Proceedings of the 3rd European Conference on Intellectual Capital

More documents

Recommendations

Info

Peter Marshall and Damian Gordon reporting or as means <strong>of</strong> critiquing <strong>the</strong> performance <strong>of</strong> o<strong>the</strong>rs. An advantage <strong>of</strong> <strong>the</strong> retrospective approach is that video can be paused or run at slow speed to allow time for <strong>the</strong> SME to provide full explanation <strong>of</strong> <strong>the</strong> decision making processes involved. This is particularly useful in dynamic domains (ibid). In <strong>the</strong>se experiments, <strong>the</strong> retrospective version is used. The SME will be presented with a series <strong>of</strong> videos <strong>of</strong> <strong>the</strong> associated techniques being performed in a competition. The expert is <strong>the</strong>n asked to provide a commentary, critiquing <strong>the</strong> techniques <strong>of</strong> o<strong>the</strong>rs as <strong>the</strong>y perform. To minimise <strong>the</strong> impact <strong>of</strong> <strong>the</strong> knowledge acquisition bottleneck, demonstrations <strong>of</strong> technique will not recorded by <strong>the</strong> knowledge engineer but randomly selected from examples <strong>of</strong> <strong>the</strong> techniques being successfully performed from <strong>the</strong> Internet. 3.4.3 Triadic elicitation using video technology Triadic elicitation is a KE technique used to capture <strong>the</strong> way in which an expert views <strong>the</strong> concepts in a domain. Traditionally, a knowledge engineer populates an experimental set with concepts from <strong>the</strong> domain under consideration during <strong>the</strong> KE session. Next <strong>the</strong> knowledge engineer presents three random concepts within <strong>the</strong> set to SME. Upon presentation <strong>of</strong> <strong>the</strong>se concepts, <strong>the</strong> SME selects two <strong>of</strong> <strong>the</strong> three that <strong>the</strong>y would consider <strong>the</strong> most similar and explain why. This differentiating factor will identify a characteristic <strong>of</strong> <strong>the</strong> overall set <strong>of</strong> concepts being considered. In this research, a modified version <strong>of</strong> this technique was presented in which <strong>the</strong> concepts from <strong>the</strong> field were replaced by video variations <strong>of</strong> <strong>the</strong> MMA technique being performed in competition. As with <strong>the</strong> Commentary technique detailed above, randomly selected videos found online will be used, thus making <strong>the</strong> experiment repeatable as well as to minimise <strong>the</strong> effect <strong>of</strong> <strong>the</strong> knowledge acquisition bottleneck. Although <strong>the</strong> traditional method <strong>of</strong> triadic elicitation does not elicit procedural knowledge, it was <strong>of</strong> interest to see how <strong>the</strong> modified version exposed different types <strong>of</strong> knowledge during <strong>the</strong> KE sessions. 3.4.4 Cognitive walk-through The Cognitive Walk-Through is a KE technique where <strong>the</strong> SME articulates <strong>the</strong>ir knowledge concurrently whilst working through a problem or a task (Wright & Ayton, 1987). Similar to <strong>the</strong> Commentary technique, this method is performed concurrently and enables knowledge engineers to observe <strong>the</strong> thought process <strong>of</strong> <strong>the</strong> SME during <strong>the</strong> performance <strong>of</strong> <strong>the</strong> task. This is a powerful technique in <strong>the</strong> identification <strong>of</strong> knowledge elements and steps. According to Ericsson and Simon (1980), <strong>the</strong> concurrent verbalisation element provides a complete report <strong>of</strong> <strong>the</strong> knowledge being demonstrated as no thought, feeling, or action is omitted from <strong>the</strong>ir account. In tasks where significant cognitive effort is required, this