Proceedings of the 3rd European Conference on Intellectual Capital

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Víctor Raúl López Ruiz et al. Table 4: Panel data relationships between intellectual capital components and GDP (per capita at constant dollars) Components Qualifications Motivation and employability Excess employability Process Relational External and internal Image Research, Development and Innovation Social and Environmental R 2 Regression 1:Human Capital Coefficient (Elasticity) 15.54272 (0.804) 18.44895 (0.056) T-Stat 49.15132 12.60838 Regression 2: Structural Capital Coefficient (Elasticity) 0.083239 (0.047) 1.670248 (0.062) 0.050209 (0.0096) 9.360531 (0.114) T-Stat 3.925446 5.644141 2.247112 3.800079 Regression 3: Intellectual Capital Coefficient (Elasticity) 14.67855 (0.803) 0.056509 (0.048) 14.22958 (0.555) 14.34199 0.936433 0.937913 0.943563 Source: Own elaboration. Pooled Least Squares. Total panel observations: 216 (3x72). 6. Conclusions T-Stat 43.84944 5.543384 GDP has traditionally been used to measure national economic development. However, in <strong>the</strong> current knowledge economy, o<strong>the</strong>r factors have a great influence on growth and do not appear to be captured by GDP. In this research, we have made an estimation <strong>of</strong> <strong>the</strong> national intellectual capital in monetary terms. It allows comparing results obtained for different years. Moreover we have estimate <strong>the</strong> value <strong>of</strong> this capital for each main component <strong>of</strong> it: human and structural capital. In valuation <strong>of</strong> national intellectual capital we have used an objective weighting (applying a princiapal componenet analysis) in <strong>the</strong> elaboration <strong>of</strong> efficiency indexes for each component that allow to obtain unbiased efficiency indicators. The more important results can be summarizing as: First <strong>of</strong> all, national intellectual capital is a divergence factor in terms <strong>of</strong> economic growth in <strong>the</strong> 2000 decade, <strong>the</strong>refore if we use this measure <strong>of</strong> wealth in a nation <strong>the</strong> difference between countries will be higher. Second, <strong>the</strong> intellectual capital allows anticipating <strong>the</strong> economic crisis beginning in 2000. In this sense, we can emphasize <strong>the</strong> important <strong>of</strong> <strong>the</strong> structural capital in <strong>the</strong> economic situation in 2008. It shows that governments have to make more attacks in <strong>the</strong>se aspects. Finally, when we analyses each components <strong>of</strong> <strong>the</strong>se capitals we can obtain that <strong>the</strong> more relevant factors in <strong>the</strong> economic growth are <strong>the</strong> human capital and within <strong>the</strong>m <strong>the</strong> qualification <strong>of</strong> its inhabitants. In structural capital are Social and Environmental (SE) and Research, Development and Innovation (R&D&I) more important factors. 7. Appendix 1 Table 2: Details: Human and structural capital per capita at constant dollars Country Human Structural Capital Capital 2000 2005 2008 2000 2005 2008 Argentina 353.879 105.513 156.935 1733.046 662.123 447.331 Armenia 17.785 43.510 95.607 31.632 58.841 134.982 Australia 1139.616 1439.686 1764.437 12662.248 21984.754 16919.565 Austria 1388.202 1952.066 2400.163 4731.839 19475.706 10314.690 Belgium 1422.765 2101.469 2588.146 25175.212 19687.283 15744.644 194
Víctor Raúl López Ruiz et al. Country Human Capital Structural Capital Bolivia 55.633 55.382 67.072 98.578 102.225 112.301 Brazil 149.202 135.745 233.260 396.342 660.161 754.862 Bulgaria 53.860 131.148 167.060 113.406 555.115 835.766 Chile 175.121 180.304 217.058 1698.798 3529.334 2659.771 China 23.810 43.693 78.320 149.497 269.172 709.930 Colombia 85.300 102.041 131.293 163.485 442.796 634.897 Costa Rica 178.032 155.555 161.299 432.660 256.936 314.814 Croatia 172.212 385.762 485.560 535.561 1727.491 3247.862 Cyprus 660.678 1208.693 1635.588 3290.089 5192.538 6503.323 Czech Republic 207.250 453.787 716.495 807.876 3044.522 3544.037 Denmark 2526.224 3612.241 4313.870 21286.094 27039.296 18072.733 Egypt. Arab Rep. 61.088 39.628 40.648 140.121 292.728 248.507 El Salvador 66.154 96.088 124.369 166.547 279.873 314.125 Estonia 218.587 427.084 576.609 744.025 2502.196 1438.515 Finland 1398.093 2281.335 2778.712 40453.530 31429.210 22373.884 France 1290.721 1843.896 2238.737 14401.738 18494.134 15297.509 Georgia 15.552 28.482 56.706 35.454 103.309 172.111 Germany 1043.883 1475.120 1850.213 11959.283 12982.296 12216.150 Greece 467.736 766.948 1022.626 5204.691 6669.898 4624.862 Hong Kong SAR. China 927.400 1185.369 1083.071 73318.562 92493.744 147111.604 Hungary 224.807 461.658 542.630 664.332 1936.244 3239.990 Iceland 1850.950 3417.784 2501.873 13922.647 65407.779 10542.946 India 14.187 16.551 18.566 34.779 104.154 121.540 Indonesia 17.037 23.025 33.786 63.938 79.147 69.969 Ireland 1121.384 1935.715 2496.013 21185.666 16033.556 9931.877 Israel 1358.669 1149.136 1635.493 7874.165 10352.475 10661.158 Italy 843.610 1182.274 1345.586 9270.297 9141.751 5648.678 Jamaica 182.695 143.578 156.178 575.185 1712.848 691.703 Japan 1351.885 1347.354 1460.808 17964.277 27722.913 20622.005 Kazakhstan 38.357 47.601 76.870 120.466 305.298 748.409 Korea. Rep. 423.670 623.181 666.526 2689.114 8879.509 5451.126 Kyrgyz Republic 8.824 20.573 39.289 6.630 17.760 31.534 Latvia 167.644 276.159 382.697 240.422 779.651 632.063 Lithuania 192.915 348.674 490.241 282.221 1703.628 966.405 Luxembourg 2982.872 3583.919 5175.282 237809.660 265558.741 216250.095 Malaysia 200.269 297.259 220.350 2556.661 4225.216 3612.219 Mauritius 144.333 166.864 156.271 542.493 699.834 810.942 Mexico 272.287 273.565 279.917 595.206 778.196 646.497 Mongolia 21.614 27.155 36.396 23.479 23.523 55.128 Morocco 52.390 94.813 123.244 127.719 399.968 771.511 Ne<strong>the</strong>rlands 1218.734 1905.737 2429.637 31437.134 27978.451 17351.743 New Zealand 920.267 1606.195 1395.906 3805.844 7328.451 4486.284 Norway 2483.049 3921.874 4452.013 17667.880 36965.611 25447.471 Pakistan 5.177 7.784 10.477 19.086 73.855 34.613 Panama 176.592 157.110 202.402 597.068 1069.091 1191.136 Paraguay 67.347 29.807 45.457 78.150 39.633 45.539 Peru 70.412 72.255 101.777 129.798 474.370 674.102 Poland 224.529 394.288 568.930 451.636 1415.589 1450.004 Portugal 727.484 914.703 1083.130 3604.436 4020.269 3981.966 Romania 41.781 60.075 109.519 65.333 240.185 230.173 195
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Proceedings <stron
