the good, the bad, and the framed
TRANSCRIPT
The good, the bad, and the framedA study of behavioral economics and the framing effect on tobacco free snus
William Muleba
Business and Economics, master's level
2020
Luleå University of Technology
Department of Business Administration, Technology and Social Sciences
Abstract
This study sets out to explore attitudes and intentions towards nicotine product goods and how it
is affected by the goal framing effect. The presence of this effect has been shown in the advertising
of both green products and everyday products. The aim of this research is to explore whether or
not this also holds true when it comes to unhealthy products, more precisely tobacco free all white
nicotine products.
A quasi experimental study was conducted with the use of a fictitious brand of All-white tobacco.
All 63 participants in the three different experimental groups of the study met the mandatory age
requirement. One of the three groups received a positively framed advert, another received a
negatively framed advert, whereas the last group acted as the control group and therefore received
a neutral advertisement stimuli. All participants submitted their answers on a questionnaire created
for this study, which was validated using Cronbach’s alpha and factor analysis.
The results suggest that the use of goal framing is beneficial when advertising nicotine product
goods. Both positive and negative goal-framing showed a greater effect on purchase intention and
product attitude than the control group. The negative goal-framing advert proved to be statistically
different than the control group when measuring product attitude. Furthermore, the positively
framed advert showed a statistically significant difference in effect on both product attitude and
purchase intention compared to the control group.
The findings suggest that positively framed goal-framing has an effect on both attitudes and
purchase intention, compared to the neutral stimuli. The negatively framed goal-framing had an
effect on attitudes, compared to the neutral stimuli. The practical implication of this study could
possibly be that when constructing advertisements for tobacco free snus products, it could be
preferable to make use of the positive goal-framing effect in order to affect the consumers purchase
intention and attitude towards the product.
This study has confirmed to some extent that the framing effect is a factor prevalent in the
advertising of tobacco free products. For further research it would be highly interesting to delve
deeper in comparing positive and negative goal-framing in order to find further evidence of which
one has greater effect on consumers.
List of relevant keywords and authors names
“Behavioral economics”; “Goal-Framing”; “Framing effect”; “Advertising”;”Marketing”;
“Communication”; “Consumer marketing”; “Tobacco”;” Attitudes”; “Purchase intention”;
“Kahneman, Daniel” ; “Tversky, Amos”; “Thaler, Richard” ; “Sunstein, Cass”; “Shiller, Robert”
; “Ajzen, I”;“Putrevu, S” ; “Simon. A, Herbert”; “Camerer, C. F., & Loewenstein”
Table of contents
Contents 1. Introduction 1
1.1 Background 1
1.1.1 All white snus 3
1.1.2 British American Tobacco (BAT) 3
1.1.3 Japan Tobacco Inc. (JTI) - Nordic Spirit 3
1.1.4 Swedish Match - Zyn 3
1.1.5 Market figures. 4
1.2 Problem discussion 5
1.3 Research purpose 7
1.4 Research question 7
1.5 Delimitations 7
2. Literature review 8
2.1 The Evaluation system 8
2.2 Framing effect 9
2.2.1 Attribute framing 10
2.2.2 Goal framing 11
2.2.3 Temporal framing 12
2.2.4 Framing effect and advertising 13
2.3 Theory of planned behavior 14
2.3.1 Behavioral Control 15
2.3.2 Attitude toward the behavior 15
2.3.3 Subjective norm 15
2.4 Behavioral economics 16
2.5 Frame of reference 17
3. Methodology 18
3.1 Research purpose 18
3.2 Research approach 19
3.3 Deductive approach 20
3.4 Quantitative method 20
3.5 Questionnaire 22
3.6 Reliability 24
3.6.1 Factor analysis 24
3.7 Validity 25
3.8 Descriptive statistics 25
3.9 Inferential statistics 25
3.10 Manipulation checks 26
3.11 Sample selection 26
3.12 Methodology summary 28
4. Results 30
4.1 Factor analysis 30
4.2 Cronbach’s alpha 31
4.3 Descriptive statistics 32
4.4 Inferential statistics 33
4.4.1 Product attitude 34
4.4.1.1 Product attitude: Negative framing vs Neutral stimuli 34
4.4.1.2 Product attitude: Positive framing vs Neutral Stimuli 35
4.4.1.3 Product attitude: Negative framing vs positive framing 36
4.4.2 Purchase intention 37
4.4.2.1 Purchase intention: Negative framing vs Neutral stimuli 37
4.4.2.2 Purchase intention: Positive framing vs Neutral stimuli 38
4.4.2.3 Purchase intention: Negative framing vs Positive Framing 39
4.4.3 Manipulation checks 40
4.4.3.1 Negative goal-framing paired t-test 40
4.4.3.2 Positive goal-framing paired t-test 41
5. Analysis 42
6. Conclusions 44
6.1 Practical implications 45
6.2 Theoretical implications 45
6.3 Limitations 46
6.4 Suggestions for further research 47
7. Appendix 51
7.1 Appendix 1 - Questionnaire 51
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1. Introduction
This thesis will be about behavioral economics, more precisely the framing effect and how it
affects consumers' purchase intention and attitude towards the product when purchasing harmful
goods. In this case tobacco free snus.
