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Record W3121676795 · doi:10.1108/14635771211224509

The impact of company learning and growth capabilities on the customer‐related performance

2012· article· en· W3121676795 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

VenueBenchmarking An International Journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsConcordia University
Fundersnot available
KeywordsBalanced scorecardBusinessService (business)Knowledge managementCustomer retentionOriginalityCustomer advocacySample (material)MarketingProcess managementComputer scienceService quality

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate the relationship between a company's customer‐related performance and its learning and growth capabilities. Design/methodology/approach Four banks in Taiwan – Citibank, Chinatrust, Taipei Fubon Bank and Taiwan HSBC – have recently applied the Balanced Scorecard (BSC) perspective to their customer service. This research was designed to use the data of these sample banks and analyze it to build the theoretical relationship. Findings The results reveal that a company's customer‐related performance is positively associated with the interactions of its Human Resource Service Capability (HRSC), Information Technology Service Capability (ITSC) and Marketing Service Capability (MKSC). Originality/value This paper contributes to the literature by providing empirical evidence that when an organization establishes and raises levels of company learning and growth capabilities by using HR‐service capability, IT‐service capability, and MK‐service capability, conjoint effects of these result in a favorable interaction relationship and thus can help achieve a higher level of customer‐related performance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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