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Record W4284886200 · doi:10.3390/jrfm15070300

Crowd Reactions to Entrepreneurial Failure in Rewards-Based Crowdfunding: A Psychological Contract Theory Perspective

2022· article· en· W4284886200 on OpenAlexvenueno aff
Swati Oberoi, Smita Srivastava, Vishal K. Gupta, Rohit Joshi, Atul C. Mehta

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological contractPopularityTransactional leadershipCrowdsPsychologySocial psychologyPerspective (graphical)Public relationsBusinessPolitical science

Abstract

fetched live from OpenAlex

Rewards-based crowdfunding (RBC) has recently gained popularity as an alternative means of finance to help entrepreneurs bring novel projects to life. We theorize that crowdfunding backers perceive an implicit psychological contract with campaign creators. When promised rewards fail to materialize post fundraising, backers may perceive entrepreneurs’ failure to deliver rewards as a violation of their psychological contract with him or her. Drawing on psychological contract theory and using Eisenhardt’s comparative case methodology, we generate insights about crowd reactions to creators’ failure to deliver rewards to backers. Our research generates the novel insight that in the event of delivery failure, backers who perceive a transactional psychological contract with creators are more likely to display negative emotional reactions, while backers who perceive a relational psychological contract are more likely to display positive emotional reactions. Furthermore, we identify three progressive stages of backers’ interaction with creators in failed RBC campaigns, ‘committing’, ‘crisis handling’, and ‘coping-up’ and highlight the crowds’ emotional valence associated with each stage. Our analyses of the campaign comments reveal insights of interest to RBC players and hold implications for the future development of crowdfunding.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.235 · 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 designTheoretical or conceptual
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

Citations8
Published2022
Admission routes1
Has abstractyes

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