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Record W1969287165 · doi:10.1108/09604520010336669

Service quality in consulting: what is engagement success?

2000· article· en· W1969287165 on OpenAlexaff
Ron McLachlin

Bibliographic record

VenueManaging Service Quality · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBusinessReputationInformation technology consultingMarketingRevenueService (business)Customer engagementQuality (philosophy)Exploratory researchService qualityPublic relationsServices marketingSociologyComputer scienceAccountingInformation system

Abstract

fetched live from OpenAlex

There are various views about the nature of service quality in a consulting engagement. This paper utilises literature from a number of disciplines, along with exploratory interviews with seven consultants and one client, to address one question, namely, “What is engagement success in consulting, from both the client and consultant points of view?”. In addressing this question, the paper considers distinctions between types of consulting, client expectations and needs, and short‐ and long‐term revenue streams. It concludes by suggesting that a consulting engagement is successful if the consultant has met client expectations (by improving one or more of client performance, client capabilities, or organisational culture, without making any category worse) – whether or not a core need has been addressed – and the consultant has enhanced his/her reputation, with expectations of future revenue streams – whether or not any immediate income has been received.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0070.012
Scholarly communication0.0180.008
Open science0.0020.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.036
GPT teacher head0.293
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations58
Published2000
Admission routes1
Has abstractyes

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