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Record W3119673027 · doi:10.1177/0022242921990351

The Control–Effort Trade-Off in Participative Pricing: How Easing Pricing Decisions Enhances Purchase Outcomes

2021· article· en· W3119673027 on OpenAlex
Cindy Xin Wang, Joshua T. Beck, Hong Yuan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Marketing · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsBooth University College
Fundersnot available
KeywordsDelegationPricing strategiesBusinessControl (management)Set (abstract data type)MarketingPurchasingMicroeconomicsEconomicsComputer science

Abstract

fetched live from OpenAlex

Participative pricing strategies may influence consumer purchase decisions; this research proposes specifically that firms’ delegation of pricing decisions to consumers can create a control–effort trade-off. Consumers favor greater pricing control but are deterred by the effort involved in deciding what to pay. Strategies such as pay what you want in turn might reduce purchase intentions due to the effort involved. In contrast, strategies that increase feelings of control but not perceived effort, such as pick your price options that let consumers choose from a limited set of prices, could enhance pricing outcomes. A field study and four laboratory experiments confirm these propositions. The findings demonstrate the mixed effects of participative pricing, identify mediating mechanisms that explain these effects, and specify common moderating conditions that shape the outcomes of participative pricing. These results have notable implications for pricing theory and practice.

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.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.539
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.037
GPT teacher head0.288
Teacher spread0.251 · 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