method may interfere with <strong>the</strong> SME‟s performance. As a means <strong>of</strong> negating this limitation, a retrospective variant can be employed where <strong>the</strong> SME provides a commentary <strong>of</strong> <strong>the</strong>ir thought processes whilst observing <strong>the</strong>mselves performing <strong>the</strong> task. However this variant does not provide <strong>the</strong> knowledge engineer direct access to <strong>the</strong> decision making process as it happens. For <strong>the</strong> purpose <strong>of</strong> this research, <strong>the</strong> concurrent version <strong>of</strong> <strong>the</strong> technique was employed. 3.5 Analysis <strong>of</strong> captured knowledge Once <strong>the</strong> KE sessions were completed, <strong>the</strong> next step was to analyse <strong>the</strong> produced protocol from <strong>the</strong> KE sessions. For each technique, a transcript was produced from <strong>the</strong> resulting protocol. The next step in will be to analyse <strong>the</strong> non-verbal articulations, <strong>the</strong> movements used by SME in <strong>the</strong> demonstration <strong>of</strong> technique were articulated and <strong>the</strong>n merged. From this, <strong>the</strong> intersections from this produce a model <strong>of</strong> <strong>the</strong> knowledge being demonstrated. 3.6 Metrics for comparison Once <strong>the</strong> individual knowledge artefacts had been analysed <strong>the</strong> next step will be to compare <strong>the</strong> different techniques against o<strong>the</strong>r. In this we can make interesting comparisons between <strong>the</strong> KE elicitation techniques as well as <strong>the</strong> initial demonstration <strong>of</strong> technique. As described above two types <strong>of</strong> knowledge were identified as being <strong>the</strong> aim <strong>of</strong> <strong>the</strong> elicitation sessions, procedural knowledge, in terms <strong>of</strong> <strong>the</strong> steps required to perform <strong>the</strong> techniques, and strategic knowledge, in terms <strong>of</strong> <strong>the</strong> situations <strong>of</strong> when <strong>the</strong> <strong>the</strong>se techniques could be performed. The following measures were extracted from <strong>the</strong> elicitation sessions. Procedural Knowledge articulated verbally Procedural knowledge articulated non-verbally Strategic knowledge articulated verbally 226
Strategic knowledge articulated non-verbally Peter Marshall and Damian Gordon From <strong>the</strong>se findings, a series <strong>of</strong> graphs will be created as a means <strong>of</strong> comparison. Procedural steps required to perform <strong>the</strong> arm-bar technique Strategic knowledge found for <strong>the</strong> arm-bar technique Procedural steps required to perform <strong>the</strong> tai-otoshi throw Strategic knowledge found for <strong>the</strong> tai-otoshi throw 4. Results The results are presented considering <strong>the</strong> two techniques (Arm-Bar and Tai-Otoshi) from <strong>the</strong> perspectives <strong>of</strong> both Procedural knowledge and Strategic knowledge. 4.1 Elicitation <strong>of</strong> procedural knowledge for <strong>the</strong> Arm-Bar technique The first comparison <strong>of</strong> elicited knowledge was <strong>the</strong> comparison <strong>of</strong> techniques in eliciting procedural steps required to perform <strong>the</strong> arm bar technique. From <strong>the</strong> results in figure 1, <strong>the</strong> initial demonstration <strong>of</strong> technique identified nine steps, six <strong>of</strong> which were articulated verbally and three non-verbally. The cognitive walkthrough not only identified <strong>the</strong> nine steps but <strong>the</strong> steps were verbally identified. With <strong>the</strong> OIM demonstration, only five <strong>of</strong> <strong>the</strong> nine steps were identified and only one <strong>of</strong> those articulated verbally. The triadic elicitation and commentary methods failed to identify any <strong>of</strong> <strong>the</strong> steps. Procedural Steps Identified Verbal Non Verbal Results From Elicitation 0 2 4 6 8 10 Number <strong>of</strong> Steps Identified Cognitive WalkThrough Triadic Elicitation Commentary OIM Initial Demonstration Figure 1: Comparison <strong>of</strong> verbal and non-verbal procedural steps elicited for <strong>the</strong> Arm Bar Technique 4.2 Elicitation <strong>of</strong> procedural knowledge for <strong>the</strong> Tai-Otoshi technique The next comparison <strong>of</strong> elicited knowledge was <strong>the</strong> comparison <strong>of</strong> techniques in eliciting procedural steps required to perform <strong>the</strong> tai-otoshi throw. From <strong>the</strong> results in figure 2, <strong>the</strong> initial demonstration <strong>of</strong> technique identified seven steps, five <strong>of</strong> which were articulated verbally and two non-verbally. As can be seen in <strong>the</strong> results, <strong>the</strong> initial demonstration was superior to <strong>the</strong> elicitation methods. In <strong>the</strong> cognitive walkthrough, two were verbally identified and <strong>the</strong> o<strong>the</strong>r five, non-verbally. In <strong>the</strong> OIM demonstration, four were identified and three non-verbally. Once again, <strong>the</strong> triadic elicitation and commentary methods failed to identify any <strong>of</strong> <strong>the</strong> steps. 4.3 Elicitation <strong>of</strong> strategic knowledge for <strong>the</strong> Arm-bar technique The third comparison <strong>of</strong> elicited knowledge was <strong>the</strong> comparison <strong>of</strong> techniques in eliciting strategic knowledge required to perform <strong>the</strong> arm-bar technique. In <strong>the</strong> initial demonstration, three methods were identified non-verbally. In <strong>the</strong> OIM method, two techniques were shown both <strong>of</strong> which were 227
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. Hier
Page 7 and 8:
Preface These proceedings represent
Page 9 and 10:
educating through ‘hands on’ co
Page 11 and 12:
Ibrahim Elbeltagi is a Senior lectu
Page 13 and 14:
Theodora Ngosi has a PhD in Compute
Page 15:
José Vale is an invited assistant
Page 19 and 20:
Social Knowledge: Are you Ready? 1
Page 21 and 22:
John Girard don’t know.” (Benne
Page 23 and 24:
John Girard The real question becom
Page 25 and 26:
John Girard our belief that many le
Page 27 and 28:
John Girard organizations to gain k
Page 29 and 30:
Intellectual Capital Accounting - H
Page 31:
Academic Research Papers 15
Page 34 and 35:
Khodayar Abili and Mahyar Abili The
Page 36 and 37:
Khodayar Abili and Mahyar Abili Nah
Page 38 and 39:
Khodayar Abili and Mahyar Abili Tim
Page 40 and 41:
Knowledge Transfer in Romanian Univ
Page 42 and 43:
Simona Agoston et al. Table 1: Know
Page 44 and 45:
Simona Agoston et al. homoscedastic
Page 46 and 47:
Simona Agoston et al. included in <
Page 48 and 49:
A Modeling Approach to Intellectual
Page 50 and 51:
Eckhard Ammann (referring to <stron
Page 52 and 53:
Eckhard Ammann Figure 2: The IC spa
Page 54 and 55:
Eckhard Ammann section 2. They are
Page 56 and 57:
Eckhard Ammann From an overall pers
Page 58 and 59:
Sorin Anagnoste and Gabriela Dumitr
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Gabriela Atanasiu and Florin Leon T
Page 72 and 73:
Gabriela Atanasiu and Florin Leon
Page 74 and 75:
Gabriela Atanasiu and Florin Leon B
Page 76 and 77:
Gabriela Atanasiu and Florin Leon a
Page 78 and 79:
Developing and Implementing Strateg
Page 80 and 81:
2. Technology, faculty, and staff B
Page 82 and 83:
Learning programs Individual poten
Page 84 and 85:
Bob Barrett facts with the<
Page 86 and 87:
Intellectual Capital Dynamics withi
Page 88 and 89:
Ruxandra Bejinaru and Stefan Iordac
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Education and Training Practice Str
Page 96 and 97:
Andrea Bencsik et al. We wanted to
Page 98 and 99:
Andrea Bencsik et al. In this sampl
Page 100 and 101:
Table 4: Types of
Page 102 and 103:
References Andrea Bencsik et al. An
Page 104 and 105:
Ayşen Berberoğlu and Emine Ünar
Page 106 and 107:
