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Record W6902892488 · doi:10.1016/j.enpol.2025.114770

What drives consumers to switch retailers? Evidence from the Alberta electricity market

2025· article· en· W6902892488 on OpenAlexaffabout

Bibliographic record

VenueEnergy Policy · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsCarleton UniversityUniversity of CalgarySGS (Canada)
Fundersnot available
KeywordsElectricityPreferenceElectricity retailingProduct (mathematics)Consumer behaviourConsumption (sociology)Consumer demand

Abstract

fetched live from OpenAlex

This study examines consumer switching behaviour in Alberta’s retail electricity market, focusing on the impact of rate volatility, income, education, and age on search rates and product premia. The analysis reveals significant consumer inertia, with many customers remaining on default rate even when competitive alternatives offer lower rates. Higher electricity rates were found to significantly increase search activity and switching rates. Contrary to expectations, consumers living in higher-income and higher-educated areas more often made less cost-effective choices, often opting for the more expensive default rate, called the Regulated Rate Option (RRO). Consumers living in lower-income areas, despite being presumptively more price-sensitive, also showed a preference for the RRO, indicating potential market misjudgment. The findings highlight the importance of consumer information to foster a more competitive and efficient market, as many consumers appear to make non-cost-minimizing or suboptimal switching decisions, particularly during periods of rapid rate increases. • Examination of consumer switching behaviour in Alberta’s retail electricity market. • There is significant consumer inertia but rate shocks cause consumers to search. • Consumers in high-income and high-education areas often do not cost minimize. • A variety of consumer groups place a premium on the regulated default product. • The findings highlight the importance of consumer information.

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)0.001
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 score0.461
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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