Cited by 171Decision-Making and Behavioral EconomicsExperimental Behavioral Economics StudiesConsumer Market Behavior and Pricing

The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.

Medium Maximization

Open access
Hsee, Christopher K., Yu, Fang, Zhang, Jiao, Zhang, Yan·2003·Journal of Consumer Research

Abstract

A medium—for example, points or money—is a token people receive as the immediate reward of their effort. It has no value in and of itself, but it can be traded for a desired outcome. Experiments demonstrate that, when people are faced with options entailing different outcomes, the presence of a medium can alter what option they choose. This effect occurs because the medium presents an illusion of advantage to an otherwise not so advantageous option, an illusion of certainty to an otherwise uncertain option, or an illusion of linearity to an otherwise concave effort-outcome return relationship. This work has implications for how points influence consumer choice and how money influences human behavior.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1086/374702

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 37 references

Research works printed but not linked: 5

  1. Armus, Harvard L. (1982), “Secondary Reinforcement Strength and Acquisition Reinforcement Schedule,” Bulletin of the Psychonomic Society, 20 (3), 129
  2. Frank, Robert (1999), Luxury Fever: Why Money Fails to Satisfy in an Era of Excess, New York: Free Press.
  3. ——— (1994), “Creativity in Decision Making with Value-Focused Thinking,” Sloan Management Review, 35 (Summer), 33–41.
  4. Keeney, Ralph L. and Howard Raiffa (1976), Decisions with Multiple Objectives: Preferences and Value Tradeoffs, New York: Wiley.
  5. ——— (2000), “Mental Accounting Matters,” in Choices, Values and Frames, ed. Daniel Kahneman and Amos Tversky, Cambridge: Cambridge University Press.

Cited by

Sorted by citations

Items 101–171 of 171

Metadata

Authors
Hsee, C. K., Yu, F., Zhang, J., & Zhang, Y.
Venue
Journal of Consumer Research
Published
2003-06
Volume
30
Issue
1
Pages
1-14
ISSN
  • 0093-5301 (ISSN-L)
  • 1537-5277
OpenAlex
W4234925279
Cited by
171
References
37
Citations by year
2026: 3 · 2025: 4 · 2024: 9 · 2023: 8 · 2022: 9 · 2021: 10 · 2020: 13 · 2019: 7 · 2018: 9 · 2017: 9 · 2016: 8 · 2015: 9 · 2014: 15 · 2013: 10 · 2012: 6
FWCI
2.71
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of Consumer Research (publishedVersion)
  • CEIBS Institutional Repository (submittedVersion, cc-by-nc-nd)
Funders
National Science Foundation
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
Keywords
reward, consumer choice, money, decision-making

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review