Journal of Agricultural Economics and Development Vol. 2(8), pp. 324-332, August 2013
Available online at http://academeresearchjournals.org/journal/jaed
ISSN 2327-3151 ©2013 Academe Research Journals
Full Length Research Paper
The influence of incentives in eliminating hypothetical
bias: Evidence from a choice-based conjoint
experiment for beef products in Iringa and Mbeya
Regions in Tanzania
S. W. Nandonde1*, E. E. Msuya1, L. A. Mtenga2, F. T. Kilima1 and R. Alphonce1
1
Department of Agricultural Economics and Agribusiness, Sokoine University of Agriculture, P. O. Box 3007,
Morogoro Tanzania.
2
Department of Animal Science and Production, Sokoine University of Agriculture, P. O. Box 3014, Morogoro, Tanzania.
Accepted 6 June, 2013
Consumer responses were observed for a within-sample comparison of preference and willingness-topay (WTP) estimates for tenderness, leanness, freshness and hygiene for beef products from finished
cattle and non-finished cattle (status quo). This comparison was conducted through two sessions of
repeated choice-based conjoint experiments (CBC), starting with a hypothetical choice-based conjoint
(HCBC) experiment that involved cheap talk only followed by a real choice-based conjoint (RCBC)
experiment that involved the actual purchase of experimental products with real money. Consumers
prefer more tender, less fatty, chilled beef and clean retailing premises, regardless of the choice
session; however, the estimated coefficients were not equal (p<0.001). The selection was motivated by
alternatives in HCBC where finished beef constituted 76% of all choices made. The selection of finished
beef dropped to 67% in RCBC where consumers were sensitive to the price and quality content of
alternative products. Consumers overestimate the WTP for hygiene in HCBC (p=0.014); however, there
are no significant differences in WTP estimates for other attributes. Therefore, it is concluded that
monetary incentives can reduce hypothetical choice bias and provide more trustworthy WTP estimates
for all attributes.
Key words: Beef, finishing, chilling, hygiene, tenderness, preferences, willingness to pay.
INTRODUCTION
The development and shaping of the beef industry
depend on customer desires and preferences for various
ranges of beef quality attributes. The importance of beef
consumers in the entire beef industry worldwide has
necessitated the measuring of information concerning
consumer preferences and willingness to pay (WTP) for
attributes of interest, such as freshness, tenderness, fat
content and safety or hygiene, using the conjoint
technique as an appropriate marketing research tool
because it reflects the real market scenario (Hensher et
al., 2005). The technique has recently been adopted in
market research in developing countries, including
Tanzania (Jabbar et al., 2010; Nandonde et al., 2013a).
Intuitively, conjoint experiments have suffered from
consumer bias to the extent of losing financial meaningful
to marketers particularly when the experiment is basically
too hypothetical for consumer to put cognitive effort into
their decisions as they make choices out of habit
(Dawnay and Shah, 2005; Lusk et al., 2008; Alfenes et
al., 2009). This issue has led to the reframing of choice
experiments by conducting field-oriented experiments
where real subjects can be found instead of the typical
laboratory experiments that involve mainly students (List
2011). In addition, such incentives as cheap talk scripts
and real purchase arrangements of the products provide
*Corresponding author. E-mail:
[email protected]. Tel:
+255787772222.
J. Agric. Econ. Dev.
chances for accruing more truthful information from
consumers (Lusk, 2003; Alfenes et al., 2006; Lusk and
Shroeder, 2004; Lusk et al., 2008; Genon et al., 2011).
The use of purchase arrangements with real money
provides a baseline to differentiate between hypothetical
and real choices (Carlsson and Martinsson, 2001; Ding,
2007). According to Alfenes et al. (2006), using real
money in choice-based experiments might be favoured
because it reflects a common practise in most retail
markets whereby sellers post the price and consumers
choose which product to buy.
