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CHAPTER 6
EXPERIMENTAL STUDIES
6.1 OVERVIEW
This chapter provides a detailed description of the both the studies
and their associated results. Initially, the pretests are discussed, followed by a
description of the experiments in both the studies. Finally the results of the
experiments are examined and a discussion of the results is presented.
6.2 PRETESTS
Two pretests were conducted. The first pretest was done to identify
the products that were familiar and of interest to the study population. The
second pretest was done to identify the country that was considered proximal
to the target population. The second pretest was conducted to develop stimuli
for the experiments in Study 1. Both the pretests were conducted on forty four
MBA (first year) students in a large South Indian University. The average age
of the students was 20 and 56% of them were male. The questionnaire Q1 is
shown in Appendix 2.
6.2.1 Product Category
This pretest was conducted to identify products that were relevant
to the target population. A set of 10 products were selected for the pretest.
The selection was based on products used in extant green advertising studies
and popular products that used green advertisements in India (chosen from
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Table 4.4 in Chapter 4). The ten products were laundry detergent (Schuhwerk
& Lefkoff-Hagius 1995; Kong & Zhang 2013), shampoo (Chang 2011),
mobile phone (Paladino & Ng 2013), mineral water (Grimmer & Woolley
2012), jeans, laptop, skin whiteners, scooter, notebooks and wristwatch.
Consumer involvement scale (Traylor & Joseph 1984) - a six item seven-
point scale that is used to gauge consumers’ involvement across product
categories was used to measure consumer involvement with the selected
products (The scale Q1a is shown in Appendix 2). The results of the pretest
are shown in Table 6.1a. Mobile phones (M=17.45, S.D=7.949) and
wristwatches (M=15.93, S.D=5.699) were ranked high by the consumers.
Table 6.1a Results of pretest for product preferrences
Det
erge
nt
Sham
poo
Mob
ile
Jean
s
Lapt
op
Wat
erBo
ttle
Skin
whi
tene
r
Scoo
ter
Not
eboo
k
Wri
stw
atch
Mean 28.02 26.55 17.45 19.14 18.45 24.93 22.77 19.57 23.50 15.93
N 44 44 44 44 44 44 44 44 44 44
Std. Deviation
8.245 7.866 7.949 7.438 6.670 8.445 8.523 7.053 6.743 5.699
A t-test was also conducted to verify if there was any relationship between
gender and product preferences. The results of the t-tests are shown in Table
6.1b. The results show that there were no gender differences in the product
preferences. Based on this pretest, mobile phones and wristwatches were
chosen as the products for the experiments.
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Table 6.1b Results of independent samples t-test to test gender
difference in product preferrences
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t dfSig. *
(2-tailed)
MeanDifference
Std. Error Difference
95% Confidence Interval of
the Difference
Lower UpperDetergent Equal
variances assumed
.076 .784 .238 42 .813 .600 2.524 -4.494 5.694
Equalvariances not assumed
.241 41.889 .811 .600 2.493 -4.431 5.631
Shampoo Equalvariances assumed
1.420 .240 1.446 42 .156 3.400 2.352 -1.347 8.147
Equalvariances not assumed
1.475 41.946 .148 3.400 2.305 -1.251 8.051
Mobile Equalvariances assumed
.725 .399 .072 42 .943 .175 2.435 -4.739 5.089
Equalvariances not assumed
.074 40.911 .941 .175 2.359 -4.589 4.939
Jeans Equalvariances assumed
.408 .527 .333 42 .741 .758 2.276 -3.834 5.351
Equalvariances not assumed
.330 38.783 .743 .758 2.298 -3.890 5.406
Laptop Equalvariances assumed
.444 .509 .333 42 .740 .633 1.899 -3.199 4.466
Equalvariances not assumed
.326 35.889 .746 .633 1.940 -3.303 4.569
100
Table 6.1b (Continued)
Levene's Test for
Equality of Variances
t-test for Equality of Means
F Sig. t dfSig. *
(2-tailed)
MeanDifference
Std. Error Difference
95% Confidence Interval of
the Difference
Lower UpperWater_Bottle Equal
variances assumed
.250 .619 1.277 42 .209 3.242 2.538 -1.881 8.364
Equalvariances not assumed
1.284 41.320 .206 3.242 2.525 -1.856 8.339
Skin_whitener Equalvariances assumed
.004 .952 -.616 42 .542 -1.600 2.599 -6.846 3.646
Equalvariances not assumed
-.616 40.772 .541 -1.600 2.596 -6.844 3.644
Scooter Equalvariances assumed
4.590 .038 .324 42 .747 .700 2.158 -3.655 5.055
Equalvariances not assumed
.339 38.753 .737 .700 2.068 -3.483 4.883
Notebook Equalvariances assumed
1.025 .317 -.669 42 .507 -1.375 2.055 -5.522 2.772
Equalvariances not assumed
-.682 41.976 .499 -1.375 2.015 -5.442 2.692
Wristwatch Equal variances assumed
.044 .835 .975 42 .335 1.683 1.727 -1.801 5.168
Equalvariances not assumed
.963 38.160 .342 1.683 1.748 -1.855 5.221
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6.2.2 Geographical Proximity of Environmental Issues
Students rated the relevancy of environmental issues based on
geographical (spatial) proximity using seven point scales from ‘relevant to
me’ to ‘irrelevant to me’ (Chang 2012) (The scale Q1b is shown in
Appendix 2). Table 6.2 shows the results of this pretest.
It can be seen that issues related to South India (M=1.93
S.D=1.676) were considered highly relevant when compared to North India
and other countries (China, USA and Australia). Environmental issues in
Australia were considered least important (M=4.30 S.D=1.960).
Table 6.2 Pretest for geographical proximity
China North_India USA South_India AustraliaMean 3.75 2.57 3.98 1.93 4.30
N 44 44 44 44 44
Std. Deviation 1.754 1.485 1.886 1.676 1.960
6.3 STUDY 1: EXPERIMENT 1: TEMPORAL AND
GEOGRAPHICAL FRAMING OF THREAT
This study was conducted to evaluate the effect of temporal and
geographical framing of threat on the PMT variables. The effect of the PMT
variables on involvement and the subsequent influence of involvement on
attitudes and purchase intention was also evaluated. The stimuli were
developed based on the products chosen using the pretests i.e mobile phone
and wristwatch. The experiment was also used to assess the stimuli, content
and face validity of the instrument.
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6.3.1 Experimental Design
A 2 (temporal proximity of health threat: day vs. year) x 2
(geographical proximity of the health threat: local vs. global) between
subjects experimental design was utilized to investigate the hypotheses. This
resulted in four possible combinations of the factors. Fifty nine valid
responses were obtained from MBA students from a large South Indian
University (39 % male, median age=22). The students were randomly
assigned to the four possible conditions for the mobile phone stimuli.
Similarly, forty one valid responses were obtained from MBA students from a
large South Indian University (62 % male, median age=23). The students
were randomly assigned to the four possible conditions for the watch stimuli.
Data collection was through a paper and pencil questionnaire (Q2
shown in Appendix 3). Students first filled the questionnaire containing the
major dependent variables. Next, they were asked to answer a filler
questionnaire which asked them to describe their favourite celebrity. This was
a filler task designed to distract the respondents from associating the
personality variables question with the next questionnaire. On completion,
they filled counterbalanced questionnaires containing the questions for the
variables related to the environment (environmental concern, environmental
knowledge) and the personality variable (consideration for future
consequences).
6.3.2 Stimuli
A total of four print advertisements were developed for the four
cells: temporally proximal threat and geographically proximal threat;
temporally proximal threat and geographically distant threat; temporally
distant threat and geographically proximal threat; temporally distant threat
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and geographically distant threat. The advertisements also listed the
environment friendly features of the wristwatch or the mobile phone.
In the temporal threat conditions, the advertisement distinguished
between a day vs. year. Either day or year was used as the reference to specify
the number of people who suffer from respiratory diseases and cancers due to
toxins from either plastic waste (in case of watch) or electronic waste (in case
of mobiles) (Chandran & Menon 2004). In terms of geographical proximity
“India” was used to denote proximity and “world” was used to denote
geographically distant threats (Chang 2012). Although Australia was shown
as the location that is most geographically distant in the pretests, it was not
meaningful to represent an equivalent message that presented a threat in
Australia as part of the stimuli. Hence a more generic “world” was used to
denote a geographically distant threat. The ad was similar to those appearing
in the current Indian print media. The layout and format were not distinct and
contained basic information about the mobile phone or watch. The ad showed
a photograph of the mobile phone/watch and contained a description of the
product features and its environmental attributes. The mobile phone ad
contained a description of the display unit, talk time, standby time, OS and
memory. The mobile phone ad also specified that it contained recyclable
materials and avoided toxic components. The wristwatch ad contained details
about the materials and components used. The ad for the wristwatch also
highlighted its biodegradability.
6.3.3 Treatment Validity
The four print advertisements were analyzed by an expert panel to
assess if it contained the necessary variations in the temporal proximity and
the geographical proximity. This panel consisted of 3 marketing professors
and 2 Phd students who were familiar with marketing literature on fear
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appeals, PMT and temporal framing. The panel suggested changes to the
presentation format and the final version of the advertisements are shown in
Appendix 4 (Figure A4.1, Figure A4.2, Figure A4.3, Figure A4.4, Figure
A4.5, Figure A4.6, Figure A4.7 and Figure A4.8)
6.3.4 Manipulation Checks
Manipulation checks were conducted by adding questions to verify
if the manipulations were successful. To this end, two questions were
included in the questionnaire. The temporal manipulation was checked asking
the question: “How long do you think it takes for plastic waste pollution to
cause respiratory diseases or cancer?”. The response to this item was
measured using seven point semantic scales anchored from 1 = the near future
and 7 = Distant future. Geographical manipulation was checked asking the
question: “Is the issue of plastic pollution relevant to your country?”. The
response to this item was measured using seven point semantic scales
anchored from 1 = Relevant to my country and 7 = Irrelevant to my country.
6.3.5 Dependent Variables
The study has mostly used previously validated instruments to
measure the constructs. The dependent variables include perceived severity,
perceived vulnerability, perceived self-efficacy, perceived response-efficacy,
fear, message involvement, attitude towards the advertisement, attitude
towards the brand and purchase intention. The other variables related to
individual characteristics included environmental concern, objective
environmental knowledge and consideration for future consequences. The
sources and scales are described in detail below and shown in Appendix 3 as
discussed previously.
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6.3.5.1 Protection motivation theory variables
The PMT variables were adapted from Milne et al (2002). They are
described in detail below:
Perceived severity
Perceived severity was measured using a four item seven point
scale where 1 = Strongly Disagree and 7 = Strongly Agree. Participants were
asked to indicate their responses on the following statements: “I believe that
plastic waste / e-waste in the environment may cause severe health issues like
respiratory diseases and cancer”, “I believe that plastic waste / e-waste
pollution is a serious threat to human health”, “I do not think that plastic
waste /e-waste will affect our health”, “I believe plastic waste / e-waste
pollution is a significant problem”. In the case of wristwatch “plastic waste”
was used and “e-waste” was used with mobile phone stimuli.
Perceived vulnerability
Perceived vulnerability was measured using a three item seven
point scale where 1 = Strongly Disagree and 7 = Strongly Agree to determine
the participants’ susceptibility to the threat. Participants were asked to
indicate their responses to the following statements: “I am worried that I
might get respiratory illness or cancer because of plastic waste / e-waste”,
“Plastic waste /E-waste pollution is a big concern for me as it might affect my
health”, “It is possible that I am at risk of being affected by respiratory illness
or cancer because of plastic waste / e-waste”.
Response efficacy
Response efficacy was also measured using a three item seven
point scale where 1 = Strongly Disagree and 7 = Strongly Agree to determine
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whether participants’ believed if purchasing recyclable / biodegradable
products averted the threat. Participants rated their responses on the following
statements: “Buying biodegradable / recyclable products is highly effective in
preventing diseases due to plastic/e-waste pollution”, “Buying biodegradable /
recyclable products will significantly lower the risk of being affected by
respiratory diseases and cancer”, “Buying biodegradable products is an
effective method of reducing respiratory illness and cancer in humans”. In the
case of mobile phone, the word “biodegradable” was changed to “recyclable”.
Self efficacy
Self efficacy was measured using a three item seven point scale
where 1 = Strongly Disagree and 7 = Strongly Agree to determine whether
participants’ believed if they were capable of averting the threat. Participants
rated their responses to the following statements: “It would be easy for me to
identify a biodegradable watch/ mobile made of recycled materials”, “It is not
difficult for me to check if the watch contains plastic or not / mobile is made
of recycled materials or not”, “I can easily identify a biodegradable watch /
mobile made of recycled materials”.
Fear
Fear is an affective response to the threat levels presented in the
stimuli. Participants rated their emotions (the extent to which they
experienced each of the emotions afraid, scared, fearful, anxious and worried)
while viewing the advertisement on a seven item seven point Likert Scale (1 =
Strongly Disagree and 7 = Strongly Agree). This measure is similar to fear
measures used in previous studies that employ the protection motivation
theory and the scale had a high internal reliability (Hartmann et al 2013).
107
6.3.5.2 Message involvement
Participants reported agreement with six statements (on a seven
point Likert scale) adapted from Cox & Cox (2001) : “I got involved in what
the advertisement had to say,” “The ad's message seemed relevant to me,”
“This ad really made me think”, “This ad was thought-provoking” , “The ad
was very interesting,” and “I felt strong emotions while reading this ad.” This
scale had a good internal reliability score in previous studies (Cox & Cox
2001; Cauberghe et al 2009).
6.3.5.3 Attitudes and Intentions
Attitude towards ad
Attitude towards the ad was measured by using three seven point
semantic differential scales: good/bad, pleasant/unpleasant, and
favorable/unfavorable ( =0.88) (Mackenzie & Lutz 1989).
Attitude towards the brand
Attitude towards the brand was measured by using three seven
point semantic differential scales: good/bad, pleasant/unpleasant, and
favorable/unfavorable ( =0.93) (Muehling & Laczniak 1988).
Purchase intention
Participants were asked to respond to three sets of bipolar
adjectives (unlikely-likely, definitely would-definitely would not, improbable-
probable) placed on seven point scales to indicate how likely they were to
purchase the advertised brand. This scale was also adapted from previous
research (MacKenzie et al 1986).
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6.3.5.4 Variables related to the environment and personality
Environmental concern
Participants’ environmental concern was measured using the scale
proposed by Schultz (2001). The scale requires the participants to rank their
environmental concerns from one to seven on sub-categories namely
biospheric concerns (plants, marine life, birds, and animals), altruistic
concerns (humanity, children, people in the country, future generations) and
egoistic concerns (me, my future, my health, my lifestyle).
Objective environmental knowledge
Objective environmental knowledge was measured using a set of
fifteen questions similar to the MEAK subscale on environmental knowledge
(Maloney et al 1975). The questions were based on combination of general
questions about environmental awareness (for instance, impact of climate
change, pollutants in batteries and CFLs) and issues specific to India (for
example, Bhopal disaster, maximum greenhouse emissions in India). Some of
the questions were taken from an online quiz (http://edugreen.teri.res.in/
explore/quiz/quiz.htm). The scale is in a quiz format and the correct answers
are summed to form the objective environmental knowledge score. Higher
scores reveal a high degree of factual knowledge about the environment and
vice-versa.
Consideration for future consequences (CFC)
Individual’s temporal orientation was measured using the
consideration of future consequences fourteen item scale (Joireman et al
2012). The scale has two components measuring the concern with future
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consequences and concern with immediate consequences. The consideration
for future consequences score was determined after recoding the immediate
items (3, 4, 5, 9, 10, 11, 12).
6.3.6 Results of Experiment 1
The experiment was conducted with the mobile phone and
wristwatch stimuli to evaluate the effect of temporal and geographical
framing of threat on the PMT variables. The effect of PMT variables on
involvement and the subsequent influence of involvement on attitudes and
intention was also evaluated. The experiment was also used to assess the
content and face validity of the instrument. The results are discussed below.
6.3.6.1 Manipulation check
Mobile phone stimuli
Temporal proximity manipulations were not successful as there was
no difference in the way participants evaluated the temporal proximity of the
threat. There was no significant main effect of the temporal proximity
manipulation (day vs. year) in both the proximal (M = 3.66) and distal (M =
3.87) conditions (F (1,57) = 0.224 ; p>0.5). The mean values and the ANOVA
tests are shown in Table 6.3a and Table 6.3b.
Table 6.3a shows that the mean values are very close in value. It
can also be seen from Table 6.3b that the temporal proximity did not have any
effect on the manipulation check variable. Geographical manipulations were
checked next.
110
Table 6.3a Experiment 1: Mean values of the manipulation check variable for temporal distance of threat (mobile phone stimuli)
MC_TIME
N Mean Std.Deviation
Std.Error
95%Confidence Interval for
Mean Minimum Maximum
Lower Bound
Upper Bound
Day 29 3.66 1.610 .299 3.04 4.27 1 6Year 30 3.87 1.814 .331 3.19 4.54 1 7Total 59 3.76 1.705 .222 3.32 4.21 1 7
Table 6.3b Experiment 1: One way ANOVA - manipulation check variable for temporal distance of threat (mobile phone stimuli)
ANOVAMC_TIME
Sum of Squares df Mean Square F Sig. Between Groups .660 1 .660 .224 .638Within Groups 168.018 57 2.948Total 168.678 58
Similarly geographical proximity manipulations were also not successful as
there was no significant main effect of the geographical proximity
manipulations as proximity manipulation (India vs. World) did not produce
any statistically significant effect in both the proximal (M = 2.37) and distal
(M = 1.90) conditions (F (1,57) = 2.552 ; p>0.05). The results of the
geographical manipulation checls are shown in Tables 6.4a and 6.4b.
111
Table 6.4a Experiment 1: Mean value of the manipulation check variable for geographical distance of threat (mobile phone stimuli)
Descriptives MC_GEOGRAPHY
N Mean Std.Deviation
Std. Error
95% Confidence Interval for Mean
Minimum MaximumLower Bound
Upper Bound
India 30 2.37 1.299 .237 1.88 2.85 1 6World 29 1.90 .939 .174 1.54 2.25 1 4Total 59 2.14 1.152 .150 1.84 2.44 1 6
Table 6.4b Experiment 1: One way ANOVA of the manipulation check variable for geographical distance of threat (mobile phone stimuli)
ANOVAMC_GEOGRAPHY
Sum of Squares df Mean
Square F Sig.
Between Groups 3.259 1 3.259 2.522 .118Within Groups 73.656 57 1.292Total 76.915 58
Watch stimuli
Temporal proximity manipulations were not successful for the
watch stimuli as there was no significant main effect of the temporal
proximity manipulation (day vs. year) in both the proximal (M = 4.05) and
distal (M = 3.55) conditions (F (1, 40) = 0.990; p>0.05). The results are
shown in Tables 6.5a, 6.5b.
112
Table 6.5a Experiment 1: Mean values of the manipulation check variable for temporal distance of threat (watch stimuli)
Descriptives MC_TIME
N Mean Std.Deviation
Std. Error
95% Confidence Interval for Mean
Minimum MaximumLower Bound
Upper Bound
Day 22 4.05 1.704 .363 3.29 4.80 1 7Year 20 3.55 1.504 .336 2.85 4.25 1 7Total 42 3.81 1.612 .249 3.31 4.31 1 7
Table 6.5b Experiment 1: One way ANOVA of the manipulation check variable for temporal distance of threat (watch stimuli)
ANOVA
MC_TIME
Sum of Squares
df Mean
Square F Sig.
Between Groups 2.572 1 2.572 .990 .326
Within Groups 103.905 40 2.598
Total 106.476 41
Similarly geographical proximity manipulations were also not successful as
there was no significant main effect of the geographical proximity
manipulations (India vs. World) in both the proximal (M = 1.96) and distal
(M = 2.24) conditions (F(1,40) = 0.414 ; p>0.05). Tables 6.5c and 6.5d show
these results.
113
Table 6.5c Experiment 1: Mean values of the manipulation check variable for geographical distance of threat (watch stimuli)
Descriptives
MC_GEOGRAPHY
N MeanStd.
DeviationStd.
Error
95% Confidence Interval for Mean
Minimum MaximumLower Bound
Upper Bound
India 25 1.96 1.306 .261 1.42 2.50 1 5
World 17 2.24 1.437 .349 1.50 2.97 1 5
Total 42 2.07 1.351 .208 1.65 2.49 1 5
Table 6.5d Experiment 1: One way ANOVA of the manipulation check variable for geographical distance of threat (watch stimuli)
ANOVA
MC_GEOGRAPHY
Sum of Squares
df Mean
Square F Sig.
Between Groups .767 1 .767 .414 .523
Within Groups 74.019 40 1.850
Total 74.786 41
A failed manipulation check in social psychology research is not of
great concern and does not indicate that the manipulation of the independent
variable failed (Sigall & Mills 1998). Therefore further analyses on the data
were conducted.
114
6.3.6.2 Scale reliability
Mobile phone stimuli
The internal consistency of the scales was assessed using Cronbach
. Table 6.6 below shows the reliability scores. Almost all the constructs
meet and exceed 0.6 – the rule of thumb criteria suggested by Nunnally
(Nunnally 1970) indicating that the instrument is reasonably reliable.
Although self-efficacy has a lower reliability score, most PMT studies report
such low score. Since > 0.5 is acceptable, the same measure was used and
the reliabilities are deemed acceptable. As this study was also used to evaluate
the scales, the scale was retained. Environmental knowledge is treated as a
single formative indicator and therefore reliability score was not calculated
for this measure as it is illogical to check correlations between the indicators
for such a construct (Chin 1998).
Table 6.6 Experiment 1: Reliability scores using mobile phone stimulus
Construct Cronbach Perceived severity 0.64Perceived vulnerability 0.88Response Efficacy 0.82Self Efficacy 0.52Message involvement 0.82Fear 0.89Attitude towards ad 0.81Attitude towards brand 0.90Purchase intention 0.96Environmental Knowledge Environmental concern 0.86Consideration for future consequences 0.85
115
Watch stimuli
Table 6.7 below shows the reliability scores for the watch stimuli.
Table 6.7 Experiment 1: Reliability scores using watch stimulus
Construct Cronbach
Perceived severity 0.57
Perceived vulnerability 0.90
Response Efficacy 0.88
Self Efficacy 0.41
Message involvement 0.82
Fear 0.89
Attitude towards ad 0.82
Attitude towards brand 0.94
Purchase intention 0.93
Environmental concern 0.89
Consideration for future consequences 0.78
It can be seen from Table 6.7 that almost all the constructs meet
and exceed 0.6 Nunnally’s rule of thumb (1970). The instrument is therefore
reasonable reliable. Self-efficacy has a lower reliability score and < 0.5 is
unacceptable. Therefore, this measure was not used for further analysis with
the watch stimulus.
6.3.6.3 Hypotheses tests of the effect of manipulations on PMT
variables
The hypothesized effect of temporal and geographical
manipulations on the PMT variables was analyzed using MANOVA or
MANCOVA as appropriate. After checking for missing data and outliers, it
116
was found that most of the PMT variables were negatively skewed.
