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

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Maria Cristina Morariu M6. ROE = β1VAHU+β2SCVA+β3VACA+β4FSIZE+β5INDUSTRY 4. Results and analysis 4.1 Descriptive statistics From <strong>the</strong> output (Table 3 below) we see that most <strong>of</strong> <strong>the</strong> variables have skewness values between -1 and 1, but three do not (VAHU, VACA, MB). Never<strong>the</strong>less, if we consider <strong>the</strong> mean and median for <strong>the</strong>se three variables we can observe that <strong>the</strong>y are approximately equal so we can assume that <strong>the</strong> distribution is approximately normal for <strong>the</strong>se variables too. The comparison between VAIN and VACA values suggests that during 2009 <strong>the</strong> sample companies were generally more effective in creating VA from <strong>the</strong>ir IC (VAIN = 2.0533) than from physical and financial capital employed (VACA = .9337). Table 3: Descriptive statistics Statistics N Std. Valid Missing Mean Median Deviation Skewness Minimum Maximum VAHU_LnVA 14 1 1.0869 1.0423 .08879 1.220 1.01 1.30 SCVA_Rec 14 1 .9664 .9699 .02783 -.187 .92 1.00 VAIN_Rec 14 1 2.0533 2.0119 .11328 .881 1.93 2.30 VACA_rec 14 1 .9337 .9332 .03849 1.287 .87 1.04 VAICTM_rec 14 1 2.986964 2.946004 .1072158 .361 2.8495 3.1636 ROE_rec 14 1 .8838 .8968 .06583 -.751 .72 1.01 ATO_Rec 14 1 .9565 .9569 .02725 -.483 .89 1.01 MB_Rec 14 1 1.0072 1.0074 .04243 1.518 .95 1.13 LN_SALES 14 1 20.4010 19.9050 1.75987 .582 18.02 23.53 To compare between groups <strong>of</strong> industries, a one-way ANOVA was conducted on <strong>the</strong> two industries. Results (Table 4) suggest that both groups were generally more effective in creating VA from its IC than from physical and financial capital employed (VAIN > VACA). This finding suggests that, even within traditional industry sectors, <strong>the</strong> conventional underlying factors <strong>of</strong> production such as physical and financial capital lost much <strong>of</strong> <strong>the</strong>ir importance in favour <strong>of</strong> IC. In addition <strong>the</strong> results shows that KI sectors were generally more effective in creating VA from <strong>the</strong>ir intellectual, physical and financial resources (VAICTM = 3.032814) than TI group (VAICTM = 2.904434), F (1, 12) = 6.59, p < .05. Table 4: ANOVA summary A one way between groups ANOVA Knowledge Intensive Traditional Industry Total Std. Std. N Mean Deviation N Mean Deviation F Sig. VAHU_LnVA 9 1.1196 .09674 5 1.0281 .01324 4.276 .061 SCVA_Rec 9 .9746 .03009 5 .9516 .01684 2.420 .146 VAIN_Rec 9 2.0941 .12305 5 1.9797 .02992 4.052 .067 VACA_rec 9 .9387 .04599 5 .9247 .02073 .401 .538 VAICTM_rec 9 3.032814 .1057652 5 2.904434 .0417484 6.590 .025 ROE_rec 9 .9087 .04883 5 .8389 .07348 4.622 .053 ATO_Rec 9 .9447 .02343 5 .9777 .02095 6.846 .023 MB_Rec 9 1.0212 .04446 5 .9821 .02582 3.196 .099 388
4.2 Correlation analysis Maria Cristina Morariu Correlation analysis is <strong>the</strong> initial statistical technique used to analyse <strong>the</strong> association between <strong>the</strong> dependent and <strong>the</strong> independent variables. Table 7 shows <strong>the</strong> findings from Pearson correlation matrix analysis. As a start, <strong>the</strong> Pearson’s correlation coefficients were analysed to check for <strong>the</strong> absence <strong>of</strong> multicollinearity problems. First we can observe that a significant positive correlation exists between two independent variables VAHU and SCVA, r (12) = .844. This multicolinearity problem was eliminated after finding <strong>the</strong> results <strong>of</strong> collinearity test that we ran (Table 5 & 6; see VIF < 3). Table 5: Multicollinearity VAHU Table 6: Multicollinearity SCVA Coefficients a Collinearity Statistics Model Tolerance VIF 1 SCVA_Rec .725 1.379 VACA_rec .670 1.493 LN_SALES .517 1.935 a. Dependent Variable: VAHU_LnVA Coefficients a Collinearity Statistics Model Tolerance VIF 1 VACA_rec .698 1.433 LN_SALES .551 1.815 VAHU_LnVA .667 1.499 a. Dependent Variable: SCVA_Rec Considering <strong>the</strong> correlations between independent variables and dependent variables, only nine are statistically significant. VACA is significantly positively associated with ROE, r (12) = .615, p < .05 and with MB, r (12) = .858, p < .01.This means that companies with relatively high VACA were likely to have high ROE and MB. No significant correlation is identified between VACA and ATO. SCVA is significantly positively correlated with ROE, r (12) = .563, p < 0.05 but negatively correlated with ATO, r (12) = -.570, p < 0.05. This means that companies that have efficiently used <strong>the</strong>ir SC were likely to have recorded a high ROE but a small ATO. No significant correlation is between SCVA and MB. VAHU is significantly negatively correlated with ATO, r (12) = -.746, p < 0.01. VAHU is not significantly correlated with <strong>the</strong> remaining two dependent variables (ROE and MB). This means that when