1.1 Background
The main objective of marketing communications is to influence the individual's perception of an
organization and its products (Baines, Fill, & Rosengren, 2017). This is most commonly done with
the use of a communication tool such as advertising and a form of media where the communication
message is delivered (Baines et al., 2017).
There are communication models that aim to describe the process of communication between
sender and receiver, i.e. organization and customer. Kotler & Keller (2016) portray one such model
called the communications process model. The authors portray a model in which the sender
encodes their message and delivers it in a form of media, the encoded message is then decoded by
the receiver followed by their response and feedback. The final component of the model is the
noise which for instance can be rival messages interfering with the communication. The goal of
advertising is to act as an external stimulus to trigger the consumers buying decision process.
(Kotler & Keller, 2016). However all consumers do not follow the linear buying decision process
model, some consumers skip a few steps and go directly to the purchase decisions. This irrational
behavior by consumers is further explored in the field of behavioral economics. (Kotler & Keller,
2016). According to Camerer and Loewenstein (2003) behavioral economics increases the
explanatory power of economics by providing it with more realistic psychological foundations.
Within behavioral economics plenty of research has been conducted on the effects of messaging
on decision making. Behavioral economics challenges certain aspects of the neoclassical view of
economics, more importantly for this thesis, the aspects of the irrationality of human decision
making (Wilkinson & Klaes, 2012). This is done by weighing in human psychology in economic
theories. This allows for greater understanding of human decision making. Two of the most
prominent authors on the subject of behavioral economics and decision making are Amos Tversky
and Daniel Kahneman, with their findings on areas such as prospect theory (Kahneman & Tversky,
1979).
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Thanks to these findings more research has been done on decision making and what influences
individuals. Their findings include more evidence that challenges the rationality of individual
decision making, such as the framing effect (Kahneman, D., 2003).
According to Kahneman and Tversky the framing effect is a bias which appears when convictions
and preferences are gratuitously affected by the way in which a problem is formulated (Kahneman,
2013). Another explanation is that the framing effect is the idea that one part of decision making is
the way in which the problem or question is delivered. In essence the selected decision can change
depending on whether a problem is framed in terms of losses or gains. (Thaler & Sunstein, 2009).
Seeing as decision making is such a fundamental aspect of everyday life, the fact that such a simple
component as the phrasing of the problem has major influence on the outcome is interesting. It
becomes even more interesting if one considers the possible implications it has on the usage of
nicotine products.
The following section aims to highlight a few of the main competitors in the snus segment on the
Swedish market. The reason for choosing these particular companies in an effort to portray
different market participants is that they have established a considerable share of the market.
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1.1.1 All white snus
1.1.2 British American Tobacco (BAT)
British American Tobacco has previously been focused solely on the manufacturing and sale of
cigarettes but after the acquisition of Skandinavisk Tobakskompagni in 2008 expanded into the
snus market (Avanza, 2020). The company now has a tobacco free nicotine pouch product offering
available in the US, Scandinavian and Swiss market under the product name Lyft. BAT’s market
share in the segment in Sweden as of 2018 was 12% and in Norway the market share was 8 %.(
British American Tobacco, 2020).
1.1.3 Japan Tobacco Inc. (JTI) - Nordic Spirit
Japan Tobacco Inc. is a global tobacco giant founded in 1949 and employs 61 975 people
worldwide (Japan Tobacco Inc., 2020a). JTI entered the snus market in 2007 when they acquired
Gallaher Group PLC. Their contender for market share in the tobacco free segment is the Nordic
Spirit which was launched in 2018 (Japan Tobacco Inc., 2020b) In 2019 they expanded further into
the market by making the product available in seven different countries including the UK (Japan
Tobacco Inc., 2020b).
