PETROM* 3.2.1 Dependent variables The three traditional financial indicators used as dependent variables are market-to-book value (MB) for market valuation, asset turnover (ATO) for productivity and return on equity (ROE) for pr<strong>of</strong>itability. The following general formulas were considered for computation: MB = Market Capitalization/Shareholders’ Equity = Ln_MkCap/Ln_Equity 386
Maria Cristina Morariu ATO = Turnover/Total Assets =Ln_Sales/Ln_TotalAssets ROE = Net Pr<strong>of</strong>it/Shareholders’ Equity = Ln_NetPr<strong>of</strong>it/Ln_Equity 3.2.2 Independent variables The literature review revealed <strong>the</strong> use <strong>of</strong> VAIC as both a dependent and an independent variable. It will be included in this study as independent variable, in order to test <strong>the</strong> extent to which it may be related to a company’s performance. Using <strong>the</strong> VAICTM method measures, three coefficients were selected to measure <strong>the</strong> independent variables under consideration: (1) VAHU: Value added human capital coefficient VAHU = VA/HC; VA = OUT – INPUT = Ln_Sales – Ln_Input HC = all <strong>the</strong> expenditures for employees (total salaries and wages) = Ln_HC (2) SCVA: Value added structural capital coefficient SCVA = SC/VA; SC = Ln_VA – Ln_HC; VAIN: <strong>the</strong> value added IC coefficient VAIN = Ln_VAHU + Ln_SCVA (3) VACA: <strong>the</strong> value added capital employed coefficient VACA = VA/CA CA = book value <strong>of</strong> <strong>the</strong> net assets (or equity) = Ln_Equity In <strong>the</strong> end, VAIC = VAHU + SCVA + VACA 3.2.3 Control variables In conducting <strong>the</strong> multiple regression analysis, two control variables (firm size and industry type) are generally included. Proxy measures are briefly described as follows: 1. Size <strong>of</strong> <strong>the</strong> Firm (Ln_Sales) = natural logarithm <strong>of</strong> total sales; 2. Industry Type: dummy variable representing 2 major industries: KI and TI 3.3 Regression models Six regression models were used to investigate our 12 hypo<strong>the</strong>ses. The first three models investigate <strong>the</strong> association between VAIC and <strong>the</strong> three dependent variables; <strong>the</strong> remaining three models investigate on <strong>the</strong> association between VAIC components and dependent variables. M1. MB = β1VAICTM + β2FSIZE + β3INDUSTRY M2. ATO = β1VAICTM + β2FSIZE + β3INDUSTRY M3. ROE = β1VAICTM+β2FSIZE+β3INDUSTRY M4. MB = β1VAHU+β2SCVA+β3VACA+β4FSIZE+β5INDUSTRY M5. ATO =β1VAHU+β2SCVA+β3VACA+β4FSIZE+β5INDUSTRY 387
Page 1 and 2:
Proceedings <stron
Page 3 and 4:
Contents Paper Title Author(s) Page
Page 5 and 6:
Paper Title Author(s) Page No. Main
Page 7 and 8:
Paper Title Author(s) Page No. Know
Page 9 and 10:
Preface These proceedings represent
Page 11 and 12:
TU Dortmund, Germany, ); Wing Bun L
Page 13 and 14:
William Halal is Prof</stro
Page 15 and 16:
