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

More documents

Recommendations

Info

Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser Table 4: Most frequently used terms in <strong>the</strong> questionnaire Action specified by participant Occurrences Search 65 Browse 57 Read 41 Open/access 34 Author/Write 31 Create 28 Communicate 22 Send 20 Use 16 Copied 13 Save 11 Organize 10 Examine 9 Calculate 8 Execute 5 Table 5: Mapping user action words to desktop operations and knowledge actions 2.3 Study summary Action specified by participant Desktop Operations Knowledge Action Search Browse Browse Browse Read Consume open/access Open author/write Author Create Create Communicate Communicate Send Communicate Use Focus Copied Copy Save Save Organize Organize Examine Consume Calculate Execute Execute Execute Renaming Deleting Cutting Pasting Printing The previous sections have shown that knowledge work execution can be captured by core execution processes based on a vocabulary <strong>of</strong> hierarchically connected desktop operations and knowledge actions. Knowledge actions are work techniques that require desktop operations as application usage to execute tasks. The different applications and thus <strong>the</strong> different operations guided by <strong>the</strong> actions are connected by transitions that occur during task execution in <strong>the</strong> form <strong>of</strong> application switches. As <strong>the</strong> actions and operations can be applied in different tasks <strong>the</strong>y do not support <strong>the</strong> identification <strong>of</strong> task instances in work execution. Information about differences <strong>of</strong> tasks is assumed to manifest in <strong>the</strong> textual content that is accessed during task execution. The following section presents a study that focuses on textual content. 894
Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser 3. Study <strong>of</strong> text involved in task execution processes Whereas, <strong>the</strong> previous section has identified operations and actions that are mixed to create work processes, <strong>the</strong> following section examines <strong>the</strong> textual content accessed during work execution and its significance for task identification. When a user works, different content is on <strong>the</strong> screen, tables, texts etc. The displayed content changes frequently due to application switches or actions on resources like opening and closing. Tracking <strong>the</strong> screen content shows a meta-text <strong>of</strong> work execution, comprised <strong>of</strong> different information types. In <strong>the</strong> following, a second study is presented (section 3.1). The focus <strong>of</strong> <strong>the</strong> examination is set on <strong>the</strong> textual content that is described (section 3.2) and analyzed with respect to similarity <strong>of</strong> involved information and <strong>the</strong> possibility to find topics as guiding <strong>the</strong>me for task execution processes. Finally, a summary <strong>of</strong> <strong>the</strong> study is given (section 3.3). 3.1 Methodology To make assumptions about <strong>the</strong> relevance <strong>of</strong> information involved in work execution, a dataset was created through a user study. Eight knowledge workers, four PhD students and four post-docs in computer science working in <strong>the</strong> IT research <strong>of</strong> a big s<strong>of</strong>tware company executed five different tasks listed in table 6 (avg. execution time was one hour). The tasks were given in random order to <strong>the</strong> participants. To correspond to <strong>the</strong> dynamic and highly individual execution <strong>of</strong> knowledge-intensive tasks in a real work setting, task switches were stimulated by giving a new task to a participant before a former task was finalized. An example is given in Figure 4 with interruptions for task 2, task 5 and task 3. Figure 4: Example for task execution with disruption, as applied in <strong>the</strong> study setting The resulting dataset contains eight interaction histories. Each interaction