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Record W7128121332

How do Finns choose a bank? - A study on choice criteria and information channels in the south of Finland

2019· other· en· W7128121332 on OpenAlexaboutno aff
Jyri Alakukku

Bibliographic record

VenueAaltodoc (Aalto University) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Bank accountMobile bankingInstitutionFinancial institutionPerspective (graphical)Value (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper is to reveal what criteria have the most impact on Finns when they are choosing a bank. Additionally, I aim to discover what information channels Finns value and how trusting they are towards bank advertisements. Surprisingly nearly a quarter of those who answered are exclusively customers at the bank in which their parents opened their first bank account. The most common reason as to why they haven’t changed banks is that they simply have not seen a reason to do so. From a bank’s perspective it would be prudent to find out how willing these individuals are to change banks if they were to discover that some other institution offers better services. Out of those who answered 40,80% have changed their primary bank and the most common reason for doing so is better mortgage terms. For 53,66% better mortgage terms have played a part in their decision and for 35,37% it was the only reason that has made them change banks. The most important choice criteria are easy to use online banking services and diverse online banking services. This indicates that banks must especially focus on developing their online services. What makes this difficult is that people seem to demand that they be able to do most of their banking on their own computer but at the same time it must be simple. There were no major differences in the importance placed on these services between genders, different age groups, or different income levels. Surprisingly mobile apps do not seem to be as important as I originally thought. Easy to use mobile app is only the fifth most important criterion and a diverse mobile app is only the ninth most important. But there are differences. For those aged under 50 years these criteria are both in the top five whereas those aged over 50 years ranked them as 11th and 13th. This indicates that the importance of mobile apps will further increase in the coming years and in order to stay competitive banks must focus on developing their mobile services. Easy access to service and the quality of service are ranked third and sixth respectively. Even though younger generations seem to place slightly less value on service it is still one of the most important criteria. For banks this means that even though the importance of online and mobile banking is going to be even more significant than it is now they should not make compromises in the quality and accessibility of traditional service. Branch location occupies the 18th place and is thus the least most important criterion. Those aged over 50 years ranked it as the 14th most important but it is still clear that it`s importance has radically decreased. Insurance services ranked surprisingly low. Access to insurance services is the 17th most important criterion while the quality of insurance services is 16th. The most important information channel is individual`s own research. The second most important is word of mouth, third is news media, and only fourth most important is bank advertising. In addition to ranking it so low Finns also don’t seem to especially trust bank advertisements giving it an average score of 5,36 on a scale from zero to ten. This seems to indicate that Finnish bank advertising has very little effect on conscious decision making.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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