Segmentation Analysis of Grocery Shoppers in Alabama

Using survey responses from over 500 responses, this paper conducts a market segmentation of grocery shoppers in Alabama. By employing cluster analysis technique, Alabama grocery shoppers are segmented into three different groups based on the relative importance of factors that describe their shopping experiences.Food Consumption/Nutrition/Food Safety,

Consumer Purchasing Behaviors and Attitudes toward Shopping at Public Markets

This paper identifies and empirically evaluates factors that explain the variations in consumers’ attitudes toward shopping at farmers markets in general and public markets in particular. The analysis draws on data from a telephone survey conducted in Jefferson County, Alabama. Logit model results point to several factors that seem to be strongly correlated with consumer purchasing behaviors and attitudes toward shopping at public markets, including income, education, age of household head, household size, and price and quality of produce. The insights gained from the study should help farmers increase the profitability of their operations and improve the likelihood that they will continue farming.Consumer/Household Economics,

Consumer Purchasing Behaviors and Attitudes toward Shopping at Public Markets

This paper identifies and empirically evaluates factors that explain the variations in consumers’ attitudes toward shopping at farmers markets in general and public markets in particular. The analysis draws on data from a telephone survey conducted in Jefferson County, Alabama. Logit model results point to several factors that seem to be strongly correlated with consumer purchasing behaviors and attitudes toward shopping at public markets, including income, education, age of household head, household size, and price and quality of produce. The insights gained from the study should help farmers increase the profitability of their operations and improve the likelihood that they will continue farming

Segmentation Analysis of Grocery Shoppers in Alabama

Using survey responses from over 500 responses, this paper conducts a market segmentation of grocery shoppers in Alabama. By employing cluster analysis technique, Alabama grocery shoppers are segmented into three different groups based on the relative importance of factors that describe their shopping experiences

CONSUMER PURCHASING BEHAVIORS AND ATTITUDES TOWARD SHOPPING AT PUBLIC MARKETS IN BIRMINGHAM, ALABAMA

The purpose of this study was to obtain information that could help farmers increase the profitability of their operations and improve the likelihood that they would continue farming. The specific objective was to empirically evaluate which socio-demographic characteristics and purchasing behaviors encourage consumers to patronize farmers markets, in general and public markets, in particular. Data was drawn from telephone survey responses from 502 potential food shoppers in Alabama. Logit model results point to several factors that seem to be strongly correlated with consumer purchasing behaviors and attitudes toward shopping at public markets, including income, education, age of household head, household size, price and quality of produce

CONSUMER PURCHASING BEHAVIORS AND ATTITUDES TOWARD SHOPPING AT PUBLIC MARKETS IN BIRMINGHAM, ALABAMA

The purpose of this study was to obtain information that could help farmers increase the profitability of their operations and improve the likelihood that they would continue farming. The specific objective was to empirically evaluate which socio-demographic characteristics and purchasing behaviors encourage consumers to patronize farmers markets, in general and public markets, in particular. Data was drawn from telephone survey responses from 502 potential food shoppers in Alabama. Logit model results point to several factors that seem to be strongly correlated with consumer purchasing behaviors and attitudes toward shopping at public markets, including income, education, age of household head, household size, price and quality of produce.Consumer/Household Economics,

MasakhaNER 2.0:Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages