Linking consumers' food choice motives to their preferences for insect-based food products: An application of Integrated Choice and Latent Variable model in an African context

Mohammed H. Alemu and Søren B. OlsenDepartment of Food and Resource Economics, University of Copenhagen, Rolighedsvej 25, 1958 Frederiksberg C,

Denmark

Abstract

A large body of literature shows that consumers' attitudes, perceptions, personalities as well as motives

play an important role in shaping their food choices. As these factors are not fully observed by analysts

they are referred to as latent variables. A number of economic studies treated such variables as a direct

measure of consumers' latent food choice behavior without acknowledging that this could risk

introducing measurement error as well as endogeniety bias. In this study, we aim to investigate the

latent link between consumers' preferences and food choice motives (FCMs) in an African context. We

employ an integrated choice and latent variable (ICLV) model specification for data analysis to

recognize the latent nature of the FCMs and to address the above problems. The data originates from an

incentivized discrete choice experiment conducted in Kenya to elicit consumers' preferences for insect-

based foods. The findings show that consumers' preferences as well as responses to the FCMs are

influenced by their latent motivational orientation. The results generally indicate the benefit of using

the ICLV models in accounting for consumers' latent preference constructs in food choice and

valuation research.

Keywords: Discrete choice experiment, Food choice motives, Integrated choice and latent variable

model, Edible insects, Kenya

1. Introduction

Food choice is a multifaceted phenomenon and consumers make a range of food choices to satisfy their

consumption needs. They consider a number of factors and the taste, nutritional content, food safety,

origin, brand, packaging and price aspects of the food products are some of the most important ones for

them (Lau et al., 1984; Grunert, 2003, 2005; Jaeger, 2006; Köster, 2009; Unnevehr et al., 2009;

Alphonce and Alfnes, 2012). The contribution of the food and consumer science as well as the

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marketing and economic disciplines is great in terms of studying the association of such factors with

consumers' food choice behaviour (see e.g. Glanz et al., 1998; Tuorila and Cardello, 2002; Semeijn et

al., 2004; Rosas-Nexticapa et al., 2005; Loureiro and Umberger, 2007; Garcia-Bello et al., 2008;

Gracia et al., 2009; Lusk and Briggeman, 2009; Cranfield et al., 2011; De Groote et al., 2014; Alemu et

al., 2017a). There is increasing attention on further understanding the association between consumers'

motives and their food choice behaviour (see Prescott et al., 2002; Cranfield et al., 2011; Eertmans et

al., 2005; Honkanen et al., 2006). This is because consumers' choice of food products, especially of

new food products is dependent not only on the characteristics of the product but also on the socio-

demographic as well as the personal motives of consumers.

Specifically, with no or limited consumption experiences with new food products, the main

driver of consumers' food choice would be their food choice motives (FCM). This is acknowledged by

several authors (e.g. Steptoe et al., 1995; Eertmans et al., 2005; Grunert, 2005; Martins and Pliner,

2005; Honkanen et al., 2006; de Boer et al., 2007; Cranfield et al., 2011; Tan et al., 2015; Caparros-

Megido et al., 2014). Nevertheless, little is known about the link between consumers' FCM and their

preferences for novel food attributes. In this study, we aim to contribute to the literature by

investigating this link in the context of Kenyan consumers' preferences for novel insect-based food

products. Our hypothesis is that consumers' preferences for such food poducts can be driven by their

FCMs. While consumers could have different motives regarding food choice, we focus on the FCMs

developed by de Boer et al., (2007) and later extended by de Boer et al., (2013).

Drawing upon the works of Higgins, (1997) and Higgins et al., (2001); de Boer et al., (2007)

validated the FCMs to specifically study consumers' motivational orientations in terms of promotional

and prevention oriented inclination to food products. According to these authors, the former may refer

to a behavioral orientation involving a tendency to positively appreciate the value of food for humans.

It may also include willingness to taste new food products and respect everything that is edible. The

latter represents a behavioral reflection that entails avoidance of some food products which are

percieved to be unconventional and harmful and focus only on those that are obligatory to eat. The

items in the FCMs are constructed to represent these two main motivational orientations. For example,

the item "She likes to vary her meal. She is curious about new tastes" can be considered to be

promotional orientation whereas "She eats because she has to. Meals are not important to her" reflects

a prevention oriented attitude.

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In this study, we also aim to examine consumer behaviour in terms of these orientations.

However, unlike de Boer et al., (2007), we focus on linking these orientations to consumers'

preferences for the attributes of novel foods.We elicit consumers' preferences for buns made with

cricket flour using incentivized discrete choice experiments (DCEs). DCE has been extensively used in

economic studies investigating food choices and valuation. It was developed to quantify the relative

importance of attributes of a product in determining consumers behaviour using a multi-attribute utility

expression (Lancaster, 1996). In DCEs, consumers make choices among given alternatives described

by specific attributes, and the alternatives differ in the levels that the attributes take. This resembles the

everyday choices they make in e.g. stores and marketplaces. The popularity of the DCE aproach – as

compared to oher related approaches such as experimental auctions – is linked to this fact (Gracia,

2014).

Most DCE consumer studies on food preferences have been conducted in developed countries.

There are however a few investigations undertaken in developing countries, e.g. in an African context.

For example, Alphonce and Alfnes, (2012, 2017) used DCE to elicit willingness to pay (WTP) for

conventional, organic and food safety inspected tomatoes in a traditional market in Tanzania. Probst et

al., (2012) and Lagerkvist et al., (2013) investigated preferences and WTP for safer vegetables in west

and east African countries, respectively. In the contex of biofortified foods, Naico and Lusk, (2010),

Chowdhury et al., (2011), Meenakshi et al., (2012) and Birol et al., (2015) explored consumers'

preferences and WTP in different African countries. DCE has also been applied to assess demand for

genetically modified food products in an African setting (e.g. Kikulwe et al., 2011). In the context of

introducing new insects-based food products in Africa, to the authors' knowledge, Alemu et al., (2017a,

b) are the only DCE studies examining consumers' preferences and WTP for such food products. An

additional aim of this study is, therefore, to contribute to this line of research by exploring consumer

preferences for cricket flour buns in an African setting.

Parallel to the development of the DCE method, the field of choice modelling is advancing to

improve the existing modelling approaches and introduce new ones (Hensher and Greene, 2003; Hess

and Daly, 2010). This gives DCE researchers flexibility in analysing their data and explaining

consumer behavior in a plausible manner. It is now possible to capture preference heterogeneity across

individuals using random coefficient models as a result of significant progress in computational

capacity (Hess, 2012). This has shifted the focus of analysts to capture preference heterogeneity caused

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by variation in sensitivities to attributes in the DCE. However, other factors, such as consumer

attitudes would also play an important role in driving preference heterogeneity (Ben-Akiva et al., 2002;

Hess, 2012; Bechtold and Abdulai, 2014, Sok et al., in press). Thus, we are witnessing an increasing

interest in incorporating such factors into choice models. Some authors include these data in DCE

models by interacting them with attribute levels or by incroporating them into a latent class model

framework to identify segments of consumers grouped according to their latent preference structure

(Burton et al., 2001; Bitzios et al., 2011; Kikulwe et al., 2011; Scarpa and Thiene, 2011; Grebitus et al.,

2013; Bechtold and Abdulai, 2014; Koistinen et al., 2013 and Alemu et al., 2017a). Nevertheless, it is

important to recognize that consumer attitudes have a latent nature, and treating them as explanatory

variables is likely to lead to a measurement error problem (Ashok et al., 2002; Hess, 2012). In addition,

the latent variables can be endogenously correlated with other unobserved factors in the model (Daly et

al., 2012).

Therefore, a more general analytical approach that systematically links choice decisions to

consumers' attitudes such as FCMs is required to improve the credebility of results and behavioral

predictions (Ashok et al., 2002; Ben-Akiva et al., 2002; Hess, 2012; Lusk et al., 2014; O’Neill et al.,

2014; Yangui et al., 2016; Sok et al., in press). Employing an ICLV model, which is sometimes

referred to as a hybrid choice model (Ben-Akiva et al., 2002a, b; Bolduc et al., 2005), can serve this

purpose. These models have been used in several transportation studies (e.g. Fosgerau and Bjørner,

2006; Abou-Zeid et al., 2010; Yañez et al., 2010; Daly et al., 2012; Kim et al., 2014) as well as in DCE

applications in environmental economics (Hess and Beharry-Borg, 2012; Lundhede et al., 2015),

marketing (Ashok et al., 2002), agricultural economics (Sok et al., in press) and health economics

(Kløjgaard andd Hess, 2014). To the authors' knowledge, there are only few applications in the food

choice and valuation literature, i.e. O’Neill et al., (2014) and Yangui et al., (2016), that employed the

ICLV model. Therefore, a further contribution of this study is to add to this sparse literature by using

an ICLV model to relate consumers' FCMs to their choices in an African context. Like that of

consumers' attitudes, FCMs involve psychological dispositions with latent constructs. Thus,

incorporating them into choice models using the ICLV model would improve behavioral explanation

(Sok et al., in press). To the auhors' knowledge, our study is the first in food DCE studies to apply the

ICLV model in an Arfican setting.

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The remainder of the paper is structured as follows. Section 2 describes the methodological

approach, section 3 presents the results of the study, and section 4 contains the discusion and

concluding remarks.

2. Experimental setup and Methodology1

2.1 Study design

2.1.1 Sample

The data used in this study originates from a field experiment in Kenya based on an incentivized DCE.

The sample was drawn from three counties in Kenya as shown in table 1. Most of the Kenyan

population settle in rural areas, and this is acknowledge by choosing two rural counties (Siaya and

Nandi) and one urban county (Nairobi). Ethnicity was another criterion to choose the sample as the

Kenyan population comprises of various ethnic backgrounds. Nairobi, which is the capital city of

Kenya, is home to more than 3 million people of diverse backgrounds, representing the national

population in terms of socio-economic, ethnic and cultural composition. In addition to representing the

rural segment, the inclusion of the Nandi county as one of the study sites was based on the fact that it is

part of the Rift valley region which is the largest region in Kenya, accommodating more than 10

million people based on the 2009 Kenyan population and housing census (KNBS, 2009). The tradition

of insect consumption was considered in order to enable studying the responses of people who live in

areas with this tradition and without2. A sample of consumers drawn from Siaya county (with a

population of around 800,000) represented the former whereas samples of consumers recruited from

Nandi and Nairobi counties formed the latter. A random sampling procedure resulted in a total sample

of 116 participants from the three counties. While five locations3 were randomly selected in Siaya and

Nandi counties each (the rural segments), three locations were randomly chosen from Nairobi (the

urban segment). Subsequently, two villages and three estates were randomly chosen from each location

in the rural segments and in the urban segment, respectively. Collaboration with the chiefs and sub-

chiefs of each administrative location was necessary for recruiting subjects since a proper sampling

1 This section partly overlaps with the other paper, i.e. Alemu et al., (2017b), since they share the same data. 2 Note that the results concerning the responses of people across areas with and without the tradition of insect consumption have been reported elsewhere in Alemu et al., (2017b). 3 In Kenya, locations are third level administrative unit.

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frame in terms of e.g. a household database was not available. To ensure that data was collected from

those who make dietary decisions in the households, the participants in the experiment were either

household heads or spouses.

Table 1Sample distribution

County Ethnic affiliation Number of respondents Siaya – rural region with insect eating tradition Luo 40Nandi – rural region without insect eating tradition Kalenjin and Nandi 34Nairobi – urban region without insect eating tradition Mixed 42Total 116

Source: Alemu et al., (2017b)

2.1.2 The product contexts – insect-based food products

There is an increasing interest in exploring the benefits of edible insects as food (FAO, 2013). Edible

insects are traditionally used as food by many communities worldwide mainly in Africa, Latin America

and Asia (van Huis, 2013). They are an important source of essential nutrients for human consumption

and they are generally cheap to produce (FAO, 2013, Rumpold and Schlüter, 2013). Considering the

poor and food insecure households in Africa, insect farming for food can contribute to improving their

nutritional status using locally available and cheap resources (van Huis, 2003, Ayieko et al., 2013).

Insects have low requirements of agricultural inputs such as land and water, and they are

environmentally friendly due to their property of high feed-conversion ratios (Oonincx et al., 2010;

Halloran et al., 2017). This is relevant in an African context given that drought and lack of agricultural

inputs are the limiting factors for agricultural production (IFPRI, 2014). Additionally, insect farming

can be used to empower African women in rural areas as it can create employment and be a source of

income (FAO, 2013).