MANOVA is “robust to violations of multivariate normality and to violations
of homogeneity of variance/covariance matrices if groups are of nearly equal
size” (Leech et al 2011). The dependent variables (perceived severity,
perceived vulnerability, fear, response-efficacy and self-efficacy) were
moderately correlated (0.27 – 0.49) and therefore there was no risk of
multicollinearity to pose a hindrance to conductiong MANOVA.
Mobile phone stimuli
Table 6.8a shows the distribution characteristics of the protection
motivation variables and Table 6.8b shows the group wise means. It can be
seen that most of the variables have a mean value that is closer to the highest
score on the scale i.e. 7. Perceived severity ranks high among the threat
appraisal variables with a mean value of 5.91.
Table 6.8a Experiment 1: Distribution characteristics of the protection motivation variables (mobile phone stimuli)
Variable Minimum Maximum Mean Std. DeviationPERC_SEV 4.00 7.00 5.91 0.73PERC_VUL 1.00 7.00 4.68 1.22RESP_EFFICACY 2.00 7.00 4.98 1.25SELF_EFFICACY 3.50 7.00 4.41 1.72FEAR 2.00 6.14 3.60 0.92
Table 6.8b below does not show much variation across the groups
either. The perceived severity and perceived vulnerability scores appear close
in almost all the conditions.
117
Table 6.8b Experiment 1: Group wise mean values of protection motivation variables for the mobile phone stimuli
Factor PerceivedSeverity
PerceivedVulnerability
Response Efficacy
Self Efficacy
Fear
Temporal proximity: Day
6.12 4.93 4.82 4.31 3.70
Temporal proximity: Year
5.72 4.45 5.13 4.52 3.49
Geographical proximity: India
5.83 4.50 5.13 4.74 3.53
Geographical proximity: World
6.00 4.88 4.82 4.08 3.67
Hypothesis 1 stated that consumers who viewed advertisements that
contained threats proximal in time would perceive higher severity and high
vulnerability when compared to consumers who viewed threats that were
distant in time. A one-way MANOVA was conducted to ascertain if there
were significant differences regarding perceived severity and perceived
vulnerability, among the groups in response to manipulation of the temporal
proximity of the threat. Cell sizes were approximately equal (29 and 30) and
the Box's Test indicated that the assumptions of normality were not violated
as there was no significant differences between the covariance matrices. The
one-way MANOVA results were: Pillai’s Trace=0.078; Wilks’ lambda =
0.992; Hotelling’s Trace and Roy’s Largest Root = 0.085, F(2,56)=2.379 as
shown in Table 6.9a. Since the results of the multivariate tests were not
significant, the dependent variables are not significantly dependent on the
temporal proximity of the threat. The results indicate that there was no
statistically significant difference in severity or vulnerability based on
temporal proximity. Therefore hypothesis 1 (H1) was not supported. Table
6.9a and 6.9b shows the results of the test. Although the multivariate tests
were not significant, Table 6.9b showed the possibility of an influence of the
time factor on perceived severity of the threat.
118
Table 6.9a Experiment1: Hypothesis 1: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value F Hypothesis df
Error df Sig.
Intercept Pillai's Trace .986 1980.986a 2.000 56.000 .000Wilks' Lambda .014 1980.986a 2.000 56.000 .000Hotelling's Trace 70.749 1980.986a 2.000 56.000 .000Roy's Largest Root 70.749 1980.986a 2.000 56.000 .000
Multivariate Testsb
Effect Value F Hypothesis df
Error df Sig.
TIME_FACTORPillai's Trace .078 2.379a 2.000 56.000 .102Wilks' Lambda .922 2.379a 2.000 56.000 .102Hotelling's Trace .085 2.379a 2.000 56.000 .102Roy's Largest Root .085 2.379a 2.000 56.000 .102
a. Exact statistic b. Design: Intercept + TIME_FACTOR
Table 6.9b Experiment 1: Hypothesis 1: tests of between-subjects effects (mobile phone stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df MeanSquare F Sig.
Corrected Model PERC_SEV 2.309a 1 2.309 4.500 .038PERC_VUL 3.334b 1 3.334 2.257 .139
Intercept PERC_SEV 2069.131 1 2069.131 4032.657 .000PERC_VUL 1299.221 1 1299.221 879.607 .000
TIME_FACTOR PERC_SEV 2.309 1 2.309 4.500 .038PERC_VUL 3.334 1 3.334 2.257 .139
Error PERC_SEV 29.246 57 .513PERC_VUL 84.192 57 1.477
Total PERC_SEV 2098.938 59PERC_VUL 1384.889 59
Corrected Total PERC_SEV 31.555 58PERC_VUL 87.525 58
a. R Squared = .073 (Adjusted R Squared = .057) b. R Squared = .038 (Adjusted R Squared = .021)
119
A follow up one-way ANOVA was hence conducted to test if the
temporal proximity of the threat had an effect on the perceived severity.
Table 6.9c shows the results of the test. There was a statistically significant
difference between groups as determined by one-way ANOVA
(F(1,57) = 4.500, p <.05). It can be seen from Table 6.9c that temporal
proximity of the threat influenced the perceived severity of the threat. A plot
was producted to check the effect. Figure 6.1 shows that participants who
viewed threats that were closer in time perceived higher levels of severity.
This implies that the temporal proximity of the threat has an effect on the
perceived severity.
Table 6.9c Experiment 1: Tests of between-subjects effects of the temporal proximity of threat on perceived severity (mobile phone stimuli)
Tests of Between-Subjects Effects
Dependent Variable:PERC_SEV
Source Type III Sum of Squares
dfMean
SquareF Sig
Partial Eta
Squared
Noncent Parameter
Observed Powerb
Corrected Model 2309a 1 2.309 4.500 .038 .073 4.500 .550
Intercept 2069.131 1 2069.131 4032.657 .000 .986 4032.657 1.000
TIME_FACTOR 2.309 1 2.309 4.500 .038 .073 4.500 .550
Error 29.246 57 .513
Total 2098.938 59
CorrectedTotal 31.555 58
a.RSquared=.073 (Adjusted R Squared = .057)
b. Computed using alpha = .05
120
Figure 6.1 Experiment 1 – effect of framing a temporally and geographically proximal threat on perceived severity
The main effect of geographical proximity of threat on perceived
severity and vulnerability to the threat was investigated using one-way
MANOVA. Hypothesis 2 (H2) was also not supported as there was no
statistically significant difference in severity and vulnerability based on the
geographical proximity of threat. The one-way MANOVA results were:
Pillai’s Trace=0.027; Wilks’ lambda = 0.876; Hotelling’s Trace and Roy’s
Largest Root = 0.028, F(2,56)=0.787. The results are shown in Table 6.10a
and 6.10b.
121
Table 6.10a Experiment 1: Hypothesis 2: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value F Hypothesis df Error df Sig.
Intercept Pillai's Trace .985 1861.603a 2.000 56.000 .000
Wilks' Lambda .015 1861.603a 2.000 56.000 .000
Hotelling's Trace 66.486 1861.603a 2.000 56.000 .000
Roy's Largest Root 66.486 1861.603a 2.000 56.000 .000
GEOG_FACTOR Pillai's Trace .027 .787a 2.000 56.000 .460
Wilks' Lambda .973 .787a 2.000 56.000 .460
Hotelling's Trace .028 .787a 2.000 56.000 .460
Roy's Largest Root .028 .787a 2.000 56.000 .460
a. Exact statistic
b. Design: Intercept + GEOG_FACTOR
Table 6.10b Experiment 1: Hypothesis 2: tests of between-subjects effects (mobile phone stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model PERC_SEV .453a 1 .453 .830 .366
PERC_VUL 2.186b 1 2.186 1.460 .232
Intercept PERC_SEV 2067.826 1 2067.826 3789.661 .000
PERC_VUL 1298.797 1 1298.797 867.497 .000
GEOG_FACTOR PERC_SEV .453 1 .453 .830 .366
PERC_VUL 2.186 1 2.186 1.460 .232
Error PERC_SEV 31.102 57 .546
PERC_VUL 85.339 57 1.497
122
Table 6.10b (Continued)
Source Dependent
Variable
Type III
Sum of
Squares
df Mean
Square
F Sig.
Total PERC_SEV 2098.938 59
PERC_VUL 1384.889 59
Corrected Total PERC_SEV 31.555 58
PERC_VUL 87.525 58
a. R Squared = .014 (Adjusted R Squared = -.003)
b. R Squared = .025 (Adjusted R Squared = .008)
Hypothesis 3 predicted interaction effects and stated that an
interaction between temporal proximity and geographical proximity would
cause perception of higher levels of severity and vulnerability under proximal
conditions. A 2 (temporal proximity of threat:day vs.year) x 2 (geographical
proximity of threat: India vs. World) multivariate analysis of variance
(MANOVA) was conducted to examine this. The results also indicated that no
interaction effect exists between temporal proximity of threat and
geographical proximity of threat on both the PMT variables (Pillai’s
Trace=0.016; Wilks’ lambda = 0.984; Hotelling’s Trace and Roy’s Largest
Root = 0.017, F(2,54)=0.450). The results are shown in Table 6.11a and Table
6.11b. Therefore, Hypothesis 3 was not accepted.
123
Table 6.11a Experiment 1: Hypothesis 3: multivariate tests (mobile phone stimuli)
Multivariate Tests b
Effect Value FHypothesis
df Error
df Sig.
Intercept Pillai's Trace .986 1972.206a 2.000 54.000 .000Wilks' Lambda
.014 1972.206a 2.000 54.000 .000
Hotelling's Trace
73.045 1972.206a 2.000 54.000 .000
Roy's Largest Root
73.045 1972.206a 2.000 54.000 .000
TIME_FACTOR Pillai's Trace .082 2.398a 2.000 54.000 .101Wilks' Lambda
.918 2.398a 2.000 54.000 .101
Hotelling's Trace
.089 2.398a 2.000 54.000 .101
Roy's Largest Root
.089 2.398a 2.000 54.000 .101
GEOG_FACTOR Pillai's Trace .030 .846a 2.000 54.000 .435Wilks' Lambda
.970 .846a 2.000 54.000 .435
Hotelling's Trace
.031 .846a 2.000 54.000 .435
Roy's Largest Root
.031 .846a 2.000 54.000 .435
TIME_FACTOR * GEOG_FACTOR
Pillai's Trace .016 .450a 2.000 54.000 .640Wilks' Lambda
.984 .450a 2.000 54.000 .640
Hotelling's Trace
.017 .450a 2.000 54.000 .640
Roy's Largest Root
.017 .450a 2.000 54.000 .640
a. Exact statistic b. Design: Intercept + TIME_FACTOR + GEOG_FACTOR + TIME_FACTOR * GEOG_FACTOR
124
Table 6.11b Experiment 1: Hypothesis 3: tests of between-subjects effects (mobile phone stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model PERC_SEV 3.228a 3 1.076 2.089 .112PERC_VUL 6.267b 3 2.089 1.414 .249
Intercept PERC_SEV 2067.929 1 2067.929 4015.101 .000PERC_VUL 1299.290 1 1299.290 879.425 .000
TIME_FACTOR PERC_SEV 2.309 1 2.309 4.483 .039PERC_VUL 3.375 1 3.375 2.284 .136
GEOG_FACTOR PERC_SEV .473 1 .473 .919 .342PERC_VUL 2.238 1 2.238 1.515 .224
TIME_FACTOR * GEOG_FACTOR
PERC_SEV .430 1 .430 .835 .365PERC_VUL .652 1 .652 .441 .509
Error PERC_SEV 28.327 55 .515PERC_VUL 81.259 55 1.477
Total PERC_SEV 2098.938 59PERC_VUL 1384.889 59
Corrected Total PERC_SEV 31.555 58PERC_VUL 87.525 58
a. R Squared = .102 (Adjusted R Squared = .053) b. R Squared = .072 (Adjusted R Squared = .021)
To test H4, a one way MANCOVA with perceived severity and
perceived vulnerability as dependent variable and CFC as the covariate was
conducted. The temporal proximity of the threat was the independent variable.
The assumptions for MANCOVA were met. In particular, the homogeneity of
the regression effect was evident for the covariate, and the covariate was
linearly related to the dependent measure. The one-way MANCOVA results
were as follows: (Pillai’s Trace=0.064; Wilks’ lambda = 0.936; Hotelling’s
Trace and Roy’s Largest Root = 0.068, F(2,54)=1.846). There were no
125
interaction effects and therefore H4 was not supported. The results are shown
in Table 6.12a and 6.12b below. It can be seen from Table 6.12a that the
temporal proximity of the threat did not have a significant effect on the
hypothesized variables. Hence Table 6.12b was not interpreted.
Table 6.12a Experiment 1: Hypothesis 4: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value F Hypothesis df
Errordf Sig.
Intercept Pillai's Trace .764 87.364a 2.000 54.000 .000Wilks' Lambda
.236 87.364a 2.000 54.000 .000
Hotelling's Trace
3.236 87.364a 2.000 54.000 .000
Roy's Largest Root
3.236 87.364a 2.000 54.000 .000
TIME_FACTOR Pillai's Trace .087 2.585a 2.000 54.000 .085Wilks' Lambda
.913 2.585a 2.000 54.000 .085
Hotelling's Trace
.096 2.585a 2.000 54.000 .085
Roy's Largest Root
.096 2.585a 2.000 54.000 .085
TIME_FACTOR * CFC_TOTAL
Pillai's Trace .064 1.846a 2.000 54.000 .168Wilks' Lambda
.936 1.846a 2.000 54.000 .168
Hotelling's Trace
.068 1.846a 2.000 54.000 .168
Roy's Largest Root
.068 1.846a 2.000 54.000 .168
CFC_TOTAL Pillai's Trace .097 2.902a 2.000 54.000 .063Wilks' Lambda
.903 2.902a 2.000 54.000 .063
Hotelling's Trace
.107 2.902a 2.000 54.000 .063
Roy's Largest Root
.107 2.902a 2.000 54.000 .063
a. Exact statistic b. Design: Intercept + TIME_FACTOR + TIME_FACTOR * CFC_TOTAL + CFC_TOTAL
126
Table 6.12b Experiment 1: Hypothesis 4: tests of between-subjects effects
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model PERC_SEV 5.611a 3 1.870 3.965 .012
PERC_VUL 15.982b 3 5.327 4.096 .011
Intercept PERC_SEV 83.946 1 83.946 177.959 .000
PERC_VUL 34.893 1 34.893 26.824 .000
TIME_FACTOR PERC_SEV 1.585 1 1.585 3.360 .072
PERC_VUL 5.090 1 5.090 3.913 .053
TIME_FACTOR * CFC_TOTAL
PERC_SEV .984 1 .984 2.086 .154
PERC_VUL 3.981 1 3.981 3.060 .086
CFC_TOTAL PERC_SEV 1.640 1 1.640 3.476 .068
PERC_VUL 6.052 1 6.052 4.653 .035
Error PERC_SEV 25.944 55 .472
PERC_VUL 71.543 55 1.301
Total PERC_SEV 2098.938 59
PERC_VUL 1384.889 59
Corrected Total PERC_SEV 31.555 58
PERC_VUL 87.525 58
a. R Squared = .178 (Adjusted R Squared = .133) b. R Squared = .183 (Adjusted R Squared = .138)
Since the hypothesis testing did not yield any specific results, a
three way ANOVA was conducted to examine the interactions among the
factors and covariates on the individual dependent variables (perceived
severity and perceived vulnerability). Tables 6.13a and 6.13b show the result.
The results showed significant interactions of the factors and the covariate
CFC on perceived severity and perceived vulnerability of the threat.
127
Table 6.13a Experiment 1: Tests of between-subjects effects (effect of temporal, geographical proximity of threat and CFC on perceived severity)
Tests of Between-Subjects Effects
Dependent Variable:PERC_SEV
Source Type III Sum of Squares
dfMean
SquareF Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Corrected Model 9.538a 7 1.363 3.156 .008 .302 22.093 .918
Intercept 76.799 1 76.799 177.895 .000 .777 177.895 1.000
TIME_FACTOR 3.449 1 3.449 7.988 .007 .135 7.988 .792
GEOG_FACTOR 2.536 1 2.536 5.875 .019 .103 5.875 .662
CFC_TOTAL .209 1 .209 .485 .489 .009 .485 .105
TIME_FACTOR * CFC_TOTAL
2.492 1 2.492 5.773 .020 .102 5.773 .654
GEOG_FACTOR * CFC_TOTAL
2.093 1 2.093 4.849 .032 .087 4.849 .579
TIME_FACTOR * GEOG_FACTOR * CFC_TOTAL
1.875 1 1.875 4.343 .042 .078 4.343 .534
TIME_FACTOR * GEOG_FACTOR
1.545 1 1.545 3.579 .064 .066 3.579 .459
Error 22.017 51 .432
Total 2098.938 59
Corrected Total 31.555 58
a. R Squared = .302 (Adjusted R Squared = .206)
b. Computed using alpha = .05
128
Table 6.13b Experiment 1: Tests of between-subjects effects (effect of temporal, geographical proximity of threat and cfc on perceived vulnerability)
Tests of Between-Subjects Effects
Dependent Variable:PERC_VUL
Source Type III Sum of Squares
dfMean
SquareF Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Corrected Model 28.190a 7 4.027 3.461 .004 .322 24.230 .943
Intercept 43.236 1 43.236 37.162 .000 .422 37.162 1.000
TIME_FACTOR 10.796 1 10.796 9.279 .004 .154 9.279 .848
GEOG_FACTOR 9.775 1 9.775 8.402 .006 .141 8.402 .812
CFC_TOTAL .783 1 .783 .673 .416 .013 .673 .127
TIME_FACTOR * CFC_TOTAL
8.870 1 8.870 7.624 .008 .130 7.624 .773
GEOG_FACTOR * CFC_TOTAL
8.071 1 8.071 6.937 .011 .120 6.937 .734
TIME_FACTOR * GEOG_FACTOR * CFC_TOTAL
3.782 1 3.782 3.251 .077 .060 3.251 .424
TIME_FACTOR * GEOG_FACTOR
3.343 1 3.343 2.874 .096 .053 2.874 .384
Error 59.336 51 1.163
Total 1384.889 59
Corrected Total 87.525 58
a. R Squared = .322 (Adjusted R Squared = .229)
b. Computed using alpha = .05
The following Table (Table 6.14) summarizes the results of the
hypotheses testing for the effect of the stimuli on the PMT variables (H1-H4).
129
Table 6.14 Experiment 1: Summary of the hypotheses (H1 – H4) –mobile phone stimulus
Hypothesis Factor Perceivedseverity
Perceivedvulnerability
H1 Temporal proximity (H1) X X
H2 Geographical proximity (H2) X X
H3 Temporal proximity * Geographical proximity (H3)
X X
H4 CFC (H4) X X
X – no effect - Effect present
Watch stimuli
Table 6.15 shows the distribution characteristics of the protection
motivation variables. Similar to the mobile phone stimulus, most of the values
were negatively skewed. Group averages for the variables are shown in Table
6.16. Self efficacy was not included in the analysis as the scale reliability was
very low.
Table 6.15 Experiment 1: Distribution characteristics of the protection motivation variables for the watch stimuli
Variable Minimum Maximum Mean Std. Deviation
Perceived Severity 2.00 7.00 6.39 0.92
Perceived Vulnerability 1.00 7.00 4.73 1.43
Response Efficacy 2.33 7.00 5.15 1.24
Fear 1.83 6.50 3.86 0.99
130
Table 6.16 Experiment 1: Group wise mean values of protection motivation variables for the watch stimuli
Factor PerceivedSeverity
PerceivedVulnerability
Response Efficacy
SelfEfficacy
Fear
Temporal proximity: Day
6.27 4.60 5.13 3.87
Temporal proximity: Year 6.52 4.86 5.16 3.84
Geographical proximity: India 6.60 4.64 5.18 3.80
Geographical proximity: World 6.08 4.86 5.09 3.94
A one-way MANOVA was conducted to verify H1 which stated
that there will be significant differences in perceived severity and perceived
vulnerability among the groups in response to manipulation of the temporal
proximity of the threat. H1 was not accepted (Pillai’s Trace=0.021; Wilks’
lambda = 0.979; Hotelling’s Trace and Roy’s Largest Root = 0.021,
F(2,39)=0.415. Table 6.17a and 6.17b show the results.
Table 6.17a Experiment 1: Hypothesis 1: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value F Hypothesis df Error df Sig.Intercept Pillai's Trace .981 989.360a 2.000 39.000 .000
Wilks' Lambda .019 989.360a 2.000 39.000 .000Hotelling's Trace 50.736 989.360a 2.000 39.000 .000Roy's Largest Root 50.736 989.360a 2.000 39.000 .000
TIME_FACTOR Pillai's Trace .021 .415a 2.000 39.000 .664Wilks' Lambda .979 .415a 2.000 39.000 .664Hotelling's Trace .021 .415a 2.000 39.000 .664Roy's Largest Root .021 .415a 2.000 39.000 .664
a. Exact statistic b. Design: Intercept + TIME_FACTOR
131
Table 6.17b Experiment 1: Hypothesis 1: tests of between-subjects effects (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model PERC_SEV .667a 1 .667 .782 .382
PERC_VUL .711b 1 .711 .343 .561
Intercept PERC_SEV 1715.810 1 1715.810 2012.613 .000
PERC_VUL 940.055 1 940.055 453.602 .000
TIME_FACTOR PERC_SEV .667 1 .667 .782 .382
PERC_VUL .711 1 .711 .343 .561
Error PERC_SEV 34.101 40 .853
PERC_VUL 82.897 40 2.072
Total PERC_SEV 1751.250 42
PERC_VUL 1023.333 42
Corrected Total PERC_SEV 34.768 41
PERC_VUL 83.608 41
a. R Squared = .019 (Adjusted R Squared = -.005)
b. R Squared = .009 (Adjusted R Squared = -.016)
Hypothesis 2 was not supported as geographical proximity had no
effect on perceived severity and vulnerability (Pillai’s Trace=0.118; Wilks’
lambda = 0.882; Hotelling’s Trace and Roy’s Largest Root = 0.134,
F(2,39)=2.60). The results are shown in Tables 6.18a and 6.18b.
132
Table 6.18a Experiment 1: Hypothesis 2: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value F Hypothesis df Error df Sig.Intercept Pillai's Trace .981 990.045a 2.000 39.000 .000
Wilks' Lambda .019 990.045a 2.000 39.000 .000Hotelling's Trace 50.772 990.045a 2.000 39.000 .000Roy's Largest Root 50.772 990.045a 2.000 39.000 .000
GEOG_FACTOR Pillai's Trace .118 2.607a 2.000 39.000 .087Wilks' Lambda .882 2.607a 2.000 39.000 .087Hotelling's Trace .134 2.607a 2.000 39.000 .087Roy's Largest Root .134 2.607a 2.000 39.000 .087
a. Exact statistic b. Design: Intercept + GEOG_FACTOR
Table 6.18b Experiment 1: Hypothesis 2: tests of between-subjects effects (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
dfMean
Square F Sig.
