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Record W2560479638

Wal-Mart N'est Pas Une Banque

2016· article· fr· W2560479638 on OpenAlexaboutno aff
Marco Pagani, Asbjørn Osland, Andrew Borchers

Bibliographic record

VenueJournal of case studies · 2016
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinancial servicesMarketingValue propositionValue (mathematics)FinTechAdvertisingCommerceFinance
DOInot available

Abstract

fetched live from OpenAlex

Introduction Ceci n'est pas une pipe (i.e., this is not a pipe) (Magritte, 1928-29) was a playful painting that represented an image of a pipe. In like manner, Wal-Mart in the U.S. was not a bank. Nevertheless, it has offered U.S. customers a large array of financial services for more than a decade. Moreover, Wal-Mart owned commercial banks in Mexico and Canada (Wal-Mart, 2014). How did Wal-Mart start offering financial services? What have been the differences in the offering of financial services between the US and other North-American markets? How did these services help Wal-Mart meet its strategic objectives? Rather than taking the trouble to establish financial services in a piece-meal fashion, why not just invite established banks to open small branches in its stores? Financial services offered by Wal-Mart in the US market Historically, Wal-Mart focused on providing value to its customers through cost-cutting, innovation, technology and widespread geographical presence. The company always targeted a customer-centric approach and provided a friendly shopping experience. In fact, Wal-Mart stated (FORM 10-K Annual Report, 1 April 2015) that its value proposition was as follows: Wal-Mart ... helps people around the world save money and live better--anytime and anywhere--in retail stores or through our e-commerce and mobile capabilities. Through innovation, we are striving to create a customer-centric experience that seamlessly integrates digital and physical shopping.... Our strategy is to lead on price, invest to differentiate on access, be competitive on assortment and deliver a great experience. The presence of a large group of potential customers at the margins of the financial services industry was very appealing for many corporations aiming to increase the number of customers. According to the FDIC (2014, p. 3, Executive Summary): The existence of unbanked and underbanked households presents an opportunity for banks to expand access to their products and services and forge relationships with these underserved groups, ultimately increasing economic inclusion. Such economic inclusion was crucial for a company like Wal-Mart to attract new shoppers and create new sources of revenue. In the late 1990s, Wal-Mart started offering financial services in response to customer demand especially from customers who did not have access to traditional banking products (Manning, 2010). Many consumers, rejected by traditional depository institutions (commercial banks and credit unions) or who were unhappy with the high costs of checking accounts, started turning toward Wal-Mart for basic financial transactions. The unbanked and under-banked, who represented twenty-two percent of U.S. consumers (Gross, Hogarth & Schmeiser, 2012), were usually individuals with limited financial means who could not afford traditional banking products. However, some were well-paid individuals considered as credit risks, according to credit bureaus like ChexSystems, Inc., 2014. The unbanked had no traditional bank accounts and, according to the FDIC, the under-banked were those that had accounts but also used alternative financial services outside of the banking system (FDIC, 2014). Wal-Mart initially started offering check cashing and bill payments as ancillary customer services. Then, given the success of its initial foray into financial services, it opened in-store MoneyCenters where customers could benefit from a vast array of financial services like prepaid cards, debit cards, credit cards, and small business loans. Lately, Wal-Mart has strengthened its involvement in financial and banking services with the Wal-Mart2Wal-Mart funds transfer service (Berr, 2014) and the offering of a checking account in conjunction with Green Dot. The Wal-Mart2Wal-Mart money transfer service allowed clients to send or receive up to $900 at any US Wal-Mart store. …

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.272
Teacher spread0.231 · 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 designNot applicable
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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