In Kenya, some insect species are traditionally consumed as food (Christensen et al., 2006;

Ayieko et al., 2010; Alemu et al., 2017a; Pambo et al., 2017). The main ones are termites,

grasshoppers, lake flies and black ants. People collect these insects from the wild and eat them whole

as a snack or with bread, cassava or stiff porridge once they are sun-dried and toasted (Kinyuru et al.,

2009). The increasing interest in utilizing insects as food in the world galvanized the idea of insect

production for human consumption in Kenya. In this regard, cricket production is becoming popular

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among small-scale farmers and business practitioners in Kenya. This is tied to the importance of

crickets as a source of important nutrients, and the ability to mass-rear as well as ease of handling them

during production (Ayieko et al., 2016). Crickets can be consumed as whole or flour (grinded) forms.

In this study, we decided to focus on the latter as it has previously been shown that consumers are

likely to react positively to less visible and grinded insects (Ayieko et al., 2010; FAO, 2013). In Africa,

where low protein cereals are commonly used as staple foods (Stevens and Winter-Nelson, 2008; De

Groote et al., 2011), using cricket flour (CF) as an ingredient by mixing it with such foods can increase

their protein contents. CF being one of the most produced and sold insect-based food products in the

world provides a great opportunity to introduce it in Kenya with a potential knowledge and technology

transfer from other experienced countries such as Thailand.

The insect-based food products used in this DCE are buns baked by adding CF to wheat flour.

The affordability as well as the availability of regular buns in virtually all Kenyan markets from small

kiosks to big supermarkets supported the decision to use them as the product contexts. In addition,

information gathered from focus groups and key informant discussions suggested that wheat products

including buns fall under the staple food items in Kenya. The buns being small in their size as

compared to other bread products facilitated the practical implementation of the experiment in the field

in terms of reducing logistic challenges to produce and transport them. Three types of bun products

were baked for the field experiment. Table 2 reports the ingredient composition of the products.

Table 2Ingredient composition of the buns

BunsAmount of wheat flour (g)

Amount of cricket flour (g)

Amount of fortified wheat flour (g)

Baking fat (g)

Salt (g)

Sugar (g)

Yeast (g)

Aceticacid (ml)

Control bun 125 0 0 7.5 1.25 5 2.5 0.125Fortified control bun 75 0 50 7.5 1.25 5 2.5 0.125Medium CF bun 118.75 6.25 0 7.5 1.25 5 2.5 0.125Fortified Medium CF bun 68.75 6.25 50 7.5 1.25 5 2.5 0.125High CF bun 112.5 12.5 0 7.5 1.25 5 2.5 0.125Fortified High CF bun 62.5 12.5 50 7.5 1.25 5 2.5 0.125

Notes: 'g' refers to grams, and 'ml' refers to millilitres.Source: Alemu et al., (2017b)

The first product involves one without CF whereas the second and the third contain 6.25g and 12.5g of

CF, respectively. These products were developed by modifying the recipe specification used in

Kinyuru et al., (2009) and by consulting food processing technicians as well as a nutrition scientist at

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the Jomo Kenyatta University of Agriculture and Technology (JKAUT). Additionally, focus groups

consisting of 10 participants were interviewed to evaluate the colour, texture, flavour and taste of trial

buns baked for this purpose. The outcomes indicated that consumers could differentiate between the

different buns based on these sensory characteristics and the recipe specification was valid in terms of

giving rise to bun products similar to those available in the market except their content of an insect

component. As noted in Alemu et al., (2017b), the buns were baked to have an active shelf life of 7

days using acetic acid which is used in bakery products for preservation purposes (Latou et al., 2010).

2.1.3 DCE design

The bun products were used in the DCE to elicit consumers' preferences for the CF-based buns. Three

attributes including a price attribute were used in the DCE. The attributes and levels are presented in

table 3. The identification of attributes and their levels relied on expert opinions, a literature review,

several focus group discussions, and observations of actual market prices for regular buns. In addition,

two pilot studies were conducted to assess whether respondents could understand and make trade-offs

between the identified attributes in the DCE.

The first attribute is the amount of CF in the buns which is used to investigate consumers'

preferences for the insect component of the buns. It has three levels which are 0 grams of CF (standard

buns with no CF), 6.25 grams of CF (Medium CF buns), and 12.5 grams of CF (High CF buns). As

indicated above, we employed a non-hypothetical DCE which entails providing the actual products to

consumers. The identification of the above levels was dependent on our ability in terms of financial and

logistic availability to bake as many buns as needed for the experiment. The second attribute is whether

some proportion of the wheat flour is fortified or not (Fortified). It has two levels which are 0 grams

and 50 grams. Again, these levels were identified based on the practical manageability of the baking of

the buns as pointed out above. Food fortification is part of the Kenyan government program to combat

micronutrient deficiency in the country via the Kenya National Food Fortification Alliance (KNFFA)

program. Industrial food products such as salt, wheat and maize flour as well as cooking oils are

fortified with vitamins, iron, folic acid, magnesium, zinc, etc (KNFFA, 2011). Studying consumer

preferences for fortified foods would provide important information to support policy programs

targeted at food fortification in Kenya. While the focus of this study is on eliciting preferences for

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insect-based food products, a more realistic purchase decision reflects the fact that consumers evaluate

a range of factors when they make food choice and purchase decisions. Therefore, the inclusion of the

Fortified attribute increases the number of attributes to be evaluated in our DCE. The third attribute is

price which we define to have six levels. This attribute represents the cost of a bag of buns with three

pieces of buns in it. A pre-survey assessment of market prices showed that regular buns were sold at

around 15 to 30 Kenyan Shillings (KShs) for a similar amount of buns. Combined with focus group

discussions, this information led us to set the minimum price at 20 KShs which is used as a lower

bound for the price attribute. The levels for price are 20, 25, 30, 40, 60 and 90 KShs.