1.1.4 Swedish Match - Zyn
Swedish Match is mainly successful in the premium segment in Sweden. In recent years they have
expanded their product portfolio to include their tobacco free nicotine pouch named Zyn. The
product is available in 67 000 stores in America and volume has increased from 12 million cans to
50 million. (Swedish Match. 2020). In 2019 the American Food and Drug administration
designated Swedish Match products to be a “Modified Risk Tobacco Product”, which is a
designation given to tobacco products considered to reduce the harm and risk of tobacco related
diseases. This results in Swedish Match being able to market its products as a less harmful
alternative than traditional tobacco products in the US. (Swedish Match. 2020).
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1.1.5 Market figures.
The figures on snus consumption in Sweden shows that 24% of males and 7% of females use snus
on a regular basis amounting to a total of 1 million consumers. The Swedish market saw a growth
of 7% in 2019 and the Norwegian market grew by an estimated 5%. Looking at the US nicotine
pouch market the volume, measured in cans, saw a growth of 47 million cans in 2019 amounting
to 60 million cans in total. The total snus market in America amounted to 1.5 billion cans, while
showing a decline of 1% in 2019. The market of pouch snus is estimated to grow by 3% per year
in the US. (Swedish Match. 2020).
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1.2 Problem discussion
Numerous studies have been conducted on the framing effect in various areas. Such as one study
that measured the framing effect on brand attitudes towards a fictitious brand of toothpaste. In the
experiment the messaging was manipulated as to differ between: “99% of caries removed” and
“1% of caries not removed”. (Burböck, Kubli, Maček, & Bobek, 2019). Tu, Kao, and Tu, (2013)
have done experiments on how different kinds of framing effects affect consumers in areas such as
green marketing. Buda and Zhang (2000) have done research on the prevalence of framing effect
and whether negative or positive messaging is preferable. What they found was that not only must
marketers pay attention to what they say and how they say it, they must also weigh in aspects of
product attributes when constructing the messaging. A lot has been done on theoretical aspects and
fictitious goods as well as green marketing in different areas. There have also been studies that
examine the most successful way to make individuals quit smoking, by comparing messages in
terms of losses of continued smoking or the gains of quitting. (Toll et al., 2008).
The utilization of advertisement for the tobacco industry has been substantial throughout the ages.
A common aspect of movies in the past has been the notion that cigarette usage has been a common
component. The advertisement of tobacco products is ever changing, flowing from different claims
of health benefits of smoking to risk reduction. As regulation of the advertisement of nicotine and
smoking products becomes stricter the companies must find new ways to market their products.
As the studies of the negative health consequences associated with cigarettes shifted the sentiment
towards reducing the demand for cigarette products, new opportunities for revenue have emerged.
One such area are the various forms of reduced-risk products on offer from some of the largest
cigarette manufacturers. Some tobacco companies have chosen vaping as their alternative in the
reduced risk segment (PMI, 2020). Others have gone for the All white snus alternative. All white
snus is an oral alternative to nicotine users. Free from tobacco and is therefore considered by the
American regulators to be less harmful than other oral counterparts and particularly less harmful
than cigarettes (Swedish Match. 2020). Investments in the all-white snus segment increases as
shown previously in the market analysis. As previously mentioned there have been studies
conducted on cigarette cessation but this brand new segment of nicotine consumption has not been
studied as carefully as cigarettes or vaping. To some extent the research on the prevalence of the
framing effect in the field of advertising nicotine products is seemingly nonexistent. Is there a
framing effect even for the goods that have an inherently negative aspect of their own? Goods that
are viewed as detrimental to health and even have addictive characteristics. Do the implications of
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the framing effect hold true even there and if so does it affect attitudes and purchase intentions of
those reached by the advertising. Therefore the aim of this research is to explore whether or not
the same sort of theoretical aspects that are true when it comes to green products or everyday
products also hold true when it comes to unhealthy products, more precisely tobacco free all white
nicotine products.
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1.3 Research purpose
This study sets out to explore attitudes and intentions towards nicotine product goods and how it is
affected by the goal framing effect.
1.4 Research question
Research question: Is there a framing effect when advertising tobacco free snus?
Hypothesis: There is a significant difference in effect on attitudes and intention between using
positive or negative goal-framing when advertising tobacco free snus products.
H0:μ1 – μ2 = 0 There is no significant difference in effect on attitudes and intention between using
positive or negative goal-framing when advertising tobacco free snus products
1.5 Delimitations
The delimitations are that this study will only focus on the Swedish market and that of tobacco free
snus products. Because of the present COVID-19 pandemic the ways of administering the
questionnaire in order to collect a data sample will be affected. The preferred way of administering
the questionnaire where the researcher is positioned in a central position on campus and hands out
the questionnaire to subjects that pass through the area is not suitable in the current situation as it
is considered a health hazard and the university is in lockdown. Therefore alternative ways of
questionnaire administration is adopted, which could affect the quality and size of the sample,
seeing as the randomization of the administration and thus sample is limited.
