The Impact of Customer Review on Consumer Preference for Fresh Produce: A
Choice Experiment Approach
Chenyi He
Graduate Student
Food and Resource Economics Department
University of Florida
Zhifeng Gao
Associate Professor
Food and Resource Economics Department
University of Florida
Selected Paper prepared for presentation at the 2015 Agricultural & Applied
Economics Association and Western Agricultural Economics Association Annual
Meeting, San Francisco, CA, July 26-28, 2015.
Copyright 2015 by Chenyi He, Zhifeng Gao. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided this copyright notice appears on all such copies.
Abstract Using food labels to promote products has expanded among fresh producers and retailers. Labels are becoming a key factor to differentiate products in fresh market. Products are normally differentiated by labels that reveal nutrient attributes, health claims, production or processing methods etc. Customers’ reviews are traditionally important for electronic products; however, they become increasingly important for fresh produce because of the new trend of online shopping of fresh produce. Examining the role of customer review on consumer preference and WTP for fresh produce will provide important information to growers, retailers, online companies or even policy makers regarding customer review management for fresh produce, which has been an important component of sale and marketing strategies for other products. Using a choice experiment approach, this study fills this gap by determining the relative importance of customer review to other popular attributes of fresh produce. The study also elicits consumer WTP for better customer reviews, which shed light on the potential resources needed to manage customer review for fresh produce.
Keyword: Strawberry, WTP, Fresh Produce, Choice Experiment, Mixed Logit, Consumer Review
Introduction
With the increased penetration of Internet rates as well as advances in Internet technology the use of Internet as a shopping and purchasing medium have grown tremendously.
Online shopping as one of the key components of electronic commerce (E-commerce) provides good opportunities for both manufactures and retailers to reach existing and potential customers more efficiently (Limayem, 2000). Revenue from online purchasing continues to grow and is one of the fastest growing forms of trade in these days (Levy,
2004). From 2006 to 2014, the average annual growth rate of total retail sale is 2.3 percent, while the growth rate is 11.9 percent for E-commerce. And in 2014, the sale from E-commerce reached to 300 billion dollars the first time (US Department of
Commerce, 2015).
More people are starting shopping online because it is convenient and usually offers a wider choice and better prices. In addition, they are not subject to upselling or impulse buying, which often happens in retail store. In general E-commerce offers several competitive advantages over traditional in store retail sales. Online shopping environment enables consumers to search more information of products they want to purchase, allows them to compare multiple products with ease and let them to purchase products or services without travel to stores. The growth of electronic commerce and business is rapid and the reason why although online groceries now are starting to make sense for most consumers and widely used by consumers, the difficulties facing internet retailers are the sale of groceries, especially the online sale of fresh produce. Few successes have been made in fresh produce online shopping and a majority of grocery shopping still takes place in the tradition brick-and-mortar stores. As of 2012, only 14% consumers in
U.S. shopped for groceries online (The Hartman Group, 2015). The main reason preventing consumers from purchasing groceries online may be the time sensitivity of the products’ properties. Limited selection, high cost and inconvenient delivery timing has kept online grocery shopping as a niche business mostly in urban areas.
Purchasing in cyberspace is very different from purchasing in physical markets. Usually, consumers make their online decision based on the pictures, images, quality information and some short videos samples if available on the shopping website instead of actual experience (Kolesar and Galbraith, 2000). Online shopping experience seems like paper catalog shopping since consumers cannot see, touch or smell the items and the they cannot tell whether the item satisfy them directly. Instead, consumers depend more on the characteristics of information presentation, navigation, quality of customer service and order fulfillment (Reynolds, 2000). Although the online shopping environment are suitable for many products such as electronic and fashion products, the lack of direct contact with physical product in online shopping make it difficult for grocery shopping, particularly fresh produce shopping. Consumers of fresh produce usually pay more attentions on the appearance attributes, sensory attributes and whether they are freshness.