Evgeny Blagov has obtained a specia
Page 17 and 18:
science and mathem
Page 19 and 20:
George Majdalany is a PhD candidate
Page 21 and 22:
Joyce Nesamani Simson is a PhD cand
Page 23 and 24:
Manpower Discipline Based on Knowle
Page 25 and 26:
Peyman Akhavan et al. brains, knowl
Page 27 and 28:
Peyman Akhavan et al. showed that o
Page 29 and 30:
Peyman Akhavan et al. appropriate l
Page 31 and 32:
Peyman Akhavan et al. sources for c
Page 33 and 34:
Attitudes Towards Changes and Organ
Page 35 and 36:
Ruth Alas et al. During the
Page 37 and 38:
Professional inter
Page 39 and 40:
Ruth Alas et al. Table 4: Correlati
Page 41 and 42:
Research was supported by ETF Grant
Page 43 and 44:
Rivadávia Correa Drummond de Alvar
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
5. Conclusions Rivadávia Correa Dr
Page 53 and 54:
Page 55 and 56:
Toward a Meta-Synthe</stron
Page 57 and 58:
Xiaomi An et al 2.3 Reasons for dev
Page 59 and 60:
Xiaomi An et al PTC works closely w
Page 61 and 62:
Xiaomi An et al The first is an adm
Page 63 and 64:
Xiaomi An et al policy reflections
Page 65 and 66:
Figure 1: Transmission gear Marko A
Page 67 and 68:
Marko Anzelak et al. knowledge basi
Page 69 and 70:
Marko Anzelak et al. 72% recognise
Page 71 and 72:
Marko Anzelak et al. virtually all
Page 73 and 74:
Gabriel Prananingrum Aryanindita an
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Rita Babiliūtė and Kęstutis Kri
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Knowledge Management, Cultural Know
Page 89 and 90:
Leonard Barnett and Edward Carter 2
Page 91 and 92:
Leonard Barnett and Edward Carter p
Page 93 and 94:
Leonard Barnett and Edward Carter p
Page 95 and 96:
Leonard Barnett and Edward Carter d
Page 97 and 98:
Impact of Knowledg
Page 99 and 100:
Bob Barrett shorter product life-cy
Page 101 and 102:
Bob Barrett 6. Concept of</
Page 103 and 104:
Bob Barrett Brown, J.S. and Duguid
Page 105 and 106:
Aurilla Aurelie Bechina and Eli Hus
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Lorne Booker et al. Knowledge is us
Page 115 and 116:
Figure 1: Business Valuation Knowle
Page 117 and 118:
3.4 Practitioner knowledge market L
Page 119 and 120:
4. Implications Lorne Booker et al.
Page 121 and 122:
Strategic Thinking Dimensions in Em
Page 123 and 124:
Constantin Bratianu et al. shows <s
Page 125 and 126:
Constantin Bratianu et al. Academy
Page 127 and 128:
Constantin Bratianu et al. cases wi
Page 129 and 130:
Constantin Bratianu et al. Pinker,
Page 131 and 132:
Delio Ignacio Castañeda and Sandra
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Quality in Teacher Education: A Sys
Page 139 and 140:
Rajendra Chetty The mechanism <stro
Page 141 and 142:
Academic leadership Rajendra Chetty
Page 143 and 144:
Rajendra Chetty There must be a re-
Page 145 and 146:
Developing Personal Knowledge Manag
Page 147 and 148:
Thirathep Chonmait
Page 149 and 150:
Thirathep Chonmait
Page 151 and 152:
The Relationship Between Knowledge
Page 153 and 154:
Samuel Kai Wah Chu et al. A knowled
