a study on consumer characteristics of processed...
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A study on consumer characteristics of processed rice and meat products
on food-related lifestyles using beta regression model
Jin Hyeung Kim
Seoul National University
Agricultural Economics and Rural Development
Sung Ho Park,
Rural Development Administration (RDA)
Young Chan Choe
Seoul National University
Agricultural Economics and Rural Development
Selected Paper prepared for presentation at the 2016 Agricultural & Applied
Economics Association
Annual Meeting, Boston, Massachusetts, July 31-August 2
Copyright 2016 by Jin Hyeung, Sung Ho Park, and Young Chan Choe. All rights reserved.
Readers may make verbatim copies of this document for non-commercial purposes by any
means, provided that this copyright notice appears on all such copies.
Abstract
This study compares consumer characteristics of processed rice and meat products on food-
related lifestyles. As the social environment changes and household income improves, the trend
of diet change and single-person households is diversifying the food consumption patterns of
consumers. The food industry and production technology has developed and the consumption
of processed or convenience food is increasing. The existing studies are mostly related to the
riskiness and disease-causing factors of processed food. In the Asian markets such as Korea
and Japan and the North American and European markets, the processed rice industry is rapidly
developing. In this study, we analyze the purchasing characteristics of processed rice
consumption as compared to processed meat products.
We used consumer panel purchase data of 703 housewives in South Korea from the Rural
Development Administration. The data includes household purchasing scanner panel data for
the year 2014 and questionnaires related to food-related lifestyle. We used confirmatory factor
analysis and the beta regression model to identify processed food consumer characteristics. The
purchase rate of processed food over the total amount has a range from ‘0’ to ‘1’. To correct
for heteroscedasticity and statistical errors in the dependent variable, we use the beta regression
model for the rate from ‘0’ to ‘1’.
. The social democratic variables such as age and number of family members from the beta
regression have a negative relationship and the eating out purchase amount has a positive
relationship with both processed rice and meat products (p <0.05). The food-related lifestyles
variables of the price criteria for quality and the cost performance for ways of shopping and
eating out in a consumption situation increased the rate of purchasing processed meat. However,
health quality decreased the consumption rate of processed meat (p <0.05). In the case of
processed rice, the propensity to seek a cooking method increased the rate of purchasing
processed rice (p <0.05), and the price criteria for ways to shop and propensity to cook with a
plan decreased the rate of purchasing amount (p <0.05).
According to the results, consumers who purchase a high proportion of processed meat
usually consider more price information and consume relatively less healthy products.
However, consumers of processed rice tend to purchasing for a new cooking method and
consider less a price criteria and cooking plan for consumption.
Key words: processed rice products, processed meat products, food-related-lifestyle, beta
regression model
Introduction
As the social environment changes and household income improves, trends in lifestyle
changes are diversifying food consumption patterns (Cowan et al., 2001; Lambert, 2001). Food
lifestyle changes in gentrification, diversification and externalization can affect all
circumstances, including the agri-food sectors. Food lifestyles are changing from family-
centered to individual-centered. The dining area has also shift from a homemade to an eating
out meal (Park et al., 2014). As the food industry and production technology have developed,
the consumption of processed or convenience foods is increasing (Euro Monitor International,
2014). The changing food lifestyle and development of food processing technology could
influence growth in the processed food market in regard to convenience and functionality (Park
et al., 2014).
Processed food can be generally defined in terms of food, manufacturing and
processing characteristics. The scope of processed food is too large, and thus too difficult to
perfectly designate scope. The U.S. Department of Agriculture and the U.S. Department of
Health and Human Services defined processed food as “any food other than a raw agricultural
commodity, including procedures that alter the food from its natural state such as washing,
cleaning, milling, cutting, chopping, …” (Fox, 2011). The International Food Information
Council divided processed food into five categories: minimally processed foods (eg., washed,
packaged fruits and vegetables), processed for preservation (eg., canned/frozen fruits and
vegetables), mixtures of combined ingredients (eg., cake mixes, salad dressings), ready-to-eat
foods (eg., breakfast cereals, lunch meats, carbonated beverages) and convenience foods (eg.,
frozen meals/pizzas) (Fox, 2011). The Fox study compared processed rice and meat with food-
related lifestyles, which is related to ready-to-eat convenience foods and divided into five
categories.
The global processed food industry increased from $2,132 billion U.S. dollars
(hereafter, dollars are based on U.S. dollars) in 2008 to $2,411 billion in 2012 (an increase of
$310 billion) according to the England Research Institute Datamonitor. In the past five years,
the average annual growth rate of the global processed food market has been consistent at 3.4%.
The European market accounted for 34% of the total processed food market and followed by
North and South America (32.5%), Asia and the Pacific Rim (31.3%) and Africa and the
Middle East (2.2%) (APEDA, 2012). The processed food market in the U.S. accounted for 18.8%
($457 billion) of the total market.