Those attributes varies among varieties and are all time sensitivity. Consumers’ perceptions of quality are influenced by the intrinsic attributes, extrinsic indicators, information as well as cues which provided by sellers (Caswell, 2002). The fresh produce can be classified by different levels of search, experience and credence properties. The search properties can be evaluate before purchase (e.g., price, dimension, size, color), while the experience properties can be determined only after consumption (e.g., tasty).
Credence properties are those attribute information is difficult to ascertain directly by
consumers at any stage of purchase. For those reason, purchasing fresh produce in brick- and-mortar stores is widely accepted by consumers because they can pick the products by themselves own avoid to get bad products or the one is not fresh. They could also make their purchasing decision by rating all the important attributes instead of looking the pictures and products descriptions online. Besides, consumers cannot get the fresh products immediately which it usually take up to 1 day to get the order and the shipping cost is high if you choose next day shipping.
The potential market of grocery shopping is huge and according to industry experts, this pattern may change during the next decades and fresh produce will play an important role in groceries online shopping. Many grocery stores now offer a shopping experience which approaches the selection and convenience we’ve come to expect from the internet.
Amazon is running its grocery service called “Amazon Fresh” in California, metro New
York and Philadelphia. Walmart expanded its service “Walmart To Go” Service to San
Francisco and Denver. “FreshDirect”, “Instacart” and “Peapod” are famous online fresh produce shopping website which are looking to shake up the market as well.
Using food labels to promote products has expanded among fresh producers and retailers.
Labels are becoming a key factor to differentiate products in fresh market (James,
Product Differentiation and Market Segmentation in Applesauce: Using a Choice
Experiment to Asess the Value of Organic, Local, and Nutrition Attributes, 2009).
Products are normally differentiated by labels that reveal nutrient attributes, health claims, production or processing methods, customers’ reviews and geographical indicators, etc. Labels about Organic, naturally grown, locally produced or GMO-Free are commonly used for fresh fruits and vegetables. Geographical indicators are important
for some fruits and vegetables which are known for better quality or flavor if grown in some specific countries or regions. Customers’ reviews are traditionally important for electronic products; however, they become increasingly important for fresh produce because of the new trend of online shopping of fresh produce. With the exponential growth of e-commerce, consumers create a huge amount of information which may influences other consumers as well (Chen and Xie, 2008). Previous studies suggests that reviews created by consumers have become a rather important influence for consumers behaviors such as making purchase decisions. Online consumer reviews (OCRs) have already been part of consumer-created information by people who purchased the target product. Information and recommendations of the producrs invovled in OCRs from consumers’ perspective (Park, 2007). Recent studies have investigated OCRs influences consumers purchase behavior as electroinc word-of-mouth and consumers make purchasing decisions based on the OCRs over the Internet (Chatterjee, 2001; Chen. and
Xie, 2008; Dellarocas, 2003; Godes, 2004)
Online retailers have discovered the effects of OCRs and are trying to use OCRs to promote their products online as consumers’ endorsements. Consumer endorsement is a useful strategy in advertising because typical consumer endorsements can improve consumers’ overall attitude to the product and they are willing to pay more for the products have high rating in reviews (Fireworker, 1977). Besides, online retailers can simply quote the certain parts or the reviews level of OCRs as advertisement without incurring any cost. Seller could their quote the entire OCR in the advertisement (e.g.
Very good! Exactly what I anticipated) or just put the consumer rating (e.g. Three stars
out of five). In this study, OCRs are used in online advertisements are defines as consumer rating
There are several previous studies focus on the demographic of internet shoppers.
(Donthu, 1999) found that internet shoppers usually are older than non-internet shoppers while the internet shoppers make more money as well. The online grocery shopping is going to get bigger and bigger. Major players like Google, Amazon and Walmart expanded their delivery services to new cities while Insracart got consumers’ attention with its meteoric growth. Investors have confidence to invest food e-commerce star-ups and want to expand to new cities. There is an uplifting trend for the future of public health. Because of the proliferation of technology and social media, consumers are more up-to-date on health food trends and more inclined towards label information than ever before. Niche produce will benefit from fresh produce online shopping because better availability of information and consumer may pay more attention on the label information since the access of search and experience attributes are limited.