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Contents Paper Title Author(s) Page
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Paper Title Author(s) Page No. Hier
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Preface These proceedings represent
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educating through ‘hands on’ co
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Ibrahim Elbeltagi is a Senior lectu
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Theodora Ngosi has a PhD in Compute
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José Vale is an invited assistant
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Social Knowledge: Are you Ready? 1
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John Girard don’t know.” (Benne
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John Girard The real question becom
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John Girard our belief that many le
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John Girard organizations to gain k
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Intellectual Capital Accounting - H
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Academic Research Papers 15
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Khodayar Abili and Mahyar Abili The
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Khodayar Abili and Mahyar Abili Nah
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Khodayar Abili and Mahyar Abili Tim
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Knowledge Transfer in Romanian Univ
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Simona Agoston et al. Table 1: Know
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Simona Agoston et al. homoscedastic
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Simona Agoston et al. included in <
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A Modeling Approach to Intellectual
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Eckhard Ammann (referring to <stron
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Eckhard Ammann Figure 2: The IC spa
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Eckhard Ammann section 2. They are
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Eckhard Ammann From an overall pers
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Sorin Anagnoste and Gabriela Dumitr
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Gabriela Atanasiu and Florin Leon T
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Gabriela Atanasiu and Florin Leon
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Gabriela Atanasiu and Florin Leon B
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Gabriela Atanasiu and Florin Leon a
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Developing and Implementing Strateg
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2. Technology, faculty, and staff B
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Learning programs Individual poten
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Bob Barrett facts with the<
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Intellectual Capital Dynamics withi
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Ruxandra Bejinaru and Stefan Iordac
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Education and Training Practice Str
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Andrea Bencsik et al. We wanted to
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Andrea Bencsik et al. In this sampl
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Table 4: Types of
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References Andrea Bencsik et al. An
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Ayşen Berberoğlu and Emine Ünar
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Ayşen Berberoğlu and Emine Ünar
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Table 7: Descriptive statistics Ay
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Knowledge Dynamics Modeling Using A
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Constantin Bratianu et al. will hav
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Constantin Bratianu et al. 5. a) Gi
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Constantin Bratianu et al. 5. Data
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Constantin Bratianu et al. Nissen,
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2. Technology and infrastructure Sh
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Sheryl Buckley and Apostolos Gianna
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Donley Carrington and Mike Tayles t
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Donley Carrington and Mike Tayles f
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Donley Carrington and Mike Tayles l
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Donley Carrington and Mike Tayles i
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Donley Carrington and Mike Tayles G
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John Dumay and Jim Rooney on a more
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John Dumay and Jim Rooney Taking <s
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John Dumay and Jim Rooney “For ex
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John Dumay and Jim Rooney Mouritsen
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Marziye Ehrami et al. 1. Creation
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Marziye Ehrami et al. Note: In 2005
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Earnings Quality and Othe</
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Magdi El-Bannany resource-based per
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Magdi El-Bannany Based on this argu
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Magdi El-Bannany Table 2: Descripti
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Page 174 and 175: Albrecht Fritzsche and Rebecca Geig
Page 178 and 179: Table 4: Applicability of</
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Page 184 and 185: Tatiana Garanina and Yana Pavlova T
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Page 192 and 193: Impact of Investme
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Page 222 and 223: Intellectual Capital and Corporate
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Page 238 and 239: Eliciting Tacit Knowledge From a Do
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Page 248 and 249: Maurizio Massaro et al. crucial fac
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Page 256 and 257: Intellectual Capital Management: Ca
Page 258 and 259: Florinda Matos et al. These models
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Complaints System dos clients New
