Seeking success in e-business : a multidisciplinary approach : IFIP TC8/WG8.4 second Working Conference on E-Business: multidisciplinary research and practice, June 9-11, 2002, Copenhagen, Denmark
Bibliographic record
Abstract
Foreword. If e-Business is Different, then so is Research in e-Business R. Clarke. The Internet and an Opportunity to Re-invent the Banking System M.S.H. Heng, S.C.A. Peters. Failing with success F. Ulbrich. Models of Trust in Business-to-Consumer Electronic Commerce V. Farrell, R. Scheepers, P. Joyce. Conceptual frames of reference and their influence on E-Commerce System Development G.L. van der Vyver, M.S. Lane. An Evaluation of Intelligent Agent based Innovation in the Wholesale Financial Services Industry M.-A. Williams, S. Elliot. Enhancing Mobile Commerce: Instant Music Purchasing Over the Air J.F. Hampe, G. Schwabe. E-Business and Dot.com Driven Transformation - a Comparison of Australian And Indian Experiences in the Telecom Sector C.R. Unnithan, P.M.C. Swatman. A Strategic Comprehensive Framework for Evaluating is Investments A. Wassenaar. Supporting CLEAR: A Strategy for Small and Medium Size Enterprise Adoption of e-Business Practices in Atlantic Canada D. Jutla, T. Weatherbee. Multidisciplinary E-Forensics Methodology Development to Assist in the Investigation of e-Crime A. Tennyenhuis, R. Jamieson. The Potentialities of Focus Groups in e-Business Research: Theory Validation S. Lichtenstein, P.M.C. Swatman. Use of the Web for Destination Marketing by Regional Tourism Organisations in the Asia-Pacific Region L. Burgess, J. Cooper, C. Alcock, K. McNamee, W. Doolin. Internet Marketing Communications: Interactivity and Integration B. Lawton, S. Gregor. E-Business and the Formation of Strategies S. Junghagen, H.C.J. Linderoth. Managing a portfolio of supplier relations in internet-driven electronic market places M. Grieger, H. Kotzab, T. Skjott-Larsen. Adoption of E-Commerce in SMEs: Lessons from Stage Models A. Scupola.E-Government Business Strategies and Services to Citizens J. Chamberlain, T. Castleman. The Implications of E-Commerce for Software Project Risk: A preliminary investigation K.J. Stevens, G.T. Timbrell. Framework for Participants' Recognition of Key Success Factors in Electronic Marketplaces R. Stockdale, C. Standing. Integrating E-commerce into the Retail Supply Chain B. Roberts, G. Thomas. Integration of an Internet-Driven Supply Chain for a Medium-Size E-commerce Company R. Maurici, J.L. Cybulski. Consumer Choice, Information Product Quality, and Market Implications G. Kazakevitch, L. Torlina. On-line Retailing: An Investigation into the Success Factors W. Golden, M. Hughes, P. Gallagher. Governance for E-business Knowledge Management Systems R. Jamieson. The Value of E-procurement Models in B2B Exchanges - An Australian Experience D. Thomson, M. Singh. E-commerce and Human Resource Management: Theoretical Approaches and Issues for the Banking Industry Y. Blount, T. Castleman, P.M.C. Swatman. When e-business becomes k-business...will it be 'a natural act'? D. Binney, M. Ishak. Designing an Online Self-Assessment Tool Utilizing Confidence Measurement G. Farrell, Ying K. Leung. Strategizing for Distributed Knowledge Management M. Holm Larsen, M. Kuhn Pedersen. Software Packages J. Damsgaard, J. Karlsbjerg. Understanding e-business competencies in SMEs T.R. Eikebrokk, D.H. Olsen. Traditional Australian Media Organisations Adoption of the World Wide Web D. Burden, P. Joyce, J. Mustard. The Nature of Work for Employees in a Virtual Organisation: The Virtual Worker Vi-Lam Truong, B.J. Corbitt. Mobile Commerce - The Challenges N. Wickramasinghe.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".