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2. Literature review
The chapter contains a review of past literature regarding behavioral economics. The purpose of
this literature review is to establish the main theoretical area. The aim is to find articles and
mediums that are most relevant for the research purpose.
2.1 The Evaluation system
Jobber and Lancaster (2015) present a system that details the different steps involved with the
consumers’ selection and evaluation of different choices for the best solution. This system is
presented in figure 1 below.
Figure 1: Evaluation system
Source: Adapted from Jobber & Lancaster
(2015)
The system consists of four different components, more specifically; (1) the evaluative criteria
which are dimensions used to evaluate products by the consumers. (2) Beliefs, which are whether
or not a product holds certain characteristics according to the consumer. (3) Attitudes, which are
described by Jobber and Lancaster as liking or disliking a product, furthermore the authors state
that attitudes are dependent on the evaluative criteria. And lastly (4) intentions, which are described
as a measurement of the probability that the attitudes are put into action.
The evaluation system by Jobber and Lancaster (2015) is a valuable addition to the thesis seeing
as the authors also state the assumption that favorable attitudes increase the purchase intentions.
Evaluative Criteria
Beliefs
Attitudes
Intentions
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2.2 Framing effect
As previously explained in the introduction, the framing effect is a bias which appears when
convictions and preferences are gratuitously affected by the way in which a problem is formulated
(Kahneman, Daniel, 2013). There are different variations of framing effects.
Levin, Schneider, and Gaeth (1998) have developed a typology to distinguish three different kinds
of valence framing effects; (1) risky choice framing, (2) attribute framing and (3) goal framing.
The authors define valence framing effects as frames that present the same information in either a
positive or negative way. They first present risky choice framing, which is introduced by Tversky
& Kahneman (1981) where the result of a potential choice involving options with different risk
levels are described in different ways. Levin, Schneider and Gaeth (1998) present a second form
of the framing effect that they call the attribute framing effect. Wherein certain characteristics of
an object or event is the focus of the actual framing manipulation. The third and final form of
framing effect introduced in the article is the goal framing effect, where the framing is the goal of
an action or behavior. Examples of attribute framing and goal framing will follow in the segment
below.
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2.2.1 Attribute framing
When certain attributes are the subject for framing manipulation it is defined as attribute framing
(Levin et al., 1998). Studies by Levin, Schneider and Gaeth (1998) on the attribute framing effect
on the labeling of beef compared the labeling of minced meat, 75% lean and 25% fat and found
that the positive labeling 75% lean resulted in better rating for the product. There are other options
such as the choice between survival rates or mortality rates when suggesting medical treatments
(Wilson, Kaplan, & Schneiderman, 1987). What the studies show is that in order to retain a
favorable rating a positive attribute frame is to be chosen instead of a negative attribute frame. In
Figure 2 below, the attribute framing process is presented. The figure displays the importance of
the process of whether or not to use the positive frame, and highlight the chance of success, or the
negative frame and highlight the risk of failure.
Figure 2: Attribute framing effect paradigm
Source: Adapted from Levin et al (1998)
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2.2.2 Goal framing
According to Levin et al. (1998) goal framing is concerned with focusing on the consequences of
performing an action. This means that the positive goal frame is centered on gaining something
whereas the negative goal frame is centered on losing something. For instance when looking at
smoking cessation, the positive goal frame could be the health benefits gained by quitting and the
negative goal frame could be the functional health loss associated with not quitting smoking.
According to research on goal framing, laying emphasis on the negative consequences of not
performing an action has the most effect. This supposedly is because of the fact that individuals
are more inclined to avoid losses. (Meyerowitz & Chaiken, 1987). Contradictory to their findings,
studies conducted by Burböck et al. (2019) showed that positive goal framing had the most effect.
Figure 3 below maps the process of determining the effect of the goal framing, where the choice is
between the positively framed option where the gain of performing an action is highlighted versus
the loss consequence of not performing the behavior is highlighted in the negative frame.
Figure 3: Basic goal framing paradigm.
Source: Adapted from Levin et al (1998)
The two different framing effects attribute framing and goal framing were measured by Burböck,
Kubli, Maček, and Bobek, (2019) on three different parameters: purchase intentions, product
attitude and ad favorability. The authors conducted experiments with a fictitious brand of
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toothpaste. The messaging of the advertising was alternated between different positive and negative
frames when comparing attribute and goal framing. As in 99% caries removed in the positive
attribute frame and 1% of caries not removed in the negative attribute frame. The authors phrased
the positive goal frame with the sentence “using dentagold leads to fresh breath and white teeth”.