history contains an instance <strong>of</strong> each task. Each interaction history especially contains <strong>the</strong> textual content that is visible in <strong>the</strong> focused window <strong>of</strong> an application. For <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> dataset, <strong>the</strong> interaction history events were manually labeled with <strong>the</strong> tasks, <strong>the</strong>y belonged to. To label <strong>the</strong> dataset <strong>the</strong> two study supervisors reviewed <strong>the</strong> logs and decided on <strong>the</strong> belonging <strong>of</strong> a sensor event based on notes taken during <strong>the</strong> study and <strong>the</strong>ir knowledge <strong>of</strong> <strong>the</strong> tasks. Table 6: Tasks for <strong>the</strong> user study on text used in <strong>the</strong> execution process Task Short Description/Name Task 1 Plan a trip and inform your colleague Task 2 Summarize and present <strong>the</strong> content <strong>of</strong> research papers Task 3 Consolidate a paper using external resources Task 4 Create an application partner catalogue Task 5 Decide on applicant invitations and communicate your decision 3.2 Text involved in task execution Textual content <strong>of</strong> a work process involves a large numbers <strong>of</strong> words (e.g. <strong>the</strong> study data contains on avg. 60000 nouns and verbs for 1 hour <strong>of</strong> desktop work per user). The textual content itself is a mixture <strong>of</strong> highly structured textual elements (e.g. hyperlinks), weakly structured textual elements (e.g. 895
Page 1 and 2:
Proceedings <stron
Page 3 and 4:
Contents Title Author(s) Picture <s
Page 5 and 6:
Title Author(s) Picture of<
Page 7 and 8:
Volume Two The Influence of
Page 9 and 10:
The Symbolic Innovation of<
Page 11 and 12:
Biographies of <st
Page 13 and 14:
Jan Pawlowski works as Prof
Page 15 and 16:
in academia. He holds the</
Page 17 and 18:
Biomedical Engineering Research Cen
Page 19 and 20:
Mathematics and Ph
Page 21 and 22:
Thi Hai Hang Nguyen is a lecturer a
Page 23 and 24:
Dan Savescu graduate TCM Faculty, p
Page 25:
Teaches Business Computing and Stat
Page 28 and 29:
Inocencia Mª Martínez-León and I
Page 30 and 31:
Page 32 and 33:
5.2 Measures Inocencia Mª Martíne
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Maurizio Massaro and Roland Bardy p
Page 40 and 41:
Maurizio Massaro and Roland Bardy T
Page 42 and 43:
Maurizio Massaro and Roland Bardy i
Page 44 and 45:
Maurizio Massaro and Roland Bardy T
Page 46 and 47:
Florian Matthes an
Page 48 and 49:
Florian Matthes an
Page 50 and 51:
Florian Matthes an
Page 52 and 53:
Florian Matthes an
Page 54 and 55:
Germany - Towards a Knowledge-Based
Page 56 and 57:
Kai Mertins, Sven Wuscher and Marku
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Sandra Moffett, Ro
Page 68 and 69:
Sandra Moffett, Ro
Page 70 and 71:
Sandra Moffett, Ro
Page 72 and 73:
Sandra Moffett, Ro
Page 74 and 75:
References Sandra Mof</stro
Page 76 and 77:
Syed Moneeb Ali, Mark Woodman and A
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
5. Evaluation Syed Moneeb Ali, Mark
Page 84 and 85:
Page 86 and 87:
Mohammad Mirkazemi Mood, Farhad Dan
Page 88 and 89:
Page 90 and 91:
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 100 and 101:
Page 102 and 103:
Page 104 and 105:
The University Institution’s Impr
Page 106 and 107:
Oliver Moravcik, Dagmar Caganova an
Page 108 and 109:
Figure 9: Researchers at th
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Martina Mullally et al A self-learn
Page 118 and 119:
Martina Mullally et al In t
Page 120 and 121:
Martina Mullally et al contacted, <
Page 122 and 123:
Martina Mullally et al Choo, C. W (
Page 124 and 125:
Fattah Nazem Organizational level
Page 126 and 127:
3. Method of <stro
Page 128 and 129:
Fattah Nazem strengthe</str
Page 130 and 131:
Fattah Nazem Miller, R. (1996). Lea
Page 132 and 133:
Fattah Nazem 1991). Intellectual ca
Page 134 and 135:
Fattah Nazem Table 1: Rotated Compo
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
Page 144 and 145:
Gaby Neumann and Eduardo Tomé inst
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).
