Funding marketing resources and capabilities during a recession: an empirical examination of top corporate advertisers
Bibliographic record
Abstract
Purpose This study aims to ask whether the funding behaviour of companies is different during a recession. Specifically, the authors study whether firms fund marketing resources and capabilities with internal or external financing during a recession and under which conditions of strategic financial flexibility debt might be used to fund marketing resources and capabilities in recessions. Design/methodology/approach This study estimates empirical models using a newly merged data set covering 17 years, from 2000 to 2016. The authors merge firms’ marketing and financial information from Advertising Age, the American Customer Satisfaction Index, Compustat and the Centre for Research in Security Prices. The sample includes a panel of 653 firm-years of 67 top corporate advertisers. Findings The results indicate that firms take recessions as opportunities to be proactive and invest in short- and long-term marketing capabilities, companies with higher strategic financial flexibility relative to their industry peers tend to rely more on debt to fund short- and long-term marketing capabilities during recessions, firms use internal financing to fund their marketing budgets and short-term marketing capabilities in recessionary and non-recessionary periods and firms use internal financing and signals from past stock returns as mechanisms to fund long-term marketing capabilities. Research limitations/implications The findings contribute to the body of knowledge on the antecedents of marketing resources and capabilities. The results extend the pecking order theory to include recessions and provide nuances of the financing drivers of resources and capabilities. Practical implications Companies should be proactive during recessions and invest in short- and long-term marketing capabilities. When negotiating marketing budgets with chief financial officers, marketing practitioners could suggest the sources to finance specific marketing resources and capabilities. Based on the results of top corporate advertisers, the authors recommend companies to fund marketing capabilities with internal resources (e.g. cash flows, retained earnings), and if cash is not available, companies need to rely on their superior strategic financial flexibility to access long-term debt and fund investments in marketing capabilities. The authors also recommend companies to fund long-term marketing capabilities by re-allocating investments. As well, signals from past performance are an important source to gain access to capital and fund investments in long-term marketing capabilities. Originality/value This study provides a more complete picture of the financial antecedents of marketing resources and capabilities in general and during a recession. The authors provide light on the moderating role of strategic financial flexibility during recessions. This study also clarifies the potential signalling of past performance for funding marketing resources and capabilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".