The processed food market in South Korea (hereafter, Korea) greatly increased the
export of processed food (aT Center, 2014). In 1995 and 2010, the proportion of processed food
to the total amount of food export was 37% and 54%, respectively. Recently, growth in
processed food export might be caused by continuous and increasing awareness, and the
“Korean Wave” in the emerging market of China, Southeast Asia and the Middle East. The
Korean Wave could positively have an effect on the brand image and food sectors of Korea
(Korea Customs Service, 2010).
Especially, consumption of processed rice in Korea has been the focus for an
increasing processed food market in the food export. In some developed countries, the interest
of processed rice for health and convenience is increasing (Williams et al., 2007). There is a
growing interest on diet and health using premium products. To substitute for carbohydrates,
consumers prefer rice products such as noodles, crackers, and cakes (Williams et al., 2007).
Recently, consumers were aware that processed food is not good for health (Jongwanich, 2009).
Existing studies are mostly related to the riskiness and disease-causing factors of processed
food (Ragaert et al., 2004). Particularly, the World Health Organization (WHO) announced that
red and processed meat can cause cancer (Oostindjer et al., 2014). However, consumers can
relatively trust processed rice food products and worry less about their health (Suwannaporn et
al., 2008; Han & Gouk, 2014). The growth of the processed rice industry gives us a solution to
increase the consumption of rice and contributes to the increase of exports in the global market
(Suwannaporn et al., 2008).
Consumers consider healthy eating by avoiding processed meat consumption
(Verneau et al., 2014). Thus, consumers who eat processed rice can have varied characteristics.
In our study, we will investigate how the consumption of selected processed foods is related to
consumer’s lifestyle.
Literature Review
1. Consumer characteristics of processed food
In the growing processed food consumption market, issues on safety and sanitation
have received preferential attention (Wei et al., 2015). Studies on consumer awareness and
purchasing behaviors have also been conducted (Ruth et al., 2001). Specifically, there were
many studies on processed meat, which were related to consumer characteristics and food
lifestyles (Grunert, 2006). Studies on a specific class of consumer such as homemade
processed chicken (Kim et al. 2001), processed plum (Kim et al. 2006) and the propensity to
consume with a consumer lifestyle (Kim et al, 2014) were conducted in Korea. However,
empirical studies using purchase history data for processed food consumption have not been
conducted.
2. Food-related lifestyles
Food-related lifestyle is defined as “consumers perceive a food product to hold value
to the extent that its consumption will lead to self-relevant consequences” (Grunert et al., 1999).
The instrument developed by Grunert et al. provides information on consumer behavior based
on lifestyle characteristics and reflects a basic appetite on food lifestyle, and proposes some
marketing insights of consumer segmentation (Divine et al., 2005). Existing socio-economic
factors do not sufficiently explain food consumption behavior. Thus, we should consider
lifestyle factors that apply consumer’s inherent value and propensity (Kotler et al., 2001). In
addition, some research use a food-related lifestyle (FRL) measure that was validated and
confirmed in non-western countries (Grunert et al., 2011).
3. Food purchasing amount
Existing studies on food purchasing amount mostly relate to an expenditure decision
factor and the elasticity of factors. In early studies, researchers explained by only using socio-
economic variables (i.e., income, number of family members, education level and working
wives). Recently, studies have been conducted that consider lifestyle and purchasing attitude
(Prochaska et al., 1973). Especially, some studies focused on working wives and the eating out
expenditure for food purchasing (Jensen et al., 1995; Nayga, 1995). However, there are only a
few studies that apply consumer expenditures of processed food. Therefore, we will analyze
processed food consumer characteristics with purchasing expenditure and food lifestyle
variables.
Data collection
We used consumer panel purchase data of 703 housewives from the Rural
Development Administration of Korea. The data includes household purchasing scanner panel
data for 2014 and questionnaires related to food-related lifestyle. We divided the purchase
amount into processed and unprocessed rice and meat. Additionally, we used a food-related
lifestyle instrument (Scholderer et al., 2001). As dependent variables, consumption of
processed rice and meat indicates the purchase rates of processed food among the total amount
(Scholderer et al., 2001). Processed rice includes rice-based products such as noodles, cakes,
puffs, crackers, and porridges. Processed meats consist of beef and pork products such as bacon,
ham, and sausages. In this study, the purchase rate of processed food over the total amount as
the dependent variable has a range from ‘0’ to ‘1’. The average purchase of processed rice and
processed meat is $148.62 and $414.01, respectively. The purchase rate of processed rice and
processed meat over the total amount is 0.517 and 0.368, respectively.
Table 1. Descriptive statistics of dependent variables
We used socioeconomic variables such as age, income, number of family, and the sum
of eating out expenditure and the use of food-related lifestyles conditions as independent
variables. In regard to the demographic variables, the average household age was 41.4 and the
average monthly income was $3,146. The average number of family members is 3.78 and the
average monthly expense for eating out was 81.68 dollars.