Page 156 and 157:
Kaspars Osis and Janis Grundspenkis
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Taha Osman, Dhavalkumar Thakker and
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Getting Ready for Knowledge Managem
Page 176 and 177:
Paul Parboteeah, Thomas Jackson and
Page 178 and 179:
Figure 1 Information Management Str
Page 180 and 181:
Knowledge Transfer, Knowledge Shari
Page 182 and 183:
2.2 KBs Dan Paulin and Kaj Suneson
Page 184 and 185:
Dan Paulin and Kaj Suneson Organiz
Page 186 and 187:
Dan Paulin and Kaj Suneson The R&D
Page 188 and 189:
Dan Paulin and Kaj Suneson Paulin,
Page 190 and 191:
Jan Pawlowski and Markus Bick frame
Page 192 and 193:
3.1 Methodology Jan Pawlowski and M
Page 194 and 195:
Jan Pawlowski and Markus Bick aware
Page 196 and 197:
Jan Pawlowski and Markus Bick This
Page 198 and 199:
Jan Pawlowski and Markus Bick Dawes
Page 200 and 201:
Understanding inter-firm networks a
Page 202 and 203:
3.1 Structural properties 3.1.1 Bet
Page 204 and 205:
Meysam Poorkavoos, Yanqing Duan and
Page 206 and 207:
Meysam Poorkavoos, Yanqing Duan and
Page 208 and 209:
Institutional Matrix of</st
Page 210 and 211:
Evgeny Popov, Maxim Vlasov and Anna
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
References Evgeny Popov, Maxim Vlas
Page 218 and 219:
Evgeny Popov, Maxim Vlasov and Mari
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Farnaz Rahimi and Mohamad Ebrahim M
Page 232 and 233:
4. Research design Farnaz Rahimi an
Page 234 and 235:
An Intelligent Model to Asses Organ
Page 236 and 237:
Kamaladdin Rahmani Youshanloui et a
Page 238 and 239:
Kamaladdin Rahmani Youshanloui et a
Page 240 and 241:
4. Conclusion Kamaladdin Rahmani Yo
Page 242 and 243:
Risk Analysis for Knowledge Managem
Page 244 and 245:
4. Risk management Lila Rajabion an
Page 246 and 247:
Lila Rajabion and Jafar Zanganeh po
Page 248 and 249:
Lila Rajabion and Jafar Zanganeh D
Page 250 and 251:
Lila Rajabion and Jafar Zanganeh th
Page 252 and 253:
References Lila Rajabion and Jafar
Page 254 and 255:
Anna Maria Costanza Rinaldi state t
Page 256 and 257:
Anna Maria Costanza Rinaldi single
Page 258 and 259:
Anna Maria Costanza Rinaldi With ho
Page 260 and 261:
Figure 3 : Group k - Leathe
Page 262 and 263:
Anna Maria Costanza Rinaldi determi
Page 264 and 265:
Country Strategic Risk and Knowledg
Page 266 and 267:
Eduardo Rodriguez, John Edwards and
Page 268 and 269:
Page 270 and 271:
Page 272 and 273: Eduardo Rodriguez, John Edwards and
Page 278 and 279: Max Erik Rohde and David Sundaram i
Page 280 and 281: Max Erik Rohde and David Sundaram
Page 282 and 283: Max Erik Rohde and David Sundaram S
Page 284 and 285: Max Erik Rohde and David Sundaram 6
Page 286 and 287: A Ranking Method for Identification
Page 288 and 289: Inès Saad criteria are decomposed
Page 290 and 291: Inès Saad K3 (Crucial knowledge)
Page 292 and 293: Josune Sáenz, Nekane Aramburu and
Page 300 and 301: Social Media Mindset and Knowledge
Page 302 and 303: Risto Säntti social media mindset
Page 304 and 305: Risto Säntti 4. The social media m
Page 306 and 307: social media mindsets. Risto Säntt