The negative goal frame was phrased with the sentence “not using dentagold can lead to bad breath
and yellow teeth”. Results showed that both attribute and goal framing was most preferable when
framed in a positive way. The authors conclude that the content of the messaging alone is not what
influences decision behavior but how the message is presented to the consumer.
2.2.3 Temporal framing
Studies have also been conducted by Kees (2011) on the time aspect of framing, described as
temporal framing which is when a temporal aspect is applied to a neutral reference point. For
example instead of the message being that a lot of people die from cancer, the temporal aspect of
“every hour” or “daily” is added to the message. In his research he investigated individuals with
an inability to properly assess the future consequences of their behavior. More specifically their
consumption of unhealthy food items. He discovered that present-oriented consumers were more
susceptible to messages of long term risk if they were framed in a more proximal way, also his
studies suggest framing long term risk messaging in a promotion frame rather than a prevention
frame.
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2.2.4 Framing effect and advertising
In an attempt to research the applicability and effect of the framing effect in advertising
Tu, Kao, and Tu (2013) examined the framing effect and green marketing on green advertising.
The study found a preference of positively framed green product advertising over negatively
framed advertising. Their findings are supported by Tversky and Kahneman (1981) who propose
that individual preference is concise advertisements and with a risk averse attitude when presented
with positive frames based on gaining something. In another study on the effectiveness of framing
effects on ad messaging, Lee, Liu, and Cheng (2018) found results that indicate that ads where
messages were positively framed are more effective than those with messages that are negatively
framed for consumers who are promotion-focused. They also examined product types such as
utilitarian products and hedonic products and found that it is more effective to match the framing
of the message with the advertised products. Furthermore, Buda and Zhang (2000) examined the
interactive effect of message framing, presentation order and source credibility. They examined
this through experiments, the experiments showed that those who received a positively framed
message rated product attitudes significantly more positive than those who received a negatively
framed message. They found an indication that there was significant influence on consumer’s
decisions from the framing effect.
So far awareness has been raised that the framing effect appears to be effective and applicable to
different products and scenarios. The authors have researched some interesting topics and found
indications that support their claims for the product categories and the influence on consumer’s
decisions. The following segment will attempt to present the current state of the research on human
behavior.
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2.3 Theory of planned behavior
Understanding human behavior in different contexts has been the interest of many scholars
throughout the ages. Icek Ajzen, together with Martin Fishbein have studied and proposed ways of
understanding human behavior. One such proposal is the theory of planned behavior, which is built
on the theory of reasoned action (Ajzen, 1991). Ajzen (1991) states that the theory of planned
behavior is: “…a theory designed to predict and explain human behavior in specific contexts”
(p.181). The theory achieves this by viewing intentions as central to the performance of behavior.
Intentions are explained as the antecedents of behavior that encapsulate various motivators, simply
put it’s more likely that a behavior is performed if the intentions are strong. In an effort to highlight
the different components that influence behavior they are included in the figure 4 below, in an
adaptation of the theory of planned behavior.
Figure 4: Theory of planned behavior
Source: Adapted from Ajzen (1991)
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2.3.1 Behavioral Control
In an attempt to dissect the different components of the theory of planned behavior the first step
will be to examine behavioral control. Azjen (1991) explains behavioral control by first separating
actual behavioral control and perceived behavioral control, where perceived behavioral control
concerns the perception of the individual's ability to perform the behavior in question. Perceived
behavioral control is also presented as different from the theory of locus of control. The difference
being that perceived behavioral control has a variance depending on the situation whereas locus of
control remains relatively stable (Ajzen, 1991). In order to further explain the difference between
locus of control and perceived behavioral control Ajzen (1991) presents an example where an
individuals perceived behavioral control can be low, in the case of the chances of becoming a pilot
but at the same time the individual can have a belief that the outcomes are determined by the
individual's behavior, and thus having an internal locus of control. The theory of planned behavior
states that it is possible to predict behavioral achievement with perceived behavioral control
together with behavioral intentions.
2.3.2 Attitude toward the behavior
The second determinant component of intention is the attitude toward the behavior. This
component concerns the individual's evaluation of the behavior and whether it is unfavorable or
favorable (Azjen, 1991). There is a difference between attitude toward objects and attitude toward
behavior. The former stems from beliefs that are associations with other attributes with for instance
other objects. The latter are beliefs tied to certain outcomes of performing the behavior. Behavior
that has desirable consequences is favored while behavior that is associated with undesirable
consequences is unfavored. (Ajzen, 1991).