Nevertheless, the reframing of the choice session and
the extent of bias to CBC is relatively complicated
because in certain cases, no bias has been observed,
and in certain other cases, substantial differences have
been observed (Carlsson and Martinsson, 2001;
Chowdhury et al., 2009). These ambiguities could be
attributed to the type of product in question, the exposure
of respondents to choice experiments and the bargain
technique to reflect the rational decision in the
respondents’ choices (Carlsson and Martinson, 2001;
Chowdhury et al., 2009; Ginon et al., 2011; Cecchi and
Bulte, 2013). This issue has created the need for
researchers to understand the potential differences that
exist in hypothetical and real choice set ups (List, 2006).
This quest is conducted not only for the sake of
understanding the presence of choice bias and its
influence on the implicit price measures but also to find
the necessity of conducting such relatively expensive real
choice experiments in countries such as Tanzania where
beef consumers/respondents are not exposed to such a
methodology compared to ordinary surveys using
questionnaires. Previous conjoint studies from Tanzania
have been dedicated to findings regarding monetary
incentives (Alphonce and Alfenes, 2012; Nandonde et al.,
2013a; Nandonde et al., 2013b) while studies regarding
beef product in nearby countries mostly have been too
hypothetical to address the differences (Jabbar et al.,
2010). This paper is aimed at examining the presence of
hypothetical bias and its consequences on the
willingness-to-pay estimates for quality-improving beef
attributes of freshness, leanness, tenderness and
hygiene of retailing outlets in the Iringa and Mbeya
Regions in Tanzania (Figure 1).
MATERIALS AND METHODS
Econometric modelling of choice experiments
The conceptual foundation of choice experiments relies
on Lancaster’s theory of value, which proposes that
utilities for goods and services can be decomposed into
separate utilities for their characteristics or attributes
(Lancaster, 1966), and random utility theory, which
explains the dominance judgments made between a pair
of offering (Thurstone, 1927). Within this framework,
325
subjects (consumers) choose among alternatives
according to a utility function that comprises two
components (Equation 1).
U ni Vni ni ,
(1)
Where Uni is the utility obtained by individual n by
selecting alternative i from a finite set of J alternatives in
choice set C in situation t. Vni represents the systematic
component of the utility (the indirect utility), which is a
function of the attributes of an alternative [V ni = f (Xni)]; Xni
is the vector of alternative i. εni is the random term.
Therefore, the individual n will choose alternative i if it’s
utility is higher than that of its alternative k within the
same choice set; hence, his probability (P ni) is shown in
Equation 2.
Pni Pr obVni ni Vnj nk : k C
(2)
Various logit probabilistic models are used in analysing
choice experiment data, such as the hybrid conditional
logit models, the random coefficient models, the
hierarchical Bayes model and the latent class model,
depending on the extent of the model to reveal more
preference heterogeneity compared to ordinary or basic
probabilistic models that assume that consumers are
homogenous in their selection or preferences (Kallas et
al., 2007 and Ortega et al., 2010). The hybrid condition
logit model or the random effect logistic model are
preferred as basic models in handling repeated choice
data and allow flexibility through the interaction of the
subjects’ socio-economic characteristics through the
indirect utility function (Radder and le-Roux, 2005; Kallas
et al., 2007; Hole 2008).
Product alternatives and attributes for experimental
design
Our experiment was designed based on the on-going
situation on the ground, where most beef is sourced from
a local unimproved (non-finished) production system,
which was treated as the status quo. The assumption
was that both local and cross-breed cattle can be
improved to produce the same quality beef with respect
to tenderness; however, a difference might arise
regarding the possibility of having higher adipose fat
content from local cattle compared to crossbred cattle.
2
2
1
The full factorial of 2 × 3 × 5 = 180 choice combinations
was reduced to 10 (Table 1). With the exception of
hygiene of retailing outlet, attributes attached to beef
itself that count for over 85% of all attributes were
presented to consumers in their real forms using actual
beef products stored in a refrigerator and a visible photo
display of a whole carcass. The experiments employed
the prevailing market price for the status quo beef for the
Nandonde et al
326
Figure 1. Map of Iringa and Mbeya Regions with the specific study areas.
meat-on-bone (Mchanganyiko) cut basis, while the price
for finished beef was suggested on the same basis by
key informants who were prominent beef dealers and
cattle finishers in Iringa and Mbeya Regions.