Table 3

DCE attributes and levelsAttribute Level Name of the level Coding Amount of cricket flour None (0%)

6.25g (5%) 12.5g (10%)

Reference Medium CF High CF

Reference Dummy Dummy

Some portion of the wheat flour is fortified

No (0g) Yes (50g)

Reference Fortified

Reference Dummy

The cost of a bag of buns in KShs 20,25,30,40,60,90 Price Continuous Source: Alemu et al., (2017b)

In DCE, one has to produce choice sets consisting of different alternatives from which

consumers make choices. This requires combining the attribute levels based on a DCE design theory

(Louviere et al., 2000; Scarpa and Rose, 2008; ChoiceMetrics, 2012). In this study, three alternatives

are included in a choice set, i.e. two new designed alternatives and one specific alternative that is

constant across choice sets. The new alternatives represent CF buns whereas the constant alternative is

a standard bun that is currently available in the market. A full factorial design would require 36

alternatives to be presented to respondents. As this would be beyond the cognitive capacity of a

respondent, 24 alternatives were identified in 12 choice sets for the survey using a D-efficient fractional

factorial design. Parameter estimates from two pilot studies were used as priors for the efficient design

which was generated using Ngene (ChoiceMetrics, 2012).

In this study, we implemented an approach called nonhypothetical or incentivized DCE with

real products and an economic incentivization scheme. This type of DCE was also employed by e.g.

Lusk and Schroeder, (2004), Ding et al., (2005), Gracia et al., (2011), Mørkbak et al., (2012), and

Alphonce and Alfnes, (2017). The decision to use incentivized DCE was motivated by hypothetical

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(stated) choices commonly being associated with hypothetical bias concerns (e.g. Murphy et al., 2005;

Vossler et al., 2012). With increasing innovative diversification and emergence of new food attributes,

policy makers and agribusiness practitioners would seek reliable preference information to make

successful and informed decision. In this case, DCEs involving exchange of products for money are

preferred because consumers have more incentive to tell the truth than in purely hypothetical DCEs

(Lusk et al., 2004; Lusk and Hudson, 2004).

2.2 Questionnaire and data collection procedure

The data used in this study was collected using a paper-based questionnaire. Focus group discussions

were conducted using an equal number of females and males in separate sessions to develop the

questionnaire and test whether respondents could understand its contents. In a further attempt to

validate it, two pilot tests were carried our using 42 respondents in total. The final questionnaire

contains several sections. The first part of it presents questions and instructions to taste the different

samples of the bun products based on a 9-point hedonic scale. Then a likelihood of preparing CF-based

buns at home was asked using a 5-point scale from very unlikely to very likely. The DCE part of the

questionnaire starts by presenting background information concerning the importance of insect

production for human consumption. It then describes the products, the attributes and their levels as well

as the overall scenario of the DCE. The questionnaire also contains questions on FCMs similar to de

Boer et al., (2007) (see table 4). Using a 6-point scale from "Not like me at all" to "Very much like me",

consumers were asked to respond to eleven items in the FCMs. The question reads as: How much like

you is this person? Notice that in this question the person is portrayed as a 'female'. While the original

FCM was developed using a sample of western consumers to identify their motivational orientation

with respect to food choices and involvement, we apply it to an African sample of consumers to assess

if it reveals important information regarding consumers' motivational orientation related to novel food

consumption. An additional aim is to understand whether peoples' attitude in terms of their

motivational orientation can be linked to their preferences for insect-based food attributes. This is

because peoples' psychological orientation plays a big role in shaping their food choice behavior.

Understanding such aspects is relevant for market segmentation as well as consumer-specific product

development (Lähteenmäki, 2013; Cranfield et al., 2011).

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Table 4Items of the food choice motives

ItemsNot like me at all

Not like me

A little like me

Somewhat like me

Like me

Very much like me

She likes to vary her meal. She is curious about new tastes She eats because she has to. Meals are not important to her She enjoys eating well. In her view every meal should be festive

Food does not bother her. She has no special demand on it She feels proud of her taste. She believes that her food choices are very attractive

She likes many different foods. She is also a great taster She prefers an ordinary meal. She is happy with existing foods she used to eat

She is grateful for her meal. In her view everything that is edible deserves respect

She is very mindful of food. She wants to eat sensibly She is a big eater. She loves to have plenty of palatable foods

She always stick to her usual food choice Source: de Boer et al., (2007).

Enumerators were recruited and trained to conduct the field experiments and record responses.

They explained the procedures to participants one by one. As the experiment involves incentivized

DCE, participants were required to buy one of the products. Therefore, they were given 90 KShs as

well as a show up fee of 30 KShs. It is fairly common to provide such financial incentives in economic

experiments (Wilcox 1993; Hertwig and Ortmann 2001; Lusk and Schroeder 2004; Read, 2005;

Harrison 2006; Chang et al. 2009). The next task was conducting sensory evaluations where

participants were asked to taste the bun products and fill in a 9-point hedonic scale. Here, the products

were presented in random orders to mitigate the potential ordering effect. Another consideration was

related to the impact of the taste of the food consumed before the sensory evaluation. We exerted a

great effort to control this by administering the tasting of the buns between 10.0h and 12.0h in the

morning and 14.0h and 16.0h in the afternoon as also stated in Alemu et al., (2017b). The DCE then

follows and participants were asked to make choices between alternative bun products explained by the

different attributes. After the 12 choice tasks were completed, they bought their chosen products based

on a procedure used in Alfnes et al., (2006) and Gracia et al., (2011). The whole procedure regarding

the DCE was explained in the following subject instruction which is adapted from Johansson-Stenman

and Svedsater, (2008), Chang et al., (2009) and Mørkbak et al., (2014).

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"You will be provided with twelve different choice scenarios within which three bags (Bag 1, Bag 2,

and Bag 3) of buns are included. Bag 1 and Bag 2 may contain buns made from wheat flour mixed with

cricket flour. Some portion of the wheat flour can be fortified. Bag 3 contains only buns made from the

wheat flour which was not fortified. In each scenario, you should choose ONE of the bags you would

like to purchase (Bag 1 or Bag 2) or you can choose Bag 3 if you would not like to purchase Bag1 or

Bag2. After you complete all 12 shopping scenarios, we will ask you to draw a number (1 to 12) from

an envelope to determine which shopping scenario will be binding. In the envelope are numbers 1

through 12. If the number 1 is drawn then the first shopping scenario will be binding, and so on. For

the binding scenario, we will look at the product you have chosen, give you your chosen product, and

you will pay the listed price in that scenario. You should use the 90 KShs for the purchase. The most

expensive alternatives cost 90 KShs. Although only one of the 12 shopping scenarios will be binding

there is an equal chance of any shopping scenario being selected as binding, so think about each

answer carefully."