2.3.3 Subjective norm
The third component is the subjective norm which is the perception of the social pressure regarding
whether or not to perform the behavior in question (Ajzen, 1991). One approach in order to measure
the subjective norm is to question respondents regarding to what extent the approval or disapproval
of performing a given behavior could be acquired from important others (Azjen, 1991).
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2.4 Behavioral economics
Costa, Carvalho, & Moreira, Bruno César de Melo (2019) define behavioral economics as the
process of including psychological aspects into the decision-making process of economics. The
field of behavioral finance is a branch from behavioral economics, which they found in their
bibliometric analysis. Where they found the first works on behavioral economics being published
in 1967 whereas the behavioral finance works first saw the light of day in 1987. Also they found
that, measured in citations among the sample, the most prominent authors were Amos Tversky and
Daniel Kahneman. Perhaps the most interesting aspect of their bibliometric analysis is the fact that
among commonly addressed subject’s practical implications of irrational behavior is
underrepresented.
According to Camerer and Loewenstein (2003) the core fundamentals of behavioral economics is
a foundation on which theoretical insights are generated which allows for better predictions of field
phenomena, this is done by increasing the realism of the psychological underpinnings of economic
analysis (Camerer, Loewenstein. 2003). In their book they present the common research scheme in
behavioral economics. The first step is to identify normative models with widespread use within
economics. The second step is to identify anomalies which are not attributed to alternative
explanations. Followed by that is the third step which is to employ the anomalies in the creation of
generalized alternatives for current models. Lastly the fourth step is to use the alternative models
in the third step to test the new economic behavioral models. (Camerer, Loewenstein, 2003).
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2.5 Frame of reference
In order to further investigate the practical implications of the irrational behavior, which was found
to be underrepresented in the bibliometric analysis by Costa, Carvalho, & Moreira, Bruno César
de Melo (2019), and to successfully provide another perspective on the framing effect and
advertising, a frame of reference has been created within the literature review. The following model
intends to visualize the frame of reference and the intended areas that are to be studied in this thesis.
Starting with the framing effect from behavioral economics and specifically the goal framing which
will be examined through manipulating advertising which is a form of communication. The
manipulated communication will be reviewed to see if it has successfully had an effect on the
attitudes of the survey respondents which could lead to a change in their purchase intention which
is a result of their attitudes. The study does not aim to research actual behavior only behavioral
intentions, seeing as in order to measure behavior an actual purchase would have to take place.
Therefore the frame of reference is illustrated as follows in figure 5 below.
Figure 5: Frame of reference
Source: Authors own construct
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3. Methodology
3.1 Research purpose
In the effort to conduct research successfully one must first come to an understanding of the actual
purpose of the study. The following are common research purposes and corresponding examples
as introduced by Black (1999); explorative research purposes answers questions such as “Which
characteristics or details relate to observed events, phenomena or reasoning?” Explorative studies
might have a research question that examines if there is a relationship between age and perception
of quality of music. The second research purpose example introduced by Black (1999) is the
descriptive purpose. The descriptive purpose investigates aspects such as: What? Or How? For
instance it might examine events that are occurring or the prevalence of a phenomena. An example
of such a research question is: “How prevalent are sleep disorders among middle management
personnel?” The third common research purpose is the explanatory purpose. Explanatory research
examines causes of an observed outcome. The main interest of such research lies in causal
relationships and testing them. The effects of independent variables on dependent variables is a
key concept of explanatory research. One example that is portrayed by Black (1999) is: “Which
side of the brain is predominantly responsible for computer mouse manipulation?”
This research purpose is explanatory. The reason for choosing an explanatory purpose is since the
main interest of the study is to examine causes of the outcome of the experiment. Seeing as the
purpose of the study is to examine the causal relationship of framing effect on human behavior.
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3.2 Research approach
After formulating and classifying the research question one must decide on an appropriate approach
that contains the correct tools in order to answer said question. Below is a few such approaches as
portrayed by Black (1999);
Descriptive statistics involves the descriptive presentation of quantitative data. One form of
descriptive presentation is in a graph or a chart. The downside with descriptive statistics according
to Black (1999) is that causal and correlational relationships cannot be formed with descriptive
statistics. Black (1999) also presents the correlative approach. The correlative approach is a
quantitative approach where relationships are measured in pairs of variables in order to find out if
and how they vary to one and other. The correlative approach inherits the same downsides as
descriptive statistics in the form of its inability to show causality. Lastly Black (1999) presents the
experimental/quasi-experimental approach. The experimental/quasi-experimental approach is the
approach when the aim is to manage variables in controlled environments in order to achieve
causality. Black (1999) points out that in environments when the sampling is not completely
random the generalizability is reduced and the experiment is then referred to as quasi experimental.