There are a lot of studies focusing on the impact of customers’ reviews on sale. (Godes et al. 2005) found consumers tend to be influence by their social interaction with others and can learn from or be affected by other consumers’ opinion when they make their purchase decisions. For example, when they choosing between two restaurants, they might be heavily influence by the opinions and experiencs of friends or simply by the consumers reviews on yelp. Research has demonsrated the connection between the subsequent sales on a site of a product such as a book or movie and the consumers’ reviews on this site
(Dellarocas et al., 2005, Chevalier, 2006). (Duan, 2008) and (Liu, 2006) found an association between review volume and sales. From consumers’ side, they think the
online reviews are more balanced and unbiased because it allows divergent opinions to be presented simulataneously on the same website from different consumers (Lee, 2008,
Senecal, 2004). However few researches has examined the impact of customer review on the consumer purchase of fresh produce, partially because these products are most time purchased in store where consumers can use information such as color, size, freshness to infer product quality. Previous studies have also examined consumers’ preference and
WTP for fresh produce labeled with country of origin, locally produced, organically produced and naturally grown etc. In the meanwhile, a large body of literature shows that customer review is one of the most important factors that determine consumer purchase of non-fresh products, such as electronics, apparels, books, toys and others, either online of in store. With increased popularity of online sale of fresh produce, consumers no longer be able to have direct contact with the products when purchase. Therefore, online fresh produce consumers must rely on external information such as customer review to infer fresh produce quality. Examining the role of customer review on consumer preference and WTP for fresh produce will provide important information to growers, retailers, online companies or even policy makers regarding customer review management for fresh produce, which has been an important component of sale and marketing strategies for other products. Using a choice experiment approach, this study fills this gap by determining the relative importance of customer review to other popular attributes of fresh produce. The study also elicits consumer WTP for better customer reviews, which shed light on the potential resources needed to manage customer review for fresh produce.
Experimental Design
The choice experiment comprises several choice sets and each choice set presents a purchase scenario in which participants need to choose one from two boxes of 16oz strawberries. The two boxes of strawberries vary by attribute labels and price. The price has four levels and its range is consistent with the market price. The Base price was
$2.99/box which is roughly matched the retail price of strawberry in grocery stores.
Three higher prices were obtained by increasing the base price by $1 and $2 while a lower price was set by decreasing $1 from the base price. There are four additional attributes of the berries in the CE, including origin, production method, customer review and best use by days. Origin has five levels which include California, Florida, Mexico,
Locally Produce and US. Production methods have three levels including “Organically
Produced”, “Naturally Grown” and “Conventional”. Customer review has three levels, including one, two and three stars. Three stars mean consumers are very satisfied with the strawberries, and less number of stars indicates consumers are less satisfied with the strawberries. Best use by days has three levels, including 3 days, 5 days and 7 days, which can be an indicator of freshness of the strawberries. This design generated in total
4*5*3*3*3=540 full factorial choice sets. Fractional factorial design that maximizes the
D-efficiency of design matrix is used to generate the choice by SAS, which results in a
CE with 14 saturated choice sets. For each choice set, respondents are asked to choose one boxes of 16oz strawberries to maximize their utility. If none of the strawberries meet their demands, respondents can choose “I would not choose either product” just like what they will do in real shopping. Decision made in each choice set is independent and all the choice sets are randomly ordered so that order effects can be minimized.
