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Record W3137286238 · doi:10.1108/ijse-08-2020-0576

Perception and willingness to contribute towards food banking in the Ashanti Region of Ghana

2021· article· en· W3137286238 on OpenAlexaboutno aff
Nicholas Oppong Mensah, Ernest Christlieb Amrago, Jeffery Kofi Asare, Anthony Donkor, Frank Osei Tutu, Emmanuella Owusu Ansah

Bibliographic record

VenueInternational Journal of Social Economics · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionQuantileQuantile regressionAgricultureGovernment (linguistics)MarketingBusinessPublic economicsEconomicsPsychologyGeography

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine the perception and willingness to contribute towards food banking in the Ashanti Region of Ghana. Design/methodology/approach Structured questionnaire was used to elicit primary data for the study from 385 respondents via the multistage sampling approach. The quantile regression model was used to analyse the factors that influence the willingness to contribute towards food banks across quantiles of contribution. Factor analysis was further used to examine the perception of food banking. Findings Gender, education and awareness influence the quantiles of contribution. Gender positively influences contribution at the 0.50 quantile. Education negatively affects contribution at the 0.25 and 0.50 quantiles whereas awareness influences contribution at the 0.75 quantiles. The benefit perception of the user and the social status perception of receiving food from food banks convey a sense of positive knowledge concerning what food banking should entail. Research limitations/implications The study provides insights on the determinants affecting the contribution towards food banking across quantiles of contribution. However, it worth noting that, the study uses cross-sectional data which fail to account for the changes over time. A Longitudinal study would therefore be imperative concerning the implementation of food banking. Practical implications The perceived positive knowledge of food banking is suggestive that, the Government of Ghana through the Ministry of Food and Agriculture (MOFA) should strengthen measures directed towards the implementation of food banking. Moving forward, non-governmental organisations on the verge of conducting a pilot implementation of food banks should give critical focus to the given area of study as the inhabitants are most likely to be attuned to such a course. Finally, to champion contribution amongst the inhabitants, leaders of food banking initiatives and other stakeholders should work in conjunction with residents that are aware of food banks at the high-income class. This procedure would aid in reducing the chances of low contributions to the implementation of food banking. Social implications This paper provides empirical implications for the development of food banks in Ghana. The findings emanating from this study has substantial social implications, because it serves as an instrumental guide to the implementation of food banks by the MOFA, and when implemented would assuage the poor living conditions of individuals that do not meet a three-square meal per day. Originality/value In this research, the authors add to the body of knowledge by employing a quantitative approach. Moreover, the authors extend the frontiers of the methodological approach by using the quantile regression model to understand the factors that influence the contribution towards food banking across quantiles of contribution. Furthermore, several studies in the developed world have been geographically limited to UK, USA, Canada and Germany with few studies in Ghana. Besides, there is limited rigorous empirical study of the perception and willingness to contribute towards food banking in Ghana.

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 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 score0.621
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.138
GPT teacher head0.431
Teacher spread0.293 · 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 teacher head, 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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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