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Record W2518733493 · doi:10.1177/2277977915574036

Magnetrol International, Incorporated: A Case Study in the Use of Appreciative Inquiry

2015· article· en· W2518733493 on OpenAlex
E. John Heiser, Jeffrey K. Swallow

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

VenueSouth Asian Journal of Business and Management Cases · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsMagna International (Canada)
FundersUtah Agricultural Experiment Station
KeywordsAppreciative inquiryMultinational corporationWorkforceWorld classChinaService (business)Action researchBusinessPublic relationsMarketingSociologyManagementPolitical sciencePedagogyEconomicsEngineeringEconomic growth

Abstract

fetched live from OpenAlex

This case examines the use of appreciative inquiry by a multinational company to gain market differentiation by developing a world-class global service and technical support organization. Magnetrol International, Incorporated is a family-owned manufacturing company headquartered outside Chicago, Illinois USA, with manufacturing facilities in the US, Belgium, Brazil, China and the UAE. The purpose of the article is to demonstrate how the appreciative inquiry framework was used to drive innovation in the creation of a world-class global service organization through the use of positive discourse and employee engagement. Data was collected over a 5-month period including during two appreciative inquiry (AI) summits, one in the US and one in Belgium. The article seeks to demonstrate that positive discourse conducted in an inclusive environment can lead to positive, innovative action with an ensuing benefit of a more engaged, committed workforce.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.144
GPT teacher head0.278
Teacher spread0.134 · 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