Choice implementation and data analysis
Six experiments were conducted, specifically, one in
each rural area (Mafinga and Tunduma Townships) and
two in each urban centre (Iringa Municipal and Mbeya
City) in February and March 2012 (Figure 1). The
experiments were conducted in two sessions in one day
starting with HCBC, whereby the cheap talk technique
was used to ensure that consumers are sincere about
what they are supposed to do and avoid haphazard
responses (Lusk et al., 2007). The second session was
conducted for RCBC whereby in addition to a cheap talk
script; consumers were obliged to purchase one of the
ten choices made using their own money (Ding, 2007;
Nandonde et al., 2013a, Nandonde et al., 2013b). The
experiments were conducted on weekends (Saturday
and/or Sunday) and all sessions took 2 to 3 h with a 30
min break in between sessions. The filled-in choice forms
were submitted to the researcher by the respondents
before proceeding with the next session, and the subjects
were not aware of the upcoming session. Consumers
were given adequate time to concentrate on the choice
by not asking them about their socio-economic
background because this information was gathered after
the completion of the choice experiments. Choice data
J. Agric. Econ. Dev.
327
Table 1. Choice alternatives with attributes and attribute levels.
Choice alternatives for beef with attribute levels
Non-finished
Local finished
Crossbred finished
Chilled
Chilled
Chilled
Not Chilled
Not Chilled
Not Chilled
Attributes
1. Chilling
2. Adipose fat content
Medium
High
Medium
High
Low
Medium
3. Tenderness
Low
Medium
Medium
High
Medium
High
4. Hygiene of outlet
Clean
Not clean
Clean
Not clean
Clean
Not clean
5. Price (Tshs/kg)
4,500.00
5,000.00
5,000.00
5,500.00
6,000.00
6,500.00
Table 2. Parameter (utility) estimates from choice sessions and pooled data.
Choice
Chilled
Medium fat
High fat
Medium tender
High tender
Clean
Price (x1000Tshs)
Constant (ASC)
Choice experiment session
Hypothetical
Real
Coefficient
P-Value
Coefficient
P-value
0.431
0.000
0.331
0.000
0.419
0.000
0.423
0.000
-0.117
0.178
-0.066
0.457
0.229
0.002
0.159
0.020
0.444
0.000
0.37
0.000
0.752
0.000
0.417
0.000
0.171
0.000
-0.317
0.000
-2.69
0.000
0.23
0.412
Log likelihood*
Respondents (n)
Observations
-5588.58
308
9240
*Log likelihood Ratio test = -2[lnLpool – (lnLhypothetical+lnLreal)] = 146.97
-5724.8
308
9240
Preferences regarding product attributes and price
The utility model estimates for all attributes in two choice
Coefficient
0.378
0.415
-0.096
0.188
0.399
0.578
-0.074
-1.203
P-value
0.000
0.000
0.121
0.000
0.000
0.000
0.024
0.000
-11387
616
18480
.
were stored in spreadsheets and analysed using a panel
logistic regression in STATA 12 for utility model
estimation. The model estimates were used to calculate
the WTP and its standard error using the Jackknife
procedure with the nlcom command in STATA (Hole,
2007). Detailed information on respondents’ socioeconomic characteristics can be found in Nandonde et al.
(2013b). For the focus of the present paper we only
considered interaction of product attributes with
consumer’s gender, age, income and residence.
RESULTS AND DISCUSSION
Pool
sessions and the pooled (combined) data are shown in
Table 2. The preference directions for non-price attributes
were the same in all choice sessions, indicating that
consumers prefer chilled, medium adipose fat, medium
tender and high tender beef sold in clean premises,
regardless of whether the experimental set up was
hypothetical or real. This result also implies the existence
of one extreme favourite regarding eating and safety
attributes. This finding might be attributable to the
involvement of authentic consumers as opposed to
typical laboratory experiments where students or less
experienced consumers are involved in making choices
(List, 2011).
The market attribute (price) at hand had a positive and
a negative coefficient in hypothetical and real choices,
respectively, and both coefficients are significant (Table
Nandonde et al
328
Table 3. Choice responses for different beef alternative sources.
