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Ridha Derrouiche et al. as 40 percentages and 80 percentages, respectively. Consequently, <strong>the</strong> sequencing association prototype model delivered that <strong>the</strong> attentive rules have also purely taken out following 40 percentages <strong>of</strong> minimum support and 80 percentages <strong>of</strong> minimum confidence construction element <strong>of</strong> sequencing association rule induction. 5. Extended framework: Exploring value creation using balanced scorecard perspective This section introduces and explains <strong>the</strong> concept <strong>of</strong> measuring value added along <strong>the</strong> supply chain from a collaborative perspective. The extended model is adapted from our previous model (Figure 2) and from Balanced Score Card (BSC) model (Kaplan and Norton, 1996) and (Brewer and Speh, 2000). It provides a tool to measure tangible and intangible value between partners (Figure 6). According to Clements and Price (2007), Value added refers to any additional value created at a particular stage <strong>of</strong> production by key production factors including; tangible value added through raw material transformation, labour and capital goods and intangible value added through intellectual capital and relational exchange i.e. <strong>the</strong> building <strong>of</strong> collaborative relationships. Table 2: PMM and performance cluster model results The BSC proposed by (Kaplan and Norton, 1996) is introduced by Brewer and Speh (2000) as a viable basis to develop a framework which assists in <strong>the</strong> measurement <strong>of</strong> tangible and intangible value added in supply chains from <strong>the</strong> transfer pricing perspective. Brewer and Speh (2000) highlight how a BSC approach can improve <strong>the</strong> supply chain through improving collaboration and leveraging <strong>the</strong> knowledge <strong>of</strong> supply chain partners, redesigning products and processes, improving information management to compliment decision making and better monitor <strong>the</strong> external market. In our context, we introduce Brewer and Speh (2000) framework’s to replace <strong>the</strong> second level <strong>of</strong> our previous framework “Performances perceived: Perceived satisfaction and Perceived effectiveness” by “Total Value-Added: Tangible Value-added and Intangible Value-added”. Figure 6: A collaborative supply chain balanced scorecard framework 146
Ridha Derrouiche et al. Of course, this is just a first draft <strong>of</strong> a tool to measure a total value-added tangible and intangible value between partners. The problem <strong>of</strong> <strong>the</strong> performance is very difficult to fix in such a case, because in most cases, it is not possible to get a quantitative measure <strong>of</strong> <strong>the</strong> performance <strong>of</strong> one interaction. This point could be improved by proposing Key Performance Indicators, not for a process or an activity, but for an interaction. 6. Conclusion This article has designated and constructed <strong>the</strong> novel application <strong>of</strong> holistic performance enhancement road map using K-Means base on PSO and association rules for B2B-SCM, developing relationship rules <strong>of</strong> <strong>the</strong> holistic performance attribute enhancement road map. Subsequently <strong>the</strong> implementation, <strong>the</strong> result is prolonged to <strong>the</strong> B2B-SCM development road map strategies to endorse <strong>the</strong> right way for holistic performance upgrading, to solve <strong>the</strong> conflict among <strong>the</strong> attributes and eradicate <strong>the</strong> weakness issues. It is powerful and easy for <strong>the</strong> B2B-SCM invokers to implement since <strong>the</strong> outcomes are revealed in <strong>the</strong> energetic rules and <strong>the</strong> basic graphical matrix plot, which derived from <strong>the</strong> results <strong>of</strong> binning and K-Means base on PSO and expert validation. This demonstration is constructed by a qualitative and very subjective method on in what way domain managers reflect <strong>the</strong> role <strong>of</strong> each performance perspective and its influence on <strong>the</strong> performance. There are many regarding facts from <strong>the</strong> association rules procedure for instance <strong>the</strong> fundamental success aspect <strong>of</strong> this method relies on <strong>the</strong> quality <strong>of</strong> <strong>the</strong> primary database, data cleaning, and data preparation. The fuzzy <strong>the</strong>ory can be practical to model <strong>the</strong> subjective perspectives before <strong>the</strong> association rule process. The future research is to modify and upgrade this framework using <strong>the</strong> o<strong>the</strong>r modern evolutionary and fuzzy <strong>the</strong>ory. This paper has introduced <strong>the</strong> concept <strong>of</strong> measuring value added along <strong>the</strong> supply chain from a collaborative perspective. The extended model was adapted from our previous model and from Balanced Score Card. Of course, it was just a first draft <strong>of</strong> a tool to measure tangible and intangible value between partners. The problem <strong>of</strong> <strong>the</strong> performance is very difficult to fix in such a case, because in most cases, it is not possible to get a quantitative measure <strong>of</strong> <strong>the</strong> performance <strong>of</strong> one interaction. This point could be improved by proposing Key Performance Indicators, not for a process or an activity, but for an interaction. References Agrawal, R., Imielinski, T. and Swami, A. (1993), Mining association rules between sets <strong>of</strong> items in large databases. SIGMOD Record (ACM Special Interest Group on Management <strong>of</strong> Data), 207 – 216. Angeles R., Nath R., (2001), Partner Congruence in electronic data interchange enabled relationships, Journal <strong>of</strong> Business Logistics, Vol. 22, n° 2; p 109-127. Ben-David, S., Pl, D. and Simon, H.U. (2007), Stability <strong>of</strong> k-means clustering. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4539 (LNAI), 20-34. Brewer, P. C. and Speh, T. W. (2000) Using <strong>the</strong> Balanced Scorecard to Measure Supply Chain Performance, Journal <strong>of</strong> Business Logistics, 21 (1), pp.75-93. Borah, S. and Ghose M.K. (2009), Performance analysis <strong>of</strong> AIM-K-Means and K-Means in quality cluster generation. Journal <strong>of</strong> Computing, 1(1), pp 175-178. Chandra, S.N., Satapathy, A. and Orissa, B. (2009), Performance Comparisons <strong>of</strong> PSO based Clustering. InterJRI Computer Science and Networking, 1, pp18-23. Ching A. et al., (1996 ), Toward IT support for coordination in network organizations”, Information and Management, Vol 30, pp 179-199. Clements Michael D.J. and Price Nigel J., (2007), A Transfer Pricing Apparatus for Measuring Value Added along <strong>the</strong> Supply Chain: Reflections for Internet based Inter-Organisational Relationships, 4, pp 2-20. Crowston K, (1994), A taxonomy <strong>of</strong> organizational dependencies and coordination mechanism, (http://ccs.mit.edu/papers/CCSWP174.html , <strong>the</strong> date <strong>of</strong> last access: July 2011) Derrouiche R., Neubert G., Bouras A., Savino M., (2010), B2B Relationship Management: A Framework to Explore Impact <strong>of</strong> Collaboration, <strong>International</strong> Journal <strong>of</strong> Production Planning & Control (IJPPC), Volume 21, Issue 6, pages 528 – 546. Derrouiche R., P. Holimchayachotikul, N. Chakpitak, G. Neubert and A. Bouras, (2009), Enhancing Performance in B2B Supply Chain Collaboration Using Simulation, <strong>International</strong> conference on Integrated Design and Production (CPI’09), Oct 19-21, Fes, Morocco. Doney PM., Cannon JP., (1997), An examination <strong>of</strong> <strong>the</strong> nature <strong>of</strong> trust in buyer–seller relationships, Journal <strong>of</strong> Marketing, Vol. 61, April, p 35-51. Eberhart, R. and Kennedy, J. (1995), New optimizer using particle swarm <strong>the</strong>ory. in <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> <strong>International</strong> Symposium on Micro Machine and Human Science, pp 39-43. Ganesan S., (1994), Determinants <strong>of</strong> long term orientation in buyer–seller relationships, Journal <strong>of</strong> Marketing, Vol. 58, n°2, pp 1-19. 147
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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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Page 121 and 122: Strategic Thinking Dimensions in Em
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Page 127 and 128: Constantin Bratianu et al. cases wi
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Page 131 and 132: Delio Ignacio Castañeda and Sandra
Page 137 and 138: Quality in Teacher Education: A Sys
Page 139 and 140: Rajendra Chetty The mechanism <stro
Page 141 and 142: Academic leadership Rajendra Chetty
Page 143 and 144: Rajendra Chetty There must be a re-
Page 145 and 146: Developing Personal Knowledge Manag
Page 147 and 148: Thirathep Chonmait
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Page 151 and 152: The Relationship Between Knowledge
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Page 157 and 158: Samuel Kai Wah Chu et al. Questionn
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Page 161 and 162: Value Creation Through Collaborativ
Page 163 and 164: Ridha Derrouiche et al. level for e
Page 165 and 166: Ridha Derrouiche et al. best, <stro
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Page 171 and 172: Organizational Learning and Transfo
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Page 181 and 182: Ikechukwu Diugwu articulated and st
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Page 187 and 188: Eva Eckenhofer cou
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Page 191 and 192: Eva Eckenhofer The
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Page 195 and 196: 1.2 Intellectual capital and innova
Page 197 and 198: 3.2.2 Reliability Irene Y.H. Fan an
Page 199 and 200: Figure 4: latent variable coefficie
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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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Pornpen Lertthasanawong and Nopasit
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2. Literature review Ya-Hui Ling 2.
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Ya-Hui Ling action of</stro
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Intellectual Capital Intellectual C
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Ya-Hui Ling Hitt, M.A., Hoskisson,
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Maksim Maksimovic et al paper prese
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Maksim Maksimovic et al 4. Lean kno
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Maksim Maksimovic et al of<
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Maurizio Massaro and Andrea Moro <s
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Maurizio Massaro and Andrea Moro Pr
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Maurizio Massaro and Andrea Moro hi
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Maurizio Massaro and Andrea Moro Si
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Helen Mitchell and Dennis Viehland
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Figure 1: KMS Framework for collabo
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Mohd Zali Mohd Nor et al. Ano<stron
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Mohd Zali Mohd Nor et al. Table 4:
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Mohd Zali Mohd Nor et al. KQ8 provi
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Maria Cristina Morariu economy and
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Table 1: Literature review summary
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Maria Cristina Morariu ATO = Turnov
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4.2 Correlation analysis Maria Cris
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4.3 Regression analysis Maria Crist
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Table 13: Regression analysis_M6 Mo
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The Arrival of <st
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Bertrand Pauget and Ahmed Dammak Mo
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Bertrand Pauget and Ahmed Dammak no
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Bertrand Pauget and Ahmed Dammak th
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Bertrand Pauget and Ahmed Dammak Sa
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Wannapa Pipattanawong et al. cannot
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Wannapa Pipattanawong et al. 3.2 Do
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Wannapa Pipattanawong et al. Moreov
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The Dark Side of S
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Siwarit Pongsakornrungsilp vans, ro
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Siwarit Pongsakornrungsilp Kreng Ja
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Siwarit Pongsakornrungsilp Lave, J.
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