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

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Mohd Zali Mohd Nor et al. components) that are significantly better on ei<strong>the</strong>r SmCAT or Rodriguez KMS. The SmCAT prototype, being an integrated tool that covers more areas <strong>of</strong> SM, is expected to be better received by maintainers compared to Rodriguez system. Therefore we hypo<strong>the</strong>size: H1: The extent <strong>of</strong> overall success <strong>of</strong> SmCAT is better than Rodriguez KMS 4. Results and discussion The summary statistics provided by Rasch, via Winsteps s<strong>of</strong>tware, are listed in Table 1, Table 2, Table 3 and Table 4. The summary <strong>of</strong> measured persons are centered with Mean=0, to allow comparison between items <strong>of</strong> SmCAT and Rodriguez KMS. The summary shall also provide <strong>the</strong> reliability <strong>of</strong> data (via Person/Item reliability and Cronbach Alpha), Raw Score, Range (Max. measure – Min. measure), Mean measure, Model errors, Infit MNSQ and ZStd and Outfit MNSQ and ZStd. All measures are in logit scale, where <strong>the</strong> higher <strong>the</strong> logit value, <strong>the</strong> more difficult for respondents to agree on <strong>the</strong> corresponding questionnaire items. Table 1: SmCAT - summary <strong>of</strong> 15 measured persons Raw Score Count Measure Model Error Infit MNSQ ZSTD Outfit MNSQ ZSTD MEAN 91 29 0.00 0.47 1.01 0.00 1.00 0.00 S.D. 8 0 1.58 0.04 0.36 1.10 0.47 0.80 MAX. 112 29 4.11 0.58 1.78 2.10 1.92 1.80 MIN. 80 29 -2.44 0.41 0.55 -1.70 0.41 -1.20 REAL RMSE .51 Adj.SD 1.50 Separation 2.95 Person Reliability .90 MODEL RMSE .47 Adj.SD 1.51 Separation 3.22 Person Reliability .91 S.E. OF Person MEAN = .42 Person Raw Score-to-Measure Correlation = 1.00 Cronbach Alpha (KR-20) Person Raw Score Reliability = .92 Table 2: SmCAT - summary <strong>of</strong> 29 measured items Raw Score Count Measure Model Error Infit MNSQ ZSTD Outfit MNSQ ZSTD MEAN 47 15 0.77 0.69 0.95 0.00 1.00 0.10 S.D. 4 0 1.34 0.19 0.37 1.00 0.75 1.00 MAX. 55 15 3.85 1.25 1.74 2.50 3.22 2.20 MIN. 36 15 -2.02 0.49 0.39 -1.60 0.09 -1.50 REAL RMSE .76 Adj.SD 1.11 Separation 1.46 Item Reliability .68 MODEL RMSE .72 Adj.SD 1.13 Separation 1.58 Item Reliability .71 S.E. OF Item MEAN = .25 Table 3: Rodriguez KMS - summary <strong>of</strong> 15 measured person Raw Score Count Measure Model Error Infit Outfit MNSQ ZSTD MNSQ ZSTD MEAN 79 29 0.00 0.51 0.96 -0.20 0.96 0.10 S.D. 10 0 2.31 0.13 0.28 1.00 0.68 0.80 MAX. 111 29 7.79 1.00 1.55 1.40 3.20 2.30 MIN. 71 29 -1.80 0.46 0.47 -2.30 0.34 -1.00 REAL RMSE .55 Adj.SD 2.25 Separation 4.09 Person Reliability .94 MODEL RMSE .53 Adj.SD 2.25 Separation 4.25 Person Reliability .95 S.E. OF Person MEAN = .62 Person Raw Score-to-Measure Correlation = 1.00 Cronbach Alpha (KR-20) Person Raw Score Reliability = .95 378
Mohd Zali Mohd Nor et al. Table 4: Rodriguez KMS - summary <strong>of</strong> 29 measured items Raw Score Count Measure Model Error Infit Outfit MNSQ ZSTD MNSQ ZSTD MEAN 41 15 1.51 0.83 0.87 -0.10 0.96 0.20 S.D. 5 0 1.72 0.51 0.39 0.70 0.77 0.90 MAX. 53 15 5.26 2.62 2.06 1.80 3.63 2.40 MIN. 31 15 -2.12 0.48 0.05 -1.40 0.01 -1.20 REAL RMSE 1.00 Adj.SD 1.40 Separation 1.41 Item Reliability .66 MODEL RMSE .98 Adj.SD 1.42 Separation 1.45 Item Reliability .68 S.E. OF Item MEAN = .33 The summary <strong>of</strong> items’ measures for SmCAT and Rodriguez KMS is listed in Appendix A. 4.1 Data reliability and fitness Based on <strong>the</strong> above summary tables, <strong>the</strong> SmCAT person reliability and item reliability are 0.90 and 0.68, which are very good and fair, respectively. The Cronbach-Alpha person raw score reliability is 