The approach of this thesis will endeavor to be experimental seeing as causality between variables
are of interest and generalizability of the results is the goal. However some measures of descriptive
and inferential statistics will be involved in analyzing the data. When the delimitations of the study
are taken into the consideration of research approach the chosen approach will likely be quasi-
experimental.
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3.3 Deductive approach
When choosing a research approach there are a few paths to choose between. There is induction
which is described by Black (1999) as a process where relationships are proposed lastly, after
observations and data is collected. This is also referred to as theory building. On the other hand
there is deduction, in the sense that relationships are firstly proposed and the data is then collected
in an effort to test theory. Since this thesis is built upon established theories of behavioral
economics and previous studies in the area and aims to test the applicability in a specific scenario
it is recognized as a deductive approach.
3.4 Quantitative method
Seeing as this thesis will include experiments where the responses will be collected through
questionnaires resulting in numerical data the chosen method is quantitative, as opposed to
qualitative. Qualitative methods are concerned with aspects that are described with words and are
usually used for deep thorough research of a narrow topic (Eliasson, 2010). Two common forms
of collecting qualitative data are through observations and interviews. According to Eliasson
(2010) qualitative methods are primarily suitable because of their flexible nature, seeing as the
researcher is able to combine methods of gathering data, prolonging the data collection until they
see fit, and when the researcher wants to investigate contexts that demand an understanding that
becomes clear as the research proceeds. According to Eliasson (2010) quantitative methods are
especially suitable for making generalizations from smaller groups which is also the one of the
aims of this thesis. Furthermore Eliasson (2010) explains that when comparing quantitative and
qualitative research, qualitative research is regarded as more in depth which makes it less
generalizable. Whereas quantitative research is broader which allows for generalizations from for
instance small groups to larger groups.
One way of collecting quantitative data is through the use of questionnaires. Eliasson (2010) states
that a well-constructed questionnaire contains questions that emanate from variables that emerge
from the operationalization. Eliasson (2010) also suggests the following benefits for using a
quantitative method: The post-process is relatively quick, a well-structured questionnaire allows
for a computer based codification. Quantitative methods allow for results that are generalizable,
that speak for larger groups. Even though the resources might only allow for a smaller study of a
smaller group. (Eliasson, 2010).
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Two common methods to collect quantitative data is either by conducting interviews or by
conducting a survey. These two methods have one thing in common and that is the use of
questionnaires. As previously mentioned the questionnaire is created emanating from the variables
that are to be researched in the thesis. The bottom line is that the aim of the research methodology
should aim to acquire as many answers as possible considering the resources available. Of the two
methods, interviews are considered more resource demanding. It is also susceptible to whether or
not the interviewer is able to reach the subject at a good time. The benefits of an interview are that
a successful interview tends to generate plenty of answers and it also gives the interviewer to
explain the questions if need be. The fact that the interviewer has knowledge of how to fill in the
questionnaire means that the reliability is increased when conducting interviews. (Eliasson, 2010).
When conducting a survey the main benefits are the time and money saved seeing as there are
many digital solutions available that offer online surveys. Another important benefit compared to
interviews is that the recipient can choose when to take part of the survey at a time that suits them
(Eliasson, 2010).
There are of course downsides even with surveys and the main downside is the fact that the
response ratio is quite low. However this can be mitigated in a number of ways, one solution is to
send reminders to the recipients and encourage them to answer the survey. There is also an
increased risk of misunderstandings as far as understanding the questions of the questionnaire. The
amount of misunderstanding is difficult to remove completely but one can make an effort to create
a survey that is as understandable as possible in order to increase the response ratio. One way of
making sure that the survey is understandable and free from misunderstandings is to test the survey
with a smaller sample in order to rule out and mend any misconceptions. (Eliasson, 2010).
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3.5 Questionnaire
A questionnaire was created with questions adapted from the questionnaire used by Burböck et al.
(2019). The reason for adapting a previously used questionnaire is that the experiment that it was
used in has high similarity with this study and it will measure similar variables. The construction
of questionnaires is a detailed task and requires careful testing of constructs and measurements in
order to reach validity (Black, 1999). The questionnaire in question can be viewed in the appendix
1. The questionnaire contained 11 questions in total. Three of the questions were regarding
purchase intention, they were: “I would buy this product”, “I would advise my friend to buy the
product”, and “I would buy this product even if I already possess another one”. The following three
questions measured the respondents' attitude towards the product; “The product indicates high-
quality”, “The product seems valuable”, “The product seems attractive”.