Statistic summary
Hypothetical
Observations
Choices made
% of choices made
Real purchase
Observations
Choices made
% of choices made
Source of cattle for the carcass
Local non finished
Local finished
Crossbred finished
3080
3080
3080
748
1187
1145
24.29
38.54
37.18
3080
1018
33.05
3080
1204
39.09
3080
858
27.86
Total
9240
3080
100.00
9240
3080
100.00
Precentage (%)
Choice session
Figure 2. Percentage of selected alternatives in two choice sessions with pooled data.
2). This result implies that beef product with higher prices
had a higher chance of being chosen in hypothetical
choices and that the price was affordable for most
consumers. Despite the directional similarity and
differences of non-price and price attributes, respectively,
the log likelihood ratio test shows that the magnitudes of
the estimated coefficients in two choice sessions were
not equal (p<0.05), thus rejecting the null hypothesis of
equal coefficients. This result is different from the findings
obtained by Carlsson and Martinnson (2001) regarding
public goods.
The direction of the coefficient of the alternative specific
constant (ASC) parameter (Table 2) does not have a
meaningful interpretation in our case; however, its
significance in HCBC implies that consumers made their
choices motivated by alternative sources rather than by
its quality contents. The ASC in connection with the
positive price coefficient in HCBC indicates that finished
beef was highly preferred to non finished beef. In HCBC,
choices made for beef from local finished cattle and
crossbred finished cattle outnumbered the status quo
(Table 3 and Figure 2). This result was different in RCBC,
where choices were made with respect to attributes and
consumers were sensitive to price (Table 2), as also
indicated by the sharp increase and decrease of the
percentage of choices made for the status quo and
crossbred finished cattle with a slight increase of finished
local cattle (Figure 2). This finding is in keeping with
reports from researchers (Ding, 2007; Lusk et al., 2008;
Miller et al., 2011; Cecchi and Bulte, 2013), suggesting
that consumers become more price sensitive and expose
their true behavioural experience in the choice of the
product and price bargaining under real market pressure.
Cheap talk has been found not to exert adequate
pressure on subjects to reveal their true preference for
price compared to when real money was involved. This
result is similar to the finding by Chowdhury et al. (2009).
In the context of preference direction, it is the price that
was adversely affected compared to other attributes that
remain almost constant. This result implies that the utility
of the price depends on the nature of the choice session
and consumers do not appear to be genuinely concerned
J. Agric. Econ. Dev.
329
Table 4. Implicit price (WTP) estimates for different sessions and the pooled data.
Variable
Attribute
Chilled
Medium fat
High fat
Medium tender
High tender
Clean outlet
1
WTP
2518
2453
-685
1339
2598
4396
Choice session
Hypothetical
1
se
P-value
WTP
733
0.001
1059
613
0.000
1335
634
0.280
-208
650
0.039
501
1009
0.010
1168
1225
0.000
1316
Real
se
211
372
264
203
227
241
Pool
P-value
0.000
0.000
0.430
0.014
0.000
0.000
1
WTP
5089
5589
-1287
2534
5374
7781
se
2298
2854
693
1098
2160
3475
P-value
0.027
0.050
0.063
0.021
0.013
0.025
WTP1, estimates are in Tanzania shillings.
Table 5. Comparison of WTP estimate differences.
II
Attribute
Chilled
Medium adipose fat
High adipose fat
Medium tender
High tender
Clean retail outlet
Difference (%)
I
57.98
45.58
69.63
62.58
55.04
70.06
Two sample t test
t value
P- value
1.9280
0.0562
1.5592
0.1175
0.6946
0.4876
1.2304
0.2189
1.3827
0.1673
2.4670
0.0139
III
Pooled t test
t value
P- value
0.3174
>0.05
0.1959
>0.05
0.3441
>0.05
0.3816
>0.05
0.3310
>0.05
0.4431
>0.05
I
Actual value difference for WTP estimates in HCBC and RCBC only. IIWTP estimate comparison for HCBC and RCBC only and as
independent treatments. IIIt test determined with the inclusion of pooled variance.
in no-purchase situations.