Page 155 and 156:
Samuel Kai Wah Chu et al. negative
Page 157 and 158:
Samuel Kai Wah Chu et al. Questionn
Page 159 and 160:
Samuel Kai Wah Chu et al. few done
Page 161 and 162:
Value Creation Through Collaborativ
Page 163 and 164:
Ridha Derrouiche et al. level for e
Page 165 and 166:
Ridha Derrouiche et al. best, <stro
Page 167 and 168:
Ridha Derrouiche et al. experience
Page 169 and 170:
Ridha Derrouiche et al. Of course,
Page 171 and 172:
Organizational Learning and Transfo
Page 173 and 174:
Irra Chrisyanti Dewi was carried ou
Page 175 and 176:
Irra Chrisyanti Dewi Table 2: Valid
Page 177 and 178:
Irra Chrisyanti Dewi Library is exp
Page 179 and 180:
Knowledge Acquisition and Sharing:
Page 181 and 182:
Ikechukwu Diugwu articulated and st
Page 183 and 184:
Ikechukwu Diugwu establishing setti
Page 185 and 186:
Ikechukwu Diugwu Mentzer, J. T., Mi
Page 187 and 188:
Eva Eckenhofer cou
Page 189 and 190:
Eva Eckenhofer The
Page 191 and 192:
Eva Eckenhofer The
Page 193 and 194:
Eva Eckenhofer Bur
Page 195 and 196:
1.2 Intellectual capital and innova
Page 197 and 198:
3.2.2 Reliability Irene Y.H. Fan an
Page 199 and 200:
Figure 4: latent variable coefficie
Page 201 and 202:
Irene Y.H. Fan and Rongbin W.B. Lee
Page 203 and 204:
Irene Y.H. Fan and Rongbin W.B. Lee
Page 205 and 206:
2.1 Appropriation Ronny Gey From a
Page 207 and 208:
Ronny Gey Table 1: Characterization
Page 209 and 210:
Ronny Gey The developers feel that
Page 211 and 212:
Ronny Gey A first limitation we wer
Page 213 and 214:
Kenneth Grant part of</stro
Page 215 and 216:
Kenneth Grant The diffusion was stu
Page 217 and 218:
Kenneth Grant A detailed computer-s
Page 219 and 220:
Kenneth Grant did not come to <stro
Page 221 and 222:
Kenneth Grant of m
Page 223 and 224:
Michel Grundstein although hundred
Page 225 and 226:
Michel Grundstein Figure 1: DITEK p
Page 227 and 228:
Michel Grundstein 4.1.1 Knowledge i
Page 229 and 230:
Michel Grundstein share. However <s
Page 231 and 232:
6. Conclusions and perspectives Mic
Page 233 and 234:
Intellectual Property Valuation Dec
Page 235 and 236:
Hezlin Harris et al. (Burrone and S
Page 237 and 238:
Hezlin Harris et al. The need for s
Page 239 and 240:
Acknowledgements Hezlin Harris et a
Page 241 and 242:
Managing an Assessment Process <str
Page 243 and 244:
Table 1: List of N
Page 245 and 246:
Shaheed Hartley Table 3: OEQ respon
Page 247 and 248:
Shaheed Hartley It is very difficul
Page 249 and 250:
A Community Framework for Knowledge
Page 251 and 252:
Igor Hawryszkiewycz between communi
Page 253 and 254:
Figure 4: Modeling knowledge subsys
Page 255 and 256:
Igor Hawryszkiewycz Prahalad, C.K.,
Page 257 and 258:
Eli Hustad and Aurilla Aurelie Bech
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Does Knowledge Stickiness Affect a
Page 267 and 268:
Nurul Indarti knowledge (Nelson and
Page 269 and 270:
3. Research methods 3.1 Research in
Page 271 and 272:
Coded knowledge Nurul Indarti The f
Page 273 and 274:
Nurul Indarti Arrow, K. J. (1969).