companies used efficiently <strong>the</strong>ir HC <strong>the</strong>y recorded a small ATO. Regarding VAIN, we notice a negative significant correlation with ATO, r (12) = -.724, p < 0.01 and no significant correlation between this variable and MB or ROE. Considering VAIC <strong>the</strong> findings show a significant positive correlation with ROE, r (12) = .621, p < 0.05, but a negative significant correlation with ATO, r (12) = .666, p < 0.01. No significant correlation is between this independent variable and MB. Overall, correlation results imply sample firms with a higher-level VACA were associated with higher levels <strong>of</strong> productivity and higher levels <strong>of</strong> market valuation. Fur<strong>the</strong>r, sample firms with higher levels <strong>of</strong> VAHU were associated with lower levels <strong>of</strong> productivity and, sample firms with higher levels <strong>of</strong> SCVA were associated with higher levels <strong>of</strong> pr<strong>of</strong>itability but lower level <strong>of</strong> productivity. Consequently, in 389
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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. Main
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Paper Title Author(s) Page No. Know
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Preface These proceedings represent
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TU Dortmund, Germany, ); Wing Bun L
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William Halal is Prof</stro
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Evgeny Blagov has obtained a specia
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science and mathem
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George Majdalany is a PhD candidate
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Joyce Nesamani Simson is a PhD cand
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Manpower Discipline Based on Knowle
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Peyman Akhavan et al. brains, knowl
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Peyman Akhavan et al. showed that o
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Peyman Akhavan et al. appropriate l
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Peyman Akhavan et al. sources for c
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Attitudes Towards Changes and Organ
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Ruth Alas et al. During the
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Professional inter
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Ruth Alas et al. Table 4: Correlati
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Research was supported by ETF Grant
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Rivadávia Correa Drummond de Alvar
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5. Conclusions Rivadávia Correa Dr
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Toward a Meta-Synthe</stron
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Xiaomi An et al 2.3 Reasons for dev
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Xiaomi An et al PTC works closely w
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Xiaomi An et al The first is an adm
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Xiaomi An et al policy reflections
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Figure 1: Transmission gear Marko A
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Marko Anzelak et al. knowledge basi
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Marko Anzelak et al. 72% recognise
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Marko Anzelak et al. virtually all
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Gabriel Prananingrum Aryanindita an
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Rita Babiliūtė and Kęstutis Kri
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Knowledge Management, Cultural Know
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Leonard Barnett and Edward Carter 2
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Leonard Barnett and Edward Carter p
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Leonard Barnett and Edward Carter p
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Leonard Barnett and Edward Carter d
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Impact of Knowledg
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Bob Barrett shorter product life-cy
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Bob Barrett 6. Concept of</
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Bob Barrett Brown, J.S. and Duguid
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Aurilla Aurelie Bechina and Eli Hus
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Lorne Booker et al. Knowledge is us
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Figure 1: Business Valuation Knowle
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3.4 Practitioner knowledge market L
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4. Implications Lorne Booker et al.