Corrected Model PERC_SEV 2.650a 1 2.650 3.301 .077PERC_VUL .502b 1 .502 .242 .626
Intercept PERC_SEV 1629.079 1 1629.079 2028.889 .000
PERC_VUL 913.772 1 913.772 439.808 .000GEOG_FACTOR PERC_SEV 2.650 1 2.650 3.301 .077
PERC_VUL .502 1 .502 .242 .626Error PERC_SEV 32.118 40 .803
PERC_VUL 83.106 40 2.078
Total PERC_SEV 1751.250 42PERC_VUL 1023.333 42
Corrected Total PERC_SEV 34.768 41PERC_VUL 83.608 41
a. R Squared = .076 (Adjusted R Squared = .053) b. R Squared = .006 (Adjusted R Squared = -.019)
133
A 2 (temporal proximity of threat: day vs.year) x 2 (geographical
proximity of threat: India vs. World) multivariate analysis of variance
(MANOVA) was conducted to examine the interaction effects between
temporal proximity and geographical proximity to verify H3. It can be seen
from Table 6.19a and Table 6.19b that no interaction effect exists between
temporal proximity of threat and geographical proximity of threat on
perceived severity and vulnerability (Pillai’s Trace=0.044; Wilks’ lambda =
0.956; Hotelling’s Trace and Roy’s Largest Root = 0.046, F(2,37)=0.858).
Therefore, H3 was not accepted and the results are shown in
Table 6.19a Experiment 1: Hypothesis 3: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value FHypothesis
dfError
df Sig.
Intercept Pillai's Trace .982 1003.743a 2.000 37.000 .000Wilks' Lambda .018 1003.743a 2.000 37.000 .000Hotelling's Trace 54.256 1003.743a 2.000 37.000 .000Roy's Largest Root 54.256 1003.743a 2.000 37.000 .000
GEOG_FACTOR Pillai's Trace .129 2.735a 2.000 37.000 .078Wilks' Lambda .871 2.735a 2.000 37.000 .078Hotelling's Trace .148 2.735a 2.000 37.000 .078Roy's Largest Root .148 2.735a 2.000 37.000 .078
TIME_FACTOR Pillai's Trace .044 .858a 2.000 37.000 .432Wilks' Lambda .956 .858a 2.000 37.000 .432Hotelling's Trace .046 .858a 2.000 37.000 .432Roy's Largest Root .046 .858a 2.000 37.000 .432
GEOG_FACTOR * TIME_FACTOR
Pillai's Trace .052 1.017a 2.000 37.000 .372Wilks' Lambda .948 1.017a 2.000 37.000 .372Hotelling's Trace .055 1.017a 2.000 37.000 .372Roy's Largest Root .055 1.017a 2.000 37.000 .372
a. Exact statistic b. Design: Intercept + GEOG_FACTOR + TIME_FACTOR + GEOG_FACTOR * TIME_FACTOR
134
Table 6.19b Experiment 1: Hypothesis 3: tests of between-subjects effects (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model PERC_SEV 5.079a 3 1.693 2.167 .108
PERC_VUL 1.233b 3 .411 .190 .903
Intercept PERC_SEV 1610.267 1 1610.267 2061.013 .000
PERC_VUL 906.243 1 906.243 418.050 .000
GEOG_FACTOR PERC_SEV 2.853 1 2.853 3.651 .064
PERC_VUL .412 1 .412 .190 .665
TIME_FACTOR PERC_SEV 1.377 1 1.377 1.762 .192
PERC_VUL .695 1 .695 .321 .575
GEOG_FACTOR * TIME_FACTOR
PERC_SEV 1.500 1 1.500 1.920 .174
PERC_VUL .116 1 .116 .053 .819
Error PERC_SEV 29.689 38 .781
PERC_VUL 82.376 38 2.168
Total PERC_SEV 1751.250 42
PERC_VUL 1023.333 42
Corrected Total PERC_SEV 34.768 41
PERC_VUL 83.608 41
a. R Squared = .146 (Adjusted R Squared = .079) b. R Squared = .015 (Adjusted R Squared = -.063)
To test H4 a one-way MANCOVA was conducted with perceived
severity and perceived vulnerability as dependent variables and CFC as the
covariate. It can be inferred from Tables 6.20a and 6.20b that there was no
statistically significant difference between the groups and hence H4 was also
not supported.
135
Table 6.20a Experiment 1: Hypothesis 4: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value F Hypothesis df Error df Sig.Intercept Pillai's Trace .587 26.961a 2.000 38.000 .000
Wilks' Lambda .413 26.961a 2.000 38.000 .000Hotelling's Trace 1.419 26.961a 2.000 38.000 .000Roy's Largest Root 1.419 26.961a 2.000 38.000 .000
CFC_TOTAL Pillai's Trace .086 1.791a 2.000 38.000 .181Wilks' Lambda .914 1.791a 2.000 38.000 .181Hotelling's Trace .094 1.791a 2.000 38.000 .181Roy's Largest Root .094 1.791a 2.000 38.000 .181
TIME_FACTOR Pillai's Trace .024 .467a 2.000 38.000 .630Wilks' Lambda .976 .467a 2.000 38.000 .630Hotelling's Trace .025 .467a 2.000 38.000 .630Roy's Largest Root .025 .467a 2.000 38.000 .630
a. Exact statistic b. Design: Intercept + CFC_TOTAL + TIME_FACTOR
Table 6.20b Experiment 1: Hypothesis 4: tests of between-subjects effects (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares df Mean
Square F Sig.
Corrected Model PERC_SEV 1.441a 2 .720 .843 .438PERC_VUL 7.717b 2 3.859 1.983 .151
Intercept PERC_SEV 47.057 1 47.057 55.068 .000PERC_VUL 9.716 1 9.716 4.993 .031
CFC_TOTAL PERC_SEV .774 1 .774 .906 .347PERC_VUL 7.006 1 7.006 3.600 .065
TIME_FACTOR PERC_SEV .712 1 .712 .834 .367PERC_VUL .859 1 .859 .441 .510
Error PERC_SEV 33.327 39 .855PERC_VUL 75.891 39 1.946
Total PERC_SEV 1751.250 42PERC_VUL 1023.333 42
Corrected Total PERC_SEV 34.768 41PERC_VUL 83.608 41
a. R Squared = .041 (Adjusted R Squared = -.008) b. R Squared = .092 (Adjusted R Squared = .046)
136
Similar to the previous experiment, a three way ANOVA was
conducted to analyse the effect of the independent variables and covariate on
perceived severity and perceived vulnerability. The results are shown in
Tables 6.21a and 6.21b. Unlike the mobile phone stimuli, the factors and the
covariate did not influence perceived severity or perceived vulnerability.
Table 6.21a Experiment 1: Tests of between-subjects effects (effect of temporal, geographical proximity of threat and cfc on perceived severity)
Tests of Between-Subjects Effects
Dependent Variable:PERC_SEV
Source Type III Sum of Squares
dfMean
SquareF Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Corrected Model 6.298a 6 1.050 1.291 .287 .181 7.743 .437
Intercept 15.534 1 15.534 19.097 .000 .353 19.097 .989
TIME_FACTOR .084 1 .084 .103 .750 .003 .103 .061
GEOG_FACTOR .608 1 .608 .747 .393 .021 .747 .134
CFC_TOTAL 1.295 1 1.295 1.591 .215 .043 1.591 .233
TIME_FACTOR * CFC_TOTAL
.266 1 .266 .328 .571 .009 .328 .086
GEOG_FACTOR * CFC_TOTAL
.321 1 .321 .395 .534 .011 .395 .094
TIME_FACTOR* GEOG_FACTOR * CFC_TOTAL
1.520 1 1.520 1.869 .180 .051 1.869 .265
Error 28.470 35 .813
Total 1751.250 42
Corrected Total 34.768 41
a. R Squared = .181 (Adjusted R Squared = .041) b. Computed using alpha = .05
137
Table 6.21b Experiment 1: Tests of between-subjects effects (effect of temporal, geographical proximity of threat and cfc on perceived vulnerability)
Tests of Between-Subjects Effects
Dependent Variable: PERC_VUL
Source Type III Sum of Squares
dfMean
SquareF Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Corrected Model 11.303a 6 1.884 .912 .498 .135 5.471 .310
Intercept 5.637 1 5.637 2.729 .108 .072 2.729 .362
TIME_FACTOR 3.258 1 3.258 1.577 .218 .043 1.577 .231
GEOG_FACTOR .017 1 .017 .008 .928 .000 .008 .051
CFC_TOTAL 2.046 1 2.046 .990 .327 .028 .990 .162
TIME_FACTOR * CFC_TOTAL
2.738 1 2.738 1.326 .257 .036 1.326 .201
GEOG_FACTOR * CFC_TOTAL
.040 1 .040 .019 .890 .001 .019 .052
TIME_FACTOR * GEOG_FACTOR * CFC_TOTAL
.304 1 .304 .147 .703 .004 .147 .066
Error 72.305 35 2.066
Total 1023.333 42
Corrected Total 83.608 41
a. R Squared = .135 (Adjusted R Squared = -.013)
b. Computed using alpha = .05
The following table (Table 6.22) summarizes the effects of the
manipulations on the PMT variables.
138
Table 6.22 Experiment 1: Summary of the hypotheses (H1 – H4) -watch stimulus
Hypothesis Factor Perceived severity
Perceived vulnerability
H1 Temporal proximity X X
H2 Geographical proximity X X
H3 Temporal proximity * Geographical proximity
X X
H4 CFC X X
X – no effect - Effect present
6.3.6.4 Hypotheses tests of the relationship among PMT variables,
involvement, attitudes and intentions
Standard simple or multiple linear regression (ordinary least
squares (OLS)) analysis was run to ascertain the relationship between the
PMT variables, involvement, attitudes and intention variables. Only one or
two predictors were considered at a time. Similarly assumptions regarding
multicollinearity, homoscedascity, linearity and normality of residuals were
met in all the scenarios
Mobile phone stimuli
A multiple regression analysis was conducted to evaluate how well
the perceived severity and perceived vulnerability predicted fear and the
efficacy variables. Neither perceived severity nor perceived vulnerability
predicted the dependent variables. Therefore H9a, H9b and H9c were not
supported. The results are shown in Table 6.23a, 6.23b and 6.23c. It can
therefore be inferred that the perceived threat levels did not affect fear.
139
Table 6.23a Experiment 1: Hypothesis 9a: effect of threat appraisal components on fear (mobile phone stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .280a .079 .046 .90543a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 3.919 2 1.960 2.390 .101a
Residual 45.909 56 .820Total 49.828 58
a. Predictors: (Constant), PERC_VUL, PERC_SEV b. Dependent Variable: FEAR
Table 6.23b Experiment 1: Hypothesis 9b: effect of threat appraisal components on response efficacy (mobile phone stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .247a .061 .027 1.23681a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 5.543 2 2.771 1.812 .173a
Residual 85.663 56 1.530Total 91.205 58
a. Predictors: (Constant), PERC_VUL, PERC_SEV b. Dependent Variable: RESP_EFFICACY
140
Table 6.23c Experiment 1: Hypothesis 9c: effect of threat appraisal components on self efficacy (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .074a 0.005 -0.030 1.16446
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .419 2 .210 .155 .857a
Residual 75.935 56 1.356
Total 76.354 58
a. Predictors: (Constant), PERC_VUL, PERC_SEV
b. Dependent Variable: SELF_EFFICACY
Simple linear regression analyses was conducted to evaluate
how well fear and the other PMT variables (perceived severity, vulnerability,
response efficacy and self efficacy) predict message involvement. The results
are shown in the following tables (Tables 6.24a, 6.24b, 6.24c, 6.24d and
6.24e). It can be seen from Table 6.24b that perceived vulnerability predicts
message involvement ( =0.30 t(58)=2.43, p<0.05, R2=0.09). From Table
6.24d, it can be seen that response efficacy predicted message involvement
=0.30 t(58)=2.41, p<0.05, R2=0.09). Therefore only H10b and H10d were
supported. The other PMT variables did not have any effect on message
involvement. Hence only perceived vulnerability and response efficacy have
an effect on message involvement.
141
Table 6.24a Experiment 1: Hypothesis 10a: effect of perceived severity on message involvement (mobile phone stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .197a 0.039 0.022 1.06258a. Predictors: (Constant), PERC_SEV
ANOVAb
Model Sum of Squares df
MeanSquare F Sig.
1 Regression 2.596 1 2.596 2.299 .135a
Residual 64.357 57 1.129Total 66.953 58
a. Predictors: (Constant), PERC_SEV b. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.24b Experiment 1: Hypothesis 10b: effect of perceived vulnerability on message involvement (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R
Square Std. Error of the
Estimate 1 .307a 0.094 0.078 1.03138
a. Predictors: (Constant), PERC_VUL
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.319 1 6.319 5.941 .018a
Residual 60.634 57 1.064Total 66.953 58
a. Predictors: (Constant), PERC_VUL b. Dependent Variable: MESSAGE_INVOLVEMENT
142
Table 6.24b (Continued)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.602 .534 6.743 .000
PERC_VUL .269 .110 .307 2.437 .018
a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.24c Experiment 1: Hypothesis 10c: effect of fear on message involvement (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R SquareStd. Error of the
Estimate
1 .193a 0.037 0.020 1.06344
a. Predictors: (Constant), FEAR
ANOVAb
Model Sum of Squares
dfMean
Square F Sig.
1 Regression 2.491 1 2.491 2.203 .143a
Residual 64.461 57 1.131
Total 66.953 58
a. Predictors: (Constant), FEAR
b. Dependent Variable: MESSAGE_INVOLVEMENT
143
Table 6.24d Experiment 1: Hypothesis 10d: effect of response efficacy on message involvement (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .305a 0.093 0.077 1.032228
a. Predictors: (Constant), RESP_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.214 1 6.214 5.831 .019a
Residual 60.739 57 1.066
Total 66.953 58
a. Predictors: (Constant), RESP_EFFICACY
b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.561 .555 6.415 .000
RESP_EFFICACY .261 .108 .305 2.415 .019
a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.24e Experiment 1: Hypothesis 10e: effect of self efficacy on message involvement (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .167a 0.028 0.011 1.06864
a. Predictors: (Constant), SELF_EFFICACY
144
Table 6.24e (Continued)
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 1.859 1 1.859 1.628 .207a
Residual 65.094 57 1.142Total 66.953 58
a. Predictors: (Constant), SELF_EFFICACY b. Dependent Variable: MESSAGE_INVOLVEMENT
Simple regression analysis was conducted to evaluate how well coping variables predicted attitude towards the ad and purchase intention. It
can be seen from Table 6.25a and Table 6.25c that response efficacy significantly predicted attitude towards the ad ( =0.32 t(58)=2.550, p<0.05) and purchase intention ( =0.297 t(58)=2.352, p<0.05 R2=0.08) while self-efficacy did not predict both the variables (Tables 6.25b and Table 6.25d).
Therefore H11a and H11c were supported and H11b and H11d were not supported.
Table 6.25a Experiment 1: Hypothesis 11a: effect of response efficacy on attitude towards ad (mobile phone stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .320a 0.102 0.087 1.04128a. Predictors: (Constant), RESP_EFFICACY
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 7.048 1 7.048 6.500 .013a
Residual 61.803 57 1.084Total 68.851 58
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: ATTITUDE_AD
145
Table 6.25a (Continued)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.858 .560 6.889 .000
RESP_EFFICACY .278 .109 .320 2.550 .013
a. Dependent Variable: ATTITUDE_AD
Table 6.25b Experiment 1: Hypothesis 11b: effect of self efficacy on attitude towards ad (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .126a 0.016 -0.001 1.09033
a. Predictors: (Constant), SELF_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 1.089 1 1.089 .916 .343a
Residual 67.762 57 1.189
Total 68.851 58
a. Predictors: (Constant), SELF_EFFICACY b. Dependent Variable: ATTITUDE_AD
146
Table 6.25c Experiment 1: Hypothesis 11c: effect of response efficacy on purchase intention (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .297a 0.088 0.072 1.66704
a. Predictors: (Constant), RESP_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 15.373 1 15.373 5.532 .022a
Residual 158.405 57 2.779
Total 173.778 58
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: PURCHASE_INTENTION
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B
Std. Error
Beta
1 (Constant) 1.954 .896 2.180 .033
RESP_EFFICACY .411 .175 .297 2.352 .022
a. Dependent Variable: PURCHASE_INTENTION
Table 6.25d Hypothesis 11d: effect of self efficacy on purchase intention (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R SquareStd. Error of the
Estimate
1 .0.215a 0.46 0.030 1.70519
a. Predictors: (Constant), SELF_EFFICACY
147
Table 6.25d (Continued)
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 8.041 1 8.041 2.765 .102a
Residual 165.737 57 2.908Total 173.778 58
a. Predictors: (Constant), SELF_EFFICACY b. Dependent Variable: PURCHASE_INTENTION
H12a was not supported as environmental knowledge predicted perceived severity in the direction opposite to the one hypothesized. H12b and H12c were not supported as simple regression analyses revealed that environmental knowledge did not predict perceived vulnerability and fear. Environmental knowledge did not predict message involvement and therefore H12d was not supported. Tables 6.26a, 6.26b 6.26c, 6.26d illustrate the results.
Table 6.26a Experiment 1: Hypothesis 12a: effect of environmental knowledge on perceived severity (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.333a 0.111 0.095 0.70162
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 3.496 1 3.496 7.102 .010a
Residual 28.059 57 .492
Total 31.555 58
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: PERC_SEV
148
Table 6.26a (Continued)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B
Std.Error
Beta
1 (Constant) 5.053 .338 14.954 .000
ENV_KNOW .108 .041 .333 2.665 .010
a. Dependent Variable: PERC_SEV
Table 6.26b Experiment 1: Hypothesis 12b: effect of environmental knowledge on perceived vulnerability (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.033a 0.001 -0.016 1.23849
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .096 1 .096 .063 .803a
Residual 87.429 57 1.534
Total 87.525 58
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: PERC_VUL
The results show that environmental knowledge played no role in
influencing threat perception.
149
Table 6.26c Experiment 1: Hypothesis 12c: effect of environmental knowledge on fear (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.045a 0.002 -0.015 0.93401
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares df
MeanSquare F Sig.
1 Regression .103 1 .103 .118 .733a
Residual 49.725 57 .872Total 49.828 58
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: FEAR
Table 6.26d Experiment 1: Hypothesis 12d: effect of environmental knowledge on message involvement (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.065a 0.004 -0.013 1.08150
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .283 1 .283 .242 .625a
Residual 66.670 57 1.170Total 66.953 58
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: MESSAGE_INVOLVEMENT
150
Similar regression analysis was conducted to evaluate the
relationship among involvement variables and attitude towards ad.
Environmental concern was positively related to message involvement and
therefore H13a was supported ( =0.267, t(58)=2.055, p<0.05 R2=0.07).
However it did not predict attitude towards the ad and purchase intention.
Therefore H13b and H13c were not supported. The following tables (Tables
6.27a, 6.27b, 6.27c) show the regression results which clearly show that
environmental concern was related only to message involvement.
Table 6.27a Experiment 1: Hypothesis 13a: effect of environmental concern (enduring involvement with the environment) on message involvement (mobile phone stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.267a 0.071 0.054 1.01307a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 4.336 1 4.336 4.225 .045a
Residual 56.447 55 1.026Total 60.784 56
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B
Std.Error
Beta
1 (Constant) 3.021 .934 3.234 .002
TOTAL_ENV_CONCERN .324 .158 .267 2.055 .045a. Dependent Variable: MESSAGE_INVOLVEMENT
151
Table 6.27b Experiment 1: Hypothesis 13b: effect of environmental concern (enduring involvement with the environment) on attitude towards the ad (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.119a 0.014 -0.004 1.09816
a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .947 1 .947 .785 .379a
Residual 66.328 55 1.206
Total 67.275 56
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: ATTITUDE_AD
Table 6.27c Experiment 1: Hypothesis 13c: effect of environmental concern (enduring involvement with the environment) on purchase intention (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.012a 0.000 -0.018 1.70730
a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .022 1 .022 .007 .932a
Residual 160.318 55 2.915
Total 160.339 56
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: PURCHASE_INTENTION
152
H14 was supported as message involvement predicted attitude
towards ad ( =0.616, t(58)=5.902, p<0.001). Overall model fit was R2=0.359.
Table 6.28 shows the results.
Table 6.28 Experiment 1: Hypothesis 14: effect of message involvement on attitude towards ad (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.616a 0.359 0.368 0.86585
a. Predictors: (Constant), MESSAGE_INVOLVEMENT
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 26.118 1 26.118 34.838 .000a
Residual 42.733 57 .750
Total 68.851 58
a. Predictors: (Constant), MESSAGE_INVOLVEMENT b. Dependent Variable: ATTITUDE_AD
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.207 .527 4.190 .000
MESSAGE_INVOLVEMENT .625 .106 .616 5.902 .000
a. Dependent Variable: ATTITUDE_AD
It can be seen from Tables 6.29 and 6.30 that H15 and H16 were
also supported as attitude towards the ad significantly predicted the attitude
towards the brand ( =0.58 t(58)=5.412, p<0.001, R2=0.33) and attitude
153
towards the brand significantly predicted the purchase intention ( =0.517,
t(58)=4.557, p<0.001, R2=0.267).
Table 6.29 Experiment 1: Hypothesis 15: effect of attitude towards adon attitude towards brand (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.583a 0.339 0.328 0.91829
a. Predictors: (Constant), ATTITUDE_AD
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 24.703 1 24.703 29.295 .000a
Residual 48.065 57 .843
Total 72.768 58
a. Predictors: (Constant), ATTITUDE_AD
b. Dependent Variable: ATTITUDE_BRAND
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 1.905 .592 3.215 .002
ATTITUDE_AD .599 .111 .583 5.412 .000
a. Dependent Variable: ATTITUDE_BRAND
154
Table 6.30 Experiment 1: Hypothesis 16: effect of attitude towards brand on purchase intention (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.517 0.267 0.254 1.49485
a. Predictors: (Constant), ATTITUDE_BRAND
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 46.406 1 46.406 20.767 .000a
Residuals 127.372 57 2.235
Total 173.778 58
a. Predictors: (Constant), ATTITUDE_BRAND
b. Dependent Variable: PURCHASE_INTENTION
Coefficientsa
Model Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -.029 .905 -.032 .975
ATTITUDE_BRAND .799 .175 .517 4.557 .000
a. Dependent Variable: PURCHASE_INTENTION
The results of the hypothesis tests regarding the relationship
between PMT variables, involvement, attitudes and intentions for the mobile
phone stimuli are summarised in Table 6.31.
