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Record W2480196260 · doi:10.5539/ijms.v8n4p20

Understanding the Customer Journey to Create Excellent Customer Experiences in Bookshops

2016· article· en· W2480196260 on OpenAlexvenueno aff
Michela Addis

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer experienceBusinessMarketingAdvertisingPersonalizationCustomer advocacyCustomer to customerCustomer retentionCustomer intelligenceComputer scienceService (business)Service quality

Abstract

fetched live from OpenAlex

This study investigates the customer journey and identifies the drivers of excellent customer experience in bookshops. Five research methods—in-depth interview, focus group, participant observation, Zaltman metaphors elicitation technique, and collective stereographic photo essay—were run on eleven Italian bookshops involving more than 1 100 individuals overall. The contribution of this study is twofold. First, it illustrates the process to adopt when mapping the customer journey and analyzing the customer experience. Specifically, it proposes that customer experience can be deeply understood only via a broad research design involving several different profiles of participants, that are managers and booksellers, customers of different familiarity with bookshops (infrequent, frequent and loyal customers), people that were not familiar with the investigated bookshops but that have been invited on purpose, and people that have special interactions (café and events) with the bookshops. Second, results show three key aspects of the topic: (1) The customer experience world, based on rituals not on transactions; (2) The drivers of excellent customer experience in bookshops, which are customization, integration, and participation; (3) The complex role and broad competences of the ideal bookseller.

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)0.005
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 score0.325
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.146
GPT teacher head0.339
Teacher spread0.193 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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