If a picture is worth 1,000 words, what words are they? A ... · 10/07/2017 · Best Worst...

DRAFT: NOT FOR CIRCULATION OR ATTRIBUTION

1

If a picture is worth 1,000 words, what words are they? A pictorial best-worst scaling of

food production systems

Courtney Bir, John Lai, Nathanael M. Thompson, Nicole Olynk Widmar

Abstract: Through a nationally representative (U.S.) sample of 1,200 residents and two best-worst scaling (BWS) experiments (one including only pictures of tomatoes and one including the same pictures with words/labels) this analysis ranks consumer preferences for tomato attributes. To the authors’ knowledge, this is the first fully pictorial-based BWS experiment implemented. This study explores stated consumer preferences for local foods, in conjunction with the connotation of a picture, to analyze how appealing respondents found pictures of tomatoes in different shopping or production locations. The experiment resulted in relative rankings that were statistically different between the two BWS experiments. Key Words: Best Worst Scaling, farmers’ markets, local foods, pictorial-best worst, production preferences, tomatoes Psychology literature has considered the impacts of pictures on human comprehension

and how these impacts differ from words. For example, in a physiological study Smith et al.

(1980) found that semantic and articulatory information differed between pictures and words.

Previous studies concluded picture naming was easily disrupted by the presence of a word, but

they found named pictures were better recognized than categorized pictures (Smith et al., 1980).

Although communication via pictures has long been the focus of psychological studies, there is

demand for continued work on pictorial representations in the context of economic experiments

that seek to mimic real purchasing decisions. The need to control extraneous variables to reach a

realistic and interpretable conclusion can be confounded by the use of pictures, for which

respondents may bring their own biases and opinions.

From a marketing perspective, pictures are often used alongside words to improve

attribute understanding and recall (Unnava et al., 1991). Unnava et al. (1991) considered high

imagery provoking words, words that can easily be imagined as a tangible item (such as table)

and low imagery provoking words (such as freedom), and the impact of including pictures with


2

those attributes. The addition of a picture to low imagery provoking words increased recall,

while the addition of a picture to high imagery provoking words did not (Unnava et al., 1991).

Therefore, as is often seen in advertising, the less tangible the idea or attribute the more potential

value, in terms of recall ability, that can be achieved by adding a picture.

The power of visual stimulus in the form of pictures is especially strong with food.

Simmons et al. (2005) found that the same neural circuit representing food knowledge that is

completed when tasting food is also completed when viewing pictures of food. This pathway

allows the person to produce conceptual inferences about taste (Simmons et al., 2005). In part for

this reason, presenting pictures along with food names is a common occurrence on restaurant

menus (Hou et al., 2017). Increases in consumers’ attitudes, purchasing intentions, and

willingness to pay were found by Hou et al., (2017) when a picture was included alongside a

common description of a food item on a menu.

Best worst scaling (BWS) has been used to evaluate consumer preferences for food

attributes and agricultural production practices. Lusk and Briggeman (2009) included food

values such as nutrition, taste, and appearance in their BWS model, while Risius, Janssen, and

Hamm (2017) studied food labeling and sustainability claims to determine consumer preferences

for aquaculture. Visual appeal of food items is critical for shopping and selecting items,

evidenced both in practice (i.e. retail supermarket displays, lighting, showcases) and in the

literature across marketing, retailing, and various consumer sciences. Consumer studies have

indicated that food appearance is important to consumers when making a purchasing decision. In

a study of vegetable preferences among West African consumers, restaurant owners, and

vendors, Probst et al. (2012) found that appearance of the product was central to choice (Probst

et al. 2012). For Slovenian consumers, the most important attributes of organic and integrated


3

fruit and vegetables were availability, consumers’ income, health and environmental

considerations, and visual attractiveness (Kuhar and Juvancic 2010).

To the authors’ knowledge the use of pictures exclusively in a BWS choice experiment

has not been done. However, previous studies have used pictures to help augment choice

scenarios with the inclusion of words. Baker (1999) used pictures of apples to illustrate three

different levels of damage in a conjoint analysis study. Baker noted the importance of choosing

pictures of apples that were of a similar size and color and only differed in the amount of damage

to avoid confounding errors based on the consumers’ preferences. Similarly, Maples et al. (2018)

included images of steaks with listed attributes in a willingness-to-pay choice experiment to

determine consumer preferences for steak thickness and cut. Maples et al. (2018) chose to use

two photographs of steaks that were then edited to vary by size in order to minimize the

differences of other variables, such as marbling, which could be introduced by using a variety of

steaks. By holding other attributes constant, and carefully controlling the photos associated with

the choice experiments alongside words, authors have been able to augment choice scenarios

with visuals while maintaining focus on specific attributes. In these scenarios, it was unclear if

the respondents were responding to the written attributes or the visuals themselves. Many real

life shopping scenarios do not include written attributes, and consumers use visual cues and their

past experiences related to those visuals to make their purchasing decisions. Having a better

understanding of the assumptions consumers make regarding visuals may help researchers more

accurately represent real shopping scenarios in experiments.

Building on these previous findings, using pictures in a BWS choice experiment to

entirely replace versus augment descriptions provided in words for food purchases is a natural

progression. Additionally, this pictorial experiment contributes to the understanding of the


4

impact of visuals when making decisions, and the understanding of the consistency (or lack

thereof) between individuals when determining the attributes associated with a visual.