155
Table 6.31 Experiment 1: Summary of hypotheses tests regarding relationship between PMT variables, involvement, attitudes and intentions (H9 – H16) (mobile phone stimuli)
Hypothesis Predictor Dependent
variable R2 Adjusted
R2Unstandardised
coefficient B Standardised coefficient
H9a Perceived Severity and Perceived vulnerability
Fear Not Significant
H9b Perceived Severity and Perceived vulnerability
Response Efficacy
Not Significant
H9c Perceived Severity and Perceived vulnerability
Self Efficacy Not Significant
H10a Perceived Severity
Message Involvement
Not Significant
H10b Perceived Vulnerability
Message Involvement
0.09 0.07 0.26 0.30*
H10c Fear Message Involvement
Not Significant
H10d Response Efficacy
Message Involvement
0.09 0.07 0.26 0.30*
H10e Self Efficacy Message Involvement
Not Significant
H11a Response efficacy
Attitude towards ad
0.10 0.08 0.27 0.32*
H11b Self Efficacy Attitude towards ad
Not Significant
H11c Response efficacy
Purchase Intention
0.08 0.07 0.41 0.29*
H11d Self Efficacy Purchase Intention
Not Significant
H12a EnvironmentalKnowledge
Perceived Severity
0.11 0.09 0.10 0.33*
156
Table 6.31 (Continued)
H12b EnvironmentalKnowledge
Perceived Vulnerability
Not Significant
H12c EnvironmentalKnowledge
Fear Not Significant
H12d EnvironmentalKnowledge
Message Involvement
Not Significant
H13a Environmental concern
Message Involvement
0.07 0.05 0.32 0.26*
H13b Environmental concern
Attitude towards ad
Not significant
H13c Environmental concern
Purchase Intention
Not significant
H14 Message Involvement
Attitude towards ad
0.37 0.36 0.625 0.616***
H15 Attitude towards ad
Attitude towards brand
0.33 0.32 0.59 0.58***
H16 Attitude towards brand
Purchase Intention
0.26 0.25 0.79 0.51***
***p <.001 **p <.01 *p <.05; n= 58
Watch stimuli
A multiple regression analysis was conducted to evaluate how well
perceived severity and perceived vulnerability predicted fear and response
efficacy. Table 6.32a shows that H9a was not supported as the model was not
significant. H9b was partially supported as perceived vulnerability positively
influenced response efficacy ( =0.595 t(39)=4.099, p<0.001, R2 =0.309). H9c
was not tested because of the low reliability values for self efficacy. The
results of the regression analysis are shown below in Table 6.32b.
157
Table 6.32a Experiment 1: Hypothesis 9a: effect of threat appraisal components on fear (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .153a 0.023 -0.27 1.00674
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .949 2 .474 .468 .630a
Residual 39.527 39 1.014
Total 40.476 41
a. Predictors: (Constant), PERC_VUL, PERC_SEV b. Dependent Variable: FEAR
Table 6.32b Experiment 1: Hypothesis 9b: effect of threat appraisal components on response efficacy (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .556a 0.309 0.273 1.05815
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 19.489 2 9.744 8.703 .001a
Residual 43.667 39 1.120
Total 63.156 41
a. Predictors: (Constant), PERC_VUL, PERC_SEV b. Dependent Variable: RESP_EFFICACY
158
Table 6.32b (Continued)
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.854 1.163 3.313 .002
PERC_SEV -.180 .196 -.133 -.919 .364
PERC_VUL .517 .126 .595 4.099 .000
a. Dependent Variable: RESP_EFFICACY
H10a was supported as perceived severity predicted message
involvement ( =0.37 t(39)=2.496, p<0.05, R2 = 0.367). H10b and H10d were
supported, as simple linear regression analyses revealed that among the PMT
variables only perceived vulnerability ( =0.54 t(39)=4.108, p<0.001, R2 =
0.29) and response efficacy ( =0.39 t(39)=2.717, p<0.05,R2 = 0.156)
significantly predicted message involvement. The results are shown in the
following tables (Tables 6.33a, 6.33b, 6.33c and 6.33d). H10e was not tested
because it involved self efficacy.
Table 6.33a Experiment 1: Hypothesis 10a: effect of perceived severity on message involvement (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .367a 0.135 0.113 1.02271a. Predictors: (Constant), PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.518 1 6.518 6.232 .017a
Residual 41.837 40 1.046
159
Table 6.33a (Continued)
Model Sum of Squares
df Mean
Square F Sig.
Total 48.355 41a. Predictors: (Constant), PERC_SEV b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.077 1.120 1.855 .071
PERC_SEV .433 .173 .367 2.496 .017
a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.33b Experiment 1: Hypothesis 10b: effect of perceived vulnerability on message involvement (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .545a 0.297 0.279 0.92206a. Predictors: (Constant), PERC_VUL
ANOVAb
Model Sum of Squares df
MeanSquare F Sig.
1 Regression 14.348 1 14.348 16.876 .000a
Residual 34.008 40 .850Total 48.355 41
a. Predictors: (Constant), PERC_VUL b. Dependent Variable: MESSAGE_INVOLVEMENT
160
Table 6.33b (Continued)
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 2.886 .498 5.798 .000
PERC_VUL .414 .101 .545 4.108 .000a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.33c Experiment 1: Hypothesis 10c: effect of fear on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .248a 0.061 0.038 1.06524
a. Predictors: (Constant), FEAR
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 2.966 1 2.966 2.614 .114a
Residual 45.389 40 1.135
Total 48.355 41
a. Predictors: (Constant), FEAR b. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.33d Experiment 1: Hypothesis 10d: effect of response efficacy on message involvement (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .395a 0.156 0.135 1.01021a. Predictors: (Constant), RESP_EFFICACY
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Table 6.33d (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 7.534 1 7.534 7.383 .010a
Residual 40.821 40 1.021
Total 48.355 41
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.066 .673 4.556 .000
RESP_EFFICACY .345 .127 .395 2.717 .010
a. Dependent Variable: MESSAGE_INVOLVEMENT
Results of the simple regression analysis revealed the effect of
response efficacy on attitude towards the ad and purchase intention. The
model was not significant. Table 6.34a and 6.34b show that H11a and H11c
were not supported as response efficacy did not predict attitude towards the ad
and purchase intention.
Table 6.34a Experiment 1: Hypothesis 11a: effect of response efficacy on attitude towards ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .200a 0.040 0.016 1.01291
a. Predictors: (Constant), RESP_EFFICACY
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Table 6.34a (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 1.714 1 1.714 1.671 .204a
Residual 41.040 40 1.026
Total 42.754 41
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: ATTITUDE_AD
Table 6.34b Experiment 1: Hypothesis 11c: effect of response efficacy on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .266a 0.071 0.047 1.50427
a. Predictors: (Constant), RESP_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.865 1 6.865 3.034 .089a
Residual 90.513 40 2.263Total 97.378 41
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: PURCHASE_INTENTION
H12a, H12b, H12c and H12d were not supported environmental knowledge did not predict the hypothesized dependent variables. Tables 6.35a, 6.35b 6.35c, 6.35d illustrate the results.
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Table 6.35a Experiment 1: Hypothesis 12a: effect of environmental knowledge on perceived severity (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.247a 0.061 0.038 0.90330a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 2.130 1 2.130 2.610 .114a
Residual 32.638 40 .816Total 34.768 41
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: PERC_SEV
Table 6.35b Experiment 1: Hypothesis 12a: effect of environmental knowledge on perceived vulnerability (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate1 .0.219a 0.048 0.024 1.41063a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 4.014 1 4.014 2.017 .163a
Residual 79.595 40 1.990Total 83.608 41
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: PERC_VUL
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Table 6.35c Experiment 1: Hypothesis 12a: effect of environmental knowledge on fear (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.123a 0.015 -0.10 0.99834
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .609 1 .609 .611 .439a
Residual 39.867 40 .997
Total 40.476 41
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: FEAR
Table 6.35d Experiment 1: Hypothesis 12b: effect of environmental knowledge on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.010a 0.000 -0.025 1.09944
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .005 1 .005 .004 .950a
Residual 48.350 40 1.209
Total 48.355 41
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: MESSAGE_INVOLVEMENT
165
Simple regression analysis was conducted to evaluate if
involvement predicted attitude towards ad. It can be seen from Tables 6.36a,
6.36b, 6.36c that the regression model was not significant. Unlike the
previous experiment with mobile stimuli, environmental concern was not
related to message involvement, attitude towards ad or purchases intentions.
Therefore H13a, H13b and H13c were not supported.
Table 6.36a Experiment 1: Hypothesis 13a: effect of environmental concern (enduring involvement with the environment) on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.227a 0.051 0.028 1.07086
a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 2.485 1 2.485 2.167 .149a
Residual 45.870 40 1.147
Total 48.355 41
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.36b Experiment 1: Hypothesis 13b: effect of environmental concern (enduring involvement with the environment) on attitude towards the ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.157a 0.025 0.000 1.02015
a. Predictors: (Constant), TOTAL_ENV_CONCERN
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Table 6.36b (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 1.052 1 1.052 1.009 .321a
Residual 41.702 40 1.043
Total 42.754 41
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: ATTITUDE_AD
Table 6.36c Experiment 1: Hypothesis 13c: effect of environmental concern (enduring involvement with the environment) on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.233a 0.054 0.031 1.51735
a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 5.284 1 5.284 2.295 .138a
Residual 92.094 40 2.302
Total 97.378 41
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: PURCHASE_INTENTION
H14 was supported as message involvement significantly predicted
attitude towards ad ( =0.716, t(39)=6.482, p<0.001). Model fit was good as
R2=0.51. Table 6.37 shows the results.
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Table 6.37 Experiment 1: Hypothesis 14: effect of message involvement on attitude towards ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.716a 0.512 0.500 0.72201
a. Predictors: (Constant), MESSAGE_INVOLVEMENT
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 21.902 1 21.902 42.015 .000a
Residual 20.852 40 .521
Total 42.754 41
a. Predictors: (Constant), MESSAGE_INVOLVEMENT b. Dependent Variable: ATTITUDE_AD
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.096 .515 4.068 .000
MESSAGE_INVOLVEMENT .673 .104 .716 6.482 .000
a. Dependent Variable: ATTITUDE_AD
H15 and H16 were also supported as attitude towards the ad
significantly predicted the attitude towards the brand ( =0.709, t(39)=6.367,
p<0.001, R2=0.50) and attitude towards the brand significantly predicted the
purchase intention ( =0.744, t(39)=7.037, p<0.001, R2=0.55). Tables 6.38 and
6.39 show the regression results for H15 and H16.
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Table 6.38 Experiment 1: Hypothesis 15: effect of attitude towards adon attitude towards brand (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.709a 0.503 0.491 0.90473
a. Predictors: (Constant), ATTITUDE_AD
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 33.185 1 33.185 40.542 .000a
Residual 32.741 40 .819
Total 65.926 41
a. Predictors: (Constant), ATTITUDE_AD b. Dependent Variable: ATTITUDE_BRAND
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .503 .754 .666 .509
ATTITUDE_AD .881 .138 .709 6.367 .000
a. Dependent Variable: ATTITUDE_BRAND
Table 6.39 Experiment 1: Hypothesis 16: effect of attitude towards brand on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.744a 0.553 0.542 1.04296
a. Predictors: (Constant), ATTITUDE_BRAND
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Table 6.39 (Continued)
ANOVAb
Model Sum of Squares
dfMean
Square F Sig.
1 Regression 53.868 1 53.868 49.521 .000a
Residual 43.511 40 1.088
Total 97.378 41
a. Predictors: (Constant), ATTITUDE_BRAND b. Dependent Variable: PURCHASE_INTENTION
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .097 .690 .141 .889
ATTITUDE_BRAND .904 .128 .744 7.037 .000
a. Dependent Variable: PURCHASE_INTENTION
Table 6.40 shows the summary of the results regarding the relationship between the PMT variable, involvement, attitudes and purchase intention for the watch stimuli.
Table 6.40 Experiment 1: Summary of hypotheses tests regarding relationship between PMT variables, involvement, attitudes and intentions (H9 – H16) (watch stimuli)
Hypothesis Predictor Dependent
variable R2 Adjusted
R2Unstandardised
coefficient B Standardised coefficient
H9a Perceived Severity and Perceived vulnerability
Fear Not Significant
H9a Perceived Severity and Perceived vulnerability
Response Efficacy
Not Significant
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Table 6.40 (Continued)
Hypothesis Predictor Dependent variable R2 Adjusted
R2Unstandardised
coefficient B Standardised coefficient
H9c Perceived Severity and Perceived vulnerability
Self Efficacy Not Tested
H10a Perceived Severity
Message Involvement
0.135 0.113 0.433 0.367*
H10b PerceivedVulnerability
MessageInvolvement
0.29 0.27 0.41 0.54***
H10c Fear Message Involvement
Not Significant
H10d Response Efficacy
Message Involvement
0.15 0.14 0.345 0.39*
H10e Self Efficacy Message Involvement
Not tested
H11a Response efficacy
Attitude towards ad
NotSignificant
H11b Self Efficacy Attitude towards ad
Not tested
H11c Response efficacy
Purchase Intention
Not Significant
H11d Self Efficacy Purchase Intention
Not tested
H12a EnvironmentalKnowledge
Perceived Severity
Not Significant
H12a EnvironmentalKnowledge
Perceived Vulnerability
Not Significant
H12a EnvironmentalKnowledge
Fear Not Significant
H12b EnvironmentalKnowledge
Message Involvement
Not Significant
H13a Environmental concern
Message Involvement
Not Significant
H13b Environmental concern
Attitude towards ad
Not significant
H13c Environmental concern
Purchase Intention
Not significant
H14 Message Involvement
Attitude towards ad
0.51 0.50 0.673 0.716***
H15 Attitude towards ad
Attitude towards brand
0.50 0.49 0.88 0.70***
H16 Attitude towards brand
Purchase Intention
0.55 0.54 0.90 0.74***
***p <.001 **p <.01 *p <.05; n= 41
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6.3.7 Conclusions from Experiment 1
This experiment examined the effects of temporal and geographical
framing of threat on PMT variables and the subsequent effects of the PMT
variables on message involvement, attitudes and purchase intention.
Although the hypothesized relationship regarding the main effects were not
significant, it was seen that temporal proximity of the threat had a significant
effect on perceived severity in the case of mobile phone stimuli. This finding
supports current research that argue that threats in “day” terms are considered
more closer that those that are presented in “year” terms (Chandran & Menon
2004). It also shows that the self-positivity bias is reduced (Gilovich et al
1993; Raghubir & Menon 1998) as people find a temporally closer threat
relevant. However this effect was not observed with the watch stimuli. This
could be because plastic waste pollution was viewed as a severe threat by
most participants as the mean value of perceived severity was very high
(6.39) when compared to the problem of e-waste (5.9). Plastic waste can be a
more familiar issue in India as the consumer encounters regular mandatory
governmental instructions and news articles on this issue. Hence for a familiar
issue, perceived severity is rated high when compared to an unfamiliar issue
like e-waste. This difference in the arousal of fear based on issue familiarity
has been discussed by Pelsmacker et al (2011). However, only perceived
severity was viewed differently in this experiment. In a similar vein,
Obermiller (1995) also found that different appeals worked for familiar and
unfamiliar issues. The reported perceived vulnerability and fear were almost
similar in both the cases. This could be again because of issue familiarity as
the watch stimulus highlights the threat of plastic waste. Therefore consumers
are more aware of the issue of plastic waste when compared to e-waste. Of
late, the government of India mandates the pricing of plastic bags that are
used to packing the goods sold by a number of retail stores. The stores also
prominently display statutory messages advocating the reduction of plastic.
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Therefore, for a familiar issue, perceived severity and perceived vulnerability
could have had a greater impact when compared to an unfamiliar issue like e-
waste disposal.
The hypothesized effect of CFC was not supported. However, in
the case of mobile phone stimulus, CFC interacted with the factors to produce
significant effects on perceived severity and perceived vulnerability. This
finding supports existing literature that consumers perception of risk varied
based on their temporal orientation (Orbell et al 2004 & Orbell & Hagger
2006; Morison et al 2010). There were no statistically significant interaction
effects of temporal proximity and geographical proximity of the threat on the
PMT variables for both the watch and mobile phone stimuli. Most participants
exhibited high levels of perceived severity, vulnerability and fear towards
environmental threats.
In both the cases, perceived vulnerability and response efficacy
significantly predicted message involvement. This is similar to the finding by
Cauberghe et al (2009). Response efficacy is a variable that is linked to
“Perceived consumer effectiveness” (PCE) and is shown to be related to
consumer’s environmental behaviour (Gilg et al 2005).
Unlike previous studies environmental knowledge did not predict
severity or fear as hypothesized. In the case of watch stimuli, environmental
knowledge predicted perceived severity in the positive direction contrary to
the hypothesized nature of the relationship, but in the case of mobile phone
stimuli environmental knowledge did not have any effect.
Most significantly, environmental concern did not have any effect
on attitudes, message involvement or purchase intention. This is in direct
contrast to the propositions put forward by the ELM (Petty & Cacioppo 1986).
Issue involvement is supposed to activate message elaboration and therefore
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increase involvement with the message (Maheswaran & Meyers-Levy 1990).
However consumers who reported high environmental concern (issue
involvement) did not exhibit this behaviour. This highlights the fact that better
measures are needed to assess environmental concern or enduring
involvement with the environment. Most respondents probably report
environmental concern as a socially desirable response (Bord et al 1998;
Ewert & Baker 2001; Ewert & Galloway 2009) and hence the results did not
support the related hypothesis.
Attitude towards the advertisement significantly predicted attitude
towards the brand and attitude towards the brand significantly predicted
purchase intentions in both the scenarios. This is in congruence with the dual
mediation hypothesis that states that attitude related cognitions affect the
attitude towards the ad, brand and in turn behaviour related to purchase
intentions (MacKenzie et al 1986; Teng et al 2007). The results support the
findings from most advertising studies that show that attitude towards
advertising has a strong influence on attitude towards the brand under high
involvement conditions (Gardner 1985; Park & Young 1986; Muehling &
Laczniak 1988).
This experiment showed that response efficacy and perceived
vulnerability greatly increased message involvement and message
involvement subsequently influenced attitudes and intentions. However
perceived severity and vulnerability did not cause fear arousal in both the
scenarios. Therefore it is necessary to investigate if fear arousal can be
obtained using a different message frame. Hence, study 2 was conducted
using goal frames and threat level as factors to increase the success of the
manipulations. Although it is not essential to conduct the two studies (study1
and study2) sequentially, they were conducted one after another in this
research to examine the effect of the framing manipulations.
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6.4 STUDY 2: EXPERIMENT 2: THREAT LEVELS AND GOAL
FRAMING (WATCH STIMULI)
Experiment 2 was designed to use threat levels and goal frames to
influence the PMT variables. Plastic waste seemed to generate higher levels
of scores for the threat appraisal variables in the previous study. Therefore the
experiment was conducted with wristwatch as the chosen product. The
advertisements were designed for this product (Ad3). The results of this
experiment were used to design the stimuli for the next experiment.
6.4.1 Experimental Design
A 2 (threat level: high vs. low) x 2 (goal frame: loss vs. gain)
between subjects experimental design was utilized to investigate the
hypotheses. This resulted in four possible combinations of the stimuli. Sixty
nine postgraduate M.E. students from a large South Indian University (95.7 %
male, median age=22) were randomly assigned to the four possible conditions
for the watch stimuli.
The experimental procedure was the same as Experiment 1. Data
collection was through a paper and pencil questionnaire. Students first filled
the questionnaire (Q3) containing dependent variables (Appendix 5). Next,
they were asked to answer the filler task which asked them to list the reason
why they liked their favourite celebrity similar to Experiment 1. On
completion, they filled counterbalanced questionnaires on environmental
concern and objective environmental knowledge. This was similar to the
questions in Experiment 1. The personality variable CFC was not included in
this questionnaire as it was related only to temporal framing.
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6.4.2 Stimuli
A total of four print advertisements were developed for the four
cells: high threat level and loss frame, high threat level and gain frame, low
threat level and loss frame and low threat level and gain frame. The
advertisements also listed the environment friendly features of the watch.
In the low threat conditions, the advertisement highlighted the fact
that burning plastic waste may cause various health problems. In the high
threat condition, the threat was specific and vivid language was used to
indicate that toxins from burning waste may cause cancer or respiratory
problems. The loss frame mentioned that choosing plastic products will
accelerate air pollution and increase the chances of health hazards caused by
pollution. The gain frame emphasized that by choosing a green product one
can slow down air pollution and reduce the chances of health hazards caused
by pollution. The watch advertisement contained further details about its
biodegradability.
6.4.3 Treatment Validity
The four print advertisements were analyzed by an expert panel to
assess if it contained the necessary variations in the threat level and message
frames. This panel consisted of 3 marketing professors. The changes
suggested by the panel were made and the final versions of the advertisements
are shown in Appendix 6 (Figure A6.1, Figure A6.2, Figure A6.3, and Figure
A6.4).
6.4.4 Manipulation Checks
Two questions were included in the questionnaire to check the
manipulations. Threat level manipulations were checked by including a
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multiple response question: “What health problems did the advertisement
highlight? Choose only one answer”. The options were (1) Diseases (2)
Cancer (3) Respiratory problems (4) Cancer and respiratory problems. Frame
manipulations were checked asking the respondents to rate the following
questions: “I can gain health benefits by buying biodegradable products”, “I
can lose important health benefits if I don’t buy biodegradable products”. The
response to these items was measured using seven point Likert scales
anchored from 1 = Strongly Disagree and 7 = Strongly Agree.
6.4.5 Modified Dependent Variables
The study used the same dependent variables as Experiment 1.
However some of the PMT variables were modified to improve their
reliabilities. Perceived severity and self-efficacy variables were modified as
the reliabilities were low in Experiment1. Response efficacy and perceived
vulnerability were also changed to reflect the changes in the independent
factors. The variables were now adapted from the risk behaviour diagnosis
scale (Witte et al 1996), as this scale is widely used in measuring risks
associated with health messages. Apart from these changes, since the threat
levels varied in the advertisements, efficacy variables were changed to
measure generic diseases rather than specifying particular diseases like
respiratory diseases and cancer. The changed variables are described below
and the entire questionnaire (Q3) is shown in Appendix 5.
Perceived severity
Perceived severity was measured using a three item seven point
scale where 1 = Strongly Disagree and 7 = Strongly Agree. Participants were
asked to indicate their responses on the following statements: “I believe that
plastic waste pollution is a serious threat to human health”, “I believe that
177
plastic waste disposal may cause severe health issues.”, “I believe that plastic
waste pollution is extremely harmful”.
Perceived vulnerability
Perceived vulnerability was measured using a three item seven
point scale where 1 = Strongly Disagree and 7 = Strongly Agree to determine
the participants’ perceived susceptibility to the threat. Participants were asked
to indicate their responses on the following statements. “It is possible that I
might get affected by diseases caused by plastic waste pollution.”, “It is
probable that I will suffer from various diseases caused by plastic waste
pollution.”, “I am at risk for getting health problems caused by plastic waste
pollution.” These items were collapsed into a single perceived vulnerability
score.
Response efficacy
Response efficacy was measured using a three item seven point
scale where 1 = Strongly Disagree and 7 = Strongly Agree to determine
whether participants’ believed if purchasing biodegradable products averted
the threat. Participants rated their responses on the following statements:
“Purchasing biodegradable products is a highly effective way of preventing
diseases due to plastic pollution”, “Buying biodegradable products will
significantly lower my risk of being affected by diseases caused by plastic
pollution”, “Buying biodegradable products is an effective method of
reducing threats caused by plastic pollution to human health”. These items
were combined into a single response efficacy score.
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Self efficacy
Self efficacy was measured using a three item seven point scales
where 1 = Strongly Disagree and 7 = Strongly Agree to determine whether
participants’ believed if they were capable of averting the threat. Participants
rated their responses on the following statements: “I am capable of identifying
and purchasing biodegradable products”, “I can easily switch over to
biodegradable products to prevent future health problems”, “It is not difficult
for me to check if products contain plastic or not”.
6.4.6 Results of Experiment 2 - Threat Levels and Goal Framing
This study was conducted with the watch stimuli to evaluate the
effect of different threat levels (low/high) and goal frames (gain vs. loss) on
the PMT variables. The effect of PMT variables on involvement and the
subsequent influence of involvement on attitudes and purchase intention were
also evaluated.
6.4.6.1 Manipulation checks
If the participant under high threat condition chose any other
answer, apart from the generic “diseases”, the manipulation was considered
successful. A chi-square test, comparing the observed frequencies of cases
with the correct evaluation of the threat with the expected frequencies,
revealed that the threat manipulation was successful only in the high threat
condition. The results can be seen in Table 6.41 below. The threat condition
that was assigned to them was correctly identified by 94.2% of the
participants in the high threat condition. This showed that the manipulation
worked for the high threat condition. Even under low threat condition
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participants viewed the threat as high (64.7% of them viewed the threat as
high).
Table 6.41 Experiment 2: Manipulation check for threat levels
Threat_level * MC_THREAT Crosstabulation Count
MC_THREAT TotalCancer and Resp Generic
Threat_level high 31 4 35low 22 12 34
Total 53 16 69
Chi-Square TestsValue df Asymp. Sig. (2-
sided) Exact Sig. (2-sided)
Exact Sig. (1-sided)
Pearson Chi-Square
5.515a 1 .019
Continuity Correctionb
4.256 1 .039
Likelihood Ratio 5.707 1 .017Fisher's Exact Test
.024 .019
N of Valid Cases 69 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 7.88. b. Computed only for a 2x2 table
The frame manipulation was not successful as there was no significant main effect of the frame manipulation (loss vs. gain) in both the conditions (Table 6.42).
Similar to Experiment 1, unsuccessful manipulation checks were
not of great concern and did not indicate that the manipulation of the
independent variable failed (Sigall & Mills 1998). Hence further analyses
were conducted.
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Table 6.42 Experiment 2: Manipulation check for frame type
ANOVASum of Squares df Mean Square F Sig.
MC_GAIN Between Groups 1.503 1 1.503 .535 .467Within Groups 188.265 67 2.810Total 189.768 68
MC_LOSS Between Groups 4.304 1 4.304 2.247 .139Within Groups 128.333 67 1.915Total 132.638 68
6.4.6.2 Scale reliability
The internal consistency of the scales was assessed using Cronbach
. The Table 6.43 below shows the reliability scores.
Table 6.43 Experiment 2: Reliability scores using watch stimulus
Construct Cronbach
Perceived severity 0.84
Perceived vulnerability 0.71
Response Efficacy 0.64
Self Efficacy 0.56
Message involvement 0.80
Fear 0.92
Attitude towards ad 0.86
Attitude towards brand 0.91
Purchase intention 0.91
Environmental concern 0.78
All the variables except self-efficacy had reliability scores exceeding 0.6.
Self-efficacy also has adequate reliability in this case. The results suggest that
181
the instrument was reasonably reliable. The scale reliability for perceived
severity increased with the revision.
6.4.6.3 Hypotheses tests of the effect of manipulations on PMT
variables
Tables 6.44 and 6.45 show the distribution characteristics and the
group wise means of the protection motivation variables. It can be seen that
the average values for perceived severity and perceived vulnerability are high
and closer to the maximum score. The group-wise means also show that there
are not much variations in the scores across the groups.
Table 6.44 Experiment 2: Distribution characteristics of the protection motivation variables (watch stimuli)
Minimum Maximum Mean Std. Deviation
PERC_SEV 1.00 7.00 6.22 1.00
PERC_VUL 2.33 7.00 5.31 1.20
RESP_EFFICACY 3.33 7.00 5.92 0.82
SELF_EFFICACY 2.00 6.67 4.88 1.26
FEAR 1.00 7.00 4.34 1.60
Table 6.45 Experiment 2: Group wise mean values of protection motivation variables for the (watch stimuli)
Factor PerceivedSeverity
PerceivedVulnerability
Response Efficacy
Self Efficacy
Fear
Threat level: High 6.25 5.32 5.90 4.91 4.25
Threat level: Low 6.17 5.29 5.94 4.85 4.42
Goal frame: Gain 6.27 5.43 6.07 5.06 4.49
Goal frame: Loss 6.16 5.19 5.78 4.72 4.20
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To test the hypotheses regarding the effect of manipulations on the
PMT variables, MANOVA was conducted to test the hypotheses. Hypothesis
5 stated that participants who viewed advertisements with higher threats
levels would report higher severity and vulnerability when compared to
consumers who viewed weaker threats. A one-way MANOVA was conducted
to test this hypothesis. The one-way MANOVA results were: Pillai’s
Trace=0.002; Wilks’ lambda = 0.998; Hotelling’s Trace and Roy’s Largest
Root = 0.002, F(2,66)=0.057, p > 0.05) (Table 6.46a). The results indicate
that there was no statistically significant difference in severity and
vulnerability based on threat levels and hence Table 6.46b was not further
interpreted. Therefore hypothesis 5 (H5) was not supported.
Table 6.46a Experiment 2: Hypothesis 5: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value F Hypothesis df Error df Sig.
Intercept Pillai's Trace .977 1387.169a 2.000 66.000 .000
Wilks' Lambda .023 1387.169a 2.000 66.000 .000
Hotelling's Trace 42.035 1387.169a 2.000 66.000 .000
Roy's Largest Root 42.035 1387.169a 2.000 66.000 .000
Threat_level Pillai's Trace .002 .057a 2.000 66.000 .945
Wilks' Lambda .998 .057a 2.000 66.000 .945
Hotelling's Trace .002 .057a 2.000 66.000 .945
Roy's Largest Root .002 .057a 2.000 66.000 .945
a. Exact statistic b. Design: Intercept + Threat_level
183
Table 6.46b Experiment 2: Hypothesis 5: tests of between-subjects effects (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares df Mean
Square F Sig.
Corrected Model
PERC_SEV .112a 1 .112 .111 .740PERC_VUL .015b 1 .015 .010 .919
Intercept PERC_SEV 2666.199 1 2666.199 2641.484 .000PERC_VUL 1944.363 1 1944.363 1340.709 .000
Threat_level PERC_SEV .112 1 .112 .111 .740PERC_VUL .015 1 .015 .010 .919
Error PERC_SEV 67.627 67 1.009PERC_VUL 97.167 67 1.450
Total PERC_SEV 2735.000 69PERC_VUL 2042.111 69
Corrected Total
PERC_SEV 67.739 68
a. R Squared = .002 (Adjusted R Squared = -.013) b. R Squared = .000 (Adjusted R Squared = -.015)
The results also indicate that there was no statistically significant difference in severity and vulnerability based on frame type (Pillai’s Trace=0.010; Wilks’ lambda = 0.990; Hotelling’s Trace and Roy’s Largest Root = 0.010, F(2,66)=0.341, p > 0.05). Therefore hypothesis 6 (H6) was not supported. The following tables (Tables 6.47a and 6.47b) show the results.
Table 6.47a Experiment 2: Hypothesis 6: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value F Hypothesis df
Error df Sig.
Intercept Pillai's Trace .977 1392.001a 2.000 66.000 .000 Wilks' Lambda .023 1392.001a 2.000 66.000 .000
184
Table 6.47a (Continued)
Effect Value F Hypothesisdf
Error df Sig.
Hotelling's Trace 42.182 1392.001a 2.000 66.000 .000 Roy's Largest Root 42.182 1392.001a 2.000 66.000 .000
frame Pillai's Trace .010 .341a 2.000 66.000 .712Wilks' Lambda .990 .341a 2.000 66.000 .712Hotelling's Trace .010 .341a 2.000 66.000 .712Roy's Largest Root .010 .341a 2.000 66.000 .712
a. Exact statistic b. Design: Intercept + frame
Table 6.47b Experiment 2: Hypothesis 6: tests of between-subjects effects (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares df Mean
Square F Sig.
Corrected Model
PERC_SEV .194a 1 .194 .192 .663PERC_VUL .991b 1 .991 .690 .409
Intercept PERC_SEV 2664.194 1 2664.194 2642.679 .000PERC_VUL 1945.068 1 1945.068 1354.799 .000
frame PERC_SEV .194 1 .194 .192 .663PERC_VUL .991 1 .991 .690 .409
Error PERC_SEV 67.545 67 1.008PERC_VUL 96.191 67 1.436
Total PERC_SEV 2735.000 69PERC_VUL 2042.111 69
Corrected Total
PERC_SEV 67.739 68PERC_VUL 97.182 68
a. R Squared = .003 (Adjusted R Squared = -.012) b. R Squared = .010 (Adjusted R Squared = -.005)
It can be seen from Tables 6.48a and 6.48b that the proposed
interaction between threat levels and frames was also not supported (Pillai’s
185
Trace=0.079; Wilks’ lambda = 0.921; Hotelling’s Trace and Roy’s Largest
Root = 0.086 (F2,64)=2.539, p>0.05). Therefore hypothesis 7 (H7) was not
supported.
Table 6.48a Experiment 2: Hypothesis 7: multivariate tests (watch stimuli)
Multivariate Testsb
Effect Value FHypothesis
df Error
df Sig.
Intercept Pillai's Trace .977 1379.959a 2.000 64.000 .000
Wilks' Lambda .023 1379.959a 2.000 64.000 .000
Hotelling's Trace 43.124 1379.959a 2.000 64.000 .000
Roy's Largest Root
43.124 1379.959a 2.000 64.000 .000
frame Pillai's Trace .010 .325a 2.000 64.000 .724
Wilks' Lambda .990 .325a 2.000 64.000 .724
Hotelling's Trace .010 .325a 2.000 64.000 .724
Roy's Largest Root
.010 .325a 2.000 64.000 .724
Threat_level Pillai's Trace .002 .061a 2.000 64.000 .941
Wilks' Lambda .998 .061a 2.000 64.000 .941
Hotelling's Trace .002 .061a 2.000 64.000 .941
Roy's Largest Root
.002 .061a 2.000 64.000 .941
frame * Threat_level
Pillai's Trace .074 2.539a 2.000 64.000 .087
Wilks' Lambda .926 2.539a 2.000 64.000 .087
Hotelling's Trace .079 2.539a 2.000 64.000 .087
Roy's Largest Root
.079 2.539a 2.000 64.000 .087
a. Exact statistic b. Design: Intercept + frame + Threat_level + frame * Threat_level
186
From Table 6.48b it could be seen that the interaction of the independent
factors had an effect on perceived vulnerability. Hence a followup ANOVA
revealed this effect. The ANOVA results are shown in Table 6.48c. Figure
6.2 shows that gain frames and high levels of threat produced higher
vulnerability scores. Under loss frame conditions, low threat produced higher
scores of perceived vulnerability.
Table 6.48b Experiment 2: Hypothesis 7: multivariate tests (watch stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model
PERC_SEV .743a 3 .248 .240 .868
PERC_VUL 7.784b 3 2.595 1.886 .141
Intercept PERC_SEV 2661.319 1 2661.319 2582.032 .000
PERC_VUL 1940.361 1 1940.361 1410.803 .000
Frame PERC_SEV .180 1 .180 .175 .677
PERC_VUL .907 1 .907 .659 .420
Threat_level PERC_SEV .128 1 .128 .124 .726
PERC_VUL .050 1 .050 .036 .850
frame * Threat_level
PERC_SEV .442 1 .442 .428 .515
PERC_VUL 6.781 1 6.781 4.930 .030
Error PERC_SEV 66.996 65 1.031
PERC_VUL 89.398 65 1.375
Total PERC_SEV 2735.000 69
PERC_VUL 2042.111 69
Corrected Total
PERC_SEV 67.739 68
PERC_VUL 97.182 68
a. R Squared = .011 (Adjusted R Squared = -.035) b. R Squared = .080 (Adjusted R Squared = .038)
187
Table 6.48c Experiment 2: Hypothesis 7: interaction effect of goal frames and threat levels on perceived vulnerability
Tests of Between-Subjects Effects Dependent Variable:PERC_VUL
Source Type III Sum of Squares
df MeanSquare F Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Corrected Model
7.784a 3 2.595 1.886 .141 .080 5.659 .467
Intercept 1940.361 1 1940.361 1410.803 .000 .956 1410.803 1.000 Threat_level .050 1 .050 .036 .850 .001 .036 .054frame .907 1 .907 .659 .420 .010 .659 .126Threat_level * frame 6.781 1 6.781 4.930 .030 .071 4.930 .590
Error 89.398 65 1.375Total 2042.111 69Corrected Total 97.182 68
a. R Squared = .080 (Adjusted R Squared = .038) b. Computed using alpha = .05
Figure 6.2 Experiment 2 – effect of threat levels and goal on perceived vulnerability
188
In order to examine H8a regression analyses was done with three
predictors: framing, environmental concern and an interaction term of these
variables with purchase intention as the dependent variable. Framing was
dummy coded with the loss-frame message condition allocated a value of 0
and the gain-frame message condition a value of 1.
The interaction terms were calculated as a product of frame type
and environmental concern (frame x environmental concern). The hypothesis
was not supported as the interaction between the variables did not predict
purchase intention. Since the model was significant, a follow up stepwise
regression revealed that only environmental concern predicted purchase
intention. Table 6.49a shows this interaction.
Table 6.49a Experiment 2: Hypothesis 8a: interaction of frame and environmental concern on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.444a 0.197 0.147 1.25387
a. Predictors: (Constant), TOTAL_ENV_CONCERN, FRAME_CODED, ENV_CONC_X_FRAME
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 21.394 3 7.131 4.461 .007a
Residual 103.901 65 1.598
Total 125.295 68
a. Predictors: (Constant), TOTAL_ENV_CONCERN, FRAME_CODED, ENV_CONC_X_FRAME b. Dependent Variable: PURCHASE_INTENTION
189
Table 6.49a (Continued)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
BStd.
Error Beta
1 (Constant) -.688 4.717 -.146 .884
FRAME_CODED .127 3.294 .047 .039 .969
ENV_CONC_X_FRAME .007 .523 .018 .014 .989
TOTAL_ENV_CONCERN .886 .750 .401 1.181 .242
a. Dependent Variable: PURCHASE_INTENTION
Similarly H8b was also not supported as message involvement did
not interact with frame type to produce an effect on purchase intentions.
Table 6.49b shows this interaction. Since the model was significant, a follow
up stepwise regression revealed that only message involvement significantly
predicted purchase intention.
Table 6.49b Experiment 2: Hypothesis 8b: interaction of frame and message involvement on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.515a 0.266 0.232 1.18986
Predictors: (Constant), MESS_INV_X_FRAME, MESSAGE_INVOLVEMENT, FRAME_CODED
190
Table 6.49b (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 33.270 3 11.090 7.833 .000a
Residual 92.025 65 1.416
Total 125.295 68
a. Predictors: (Constant), MESS_INV_X_FRAME, MESSAGE_INVOLVEMENT, FRAME_CODED b. Dependent Variable: PURCHASE_INTENTION
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.679 2.110 1.744 .086
FRAME_CODED -1.331 1.470 -.493 -.905 .369
MESSAGE_INVOLVEMENT .254 .400 .204 .634 .528
MESS_INV_X_FRAME .270 .276 .637 .979 .331
a. Dependent Variable: PURCHASE_INTENTION
H8c was not tested as the sample consisted of mostly male subjects
(95.7%). Therefore gender based variations could not be investigated.
The summary of results is shown in Table 6.50. The table clearly
highlights the fact that the factors were not successful in producing the
hypothesized main effects or interaction effects.
191
Table 6.50 Experiment 2: Summary of the effect of manipulations on PMT variables with the watch stimulus
Hypothesis Factor Perceivedseverity
Perceivedvulnerability
H5 Threat level X X
H6 Goal frame X X
H7 Threat level * Goal Frame X X
- -
- -
Purchase Intention
H8a Environmental concern * Goal Frame
X
H8b Message Involvement * Goal Frame
X
6.4.6.4 Hypotheses tests of the relationship among PMT variables,
involvement, attitudes and intentions
Similar to Experiment 1, standard simple or multiple linear
regression (ordinary least squares (OLS)) analyses were run to ascertain the
effect of the predictor variables. Only one or two predictors were considered
at a time. Similarly assumptions regarding like multicollinearity,
homoscedascity and linearity were met in all the scenarios.
A multiple regression analysis was conducted to evaluate how well
perceived severity and perceived vulnerability predicted fear. H9a was
supported as both perceived severity ( =0.318, t(66)=2.622, p<0.05) and
perceived vulnerability ( =0.273, t(66)=2.254, p<0.05) significantly predicted
fear. The model fit was also good (R2=0.261).
192
Perceived severity also predicted both the efficacy variables.
Tables 6.51a, 6.51b and 6.51c show the regression results.
Table 6.51a Experiment 2: Hypothesis 9a: effect of threat appraisal components on fear (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .510a 0.261 0.238 1.39272
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 45.110 2 22.555 11.628 .000a
Residual 128.018 66 1.940
Total 173.128 68
a. Predictors: (Constant), PERC_VUL, PERC_SEV
b. Dependent Variable: FEAR
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -.755 1.099 -.687 .495
PERC_SEV .508 .194 .318 2.622 .011
PERC_VUL .365 .162 .273 2.254 .028
a. Dependent Variable: FEAR
193
Table 6.51b Experiment 2: Hypothesis 9b: effect of threat appraisal components on response efficacy (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .382a 0.146 0.120 0.77197
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.700 2 3.350 5.621 .006a
Residual 39.332 66 .596
Total 46.032 68
a. Predictors: (Constant), PERC_VUL, PERC_SEV b. Dependent Variable: RESP_EFFICACY
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.959 .609 6.497 .000
PERC_SEV .196 .107 .237 1.821 .073
PERC_VUL .141 .090 .205 1.569 .121
a. Dependent Variable: RESP_EFFICACY
From Table 6.51b it can be seen that although the model was
significant, the predictors were not significant. Hence H9b was not supported.
A stepwise regression was conducted to evaluate which one of the variables
contributed to the model. The results showed that perceived severity alone
predicted response efficacy as shown in Table 6.51 b1 (R2=0.114 =0.337,
t(66)=2.931, p<0.01).
194
Table 6.51b1 Experiment 2: Effect of threat appraisal components on response efficacy (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .337a 0.114 0.100 0.78036
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 5.232 1 5.232 8.592 .005a
Residual 40.800 67 .609
Total 46.032 68
a. Predictors: (Constant), PERC_SEV b. Dependent Variable: RESP_EFFICACY
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 4.195 .597 7.027 .000
PERC_SEV .278 .095 .337 2.931 .005
a. Dependent Variable: RESP_EFFICACY
Excluded Variablesb
Model Beta In t Sig. Partial
Correlation
Collinearity Statistics
Tolerance
1 PERC_VUL .205a 1.569 .121 .190 .762
a. Predictors in the Model: (Constant), PERC_SEV b. Dependent Variable: RESP_EFFICACY
195
The results showed that perceived severity alone signficantly
predicts self efficacy as shown in Table 6.51c (R2=0.090 =0.331,
t(66)=2.459, p<0.01). Hence H9c was not supported.
Table 6.51c Experiment 2: Hypothesis 9c: effect of threat appraisal components on self efficacy (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .300a 0.090 0.062 1.22004
a. Predictors: (Constant), PERC_VUL, PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 9.721 2 4.861 3.265 .044a
Residual 98.240 66 1.488
Total 107.961 68
a. Predictors: (Constant), PERC_VUL, PERC_SEV b. Dependent Variable: SELF_EFFICACY
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.734 .963 2.839 .006
PERC_SEV .418 .170 .331 2.459 .017
PERC_VUL -.084 .142 -.080 -.593 .555
a. Dependent Variable: SELF_EFFICACY
Simple regression analyses were run with each of the PMT
variables and each one of them except self-efficacy significantly predicted
message involvement. Perceived severity ( =0.261 t(67)=2.213,
196
p<0.05,R2=0.06), perceived vulnerability ( =0.334 t(67)=2.898,
p<0.001,R2=0.111), fear ( =0.554 t(67)=5.447, p<0.001,R2=0.307), and
response efficacy ( =0.458 t(67)=4.221, p<0.05,R2 = 0.210) significantly
predict message involvement. The results are shown in the following tables
Tables 6.52a, 6.52b, 6.52c and 6.52d. Therefore H10a, H10b, H10c, H10d
were supported and H10e was not supported.
Table 6.52a Experiment 2: Hypothesis 10a: effect of perceived severity on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .261a 0.068 0.054 1.06091
a. Predictors: (Constant), PERC_SEV
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 5.512 1 5.512 4.898 .030a
Residual 75.411 67 1.126
Total 80.924 68
a. Predictors: (Constant), PERC_SEV b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.420 .812 4.214 .000
PERC_SEV .285 .129 .261 2.213 .030
a. Dependent Variable: MESSAGE_INVOLVEMENT
197
Table 6.52b Experiment 2: Hypothesis 10b: effect of perceived vulnerability on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.334a 0.111 0.098 1.03598
a. Predictors: (Constant), PERC_VUL
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 9.016 1 9.016 8.400 .005a
Residual 71.908 67 1.073
Total 80.924 68
a. Predictors: (Constant), PERC_VUL b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.576 .572 6.255 .000
PERC_VUL .305 .105 .334 2.898 .005
a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.52c Experiment 2: Hypothesis 10c: effect of fear on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .554a 0.307 0.297 0.91495
a. Predictors: (Constant), FEAR
198
Table 6.52c (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 24.836 1 24.836 29.668 .000a
Residual 56.088 67 .837Total 80.924 68
a. Predictors: (Constant), FEAR b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 3.549 .321 11.042 .000
FEAR .379 .070 .554 5.447 .000
a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.52d Experiment 2: Hypothesis 10d: effect of response efficacy on message involvement (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .458a 0.210 0.198 0.97676a. Predictors: (Constant), RESP_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 17.001 1 17.001 17.820 .000a
Residual 63.922 67 .954Total 80.924 68
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: MESSAGE_INVOLVEMENT
199
Table 6.52d (Continued)
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 1.594 .861 1.852 .068
RESP_EFFICACY .608 .144 .458 4.221 .000
a. Dependent Variable: MESSAGE_INVOLVEMENT
Table 6.52e Experiment 2: Hypothesis 10e: effect of self efficacy on message involvement (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .236a 0.056 0.042 1.06800a. Predictors: (Constant), SELF_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 4.502 1 4.502 3.947 .051a
Residual 76.422 67 1.141Total 80.924 68
a. Predictors: (Constant), SELF_EFFICACY b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 4.196 .518 8.097 .000
SELF_EFFICACY .204 .103 .236 1.987 .051 a. Dependent Variable: MESSAGE_INVOLVEMENT
200
Multiple regression analysis was conducted to evaluate how well
coping variables predicted attitude towards ad and purchase intention. Both
response efficacy ( =0.474, t(67)=4.409, p<0.001, R2=0.225) and self
efficacy ( =0.271, t(67)=2.306, p<0.05,R2=0.074) predicted attitude towards
ad. However they did not predict purchase intention. Therefore H11a and
H11b were supported but H11c and H11d were not. These results can be seen
in Tables 6.53a, 6.53b, 6.53c and 6.53d.
