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Record W3200002628 · doi:10.33423/jabe.v23i2.4085

Developing Competitive Advantage for Real Estate Agents: Links to Organizational Culture, Service Quality and Behavioral Intentions

2021· article· en· W3200002628 on OpenAlexvenueno aff
Dean A. Koutroumanis

Bibliographic record

VenueJournal of Applied Business and Economics · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateBusinessCompetitive advantageQuality (philosophy)Service (business)MarketingCorporate Real EstateEstateOrganizational cultureService qualityReal estate developmentKnowledge managementIndustrial organizationFinanceEconomicsComputer scienceManagement

Abstract

fetched live from OpenAlex

This manuscript examined literature and posited theoretical tools for real estate brokerage firms and agents to use in developing successful and sustainable business practices. It added to the literature that existed on organizational culture, service quality, behavioral intentions and competitive advantage as related to the real estate industry. The literature indicated that the constructs play a crucial role in the success of real estate brokerages and agents building a successful business. The findings lead to practical applications for real estate professionals with strategies to aid in developing appropriate organizational cultures, service delivery practices and increase the number of repeat customers.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.308
Teacher spread0.250 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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