Understanding consumers’ associations with a particular food appearance or presentation has

value for making business decisions as a producer, marketer, or retailer. From a research

perspective, the potential for images alone versus images with words to elicit different responses

in survey settings is troubling. However, given the imagery inherently coupled with shopping

decisions, most of which are informed visually by inspecting items, such as produce or meats,

rather than by reading descriptions (words); one must consider that selecting amongst images

may bear a closer resemblence of consumer/shopper decision making than other means of

describing items. Having a better understanding of the impact of images compared to words on

respondents participating in experiments, and how varied respondent associations with pictures

can be, can contribute to the base knowledge needed to design experiments that more closely

resemble actual shopping experiences.

This study explores consumer preferences for local foods, as well as the connotation of a

picture, by using two BWS experiments to analyze how appealing respondents found pictures of

tomatoes in different shopping or growing locations, including farmers’ markets and roadside

stands. Despite the local food movement’s recent rise in popularity, the definition of “local” is

still ambiguous and may differ amongst consumers (McFadden, 2015). Respondents have

previously indicated their definition of local in terms of physical distance (Byrd et al., 2017;

Onozaka et al., 2010). However, it seems possible that beyond the physical definition of local,

individuals may perceive local in terms of varying attributes or consequences of local

production. This may include perceptions of the environmental impact of food supply, both in

production and transportation (Edwards, 2010). Local production is often associated with the


5

location the food is sold, for example farmers’ markets, roadside stands and community

supported agriculture (CSA) (Cranfield, 2012). Using contextual clues in determining an

intangible attribute such as production, makes using pictures of a common and locally produced

food a good starting point. Thus, tomatoes are used in this case for studying the effect of picture

usage on choice experiments and the attributes that respondents associate with particular

photographs.

Each of the two BWS experiments employed photos when collecting preference data;

however, one paired photos with words and one provided images without any additional

information. The two experimental designs allows for an impact comparison which is generated

from adding additional written context to a photograph, much like adding signage to a display or

package. Through these experiments and other questions within the survey instrument, this

research answers three questions. First, what are consumer preferences for different tomato

production methods? Second, do responses to BWS experiments differ when only pictures are

used vs. when pictures are combined with descriptor words? And third, what food attributes are

associated with pictures depicting different tomato production methods, and do the selections

differ between those who had been previously exposed to written words describing the

photographs?

Research Methodology

From July 10, 2017 through July 18, 2017 an online survey was launched using quotas in

Qualtrics to collect data on the relationship between demographic characteristics of households,

their perceptions of local food, and association and preferences for (pictures of) tomatoes in

different purchasing or production locations/types. A total of 1,200 respondents participated in

one of two BWS choice experiments used in this study. The sample is nationally representative


6

for gender, age, income, region of residence, and education. For both versions of the experiment,

each of the seven choice scenarios presented included three pictures of tomatoes. In one

experiment the pictures were also accompanied with a word meant to describe the picture,

namely conventional retail, greenhouse, roadside stand, upscale retail, personal garden, farmers’

market, and commercial retail (Table 1). The pictures used were chosen to represent the

descriptive text by a panel of experts, described as follows. A pool of photos representing each of

the different descriptive text attributes that were included in the BWS choice experiment was

created and ranged from 5 to 19 different images. Subject matter experts were then shown each

of the images and asked to rank them in order from most to least appropriate for each of the

seven descriptive attributes. Instructions were provided allowing subject matter experts enough

context to fine tune results suitable for the choice experiment setting. The highest ranked picture

amongst the experts was chosen to accompany the descriptive text attribute.

Of the 1,200 respondents, half (n=602) were randomly assigned to participate in the BWS

choice experiment that only contained pictures, and the other half (n=598) were assigned to

participate in the BWS choice experiment that contained the same pictures with descriptive

labels. After participating in the BWS choice experiment, both groups of respondents were

shown the same pictures used in the experiments without labels and were asked to select from a

list of 19 attributes the attributes they associated with the picture. Attributes included: freshness,

nutrition, food safety, pesticide-free, price, shelf life, having a relationship to the producer,

quality, convenience, production location, production method, variety, customer service, taste,

environmental impact, support of local economy, local, heirloom, certified organic, and none.


7

For both the BWS model with pictures only and the model with pictures and descriptor

only, how appealing a respondent views that tomato picture or picture with a descriptor can be

modeled for respondent i as:

(1) 𝐼𝐼𝑖𝑖𝑖𝑖 = 𝜆𝜆𝑖𝑖 + ℇ𝑖𝑖𝑖𝑖

where ℇ𝑖𝑖𝑖𝑖 denotes a random error term. The probability that the respondent 𝑖𝑖 chooses picture 𝑗𝑗

as the most appealing picture and picture 𝑘𝑘 as least appealing is the probability that the

difference between 𝐼𝐼𝑖𝑖𝑖𝑖 and 𝐼𝐼𝑖𝑖𝑖𝑖 is greater than all other potential differences available from the

choices. Assuming the error term is independently and identically distributed type I extreme

value, the probability of choosing a given most appealing-least appealing pair takes the

multinomial logit (MNL) form (Lusk & Briggeman, 2009) represented as:

(2) 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃(𝑗𝑗 = 𝑃𝑃𝑏𝑏𝑏𝑏𝑏𝑏 ⋂𝑘𝑘 = 𝑤𝑤𝑃𝑃𝑃𝑃𝑏𝑏𝑏𝑏) = 𝑒𝑒𝜆𝜆𝑗𝑗−𝜆𝜆𝑘𝑘

∑ ∑ 𝑒𝑒𝜆𝜆𝑙𝑙−𝜆𝜆𝑚𝑚−𝐽𝐽 𝐽𝐽𝑚𝑚=1

𝐽𝐽𝑙𝑙=1

.

