Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".