Methodology
CE is based on random utility theory (Hanemann, 1984; Hanley, 1998; Hanley, Wright, and Adamowicz, 1998). Based on the econometric framework for discrete choice analysis in the context of random utility models by McFadden (1974), strawberries’ characteristics can be evaluated using discrete choice models where choices are made among mutually exclusive finite alternatives within an exhaustive choice set in this study. The theoretical model shows the number of attributes changes in a consumer’s utility function and their
WTP for a specific attribute may also change. Assuming a linear random utility function, consumer utility can be defined as
Uij =Vij+εij =βi' · Xij + εij (1)
where Vij is the deterministic, Xij is a vector of attributes of product j, βi is a vector of parameters while εi is unobservable stochastic error which are assumed distributed independently and identically distributed with the Gumbel distribution. The rule of choice is utility maximization: product j is chosen by consumer i among all alternatives iff
Uij ≥ Uik ∀ j≠k (2)
Different assumptions on the structure of the stochastic component lead to a variety of specifications. In this study, we have applied the Random Parameter Logit (RPL) where a
Mixed Logit (ML) specification is obtained by allowing the set of individual preference parameters βi to be distributed across individuals according to a statistical distribution βi
~ f (β| 훽̅, η ) which is characterized by mean 훽̅ and variance-covariance matrix η .
Specifically, we denote the following equation:
βi = 훽̅ + η· μi , (3)
where 훽̅ measure the mean effect of product attributes, η is the triangular matrix and μi is independently identically distributed with certain distributions (Train, 2003). The RPL model is widely applied and has already become the standard reference for Stated Choice
Experiment (SCE) studies because of its ability to account for preference heterogeneity and its flexibility in accommodating a variety of model specifications (McFadden and
Train, 2000).
The probability Pi that consumer i may choose alternative j, conditional on a given set of values of the βi parameters is denoted as
푉 푒푥푝 푖푗 푒푥푝훽푖 ·X푖 Pi(j|βi)=Lij(βi) = = (4) 푚 푉푖푘 푚 훽푖 ·X푘 ∑푘=1 푒푥푝 ∑푘=1 푒푥푝
To estimate consumers’ WTP for product attributes, each preference parameter represents the marginal utility of the attribute need to be considered in the random utility model.
Consumer WTP for attributes xk (with corresponding coefficient βk) can be calculated as
훽푗 WTPj=− (5) 훽푝
The WTP is lognormal distrusted (Krinsky and Robb, 1986).
Data Collection and Results
Data collection and Demographics
An online survey company distributes our survey to 3,000 US residents in July 2014 to collect information regarding a range of issue related to strawberry. A sample of 1298 responses was collected. One section of the online survey includes a choice experiment
(CE) of strawberries. We have three screening questions to make sure all respondents are
17 years older. Besides, only primary shopper for food who shop more than 50% of the times in their family and had purchased strawberries in the past 6 months are qualify for participate the survey. Table 1 present the statistics of respondents ‘demographics.
Empirical Results
As stated before, a random parameter logit model is applied on this study. To specify, we denote following equation:
Uij = α1 · Pij+βi' · Xij + εij (6)
Where P is price and X are other attributes of strawberry. The coefficient of product price
α1 was estimated as a nonrandom parameter while others coefficients of other attributes were defined as random parameters with a normal distribution as equation (3). The reason why coefficient on price was not estimated as a random parameter is the normal distribution has density on both sides of zero that would allow some individuals to have upward sloping demand curves. This assumption assured the estimated WTPs for other strawberries’ attributes are normally distributed (Lusk, Roosen and Fox, 2003).
The final multinomial logit and mixed logit models are given in table 4. For mixed logit model, except the parameters of “ Natutally Grown” and “best used within 5 days”, others are significant influence consumers’ utility. The parameter of “Florida”,”Naturally
Grown”, “3 stars Review” and “Best use within 7 days” are specfied as randon
parameters. The overall goodness of fit is better for mixed logit than that of multiomial logit (MNL). Mixed logit has a strcutral advantage in selecting the mixed logit model.