The coefficients from the MNL model are not directly interpretable, so shares of

preferences are calculated to ease interpretation (Wolf & Tonsor, 2013). The shares of

preferences are calculated as:

(3) 𝑏𝑏ℎ𝑎𝑎𝑃𝑃𝑏𝑏𝑖𝑖 = 𝑒𝑒𝜆𝜆𝑗𝑗

∑ 𝑒𝑒𝜆𝜆𝑘𝑘𝐽𝐽𝑘𝑘=1

.

A likelihood ratio test was used to determine if the two experiments (the experiment with

the pictures only and the experiment with pictures and descriptor) could be pooled for analysis

(Louviere et al., 2000). It was determined that the two experiments could not be pooled (LR =

532.71; df = 6; p = <0.001). Within each BWS experiment, confidence intervals were calculated

for the estimated preference shares (comprised of coefficients from the RPL models) using the

Krinsky-Robb method (1986). Overlapping confidence intervals were used to determine if the

preference shares were statistically different (Olynk et al., 2010). The complete combinatorial


8

method as outlined by Poe, Giraud, and Loomis (2005) was used to compare the size of the

estimated preference shares arising from the RPL models used for the two experimental designs,

those who saw pictures only versus pictures with descriptors.

Results and Discussion

The demographics of the respondents for both BWS experiments were very similar to that of the

U.S. population according to the U.S. Census (Table 2) (U.S. Census Bureau, 2016), with a few

exceptions. The percentages of respondents aged 18-24 were statistically lower for both samples,

10%, when compared to the U.S. Census (13%). Both the picture only sample (21%), and the

picture with word sample (20%), had a higher percentage of respondents with an income of

$100,000 or greater when compared to the U.S. Census, 26%. Both samples had a statistically

lower percentage, 3%, of respondents who did not graduate from high school when compared to

the U.S Census, 13%. The picture with word sample had a higher percentage of respondents,

26%, who attended college but didn’t earn a degree when compared to the U.S. Census, 21%.

Both samples, 32% picture only and 33% picture and word, had higher percentages of

respondents who attended college and earned a degree, when compared to the U.S. Census, 27%.

The picture only sample had a higher percentage of respondents with a graduate degree, 15%,

when compared to the U.S Census, 12%. Finally, both samples had statistically higher

percentages of respondents from the South, and a lower percentage of respondents from the

Midwest when compared to the U.S Census.

Best Worst Choice Experiment

For respondents who participated in the picture only choice experiment, picture 1 (Table 3,

Figure 1) was allocated the largest preference share (31%). The second largest preference share

was for picture 6 (17%). The third largest preference shares were for picture 4 (12%) and picture


9

5 (13%); the fourth largest preference shares were for picture 2 (9%) and picture 3 (9%); and the

smallest preference share was for picture 7 (8%).

Respondents who participated in the experimental design that included the same pictures,

this time accompanied by descriptive words, stated starkly different preferences than did those in

the group that saw pictures only. For example, picture 5 with the labeled as personal garden

(31%) and picture 6 labeled as farmers’ market (26%) had the largest preference shares. The

second largest preference share (15%) was for picture 1 with the label conventional retail. The

third largest preference shares were for picture 2 (greenhouse), picture 3 (roadside stand), and

picture 4 (upscale retail); which had shares of 8%, 9%, and 8% respectively. Finally, the smallest

preference share, 4%, was for picture 7 with the label commercial.

Using the full combinatorial test to compare the size of the preference shares across

models (Poe, Giraud, Loomis, 2005), it was found that the majority of the preference shares for

identical pictures were statistically different across the experiments designed with and without

descriptors (words) to accompany the pictures. Respondents in the picture-only experiment had a

larger preference share (31%) for picture 1 when compared to the group who saw picture 1 with

the descriptive term conventional retail (15%). Similarly, respondents who participated in the

picture-only experiment had a larger preference share for picture 7 (8%) when compared to those

who saw picture 7 and the word commercial (4%). These findings suggest that respondents were

reacting to the terms themselves, namely conventional and commercial, in a negative way.

Previous studies have shown many consumers have a preference for non-conventionally

produced foods, such as organic and local (Meas et al., 2014), and production methods that focus

on environmental sustainability and animal welfare (Goddard et al., 2013). Although some

consumers may claim to be able to recognize non-conventionally produced foods based on


10

sensory characteristics such as taste, many rely on labels to make the distinction (Yiridoe et al.,

2005). Not only do consumers rely on labels to make the distinction between organic and non-

organic products, they have preferences for the labeling organization, and have been found to be

willing to pay different amounts for different labels (Janssen and Hamm 2012). The willingness

to pay for labels may indicate that the visual inspection of goods does not provide enough

context for an informed purchase satisfies consumer preferences.

Respondents who were presented with the picture-only experiment had statistically larger

preference shares for picture 2 (9%) when compared to those who saw picture 2 and the word

greenhouse (8%), and picture 4 (12%) when compared to those who saw picture 4 and the word

upscale retail (8%). It is likely that the decreased preference share associated with picture 4 when

the label upscale retail was included results from the higher prices often associated with higher

end retail establishments. However, it was not immediately obvious why the preference share for

picture 2 differed when the word greenhouse was included. One possible explanation may be due

to the nature of the forced tradeoffs in the BWS experiment resulting from preference shares

necessarily summing to one. It is possible that when presented with descriptors and pictures,

respondents had a significantly larger preference share for other attributes included in the

experiment which necessarily resulted in a smaller preference share for greenhouse

nomenclature.