2.3 Econometric approach: ICLV model

DCE utilizes the random utility theory (RUT) (McFadden, 1986) to examine consumers' utility of

goods and service. The RUT postulates that an individual's utility for a bundle of alternatives is

composed of both observable as well as unobservable factors.

Unj=β Xnj+ε nj (1)

where the observed component is described by β Xnj which is the product of the estimated coefficients,

β, and the attributes of the alternatives X . The unobservable behavioral component of the utility is

described by the error term, ε nj. Individual n chooses alternative j if the utility derived from this

alternative, Unj, is greater than the utility from alternative k, Unk, among J alternatives. The multinomial

logit model does not allow for variation in consumer preference because it assumes that the error terms

are identically and independently distributed (i.i.d). One way to allow for systematic heterogeneity in

preferences is by interacting attributes with socio-demographic variables. However, psychological

factors such as FCMs also play an important role in shaping preferences and purchase behaviour

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(Ashok et al., 2002; Honkanen et al., 2006; Daly et al., 2012; Hess, 2012; Grebitus et al., 2013; Lusk et

al., 2014; Costanigro et al., 2015). Variables representing these aspects cannot be treated like that of

socio-demographic variables because they are not fully observed by analysts (Ajzen, 2005). Thus, they

are referred to as latent variables. Scale-based questions using different level of disagreement or

agreement are usually employed to collect data on latent information. Responses of, for instance, "I

strongly agree" or "I strongly disagree" does not always induce a direct causality to the choices made

by consumers (Daly et al., 2012). Therefore, treating these variables as explanatory variables in choice

models would lead to a measurement error problem (Ben-Akiva et al. 2002b; Bolduc et al. 2005; Hess

and Beharry-Borg, 2012). An additional problem associated with this practice is the risk of endogeniety

bias because the latent variables are possibly correlated with unobserved factors (Hess and

Stathopoulos, 2013).

One way of tackling these problems is to use an ICLV model where latent variables are

incorporated as dependent variables instead of explanatory variables (Ben-Akiva et al., 1999, 2002b;

Daly et al., 2012). Attitudes of individuals can be influenced by their experience as well as socio-

demographic situations which can be observed by analysts (Ben-Akiva et al., 1999). This implies that

one can form a causal relationship between latent variables as dependent ones and other observed

factors as explanatory variables through what is called a structural equation formulation. A latent

variable is usually represented by different statements in a questionnaire and these statements are

referred to as indicators. Responses to the indicators are recorded using a range of scales from, for

example 1 to 5 or 1 to 7, as appropriate. A relationship between latent variables and responses to the

indicators can be formed through a measurement equation specification. On the other hand, the latent

variables combined with observable factors such as socio-demographics can affect individuals' choice

behaviour. This effect can be incorporated into the utility specification of the choice model. The unique

feature of the ICLV model is that it enables analysts to simultaneously estimate coefficients for the

structural equation, measurement equation as well as the choice model components (Bolduc et al.,

2005; Daly et al., 2012). A graphical representation of the ICLV model structure is shown in figure 1.

We follow Daly et al., (2012) in the presentation of the ICLV model. We refer the reader to this

paper for a more detailed specification of this model. Equation (1) can be reformulated to accommodate

the elements of the ICLV model.

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Figure 1. Graphical representation of the ICLV model

ε

v

Indicators (I) Attributes (X)

Choices (y)

Utility (U)

Latent Food ChoiceMotives (Z)

Consumer characteristics such as age and gender

(W)

Observable variables

Unobservable variables

Measurement equations

Structural equations

Random disturbance terms

Unj=β Xnj+Y Z¿+vnj (2)

The parameter, Y , is the impact of the latent variable, Z¿, on the utility of choosing alternative j. vnj is

the error term with i.i.d. extreme value distribution. Different latent variables can be identified and

included in the ICLV model. They can be interacted with attribute levels (see e.g. Daly et al., 2012;

Hess and Beharry-Borg, 2012; O’Neill et al., 2014) or they can be directly inserted in the utility

specifications (see e.g. Kim et al., 2014; Mariel et al., 2015; Kassahun et al., 2016). In this study,

consumers' FCM is considered as one latent variable directly incorporated into the utility functions

representing the alternatives for the CF buns. It is not included in the utility function for the standard

buns for the purpose of model identification as well as to be able to specifically assess the impact of the

latent variable on the choices of the new insect-based products. The latent variable, i.e., the latent FCM,

can be specified as a linear function of socio-demographic characteristics:

Zn=b W n+❑n (3)

where b are parameters to be estimated representing the impact of consumer characteristics, W n, on the

latent variable, Zn. The last term, ❑n, denotes the random disturbance with standard normal distribution

with mean 0 and standard deviation, σ❑ . Equation (3) is the structural equation model. Now, given the

latent variable,Z¿, is incorporated into the utility function in equation (2), a likelihood function can be

specified by integrating it over the random component of the latent variable because the latent variable,

i.e. the latent FCMs, is unknown and not fully observed. In other terms, unobserved heterogeneity can

be captured by integrating the likelihood function over the latent variable through its disturbance term.

The maximization of the likelihood function gives the estimates of β, Y , and b. The last component of

the ICLV model is the measurement equation model by which a causal relationship between the latent

variable and the different indicators can be established. The latent FCMs are represented by eleven

indicators as shown in table 4 above.

I ¿ns=d s Zn+ε s (5)

15

where I ¿ns is the sth indicator given S indicators in total (s = 1,2,3,….,11). The parameters to be

estimated are represented by ds, and ε s is a random component in the measurement equation. Each

indicator in the latent FCMs is presented to consumers using 6-point Likert-scales from 1 = "No like

me at all" to 6 = "Very much like me". As discussed in Daly et al., (2012), such responses to the

indicators, i.e. to the FCMs, can be modelled using an ordered logit model because they have an

ordered nature with different threshold points (cut-points):

I ns={1if −∞< I n , s

¿ ≤ μ1

2if μ1< I n , s¿ ≤ μ2

3 if μ2< I n , s¿ ≤ μ3

4 if μ3< I n , s¿ ≤ μ4

5 if μ4< I n , s¿ ≤ μ5

6 if μ5< I n , s¿ ≤ ∞

(6)

The likelihood function for an observed response c for the sth indicator with s = 1, 2,3,….,11 and c = 1,

2,….,6 can be specified to estimate the parameters,ds. As indicated previously, the ICLV model enables

us to estimate the three components, i.e. the structural equation model, the choice model and the

measurement model simultaneously. This means one can specify a joint log-likelihood function by

combining the likelihood functions for the sequence of choices and for the observed responses for the

indicators of the latent variable, FCM.