Ayşen Berberoğlu and Emine Ünar
Page 108 and 109:
Table 7: Descriptive statistics Ay
Page 110 and 111:
Knowledge Dynamics Modeling Using A
Page 112 and 113:
Constantin Bratianu et al. will hav
Page 114 and 115:
Constantin Bratianu et al. 5. a) Gi
Page 116 and 117:
Constantin Bratianu et al. 5. Data
Page 118 and 119:
Constantin Bratianu et al. Nissen,
Page 120 and 121:
2. Technology and infrastructure Sh
Page 122 and 123:
Sheryl Buckley and Apostolos Gianna
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Donley Carrington and Mike Tayles t
Page 132 and 133:
Donley Carrington and Mike Tayles f
Page 134 and 135:
Donley Carrington and Mike Tayles l
Page 136 and 137:
Donley Carrington and Mike Tayles i
Page 138 and 139:
Donley Carrington and Mike Tayles G
Page 140 and 141:
John Dumay and Jim Rooney on a more
Page 142 and 143:
John Dumay and Jim Rooney Taking <s
Page 144 and 145:
John Dumay and Jim Rooney “For ex
Page 146 and 147:
John Dumay and Jim Rooney Mouritsen
Page 148 and 149:
Marziye Ehrami et al. 1. Creation
Page 150 and 151:
Marziye Ehrami et al. Note: In 2005
Page 152 and 153:
Earnings Quality and Othe</
Page 154 and 155:
Magdi El-Bannany resource-based per
Page 156 and 157:
Magdi El-Bannany Based on this argu
Page 158 and 159:
Magdi El-Bannany Table 2: Descripti
Page 160 and 161:
Magdi El-Bannany variable equal to
Page 162 and 163:
Magdi El-Bannany Edvinsson, L. & Ma
Page 164 and 165:
Ahmed Elsetouhi and Ibrahim Elbelta
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Albrecht Fritzsche and Rebecca Geig
Page 176 and 177:
Page 178 and 179:
Table 4: Applicability of</
Page 180 and 181:
Page 182 and 183:
S&P 500 Market Cap ($ billions) 14,
Page 184 and 185:
Tatiana Garanina and Yana Pavlova T
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Tatiana Garanina and Yana Pavlova I
Page 192 and 193: Impact of Investme
Page 194 and 195: Lidia García-Zambrano et al. 2003;
Page 196 and 197: Lidia García-Zambrano et al. 4.2 P
Page 198 and 199: Table 7: Goodness of</stron
Page 200 and 201: Lidia García-Zambrano et al. reaso
Page 202 and 203: Lidia García-Zambrano et al. Rodri
Page 204 and 205: Víctor Raúl López Ruiz et al. co
Page 206 and 207: Víctor Raúl López Ruiz et al. NI
Page 208 and 209: EI c k i 1 w PC i ic Víctor Raúl
Page 210 and 211: Víctor Raúl López Ruiz et al. Ta
Page 212 and 213: Víctor Raúl López Ruiz et al. Co
Page 214 and 215: Víctor Raúl López Ruiz et al. At
Page 216 and 217: Maria de Lourdes Machado and Odíli
Page 222 and 223: Intellectual Capital and Corporate
Page 224 and 225: Agnes Maciocha when objects in a da
Page 226 and 227: Agnes Maciocha 4. Presentation <str
Page 228 and 229: Agnes Maciocha while in the
Page 230 and 231: Agnes Maciocha Choong K., “Intell
Page 232 and 233: The Influence of H
Page 234 and 235: Anca Mândruleanu From the<
Page 236 and 237: Anca Mândruleanu In Table 2, <stro
Page 238 and 239: Eliciting Tacit Knowledge From a Do
Page 240 and 241: Peter Marshall and Damian Gordon MM
Page 244 and 245: Peter Marshall and Damian Gordon ar
Page 246 and 247: Peter Marshall and Damian Gordon Th
Page 248 and 249: Maurizio Massaro et al. crucial fac
Page 250 and 251: Maurizio Massaro et al. exclusive (
Page 252 and 253: Maurizio Massaro et al. autonomy <s
Page 254 and 255: Maurizio Massaro et al. Chennal (20
Page 256 and 257: Intellectual Capital Management: Ca
Page 258 and 259: Florinda Matos et al. These models
Page 260 and 261: Complaints System dos clients New
Page 262 and 263: Florinda Matos et al. Finally, <str
Page 264 and 265: Florinda Matos et al. The training
Page 266 and 267: Florinda Matos et al. 47. Opinion
Page 268 and 269: Florinda Matos et al. In th
Page 270 and 271: Florinda Matos et al. The Quadrant
Page 272 and 273: Florinda Matos et al. Integrated te
Page 274 and 275: Florinda Matos et al. The networks
Page 276 and 277: Knowledge Management for Knowledge
Page 278 and 279: 3. Our research Ludmila Mládková
Page 280 and 281: Ludmila Mládková organizational s
Page 282 and 283: Ludmila Mládková mentioned only b
Page 284 and 285: Applying the VAIC
Page 286 and 287: Maria Molodchik and Anna Bykova pro
Page 288 and 289: Maria Molodchik and Anna Bykova 2.