The preference shares for those who saw picture 3, and those who saw picture 3 and the

descriptor roadside stand were not statistically different, both 9%. This result is not surprising

considering that out of the seven pictures included in this study, this picture is arguably the least

ambiguous. Perhaps even without the descriptive word roadside stand, it was clear to the

respondent some kind of roadside selling was occurring in the picture.


11

Preference shares were larger for respondents who viewed the picture and descriptive

word experiment for picture 5 (personal garden) and picture 6 (farmers’ market), 31% and 26%,

respectively, when compared to those who viewed only pictures 5 and 6 (13% and 17%,

respectively). Both personal gardens and farmers’ markets have been associated with positive

attributes. Several studies indicate that gardening has a positive impact on children, such as

increasing the consumption of vegetables (Heim et al., 2009), as well as improving other life

skills (Bir et al., 2017). Proposed benefits of local foods such as foods purchased at farmers’

markets include the positive economic and social effects of keeping the income within the local

economy (McFadden, 2015). The local food movement has also been argued to potentially

increase social capital and amenity within communities that have farmers’ markets, and increase

local development (Winfree & Watson, 2017).

Incorporating “Word Association” Exercise

In order to better understand the characteristics respondents associated with the pictures used in

the BWS experiments, after the experiment respondents were asked to select from a list of

characteristics the characteristics they associated with each picture. Although respondents who

saw both pictures and descriptive words were not permitted to return to the BWS experiment to

refresh themselves on the descriptive terms used, it was hypothesized that the descriptive terms

used in the BWS experiment may influence the characteristics respondents associated with a

picture. The number of respondents who selected a particular characteristic for each picture were

tabulated and statistically compared using Fisher’s exact test (Table 4), a variant on the chi-

square test developed for categorical variables (Fisher, 1935). In many instances statistical

differences were found. This test has generic validity and although it has greater ease of


12

computation when compared to other common unconditional tests, it does not have loss of power

and remains a conservative measure (Andres and Tejedor, 1995).

For picture 1, a high percentage of respondents selected the words convenience,

freshness, and quality. A higher percentage of respondents who saw the word conventional retail

along with picture 1 in the BWS experiment selected customer service (16% vs 11%), variety

(24% vs 18%), price (39% vs 26%), and having a relationship to producer (3% vs 1%) when

compared to those who did not see a descriptive word in the BWS experiment. Generally, these

results lack a clear intuitive explanation. For example, while it is not surprising that respondents

would associate a conventional retail setting with access to variety. However, it was not

immediately obvious how conventional retail would correlate with customer service or having a

relationship to producers in the minds of respondents. Perhaps one might associate the term

customer service to be reflective of the supermarket setting, given the common terminology of

customer service representative or associates for individuals on the retail floor. However, such

associations are not obvious given the more personal nature of customer service that one may

experience with a direct sales model, such as a roadside stand or farmers’ market. Within the

traditional supermarket setting, consumer preferences regarding courtesy of personnel, which can

be considered a proxy for customer service, differ (Carpenter, 2006). For frequent specialty

grocery store shoppers, cleanliness, selection of products, and courtesy of personnel were top

attributes, while for supercenter shoppers top attributes were cleanliness, price competitiveness,

product selection, and courtesy of personnel (Carpenter, 2006). Perhaps specialty grocery stores

were associated with conventional production methods and high levels of customer service for

some respondents. The association of relationship to producers with a conventional retail setting

is the most surprising aspect; however, many supermarkets have made considerable efforts to


13

provide displays, especially in produce sections, which create connections to farmers/producers.

The higher percentage of respondents who selected price as an attribute of the conventional

picture when they saw the word alongside picture 1 is definitively interpretable given the lack of

a positive or negative identifier. Nonetheless, it seems plausible that respondents were indicating

a positive association of price (lower prices) with a conventional retail setting.

A higher percentage of respondents who saw the word greenhouse in the BWS alongside

picture 2 selected food safety (21% vs. 16%), and variety (16% vs. 12%). However, a higher

percentage of respondents who did not see the word greenhouse with picture 2 selected freshness

when compared to those who saw picture 2 with the word greenhouse (54% vs. 48%). It remains

unclear as to why the greenhouse label decreased respondents’ association of freshness with

picture 2, further study would be necessary to determine why some respondents do not associate

greenhouse production with freshness.

For picture 3 (roadside stand) a higher percentage of respondents who saw both the

picture and the descriptor in the BWS experiment selected taste (24% vs. 16%), food safety

(13% vs. 10%), customer service (15% vs. 11%), price (28% vs. 18%), production location (18%

vs. 12%), production method (15% vs. 11%), pesticide free (10% vs. 7%), and environmental

impact (11% vs. 6%). It is generally not surprising that respondents would associate many of

these potentially positive characteristics, such as taste, pesticide free, and environmental impact,

with the novelty sentiment and entrepreneurial connotations of a roadside stands. Although the

definition of local is somewhat ambiguous, consumers frequently assume the products sold at

roadside stands have been produced in a close proximity to their home (Cranfield, 2012).

Therefore, it is logical that many would associate the produce of roadside stands with the often

believed attributes of locally produced foods. However, it is somewhat surprising that a higher


14

portion of respondents associated food safety with picture 3 when the term roadside stand was

included. Recently, there have been many studies that focus on the food safety aspects of non-

retailer food purchases. For example, in a study of an E. coli outbreak in strawberries, the illness

was statistically significantly associated with purchases from farmers’ markets and roadside

stands (Laidler et al., 2013). In a study of small farms and farmers’ markets in Georgia, Virginia,

and South Carolina, Harrison et al. (2013) found that over 42% of farmers’ markets did not have

food safety standards in place.

