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Record W4387055456 · doi:10.32782/mer.2023.101.06

PROBLEM AREA DIFFERENCES IN THE CONCEPTS OF CUSTOMER CENTRICITY AND CUSTOMER ORIENTATION

2023· article· en· W4387055456 on OpenAlexaboutno aff
Інна Рєпіна, Олена Потієнко

Bibliographic record

VenueMechanism of an economic regulation · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsScopusFocus (optics)Sample (material)Scientific literatureComputer scienceInterpretation (philosophy)Data scienceKnowledge managementInformation retrievalPolitical science

Abstract

fetched live from OpenAlex

In recent years, business literature has shifted its focus from customer focus to customer centricity, which is positioned as a fundamentally new concept, a paradigm of doing business. The purpose of this article is to understand the correlation of these concepts for the correct interpretation of the latter. A review of the scientific literature using the built-in analysis option of SCOPUS.com showed that the main components of this concept (definition, genesis, prerequisites, consequences, differences from other concepts, positives, barriers, etc.) were developed long ago - at the beginning of the 21st century. The To increase the validity and remove bias in the interpretation of the distinctive features of the concept, a conceptual computer analysis of 2 arrays of information was carried out: titles and abstracts of 270 scientific publications published in the SCOPUS scientometric database with the keyword "customer focus" and 460 scientific publications with the keyword "customer centricity". The sample comprised 5,856 publications with one of the possible terms describing the attitude towards the client (orientation, focus, focus) in the title or abstract. The text arrays were processed using WordStat 2023.0.1, a special text analysis module developed by Provalis Research (Canada). The computer analysis conducted with the help of the WordStat software allowed to establish a list of words that are most often found in scientific periodicals (articles and abstracts) included in the relevant sample; a list of phrases that are most often found in scientific periodicals (articles and abstracts) included in the relevant sample; leading topics that are considered within the formed samples (6 topics that characterise the problem field of customer focus research and 10 leading topics of publications on customer centricity). The objective data obtained proves that while at the level of words there is an identity of concepts, at the level of phrases and top topics there is a clear shift in focus from marketing issues (customer focus, customer satisfaction, customer relations, customer requirements, customer-centric service, customer-centric experience, customer relationship management, etc.). In addition to the traditional aspects (customer centricity, customer relations), the leading aspects described in the literature on customer centricity are making the right decisions in all links of the supply chain, the problems of developing appropriate information systems and technologies, creating and using social networks, developing tools for empathy with the customer, and predicting his or her behaviour - as the basis for real customer centricity. It was also found that customer centricity is recognised as a priority for the development of products and services in such new areas as energy saving and electric vehicles. Thus, the conceptual computer analysis conducted with the help of the WordStat software product allows to interpret customer centricity as a qualitatively new level of development of the customer orientation concept, which implies the extension of the concept's scope to all components and processes of the business model, the acquisition by organisations of a new status of "customer-oriented"; recognition of the customer as the most important, central figure and driving force of business development.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.833
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.257
Teacher spread0.225 · 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 designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractyes

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