Page 290 and 291: Maria Molodchik and Anna Bykova Let
Page 292 and 293:
Maria Molodchik and Anna Bykova The
Page 294 and 295:
Perspectives on the</strong
Page 296 and 297:
Maria Cristina Morariu as we consid
Page 298 and 299:
Maria Cristina Morariu Tobin’s q
Page 300 and 301:
Maria Cristina Morariu for determin
Page 302 and 303:
The Structural Model of</st
Page 304 and 305:
Fattah Nazem Ackerley (2006) reveal
Page 306 and 307:
Fattah Nazem The research instrumen
Page 308 and 309:
Fattah Nazem intellectual capital m
Page 310 and 311:
Fattah Nazem Johnson, M.J. (1986) T
Page 312 and 313:
Andrei Stefan Nestian organization.
Page 314 and 315:
Andrei Stefan Nestian Chaordic syst
Page 316 and 317:
Andrei Stefan Nestian environment,
Page 318 and 319:
Increasing Knowledge Management Mat
Page 320 and 321:
Theodora Ngosi et al. 2.2 Dominatin
Page 322 and 323:
Theodora Ngosi et al. Figure 1: Del
Page 324 and 325:
Theodora Ngosi et al. IT systems, a
Page 326 and 327:
5. Discussion and summary Theodora
Page 328 and 329:
Theodora Ngosi et al. Weill, P., an
Page 330 and 331:
Bongani Ngwenya late adopters respo
Page 332 and 333:
Bongani Ngwenya This paper extends
Page 334 and 335:
Bongani Ngwenya argument, t
Page 336 and 337:
Bongani Ngwenya DiMaggio, P. J., an
Page 338 and 339:
The Quality of Kno
Page 340 and 341:
5.2 5 4.8 4.6 4.4 4.2 4 3.8 Compete
Page 342 and 343:
Corina Pelau et al. In order to imp
Page 344 and 345:
Hierarchy and Tacit Knowledge in <s
Page 346 and 347:
Ulrica Pettersson and James Nyce in
Page 348 and 349:
Ulrica Pettersson and James Nyce mi
Page 350 and 351:
Katja Pook and Campbell Warden time
Page 352 and 353:
Katja Pook and Campbell Warden futu
Page 354 and 355:
5. Commonalities of</strong
Page 356 and 357:
Katja Pook and Campbell Warden <str
Page 358 and 359:
Katja Pook and Campbell Warden Lazl
Page 360 and 361:
Nicolae Al. Pop et al. satisfaction
Page 362 and 363:
Nicolae Al. Pop et al. the<
Page 364 and 365:
Nicolae Al. Pop et al. satisfaction
Page 366 and 367:
Knowledge Cities: A Portuguese Case
Page 368 and 369:
Katia Rodrigues and Eduardo Tomé
Page 370 and 371:
3. The empirical study 3.1 Methodol
Page 372 and 373:
Table 3: Smart cities - application
Page 374 and 375:
4.3 Suggestions to future work Kati
Page 376 and 377:
Anna Romiti and Daria Sarti 2. Orga