0.92. These indicate that SmCAT responses are reliable for fur<strong>the</strong>r analysis. Similarly, <strong>the</strong> Rodriguez KMS person reliability and item reliability are 0.94 and 0.66, respectively, with Cronbach-Alpha <strong>of</strong> 0.95. The Infit and Outfit Mean Square (MNSQ) for Person and Item means are within <strong>the</strong> limit <strong>of</strong> 0.6 to 1.4 logit (Wright & Linacre, 1994). This indicates that <strong>the</strong> data fits <strong>the</strong> Rasch rating scale model, and could be used for fur<strong>the</strong>r analysis. 4.2 T-Test To determine if we could conclude <strong>the</strong> above hypo<strong>the</strong>ses, several t-tests are performed (at 95% confidence level), with <strong>the</strong> following results: Table 5: T-Test Results Mean Mean SD SD t-test df= t-value Segment SmCAT Rodriguez SmCAT Rodriguez value (n-1) @ 95% H1 - Overall 0.69 1.40 1.36 1.72 1.74 26 1.71 Based on <strong>the</strong> above t-test results, SmCAT, in overall, is only slightly significantly better than Rodriguez KMS. However, since Rasch only provides unidimensional measures, we could not conclude if SmCAT is also better than Rodriguez KMS in different segments <strong>of</strong> Knowledge Quality, User Satisfaction, Perceived KMS Benefits, System Quality and System Use. 4.3 Common Person Equating plot Whilst <strong>the</strong> t-tests may reveal <strong>the</strong> overall and segments results, <strong>the</strong> Common Person Equating (CPE) plot (see Figure 5) is an alternative method to evaluate <strong>the</strong> individual items distribution for both SmCAT and Rodriguez KMS. CPE plots <strong>the</strong> items’ measures for SmCAT against items’ measures for Rodriguez KMS. Two standard deviation upper and lower limits are also drawn. The items outside <strong>of</strong> <strong>the</strong> limits are considered items with significant differences (at 95% confidence interval). The items above <strong>the</strong> upper limit are items which Rodriguez KMS are significantly better. Meanwhile <strong>the</strong> items below <strong>the</strong> lower limit denote <strong>the</strong> items which SmCAT are significantly better than Rodriguez KMS. Based on <strong>the</strong> CPE plot, Rodriguez excels in four KMS success indicators; KQ2 - Words And Phrases Are Consistent, KQ3 - Content Representation Logical and Fit, KQB - Provide Contextual Knowledge and SU1 - Making Decisions. Meanwhile, SmCAT is significantly better in KQ9 - Complete Knowledge Portal, KQA - Accurate Expert Directory, PB2 - Effectively Manage Knowledge, PB5 – Improves Quality, US2 - Efficient and SU5 - Share Specific Knowledge. 5. Conclusion and future works Wu and Wang’s KMS success model was used to evaluate whe<strong>the</strong>r SmCAT really help users and maintainers to share and access <strong>the</strong> knowledge and information easier. Based on <strong>the</strong> survey results, SmCAT, in overall, is significantly better than Rodriguez KMS. SmCAT is also significantly better than Rodriguez KMS in Knowledge Quality, User Satisfaction and Perceived KMS Benefits. In individual component level, SmCAT is still lacking in several key KMS components such as Content 379
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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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Page 405 and 406: Maria Cristina Morariu economy and
Page 407 and 408: Table 1: Literature review summary
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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
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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.
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