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Record W2326928967

Using Social Media for Effec tive Customer Service

2014· article· en· W2326928967 on OpenAlexfundaboutno aff
Patrick Arsenault

Bibliographic record

VenueScholarWorks -A service of University of Vermont Libraries (University of Vermont) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersMarketing Science InstituteUniversity of Ottawa
KeywordsBusinessSocial mediaService (business)Internet privacyAdvertisingComputer scienceMarketingComputer securityPublic relationsWorld Wide WebPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Colleges and universities can no longer be characterized as education institutions only, as they are increasingly facing the same challenges as traditional businesses (Anctil, 2008). More specifically, students now want to be seen as customers and thus expect more value from institutions where they choose to matriculate (Halbesleben, Becker & Buckley, 2003; Woddall, Hiller & Resnick, 2012). Offering education as a commodity no longer suffices and institutions that want to remain competitive should engage with stakeholders to create meaningful experiences, which are part of the institutions’ offer (Pine & Gilmore, 1998). Many colleges and universities show increased interest in the potential of social media as way to engage with their stakeholders (Constantinides & Stagno, 2011). This article looks at what customers’ expectations on internet are and how higher education professionals can address them to offer effective customer service via social media.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.034
GPT teacher head0.235
Teacher spread0.201 · 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 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

Citations0
Published2014
Admission routes2
Has abstractyes

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Same venueScholarWorks -A service of University of Vermont Libraries (University of Vermont)Same topicDigital Marketing and Social MediaFrench-language works237,207