Table 2. Descriptive statistics of independent variables
Methodology
We used confirmatory factor analysis and the beta regression model to identify
processed food consumer characteristics.
1. Confirmatory factor analysis
We used demographic and food-related lifestyle characteristics as independent
variables. We conducted a confirmatory factor analysis of the food-related lifestyle variables.
Factor analyses consist of both exploratory factor analysis and confirmatory factor analysis.
Exploratory factor analysis was executed when we did not have information on such factors.
In addition, confirmatory factor analysis is applied to obtain information related to the factors.
For the survey, food-related lifestyle variables were based on theory. Thus, we used
confirmatory factor analysis. The purpose of confirmatory factor analysis is to determine the
fit of the data to a hypothesized measured model. For confirmatory factor analysis, we
measured both convergent validity and discriminant validity. In convergent validity, the factor
loadings of most items were greater than 0.70 except for WS_info3 (0.6570), QA_Price3
(0.6708) and PM_secure1 (0.6895). The composite reliability scores were higher than 0.70
(Fornell & Larcker, 1981). The average variance extracted (AVE) scores were also higher than
0.50 (Fornell & Larcker, 1981).
In discriminant validity, the squared root of the AVEs is greater than all correlations
between any two constructors (Chin, 1998) and can be used to measure discriminant validities.
The results demonstrated significant discriminant validity.
Table 3. Result of CFA (Convergent Validity)
Table 4. Result of CFA (Discriminant Validity)
2. Beta regression model
We used the beta regression model with a ‘0’ to‘1’ rating to correct for
heteroscedasticity and statistical errors in the dependent variables. The beta regression model
is useful when the dependent variable is continuous and restricted to the interval from 0 to 1
(Ferrari & Cribari-Neto, 2004) and is useful for limited range variables having
heteroskedasticity and asymmetry problems. The model is beta distributed using a
parameterization of beta law by mean and parameters and can be estimated by the maximum
likelihood method (Ferrari & Cribari-Neto, 2004).
Results
First, we compared beta regression and multiple regression results. There are similarly
significant variables. Some variables such as the number of family members for processed rice,
and the health quality and price quality of processed meat are only significant at the 0.1 level.
However, in beta regression, these variables are also significant at the 0.5 level. The coefficient
of determination of beta regression is also higher than the multiple regression result. Thus, we
can explain that the beta regression has more power of explanation for the model.
Second, the results of the beta regression identified explanatory variables related to the
rate of processed rice and meat. Demographic variables such as age and the number of family
members from the beta regression have a negative relationship and the purchase amount for
eating out has a positive relationship with both processed rice and meat products (p <0.05).
The food-related lifestyles variables for price criteria forquality and the cost performance for
ways of shopping and eating out in a consumption situation increased the rate of purchasing
processed meat. However, health quality reduced the consumption rate of processed meat (p
<0.05). In the case of processed rice, the propensity to seek a cooking method increased the
rate of purchasing processed rice (p <0.05), and the price criteria for ways to shop and
propensity to cook with a plan decreased the rate of purchasing amount (p <0.05).
Table 5. Result of multiple regression analysis of processed rice and meat
According to these results, consumers who purchase a high proportion of processed
meat usually consider price information more and consume relatively less healthy products.
However, consumers of processed rice tend to purchase for a new cooking method and consider
less price criteria and cooking plan for consumption.
Table 6. Result of beta regression analysis of processed rice and meat
Discussion
We identified that processed rice may have different characteristics than processed meat.
According to the results, consumers who prefer processed rice generally consider the cooking
method. Also, price-insensitive consumers enjoy processed rice when compared with
processed meat consumers. Therefore, the results indicate target or special event marketing for
the processed rice consumer.
Processed meat is mostly served with a side dish or dessert when developing a menu.
However, processed rice can be substituted for the main dish. In addition, consumers who love
to eat out also consume a lot of processed food. These consumers might pay attention to the
method in which the meal is prepared.
As a marketing strategy, processed rice producers need to consider products that can
be easily applied and differently cooked. Thus, we will further investigate the market segment
for each individual rice product such as noodles, cakes, and crackers.
The limitations of this study are as follows. The data was only formulated in
households in Korea. Consumers from other countries and cultures can have different
characteristics using processed rice. Future research should examine the characteristics of
processed rice products for other countries and cultures and produce a sales strategy for
considering consumer characteristics of export products. In addition, the one-person household
was an important consumer target in the marketing of processed food. However, the one-person
household was only 0.6% of our analysis data. Future research should reflect this lifestyle
condition.
Acknowledgement
This work was carried out with the support of "Cooperative Research Program for Agriculture
Science & Technology Development (Project No. PJ0113902016)" Rural Development
Administration, Republic of Korea.
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