Proceedings of the 12th European Conference on Knowledge ...

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Oliver Moravcik, Dagmar Caganova and Jana Stefankova decrease in <strong>the</strong> number <strong>of</strong> students at <strong>the</strong> faculty – increasing share <strong>of</strong> <strong>the</strong> science funding versus <strong>the</strong> number <strong>of</strong> <strong>the</strong> students in <strong>the</strong> pedagogic process. 5 000 000,00 4 500 000,00 4 000 000,00 3 500 000,00 3 000 000,00 2 500 000,00 2 000 000,00 1 500 000,00 1 000 000,00 500 000,00 0,00 Figure 2: Salaries at MTF STU without taxes 2007 2008 2009 2010 Increase in <strong>the</strong> salaries financial volume is a reflection <strong>of</strong> <strong>the</strong> increased activity in <strong>the</strong> field <strong>of</strong> publications and grant fruitfulness. At <strong>the</strong> same time, it is necessary to have a regard for <strong>the</strong> generally diminishing number <strong>of</strong> staff. 1 400 000,00 1 200 000,00 1 000 000,00 800 000,00 600 000,00 400 000,00 200 000,00 Figure 3: Goods and Services at <strong>the</strong> MTF STU 0,00 2007 2008 2009 2010 It is possible to make a connection with <strong>the</strong> previous Figure 2 by analogy, because both financial circles follow very similar rules for calculating which is set by <strong>the</strong> Ministry <strong>of</strong> Education. 1 000 000,00 800 000,00 600 000,00 400 000,00 200 000,00 0,00 2007 2008 2009 2010 Figure 4: Scholarship <strong>of</strong> PhD students at <strong>the</strong> MTF STU The faculty took a gamble on <strong>the</strong> preference <strong>of</strong> <strong>the</strong> second and third degree <strong>of</strong> study under <strong>the</strong> conditions <strong>of</strong> <strong>the</strong> present decrease in <strong>the</strong> number <strong>of</strong> first-grade students due to different reasons: <strong>the</strong> demandingness coefficient, formulized financially, is <strong>the</strong> lowest on <strong>the</strong> first degree. As a research faculty, <strong>the</strong> main focus is moved to <strong>the</strong> second and PhD degree. The result <strong>of</strong> this activity is definitely due to <strong>the</strong> consequent, relevant increase in funds within state scholarships on <strong>the</strong> third degree. 678 EUR EUR EUR
Oliver Moravcik, Dagmar Caganova and Jana Stefankova 600 000,00 500 000,00 400 000,00 300 000,00 200 000,00 100 000,00 Figure 5: Program 07712 - Science & Technology 0,00 2007 2008 2009 2010 The volume <strong>of</strong> funds for <strong>the</strong> fields <strong>of</strong> science and technology shows a long-term slight increase. It is primarily characterised by <strong>the</strong> volume <strong>of</strong> funds from <strong>the</strong> grant agencies, except for projects concerning structural funds, on which <strong>the</strong> faculty was focused in preference within <strong>the</strong> given period <strong>of</strong> time. The following Figures 6, 7, 8, 9 do not prove to be negligible job cuts for particular groups <strong>of</strong> employees. On <strong>the</strong> o<strong>the</strong>r side, <strong>the</strong> number <strong>of</strong> research workers increased (Figure 9 this is a positive trend towards <strong>the</strong> high aims <strong>of</strong> <strong>the</strong> faculty in <strong>the</strong> area <strong>of</strong> science and research. 490 480 470 460 450 440 430 420 Figure 6: Employees <strong>of</strong> MTF STU 300 250 200 150 100 50 Figure 7: Teachers <strong>of</strong> <strong>the</strong> MTF STU 175 170 165 160 155 150 0 2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010 Figure 8: Administrative & technical employees <strong>of</strong> <strong>the</strong> MTF STU 679 EUR persons persons persons
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Title Author(s) Picture of<
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Inocencia Mª Martínez-León and I
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5.2 Measures Inocencia Mª Martíne
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Maurizio Massaro and Roland Bardy p
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Maurizio Massaro and Roland Bardy T
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Maurizio Massaro and Roland Bardy i
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Maurizio Massaro and Roland Bardy T
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Florian Matthes an
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Florian Matthes an
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Florian Matthes an
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Florian Matthes an
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Germany - Towards a Knowledge-Based
Page 56 and 57: Kai Mertins, Sven Wuscher and Marku
Page 66 and 67: Sandra Moffett, Ro
Page 74 and 75: References Sandra Mof</stro
Page 76 and 77: Syed Moneeb Ali, Mark Woodman and A
Page 82 and 83: 5. Evaluation Syed Moneeb Ali, Mark
Page 86 and 87: Mohammad Mirkazemi Mood, Farhad Dan
Page 92 and 93: RI = w k j Mohammad Mirkazemi Mood,
Page 94 and 95: References Mohammad Mirkazemi Mood,
Page 96 and 97: Knowledge Mapping Based on EFQM Exc
Page 98 and 99: Mahmoud Moradi, Mohammad Rahim Rama
Page 104 and 105: The University Institution’s Impr
Page 108 and 109: Figure 9: Researchers at th
Page 110 and 111: Oliver Moravcik, Dagmar Caganova an
Page 116 and 117: Martina Mullally et al A self-learn
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Page 120 and 121: Martina Mullally et al contacted, <
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Page 124 and 125: Fattah Nazem Organizational level
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Page 130 and 131: Fattah Nazem Miller, R. (1996). Lea
Page 132 and 133: Fattah Nazem 1991). Intellectual ca
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Page 136 and 137: Fattah Nazem Intellectual capital i
Page 138 and 139: The Knowledge Clinic: Concepts, Met
Page 140 and 141: Gaby Neumann and Eduardo Tomé capi
Page 142 and 143: Gaby Neumann and Eduardo Tomé impr
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Page 146 and 147: A Tenth Anniversary Assessment <str
Page 148 and 149: Company discussed in Working Knowle
Page 150 and 151: Gary R Oliver The difficulty with u
Page 152 and 153: Gary R Oliver accounting precedent
Page 154 and 155: Gary R Oliver Grant, R. M. (1996).
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Kaspars Osis and Janis Grundspenkis
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Taha Osman, Dhavalkumar Thakker and
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Getting Ready for Knowledge Managem
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Paul Parboteeah, Thomas Jackson and
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Figure 1 Information Management Str
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Knowledge Transfer, Knowledge Shari
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2.2 KBs Dan Paulin and Kaj Suneson
