The Moderating Role of Hotel Type on Advertising Expenditure Returns in Franchised Chains
Bibliographic record
Abstract
This study contributes to a deeper understanding of financial returns from the advertising of hospitality services. Specifically, we investigate how different types of franchised hotel outlets, each targeting a different customer segment, moderate the effects of various advertising expenditures on unit-level profitability. Our unique data set allows us to examine the impact of unit-level advertising expenditure allocations using line items from the profit and loss statements of more than 9,000 franchised U.S. hotel properties from 2007 to 2018. We consider the effects of investing in varied advertising activities, including the franchise advertising assessment, loyalty programs, local sales force, and local media advertising. As hypothesized, we find differential effects of advertising for outlets targeting different customer segments. Relative to traditional full-service hotels, those focused on destination-driven customer segments (i.e., destination hotels) and price-sensitive customer segments (i.e., limited-service hotels) benefit less from investing in the franchise advertising assessment, loyalty programs, and local media advertising. We also find that local sales force expenditures positively moderate performance for destination hotels. We highlight the moderating effects of hotel type as a key situational factor and provide more nuanced insight into managing the tension arising from advertising allocation at franchised hotel chains.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".