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

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Rivadávia Correa Drummond de Alvarenga Neto Perez-Bustamante, G. (1999) Knowledge Management in agile innovative organizations. Journal <strong>of</strong> Knowledge Management, 3, 6-17. Roth, J. (2003) Enabling Knowledge Creation: learning from an R&D organization. Journal <strong>of</strong> Knowledge Management, 7, 32-48. Sabherwal, R. & Becerra-Ferndandez, I. (2003) An empirical study <strong>of</strong> <strong>the</strong> effect <strong>of</strong> KM processes at individual, group, and organizational levels. Decision Sciences, 34, 225-260. Sawhney, M. & PRrandelli, E. (2000) Communities <strong>of</strong> creation: Managing distributed innovation in turbulent markets. California Management Review, 42, 24-+. Senoo, D., Magnier-Watanabe, R. & Salmador, M. (2007) Workplace reformation, active ba and knowledge creation: from a conceptual to a practical framework. European Journal <strong>of</strong> Innovation Management, 10, 296- 315. Seufert, A., Von Krogh, G. & Bach, A.(1999) Towards Knowledge Networking. Journal <strong>of</strong> Knowledge Management, 3, 180-190. Shimizu, H. (1995) Ba-principle: new logic for <strong>the</strong> real-time emergence <strong>of</strong> information,Holonics, 5(1), 67-79. Tsoukas H. (2005) Complex Knowledge: studies in organizational epistemology. New York: Oxford University Press. Tee, M.Y. (2005). Tacit knowledge in a e-learning environment: a naturalistic study. Ph.D. University <strong>of</strong> Kansas. Von Krogh, G. (1998) Care in knowledge creation. California Management Review, 40, 133-+. Von Krogh , G., Ichijo, K. & Nonaka, I. O. (2000) Enabling knowledge creation: how to unlock <strong>the</strong> mystery <strong>of</strong> tacit knowledge and release <strong>the</strong> power <strong>of</strong> innovation, Oxford ; New York, Oxford University Press. Von Krogh , G., Kim, S. & Erden, Z. (2008) Fostering <strong>the</strong> knowledge-sharing behavior <strong>of</strong> customers in interorganizational healthcare communities. IN CAO, J., Li, M. L., Weng, C. L., Xiang Y., Wang, X., Tang, H., Hong F., Liu, H. & Wang, Y. L. (Eds.) IFIP <strong>International</strong> <strong>Conference</strong> on Network and Parallel Computing. Shanghai, Peoples R China, Ieee Computer Soc. Von Krogh, G., Nonaka, I. & Ichijo, K. (1997). Develop Knowledge Activists! European Management Journal, 15, 475-483.) Wu, C. N. (2008) Knowledge creation in a supply chain. Supply Chain Management-an <strong>International</strong> Journal, 13, 241-250. Zarraga C. & Bonache J. (2005) The impact <strong>of</strong> team atmosphere on knowledge outcomes in self-managed teams. Organization Studies, 26, 661-681. Zhu, Z. (2004) KM: towards a universal concept or cross-cultural contexts? Knowledge Management Research & Practice, 2, 67-79. 32
Toward a Meta-Syn<strong>the</strong>sis Support System for Reuse <strong>of</strong> Government Information Resource on City Travel And Traffic: Knowledge Asset Management Approaches for Beijing Xiaomi An 1, 2 , Shaotong Xu 1, 2 , Yong Mu 3 , Wei Wang 3, , Xian Yang Bai 1, 2 , Andy Dawson 4 1, 2 and Hongqi Han 1 Key Laboratory <strong>of</strong> Data Engineering and Knowledge Engineering <strong>of</strong> <strong>the</strong> Ministry <strong>of</strong> Education, Renmin University <strong>of</strong> China, Beijing, China 2 School <strong>of</strong> Information Resource Management, Renmin University <strong>of</strong> China, Beijing, China 3 Beijing Information Resources Management Center, Beijing, China 4 CIBER (Department <strong>of</strong> Information Studies, UCL), London, UK anxiaomi@ruc.edu.cn xushaotong2003@126.com muy@beijingit.gov.cn wangw@bjeit.gov.cn baiyangcn@ruc.edu.cn andrew.dawson@ucl.ac.uk hanhongqi@126.com Abstract: The authors propose knowledge asset management approaches to develop a meta-syn<strong>the</strong>sis support system for <strong>the</strong> reuse <strong>of</strong> government information resources on city travel and traffic for Beijing City in China. An analysis <strong>of</strong> various existing surveys about reuse <strong>of</strong> government information resources for city travel and traffic in Beijing suggests <strong>the</strong> need for such a system. A critical review <strong>of</strong> o<strong>the</strong>r literature and <strong>of</strong> successful experiences in o<strong>the</strong>r countries suggests three fur<strong>the</strong>r justifications for <strong>the</strong> development <strong>of</strong> such a system. Firstly, it is needed in conceptual frameworks for awareness <strong>of</strong> government information data resources in administration, economy, and legal right protection as knowledge asset. Secondly, it is needed in management frameworks for proper process control <strong>of</strong> <strong>the</strong> reuse <strong>of</strong> government information resources for value added in services innovation in general. Finally, it