The estimation of the joint likelihood function requires some important assumptions and

normalizations. First, the estimation of the threshold levels, i.e. μ1, μ2,μ3, μ4, μ5, requires normalization

in terms of estimating five threshold levels out of six since we have scales from 1 to 6. This means, the

constraint μ0=−∞ and μ6=∞ should be set. Second, either a constant for each indicator function

should be estimated or it should be excluded and one of the threshold levels should be constrained to

zero. In this paper, we chose the latter approach. Third, identification of the ICLV model requires

normalization of the measurment equation model as the random component of the latent variable is

used to integrate the likelihood function. Daly et al., (2012) discussed two ways that serve this purpose.

One is the Ben-Akiva normalization and the other is the Bolduc normalization. In this study, we used

the latter and the standard deviation of the latent variable, Z, is fixed to 1. Fourth, the assumption of

16

mutual independence of the error components, v , ,∧ε, in equations (2), (3) and (5), respectively, is

maintained. The ICLV model was estimated using the R software (www.r-project.org) which enables to

incorporate the panel structure of the data into the model.

3. Results

3.1 Summary of responses to FCMs

The responses to the FCM questions are presented using a graphical representation in figure 2 below.

Most consumers respond "Very much like me" or "Like me" to the following statements of the FCMs

when asked how much this person is like you. These are: "She likes to vary her meal. She is curious

about new tastes", "She enjoys eating well. In her view every meal should be festive", "She feels proud

of her taste'. She believes that her food choices are very attractive", "She likes many different foods.

She is also a great taster", "She is grateful for her meal. In her view everything that is edible deserves

respect", and "She is very mindful of food. She wants to eat sensibly". These statements represent a

promotional oriented behaviour when considering consumers' food involvement (de Boer et al., 2007,

2013). On the other hand, the following statements of the FCMs receive responses of "Not like me at

all" or "Not like me" by some consumers. They include: "She eats because she has to. Meals are not

important to her", "Food does not bother her. She has no special demand on it", "She prefers an

ordinary meal. She is happy with existing foods she used to eat", "She is a big eater. She loves to have

plenty of palatable foods", and "she always stick to her usual food choices". These statement combined

would reflect prevention oriented behaviour (de Boer et al., 2007, 2013). Overall, the results show that

most consumers have a tendency of trying new foods and diversifying their foods. Thus, one can say

that they are promotional oriented consumers. de Boer et al., (2007, 2013) reported that such

consumers can have taste-oriented or reflection-oriented behaviour. This is in agreement with the

results of this study. That is, the FCMs statements to which most consumers respond to as "Very much

like me" or "Like me" signal that consumers would like to try new tastes and they reflect on being

proud of one's taste, sensible eating, and food being festive as well as respected. These results may

indicate consumers' inclination to the insect-based foods which can be described by the likelihood of

17

preparing the CF buns for their own consumption. When asked this using a 5-point scale from "Very

unlikely" to "Very likely", most of them respond "Very likely" or "Likely" as shown in figure 3.

18

Figure 2. Responses to statements on food choice motives

19

Curious

abou

t new

taste

s

Meals a

re no

t impo

rtant

Every m

eal sh

ould be f

estive

No speci

al de

mand o

n foo

d

Her foo

d cho

ices a

re att

ractiv

e

She i

s a gr

eat ta

ster

Happy

with ex

isting

food

s

Edible

deserves

respe

ct

She w

ants t

o eat

sensib

ly

Loves

to ha

ve pa

latab

le food

s

She s

tick t

o her

food ch

oice

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Very much like me Like me Somewhat like me A little like me Not like me Not like me at all

Indicators of Food choice motives

Perc

enta

ge o

f con

sum

ers

Very un-likely

Not likely Not sure Likely Very likely 0

10

20

30

40

50

60

70

80

102

9

24

71

How likely is it that you would prepare the buns at your home?

Num

ber

of c

onsu

mer

s

Figure 3. Likelihood of preparing CF-based buns

3.2 Estimation results

Table 5 presents the estimation results of the ICLV model. Estimation results from a random parameter

mixed logit model are presented in appendix II4. Focusing on the result from the choice model

component of the ICLV model in table 5, consumers attach positive preferences to bun products with

CF. The same is true for Fortified buns.

Table 5Estimation results from the ICLV model: choice model component

ICLV modelEstimate t-ratio

_ASC 0.88 1.10_high_CF 1.05 9.69_Medium_CF 1.71 8.92_Fortified 0.72 6.77_Cost -0.04 -13.32Y_latent variable 1.91 9.28Observations 1392Null log-likelihood -1529.3Overall log-likelihood -3103.5Log-likelihood for choice model component -1219.8Adjusted Rho-squared 0.202Notes: Additional parameter estimates from the ICLV model are presented in tables 6 and 7

4 The goodness-of-fit of the mixed model is better than the ICLV model according to the log-likelihood and Adjusted R-squared values. This is related to the fact that the former allows for preference heterogeneity across individuals in the data whereas the latter does this via a random latent variable with standard normal distribution.

20

The estimated coefficient for the latent variable is positive and highly significant meaning that it has a

positive effect on choices of the new CF buns. In other words, increasing the value of the latent

variable leads to an increase in utility for the CF buns.

