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Record W2542698714 · doi:10.1108/ejm-08-2015-0541

Commitment to marketing spending through recessions

2016· article· en· W2542698714 on OpenAlexaff
Imran S. Currim, Jooseop Lim, Yu Zhang

Bibliographic record

VenueEuropean Journal of Marketing · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsRecessionStock (firearms)EconomicsMarketingStock marketRevenueBusinessFinanceMacroeconomics

Abstract

fetched live from OpenAlex

Purpose This paper aims to address two unique and important questions. First, how do recessions directly affect firms’ marketing spending decisions? Second, and more importantly, do firms which are more committed to marketing spending through past recessions achieve better stock market returns? Design/methodology/approach This study is based on a combination of National Bureau of Economic Research, COMPUSTAT and Center for Research in Security Prices data on 6,000 firms between 1982 and 2009 which are analyzed using panel data-based regression models. Findings The authors find that firms cut marketing spending during recessions. However, firms committed to marketing spending during past recessions achieve better stock market returns. The findings are found to be robust across B2B and B2C industries, different periods and US firms which vary on the proportion of their global revenue from non-US sales. Research limitations/implications Top executives cut marketing budgets during recessions; however, if they can resist the pressures, and strategically continue to make marketing investments during recessions, they will achieve higher stock market returns. Originality/value This is the first paper to establish the longer-term (not short-term) positive stock market performance of continuous (not episodic) marketing spending through past recessions, i.e. the view that marketing spending is necessary (not discretionary) for stock returns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.236
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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