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Record W2782762104 · doi:10.1002/jcpy.1023

Discounting Humanity: When Consumers are Price Conscious, Employees Appear Less Human

2018· article· en· W2782762104 on OpenAlexaff
Alexander P. Henkel, Johannes Boegershausen, JoAndrea Hoegg, Karl Aquino, Jos Lemmink

Bibliographic record

VenueJournal of Consumer Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDehumanizationMarketingHumanityPerceptionBusinessService (business)DiscountingEconomicsPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Consumers are frequently bombarded with a myriad of marketing tactics. One tactic regularly employed by thrift‐oriented brands is to highlight low prices, discounts, and sales promotions. When consumers encounter these low‐price signals, they may adopt a price conscious mentality, that is, a singular focus on getting the cheapest deal. A price conscious mentality is likely beneficial for consumers, as it helps them save money. However, it is also possible that it has negative implications, particularly for how consumers perceive and interact with other human beings in the marketplace, such as customer service employees. The current research addresses this issue by investigating how consumers’ price conscious mentality impacts their perceptions of employees’ humanity. Results from four studies demonstrate that a price conscious mentality can lead consumers away from fully recognizing the human qualities of employees. The findings also suggest that this subtle form of dehumanization can result in harsher treatment of employees when they provide less than satisfactory service.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.055
GPT teacher head0.381
Teacher spread0.326 · 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.

Study designNot applicable
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

Citations41
Published2018
Admission routes1
Has abstractyes

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