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Florinda Matos et al. Finally, <str
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Florinda Matos et al. The training
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Florinda Matos et al. 47. Opinion
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Florinda Matos et al. In th
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Florinda Matos et al. The Quadrant
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Florinda Matos et al. Integrated te
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Florinda Matos et al. The networks
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Knowledge Management for Knowledge
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3. Our research Ludmila Mládková
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Ludmila Mládková organizational s
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Ludmila Mládková mentioned only b
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Applying the VAIC
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Maria Molodchik and Anna Bykova pro
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Maria Molodchik and Anna Bykova 2.
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Maria Molodchik and Anna Bykova Let
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Maria Molodchik and Anna Bykova The
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Perspectives on the</strong
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Maria Cristina Morariu as we consid
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Maria Cristina Morariu Tobin’s q
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Maria Cristina Morariu for determin
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The Structural Model of</st
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Fattah Nazem Ackerley (2006) reveal
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Fattah Nazem The research instrumen
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Fattah Nazem intellectual capital m
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Fattah Nazem Johnson, M.J. (1986) T
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Andrei Stefan Nestian organization.
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Andrei Stefan Nestian Chaordic syst
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Andrei Stefan Nestian environment,
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Increasing Knowledge Management Mat
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Theodora Ngosi et al. 2.2 Dominatin
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Theodora Ngosi et al. Figure 1: Del
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Theodora Ngosi et al. IT systems, a
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5. Discussion and summary Theodora
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Theodora Ngosi et al. Weill, P., an
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Bongani Ngwenya late adopters respo
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Bongani Ngwenya This paper extends
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Bongani Ngwenya argument, t
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Bongani Ngwenya DiMaggio, P. J., an
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The Quality of Kno
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5.2 5 4.8 4.6 4.4 4.2 4 3.8 Compete
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Corina Pelau et al. In order to imp
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Hierarchy and Tacit Knowledge in <s
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Ulrica Pettersson and James Nyce in
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Ulrica Pettersson and James Nyce mi
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Katja Pook and Campbell Warden time
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Katja Pook and Campbell Warden futu
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5. Commonalities of</strong
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Katja Pook and Campbell Warden <str
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Katja Pook and Campbell Warden Lazl
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Nicolae Al. Pop et al. satisfaction
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Nicolae Al. Pop et al. the<
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Nicolae Al. Pop et al. satisfaction
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Knowledge Cities: A Portuguese Case
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Katia Rodrigues and Eduardo Tomé
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3. The empirical study 3.1 Methodol
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Table 3: Smart cities - application
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4.3 Suggestions to future work Kati
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Anna Romiti and Daria Sarti 2. Orga
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Anna Romiti and Daria Sarti based o
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Anna Romiti and Daria Sarti Governa
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Anna Romiti and Daria Sarti Therefo
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Anna Romiti and Daria Sarti Valkoka
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Kent Rondeau and Terry Wagar planne
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2.1 Study measures Kent Rondeau and
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Kent Rondeau and Terry Wagar contri
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Kent Rondeau and Terry Wagar Our re
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Intellectual Capital and a Firm’s
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Figure 1: Hypothes
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Helena Santos-Rodrigues et al. Norm
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Helena Santos-Rodrigues et al. To s
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Helena Santos-Rodrigues et al. Our
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Factors Influencing the</st
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Thanaletchumi Sathasivam et al. 2.3
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Thanaletchumi Sathasivam et al. The
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Thanaletchumi Sathasivam et al. gro
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Thanaletchumi Sathasivam et al. The
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Thanaletchumi Sathasivam et al. 11.
