MétaCan
Menu
← Retour à la cohorte
Enregistrement W5393354

Evaluating the effectiveness of applying an adult learning approach to value chain management education

2012· article· en· W5393354 sur OpenAlexaboutno aff
Martin Gooch

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueEntrepreneurship Studies and Influences
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésExperiential learningAgribusinessMarketingProduct (mathematics)Value (mathematics)BusinessValue chainService (business)Work (physics)Knowledge managementSupply chainAgricultureComputer scienceEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This study examines the effectiveness of an experiential workshop designed to engender purposeful changes in the attitudes and behaviour of agribusiness managers. Delivered on 13 occasions across Canada, the workshops’ effectiveness was tested using an evaluation framework that combined a method previously used in agricultural extension, with an approach designed to improve the effectiveness of learning programs delivered in non-agricultural social settings.Businesses do not operate in isolation; they each have suppliers from whom they source a product or service. They then seek to add value to that product or service prior to its sale to a customer or a final consumer at a price that exceeds its cost of production. Thus, a series of businesses that together derive value from supplying products and services to target consumers can be thought of as a value chain. Value chain management (VCM) describes a business approach where firms in a value chain choose to work together with a focus on improving the efficiency of operations within and between firms, and the effectiveness of creating value for the end consumer. Agribusiness firms have been much slower to adopt VCM practices than firms in other industries. More widespread adoption of VCM in agribusiness requires changes in thinking and practice. This dissertation addresses the problem of identifying how agribusiness managers can be motivated to learn about VCM and then apply their newly acquired knowledge to purposely developing closer strategic relationships with other businesses. It achieved this by evaluating the effectiveness of an experiential workshop that reflects the theory of adult learning and the principles of VCM. The research is located in the paradigm of social constructivism. It employed a longitudinal case study involving 279 exit surveys of individuals immediately after each of the 13 workshops, and 109 semi-structured follow-up interviews conducted an average of 14 months later. Results show that experiential VCM workshops are effective in motivating agribusiness managers to acquire then act upon the knowledge necessary to develop closer relationships with other businesses. The majority (80%) of agribusiness managers who participated in the research changed how they managed their businesses, with 92% (56 individuals) of them attributing the changes in their behaviour to having attended a VCM workshop. In 37 cases, the changes made led to improvements in the financial performance of their businesses, 11 of which were very significant. A positive correlation exists between individuals’ level of education, experience of marketing, and/or working outside agribusiness, and their propensity to change. Most likely to embrace VCM business approaches were individuals aged 45-64 who possess university level education, with 100% of farm managers from this group changing behaviour or already being involved in a value chain initiative. Recognising ‘why’ a change in their (or members’/clients’) behaviour is warranted was found to have greater influence on motivating changes in behaviour than feeling confident about knowing ‘how’ to change. Statistically less likely to have changed behaviour were stakeholders to whom agribusiness managers look for guidance and advice, namely individuals from government and industry organisations. The most important elements of the workshops for facilitating changes in individuals’ attitude and behaviour were video case studies of successful value chain initiatives, and facilitated discussions where the audience compared and contrasted sometimes differing perspectives on what they had witnessed in the case studies, with their own situation. The workshop experience led individuals to connect emotionally with the topic of VCM in the context of their own situation, which in turn led many of them to commence an action learning cycle that resulted in changes occurring in their attitudes and behaviour. The influence of external factors on determining whether changes occurred in individuals’ attitude and behaviour was sufficiently important that it was added as a fifth element (E) to Bennett’s (1974) Knowledge, Attitude, Skills and Aspirations (KASA) framework. Bennett’s Hierarchical framework was also adapted to reflect the concept of taking a generative [versus successionist] approach to identifying opportunities to improve the effectiveness of a learning program. The theoretical contribution of this study lies in its combination of adult learning theory, the principles of VCM, and evaluation theory, to develop then test a method that proved effective in motivating and enabling agribusiness managers to adopt VCM – a non-traditional management approach. Its practical implications extend to how agribusiness training is designed, delivered and evaluated. It demonstrates the value of experiential learning for engendering purposeful changes in agribusiness managers’ attitude and behaviour. It also highlights the negative impact that external factors can have on motivating and enabling changes in individuals’ attitude and behaviour, particularly among less educated farm managers. The research also led to the development of an evaluation model that enables researchers to more thoroughly identify how to improve a program’s effectiveness by determining for whom a program might work, and why.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,039
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,070

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,039
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,312
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2012
Routes d'admission1
Résumé présentoui

Explorer davantage

Même sujetEntrepreneurship Studies and Influences→Travaux en français237 207→