The coefficients of most strawberries were different from zero at the 0.001 siginificance level. Price coefficient was negative which indicating downward sloping price-demand relationship. Consumers are sensitive to changes in price of a box of strawberry and decline in price is associated with an increased probability of purchasing. The positive coefficients of other attributes of strawberries indicated an increasing probability of consumers choosing alternatives possessing those strawberries attributes. From the results, we can see consumers are more likely to purchase strawberries which produced in
Califonia, Florida, Locally Produced or have a label “Product of USA” compare to the strawberries from Mexico. It seems the respondents dilike the strawberries from Mexico which suggesting consumers are more sentative to strawberries which are produced in
United States. Among those origins attributes, “Locally Produced” has the largest beta which means consumers are more sensitive to strawberries from local cummunity.
Besides, consumers are interested in “Naturally Grown” and “Organic” strawberries.
They are willing to pay premiun on them than conventional ones. All variables of consumers’ review have significant positive influence which means consumers are more likely to purchase the strawberries with higher review level. For the attributes related to freshness, repondents seems more sensitve to strawberries have label “ Best use within 7 days” than “Best use within 5 days”.
Table 5 shows the results of estimate WTPs for addtional attributes of strawberry. WTP estimates are the derivation of the maeginal rate of substitution between attributes and purchase price. Constraining the distribution from which the random parameters are
drawn derive behaviorally meaningful WTP values from the mixed logit model(Hensher and Greene, 2003). Consumers’WTP for most attributes of strawberries changed in an economically important way after more attributes were added to CE. There are 10 WTP estimates for strawberries. Compare the WTP of all attributes, we can find the attributes of origins have larger WTPs than those of other attributes. The WTP of attributes of customers’ review are the second largest, suggesting consumer are likely to pay more on products have higher reviews than other attributes such as “Organic”, “Naturally Grown” or “Best use with 7 days.”
Dissusion and Conclusion
With increaing development on fresh produce online shopping, the preference of consumers, behavior of consumer and the impact of crucial attributes are very important to retailers. Our study investigare the effect of custmers’ review as well as other product attributes on consumers choice decisions by measuring changes in consumer WTP for attributes. Consistant with results from other previous studies on impact of custmoers’ review, the impact of custmoers’ review influence consumers’ WTP significantly on fresh produce as well. Consumers are willing to pay more for fresh produce with higher review level. There is limition in our CE as well because no real money and actual products were used in our experiment, a higher WTP may be estimated which is typical in hypothetical conjoint analysis (Lusk and Schroeder , 2004). However, the objective of this study is to investigate whether customers’ review influence consumers’ WTP.
Besdies, we included all possible label information in our suvey and try to accurately measured the WTP for various strawberry attributes as much as possible. Our results
show that changes in WTP result from other premium attributes and such findings suggest that information on label helps consumers to differeniate products on the basis of these attributes. Consumers are leaded to increased prevalence of attributes of higher level customers’ review, more locally produced, organic certificate and longer best use by date. These reults imply that the fresh produce retailers can improve the customers’ review system as effective markeing and communications strategies to promote the sale of fresh produce.
