The Influence of Country-of-Origin Labeling for Lentils on Consumer Preference: A Study with Reference to Sri Lanka
Notice bibliographique
Résumé
Lentils have been a part of the human diet for ages, especially in the developing countries, as a source of protein, vitamins and minerals. Understanding and identifying consumers who care about the country-of-origin of these lentils are important in strategic lentil marketing. Thus, the present study aims to identify consumers who think that countryof-origin is an important attribute while purchasing red lentils by conducting an intercept survey of 300 consumers in grocery stores, supermarkets and other family-run stores in Sri Lanka between July-August 2010. This survey collected detailed information relating to demographic, socioeconomic, grocery purchasing pattern and behavior. The results obtained using logit model indicate that those who frequently consume lentils every day, who use non-packed red lentils, who use packed red lentils with store's own label, who think brand is an important attribute, who work in the government sector, and who earn between 45,001 and 65,000 Sri Lankan rupees per month are more likely to consider the country-of-origin as an important factor while purchasing red lentils.(ProQuest: ... denotes formulae omitted.)IntroductionConsumers differ in their consumption behavior with respect to taste and preference. Most of the empirical studies (Green and Sirinivasan, 1990; Hair et al., 2006; and Ares and Deliza, 2010), so far, analyzed the relationship between the quantity consumed of a specific product and different characteristics of the household. It failed to adequately address the consumer preference, such as the number of different products a household consumes in a specific time period. Understanding variety in food consumption is important for nutrition and for protection against chronic diseases (Randall et al., 1985; Krebs-Smith et al., 1987; Vecchia et al., 1997; and Hatloy et al., 1998). In a developing economy, lentils play an important role in the human diet and is often referred to as the 'poor man's meat' because it contains high amount of protein, fiber, vitamins and minerals. They are also low in sodium, fat and cholesterol. Lentils support general wellbeing and reduce the risk of illnesses and hence are good in controlling diabetes, preventing coronary and cardiovascular disease and lowering blood cholesterol levels due to their high-fiber content (Agriculture and Agri-Food Canada, 2012).Legumes naturally fix atmospheric nitrogen, which provides a nutrient for their growth, and maintains soil fertility for subsequent crop rotations. This nitrogen fixation ability of legumes helps to cut down on the application of artificial nitrogen fertilizer and promotes environmental sustainability (Muehlbauer and Tullu, 1997; Graham and Vance, 2003; and Gowda et at, 2007). As a consequence of pulse production shortfalls in recent years, the poor has been affected by increasing food prices (Akibode and Maredia, 2011; Chandrashekhar, 2011; and Prensa Libre.com, 2012). High prices limit the ability of the poor to purchase sufficient quantities (as indicated by income elasticity data). High prices can also force the poor to change their diets towards less nutritious foods- a caution issued by a recent international conference on 'Leveraging Agriculture for Improving Nutrition and Health' (IFPRI, 2010).Joshi (1998), Rao et at (2010), and Akibode and Maredia (2011) indicate four reasons for slower yield growth in grain legumes: ( 1 ) Low input use; (2) Shifts in marginal growing areas; (3) Less policy support than other commodities; and (4) Limited RSdD and dissemination of improved technology Only 25% of the grain legume crop area in the developing world is high input use area/irrigated as compared to 60% of the cereal area. Similarly, only 6% of fertilizers in Sub-Saharan Africa are used on grain legumes as compared to 26% for maize and 11% for wheat/barley (Bumb et at, 2011).In the family of legumes, lentils production was significantly influenced by South Asian countries, Europe, North America, South America and Africa (Akibode and Maredia, 2011). …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
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| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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