For picture 4 (upscale retail) a higher percentage of those who saw the picture and the

descriptor selected the characteristics nutrition (22% vs. 18%), customer service (14% vs. 9%),

variety (22% vs. 17%), heirloom (5% vs. 3%), and price (34% vs. 23%). It is possible that from

the picture alone, the respondent was not sure what kind of store the picture was taken in. Once

the descriptor up-scale retail was added, respondents may have been more confident in assuming

the store would have higher-end products such as heirloom and local foods in general, as well as

better customer service.

A higher percentage of respondents who saw picture 5 and the word personal garden

selected the tomato characteristics freshness (66% vs. 57%), nutrition (33% vs. 23%), taste (34%

vs. 20%), variety (20% vs. 12%), and having a relationship to producer (19% vs. 11%). It is

understandable that respondents may feel that a tomato from their own garden is fresher, and

they can plant a wide variety of tomato for which they can purchase available seeds. A higher

percentage of respondents who saw picture 5 and the word personal garden also selected words

associated with production including food safety (23% vs. 12%), quality (42% vs. 30%), price

(29% vs. 13%), production location (32% vs. 22%), certified organic (13% vs. 8%), pesticide

free (30% vs. 12%), support of the local economy (17% vs. 23%), environmental impact (21%


15

vs. 14%) and local (40% vs. 31%). Having a personal garden allows for more control in terms of

the production of the crop, therefore it is unsurprising attributes such as certified organic and

pesticide free were selected at a higher rate when the descriptor personal garden was added.

Although there are many benefits of home gardening, there are risks associated with the public

using gardening techniques without the proper safety education. In a study of a Salmonella

outbreak from a personal home garden, Jay-Russell et al. (2013) found that the improper use of

raw horse manure as a garden fertilizer was the source of the outbreak. Jay-Russell et al. (2013)

caution that better education surrounding the potential food safety hazards associated with home

gardening techniques is needed in the U.S., especially regarding the use of raw manure.

With the exception of production location, shelf life, and convenience, all other attributes

were selected by a higher percentage of respondents who viewed picture 6 and the word farmers’

market when compared to picture 6 alone. Respondents in this study selected quality more

frequently if they participated in the BWS experiment that included the word farmers’ market

(44% vs. 33%). Given the high association of food borne illness with farmers’ markets and

roadside stands, it is likely respondents were considering taste, color, etc. when selecting quality

(Laidler et al., 2013; Harrison et al., 2013). In one study conducted in California by Wolf et al.

(2005), they found respondents shopped at farmers’ markets because of perceived higher quality,

to socialize, and to eat. Similar to our results, reasons to not visit the farmers’ market included

inconvenience, and difficulty parking (Wolf et al., 2005). Respondents who participated in the

BWS experiment with the word farmers’ market also selected support the local economy more

frequently (36% vs. 26%). In a review by Brown and Miller (2008), it was found that even when

accounting for the loss in sales in grocery stores, the four states discussed had a positive

economic net impact from farmers’ markets. Additionally, at the level of the farmers themselves,


16

it was found in most states that farmers’ markets accounted for a large percentage of farm

income, and were seen as an opportunity to develop the farm business (Brown and Miller, 2008).

A lower percentage of respondents who viewed picture 7 and the word commercial

selected the words freshness (50% vs. 61%), having a relationship to producer (12% vs. 17%),

support of local economy (27% vs. 34%), and local (30% vs. 40%), when compared to those who

viewed picture 7 by itself. These respondents may be expressing a preference for locally grown

produce. However, even commercially produced agricultural products are defined as local for

some people. So it may be reasonable to assume that respondents do not believe commercial

agriculture (at least for tomatoes) is being undertaken in their area, so the tomato production

would not be helping respondents’ local economy and may not fit their definition of local. These

results indicate people express different preferences between pictures and labeled pictures. That

is, without labels, people are making different assumptions regarding the pictures. The effect of

the label “sticks” with the person, and further influences later attributes attributed to the pictures.

Our results show that there may be discrepancies between people regarding the content of

pictures.

Conclusion

When provided with a picture and a descriptive word, respondents preferred tomatoes grown in a

personal garden or sold at a farmers’ market. Without the descriptive word, respondents

preferred picture 1, which was labeled conventional retail in comparison experimental design.

Including pictures in best-worst scaling experiments may help respondents visualize the product

and maintain interest. However, if any words/descriptors are provided with the picture, the

results of the experiment may change. Respondents in this study associated different

characteristics with the pictures when participating in either an experiment where the picture was


17

accompanied by a descriptive word in the BWS or an experiment where the picture did not

include a descriptive word. The word used in the experiment may have influenced their later

responses to other questions, as demonstrated by the statistical differences between the responses

of those whose experiment included the picture and descriptive word and those whose

experiment included only the picture. When determining the order of questions in a survey

instrument, it is important to consider the impact of earlier questions on later responses, as it can

result in different results. It is important to consider the arrangement of questions, especially

when utilizing pictures within the survey, so the effects of previous questions can be anticipated.

Those marketing local foods or a particular production method, should take care when

using pictures in advertising. The pictures may not be conveying the desired characteristics, or if

there is verbiage being used alongside the picture, it may be overpowering any message they

hope to convey with the picture. This work builds the foundation that, at least in one instance,

people do not agree on the attributes associated with pictures, and words included with pictures

heavily influence their perspective on the attributes they associate with that picture. Future work

could further build on this foundation by comparing the impact of visuals with descriptors for

different products, markets, and other experimental designs.