Page 275 and 276:
Organizational Learning in Pr<stron
Page 277 and 278:
Brigita Janiunaite et al. learn and
Page 279 and 280:
Brigita Janiunaite et al. given dif
Page 281 and 282:
Brigita Janiunaite et al. empowered
Page 283 and 284:
Brigita Janiunaite et al. A statist
Page 285 and 286:
Brigita Janiunaite et al. learning
Page 287 and 288:
Balancing the Ince
Page 289 and 290:
Giedrius Jucevičius safeguard and
Page 291 and 292:
Giedrius Jucevičius nature <strong
Page 293 and 294:
Giedrius Jucevičius motivating fac
Page 295 and 296:
Giedrius Jucevičius Ghosh, T. (200
Page 297 and 298:
Palmira Juceviciene and Vyda Mozuri
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Robertas Jucevicius relations shoul
Page 309 and 310:
Robertas Jucevicius do not know whe
Page 311 and 312:
Robertas Jucevicius and innovation
Page 313 and 314:
Robertas Jucevicius Lawton Smith, H
Page 315 and 316:
Annukka Jyrämä et al. knowledge c
Page 317 and 318:
Annukka Jyrämä et al. Respondents
Page 319 and 320:
Annukka Jyrämä et al. various cit
Page 321 and 322:
Annukka Jyrämä et al. example hea
Page 323 and 324:
Managing Information Overload - Tea
Page 325 and 326:
Harri Ketamo conceptual structure a
Page 327 and 328:
Harri Ketamo limited to YouTube, Fl
Page 329 and 330:
Harri Ketamo Figure 8: Visualizatio
Page 331 and 332:
Farmers’ Knowledge Management in
Page 333 and 334:
2.1 Farmer knowledge Le Nguyen Doan
Page 335 and 336:
Le Nguyen Doan Khoi days needed for
Page 337 and 338:
Le Nguyen Doan Khoi dosage. Moreove
Page 339 and 340:
Le Nguyen Doan Khoi use homemade in
Page 341 and 342:
Developing Human Capital through Ne
Page 343 and 344:
Eric Kong organisation as individua
Page 345 and 346:
Eric Kong As described by Tosey and
Page 347 and 348:
Eric Kong Craft, A. (2001) "Neuro-l
Page 349 and 350:
Sophie Le Bellu and Benoit Le Blanc
Page 351 and 352:
Page 353 and 354:
Results Sophie Le Bellu and Benoit
Page 355 and 356:
Page 357 and 358: Sophie Le Bellu and Benoit Le Blanc
Page 359 and 360: Pornpen Lertthasanawong and Nopasit
Page 367 and 368: 2. Literature review Ya-Hui Ling 2.
Page 369 and 370: Ya-Hui Ling action of</stro
Page 371 and 372: Intellectual Capital Intellectual C
Page 373 and 374: Ya-Hui Ling Hitt, M.A., Hoskisson,
Page 375 and 376: Maksim Maksimovic et al paper prese
Page 377 and 378: Maksim Maksimovic et al 4. Lean kno
Page 379 and 380: Maksim Maksimovic et al of<
Page 381 and 382: Maurizio Massaro and Andrea Moro <s
Page 383 and 384: Maurizio Massaro and Andrea Moro Pr
Page 385 and 386: Maurizio Massaro and Andrea Moro hi
Page 387 and 388: Maurizio Massaro and Andrea Moro Si
Page 389 and 390: Helen Mitchell and Dennis Viehland
Page 397 and 398: Figure 1: KMS Framework for collabo
Page 399 and 400: Mohd Zali Mohd Nor et al. Ano<stron
Page 401 and 402: Mohd Zali Mohd Nor et al. Table 4:
Page 403 and 404: Mohd Zali Mohd Nor et al. KQ8 provi
Page 405 and 406: Maria Cristina Morariu economy and
Page 407: Table 1: Literature review summary
Page 411 and 412: 4.2 Correlation analysis Maria Cris
Page 413 and 414: 4.3 Regression analysis Maria Crist
Page 415 and 416: Table 13: Regression analysis_M6 Mo
Page 417 and 418: The Arrival of <st
Page 419 and 420: Bertrand Pauget and Ahmed Dammak Mo
Page 421 and 422: Bertrand Pauget and Ahmed Dammak no
Page 423 and 424: Bertrand Pauget and Ahmed Dammak th
Page 425 and 426: Bertrand Pauget and Ahmed Dammak Sa
Page 427 and 428: Wannapa Pipattanawong et al. cannot
Page 429 and 430: Wannapa Pipattanawong et al. 3.2 Do
Page 431 and 432: Wannapa Pipattanawong et al. Moreov
Page 433 and 434: The Dark Side of S
Page 435 and 436: Siwarit Pongsakornrungsilp vans, ro
Page 437 and 438: Siwarit Pongsakornrungsilp Kreng Ja
Page 439: Siwarit Pongsakornrungsilp Lave, J.
show all

Proceedings of the 8th International Conference on Intellectual ...

Create successful ePaper yourself

Delete template?

Save as template?