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Record W2039594432 · doi:10.1108/raf-12-2013-0141

Groupcentric budget goals, budget-based incentive contracts, and additive group tasks

2015· article· en· W2039594432 on OpenAlexaff
Jonathan Farrar, Theresa Libby, Linda Thorne

Bibliographic record

VenueReview of Accounting and Finance · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsYork UniversityUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsIncentiveTask (project management)Context (archaeology)OriginalityGroup (periodic table)Value (mathematics)Goal orientationPsychologySocial psychologyMicroeconomicsComputer scienceEconomicsManagement

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to examine the effects of three different types of budget goals (egocentric individual, groupcentric individual and group) on group performance of an additive task, assigned within an individual budget-based incentive contract. While previous research has established that budget-based incentive contracts motivate higher group performance than piece rate contracts for additive group tasks, no studies, which we are aware of, have considered explicitly the type of goal within this context. Design/methodology/approach – We conduct a 3 × 2 experiment in which we manipulate the presence of an individual goal (egocentric, groupcentric and absent) and a group goal (present and absent) on group performance of an additive task. Findings – Group performance is higher for groups assigned groupcentric individual goals than for groups assigned egocentric individual goals, either alone or in combination with a group goal. Practical implications – Egocentric individual goals may reinforce an individualistic orientation, which may work against the potential gains from having group members adopt more of a group focus. Originality/value – This paper considers how groupcentric individual goals may improve group performance. The management accounting literature typically examines just egocentric individual goals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.306
Teacher spread0.285 · 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 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

Citations6
Published2015
Admission routes1
Has abstractyes

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