The Misuse of Accounting-Based Approximations of Tobin’s q in a World of Market-Based Assets
Bibliographic record
Abstract
Accounting-based approximations of Tobin’s q (AATQ) are increasingly popular in marketing. AATQ differ from Tobin’s original conception in that they use accounting data to assess the replacement cost of a firm’s assets; the core problem with this is that valuable assets go unrecorded in external reports, including systematic underrecording of market-based assets. This research examines the extensive erroneous claims made about AATQ in marketing studies. We note the widespread use of the metrics and demonstrate that the AATQ used in marketing (1) are not comparable across industries, (2) do not use only tangible assets in their denominator, and (3) should not find equilibrium at 1. AATQ are often described as performance metrics and can respond appropriately to certain types of positive performance. Unfortunately, they also respond positively to performance-neutral strategic choices. Furthermore, whenever AATQ exceed 1, as is typical, they increase even with completely wasted investments. We note that AATQ are especially problematic measures of performance for marketers because they are biased toward reporting that investments in market-based assets (e.g., brand equity and customer satisfaction) are effective. The misuse of AATQ we document suggests the need for marketing scholars to pay greater attention to the theoretical underpinnings of their metrics.
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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 | 0.040 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".