Digital Business Models and Data Analytics: Essays on New Paradigms of Business Operations
Bibliographic record
Abstract
How will data as a new production factor and the underlying data-oriented technological revolutions reshape business operations and human society? This thesis collects my early research endeavors toward this broad research agenda from two different perspectives: one regarding innovative business models built upon advanced digital technologies and their profound impacts on how companies and organizations manage other production factors (e.g., labor), and the other on methodologies and applications of data analytics, the building block for modern data-driven operations. I discuss my research agenda in greater detail and how my thesis work fits into this agenda in Chapter 1. Chapters 2 and 3 document my pursuit of the agenda from the first perspective mentioned above. Both chapters investigate labor issues brought forward by innovative on-demand service (“gig”) platforms such as Uber and DoorDash, which transform the paradigm of human resource management by giving workers complete flexibility in work schedules. In Chapter 2, “Worker Classification in On-Demand Economy,” I will join the recent policy debate on whether gig workers should be classified as independent contractors or employees. I will analyze the operations of an on-demand service platform given different worker classifications and their implications for workers’ welfare. I will discuss why being classified as independent contractors can be a good idea for gig workers, issues with current regulations, and policy alternatives with potential for Pareto improvement. In Chapter 3, “Customers’ Pro-Social Behaviours in On-Demand Economy,” I study customers’ tipping on gig platforms. By analyzing the Chicago ride-hailing and taxi datasets, I will demonstrate that social events such as anti-racism protests can help reverse the declining trend of tipping and discuss the mechanisms and limitations of such an effect. Theoretically, I will show that thanks to gig workers’ flexibility in work schedules, customers’ tipping may benefit both workers and themselves. Finally, Chapter 4, ‘‘Data Analytics for Event Evaluation,’’ studies methodologies for data analytics. I will develop a framework that enables companies and organizations to dynamically evaluate the impacts of specific events using their proprietary data. For applications, I will study an emergency room redesign project at a local hospital near Toronto.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.016 | 0.041 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".