Willingness to pay
As expected, differences in coefficient estimates had a
direct impact on the estimated WTP (Table 4). The
decrease of the coefficients for attributes in RCBC had
automatically lowered consumers’ marginal WTP (Table
5). However, in view of the t test for pooled variance with
the assumption of independence of the choice session,
none of the estimated WTP differences of an attribute
was significant (Table 5), which implies a non WTP
overestimation. This finding is similar to findings by
Carlsson and Martinson (2001). By comparing two
sessions as two independent treatments without
considering the pooled variance, there was a different
WTP estimation for hygiene (p=0.0139), implying an
overestimation for the clean retail outlet attribute. This
finding is different from that of Carlsson and Martinsson
(2001) but is in line with other studies (Voelckner, 2006;
Lusk et al., 2007; Alfenes et al., 2009), suggesting that
consumers tend to overestimate their WTP in a
hypothetical
choice
scenario.
However,
the
overestimation of the clean retailing attribute in our case
might be due to a too imaginary presentation of the
hygiene attribute in the experimental set up because
there was no visible reference for a clean and unclean
outlet compared to other attributes that were directly
attached to beef and that were presented virtually in real
form through actual beef cuts (Nandonde et al., 2013a).
The log likelihood ratio test in a choice model with the
inclusion of socio-economic characteristics had shown
coefficient inequalities (P<0.05) and a bias toward
finished beef, as in the case when the analysis involves
attributes and price only (Table 6). There was a general
consistency in terms of the preference for chilled beef
exhibited by female and more educated consumers,
whereas urban residents had shown a preference for
high fat beef in the hypothetical choice session; however,
urban residents disfavoured the same choice in the real
choice sessions. The observation on interactive utility
estimates in real choices is similar to those addressed by
Nandonde et al. (2013b), which implies that some
behavioural differences are indeed contributed by
consumer characteristics and might affect the willingness
to pay for a specific attribute.
CONCLUSIONS
There is a choice bias in HCBC; however, this bias does
not affect all estimated WTPs. Socio-economic
characteristics of respondents contribute to the firmness
Nandonde et al
330
Table 6. Choice estimates with socio-economic characteristics.
Variable
Choice
Chilled
Medium fat
High fat
Medium tender
High tender
Hygiene
Chilled x Age
Chilled x Gender
Chilled x Education
Chilled x Income
Chilled x Residence
Medium fat x Age
Medium fat x Gender
Medium fat x Education
Medium fat x Income
Medium fat x Residence
High fat x Age
High fat x Gender
High fat x Education
High fat x Income
High fat x Residence
Medium tender x Age
Medium tender x Gender
Medium tender x Education
Medium tender x Income
Medium tender x Residence
High tender x Age
High tender x Gender
High tender x Education
High tender x Income
High tender x Residence
Hygiene x Age
Hygiene x Gender
Hygiene x Education
Hygiene x Income
Hygiene x Residence
Price (x 1000 Tshs)
ASC
Log likelihood*
Participants
Observations
Hypothetical
Coef.
P>|z|
0.208
0.085
0.354
0.017
0.090
0.594
0.264
0.079
0.162
0.309
1.058
0.000
-0.106
0.253
0.355
0.000
0.205
0.043
-0.225
0.025
0.117
0.238
-0.012
0.913
-0.320
0.002
-0.076
0.515
0.029
0.798
0.395
0.001
-0.011
0.927
-0.451
0.000
-0.475
0.000
0.136
0.299
0.378
0.003
0.183
0.095
0.086
0.422
-0.020
0.866
0.058
0.626
-0.269
0.022
0.308
0.007
0.122
0.271
0.212
0.089
0.128
0.295
-0.185
0.127
-0.267
0.004
0.075
0.405
0.040
0.693
-0.038
0.705
-0.293
0.003
0.169
0.000
-2.704
0.000
Real
Coef.