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Strategic Thinking Dimensions in Em
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Constantin Bratianu et al. shows <s
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Constantin Bratianu et al. Academy
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Constantin Bratianu et al. cases wi
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Constantin Bratianu et al. Pinker,
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Delio Ignacio Castañeda and Sandra
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Quality in Teacher Education: A Sys
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Rajendra Chetty The mechanism <stro
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Academic leadership Rajendra Chetty
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Rajendra Chetty There must be a re-
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Developing Personal Knowledge Manag
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Thirathep Chonmait
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Thirathep Chonmait
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The Relationship Between Knowledge
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Samuel Kai Wah Chu et al. A knowled
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Samuel Kai Wah Chu et al. negative
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Samuel Kai Wah Chu et al. Questionn
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Samuel Kai Wah Chu et al. few done
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Value Creation Through Collaborativ
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Ridha Derrouiche et al. level for e
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Ridha Derrouiche et al. best, <stro
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Ridha Derrouiche et al. experience
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Ridha Derrouiche et al. Of course,
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Organizational Learning and Transfo
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Irra Chrisyanti Dewi was carried ou
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Irra Chrisyanti Dewi Table 2: Valid
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Irra Chrisyanti Dewi Library is exp
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Knowledge Acquisition and Sharing:
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Ikechukwu Diugwu articulated and st
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Ikechukwu Diugwu establishing setti
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Ikechukwu Diugwu Mentzer, J. T., Mi
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Eva Eckenhofer cou
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Eva Eckenhofer The
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Eva Eckenhofer The
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Eva Eckenhofer Bur
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1.2 Intellectual capital and innova
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3.2.2 Reliability Irene Y.H. Fan an
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Figure 4: latent variable coefficie
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Irene Y.H. Fan and Rongbin W.B. Lee
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Irene Y.H. Fan and Rongbin W.B. Lee
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2.1 Appropriation Ronny Gey From a
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Ronny Gey Table 1: Characterization
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Ronny Gey The developers feel that
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Ronny Gey A first limitation we wer
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Kenneth Grant part of</stro
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Kenneth Grant The diffusion was stu
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Kenneth Grant A detailed computer-s
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Kenneth Grant did not come to <stro
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Kenneth Grant of m
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Michel Grundstein although hundred
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Michel Grundstein Figure 1: DITEK p
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Michel Grundstein 4.1.1 Knowledge i
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Michel Grundstein share. However <s
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6. Conclusions and perspectives Mic
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Intellectual Property Valuation Dec
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Hezlin Harris et al. (Burrone and S
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Hezlin Harris et al. The need for s
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Acknowledgements Hezlin Harris et a
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Managing an Assessment Process <str
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Table 1: List of N
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Shaheed Hartley Table 3: OEQ respon
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Shaheed Hartley It is very difficul
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A Community Framework for Knowledge
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Igor Hawryszkiewycz between communi
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Figure 4: Modeling knowledge subsys
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Igor Hawryszkiewycz Prahalad, C.K.,
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Eli Hustad and Aurilla Aurelie Bech
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Does Knowledge Stickiness Affect a
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Nurul Indarti knowledge (Nelson and
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3. Research methods 3.1 Research in
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Coded knowledge Nurul Indarti The f
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Nurul Indarti Arrow, K. J. (1969).
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Organizational Learning in Pr<stron
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Brigita Janiunaite et al. learn and
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Brigita Janiunaite et al. given dif
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Brigita Janiunaite et al. empowered
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Brigita Janiunaite et al. A statist
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Brigita Janiunaite et al. learning
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Balancing the Ince
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Giedrius Jucevičius safeguard and
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Giedrius Jucevičius nature <strong
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Giedrius Jucevičius motivating fac
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Giedrius Jucevičius Ghosh, T. (200
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Palmira Juceviciene and Vyda Mozuri
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Robertas Jucevicius relations shoul
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Robertas Jucevicius do not know whe
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Robertas Jucevicius and innovation
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Robertas Jucevicius Lawton Smith, H
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Annukka Jyrämä et al. knowledge c
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Annukka Jyrämä et al. Respondents
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Annukka Jyrämä et al. various cit
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Annukka Jyrämä et al. example hea
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Managing Information Overload - Tea
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Harri Ketamo conceptual structure a
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Harri Ketamo limited to YouTube, Fl
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Harri Ketamo Figure 8: Visualizatio
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Farmers’ Knowledge Management in
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2.1 Farmer knowledge Le Nguyen Doan
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Le Nguyen Doan Khoi days needed for
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Le Nguyen Doan Khoi dosage. Moreove
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Le Nguyen Doan Khoi use homemade in
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Developing Human Capital through Ne
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Eric Kong organisation as individua
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Eric Kong As described by Tosey and
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Eric Kong Craft, A. (2001) "Neuro-l
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Sophie Le Bellu and Benoit Le Blanc
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Results Sophie Le Bellu and Benoit
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Page 405 and 406: Maria Cristina Morariu economy and
Page 407 and 408: Table 1: Literature review summary
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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
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Page 427 and 428: Wannapa Pipattanawong et al. cannot
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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
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