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Dan Paulin and Kaj Suneson Organiz
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Dan Paulin and Kaj Suneson The R&D
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Dan Paulin and Kaj Suneson Paulin,
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Jan Pawlowski and Markus Bick frame
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3.1 Methodology Jan Pawlowski and M
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Jan Pawlowski and Markus Bick aware
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Jan Pawlowski and Markus Bick This
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Jan Pawlowski and Markus Bick Dawes
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Understanding inter-firm networks a
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3.1 Structural properties 3.1.1 Bet
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Meysam Poorkavoos, Yanqing Duan and
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Meysam Poorkavoos, Yanqing Duan and
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Institutional Matrix of</st
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Evgeny Popov, Maxim Vlasov and Anna
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References Evgeny Popov, Maxim Vlas
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Evgeny Popov, Maxim Vlasov and Mari
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Farnaz Rahimi and Mohamad Ebrahim M
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4. Research design Farnaz Rahimi an
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An Intelligent Model to Asses Organ
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Kamaladdin Rahmani Youshanloui et a
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Kamaladdin Rahmani Youshanloui et a
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4. Conclusion Kamaladdin Rahmani Yo
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Risk Analysis for Knowledge Managem
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4. Risk management Lila Rajabion an
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Lila Rajabion and Jafar Zanganeh po
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Lila Rajabion and Jafar Zanganeh D
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Lila Rajabion and Jafar Zanganeh th
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References Lila Rajabion and Jafar
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Anna Maria Costanza Rinaldi state t
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Anna Maria Costanza Rinaldi single
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Anna Maria Costanza Rinaldi With ho
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Figure 3 : Group k - Leathe
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Anna Maria Costanza Rinaldi determi
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Country Strategic Risk and Knowledg
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Eduardo Rodriguez, John Edwards and
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Max Erik Rohde and David Sundaram i
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Max Erik Rohde and David Sundaram
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Max Erik Rohde and David Sundaram S
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Max Erik Rohde and David Sundaram 6
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A Ranking Method for Identification
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Inès Saad criteria are decomposed
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Inès Saad K3 (Crucial knowledge)
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Josune Sáenz, Nekane Aramburu and
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Social Media Mindset and Knowledge
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Risto Säntti social media mindset
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Risto Säntti 4. The social media m
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social media mindsets. Risto Säntt
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Dan Săvescu, Mihaela-Georgia Sima
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Benedikt Schmidt, Todor Stoitsev an
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2.1 Web analytics evolution Alexand
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Alexander Schneider and Alexander S
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Camilo Augusto Sequeira and Eloi Fe
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9. Conclusions Camilo Augusto Seque
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Elena Shakina and Anna Bykova P. Ra
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Elena Shakina and Anna Bykova Accor
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Elena Shakina and Anna Bykova Figur
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Table 1: The sample descriptive sta
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Elena Shakina and Anna Bykova Table
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Elena Shakina and Anna Bykova It sh
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The Major Challenges of</st
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Mehdi Shami Zanjani, Hamid Rahimian
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Mehdi Shami Zanjani, Hamid Rahimian
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Evangelia Siachou, Sof</str
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Relevance of Intel
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Marta Christina Suciu et al Source:
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Marta Christina Suciu et al Apart f
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Marta Christina Suciu et al public
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Managing Intellectual Capital in SM
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Marta Christina Suciu et al We cons
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Marta Christina Suciu et al aim is
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Marta Christina Suciu et al require
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Universities as Knowledge Creation
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Marta Christina Suciu et al Luintel
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Marta Christina Suciu et al holds a
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Marta Christina Suciu et al propose
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Kaj Suneson and Ilona Heldal Many r
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Kaj Suneson and Ilona Heldal recogn
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Kaj Suneson and Ilona Heldal too ex
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Kaj Suneson and Ilona Heldal In thi
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Infrastructure of
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Natalia Tikhomirova et al global c
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Natalia Tikhomirova et al developm
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Measurement of cus