Table 6.53a Experiment 2: Hypothesis 11a: effect of response efficacy on attitude towards ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .474a 0.225 0.213 1.01172
a. Predictors: (Constant), RESP_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 19.897 1 19.897 19.439 .000a
Residual 68.579 67 1.024
Total 88.477 68
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: ATTITUDE_AD
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 1.739 .892 1.950 .055
RESP_EFFICACY .657 .149 .474 4.409 .000
a. Dependent Variable: ATTITUDE_AD
201
Table 6.53b Experiment 2: Hypothesis 11b: effect of self efficacy on attitude towards ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .271a 0.074 0.060 1.10608
a. Predictors: (Constant), SELF_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.508 1 6.508 5.320 .024a
Residual 81.968 67 1.223
Total 88.477 68
a. Predictors: (Constant), SELF_EFFICACY b. Dependent Variable: ATTITUDE_AD
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 4.434 .537 8.261 .000
SELF_EFFICACY .246 .106 .271 2.306 .024
a. Dependent Variable: ATTITUDE_AD
Table 6.53c Experiment 2: Hypothesis 11b: effect of response efficacy on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .185a 0.034 0.020 1.34386
a. Predictors: (Constant), RESP_EFFICACY
202
Table 6.53c (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 4.296 1 4.296 2.379 .128a
Residual 120.999 67 1.806
Total 125.295 68
a. Predictors: (Constant), RESP_EFFICACY b. Dependent Variable: PURCHASE_INTENTION
Table 6.53d Experiment 2: Hypothesis 11d: effect of self efficacy on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.110a 0.012 -0.003 1.35918
a. Predictors: (Constant), SELF_EFFICACY
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 1.521 1 1.521 .824 .367a
Residual 123.773 67 1.847
Total 125.295 68
a. Predictors: (Constant), SELF_EFFICACY b. Dependent Variable: PURCHASE_INTENTION
H12a, H12b, H12c and H12d were not supported as environmental
knowledge did not significantly predict perceived severity, perceived
vulnerability, fear or message involvement. Tables 6.54a , 6.54b 6.54c, 6.54d
illustrate the results.
203
Table 6.54a Experiment 2: Hypothesis 12a: effect of environmental knowledge on perceived severity (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.130a 0.017 0.002 0.99695
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 1.147 1 1.147 1.154 .286a
Residual 66.592 67 .994
Total 67.739 68
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: PERC_SEV
Table 6.54b Experiment 2: Hypothesis 12b: effect of environmental knowledge on perceived vulnerability (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.092a 0.009 -0.006 1.19921
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .829 1 .829 .577 .450a
Residual 96.353 67 1.438
Total 97.182 68
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: PERC_VUL
204
Table 6.54c Experiment 2: Hypothesis 12c: effect of environmental knowledge on fear (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.077a 0.006 -0.009 1.60273
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 1.021 1 1.021 .398 .530a
Residual 172.107 67 2.569
Total 173.128 68
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: FEAR
Table 6.54d Experiment 2: Hypothesis 12d: effect of environmental knowledge on message involvement (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.103a 0.011 -0.004 1.09322
a. Predictors: (Constant), ENV_KNOW
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression .850 1 .850 .711 .402a
Residual 80.073 67 1.195
Total 80.924 68
a. Predictors: (Constant), ENV_KNOW b. Dependent Variable: MESSAGE_INVOLVEMENT
205
Similar simple regression analysis was conducted to evaluate if
involvement predicted attitude towards ad. Environmental concern was
positively related to message involvement ( =0.457 t(66)=4.203, p<0.001,
R2=0.209) and therefore H13a was supported. H13b was also supported as
environmental concern significantly predicted attitude towards ad ( =0.499
t(66)=4.716, p<0.001, R2=0.249). Environmental concern significantly
predicted purchase intention ( =0.408 t(66)=3.661, p<0.001, R2=0.167) and
therefore H13c was supported. The following tables (Tables 6.55a, 6.55b,
6.55c) show the regression results.
Table 6.55a Experiment 2: Hypothesis 13a: effect of environmental concern (enduring involvement with the environment) on message involvement (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.457a 0.209 0.197 0.97766a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 16.883 1 16.883 17.664 .000a
Residual 64.040 67 .956Total 80.924 68
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: MESSAGE_INVOLVEMENT
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B Std.
Error Beta
1 (Constant) .112 1.215 .092 .927TOTAL_ENV_CONCERN .811 .193 .457 4.203 .000
a. Dependent Variable: MESSAGE_INVOLVEMENT
206
Table 6.55b Experiment 2: Hypothesis 13b: effect of environmental concern (enduring involvement with the environment) on attitude towards the ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.499a 0.249 0.238 0.99571
a. Predictors: (Constant), TOTAL_ENV_CONCERN
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 22.050 1 22.050 22.241 .000a
Residual 66.426 67 .991
Total 88.477 68
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: ATTITUDE_AD
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -.175 1.237 -.141 .888
TOTAL_ENV_CONCERN .927 .197 .499 4.716 .000
a. Dependent Variable: ATTITUDE_AD
Table 6.55c Experiment 2: Hypothesis 13c: effect of environmental concern (enduring involvement with the environment) on purchase intention (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.408a 0.167 0.154 1.24834
a. Predictors: (Constant), TOTAL_ENV_CONCERN
207
Table 6.55c (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 20.884 1 20.884 13.401 .000a
Residual 104.410 67 1.558
Total 125.295 68
a. Predictors: (Constant), TOTAL_ENV_CONCERN b. Dependent Variable: PURCHASE_INTENTION
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -.536 1.551 -.345 .731
TOTAL_ENV_CONCERN .902 .246 .408 3.661 .000
a. Dependent Variable: PURCHASE_INTENTION
It can be seen from Table 6.56 that H14 was supported as message
involvement predicted attitude towards ad ( =0.718 t(66)=8.452, p<0.001).
The model fit was also good with R2 =0.516.
Table 6.56 Experiment 2: Hypothesis 14: effect of message involvement on attitude towards ad (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.718a 0.516 0.509 0.79944
a. Predictors: (Constant), MESSAGE_INVOLVEMENT
208
Table 6.56 (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 45.657 1 45.657 71.439 .000a
Residual 42.820 67 .639
Total 88.477 68
a. Predictors: (Constant), MESSAGE_INVOLVEMENT b. Dependent Variable: ATTITUDE_AD
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.B
Std. Error
Beta
1 (Constant) 1.732 .471 3.674 .000
MESSAGE_INVOLVEMENT .751 .089 .718 8.452 .000
a. Dependent Variable: ATTITUDE_AD
H15 and H16 were also supported as attitude towards the ad
significantly predicted the attitude towards the brand ( =0.747 t(66)=9.210,
p<0.001, R2=0.559). Table 6.57 illustrates this result. Attitude towards the
brand significantly predicted the purchase intention as seen in Table 6.58
=0.647, t(66)=6.942, p<0.001, R2=0.418).
Table 6.57 Experiment 2: Hypothesis 15: effect of attitude towards adon attitude towards brand (watch stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.747a 0.559 0.552 0.74046
a. Predictors: (Constant), ATTITUDE_AD
209
Table 6.57 (Continued)
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 46.512 1 46.512 84.832 .000a
Residual 36.735 67 .548Total 83.246 68
a. Predictors: (Constant), ATTITUDE_AD b. Dependent Variable: ATTITUDE_BRAND
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 1.409 .452 3.115 .003
ATTITUDE_AD .725 .079 .747 9.210 .000a. Dependent Variable: ATTITUDE_BRAND
Table 6.58 Experiment 2: Hypothesis 16: effect of attitude towards brand on purchase intention (watch stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.647a 0.418 0.410 1.04293a. Predictors: (Constant), ATTITUDE_BRAND
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 52.419 1 52.419 48.192 .000a
Residual 72.876 67 1.088Total 125.295 68
a. Predictors: (Constant), ATTITUDE_BRAND b. Dependent Variable: PURCHASE_INTENTION
210
Table 6.58 (Continued)
Coefficientsa
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) .757 .640 1.183 .241
ATTITUDE_BRAND .794 .114 .647 6.942 .000a. Dependent Variable: PURCHASE_INTENTION
Table 6.59 below shows the summary of the hypotheses tests
regarding the relationship among the PMT variables, involvement, attitudes
and purchase intention.
Table 6.59 Experiment 2: Summary of hypotheses tests regarding relationship between PMT variables, involvement, attitudes and intentions (H9 – H16) (watch stimuli)
Hypothesis Predictor Dependent
variable R2 Adjusted
R2
Unstandardisedcoefficient B
Standardised coefficient
H9a Perceived Severity and Perceived vulnerability
Fear 0.261 0.238 0.5080.365
0.318*0.273*
H9b Perceived Severity and Perceived vulnerability
Response Efficacy
Not supported (Only perceived severity)
H9a Perceived Severity and Perceived vulnerability
Self efficacy 0.300 0.090 0.418 0.331*
H10a Perceived Severity
Message Involvement
0.06 0.05 0.285 0.261*
H10b Perceived Vulnerability
Message Involvement
0.11 0.09 0.305 0.334**
H10c Fear Message Involvement
0.30 0.29 0.37 0.55***
211
Table 6.59 (Continued)
Hypothesis Predictor Dependent
variable R2 Adjusted
R2Unstandardised
coefficient B Standardised coefficient
H10d Response Efficacy
Message Involvement
0.21 0.19 0.608 0.458***
H10e Self Efficacy Message Involvement
Not Significant
H11a Response efficacy
Attitude towards ad
0.22 0.21 0.65 0.47 ***
H11b Self Efficacy Attitude towards ad
0.07 0.06 0.246 0.271 *
H11c Response efficacy
Purchase Intention
Not Significant
H11d Self Efficacy Purchase Intention
Not Significant
H12a EnvironmentalKnowledge
Perceived Severity
Not Significant
H12b EnvironmentalKnowledge
Perceived Vulnerabilty
Not Significant
H12c EnvironmentalKnowledge
Fear Not Significant
H12d EnvironmentalKnowledge
Message Involvement
Not Significant
H13a Environmental concern
Message Involvement
0.20 0.19 0.81 0.45***
H13b Environmental concern
Attitude towards ad
0.25 0.24 0.92 0.49***
H13c Environmental concern
Purchase Intention
0.16 0.15 0.902 0.408***
H14 Message Involvement
Attitude towards ad
0.51 0.50 0.751 0.718***
H15 Attitude towards ad
Attitude towards brand
0.55 0.55 0.725 0.747***
H16 Attitude towards brand
Purchase Intention
0.41 0.41 0.79 0.64***
***p <.001 **p <.01 *p <.05; n= 68
212
6.4.7 Conclusions from Experiment 2
This experiment examined the effects of varying threat levels and
goal frames on PMT variables and the subsequent effects of the PMT
variables on message involvement, attitudes and purchase intention. The
manipulation checks were moderately successful as under higher threat
conditions, participants identified the threat levels correctly. Internal
consistency of the self-efficacy scale successfully improved to 0.56. There
was no main effect of threat levels on perceived severity of threat, perceived
vulnerability and fear. The goal frames did not influence the threat appraisal
variables as hypothesized. There was also no significant interaction effect
between the factors as hypothesized. However independent variables had an
interaction effect on perceived vulnerability. The interaction showed that gain
frames and higher threat levels increased feelings of perceived vulnerability.
This result confirms the results of previous studies (Rothman et al 1993;
Mann et al 2004) that gain frames work better in the case of preventive
behaviour. This result also shows that goal framing can be used to promote
pro-environmental behaviour by accentuating intrinsic goals related to health
and well-being (Lindenberg & Steg 2007; Pelletier & Sharp 2008).
In contrast to previous studies (Cox & Cox 2001; Meyers-Levy &
Maheswaran 2004; van ‘t Riet et al 2008;O’Keefe & Jensen 2009; Janssens
et al 2010; Updegraff 2013) loss frames did not increase threat perception.
Similarly there was no relationship between involvement and framing. Both
environmental concern and message involvement did not interact with frames
to produce an effect on purchase intention.
Perceived severity and perceived vulnerability significantly
predicted fear as proposed by PMT (Rogers & Prentice-Dunn 1997; Floyd et
al 2000) and other studies that apply this theory (Milne et al 2000; de Hoog et
al 2008). Since the participants judged the threat to be high, fear levels
213
increased and coping appraisal was also intiated (Maddux & Rogers 1983;
Boer & Seydel 1996; Milne et al 2000). In the case of the previous
experiment only response efficacy and perceived vulnerability predicted
message involvement. In this experiment all the PMT variables (perceived
severity, perceived vulnerability, fear and response efficacy) except self
efficacy significantly predicted message involvement. This shows that higher
levels of health risk increased elaboration and thereby increased their
involvement with the message as observed in previous studies (Bloch &
Richins 1983; Richins & Bloch 1986; Keller & Block 1996; de Hoog 2005).
Therefore fear can increase more effortful processing in environment related
communication (Meijnders et al 2001). The relationship between the PMT
variables and message involvement is similar to the results presented by
recent research (Cauberghe et al 2009). Environmental knowledge did not
have any effect on the hypothesized variables. Unlike the previous
experiment, environmental concern had a significant effect on message
involvement, attitudes and intentions. This confirms ELM’s proposition that
issue involvement has an effect on attitudes and intentions (Petty & Cacioppo
1986). Both environmental concern and message involvement had a
significant influence on attitudes and intention similar to other advertising
studies (Gardner 1985; Park & Young 1986; Muehling & Laczniak 1988).
The manipulation checks were comparatively successful than
Experiment1 for the threat level perception. Therefore Experiment 3 was
designed using these two factors (threat level and goal frames) by refining the
stimuli. Since high scores for perceived severity and vulnerability were
reported in both the scenarios, the threat levels were modified, such that the
low threat level contained very generic statements about pollution issues,
whereas the high threat levels highlighted the perceived vulnerability to the
threat, by mentioning risks of cancer and respiratory illness. The next
214
experiment used the issue of e-waste (based on mobile phone stimuli) to
observe if similar effects were observed.
6.5 STUDY 2: EXPERIMENT 3: THREAT LEVELS AND GOAL
FRAMING (MOBILE PHONE STIMULI)
Experiment 3 was designed to use threat levels and goal frames to
influence the PMT variables. This experiment was conducted with mobile
phones as the chosen product. The mobile phone stimulus was chosen for the
experiment and the changes to the stimulus were made based on the
information gained from Experiment 2.
6.5.1 Experimental Design
A 2 (frame type: gain vs. loss) x 2 (threat level: high vs. low)
between subjects experimental design was utilized to investigate the
hypotheses. This resulted in four possible combinations of the stimuli. One
hundred and ninety undergraduate engineering students from a large South
Indian University (52.4 % male, median age=20) were randomly assigned to
the four possible conditions. Males and female respondents were represented
almost equally. The respondents’ age ranged from 18-22. The participants
were exposed to the stimulus and data collection was through a paper and
pencil questionnaire.The questionnaire (Q4) is shown in Appendix 7. On
completing the questions on the dependent variables, the respondents were
given the filler questionnaire followed by the counterbalanced questionnaires
related to the environment (environmental concern and environmental
knowledge). This was similar to the questions in Experiment 1. The
personality variable CFC was also not included in this questionnaire.
215
6.5.2 Stimuli and Treatment Validity
A total of four print advertisements were developed for the four
cells: high threat level and gain frame; high threat level and loss frame; low
threat level and gain frame; low threat level and loss frame. The
advertisements listed the features of the green mobile phone and specified that
the mobile phone is 82% recyclable. In Experiment 2, the participants judged
low levels of threat as a severe threat. Hence in this experiment the threat
levels were toned down in the low threat level conditions.
In the low threat condition, the advertisement emphasized that e-
waste was difficult to dispose and did not specifically mention a health threat.
In the high threat condition, the message emphasized health threats like
respiratory illness and highlighted personal vulnerability towards the threat.
The gain frame exhorted the respondents to protect themselves and the loss
frame message highlighted the potential losses incurred when not purchasing
a green product. These were also made stronger. The advertisements were
shown to the panel as in the previous experiments to check their validity. The
advertisements are presented in Appendix 8 (Figure A8.1, Figure A8.2, Figure
A8.3 and Figure A8.4).
6.5.3 Manipulation Checks
Threat level manipulations were checked by verifying if the
perceived severity and perceived vulnerability varied for different threat
levels. Frame manipulations were checked by asking the respondents (similar
to Experiment 2) to rate the following questions: “I can gain health benefits
by buying recylable products”, “I can lose important health benefits if I don’t
buy recylable products”. The response to this items was measured using seven
point Likert scales anchored from 1 = Strongly Disagree and 7 = Strongly
Agree.
216
6.5.4 Dependent Variables
Most of the dependent variables were the same as Experiment 2.
The items were changed to reflect the issue of e-waste and changes are
described below.
6.5.4.1 Protection motivation theory variables
Perceived severity
Perceived severity was measured using a three item seven point
scale where 1 = Strongly Disagree and 7 = Strongly Agree. Participants were
asked to indicate their responses on the following statements. “I believe that
e-waste pollution is a serious threat to human health”, “I believe that e-waste
disposal may cause severe health issues”, “I believe that e-waste pollution is
extremely harmful”. The three items were averaged into a single perceived
severity score.
Perceived vulnerability
Perceived vulnerability was measured using a three item seven
point scale where 1 = Strongly Disagree and 7 = Strongly Agree to determine
the participants’ susceptibility to the threat. Participants were asked to
indicate their responses on the following statements: “It is possible that I
might get affected by diseases caused by e-waste pollution.”, “It is probable
that I will suffer from various diseases caused by e-waste pollution”, “I am at
risk for getting health problems caused by e-waste pollution”. These items
were collapsed into a single perceived vulnerability score.
217
Response efficacy
Response efficacy was also measured using a three item seven
point scale where 1 = Strongly Disagree and 7 = Strongly Agree to determine
whether participants’ believed if purchasing recyclable products averted the
threat. Participants rated their responses on the following statements:
“Purchasing recyclable products is a highly effective way of preventing
diseases caused by e-waste pollution”, “Buying recyclable products will
significantly lower my risk of being affected by diseases caused by e-waste
pollution”, “Buying recyclable products is an effective method of reducing
threats to human health caused by e-waste pollution” These items were
combined into a single response efficacy score.
Self efficacy
Self efficacy was measured using a three item seven point
scaleswhere 1 = Strongly Disagree and 7 = Strongly Agree to determine
whether participants’ believed if they were capable of averting the threat.
Participants rated their responses on the following statements: “I am capable
of checking if products contain recyclable materials”, “I can easily switch
over to recyclable products to prevent future health problems”, “I can identify
and purchase recyclable products” These items were combined into a single
self efficacy score. The rest of the dependent variables remained the same as
Experiment 2. The questionnaire (Q4) is shown in Appendix 7 as discussed
previously.
6.5.5 Demographics
There were very few demographic variables collected from the
participants, as these variables were not the major focus of the research
questions. Table 6.60 shows the demographic details of the sample based on
218
the usable responses out of the 190 students. Male and female students were
represented in almost equal proportions. Most of the students were aged 20
and above.
6.5.6 Data Screening
Unlike previous experiments, this study planned to use PLS-SEM.
Hence to ensure data integrity, data screening was conducted using SPSS 19
to ensure the validity of the data prior to hypotheses testing. Data was first
screened for missing values and outliers (univariate and multivariate). Apart
from this, other multivariate statistical assumptions (normality, linearity and
homoscedasticity) were also investigated.
Table 6.60 Demographic details
Variable Count %
Gender MaleFemale Total
9685181
4753100
Age1819202122
13596445
0.619.353.024.32.8
Course Civil Engineering Electrical and Electronics EngineeringInformation Technology Mining Engineering Printing Technology
4256311537
23.230.917.18.320.4
219
6.5.6.1 Missing Value Analysis
On inspection, there were very few missing values. Only one value
was missing in the dependent variable. Hair et al (2009) recommend mean
imputation as a suitable method for replacing missing values in such cases.
Hence mean imputation was used to replace this missing value. After
imputation of the missing value, the items were summated to determine the
composite score of the variables.
6.5.6.2 Outlier Analysis
Univariate outliers
Box plots were used to identify univariate outliers among
dependent variables. Outlier analyses were conducted on composite variables
to reduce the effect any variations that single indicators might cause. Few
outliers were identified.
Multivariate outliers
Multivariate outliers are unusual combinations of variable values
(Hair et al 2009). Mahalanobis' distance was used to identify multivariate
outliers. A conservative level of significance of 0.001 was used to identify
outliers. Four cases were identified as outliers.
6.5.6.3 Decision regarding outliers
Since there were no data entry errors or other anomalies, univariate
outliers were not deleted as deleting them might impact the generalizability of
the data (Hair et al 2009). Only the multivariate outliers were removed as they
represented a small proportion (2.1%) of the total number of cases. This
resulted in 181 usable responses.
220
6.5.6.4 Univariate normality
Skewness and kurtosis were assessed to determine univariate
normality. Certain variables (perceived severity, response efficacy, self
efficacy, message involvement) did not follow a normal distribution.
For instance, respondents perceived high severity in both high and low threat
conditions and therefore the perceived severity value was negatively skewed.
The skewness and kurtosis values are shown in Table 6.61.
Table 6.61 Univariate normality
Mean Skewness Kurtosis
Statistic Statistic Zskew Statistic Zkurtosis
PERC_SEV 5.6427 -.900 -4.97238 .696 1.938719
PERC_VUL 4.7403 -.681 -3.76243 .402 1.119777
RESP_EFFICACY 5.6446 -.816 -4.50829 1.287 3.584958
SELF_EFFICACY 4.6961 -.330 -1.8232 -.434 -1.20891
MESSAGE_INVOLVEMENT 5.1860 -.766 -4.23204 .755 2.103064
FEAR 3.9945 -.221 -1.22099 -.563 -1.56825
ATTITUDE_AD 5.3168 -.632 -3.49171 .500 1.392758
ATTITUDE_BRAND 5.1731 -.321 -1.77348 -.341 -0.94986
PURCHASE_INTENTION 4.5783 -.544 -3.00552 -.143 -0.39833
ENV_KNOW 6.6133 .360 1.98895 -.076 -0.2117
TOTAL_ENV_CONCERN 5.9982 -1.420 -7.8453 3.451 9.612813
Most of the skewness and kurtosis values range from -1 to +1,
except environmental concern. Further tests showed that Z-values of the
skewness and kurtosis values of most variables were negative. This is not
surprising as the computed mean values are very high for the threat and fear
variables. Hair et al (2009) also recommend Z-tests for testing the skewness
221
of the variables and based on significance at 0.01 levels, seven variables
exceeded the value of 2.58. Most variables are negatively skewed.
6.5.6.5 Multivariate Statistical Assumptions
Multivariate normality
Multivariate normality was assessed based on Mardia’s coefficient
(Mardia 1970) using IBM AMOS 18. A high critical ratio of the coefficient
(26.512) indicated that data was significantly not normal as it exceeded the
cut-off value of 5.0 as suggested by Bentler (2006).
Linearity and homoscedasticity
Linearity and homoscedasticity were assessed among the variables
by using the regression residual and scatter plots. The variables met the
assumptions of linearity and homoscedasticity.