0.065
0.706
0.250
0.214
-0.081
0.435
-0.184
0.283
0.328
-0.153
0.142
-0.066
-0.012
-0.344
-0.019
-0.021
0.153
0.086
-0.516
0.119
-0.264
0.026
-0.229
0.034
0.022
0.039
0.134
-0.105
0.446
0.078
0.184
0.036
-0.042
0.052
0.054
-0.106
-0.322
0.239
-5545.8
308
9240
*Log likelihood Ratio test = -2[lnLpool – (lnLhypothetical+lnLreal)] = 98.29
of their decisions. RCBC can eliminate bias and exhibit
the true preference for both price and non-price
Pool
P>|z|
0.587
0.000
0.133
0.140
0.608
0.000
0.045
0.002
0.001
0.122
0.145
0.531
0.909
0.003
0.867
0.849
0.187
0.447
0.000
0.343
0.031
0.811
0.029
0.773
0.850
0.736
0.239
0.343
0.000
0.523
0.131
0.695
0.638
0.604
0.587
0.273
0.000
0.395
-5680.91
308
9240
Coef.
0.133
0.517
0.159
0.233
0.038
0.737
-0.142
0.316
0.261
-0.184
0.127
-0.039
-0.161
-0.204
0.005
0.181
0.073
-0.171
-0.482
0.125
0.042
0.101
-0.071
0.005
0.036
-0.114
0.217
0.008
0.320
0.101
-0.002
-0.113
0.015
0.043
0.009
-0.196
0.000
-1.193
P>|z|
0.116
0.000
0.176
0.024
0.733
0.000
0.028
0.000
0.000
0.009
0.065
0.600
0.026
0.013
0.950
0.022
0.375
0.034
0.000
0.165
0.632
0.187
0.339
0.955
0.662
0.158
0.007
0.917
0.000
0.243
0.980
0.080
0.809
0.546
0.900
0.004
0.019
0.000
-11325
616
18480
.
attributes. Further consumer behavioural studies should
be conducted by employing advanced analytical models,
J. Agric. Econ. Dev.
such as latent class and mixed logit models, to capture
more heterogeneity regarding consumers’ preferences
and willingness to pay.
ACKNOWLEDGMENTS
The authors wish to acknowledge the German Academic
Exchange Service (DAAD) in collaboration with the
International Livestock Research Institute (ILRI) for their
financial and advisory support.
REFERENCES
Alfenes F, Yue C and Jehsen H (2009). Cognitive
th
dissonance as a mean of reducing hypothetical bias. 4
Nodic Conference on behavioural and experimental
economics. October 31-31, 2009. Oslo, Norway.
Alfenes F., Guttormsen AG, Steine G and Kol-stad K
(2006). Consumer willingness to pay for the colour of
Salmon: Choice Experiment with real economic
incentives. Am. J. Agric. Econ., 88: 1050-1061.
Alphonce R, Alfnes F (2012). Consumer willingness to
pay for food safety in Tanzania: an incentive-aligned
conjoint analysis. Int. J. Cons. Stud., 36: 394-400.
Carlsson F, Frykblom P, Lagerkvist C (2007).
Preferences with and without price: Does the price
attribute affect behaviour in stated preference survey?
Env. Resource Econ., 38: 155-164.
Carlsson F, Martisnson P (2001). Do hypothetical and
actual marginal willingness pay differ in Choice
experiments? J. Environ. Econ. Manage., 14: 179-192.
Checci F, Bulte E (2013). Does market experience
promote rational choices? Experimental evidence from
rural Ethiopia. Econ. Dev. and Cult. Change., 61(2):
407-429.
Chowdhury S, Meenakshi J, Tomlins K, Owori C (2009).
Are Consumers in Developing Countries Willing-to-Pay
More for Micronutrient-Dense Biofortified Foods?
Evidence from a Field Experiment in Uganda:
Contributed Paper prepared for presentation at the
International Association of Agricultural Economists
Conference, Beijing, China, August 16-22, 2009.
[www.research4development.info/PDF/.../HarvestPlus_
Working_Paper_3.pdf] visited on 25.06.2010.
Dawnay E, Shah H (2005). Behaviour economics: Seven
principals for policy makers. New Economic
Foundation.
http://www.i-re.org/fiche-analyse97_en.html.