18

References Baker, Gregory A. 1999. "Consumer preferences for food safety attributes in fresh apples:

market segments, consumer characteristics, and marketing opportunities." Journal of Agricultural and Resource Economics: 80-97.

Bir, Courtney, Nicole Olynk Widmar, Elizabeth Schlesinger-Devlin, and Ambarish Lulay. 2017. “Personal Gardens: Who is growing their own in the U.S.?” Available online at https://mdc.itap.purdue.edu/item.asp?Item_Number=EC-814-W

Brown, Cheryl, and Stacy Miller. 2008. "The impacts of local markets: a review of research on farmers markets and community supported agriculture (CSA)." American Journal of Agricultural Economics 90(5): 1298-1302.

Byrd, Elizabeth S., Nicole J. Olynk Widmar, and B. M. Gramig. 2017. "Presentation matters: Number of attributes presented impacts estimated preferences." Agribusiness 34(2): 377-389.

Carpenter, J. M., and Marguerite Moore. 2006. “Consumer demographics, store attributes, and retail format choice in the US grocery market.” International Journal of Retail & Distribution Management 34(6): 434-452.

Cranfield, J., S. Hensen, and J. Blandon. 2012. “The effect of attitudinal and sociodemographic factors on the likelihood of buying locally produced food.” Agribusiness 28(2): 205-221.

Edwards-Jones, G. 2010. “Does eating local food reduce the environmental impact of food production and enhance consumer health?” Proceedings of the Nutrition Society 69(4): 582-591.

Fisher, R. A. 1935. “The logic of inductive inference.” Journal of the Royal Statistical Society 98(1): 39-82.

Goddard, E. W., J. E. Hobbs, B. G. Innes, P. E. Romanowska, and A. D. Uzea. 2013. “Risk perceptions and preferences for ethical and safety credence attributes.” American Journal of Agricultural Economics 95(2): 390-396.

Harrison, J. A., J. W. Gaskin, M. A. Harrison, J. L. Cannon, R. R. Boyer, and G. W. Zehnder. 2013. “Survey of food safety practices on small to medium-sized farms and in farmers markets.” Journal of food protection 76(11): 1989-1993.

Heim, S., J. Stang, and M. Ireland. 2009. “A garden pilot project enhances fruit and vegetable consumption among children.” Journal of the Academy of Nutrition and Dietetics 109(7): 1220-1226.

Hou, Yuansi, Wan Yang, and Yixia Sun. 2017 "Do pictures help? The effects of pictures and food names on menu evaluations." International Journal of Hospitality Management 60: 94-103.

Janssen, M., and U. Hamm. 2012. “Product labelling in the market for organic food: Consumer preferences and willingness-to-pay for different organic certification logos.” Food quality and preference 25(1): 9-22.

Jay-‐Russell, M. T., J. E. Madigan, Y. Bengson, S. Madigan, A. F. Hake, J. E. Foley, and B. A. Byrne. 2014. “Salmonella Oranienburg isolated from horses, wild turkeys and an edible home garden fertilized with raw horse manure.” Zoonoses and public health 61(1): 64-71.

Krinsky, I., and A. L. Robb. 1986. “On Approximating the Statistical Properties of Elasticities.” The Review of Economics and Statistics 68(4):715-719.

Kuhar, A., and L. Juvancic. 2010. “Determinants of purchasing behaviour for organic and integrated fruits and vegetables in Slovenia.” Agricultural Economics Review 11(2): 70.


19

Laidler, Matthew M. R., Mathieu Tourdjman, G. L. Buser, T. Hostetler, K. K. Repp, R. Leman, M. Samadpour, and W. E. Keene. 2015. "Escherichia coli O157: H7 infections associated with consumption of locally grown strawberries contaminated by deer." Clinical Infectious Diseases 57(8): 1129-1134.

Louviere, Jordan J. J., D. A. Hensher, and J. D. Swait. 2000. Stated choice methods: analysis and applications. Cambridge university press.

Lusk, J. L., and B. C. Briggeman. 2009. “Food values.” American Journal of Agricultural Economics 91(1):184-196.

Maples, Joshua G., Jayson L. Lusk, and Derrell S. Peel. 2018. "Unintended consequences of the quest for increased efficiency in beef cattle: When bigger isn’t better." Food Policy 74: 65-73.

McFadden, D. T. 2015. “What Do We Mean by “Local Foods”?” Choices 30(1): 1-6. Meas, T., W. Hu, M. T. Batte, T. A. Woods, and S. Ernst. 2014. “Substitutes or complements?

Consumer preference for local and organic food attributes.” American Journal of Agricultural Economics 97(4): 1044-1071.

Olynk, N. J., G. T. Tonsor, and C. A. Wolf. 2010. “Consumer willingness to pay for livestock credence attribute claim verification.” Journal of Agricultural and Resource Economics 261-280.

Onozaka, Y., G. Nurse, and D. T. McFadden. 2010. “Local food consumers: how motivations and perceptions translate to buying behavior.” Choices 25(1): 1-6.

Poe, G. L., K. L. Giraud, and J. B. Loomis. 2005. “Computational methods for measuring the difference of empirical distributions.” American Journal of Agricultural Economics 87(2): 353-365.

Probst, L., E. Houedjofonon, H. M. Ayerakwa, and R. Haas.2012. “Will they buy it? The potential for marketing organic vegetables in the food vending sector to strengthen vegetable safety: A choice experiment study in three West African cities.” Food Policy 37(3):296-308.