Page 308 and 309: Dan Săvescu, Mihaela-Georgia Sima
Page 318 and 319: Benedikt Schmidt, Todor Stoitsev an
Page 328 and 329: 2.1 Web analytics evolution Alexand
Page 330 and 331: Alexander Schneider and Alexander S
Page 336 and 337: Camilo Augusto Sequeira and Eloi Fe
Page 344 and 345: 9. Conclusions Camilo Augusto Seque
Page 346 and 347: Elena Shakina and Anna Bykova P. Ra
Page 348 and 349: Elena Shakina and Anna Bykova Accor
Page 350 and 351: Elena Shakina and Anna Bykova Figur
Page 352 and 353: Table 1: The sample descriptive sta
Page 354 and 355: Elena Shakina and Anna Bykova Table
Page 356 and 357: Elena Shakina and Anna Bykova It sh
Page 358 and 359: The Major Challenges of</st
Page 360 and 361: Mehdi Shami Zanjani, Hamid Rahimian
Page 362 and 363: Mehdi Shami Zanjani, Hamid Rahimian
Page 364 and 365: Evangelia Siachou, Sof</str
Page 372 and 373:
Relevance of Intel
Page 374 and 375:
Marta Christina Suciu et al Source:
Page 376 and 377:
Marta Christina Suciu et al Apart f
Page 378 and 379:
Marta Christina Suciu et al public
Page 380 and 381:
Managing Intellectual Capital in SM
Page 382 and 383:
Marta Christina Suciu et al We cons
Page 384 and 385:
Marta Christina Suciu et al aim is
Page 386 and 387:
Marta Christina Suciu et al require
Page 388 and 389:
Universities as Knowledge Creation
Page 390 and 391:
Marta Christina Suciu et al Luintel
Page 392 and 393:
Marta Christina Suciu et al holds a
Page 394 and 395:
Marta Christina Suciu et al propose
Page 396 and 397:
Kaj Suneson and Ilona Heldal Many r
Page 398 and 399:
Kaj Suneson and Ilona Heldal recogn
Page 400 and 401:
Kaj Suneson and Ilona Heldal too ex
Page 402 and 403:
Kaj Suneson and Ilona Heldal In thi
Page 404 and 405:
Infrastructure of
Page 406 and 407:
Natalia Tikhomirova et al global c
Page 408 and 409:
Natalia Tikhomirova et al developm
Page 410 and 411:
Measurement of cus
Page 412 and 413:
Kamila Tislerova and customers, ra<
Page 414 and 415:
Kamila Tislerova as pretty poor (45
Page 416 and 417:
Multi Perspective Framework to Impr
Page 418 and 419:
Choon-Bae Yoo, Igor Hawryszkiewycz
Page 420 and 421:
Page 422 and 423:
6.4.1 Content Analysis Choon-Bae Yo
Page 424 and 425:
Page 426 and 427:
2. Research objective John T Young
Page 428 and 429:
Figure 2: Exploring and expressing
Page 430 and 431:
John T Young municipal services. Th
Page 432 and 433:
4.1.4 The Customers C John T Young
Page 434 and 435:
6.3 Procedural changes John T Young
Page 436 and 437:
8. The CATWOE Components 8.1 Transf
Page 438 and 439:
John T Young orders could be airfre
Page 440 and 441:
Alexander K. Kofin
Page 442 and 443:
Alexander K. Kofin
Page 444 and 445:
4 : Mobilization of</strong
Page 446 and 447:
Alexander K. Kofin
Page 448 and 449:
Alexander K. Kofin
Page 450 and 451:
1022
Page 452 and 453:
Nadine Amende Figure 1 shows an exa
Page 454 and 455:
Nadine Amende they
Page 456 and 457:
Figure 3: Classification of
Page 458 and 459:
Nadine Amende such as “What are y
Page 460 and 461:
Nadine Amende Davis, F. D. (1986) A
Page 462 and 463:
Evgeny Blagov internal and external
Page 464 and 465:
Evgeny Blagov oriented on t
Page 466 and 467:
Evgeny Blagov (0 - absolutely unimp
Page 468 and 469:
References Evgeny Blagov Arrow, K.J
Page 470 and 471:
Roman Kislov This research looks at
Page 472 and 473:
Roman Kislov role boundary blurring
Page 474 and 475:
Roman Kislov The following methods
Page 476 and 477:
Roman Kislov Lave, J. and Wenger, E
Page 478 and 479:
Tony K. M. Lo and Patrick S.W. Fong
Page 480 and 481:
Type P=A=L P=A, L P, A=L P=L, A P,
Page 482 and 483:
6. Case analysis Tony K. M. Lo and
Page 484 and 485:
Tony K. M. Lo and Patrick S.W. Fong
Page 486 and 487:
KM as a Solution for the</s
Page 488 and 489:
Thi Hai Hang Nguyen et al competiti
Page 490 and 491:
Thi Hai Hang Nguyen et al The varia
Page 492 and 493:
Thi Hai Hang Nguyen et al The resul
Page 494 and 495:
Knowledge Base Development
Page 496 and 497:
2. Literature review 2.1 AC: Theore
Page 498 and 499:
2.3 The inter-relations between SC
Page 500 and 501:
Omid Omidvar Jansen, J. J. P., Van
Page 502 and 503:
Sakura Shimada and that of<
Page 504 and 505:
Sakura Shimada Figure 1: Strategic
Page 506 and 507:
Sakura Shimada broader repercussion
Page 508 and 509:
Sakura Shimada Excousseau, J-L. (20
Page 510 and 511:
Simon Woll The research topic can b
Page 512 and 513:
Simon Woll UI research projects are
Page 514 and 515:
Figure 2: Research design and metho
Page 516 and 517:
1088
Page 518 and 519:
1090
Page 520 and 521:
Chris Blodgett leadership journey t
Page 522 and 523:
Figure 2: Needleman’s Knowledge P
Page 524 and 525:
Chris Blodgett Analysts turn inform
Page 526 and 527:
Figure 8: Cynefin Framework Chris B
Page 528 and 529:
Chris Blodgett IKM Level 1: Inform
Page 530 and 531:
7. Conclusion Chris Blodgett Given
Page 532 and 533:
Stefanie Dannemann However, what wo
Page 534 and 535:
Stefanie Dannemann In modern econom
Page 536 and 537:
2. Research design and methodology
Page 538 and 539:
3.4 KM Applications in IDSC Mohamed
Page 540 and 541:
Mohamed Farid 3.5 Problems with <st
Page 542 and 543:
Mohamed Farid “Enterprise collabo
Page 544 and 545:
Mohamed Farid The design of
Page 546 and 547:
Mohamed Farid For the</stro
Page 548 and 549:
Susan McIntyre and Kate Kaminska re
Page 550 and 551:
Susan McIntyre and Kate Kaminska C
Page 552 and 553:
Susan McIntyre and Kate Kaminska Th
Page 554 and 555:
Susan McIntyre and Kate Kaminska il
Page 556 and 557:
1128
Page 558 and 559:
Óscar Arias Londoño mention that
Page 560 and 561:
5.4 Tacit Knowledge Óscar Arias Lo
Page 562 and 563:
Proposal For A 2.0 Knowledge Manage
Page 564 and 565:
Lillyana María Giraldo Marín et a
Page 566 and 567:
Similarity and Accuracy of<
Page 568 and 569:
Nina Groß et al reporting and cont
Page 570 and 571:
Ravinder Singh Kahlon and Man-Chie
Page 572 and 573:
Figure 3: Non-IT and IT Kiviat Diag
Page 574 and 575:
Appendix B Ravinder Singh Kahlon an
Page 576 and 577:
Pushkal Pandey, Sandra Mof<
Page 578 and 579:
Pushkal Pandey, Sandra Mof<
Page 580 and 581:
José Vásquez Paniagua According t
Page 582 and 583:
José Vásquez Paniagua the
show all

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

Create successful ePaper yourself

Delete template?

Save as template?