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Record W5393354

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

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningAgribusinessMarketingProduct (mathematics)Value (mathematics)BusinessValue chainService (business)Work (physics)Knowledge managementSupply chainAgricultureComputer scienceEngineering
DOInot available

Abstract

fetched live from 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.

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)0.013
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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 designObservational
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".

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Citations1
Published2012
Admission routes1
Has abstractyes

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