Food Protection Trends - January/February 2018 - 40

Heuristic Information Processing Behavior was somewhat
greater among urban respondents (P = 0.018); however,
mean scores indicated that agreement with items measuring
heuristic processing was average (score range 4-20, 12 =
neutral). No other group or interaction differences were
found. In contrast, urban respondents also scored slightly
above the mean on Systematic Information Processing
Behavior (P = 0.001) and there was an interaction with
Milk Preference (P = 0.021). Urban unpasteurized and
rural pasteurized milk consumers had higher scores
within their respective residence groups. For Systematic
Information Processing Behavior, scores ranged from
8-20, with 14 = neutral.
Three constructs were used to predict the behavioral
outcome of the TPB portion of the Mental Models of
Milk Product Consumption: Behavioral Subjective Norm,
Perceived Behavioral Control and Attitude (Fig. 1).
Behavioral Subjective Norm differed by Residence (P =
0.003), with no other differences noted. As was the case
for the Informational Subjective Norm, the type of milk
products consumed was influenced by family members and
friends, and the effect was significantly higher for those in
the urban residence group (P < 0.001). The indication is
that those in the urban group perceived that they had the
support of significant others to obtain and consume the
type and amount of milk they preferred. Other differences
were not found.
Attitude toward milk consumption was measured by
respondents' beliefs and evaluation toward AttitudeConcerns, Attitude-Responsibility, and Attitude-Benefits
(Table 5). Attitude-Concerns differed by Residence (P =
0.013) and by Milk Preference (P < 0.001), and there was an
interaction (P = 0.027). Unpasteurized milk consumers felt
more concern about issues that lead the public to be cautious
of an unpasteurized food. In general, residents in urban areas
shared the concern. There was no interaction for AttitudeResponsibility, but differences were found for Residence
(P < 0.001) and Milk Preference (P = 0.011). Urban
residence and pasteurized milk consumers were more likely
to share attitudes related to responsible practices related to
unpasteurized milk consumption. Attitude-Benefits differed
only by Milk Preference (P < 0.001), with unpasteurized
milk consumers agreeing to the benefits of their choice of
milk, in contrast to pasteurized milk consumers.
Discriminant analysis (Tables 6 and 7)
All continuous-scaled variables from the RISP and
TPB models were included in the Discriminant Analysis.
Key distinguishing variables, defined as standardized
canonical discriminant function coefficients greater than
0.6, were Attitude-Responsibility and Attitude-Concerns.
Distinguishing variables (e.g., those with values between
0.3 and 0.6) were Affect, Current Knowledge, Knowledge
Threshold, Another Internet Sources, Information Seeking/

40

Food Protection Trends January/February

Avoiding, Heuristic Information Processing, AttitudeConcerns, Attitude-Responsibility, and Attitude-Benefits.
Coefficients for other continuous-scaled variables were not
considered in the construction of the Mental Models of Milk
Product Consumption.
Hierarchical linear modeling for the RISP model
(Table 6)
Three HLM models were computed with each one ending
with a RISP model behavioral outcome variable. All models
were significant (Information Seeking/Avoiding, P < 0.001,
R2 = 0.841; Heuristic Information Processing, P < 0.001, R2
= 0.771; and Systematic Information Processing, P < 0.001,
R2 = 0.559). Variables in the Individual Characteristics
construct of the RISP model (Fig. 1) were the first to be
entered into the HLM models. Hispanic/Latino ethnicity
differed for the Information Seeking/Avoiding Behavior
model (P = 0.003). Female gender (P = 0.021) and Age
18-29 years were significant for the Heuristic Information
Processing Behavior model, and Age 18-29 years was also
different for the Systematic Information Processing Behavior
model (P = 0.029). The "don't know" response option for
the variable, Relevant Hazard Experience, Self, was different
for the two information processing models (Heuristic, P =
0.001; Systematic, P = 0.016). Urban respondents were more
likely to respond that they don't know whether they have ever
had a foodborne illness. However, if they did know about
their hazard experience, urban respondents reported that
they had resolved the illness with self-care in the Information
Seeking/Avoiding Behavior model (P = 0.037). Other than
the "very liberal" response option, which was excluded
from model analyses, other response options for Political
Philosophy differed, especially for the Information Seeking/
Avoiding Behavior model and less so for the Heuristic
Information Processing Behavior model (Table 6). Urban
respondents tended to be more liberal and rural respondents
to be more conservative.
In the RISP construct, Perceived Hazard Characteristics
(Fig. 1), Institutional Trust differed in the Information
Seeking/Avoiding Behavior model (P < 0.001), whereas
in the Heuristic Information Processing Behavior model,
both Risk Judgment (P = 0.015) and Institutional Trust
differed (P = 0.005). For both models, urban respondents
scored higher on the scale than did Rural respondents,
indicating greater trust in governmental institutions and
better judgment about the risks associated with consuming
unpasteurized milk products. When asked about their
choice to consume unpasteurized milk products, both
pasteurized and unpasteurized milk consumers differed for
the response choice "Strongly Disagree" for the Systematic
Information Processing Behavior model. However, consumers
of unpasteurized milk products were more likely to mark
the "strongly disagree" option, whereas the pasteurized milk
product consumers were the least likely to choose this option.



