Going the distance: project management issues in a cycle hire project
Bibliographic record
Abstract
Research methodology The case was devised using both primary and secondary data sources. Primary sources of data consisted of in-depth interviews with individuals using the cycle hire project. The researcher also had first-hand experiences of using the cycles. The case study has been tested with undergraduate and graduate students taking management information systems courses. Case overview/synopsis This teaching case study charts the London cycle hire project, mostly from its first inception in July 2010, right through to the planned expansion of electric cycles from Summer 2022. The main aim of the case is to introduce students to project management challenges which are part of the London cycle hire project. While the project was filled with enthusiasm from its early beginnings, various challenges were encountered including issues associated with the project procurement/sourcing process, software and technical problems, as well as other project management issues. Problems became so severe in 2011 that the service provider was hit with a penalty and had to make critical project improvements. Would these accountability measures prompt the service provider to resolve these issues? How would the service provider go about undertaking a fact-finding exercise to verify the existence of the challenges and address them to ensure renewed project success? Complexity academic level The case was written for classes at both the undergraduate and graduate levels. The focus of the case is particularly well suited for exploring topics and issues relating to types of information systems, project management and accountability, multiple global supplier procurement, as well as challenges associated with hardware integration and software design. While the case was targeted at MIS students, the case study would also be effective for an introductory level project management course or a general management course. The subject of the case, the bicycle rental program, is likely to appeal to students, and the basic underlying business issues, processes and objectives of the project are easily understood.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".