6.5.6.6 Conclusions from Data Screening
There was very few missing data and data imputation was done to
handle missing data. Data analysis showed that most variables were
negatively skewed and had a non-normal distribution. MANOVA is robust to
the violations of normality (Leech et al 2011) and therefore hypotheses
involving MANOVA were conducted using IBM SPSS 19. This study was
planned to be analysed using partial least squares based SEM (PLS-SEM) and
therefore this choice ensured that non normal data distribution did not pose a
problem for further analysis. Additionally the fact that PLS also works well
for a series of cause and effect relationships is to the study’s advantage
(Bontis et al 2007). PLS bootstrap also provides a more accurate and efficient
estimation of structural model parameters when compared to MLE and
222
Bollen-Stine SEM bootstraps when there are fewer than 200 observations
(Sharma & Kim 2013).
6.5.7 Validity and reliability analyses
Validity and reliability was assessed in two different ways. For the
first level of analysis, to assess the effect of the factors on the PMT variables
MANOVA is to be used. Hence Cronbach’s & EFA (Exploratory Factor
Analysis) were used to analyse the reliability and validity of the PMT
variables.
Next, the results of path analysis were to be analysed using PLS-
SEM. Hence, the measurement model was checked to ensure the reliability
and validity criteria associated with the formative and reflective measurement
model.
6.5.7.1 Validity and reliability of the PMT variables
Exploratory Factor Analysis (EFA) was used to examine
component loadings for the PMT constructs. The prescribed minimum sample
size for EFA is 100 (Hair et al 2009). On completion of the EFA, scale
reliabilities were assessed using the reliability coefficient (Cronbach ). The
PMT constructs (perceived severity, perceived vulnerability, fear, response
efficacy and self efficacy) loaded on five factors. Since PMT hypothesizes
close relationship between the constructs, oblique rotation was used and
factor loading above 0.4 was used to interpret each factor (Wu et al 2005).
None of the items loaded on more than one factor. Next, the internal
reliabilities of the constructs were tested using Cronbach’s and were found
adequate.
223
Scale reliability of the PMT constructs
Table 6.62 lists the reliabilities of the PMT constructs used. All the
scales had adequate reliabilities except response efficacy which had a
moderate reliability of 0.56. However, other PMT studies have also used this
variable despite achieving such moderate reliability scores (e.g. Milne et al
2002; Wu et al 2005; Daley et al 2009). Hence, this variable was retained in
the study.
Table 6.62 Experiment 3: Means and reliabilities of the PMT constructs
Scale Items Item-total correlation ( ) MEAN SDPerceived Severity 0.67 5.65 0.93
Perceived Vulnerability 0.62 4.76 1.13
Response Efficacy 0.56 5.64 0.82
Self Efficacy 0.61 4.7 1.18
Fear 0.87 4.0 1.321
6.5.7.2 Reliability of the reflective constructs by assessing the
measurement/outer model
Indicator reliability and composite reliability were used to assess
the reliability of the reflective measurement model. Cronbach’s exceeded
0.6 for all constructs (Table 6.62), except response efficacy which had a
moderate reliability as discussed previously. Composite reliability was now
used to prioritise indicators during estimations. Indicator reliability was
checked by examining the indicator loadings. Table 6.63 shows the indicator
loading and the composite reliability. The loadings ranged from 0.40 to 0.94
and most of them exceeded 0.70 (Fornell & Larcker 1981). Loadings below
0.7 are candidates for deletion if deleting these indicators leads to an increase
in composite reliability above the threshold value of 0.70. However composite
224
reliability scores ranged from 0.76 to 0.95 and therefore exceeded the 0.7 cut-
off (Hair et al 2011; Hair et al 2012). Only MI2 (“The ad's message seemed
relevant to me.”) from the message involvement scale had a low value but it
was not necessary to remove this indicator as the AVE and CR values were
above requisite cut-off criteria (Hair et al 2011) .
Common method bias
Common method variance is a potential problem in social science
research as data is collected through surveys based on self reporting. This
research tried to minimise the effect of common method bias by (a) increasing
the clarity of questions by using iterative pretests (b) not collecting sensitive
personal data as it might induce social desirability responses (Herath & Rao
2009; Mohan et al 2013). Harman’s One-Factor Test was employed to test if
a single factor emerged from the analysis or if a general factor explained
majority of the variance (Podsakoff et al 2003). The results of an exploratory
factor analysis on IBM SPSS 19 showed that multiple factors were present
and the major factor accounted for only 29 % of the total variance. However
this method has its own limitations (Podsakoff et al 2003; Chin et al 2012).
Therefore the correlations matrix of the latent variables was observed and the
largest correlation was 0.65 which is lower than the correlations that suggest
common method bias (r > 0.9).
6.5.7.3 Validity
Convergent and discriminant validity was also assessed for the
measurement model. The average variance extracted (AVE) was higher than
the requisite 0.50 for all the constructs except environmental concern which is
a second order construct. The values are shown in Table 6.63. Discriminant
validity was evaluated by the examination of the cross loading of the variable
and the Forner-Larcker criterion.
225
Table 6.63 Experiment 3: Composite reliability, indicator reliability and convergent validity
Construct Indicator Outer
Loading>0.7
Composite Reliability
>0.7
AVE
>0.5Perceived Severity PS1 0.8361 0.824 0.619
PS2 0.8671PS3 0.6251
Perceived Vulnerability PV1 0.8741 0.791 0.568PV2 0.5212PV3 0.8177
Response Efficacy RE1 0.7463 0.778 0.539RE2 0.7732RE3 0.6812
Self Efficacy SE1 0.6773 0.761 0.525SE2 0.5718SE3 0.8864
Fear F1 0.8286 0.904 0.661F2 0.8871F3 0.8813F4 0.7723F5 0.6578
Message Involvement MI1 0.6895 0.859 0.512MI2 0.4081MI3 0.7837MI4 0.7461MI5 0.7964MI6 0.7968
Attitude towards brand AAB1 0.917 0.951 0.866AAB2 0.9402AAB3 0.935
Attitude towards ad AAD1 0.8815 0.901 0.753AAD2 0.8629AAD3 0.8559
226
Table 6.63 (Continued)
Construct Indicator Outer
Loading>0.7
Composite Reliability
>0.7
AVE
>0.5Purchase Intention PI1 0.9222 0.949 0.860
PI2 0.9301PI3 0.933
Biospherical-Concern Animals 0.711 0.844 0.573Birds 0.778Plants 0.803
Children 0.741Egoistic-concern Me 0.833 0.844 0.538
My Future 0.839My Health 0.708
My LifeStyle 0.643Altruistic - Others All People 0.715 0.805 0.626
My children 0.684People in
my country 0.714
Marine 0.736Environmental concern Bio 0.859 0.604
Egoistic Altruistic
The AVE of the latent construct must be greater than the latent
construct’s highest squared correlation with other constructs (Fornell &
Larcker 1981). It can be seen from Table 6.64 that the Forner-Larcker
criterion is satisfied. Table 6.65 shows the details of the second-order
construct. Here too, the criterion was met.
The main diagonal in Table 6.64 and 6.65 show the AVE of the
constructs. The scales satisfied the discriminant validity criteria. The loadings
and cross-loading of item to other constructs were also inspected to evaluate
227
discriminant validity. Items loaded more on their constructs when compared
to other constructs as required.
Table 6.64 Experiment 3: Discriminant validity
AAD AAB FEAR MI PS PV PI RE SE
AAD 0.753
AAB 0.498 0.866
FEAR 0.071 0.068 0.661
MI 0.313 0.281 0.26 0.512
PS 0.0595 0.091 0.148 0.127 0.619
PV 0.053 0.094 0.086 0.089 0.164 0.568
PI 0.243 0.428 0.132 0.231 0.075 0.034 0.86
RE 0.088 0.071 0.094 0.181 0.261 0.158 0.047961 0.539
SE 0.033 0.035 0.024 0.071 0.076 0.031 0.09 0.127449 0.525
Table 6.65 Experiment 3: Discriminant validity of the second order construct (environmental concern)
Egoistic Altruistic Biospheric
Egoistic 0.538
Altruistic 0.099 0.573
Biospheric 0.130 0.298 0.626
The analysis shows that the reflective measurement model for the
(both first-order and second-order) variables used in this research are reliable
and valid.
Validity of the formative construct
Hair et al (2011) recommend the examination of convergent
validity, collinearity among indicators and use previous theory to retain
indicators that do not have significant outer loading to asses the formative
228
constructs and indicators. The weight of the indicators was examined by
resampling using bootstrapping (181 observations per subsample, 5,000
subsamples and no sign changes) in SmartPLS as this is the primary statistic
for examining the indicators (Hair et al 2012). The t-values were significant
for most of the indicators except EK7, EK8, EK14 and EK15 (p<0.05). Hence
these indicators were removed from the model. Subsequently variance
inflation factor (VIF) was used to test the multicollinearity among the
remaining environment knowledge indicators. The results show minimal
collinearity among the indicators as the VIF of all items ranged between
1.073 and 1.33, below the common cut off value of 5. Therefore, the
assumption of multicollinearity was not violated (Chin, 2010).
6.5.8 Results of Experiment 3: threat levels and goal framing
This experiment was conducted to verify goal framing and threat
levels. The mobile phone stimulus was chosen, since electronic waste seemed
to be a less familiar issue when compared to biodegradability.
This study was conducted with the mobile phone stimuli to evaluate
the effect of different threat levels (low/high) and goal frames (gain vs. loss)
on the PMT variables. The effect of PMT variables on involvement and the
subsequent influence of involvement on attitudes and purchase intention were
evaluated using the path model.
6.5.8.1 Manipulation checks
One-way ANOVA was conducted to examine the effectiveness of
the message in manipulating the perceived severity, perceived vulnerability
based on threat levels. In both the threat levels, the mean for the perceived
severity remained above 5.5 and perceived vulnerability scores ranged above
4.9. Therefore there was no statistically significant effect of the threat level on
229
these variables. Similarly the manipulation checks of the frames indicated that
there was no statistically significant effect of the frames on the variables
included for manipulation checks. Tables 6.66a, 6.66b, 6.66c and 6.66d show
the results of the manipulation check.
Table 6.66a Experiment 3: Manipulation check: effect of threat level on perceived severity
Descriptive Statistics Dependent Variable:PERCEIVED SEVERITY
Threat Level Mean Std. Deviation Nhigh 5.7753 .88731 89low 5.5145 .96383 92
Total 5.6427 .93362 181
Tests of Between-Subjects Effects Dependent Variable:PERCEIVED SEVERITY
Source Type III Sum of Squares df Mean Square F Sig.Corrected Model 3.077a 1 3.077 3.580 .060Intercept 5765.935 1 5765.935 6709.822 .000Threat_level 3.077 1 3.077 3.580 .060Error 153.820 179 .859Total 5920.000 181Corrected Total 156.896 180a. R Squared = .020 (Adjusted R Squared = .014)
Table 6.66b Experiment 3: Manipulation check: effect of threat level on perceived vulnerability
Descriptive Statistics
Dependent Variable:PERCEIVED VUNERABILITY
Threat Level Mean Std. Deviation N
High 4.8052 1.02840 89
low 4.6775 1.24177 92
Total 4.7403 1.14049 181
230
Table 6.66b (Continued)
Tests of Between-Subjects Effects
Dependent Variable:PERCEIVED VULNERABILITY
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model .738a 1 .738 .566 .453
Intercept 4067.903 1 4067.903 3119.890 .000
Threat_level .738 1 .738 .566 .453
Error 233.391 179 1.304
Total 4301.333 181
Corrected Total 234.129 180
a. R Squared = .003 (Adjusted R Squared = -.002)
Manipulation checks also revealed that the frame manipulation was
not successful as there was no significant main effect of the frame
manipulation (loss vs. gain) in both the conditions (Table 6.66c and 6.66d).
Table 6.66c Experiment 3: Manipulation check: effect of frame type (gain)
Tests of Between-Subjects Effects
Dependent Variable:MC_GAIN
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model .816a 1 .816 .526 .469
Intercept 5045.855 1 5045.855 3251.132 .000
frame_type .816 1 .816 .526 .469
Error 277.813 179 1.552
Total 5328.000 181
Corrected Total 278.630 180
a. R Squared = .003 (Adjusted R Squared = -.003)
231
Table 6.66d Manipulation check: effect of frame type (loss)
Tests of Between-Subjects Effects
Dependent Variable:MC_LOSS
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model .021a 1 .021 .008 .928
Intercept 3916.154 1 3916.154 1520.422 .000
frame_type .021 1 .021 .008 .928
Error 461.051 179 2.576
Total 4378.000 181
Corrected Total 461.072 180
a. R Squared = .000 (Adjusted R Squared = -.006)
Similar to the other experiments , failed manipulation checks were
not of great concern and therefore further analysis on the data was conducted.
6.5.8.2 Hypotheses tests of the effect of manipulations on PMT
variables
The hypothesized effect of goal frames and threat levels on the
PMT variables was analyzed using MANOVA. The dependent variables
(perceived severity, perceived vulnerability, fear, response-efficacy and self-
efficacy) were only moderately correlated (0.21 – 0.51) and therefore there
was no risk of multicollinearity to pose a hindrance to MANOVA. Tables
6.67 and 6.68 show the distribution characteristics and the group wise means
of the protection motivation variables. Similar to previous experiments, the
perceived severity and perceived vulnerability to the threat are on the higher
side. The group wise means do not seem to differ much similar to the
previous experiments.
232
Table 6.67 Experiment 3: Distribution characteristics of the protection motivation variables watch stimuli
Minimum Maximum Mean Std. Deviation
PERC_SEV 2.67 7.00 5.64 0.93
PERC_VUL 1.00 7.00 4.74 1.14
RESP_EFFICACY 2.33 7.00 5.64 0.83
SELF_EFFICACY 1.67 7.00 4.70 1.20
FEAR 1.00 7.00 3.99 1.33
Table 6.68 Experiment 3: Group wise mean values of protection motivation variables for the mobile phone stimuli
Factor PerceivedSeverity
PerceivedVulnerability
Response Efficacy
SelfEfficacy
Fear
Threat level: High
5.77 4.80 5.71 4.78 3.98
Threat level: Low
5.51 4.67 5.57 4.60 4.00
Goal frame: Gain
5.72 4.72 5.71 4.66 3.86
Goal frame: Loss
5.56 4.75 5.57 4.72 4.12
A one-way MANOVA was conducted to test hypothesis 5 (H5) that
stated that participants who viewed advertisements with higher threats levels
would report higher levels of severity and vulnerability when compared to
consumers who viewed weaker threats. The results did not show significant
differences between the groups (Pillai’s Trace=0.20; Wilks’ lambda = 0.980;
Hotelling’s Trace and Roy’s Largest Root = 0.020, F(2,178) =1.782, p >0.05)
and hence the hypothesis was not supported (Table 6.69a and 6.69b).
233
Table 6.69a Experiment 3: Hypothesis 5: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value FHypothesis
df Error
df Sig.
Intercept Pillai's Trace .977 3712.945a 2.000 178.000 .000
Wilks' Lambda .023 3712.945a 2.000 178.000 .000
Hotelling's Trace
41.718 3712.945a 2.000 178.000 .000
Roy's Largest Root
41.718 3712.945a 2.000 178.000 .000
Threat_level Pillai's Trace .020 1.782a 2.000 178.000 .171
Wilks' Lambda .980 1.782a 2.000 178.000 .171
Hotelling's Trace
.020 1.782a 2.000 178.000 .171
Roy's Largest Root
.020 1.782a 2.000 178.000 .171
a. Exact statistic b. Design: Intercept + Threat_level
Table 6.69b Experiment 3: Hypothesis 5: tests of between-subjects effects (mobile phone stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model
Perceived severity
3.077a 1 3.077 3.580 .060
Perceived vunerability
.738b 1 .738 .566 .453
Intercept Perceived severity
5765.935 1 5765.935 6709.822 .000
Perceived vunerability
4067.903 1 4067.903 3119.890 .000
234
Table 6.69b (Continued)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Threat_level Perceived severity
3.077 1 3.077 3.580 .060
Perceived vunerability
.738 1 .738 .566 .453
Error Perceived severity
153.820 179 .859
Perceived vunerability
233.391 179 1.304
Total Perceived severity
5920.000 181
Perceived vunerability
4301.333 181
Corrected Total
Perceived severity
156.896 180
Perceived vunerability
234.129 180
a. R Squared = .020 (Adjusted R Squared = .014) b. R Squared = .003 (Adjusted R Squared = -.002)
Hypothesis 6 (H6) was not supported as the results indicated that
there was no statistically significant difference in severity and vulnerability
based on frame type (Pillai’s Trace=0.010; Wilks’ lambda = 0.990;
Hotelling’s Trace and Roy’s Largest Root = 0.010, F(2,178) = 0.872, p >0.05)
(Table 6.70a and 6.70b). Therefore frame type did not increase threat
perception.
235
Table 6.70a Experiment 3: Hypothesis 6: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value FHypothesis
df Error
df Sig.
Intercept Pillai's Trace .976 3659.959a 2.000 178.000 .000
Wilks' Lambda .024 3659.959a 2.000 178.000 .000
Hotelling's Trace
41.123 3659.959a 2.000 178.000 .000
Roy's Largest Root
41.123 3659.959a 2.000 178.000 .000
frame_type Pillai's Trace .010 .872a 2.000 178.000 .420
Wilks' Lambda .990 .872a 2.000 178.000 .420
Hotelling's Trace
.010 .872a 2.000 178.000 .420
Roy's Largest Root
.010 .872a 2.000 178.000 .420
a. Exact statistic b. Design: Intercept + frame_type
Table 6.70b Experiment 3: Hypothesis 6: tests of between-subjects effects (mobile phone stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
Corrected Model
Perceived severity
1.124a 1 1.124 1.292 .257
Perceived vunerability
.054b 1 .054 .041 .840
Intercept Perceived severity
5764.188 1 5764.188 6623.707 .000
Perceived vunerability
4065.598 1 4065.598 3109.007 .000
236
Table 6.70b (Continued)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df Mean
Square F Sig.
frame_type Perceived severity
1.124 1 1.124 1.292 .257
Perceived vunerability
.054 1 .054 .041 .840
Error Perceived severity
155.772 179 .870
Perceived vunerability
234.075 179 1.308
Total Perceived severity
5920.000 181
Perceived vunerability
4301.333 181
Corrected Total
Perceived severity
156.896 180
Perceived vunerability
234.129 180
a. R Squared = .007 (Adjusted R Squared = .002) b. R Squared = .000 (Adjusted R Squared = -.005)
The proposed interaction between threat levels and frames was also
not supported (Pillai’s Trace=0.011; Wilks’ lambda = 0.989; Hotelling’s
Trace and Roy’s Largest Root = 0.011, F(3,175) = 0.630, p >0.05).
Therefore hypothesis 7 (H7) was not supported. Tables 6.71a and 6.71b show
the MANOVA results. Therefore the factors did not interact to produce any
significant results.
237
Table 6.71a Experiment 3: Hypothesis 7: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value FHypothesis
df Error
df Sig.
Intercept Pillai's Trace .977 3720.629a 2.000 176.000 .000
Wilks' Lambda .023 3720.629a 2.000 176.000 .000
Hotelling's Trace
42.280 3720.629a 2.000 176.000 .000
Roy's Largest Root
42.280 3720.629a 2.000 176.000 .000
frame_type Pillai's Trace .010 .876a 2.000 176.000 .418
Wilks' Lambda .990 .876a 2.000 176.000 .418
Hotelling's Trace
.010 .876a 2.000 176.000 .418
Roy's Largest Root
.010 .876a 2.000 176.000 .418
Threat_level Pillai's Trace .020 1.810a 2.000 176.000 .167
Wilks' Lambda .980 1.810a 2.000 176.000 .167
Hotelling's Trace
.021 1.810a 2.000 176.000 .167
Roy's Largest Root
.021 1.810a 2.000 176.000 .167
frame_type * Threat_level
Pillai's Trace .010 .901a 2.000 176.000 .408
Wilks' Lambda .990 .901a 2.000 176.000 .408
Hotelling's Trace
.010 .901a 2.000 176.000 .408
Roy's Largest Root
.010 .901a 2.000 176.000 .408
a. Exact statistic b. Design: Intercept + frame_type + Threat_level + frame_type * Threat_level
238
Table 6.71b Experiment 3: Hypothesis 7: tests of between-subjects effects (mobile phone stimuli)
Tests of Between-Subjects Effects
Source Dependent Variable
Type III Sum of Squares
df MeanSquare F Sig.
Corrected Model
Perceived severity 5.210a 3 1.737 2.027 .112
Perceived vunerability 2.467b 3 .822 .628 .598
Intercept Perceived severity 5766.088 1 5766.088 6728.366 .000
Perceived vunerability 4065.379 1 4065.379 3106.127 .000
frame_type Perceived severity 1.140 1 1.140 1.330 .250
Perceived vunerability .046 1 .046 .035 .851
Threat_level Perceived severity 3.115 1 3.115 3.635 .058
Perceived vunerability .777 1 .777 .593 .442
frame_type * Threat_level
Perceived severity 1.029 1 1.029 1.201 .275
Perceived vunerability 1.673 1 1.673 1.278 .260
Error Perceived severity 151.686 177 .857
Perceived vunerability 231.662 177 1.309
Total Perceived severity 5920.000 181
Perceived vunerability 4301.333 181
Corrected Total Perceived severity 156.896 180
Perceived vunerability 234.129 180
a. R Squared = .033 (Adjusted R Squared = .017) b. R Squared = .011 (Adjusted R Squared = -.006)
239
A regression analysis was done with three predictors: framing,
environmental concern and the interaction term with purchase intention as the
dependent variable to test H8a. Framing was dummy coded with the loss-
frame message condition allocated a value of 0 and the gain-frame message
condition a value of 1. The interaction terms were calculated as a product of
frame type and environmental concern (frame x environmental concern) from
these variables. It can be seen from Table 6.72 that the hypothesis was not
supported as interaction between the variables did not predict purchase
intention. Hence the two variables did not have the hypothesized effect.
Table 6.72a Experiment 3: Hypothesis 8a: interaction of frame and environmental concern on purchase intention (mobile phone stimuli)
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 .0.127a 0.016 -0.001 1.45332
a. Predictors: (Constant), TOTAL_ENV_CONCERN, FRAME_CODED, ENV_CONC_X_FRAME
ANOVAb
Model Sum of Squares
df Mean
Square F Sig.
1 Regression 6.123 3 2.041 .966 .410a
Residual 373.796 177 2.112
Total 379.919 180
a. Predictors: (Constant), TOTAL_ENV_CONCERN, frame_coded, frame_x_env_concern
b. Dependent Variable: PURCHASE_INTENTION
240
Table 6.72a (Continued)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B
Std.Error
Beta
1 (Constant) 5.118 2.463 2.078 .039
frame_coded -1.237 1.682 -.427 -.735 .463
frame_x_env_concern .186 .278 .413 .668 .505
TOTAL_ENV_CONCERN -.059 .407 -.033 -.146 .884
a. Dependent Variable: PURCHASE_INTENTION
Similarly Table 6.72b shows that H8b was not supported as message involvement did not interact with frame type to produce an effect on purchase intentions. However, the model was significant, a follow up stepwise regression revealed that only message involvement significantly predicted purchase intention.