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Enhancing IC Formation by Evoking H
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Klaus Bruno Schebesch to portray <s
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Klaus Bruno Schebesch 4. Leaning an
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Klaus Bruno Schebesch We argue in f
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Klaus Bruno Schebesch come
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In Search of key F
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Karen Smits et al. contribution, wi
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Karen Smits et al. Table 1: Correla
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Karen Smits et al. staff feel that
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Marta-Christina Suciu et al. This p
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Marta-Christina Suciu et al. <stron
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Marta-Christina Suciu et al. The qu
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Marta-Christina Suciu et al. This t
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Information and Communication Techn
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Jukka Surakka et al. The participan
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Jukka Surakka et al. people. Commun
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Personal Knowledge Management (PKM)
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Marzena Świgoń provide th
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Marzena Świgoń (number and type <
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Marzena Świgoń measure self-perce
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Eduardo Tomé what we consider to b
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Eduardo Tomé IC. The intervention
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Eduardo Tomé In the</stro
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Eduardo Tomé to stress that <stron
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Eleni Magdalini Vasileiadou et al.
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José María Viedma Marti products
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Sustainable competitive advantage T
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José María Viedma Marti knowledge
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José María Viedma Marti pr<strong
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José María Viedma Marti All <stro
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ICBS I CBS José María Viedma Mart
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José María Viedma Marti Sullivan,
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2. Balanced scorecard framework Ang
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Angelos Vouldis and Angelica Kokkin
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Unclear benefits of</strong
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Angelos Vouldis and Angelica Kokkin
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9. Discussion and conclusions Angel
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The Global Position of</str
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Piotr Wisniewski safeguarding IC vi
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Piotr Wisniewski Figure 2: Ten larg
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Piotr Wisniewski suspected
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Piotr Wisniewski 2) Slow growth in
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Piotr Wisniewski US 3.8 3.8 3.9 4.3
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498
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500
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2. Hypothesis deve
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3.2 Control variables Deborah Brans
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Deborah Branswijck and Patricia Eve
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Deborah Branswijck and Patricia Eve
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Korosh Gholami et al. Durkheim beli
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Korosh Gholami et al. The effect <s
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Measure the Unmeas
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Knowledge Capital 5. Efforts for de
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Exclusive use of L
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Emmanouil Gkinoglou from th
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Project Assets Need for synergies Q
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Table 5: Sustainability checklist E
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Emmanouil Gkinoglou The next challe
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Kai Mertins and Ronald Orth generat
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Kai Mertins and Ronald Orth Never<s
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Kai Mertins and Ronald Orth <strong
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Ad 2 Structure of
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Kai Mertins and Ronald Orth Hauff,
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Maria Simonova “Concept o
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Maria Simonova The concept regardin
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Maria Simonova Becker, G. S. (1983)
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2. The aim of appl
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Nellija Titova W+I+DP+D+T+M+R (equa
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Nellija Titova performing better. I
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5. Conclusions and furthe</
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Nellija Titova Annex 2: Summary on
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Meaning in the tab
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Krishanthi Ganga Vithana et al. for
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Krishanthi Ganga Vithana et al. sti
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5. Conclusion Krishanthi Ganga Vith
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562
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564
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The Tobing Knowledge Management Arc
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Paul Lumbantobing et al. user, know
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Paul Lumbantobing et al. people con
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Paul Lumbantobing et al. influence
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Develop KM Strategy Description Dev
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Paul Lumbantobing et al. In <strong
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Yuli Purwanti et al. Figure 1: Imag
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Yuli Purwanti et al. Table below is
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582
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584
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Nargiza Abdullaeva and Campbell War
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Andreea Feraru and Nicoleta Ciucesc
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Roxana Mironescu This could be <str
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Roxana Mironescu Daft, R.L. (2002)
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Paloma Sánchez and Oihana Basilio
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Evaluating Intellectual Capital Usi
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Table 1. CRS input efficiency score
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Stelian Stancu and Anca Domnica Lup
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José Vale et al. However, it is im
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José Vale et al. Pöyhönen, A. an
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