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Kamila Tislerova and customers, ra<
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Kamila Tislerova as pretty poor (45
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Multi Perspective Framework to Impr
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Choon-Bae Yoo, Igor Hawryszkiewycz
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6.4.1 Content Analysis Choon-Bae Yo
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2. Research objective John T Young
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Figure 2: Exploring and expressing
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John T Young municipal services. Th
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4.1.4 The Customers C John T Young
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6.3 Procedural changes John T Young
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8. The CATWOE Components 8.1 Transf
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John T Young orders could be airfre
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Alexander K. Kofin
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Alexander K. Kofin
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4 : Mobilization of</strong
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Alexander K. Kofin
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Alexander K. Kofin
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1022
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Nadine Amende Figure 1 shows an exa
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Nadine Amende they
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Figure 3: Classification of
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Nadine Amende such as “What are y
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Nadine Amende Davis, F. D. (1986) A
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Evgeny Blagov internal and external
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Evgeny Blagov oriented on t
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Evgeny Blagov (0 - absolutely unimp
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References Evgeny Blagov Arrow, K.J
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Roman Kislov This research looks at
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Roman Kislov role boundary blurring
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Roman Kislov The following methods
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Roman Kislov Lave, J. and Wenger, E
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Tony K. M. Lo and Patrick S.W. Fong
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Type P=A=L P=A, L P, A=L P=L, A P,
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6. Case analysis Tony K. M. Lo and
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Tony K. M. Lo and Patrick S.W. Fong
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KM as a Solution for the</s
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Thi Hai Hang Nguyen et al competiti
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Thi Hai Hang Nguyen et al The varia
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Thi Hai Hang Nguyen et al The resul
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Knowledge Base Development
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2. Literature review 2.1 AC: Theore
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2.3 The inter-relations between SC
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Omid Omidvar Jansen, J. J. P., Van
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Sakura Shimada and that of<
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Sakura Shimada Figure 1: Strategic
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Sakura Shimada broader repercussion
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Sakura Shimada Excousseau, J-L. (20
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Simon Woll The research topic can b
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Simon Woll UI research projects are
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Figure 2: Research design and metho
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1088
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1090
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Chris Blodgett leadership journey t
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Figure 2: Needleman’s Knowledge P
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Chris Blodgett Analysts turn inform
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Figure 8: Cynefin Framework Chris B
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Chris Blodgett IKM Level 1: Inform
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7. Conclusion Chris Blodgett Given
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Stefanie Dannemann However, what wo
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Stefanie Dannemann In modern econom
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2. Research design and methodology
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3.4 KM Applications in IDSC Mohamed
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Mohamed Farid 3.5 Problems with <st
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Mohamed Farid “Enterprise collabo
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Mohamed Farid The design of
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Mohamed Farid For the</stro
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Susan McIntyre and Kate Kaminska re
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Susan McIntyre and Kate Kaminska C
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Susan McIntyre and Kate Kaminska Th
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Susan McIntyre and Kate Kaminska il
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1128
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Óscar Arias Londoño mention that
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5.4 Tacit Knowledge Óscar Arias Lo
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Proposal For A 2.0 Knowledge Manage
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Lillyana María Giraldo Marín et a
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Similarity and Accuracy of<
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Nina Groß et al reporting and cont
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Ravinder Singh Kahlon and Man-Chie
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Figure 3: Non-IT and IT Kiviat Diag
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Appendix B Ravinder Singh Kahlon an
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Pushkal Pandey, Sandra Mof<
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Pushkal Pandey, Sandra Mof<
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José Vásquez Paniagua According t
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José Vásquez Paniagua the
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