Page 378 and 379:
Anna Romiti and Daria Sarti based o
Page 380 and 381:
Anna Romiti and Daria Sarti Governa
Page 382 and 383:
Anna Romiti and Daria Sarti Therefo
Page 384 and 385:
Anna Romiti and Daria Sarti Valkoka
Page 386 and 387:
Kent Rondeau and Terry Wagar planne
Page 388 and 389:
2.1 Study measures Kent Rondeau and
Page 390 and 391:
Kent Rondeau and Terry Wagar contri
Page 392 and 393:
Kent Rondeau and Terry Wagar Our re
Page 394 and 395:
Intellectual Capital and a Firm’s
Page 396 and 397:
Figure 1: Hypothes
Page 398 and 399:
Helena Santos-Rodrigues et al. Norm
Page 400 and 401:
Helena Santos-Rodrigues et al. To s
Page 402 and 403:
Helena Santos-Rodrigues et al. Our
Page 404 and 405:
Factors Influencing the</st
Page 406 and 407:
Thanaletchumi Sathasivam et al. 2.3
Page 408 and 409:
Thanaletchumi Sathasivam et al. The
Page 410 and 411:
Thanaletchumi Sathasivam et al. gro
Page 412 and 413:
Thanaletchumi Sathasivam et al. The
Page 414 and 415:
Thanaletchumi Sathasivam et al. 11.
Page 416 and 417:
Enhancing IC Formation by Evoking H
Page 418 and 419:
Klaus Bruno Schebesch to portray <s
Page 420 and 421:
Klaus Bruno Schebesch 4. Leaning an
Page 422 and 423:
Klaus Bruno Schebesch We argue in f
Page 424 and 425:
Klaus Bruno Schebesch come
Page 426 and 427:
In Search of key F
Page 428 and 429:
Karen Smits et al. contribution, wi
Page 430 and 431:
Karen Smits et al. Table 1: Correla
Page 432 and 433:
Karen Smits et al. staff feel that
Page 434 and 435:
Marta-Christina Suciu et al. This p
Page 436 and 437:
Marta-Christina Suciu et al. <stron
Page 438 and 439:
Marta-Christina Suciu et al. The qu
Page 440 and 441:
Marta-Christina Suciu et al. This t
Page 442 and 443:
Information and Communication Techn
Page 444 and 445:
Jukka Surakka et al. The participan
Page 446 and 447:
Jukka Surakka et al. people. Commun
Page 448 and 449:
Personal Knowledge Management (PKM)
Page 450 and 451:
Marzena Świgoń provide th
Page 452 and 453:
Marzena Świgoń (number and type <
Page 454 and 455:
Marzena Świgoń measure self-perce
Page 456 and 457:
Eduardo Tomé what we consider to b
Page 458 and 459:
Eduardo Tomé IC. The intervention
Page 460 and 461:
Eduardo Tomé In the</stro
Page 462 and 463:
Eduardo Tomé to stress that <stron
Page 464 and 465:
Eleni Magdalini Vasileiadou et al.