Risius, A., M. Janssen, and U. Hamm. 2017. “Consumer preferences for sustainable aquaculture products: Evidence from in-depth interviews, think aloud protocols and choice experiments.” Appetite 113:246-254.

Simmons, W. Kyle, Alex Martin, and Lawrence W. Barsalou. 2005. "Pictures of appetizing foods activate gustatory cortices for taste and reward." Cerebral Cortex 15(10): 1602-1608.

Smith, Marilyn C., and Lochlan E. Magee. 1980. "Tracing the time course of picture–word processing." Journal of Experimental Psychology: General 109(4): 373.

U.S. Census Bureau. 2016. Annual Estimates of the Resident Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2015 (NST-EST2015-01).” Source: U.S. Census Bureau, Population Division. Release Date: June 2016.

Unnava, H. Rao, and Robert E. Burnkrant. 1991. "An imagery-processing view of the role of pictures in print advertisements." Journal of Marketing Research: 226-231.

Winfree, Jason, and Philip P. Watson. 2017. "The Welfare Economics of “Buy Local”." American Journal of Agricultural Economics 99(4): 971-987.

Wolf, C. A., and G. T. Tonsor. 2013. “Dairy farmer policy preferences.” Journal of Agricultural and Resource Economics 38(2):220-234.


20

Wolf, Marianne M., A. Spittler, and J. Ahern. 2005. "A Profile of Farmers' Market Consumers and the Perceived Advantages of Produce Sold at Farmers' Markets." Journal of Food Distribution Research 36(1).

Yiridoe, Emmanuel E. K., S. Bonti-Ankomah, and R. C. Martin. 2005. "Comparison of consumer perceptions and preference toward organic versus conventionally produced foods: a review and update of the literature." Renewable Agriculture and Food Systems 20(4): 193-205.


21

Table 1. Experimental Design, Example of Either the Pictures or Pictures and Descriptors Respondents Saw in the Best Worst Scaling Experiment

Picture only BWS Experiment Picture and Word BWS Experiment

(Picture 1)

(Picture 5)

Conventional Retail

Personal Garden

(Picture 2)

(Picture 6)

Greenhouse

Farmers’ Market

(Picture 3)

(Picture 7)

Roadside Stand

Commercial

(Picture 4)

Upscale Retail

Note: Words in parenthesis not seen by the respondent, for descriptive purposes only.


22

Table 2. Demographics of the Samples of Respondents who Participated in the Picture Only and the Picture and Descriptive Words Best Worst Scaling Experiments

Sample of Respondents Who Participated

in BWS Experiment Shown:

Demographic US Census (2015)

Pictures Only n=602

Pictures and Words n=598

Gender

Male 49% 48% 50% Female 51% 52% 50% Age

18-24 13% 10%┼ 10%┼ 25-34 18% 18% 17% 35-44 16% 16% 18% 45-54 17% 18% 18% 55-65 17% 16% 18% 65 and older 19% 22% 19% Annual Pre-Tax Income

$0-$24,999 22% 24% 22% $25,000-$49,999 23% 25% 25% $50,000-$74,999 17% 17% 19% $75,000-$99,999 12% 12% 13% $100,000+ 26% 21%┼ 20%┼ Education

Did not graduate from high school 13% 3%┼ 3%┼ Graduated from high school, did not attend college

28% 26% 25%

Attended college, no degree earned 21% 23% 26%┼ Attended college, bachelor's (B.S. or B.A.), associates or trade degree earned

27% 32%┼ 33%┼

Graduate or advanced degree (M.S., Ph.D., Law school)

12% 15%┼ 12%

Region of Residence

Northeast 18% 18% 17% South 21% 39%┼ 36%┼ Midwest 38% 21%┼ 21%┼ West 24% 22% 26%

┼indicates the percentage of respondents is statistically different than the U.S. Census.


23

Table 3. Multinomial Logit and Random Parameters Logit Results for the Picture only Best Worst Scaling Experiment and Picture and Descriptive Word Experiment

Picture Only Experiment (n=602) Picture and Word Experiment (n=598) MNL RPL MNL RPL

Element of Experiment Seen C

oeffi

cien

t (S

tand

ard

Erro

r)

Coe

ffici

ent

(Sta

ndar

d Er

ror)

Stan

dard

D

evia

tion

(Sta

ndar

d Er

ror)

Shar

es o

f Pr

efer

ence

[C

onfid

ence

In

terv

als]

Attribute Seen Coe

ffici

ent

(Sta

ndar

d Er

ror)

Coe

ffici

ent

(Sta

ndar

d Er

ror)

Stan

dard

D

evia

tion

(Sta

ndar

d Er

ror)

Shar

es o

f Pr

efer

ence

[C

onfid

ence

In

terv

als]

Picture 1 0.72*** (0.040)

1.36*** (0.096)

0.10*** (0.110)

0.31a [0.26-0.35]

Picture 1 and Conventional Retail

0.95*** (0.043)

1.40*** (0.078)

0.08*** (0.083)

0.15b [0.13-0.17]

Picture 2 0.14*** (0.039)

0.18*** (0.046)

0.05* (0.116)

0.09d [0.09-0.10]

Picture 2 and Greenhouse

0.58*** (0.043)

0.77*** (0.061)

0.06*** (0.075)

0.08c [0.07-0.09]

Picture 3 0.15*** (0.039)

0.16** (0.067)

0.07*** (0.078)

0.09d [0.08-0.09]

Picture 3 and Roadside Stand

0.65*** (0.043)

0.87*** (0.067)

0.07*** (0.074)

0.09c [0.09-0.10]

Picture 4 0.37*** (0.039)