Ding M (2007). An incentive align mechanism for conjoint
analysis. J. Market. Res., 44: 214-223.
Genon E, Chabanet C, Combris P, Issanchou S (2011).
Are decision in real choice experiment consistent with
reservation prices elicited with BDM auction? The case
of French baguettes. Food Quality and Preference.
Accepted manuscript.
Hensher D, Rose J, Greene W (2005). Applied Choice
Analysis A Primer. Cambridge, University Press.
331
Hole AR (2007). A comparison of approaches to
estimating confidence interval for willingness to pay
measures. Health Econ., 16: 827-840.
Hole AR (2008). Modelling heterogeneity in patients’
preference for the attributes of a general practiotioner
appointment. Health Econ., 27: 1078-1094.
Jabbar M, Admassu S (2009). Assessing consumer
preference for quality and safety attributes of food in
the absence of official standards: The case of beef in
Ethiopia. Contributed paper prepared for presentation
at the International Association of Agricultural
Economists Conference, Beijing China, August 16-22,
2009.
Jabbar M, Baker D, Fadiga M (2010). Demand for
livestock products in developing countries with focus on
quality and safety attributes: Evidence from Asia and
Africa. ILRI Research Report 24. Nairobi Kenya., p 168.
Kallas Z, Gomez-Limon J, Arriazza M (2007). Are citizens
willing to pay for agricultural multifunctionality? Agric.
Econ., 36: 405-419.
Lancaster K (1966). A new approach to consumer theory.
J. Pol. Econ., 74: 132-157.
List JA (2006). The behaviouralist meets the market:
Measuring Social preferences and reputations effects
in actual transaction. J. Political Econ., 114: 1.
List JA (2011). Why should economist conduct field
experiments and 14 tips for pulling one-off. J. Econ.
Perspectives, 25(3): 3-16.
Lusk J (2003). Effect of cheap talk on consumers’
willingness to pay for golden rice. Am. J. Agric. Econ.,
85: 840-856.
Lusk J, Schroeder T (2004). Are Choice Experiments
Incentive Compatible: A test with quality differentiated
beef steaks. Am. J. Agric. Econ., 86: 467-482.
Lusk JL, Fields D, Prevatt W (2008). An incentive
compatible conjoint ranking mechanism. Am. J. Agric.
Econ., 90(2): 487-498.
Lusk JL, McLaughlin L, Jaeger SR (2007). Stratergy and
response to purchase intension questions. Market Lett.,
18: 31-44.
Miller KL, Hofstetter R, Krohmer H, Zhang JZ (2011).
How should consumers’ willingness to pay be
measured? An Empirical comparison of the state-ofthe-art approaches. J. Marketing Res., 48(1): 172-184.
Nandonde SW, Msuya EE, Mtenga LA (2013b).
Assessment
of
the
influence
of
consumer
characteristics on the choice of beef quality attributes.
J. Agric. Econ. Dev., 2(3): 111-119.
Nandonde SW, Msuya EE, Mtenga LA, Issa-Zakaria A
(2013a). Assessment on consumer preference and
willingness to pay for finished and chilled tender beef in
Southern Highland Regions of Tanzania. Livestock
Res.
Rur.
Dev.,
25:
5.
http://www.lrrd.org/lrrd25/1/nand25005.htm.
Ortega D, Wang H, Wu L (2010). Modeling Heterogeneity
in Consumer Preferences for Select Food Safety
Attributes in China. Selected Paper prepared for
Nandonde et al
332
presentation at the Agricultural and Applied Economics
Association 2010 AAEA, CAES, and WAEA Joint
Annual Meeting, Colorado, July 25-27, 2010.
Radder L, le Roux R (2005). Factors affecting food
choice in relation to venison: A South African example.
J. Meat Sci., 71: 583-589.
Thurstone I (1927) A law of comparative judgement.
Psychol. Rev., 34: 273-286.
Voelckner F (2006). An empirical comparison of methods
for measuring consumer willingness to pay. Market
Lett., 17: 137-149.