Page 466 and 467:
Page 468 and 469:
Page 470 and 471:
Page 472 and 473:
Page 474 and 475:
Page 476 and 477:
Page 478 and 479:
José María Viedma Marti products
Page 480 and 481:
Sustainable competitive advantage T
Page 482 and 483:
José María Viedma Marti knowledge
Page 484 and 485:
José María Viedma Marti pr<strong
Page 486 and 487:
José María Viedma Marti All <stro
Page 488 and 489:
ICBS I CBS José María Viedma Mart
Page 490 and 491:
José María Viedma Marti Sullivan,
Page 492 and 493:
2. Balanced scorecard framework Ang
Page 494 and 495:
Angelos Vouldis and Angelica Kokkin
Page 496 and 497:
Unclear benefits of</strong
Page 498 and 499:
Angelos Vouldis and Angelica Kokkin
Page 500 and 501:
9. Discussion and conclusions Angel
Page 502 and 503:
The Global Position of</str
Page 504 and 505:
Piotr Wisniewski safeguarding IC vi
Page 506 and 507:
Piotr Wisniewski Figure 2: Ten larg
Page 508 and 509:
Piotr Wisniewski suspected
Page 510 and 511:
Piotr Wisniewski 2) Slow growth in
Page 512 and 513:
Piotr Wisniewski US 3.8 3.8 3.9 4.3
Page 514 and 515:
498
Page 516 and 517:
500
Page 518 and 519:
2. Hypothesis deve
Page 520 and 521:
3.2 Control variables Deborah Brans
Page 522 and 523:
Deborah Branswijck and Patricia Eve
Page 524 and 525:
Deborah Branswijck and Patricia Eve
Page 526 and 527:
Korosh Gholami et al. Durkheim beli
Page 528 and 529:
Korosh Gholami et al. The effect <s
Page 530 and 531:
Measure the Unmeas
Page 532 and 533:
Knowledge Capital 5. Efforts for de
Page 534 and 535:
Exclusive use of L
Page 536 and 537:
Emmanouil Gkinoglou from th
Page 538 and 539:
Project Assets Need for synergies Q
Page 540 and 541:
Table 5: Sustainability checklist E
Page 542 and 543:
Emmanouil Gkinoglou The next challe
Page 544 and 545:
Kai Mertins and Ronald Orth generat
Page 546 and 547:
Kai Mertins and Ronald Orth Never<s
Page 548 and 549:
Kai Mertins and Ronald Orth <strong
Page 550 and 551:
Ad 2 Structure of
Page 552 and 553:
Kai Mertins and Ronald Orth Hauff,
Page 554 and 555:
Maria Simonova “Concept o
Page 556 and 557:
Maria Simonova The concept regardin
Page 558 and 559:
Maria Simonova Becker, G. S. (1983)
Page 560 and 561:
2. The aim of appl
Page 562 and 563:
Nellija Titova W+I+DP+D+T+M+R (equa
Page 564 and 565:
Nellija Titova performing better. I
Page 566 and 567:
5. Conclusions and furthe</
Page 568 and 569:
Nellija Titova Annex 2: Summary on
Page 570 and 571:
Meaning in the tab
Page 572 and 573:
Krishanthi Ganga Vithana et al. for
Page 574 and 575:
Krishanthi Ganga Vithana et al. sti
Page 576 and 577:
5. Conclusion Krishanthi Ganga Vith
Page 578 and 579:
562
Page 580 and 581:
564
Page 582 and 583:
The Tobing Knowledge Management Arc
Page 584 and 585:
Paul Lumbantobing et al. user, know
Page 586 and 587:
Paul Lumbantobing et al. people con
Page 588 and 589:
Paul Lumbantobing et al. influence
Page 590 and 591:
Develop KM Strategy Description Dev
Page 592 and 593:
Paul Lumbantobing et al. In <strong
Page 594 and 595:
Yuli Purwanti et al. Figure 1: Imag
Page 596 and 597:
Yuli Purwanti et al. Table below is
Page 598 and 599:
582
Page 600 and 601:
584
Page 602 and 603:
Nargiza Abdullaeva and Campbell War
Page 604 and 605:
Page 606 and 607:
Page 608 and 609:
Andreea Feraru and Nicoleta Ciucesc
Page 610 and 611:
Page 612 and 613:
Page 614 and 615:
Roxana Mironescu This could be <str
Page 616 and 617:
Roxana Mironescu Daft, R.L. (2002)
Page 618 and 619:
Paloma Sánchez and Oihana Basilio
Page 620 and 621:
Evaluating Intellectual Capital Usi
Page 622 and 623:
Table 1. CRS input efficiency score
Page 624 and 625:
Stelian Stancu and Anca Domnica Lup
Page 626 and 627:
José Vale et al. However, it is im
Page 628:
José Vale et al. Pöyhönen, A. an
show all

Proceedings of the 3rd European Conference on Intellectual Capital

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?