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Picture 1 An example of Choice Experiment question
Table 1 Attributes in Strawberries CE design Number of Attribute Levels Level Price 4 1.99 2.99 3.99 4.99 Locally Origin 5 CA FL Mexico US Produced Certified USDA Label 3 Conventional Naturally Organic Grown Review 3 1 Star 2 Star 3 Star Best Use Date 3 3 5 7
Table 2 Attributes combination for strawberries CE design Obs Price Origin Label Review Best Use Date 1 4.99 US Conventional 3 5 2 4.99 Mexico Organic 1 7 3 4.99 FL Nature 2 7 4 4.99 CA Nature 3 3 5 3.99 US Nature 1 7 6 3.99 Locally Conventional 2 3 Produced 7 3.99 FL Organic 3 5 8 2.99 US Organic 2 3 9 2.99 Locally Nature 1 5 Produced 10 2.99 CA Conventional 3 7 11 1.99 Locally Organic 3 7 Produced 12 1.99 Mexico Nature 3 5 13 1.99 FL Conventional 1 3 14 1.99 CA Organic 2 5
Table 3. Main Summary Statistics for the Sample Demographics
Characteristics % Characteristics %
Gender Children under 18 in household Male 45% None 65% Female 55% One 18% Age Two 11% 17 or below 0% Three 4% 18-24 9% Four 1% 25-29 9% Five or more 1% 30-34 9% Current employment status 35-39 8% Employed full time 44% 40-44 10% Employed part time 12% 45-49 9% Unemployed 10% 50-54 10% Homemaker 8% 55-59 9% Student 4% 60-64 9% Retired 22% 65-69 9% Annual household income 70-74 5% Less than $14,999 10% 75-79 2% $15,000-$24,999 12% 80 or above 1% $25,000-$34,999 14% Education Level $35,000-$49,999 14% Some High School (or less) 2% $50,000-$74,999 21% High school Graduate 22% $75,000-$99,999 12% Some College 35% $100,000-$149,999 10% Bachelor's Degree 27% $150,000-$199,999 3% Post-graduate Degree 14% $200,000 or above 2% Race Weekly food expenditure Caucasia 77% Less than $49 10% Black 11% $50-$99 32% Hispanic 8% $100-$149 30% Native Hawaiian or Pacific Islander 0% $150-$199 13% Asia 6% $200-$249 5% American Indian or Alaska Native 2% $250-$299 3% Other 1% $300-$349 1% Marriage Status $350-$399 1% Single 29% $400-449 1% Married/Remarried 53% $450-$499 1% Separated 1% Above $500 1% Divorced/Widowed 14% Not Sure 1% Other 2%
Table 4. Summary of Multinomial Logit Model and Mixed Logit Model Results
Variables Multinomial Logit Mixed Logit
Price -0.538*** -0.729*** (0.011) (0.015) California 0.696*** 1.009*** (0.047) (0.064) Florida 0.719*** 0.918*** (0.039) (0.060) Locally Produced 0.897*** 1.359*** (0.434) (0.070) US 0.650*** 0.991*** (0.048) (0.076) Naturally Grown 0.225*** 0.248*** (0.030) (0.041) Organic 0.191*** 0.211*** (0.028) (0.044) Review 2 stars 0.338*** 0.522*** (0.030) (0.043) Review 3 Stars 0.571*** 0.763*** (0.030) (0.049) Best Use within 5 days 0.005 0.096** (0.033) (0.041) Best Use within 7 days 0.210*** 0.223*** (0.029) (0.036) Constant for None -1.618*** -0.223*** Option (0.059) (0.036) Standard deviations of random parameters Std. California 1.092*** (0.057) Std. Florida 1.294*** (0.053) Std. Locally Produced 1.524*** (0.061) Std. US 1.695*** (0.064) Std. Naturally Grown 0.711*** (0.049) Std. Organic 0.937*** (0.047) Std. Review 2 stars 0.722*** (0.059)
Std. Review 3 Stars 1.067*** (0.062) Std. Best Use within 5 days 0.335*** (0.051) Std. Best Use within 7 days 0.300*** (0.054)
Log- Likelihood -16402 -14437 No. of Sample 1298 1298
Note: *** indicates statistically significant at 1% significant level; ** indicates statistically significant at 5% significant level.
Table 5. WTP Estimates of Attributes
WTP for WTP
California 1.375 (1.269) Florida 1.249 (1.511) Locally Produced 1.854 (1.756) USA 1.337 (2.016) Naturally Grown 0.338 (0.787) Organic 0.316 (0.894) Review 2 stars 0.688 (0.841) Review 3 Stars 1.018 (1.214) Best Use within 5 days 0.125 (0.370) Best Use within 7 days 0.303 (0.298) Total WTP 8.604 Note: Total WTP is the sum of WTP for all attributes.