0.46*** (0.070)

0.07*** (0.083)

0.12c [0.11-0.14]

Picture 4 and Upscale Retail

0.65*** (0.043)

0.84*** (0.064)

0.06*** (0.079)

0.08c [0.08-0.09]

Picture 5 0.35*** (0.039)

0.54*** (0.059)

0.06*** (0.073)

0.13c [0.12-0.15]

Picture 5 and Personal Garden

1.30*** (0.045)

2.14*** (0.101)

0.10*** (0.117)

0.31a [0.31-0.35]

Picture 6 0.53*** (0.039)

0.77*** (0.062)

0.06*** (0.072)

0.17b [0.15-0.17]

Picture 6 and Farmers’ Market

1.33*** (0.046)

1.96*** (0.071)

0.07*** (0.086)

0.26a [0.26-0.28]

Picture 7

0.08e [0.07-0.09]

Picture 7 and Commercial

0.04d

[0.03-0.04] Note: ***, ** indicates significance at 1% and 5% levels. Letter subscripts in both shares of preference columns indicate if the shares of preference are statistically different within that column. For example in the picture only experiment shares of preference column the share of preference for picture 4 and picture 5 both have a c subscript indicating they are not statistically different from one another.


24

Table 4. Word and Picture Associations, Percentage of Respondent who Selected the Tomato Attribute when Presented with Each Picture. Picture Only BWS n=602, Picture and Word BWS n=598

Picture Shown

Word Shown

Non

e

Con

vent

iona

l R

etai

l

Non

e

Gre

enho

use

Non

e

Roa

dsid

e St

and

Non

e

Ups

cale

R

etai

l

Non

e

Pers

onal

G

arde

n

Non

e

Farm

ers

Mar

ket

Non

e

Com

mer

cial

Freshness 37% 34% 54%* 48%* 44% 47% 32% 32% 57%*** 66%*** 51%*** 62%*** 61%*** 50%*** Nutrition 20% 23% 26% 28% 20% 21% 18%* 22%* 23%*** 33%*** 25%* 30%* 23% 21% Shelf Life 21% 22% 10% 9% 9% 10% 16% 20% 9% 12% 12% 11% 7%* 11%*

Taste 20% 18% 15% 19% 16%*** 24%*** 12% 13% 20%*** 34%*** 20%*** 28%*** 21% 19% Food Safety 19% 23% 16%* 21%* 10%* 13%* 14%* 19%* 12%*** 23%*** 11%* 15%* 12% 12%

Quality 35% 33% 31% 34% 27% 30% 27% 29% 30%*** 42%*** 33%*** 44%*** 30% 28% Convenience 46% 50% 12% 13% 25% 25% 42% 42% 13%*** 37%*** 27% 23% 9%** 14%**

Customer Service 11%** 16%** 6% 8% 11%* 15%* 9%** 14%** 6% 9% 9%** 15%** 4% 5% Support of Local Economy 9% 9% 22% 21% 30% 32% 9% 9% 23%* 17%* 26%*** 36%*** 34%*** 27%***

Variety 18%* 24%* 12%* 16%* 12% 14% 17%** 22%** 12%*** 20%*** 10%*** 16%*** 9% 12% Heirloom 5% 5% 7% 6% 6% 6% 3%* 5%* 8% 9% 6%* 9%* 6% 6%

Price 26%*** 39%*** 12% 16% 18%*** 28%*** 23%*** 34%*** 13%*** 29%*** 21%** 28%** 11%** 16%** Production Location 8% 11% 26% 30% 12%** 18%** 10% 13% 22%*** 32%*** 16% 19% 29% 31% Production Method 7% 8% 31% 33% 11%* 15%* 8% 11% 24% 28% 8%** 13%** 23% 25%

Certified Organic 4% 6% 13% 15% 5% 7% 4% 6% 8%** 13%** 5%*** 11%*** 9% 8% Pesticide-Free 7% 9% 22% 24% 7%* 10%* 8% 10% 12%*** 30%*** 8%*** 15%*** 9% 10%

Having a Relationship to Producer 1%* 3%* 11% 10% 13% 14% 2% 4% 11%*** 19%*** 9%*** 17%*** 17%* 12%* Environmental Impact 4% 6% 18% 21% 6%*** 11%*** 6% 7% 14%*** 21%*** 7%*** 15%*** 17% 19%

Local 10% 12% 24% 24% 37% 38% 11% 12% 31%*** 40%*** 33%** 42%** 40%*** 30%*** Note: *the percentage of respondents in the picture only BWS group who selected that attribute to describe that picture is statistically different than the percentage of respondents in the picture and word BWS group at the .05 level, **at the .01 level, *** <0.001 level


25

.35

.30

.25

.20

.15

.10

.05

Picture 1 Picture 2 Picture 3 Picture 4 Picture 5 Picture 6 Picture 7 Figure 1. Random Parameters Logit Preference Shares for Picture Only Best Worst Scaling Experiment (n=602)

Picture 1 Picture 2 Picture 3 Picture 4 Picture 5 Picture 6 Picture 7


26

.35

.30

.25

.20

.15

.10

.05

Conventional Retail Figure 2. Random Parameters Logit Preference Shares for Picture and Word Best Worst Scaling Experiment (n=508)

Greenhouse Roadside Stand Upscale Retail Personal Garden Farmer’s Market Commercial

If a picture is worth 1,000 words, what words are they? A ... · 10/07/2017 · Best Worst...

Documents

Transcript of If a picture is worth 1,000 words, what words are they? A ... · 10/07/2017 · Best Worst...