MétaCan
Menu
Back to cohort
Record W4408447677 · doi:10.1108/mip-04-2024-0275

Service leniency: a dual logics perspective

2025· article· en· W4408447677 on OpenAlex

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

VenueMarketing Intelligence & Planning · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsPerspective (graphical)Dual (grammatical number)BusinessService (business)MarketingProcess managementComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose A pervasive yet underexplored phenomenon in service delivery is the tendency toward leniency, which can ultimately lead to negligence in service interactions. Despite its significance, we observe that the notion of leniency in service has been overlooked in the marketing literature. Therefore, this paper proposes the conceptual notion of service leniency, identifies its intrinsic and extrinsic drivers and examines its impacts on the overall service journey. Design/methodology/approach The study employs Jaakkola’s (2020) approach to conceptualize service leniency through theory synthesis and typology development. This study bases the notion of service leniency on two theoretical perspectives: service-dominant and customer-dominant logics. A review of literature within services marketing forms the basis for conceptualizing and identifying key drivers of service leniency. Findings Service leniency is defined as undue permissiveness or laxity in adhering to service standards, leading to compromised service delivery. Intrinsic drivers include permissive service culture, work role disengagement, training insufficiency, performance incentive misalignment and ambiguous service standards. Extrinsic drivers encompass assumed customer tolerance, feedback mechanism deficits, neglect of customer-driven innovations, risk aversion in service innovation and generational expectation gaps. Research limitations/implications As a conceptual study, the propositions and frameworks discussed here require empirical validation. Social implications This study highlights the potential societal implications of service leniency by emphasizing how its mitigation can foster improved public trust and satisfaction with high-quality service delivery. Originality/value This study proposes the concept of service leniency, addressing a critical phenomenon that demands attention in the marketing literature.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.291
Teacher spread0.257 · 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