Table 6Estimation results from the measurement equations component of the ICLV model

Indicator Parameter Estimate t-ratio She enjoys eating well. In her view every meal should be festive

d1 0.05 0.26

Threshold 1, μ1,1 -1.56 -5.15

Threshold 2,μ1,2 -1.09 -3.88

Threshold 3,μ1,3 -0.75 -2.76

Threshold 4,μ1,4 -0.37 -1.42

Threshold 5,μ1,5 0.93 3.39She likes to vary her meal. She is curious about new tastes

d2 0.37 1.96

Threshold 1, μ2,1 -2.43 -5.49

Threshold 2,μ2,2 -1.73 -4.74

Threshold 3,μ2,3 -1.35 -3.99

Threshold 4,μ2,4 -0.74 -2.36

Threshold 5,μ2,5 0.67 2.15She feels proud of her taste. She believes that her food choices are very attractive

d3 0.29 1.65

Threshold 1, μ3,1 -1.81 -5.13

Threshold 2,μ3,2 -0.92 -3.08

Threshold 3,μ3,3 -0.56 -1.95

Threshold 4,μ3,4 0.05 0.17

Threshold 5,μ3,5 0.99 3.27She likes many different foods. She is also a great taster

d4 0.25 1.33

Threshold 1, μ4,1 -1.94 -5.45

Threshold 2,μ4,2 -1.07 -3.61

Threshold 3,μ4,3 -0.92 -3.17

Threshold 4,μ4,4 -0.58 -2.06

Threshold 5,μ4,5 0.69 2.44She eats because she has to. Meals are not important to her

d5 -0.41 -2.00

Threshold 1, μ5,1 0.16 0.49

Threshold 2,μ5,2 1.39 3.90

Threshold 3,μ5,3 1.62 4.37

Threshold 4,μ5,4 1.80 4.69

21

Threshold 5,μ5,5 2.41 5.35Food does not bother her. She has no special demand on it

d6 -0.38 -2.12

Threshold 1, μ6,1 -0.69 -2.22

Threshold 2,μ6,2 0.14 0.46

Threshold 3,μ6,3 0.22 0.71

Threshold 4,μ6,4 0.46 1.50

Threshold 5,μ6,5 1.65 4.61She prefers an ordinary meal. She is happy with existing food she used to eat

d7 -0.43 -2.36

Threshold 1, μ7,1 -1.56 -4.56

Threshold 2,μ7,2 -0.46 -1.52

Threshold 3,μ7,3 -0.13 -0.45

Threshold 4,μ7,4 0.39 1.32

Threshold 5,μ7,5 1.05 3.28

She is very mindful of food. She wants to eat sensibly d8 0.38 2.06

Threshold 1, μ8,1 -3.02 -5.51

Threshold 2,μ8,2 -1.59 -4.56

Threshold 3,μ8,3 -1.06 -3.35

Threshold 4,μ8,4 -0.48 -1.58

Threshold 5,μ8,5 0.47 1.52She is grateful for her meal. In her view everything that is edible deserves respect

d9 0.15 0.80

Threshold 1, μ9,1 -2.47 -6.10

Threshold 2,μ9,2 -1.49 -4.88

Threshold 3,μ9,3 -1.31 -4.43

Threshold 4,μ9,4 -0.99 -3.51

Threshold 5,μ9,5 -0.45 -1.65She is a big eater. She loves to have plenty of palatable foods

d10 -0.45 -2.54

Threshold 1, μ10,1 -0.93 -2.86

Threshold 2,μ10,2 -0.00 0.000

Threshold 3,μ10,3 0.31 1.01

Threshold 4,μ10,4 0.62 1.95

Threshold 5,μ10,5 1.70 4.59She always sticks to her food choice

d11 -0.36 -1.99

Threshold 1, μ11,1 -1.39 -4.27

Threshold 2,μ11,2 -0.77 -2.56

Threshold 3,μ11,3 -0.36 -1.25

Threshold 4,μ11,4 0.21 0.74

22

Threshold 5,μ11,5 1.26 3.90

In connection with this, the results from the measurement equation model in table 6 reveal that the

latent variable has a positive impact on the indicators associated with the promotional oriented motive.

This is indicated by the positive coefficients estimated for d1, d2, d3, d4, d8 and d9, and the negative

coefficients estimated for d5, d6, d7, d10, and d11. This means higher values for the latent variable

increase the tendency to respond "very much like me" or "Like me" to the six statements: "She likes to

vary her meal. She is curious about new tastes", "She enjoys eating well. In her view every meal should

be festive", "She feels proud of her taste. She believes that her food choices are very attractive", "She

likes many different foods. She is also a great taster", "She is grateful for her meal. In her view

everything that is edible deserves respect", and "She is very mindful of food. She wants to eat sensibly".

The results reinforce the finding that consumers are promotional oriented reflecting their tendency to

try new foods and diversify their source of foods. The estimates of the thresholds levels show that the

values are increasing for all indicators from threshold 1 to 5 suggesting that the use of the ordered logit

model was appropriate in line with previous works by Daly et al., (2012); Hess and Beharry-Borg,

(2012) and O'Neill et al., (2014).

Considering the structural equation model of the ICLV, the explanatory variables that are found

to significantly affect the latent variables are age and gender. The variable urban is included even if it

is insignificant at conventional significance levels. The results in table 7 show that older people and

people from urban areas have a more positive value for the latent variable, i.e. promotional oriented

motivation. As opposed to this, being female has a negative value. Past research in food science based

on western consumers showed that females are less inclined to accept new insect-based foods (Schösler

et al., 2012; Hartmann et al., 2015; Verbeke, 2015; Caparros-Megido et al., 2014; Tan et al., 2016a, b).

Table 7Estimation results from the structural equation concerning socio-demographic components of the ICLV model

Estimate t-ratio b_Age 0.03 3.37b_female -0.70 -3.25b_urban 0.27 1.21

Table 8 reports marginal WTPs calculated using the ICLV estimates from table 5. Consumers

exhibit a positive WTP for the CF buns, and apparently they have a slightly higher WTP for the

23

Medium than the High CF level, though the 95% confidence intervals overlap. Consumers also have a

positive WTP for nutritionally fortified buns, but this appears to be of somewhat less importance than

the CF content.

Table 8 WTP estimates in KShs (95% confidence intervals in parenthesis)

WTP for ICLV estimates High CF buns 25.1*** (15.3 – 34.8)Medium CF buns 40.8*** (31.2 – 50.3)Fortified buns 17.1*** (12.7 – 21.5)

Notes: *** indicates significance level at 5% or lower.

4. Discussion and conclusion

The food choice and purchase decisions made by consumers can be driven by attitudinal factors (Ashok

et al., 2002; Hess, 2012). A large body of research has shown the importance of taking these factors

into account in food choice research to enhance our understanding of consumers' choice behaviour.

This provides important information for food marketing and food policy makers.

