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Record W4405757572 · doi:10.2478/fprj-2024-0004

Exploring the Relationships between Virtual Client Meetings, Financial Anxiety, and Trust in Financial Planning

2024· article· en· W4405757572 on OpenAlexaboutno aff
Ashlyn Rollins-Koons, Derek Lawson, Megan McCoy, Joanne Wu, Jason Anderson, E H Ludwig

Bibliographic record

VenueFinancial Planning Research Journal · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsPlannerFinanceBusinessAnxietyFinancial planPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract Tele-financial planning in Canada has become a crucial component of financial planners’ businesses, which necessitates research to understand how tele-financial planning influences client outcomes. This study uses primary data collected from Canadian financial planning clients to examine how clients engage in virtual meetings, their levels of financial anxiety, and the relationship these variables have with trust in their financial planner. The results indicate that virtual meetings and financial anxiety are negatively associated with trust. Perceived ease of use of video conferencing technology and client satisfaction with virtual meetings were positively related to trust. Findings highlight the lasting impact of virtual meetings in the field of financial planning and encourage ongoing trust-building between clients and professionals using virtual mediums.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.482
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.230
GPT teacher head0.355
Teacher spread0.125 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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