is needed in regulatory and standardized environments for accountability <strong>of</strong> stakeholders to maximized benefits to city travellers and to sustainable development <strong>of</strong> a low carbon economy. The creation and development <strong>of</strong> a meta-syn<strong>the</strong>sis system is proposed, aiming at collaboration, optimization and innovation in <strong>the</strong> reuse <strong>of</strong> government information resources on city travel and traffic for Beijing through <strong>the</strong> creation <strong>of</strong> three integrated levels <strong>of</strong> knowledge asset management systems: a quality oriented standards system for knowledge asset value creation based on information collected at micro-level; a process oriented regulatory framework to encourage knowledge asset value sharing at meso-level; and a service-oriented licensing system for knowledge asset value addition through delivery and reuse at macro-level. The suggested knowledge asset management approaches would support <strong>the</strong> efficiency, effectiveness and economy <strong>of</strong> intelligent traffic administration, support good governance <strong>of</strong> value added services based on government information resources, and support intellectual activity around smart city travel and traffic. Keywords: Government information resources, information reuse, support system, knowledge asset management, city travel and traffic, Beijing 1. Introduction Government city travel and traffic information consists <strong>of</strong> data created or collected from government functions and activities in pursuance <strong>of</strong> <strong>the</strong>ir legal obligations relevant to city travel and traffic, including location data needed for travel(e.g. commercial centres, entertainment centres, restaurants, hospitals, education and training centres, etc)and traffic data(e.g. real-time traffic, traffic events, traffic facilities, etc,. They have great societal and economic value as public data assets and can be reused by society above and beyond governmental use. A growing number <strong>of</strong> international studies have focused on reuse systems for government information resources(Chen and Huang, 2008; Chen and Ran, 2008; Chen and Wang, 2010; Ding, 2007; Ding, 2009; Gao, 2005; Mu2009, 2010; Rang 2007, 2008, 2010; Xia 2009a &b; Zhao 2007, Zhou, 2008; Zhou 2009a&b). However, most previous studies focus on single reuse systems ( e.g. for non-pr<strong>of</strong>it reuse or for commercial reuse ) and do not consider <strong>the</strong> relationships <strong>of</strong> different systems and <strong>the</strong>ir 33
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Proceedings <stron
Page 3 and 4: Contents Paper Title Author(s) Page
Page 5 and 6: Paper Title Author(s) Page No. Main
Page 7 and 8: Paper Title Author(s) Page No. Know
Page 9 and 10: Preface These proceedings represent
Page 11 and 12: TU Dortmund, Germany, ); Wing Bun L
Page 13 and 14: William Halal is Prof</stro
Page 15 and 16: Evgeny Blagov has obtained a specia
Page 17 and 18: science and mathem
Page 19 and 20: George Majdalany is a PhD candidate
Page 21 and 22: Joyce Nesamani Simson is a PhD cand
Page 23 and 24: Manpower Discipline Based on Knowle
Page 25 and 26: Peyman Akhavan et al. brains, knowl
Page 27 and 28: Peyman Akhavan et al. showed that o
Page 29 and 30: Peyman Akhavan et al. appropriate l
Page 31 and 32: Peyman Akhavan et al. sources for c
Page 33 and 34: Attitudes Towards Changes and Organ
Page 35 and 36: Ruth Alas et al. During the
Page 37 and 38: Professional inter
Page 39 and 40: Ruth Alas et al. Table 4: Correlati
Page 41 and 42: Research was supported by ETF Grant
Page 43 and 44: Rivadávia Correa Drummond de Alvar
Page 51 and 52: 5. Conclusions Rivadávia Correa Dr
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Page 57 and 58: Xiaomi An et al 2.3 Reasons for dev
Page 59 and 60: Xiaomi An et al PTC works closely w
Page 61 and 62: Xiaomi An et al The first is an adm
Page 63 and 64: Xiaomi An et al policy reflections
Page 65 and 66: Figure 1: Transmission gear Marko A
Page 67 and 68: Marko Anzelak et al. knowledge basi
Page 69 and 70: Marko Anzelak et al. 72% recognise
Page 71 and 72: Marko Anzelak et al. virtually all
Page 73 and 74: Gabriel Prananingrum Aryanindita an
Page 79 and 80: Rita Babiliūtė and Kęstutis Kri
Page 87 and 88: Knowledge Management, Cultural Know
Page 89 and 90: Leonard Barnett and Edward Carter 2
Page 91 and 92: Leonard Barnett and Edward Carter p
Page 93 and 94: Leonard Barnett and Edward Carter p
Page 95 and 96: Leonard Barnett and Edward Carter d
Page 97 and 98: Impact of Knowledg
Page 99 and 100: Bob Barrett shorter product life-cy
Page 101 and 102: Bob Barrett 6. Concept of</
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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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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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