Table of Contents for the Digital Edition of Food Protection Trends - January/February 2018

Assessing the Usage of Food Thermometers at American University Football Tailgates
An Assessment of Potential Heavy Metal Contaminants in Bivalve Shellfish from Aquaculture Zones along the Coast of New South Wales, Australia
Mental Models of Pasteurized and Unpasteurized Milk Product Consumption in the United States
Sanitary Carcass Dressing and Food Safety Practices in South Central U.S. Small and Very Small Establishments Manufacturing Fresh and Not-Ready-to-Eat Pork Products
Beyond the Bio - Joe Frank
PDG Highlight The Dairy Safety & Quality Professional Development Group
General Interest Paper The Role of the European Food Safety Authority (EFSA) in the Fight against Antimicrobial Resistance (AMR)
Industry Products
Coming Events
Food Protection Trends - January/February 2018 - Cover1
Food Protection Trends - January/February 2018 - Cover2
Food Protection Trends - January/February 2018 - 1
Food Protection Trends - January/February 2018 - 2
Food Protection Trends - January/February 2018 - 3
Food Protection Trends - January/February 2018 - 4
Food Protection Trends - January/February 2018 - 5
Food Protection Trends - January/February 2018 - 6
Food Protection Trends - January/February 2018 - 7
Food Protection Trends - January/February 2018 - Assessing the Usage of Food Thermometers at American University Football Tailgates
Food Protection Trends - January/February 2018 - 9
Food Protection Trends - January/February 2018 - 10
Food Protection Trends - January/February 2018 - 11
Food Protection Trends - January/February 2018 - 12
Food Protection Trends - January/February 2018 - 13
Food Protection Trends - January/February 2018 - 14
Food Protection Trends - January/February 2018 - 15
Food Protection Trends - January/February 2018 - 16
Food Protection Trends - January/February 2018 - 17
Food Protection Trends - January/February 2018 - An Assessment of Potential Heavy Metal Contaminants in Bivalve Shellfish from Aquaculture Zones along the Coast of New South Wales, Australia
Food Protection Trends - January/February 2018 - 19
Food Protection Trends - January/February 2018 - 20
Food Protection Trends - January/February 2018 - 21
Food Protection Trends - January/February 2018 - 22
Food Protection Trends - January/February 2018 - 23
Food Protection Trends - January/February 2018 - 24
Food Protection Trends - January/February 2018 - 25
Food Protection Trends - January/February 2018 - 26
Food Protection Trends - January/February 2018 - Mental Models of Pasteurized and Unpasteurized Milk Product Consumption in the United States
Food Protection Trends - January/February 2018 - 28
Food Protection Trends - January/February 2018 - 29
Food Protection Trends - January/February 2018 - 30
Food Protection Trends - January/February 2018 - 31
Food Protection Trends - January/February 2018 - 32
Food Protection Trends - January/February 2018 - 33
Food Protection Trends - January/February 2018 - 34
Food Protection Trends - January/February 2018 - 35
Food Protection Trends - January/February 2018 - 36
Food Protection Trends - January/February 2018 - 37
Food Protection Trends - January/February 2018 - 38
Food Protection Trends - January/February 2018 - 39
Food Protection Trends - January/February 2018 - 40
Food Protection Trends - January/February 2018 - 41
Food Protection Trends - January/February 2018 - 42
Food Protection Trends - January/February 2018 - 43
Food Protection Trends - January/February 2018 - 44
Food Protection Trends - January/February 2018 - 45
Food Protection Trends - January/February 2018 - 46
Food Protection Trends - January/February 2018 - 47
Food Protection Trends - January/February 2018 - 48
Food Protection Trends - January/February 2018 - 49
Food Protection Trends - January/February 2018 - 50
Food Protection Trends - January/February 2018 - 51
Food Protection Trends - January/February 2018 - Sanitary Carcass Dressing and Food Safety Practices in South Central U.S. Small and Very Small Establishments Manufacturing Fresh and Not-Ready-to-Eat Pork Products
Food Protection Trends - January/February 2018 - 53
Food Protection Trends - January/February 2018 - 54
Food Protection Trends - January/February 2018 - 55
Food Protection Trends - January/February 2018 - 56
Food Protection Trends - January/February 2018 - 57
Food Protection Trends - January/February 2018 - 58
Food Protection Trends - January/February 2018 - 59
Food Protection Trends - January/February 2018 - 60
Food Protection Trends - January/February 2018 - 61
Food Protection Trends - January/February 2018 - 62
Food Protection Trends - January/February 2018 - 63
Food Protection Trends - January/February 2018 - 64
Food Protection Trends - January/February 2018 - 65
Food Protection Trends - January/February 2018 - 66
Food Protection Trends - January/February 2018 - 67
Food Protection Trends - January/February 2018 - Beyond the Bio - Joe Frank
Food Protection Trends - January/February 2018 - 69
Food Protection Trends - January/February 2018 - 70
Food Protection Trends - January/February 2018 - PDG Highlight The Dairy Safety & Quality Professional Development Group
Food Protection Trends - January/February 2018 - General Interest Paper The Role of the European Food Safety Authority (EFSA) in the Fight against Antimicrobial Resistance (AMR)
Food Protection Trends - January/February 2018 - 73
Food Protection Trends - January/February 2018 - 74
Food Protection Trends - January/February 2018 - 75
Food Protection Trends - January/February 2018 - 76
Food Protection Trends - January/February 2018 - 77
Food Protection Trends - January/February 2018 - 78
Food Protection Trends - January/February 2018 - 79
Food Protection Trends - January/February 2018 - 80
Food Protection Trends - January/February 2018 - 81
Food Protection Trends - January/February 2018 - Industry Products
Food Protection Trends - January/February 2018 - 83
Food Protection Trends - January/February 2018 - 84
Food Protection Trends - January/February 2018 - 85
Food Protection Trends - January/February 2018 - 86
Food Protection Trends - January/February 2018 - 87
Food Protection Trends - January/February 2018 - Coming Events
Food Protection Trends - January/February 2018 - Cover3
Food Protection Trends - January/February 2018 - Cover4
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