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Record W2182863398 · doi:10.63963/001c.151089

The Use of Dominance Analysis to Identify Key Factors in Salespeople’s Affective Commitment Toward the Sales Manager and Organizational Commitment

2014· article· en· W2182863398 on OpenAlexaff
Stacey Schetzsle, Tanya Drollinger

Bibliographic record

VenueMarketing Management Journal · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsOrganizational commitmentBusinessSocial exchange theoryDominance (genetics)Promotion (chess)MarketingAffective events theoryOrder (exchange)Functional managerJob satisfactionPsychologyJob performanceManagementSocial psychologyJob attitudeEconomics

Abstract

fetched live from OpenAlex

Selling in the current business environment requires dedication from various members of the organization. It is important for managers and researchers to identify key factors contributing to salesperson commitment within the organization and to the organization in order to achieve sales objectives. The purpose of this study is to identify the relative importance of variables influencing salesperson affective commitment to their sales manager and the commitment to the organization. Using social exchange theory and resource exchange theory, salesperson interaction with their manager is expected to be exchanged for commitment to that manager. On an organizational level, salesperson satisfaction with the organization is expected to be exchanged for commitment to the organization. Dominance is calculated for each of the independent variables examining affective commitment to the manager (trust, integrity, consideration) and organizational commitment (job satisfaction, promotion opportunity, needs fulfillment). Dominance analysis results show relative importance for perceived trustworthiness of the manager on salesperson commitment to the manager and promotion opportunity on salesperson commitment to the organization.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.259
Teacher spread0.226 · 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 designObservational
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

Citations2
Published2014
Admission routes1
Has abstractyes

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