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Record W1975291141 · doi:10.1108/02652320110388531

Singapore’s undergraduates: how they choose which bank to patronise

2001· article· en· W1975291141 on OpenAlex
Philip Gerrard, John Cunningham

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Bank Marketing · 2001
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRanking (information retrieval)Selection (genetic algorithm)Sample (material)MarketingDimension (graph theory)Order (exchange)HomogeneousPsychologyBusinessComputer scienceMathematicsFinanceArtificial intelligence

Abstract

fetched live from OpenAlex

Undergraduates constitute an attractive segment of customers for retail banks in many countries of the world, including Singapore. This study, using a sample of Singapore’s undergraduates, sets out to establish a ranking of the various dimensions which influence their bank selection decision and seeks to determine how homogeneous undergraduates are in relation to their selection decision. Seven bank selection dimensions were identified, the most important being undergraduates should “feel secure”, while the least important dimension was “third party influences”. Responses between those “attending engineering courses and non‐engineering courses” were compared, as were those between “males and females” and “single and multiple bank users”. More significant differences were found when engineering undergraduates were compared with non‐engineering undergraduates. Irrespective of these differences, the sequencing of the seven selection dimensions was invariably in the same order.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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