The challenge in dealing with such factors is how to treat them during data analysis because

they are not directly observed by analysts. Researchers in various fields have used approaches such as

regression models, latent class and cluster analyses as well as factor and principal component analyses

in order to investigate their roles in food choices. While such studies are important to understand the

link between consumers' latent behavioural orientations and their food choices, the treatment of

attitudinal responses as a direct measure of their attitudinal orientation is prone to measurement error

and endogeneity issues (Hess, 2012). Rather, such responses should be considered as indicators of

consumers' latent behavioural constructs, and analytical methods that recognize this are thus needed.

In this study, we used the ICLV model to link Kenyan consumers' FCMs to their preferences for

novel insect-based food products using data from an incentivized DCE. The results suggest that in

addition to the attributes of the bun products, consumers' FCMs might drive consumers' preferences for

these products. This is evidenced first by the result that the latent variable has a positive influence on

the indicators that represent a tendency to try new tastes as well as diversify foods. A positive effect of

the latent variable on consumers' preferences for the CF buns, i.e. Medium CF and the High CF buns, is

additional evidence. These results are in line with results from previous studies using the ICLV model

24

(e.g. Daly et al., 2012; Hess, 2012; O'Neil et al., 2014; Mariel et al., 2015; Yangui et al., 2016; Sok et

al., in press) which underlined that attitudes, motives as well as personality can be linked to consumer

preferences for the attributes of products, and accounting for these factors in choice modelling can

enhance our understanding of behavioural heterogeneity. The results in this study further show that

consumers' age is positively associated with the latent food choice motive. Given that consumers have

promotional oriented food choice motive, the tendency to try new food products as well as varied food

products increases as age increases. This is in contrast with previous studies in food science such as

Tuorila et al., (2001), Verbeke, 2015; and Caparros-Megido et al., (2016) who found that young people

are more likely to accept novel foods. Caparros-Megido et al., (2014), however, noted that older people

are slightly more willing to try new foods and Tan et al., (2016b) found that older people have higher

willingness to buy. Other authors such as McFarlane and Pliner, (1997) and Pliner and Salvy, (2006)

indicated that older people are less hessitant to consume new foods. Females are negatively related to

the latent FCM which implies that being female reduces the tendency to try new tastes and vary one's

food. Several studies based on consumers from developed countries in the food science literature

reported similar findings (see Schösler et al., 2012; de Boer et al., 2013; Hartmann et al., 2015;

Verbeke, 2015; Caparros-Megido et al., 2106; Tan et al. 2016b) that females are neophobic towards

eating insects. Whether this could be the same for women from developing countries is subject to

further research but our result seems to suggest so.

In sum, three implications can be drawn from this study. First, this study highlights the benefits

of using the ICLV model. The study shows how ICLV models can be used in food choice research to

link consumers' latent behavior to their observed choices. Several studies employed choice modeling to

study consumers' food choices and purchase behaviors but the ICLV models are not yet commonly

used for data analysis despite their ability to avoid measurement error as well as endogenity bias. Our

study contributes to this sparse literature by highlighting the benefits of using the ICLV model

especially for studies concerned with the relationship between consumers' motives, attitudes,

perceptions, as well as personality and food choice and purchase decisions. Our study makes a novel

contribution to this literature in an African context. Second, the results of the study can be useful for

product developers and marketers in the insect-as-food sector. The positive preferences of Kenyan

consumers attached to the insect component of the bun products as well as their behavioral inclination

towards appreciating new foods suggests that commercializaton of insects could be a viable sector in

25

Kenya. A number of private sectors show interest in establishing a small-to-large scale cricket

production sector in Kenya and the results reported in this study would be relevant for them in their

effort to start operation. Third, the results of this study can have policy implications in terms of using

insects for food to increase dietary diversity thus improving the nutritional status of especially poor

households in Kenya who have poor access to animal-source foods. Local government entities and

research institutions, donors as well as regional and international food policy makers should design and

implement policy packages to realize this.

As any other empirical studies, the results reported in this study cannot be generalized since

they could be case specific. Therefore, until other similar studies come up with similar findings, our

results should be seen cautiously. Asking attitudinal questions such as the FCM questions before or

after DCEs could potentially have differential impacts on consumers' responses. In this study,

consumers were asked the FCM questions after the DCEs. In this case, consumers could somehow

frame their responses according to their answers to the DCE questions. While it is not possible to

investigate this further with the current dataset, this issue would seem worthy of future research. In this

study, we have only one latent variable in the ICLV model and it is inserted in the utility functions as

one explanatory variable. Another appraoch could be to use factor analysis to extract different latent

variables from the indicators of the FCMs and interact them with each attribute level to see their

association with each of them. We tested this in our model but this led to model complication and

convergence problems. Future studies could perform such analysis to investigate the association of

different latent variables, which are extracted from the indicators of the FCMs, with the attribute levels

of the bun products.

Appendices

Appendix I. Sample characterstics Table I

Percentage summary of consumer characteristicsConsumer characteristics Percent Age in years

18 – 34 40.5 35 - 54 47.4 54- 64 9.5Above 64 2.6

26

Household size in number of persons 1 – 4 53.4 More than 4 46.6

Number of children (<18 years)0 – 2 66.4 More than 2 33.6

Monthly household income in KShs Less than 15000 5815000 – 50000 37>50000 5

GenderFemale 50.1 Male 49.9

Education Primary education 27.6 Secondary education 28.4 Tertiary education and above 19Other educationa 25

Notes: a no education, and drop outs from primary and secondary schoolsStd.dev. refers to standard deviation

The sample covers some population variation in terms of sociodemographic variable including age,

gender, household size and education level. Almost half of the consumers who participated in the

experiment are females. Most consumers are aged between 18 and 54 years whereas a few of them are

above 54. In terms of household size, almost 55% of the consumers have 1 to 4 members in their

household and the rest of them have more than 4 members. The highest completed education is either

primary or secondary education for 55% of the consumers and it is tertiary and above for 20% of them.

The rest have other education including drop outs from school.

Appendix II: Random parameter logit model estimation results

Table IIRandom parameter logit model estimation results

Estimate t-ratioMeanASC -1.30 -8.10High CF 1.22 3.27Medium CF 2.31 6.46Fortified 0.958 5.63Cost -0.06 -14.60Standard deviation High CF 3.71 9.89Medium CF 2.93 9.63Fortified 1.20 7.57observations 1392

27

Null log likelihood -1529.3Final log-likelihood -1109.7Adjusted Rho-squared 0.269

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