Table 6.72b Experiment 3: Hypothesis 8b: interaction of frame and message involvement on purchase intention (mobile phone stimuli)
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate
1 .0.474a 0.225 0.212 1.28982Predictors: (Constant), frame_x_MI, MESSAGE_INVOLVEMENT, frame_coded
ANOVAb
Model Sum of Squares df Mean
Square F Sig.
1 Regression 85.455 3 28.485 17.122 .000a
Residual 294.464 177 1.664Total 379.919 180
a. Predictors: (Constant), frame_x_MI, MESSAGE_INVOLVEMENT, frame_coded b. Dependent Variable: PURCHASE_INTENTION
241
Table 6.72b (Continued)
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. B Std.
Error Beta
1 (Constant) .830 1.618 .513 .609frame_coded .043 1.060 .015 .040 .968MESSAGE_INVOLVEMENT .768 .307 .509 2.500 .013frame_x_MI -.038 .201 -.079 -.189 .850
a. Dependent Variable: PURCHASE_INTENTION
A one-way MANOVA analysis of the factors and gender showed
significant differences in risk perception based on gender. Therefore H8c was
supported. Tables 6.72c and 6.72d show the results. It can be seen that gender
has an effect on perceived severity and fear independently ((Pillai’s
Trace=0.058; Wilks’ lambda = 0.942; Hotelling’s Trace and Roy’s Largest
Root = 0.061, F(3,171) = 3.495, p <0.05). Gender also interacts with the
factors to produce an effect on the perceived severity of threat (Pillai’s
Trace=0.047; Wilks’ lambda = 0.953; Hotelling’s Trace and Roy’s Largest
Root = 0.050, F(3,171) = 2.834, p <0.05).
Table 6.72c Experiment 3: Hypothesis 8c: multivariate tests (mobile phone stimuli)
Multivariate Testsb
Effect Value FHypothesis
dfError df Sig.
Intercept Pillai's Trace .978 2572.816a 3.000 171.000 .000
Wilks' Lambda
.022 2572.816a 3.000 171.000 .000
Hotelling's Trace
45.137 2572.816a 3.000 171.000 .000
Roy's Largest Root
45.137 2572.816a 3.000 171.000 .000
242
Table 6.72c (Continued)
Multivariate Testsb
Effect Value FHypothesis
dfError df Sig.
frame_type Pillai's Trace .032 1.905a 3.000 171.000 .131
Wilks' Lambda
.968 1.905a 3.000 171.000 .131
Hotelling's Trace
.033 1.905a 3.000 171.000 .131
Roy's Largest Root
.033 1.905a 3.000 171.000 .131
Threat_level Pillai's Trace .025 1.471a 3.000 171.000 .224
Wilks' Lambda
.975 1.471a 3.000 171.000 .224
Hotelling's Trace
.026 1.471a 3.000 171.000 .224
Roy's Largest Root
.026 1.471a 3.000 171.000 .224
GENDER Pillai's Trace .058 3.495a 3.000 171.000 .017
Wilks' Lambda
.942 3.495a 3.000 171.000 .017
Hotelling's Trace
.061 3.495a 3.000 171.000 .017
Roy's Largest Root
.061 3.495a 3.000 171.000 .017
frame_type * Threat_level
Pillai's Trace .009 .493a 3.000 171.000 .688
Wilks' Lambda
.991 .493a 3.000 171.000 .688
Hotelling's Trace
.009 .493a 3.000 171.000 .688
Roy's Largest Root
.009 .493a 3.000 171.000 .688
243
Table 6.72c (Continued)
Multivariate Testsb
Effect Value FHypothesis
dfError df Sig.
frame_type * GENDER
Pillai's Trace .017 .966a 3.000 171.000 .410
Wilks' Lambda
.983 .966a 3.000 171.000 .410
Hotelling's Trace
.017 .966a 3.000 171.000 .410
Roy's Largest Root
.017 .966a 3.000 171.000 .410
Threat_level * GENDER
Pillai's Trace .001 .041a 3.000 171.000 .989
Wilks' Lambda
.999 .041a 3.000 171.000 .989
Hotelling's Trace
.001 .041a 3.000 171.000 .989
Roy's Largest Root
.001 .041a 3.000 171.000 .989
frame_type * Threat_level * GENDER
Pillai's Trace .047 2.834a 3.000 171.000 .040
Wilks' Lambda
.953 2.834a 3.000 171.000 .040
Hotelling's Trace
.050 2.834a 3.000 171.000 .040
Roy's Largest Root
.050 2.834a 3.000 171.000 .040
a. Exact statistic
b. Design: Intercept + frame_type + Threat_level + GENDER + frame_type * Threat_level + frame_type * GENDER + Threat_level * GENDER + frame_type * Threat_level * GENDER
244
Table 6.73d Experiment 3: Hypothesis 8c: tests of between-subjects effects (mobile phone stimuli)
Source Dependent Variable
Type III Sum of Squares
dfMean
Square F Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Corrected Model
Perceived severity
16.307a 7 2.330 2.867 .007 .104 20.066 .917
Perceived vunerability
4.223c 7 .603 .454 .866 .018 3.178 .196
Fear 19.299d 7 2.757 1.588 .142 .060 11.119 .649
Intercept Perceived severity
5760.417 1 5760.417 7088.373 .000 .976 7088.373 1.000
Perceived vunerability
4044.202 1 4044.202 3043.185 .000 .946 3043.185 1.000
Fear 2893.106 1 2893.106 1666.937 .000 .906 1666.937 1.000
Threat_level Perceived severity
3.036 1 3.036 3.736 .055 .021 3.736 .485
Perceived vunerability
.893 1 .893 .672 .414 .004 .672 .129
Fear .006 1 .006 .003 .954 .000 .003 .050
Frame_type Perceived severity
1.223 1 1.223 1.505 .222 .009 1.505 .230
Perceived vunerability
.057 1 .057 .043 .836 .000 .043 .055
Fear 3.577 1 3.577 2.061 .153 .012 2.061 .298
GENDER Perceived severity
4.382 1 4.382 5.392 .021 .030 5.392 .637
Perceived vunerability
.005 1 .005 .004 .950 .000 .004 .050
Fear 12.505 1 12.505 7.205 .008 .040 7.205 .761
Threat_level *Frame_type
Perceived severity
.559 1 .559 .688 .408 .004 .688 .131
Perceived vunerability
1.563 1 1.563 1.176 .280 .007 1.176 .190
Fear .023 1 .023 .013 .909 .000 .013 .051
Threat_level * GENDER
Perceived severity
.063 1 .063 .077 .782 .000 .077 .059
Perceived vunerability
.003 1 .003 .002 .962 .000 .002 .050
Fear .145 1 .145 .084 .773 .000 .084 .060
245
Table 6.73d (Continued)
Source Dependent Variable
Type IIISum of Squares
dfMean
Square F Sig.
Partial Eta
Squared
Noncent. Parameter
Observed Powerb
Frame_type * GENDER
Perceived severity
.075 1 .075 .093 .761 .001 .093 .061
Perceived vunerability
.776 1 .776 .584 .446 .003 .584 .118
Fear 2.937 1 2.937 1.692 .195 .010 1.692 .253
Threat_level * Frame_type * GENDER
Perceived severity
6.680 1 6.680 8.220 .005 .045 8.220 .814
Perceived vunerability
.999 1 .999 .752 .387 .004 .752 .139
Fear .406 1 .406 .234 .629 .001 .234 .077
Error Perceived severity
140.590 173 .813
Perceived vunerability
229.906 173 1.329
Fear 300.256 173 1.736
Total Perceived severity
5920.000 181
Perceived vunerability
4301.333 181
Fear 3207.560 181
Corrected Total
Perceived severity
156.896 180
Perceived vunerability
234.129 180
Fear 319.554 180
a. R Squared = .104 (Adjusted R Squared = .068) b. Computed using alpha = .05 c. R Squared = .018 (Adjusted R Squared = -.022) d. R Squared = .060 (Adjusted R Squared = .022)
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Plots were produced to check the interaction effects among the
three factors. The following figures show the various interactions. Figure 6.3
show that women perceive the environmental threat to be more severe to their
health when compared to men. Fear arousal is also greater in women when
compared to men (Figure 6.4). It can be seen from Figure 6.5 that women
perceived high severity in both the low and high threat conditions when gain
framing is used. However men perceived higher levels of severity only under
high threat conditions when gain frames are used. Figure 6.6 shows that under
loss frame condition, higher threat levels evoke higher levels of perceived
severity only in the case of women. Men do not perceive greater threat
severity under loss conditions even when high threat levels are used.
Figure 6.3 Estimated marginal means for perceived severity
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Figure 6.4 Estimated marginal means for fear
Figure 6.5 Estimated marginal means for perceived severity for gain frames
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Figure 6.6 Estimated marginal means for perceived severity for loss frames
Table 6.74 shows the effect of the manipulations.
Table 6.74 Experiment 3: Summary of the effect of manipulations on PMT variables with the mobile stimulus
Hypothesis Factor Perceivedseverity
Perceivedvulnerability
Fear
H5 Threat level X X NA
H6 Goal frame X X NA
H7 Threat level * Goal Frame X X NA
H8c Gender * Goal Frame X
Effect of Interactions on Purchase Intentions Purchase Intention
H8a Environmental concern * Goal Frame X
H8b Message Involvement * Goal Frame X
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6.5.8.3 Hypotheses tests of the relationship among PMT variables,
involvement, attitudes and intentions using the structural/ inner
model
As the measurement model was reliable and valid, the structural
model was tested with SmartPLS version 2.0.M3 (Ringle et al 2005). Initially
collinearity among the exogenous constructs was examined and there were no
multicollinearity issues. The main evaluation criterion for the structural model
is the value of the coefficient of determination (R2) as it represents the
explained variance of all the endogenous variables (Hair et al 2011). The level
and significance of the path coefficients (Hair et al 2011) are other important
criteria to judge the model.
The structural model was tested with 5000 sub-samples generated
using bootstrapping to evaluate the significance of the path co-efficients (181
observations per subsample, 5,000 subsamples and no sign changes). The
results of the structural model are shown in Figure 6.7. The significance of the
hypotheses were evaluated based on two-tailed tests (p < 0.05 (t=1.971), p <
0.01 (t= 2.598) and p < 0.001 (t= 3.334)).
The R2 values (shown in brackets) and path coefficients can be seen in Figure
6.7. R2 values greater than 0.33 are substantial and values between 0.19 and
0.33 are moderate (Chin 1998; Henseler et al 2009). Hair et al (2011) suggest
that 0.20 can be considered high for consumer behaviour studies.
Based on Chin’s criteria (Chin 1998) it can be observed that:
The coefficient of determination, R2 is 0.22 for the fear
endogenous latent variable. This means that the three latent
variables (perceived severity, perceived vulnerability, and
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environmental knowledge) moderately explain 22.0% of the
variance in fear.
Figure 6.7 Hypothesis testing using PLS-SEM
The coefficient of determination, R2 is 0.36 for the message
involvement endogenous latent variable. This means that the six
latent variables. (perceived severity, perceived vulnerability,
response efficacy, self efficacy, fear and environmental
0.25**
-0.01
-0.11
-0.04
0.22**
0.16*
0.29***
-0.22***
0.71***
0.56***
0.07
-0.01
0.02
0.11
0.07
0.35***
0.07
0.04perceived severity
perceived vulnerability
response efficacy
(0.30)
self efficacy (0.07)
fear (0.22)
message involvement (0.36)
attitude towards ad (0.32)
attitude towards brand (0.50)
purchase intention (0.46)
0.18*
environmental concern
0.03
0.62***
environmental knowledge
0.24**
0.40***
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concern) substantially explain 36.0% of the variance in message
involvement.
The coefficient of determination, R2 is 0.32 for the attitude
towards ad endogenous latent variable. This means that the four
latent variables (response efficacy, self efficacy, message
involvement and environmental concern moderately explain
32.0% of the variance in attitude towards ad.
The coefficient of determination, R2 is 0.50 for the attitude
towards brand endogenous latent variable. This means that the
the latent variable (attitude towards ad) substantially explains
50.0% of the variance in attitude towards brand.
The coefficient of determination, R2 is 0.46 for purchase
intention. This means that the latent variable - attitude towards
brand substantially explains 46.0% of the variance in purchase
intention.
Based on the inner model loadings and path co-efficients from
Figure 6.7, it can be summarised that:
The hypothesized path relationship between perceived severity,
perceived vulnerability and fear is statistically significant.
perceived severity has a comparatively stronger effect (0.29) on
fear.
perceived severity (0.40) and perceived vulnerability (0.24)
significantly predict response efficacy
The hypothesized relationship between perceived severity and
self efficacy was significant (0.25) whereas perceived
vulnerability was not significantly related to self efficacy (0.07)
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The hypothesized path relationship between perceived severity
(0.04), perceived vulnerability (0.07), self efficacy (0.11) and
message involvement was not statistically significant. However
fear (0.35) and response efficacy (0.22) had a signficiant
relationship with message involvement.
response efficacy (0.07) and self efficacy (0.02) were not related
to attitude towards ad. While response efficacy (-0.01) was not
related to purchase intention, the hypothesized relationship
between self efficacy (0.18) and purchase intention was
significant.
The hypothesized path relationship between perceived severity
(-0.11), perceived vulnerability (-0.04), message involvement
(0.07) and environmental knowledge was not statistically
significant. However fear (-0.22) had a signficiant relationship
with environmental knowledge
environmental concern was not related to any of the
hypothesized relationships. The path coefficients were not
significant with the hypothesized variables message
involvement (0.07), attitude towards ad (0.03) and purchase
intention.
message involvement (0.56) is a significant predictor of attitude
towards ad
attitude towards ad is a strong predictor (0.71) of attitude
towards brand
Similarly attitude towards brand is a significant predictor (0.62)
of purchase intention.
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Table 6.75 summarises the result of the hypotheses tests and their associated results.
Table 6.75 Experiment 3: Results of hypothesis testing using PLS-SEM
Hypothesis Path (standard)
t-value
Result
H9a Perceived severity fear 0.29 *** 4.03 Supported
H9a Perceived vulnerability fear 0.16* 2.02 Supported
H9b Perceived severity response efficacy
0.40 *** 5.92 Supported
H9b Perceived vulnerability response efficacy
0.24 ** 3.03 Supported
H9c Perceived severity self efficacy
0.25 * 2.50 Supported
H9c Perceived vulnerability self efficacy
0.07 0.58 Not supported
H10a Perceived severity message involvement
0.04 0.50 Not supported
H10b Perceived vulnerability message involvement
0.07 0.89 Not supported
H10c Fear message involvement 0.35*** 4.82 Supported
H10d Response efficacy message involvement
0.22** 2.63 Supported
H10e Self efficacy- message involvement
0.11 1.57 Not Supported
H11a Response efficacy attitude towards ad
0.07 0.92 Not Supported
H11b Self efficacy- attitude towards ad
0.02 0.35 Not Supported
H11c Response efficacy purchase intention
-0.01 0.22 Not Supported
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Table 6.75 (Continued)
Hypothesis Path (standard)
t-value
Result
H11d Self efficacy- purchase intention
0.18* 2.52 Supported
H12a Environmental knowledge perceived severity
-0.11 1.45 Not Supported
H12b Environmental knowledge perceived vulnerability
-0.04 0.62 Not Supported
H12c Environmental knowledge fear
-0.22*** 3.47 Supported
H12d Environmental knowledge message involvement
-0.08 1.349 Not Supported
H13a Environmental concern message involvement
0.07 1.08 Not Supported
H13b Environmental concern attitude towards ad
0.03 0.46 Not Supported
H13c Environmental concern purchase intention
-0.01 0.316 Not Supported
H14 Message involvement attitude towards ad
0.56 *** 9.50 Supported
H15 Attitude towards ad attitude towards brand
0.71 *** 17.7 Supported
H16 Attitude towards brand purchase intention
0.62 *** 10.3 Supported
Note: n=181; Estimates represent 5000 bootstrapping testing
*p<0.05 ; **p < 0:01; * **p <0:001
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It is also necessary to assess the predictive relevance of the inner
model and therefore the model’s predictive relevance was analyzed using
Stone-Geisser test criterion Q2 (Chin. 2010; Hair et al 2011). This was
determined using the blindfolding procedure in SmartPLS. The omission
distance was chosen as 7, since values between 5 and 10 are advantageous
(Hair et al 2012). Cross-validated measure Q2 was checked and the results are
shown in Table 6.76. The Q2 values for all the endogenous constructs were
greater than zero as required. Table 6.76 also lists the R2 values of the
endogeneous contructs.
Table 6.76 Experiment 3: Model’s predictive relevance
Endogenous Construct R2 Q2
Attitude towards ad 0.32 0.23
Attitude towards brand 0.50 0.43
Purchase intention 0.46 0.40
Message involvement 0.36 0.19
Fear 0.22 0.15
Next, the effect size was calculated to measure the impact of a
predictor on a specific endogenous construct. Effect size represents Cohen’s d.
The values of 0.02 (small), 0.15 (medium) and 0.35 (large) indicate that the
construct has a small, medium or large effect size on the criterion (dependent)
construct respectively (Cohen 1988). The effects are shown in Table 6.77.
Although fear has a small effect on message involvement the effect size 0.14
is very close to the medium threshold. Most of the other effect sizes are small.
However message involvement has a medium effect on attitude towards ad
and attitude towards brand has a medium effect on purchase intention.
Attitude towards ad has a strong effect on attitude towards brand.
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Table 6.77 Experiment 3: Effect sizes
Endogeneous VariableExogenous
Variable Effect Size f2
Effect size Interpretation
Purchase intention Attitude Towards brand 0.32 Medium
Purchase intention Self efficacy 0.05 Small
Attitude towards brand Attitude towards ad 0.43 Large
Attitude towards ad Message involvement 0.26 Medium
Attitude towards ad Response efficacy 0.01 Small
Attitude towards ad Environmental concern 0.06 Small
Message involvement Fear 0.14 Small
Message involvement Perceived vulnerability 0.01 Small
Message involvement Response efficacy 0.04 Small
Message involvement Self efficacy 0.01 Small
Message involvement Environmental concern 0.01 Small
Fear Perceived severity 0.09 Small
Fear Perceived vulnerability 0.03 Small
Fear Environmental knowledge 0.06 Small
Perceived severity Environmental knowledge 0.01 Small
6.5.9 Conclusions from Experiment 3
This experiment examined the effects of varying threat levels and
goal frames on PMT variables and the subsequent effects of the PMT
variables on message involvement, attitudes and purchase intention. The issue
of e-waste (based on mobile phone stimuli) was used to observe if the
hypothesized effects were supported. The levels of perceived severity and
vulnerability remained high in this experiment too. It can be inferred from
Table 6.67 and 6.68 that perceived severity, vulnerability and fear remain
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high for watch stimuli. Similar to Experiment 2 there was no main effect of
threat level or the goal frames. Similarly the hypothesized interaction effects
were only partially significant. Similar to Experiment 2 loss frames did not
increase threat perception as suggested by other researchers (Cox & Cox
2001; Meyers-Levy & Maheswaran 2004; van ‘t Riet et al 2008;O’Keefe &
Jensen 2009; Janssens et al 2010; Updegraff 2013).
There was no relationship between involvement and framing as
hypothesized. Both environmental concern and message involvement did not
interact with frames to produce an effect on purchase intention. This is in
contrast to the findings by other researchers who imply an effect between
involvement and framing (Maheswaran & Meyers-Levy 1990; Rothman et al
2006; Kim 2013). However gender played a significant role in predicting the
effect of the factors on the PMT variables. Women were more fearful and
perceived higher severity when facing an environmental threat when
compared to men. This confirms earlier findings by Garbarino & Strahilevitz
(2004) who find that women are more risk averse when compared to men.
The result also highlights the gender gap known to exist in environmental
threat perceptions (Flynn et al 1994; Bord & Connor 1997; McCright &
Dunlap 2011; Franzen & Vogl 2013). The interaction between frame and
gender also showed that women generate more negative thoughts when
presented with a negative goal frame when compared to men (Putrevu 2010).
Similar to the previous experiment, perceived severity and
vulnerability significantly influenced fear arousal as proposed by PMT
(Rogers & Prentice-Dunn 1997 ; Floyd et al 2000) and other studies that
apply this theory (Milne et al 2000; de Hoog et al 2008). Coping appraisal
was also initiated (Maddux & Rogers 1983; Boer & Seydel 1996; Milne et al
2000). Response efficacy was significantly influenced by perceived severity
and vulnerability. However self efficacy was only moderately influenced by
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perceived severity and not by perceived vulnerability. In this experiment too,
all the PMT variables except self-efficacy predicted message involvement.
This finding confirms that higher levels of health risk increase involvement
with the message (Bloch & Richins 1983; Richins & Bloch 1986; Keller &
Block 1996; de Hoog 2005). Fear can therefore be used to increase message
involvement in enviromental communication (Meijnders et al 2001).
Apart from this, the finding also shows that while using goal frames
and threat levels, emphasis on the response efficacy would significantly
increase consumer involvement. The results are also in contrast to the finding
by Punam & Keller (1995) who find that low efficacy promotes more effortful
processing. Recent research emphasizes the importance of response efficacy
and treats it as a key component to message acceptance (Lewis et al 2010).
However, contrary to previous research, the efficacy variables were not
related to attitudes. Self efficacy significantly predicted purchase intentions
confirming the findings of earlier research (Maibach & Murphy 1995;
Luszczynska 2004; Gaston & Prapavessis 2012; Kreausukon et al 2012).
While objective environmental knowledge did not decrease the levels of
perceived severity or vulnerability, it had a negative effect on fear (Averbeck
et al 2011). Knowledge did not affect the levels of message involvement
similar to Experiment 2. Interestingly, environmental concern did not have an
effect on any of the hypothesized variables. This is in contrast to the findings
of Experiment 2 and contrary to the ELM (Petty & Cacioppo 1986). However ,
this finding was related to Experiment1 where environmental concern did not
predict the hypothesized variables. From Table 6.77, it can also be inferred
that message involvement has a stronger effect on attitude towards the ad.
Hence, this experiment based on stimuli related to e-waste showed
similar results as Experiment 2. The major difference was the role of
environmental concern. While Experiment 2 showed an effect of
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environmental concern, this experiment showed that this variable was not
effective in influencing attitudes or intentions. Apart from the findings
related to environmental concern, there were no major differences between
the issue of e-waste and plastic waste.There were only two other differences.
In case of Experiment 2, perceived severity affected message involvement
and vulnerability affected self-efficacy. Hence, both the experiments highlight
the role of message involvement in promoting attitudes and intentions towards
green advertising.
6.5.10 Gender and Environmental Concern
Some green marketing studies claim that gender has a significant
effect on environmental concern (Shrum et al 1995; Jain & Kaur 2006;
Mostafa 2007). This was not investigated as part of hypothesis testing as it
was not part of the research objective, Surprisingly, a post hoc analysis
revealed that gender did not have any significant effect on environmental
concern in all the three experiments. This confirms the recent finding by other
researchers who do not find a link between environmental concern and gender
(e.g. D’Souza et al (2007)).