Also two questions were included in the questionnaire in order to measure the effectiveness of the
framing manipulation, they were; “The advertisement outlines the product in a rather positive light”
and “The advertisement outlines the product in a rather negative light”. Respondents entered their
response on a 7-point Likert scale ranging from strongly agree (1), to strongly disagree (7). Of the
remaining three questions, one was regarding snus usage, the remaining two were classificatory
questions regarding age and gender.
The self-reported perception of the framing manipulation was adopted from studies conducted by
Lee & Aaker (2004). Similarly to the manipulation checks carried out by Meyerowitz & Chaiken
(1987) the ones in this thesis also measured the negative and positive perception of the framing
sample with two scales and 7-point Likert scales. The method used for examining the results of the
manipulation checks will be elaborated on further in the data analysis section of the methodology
below.
In order to make sure that the questionnaire captured the response on the variables measured in an
accurate way, a variety of preexisting questionnaires were acquired and adapted. Similar studies
conducted by Scott B. MacKenzie, Richard J. Lutz, & George E. Belch (1986) were measured on
three scales. In the experiments the authors measured overall feeling towards a product and
probability of using the product using three scales and a 7 point Likert on each variable. (Scott B.
MacKenzie et al., 1986). As previously mentioned the same notion of measuring attitude and
intention using a 7 point Likert scale and capturing the answers using three scales for each variable
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was adopted in the questionnaire for this thesis. Furthermore, attitude towards a brand was
measured with three scales and a 7 point Likert scale in an experiment by Lee & Aaker (2004). The
difference compared to this study is that the scales in their study ranged from negative-positive,
favorable-unfavorable and bad-good. (Lee & Aaker, 2004). Whereas in this study the range was
the same for all scales, strongly agree-strongly disagree. Similar purchase intention measurements
were used by Putrevu (2010) who also measured purchase intention with three scales using bipolar
7-point Likert scales. Also, these scales were tested using Cronbach’s alpha to make sure they
measure their internal consistency (Black, 1999). The alpha ratings for the scales were 0,88 to 0,96,
which was deemed acceptable by Putrevu, (2010). Similar measurements for internal consistency
and factor analysis will be expanded upon below.
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3.6 Reliability
One important aspect when conducting an experiment of any sort is whether or not it is possible to
conduct the same experiment again and achieve the same result. If it is possible to perform it several
times resulting in the same outcome, then the experiment has high reliability (Eliasson, 2010).
Eliasson (2010) describes reliability as whether or not it is possible to trust that the investigation
outputs the same result after being repeated in as similar circumstances as possible. Furthermore
Eliasson (2010) also states that reliability increases when the results are repeatable. Eliasson (2010)
suggests three key concepts that help to increase reliability: Firstly by measuring the most
important variables in several different ways, so for instance in a questionnaire multiple questions
can aim to measure the same variable. Secondly, being well prepared before starting the research.
This regards such aspects as having clear instructions and routines for how the investigation should
be carried out. Lastly making sure that when the data is coded and registered that it is not registered
incorrectly. Seeing as this thesis is using a questionnaire one important aspect that will be taken
into account is the reliability of the questions. The internal consistency of the questionnaire will be
measured by using Cronbach’s alpha. According to Black (1999) Cronbach’s coefficient is an
indicator of internal consistency and can be used for questionnaires using scales. A Cronbach
coefficient equal to or above 0,68 is considered moderately reliable (Black, 1999).
3.6.1 Factor analysis
A factor analysis was conducted in order to test construct validity (Black, 1999).
In order to analyze the construct validity all questions from the questionnaire were loaded into
Minitab for computing. The first three variables were extracted from questions regarding purchase
intention, the variables after were extracted from questions regarding attitude towards the product.
Part of the aim of the factor analysis is to test how much of the variability can be explained by a
factor (Minitab, 2019). For this questionnaire two factors were thought to be present, namely
purchase intention and attitude towards product, however a three factor analysis was conducted in
order to rule out the presence of a third factor. In table 1 of the results chapter, one of three factor
analyses is presented. The similarities between the three factor analyses was high and therefore it
seemed suitable to present one as an example of the method used in this thesis. The factor analysis
will be further analyzed in the results chapter.
In essence, the researcher wants to perform the research in a way that it is possible to redo the
experiment and acquire the same results again, as previously mentioned this increases the
25
reliability. Eliasson (2010) states that increased reliability improves the conditions for high validity.
Validity will be discussed in the following section.
3.7 Validity
Another important aspect when conducting an experiment is whether or not the experiment is
measuring what the experiment is intended to measure, this is considered to be the main aspect of
validity according to Eliasson (2010). Further Eliasson (2010) presents two key aspects to increase
validity. The first key is to make sure that the operational definitions of the concepts match theory
as closely as possible. The second key aspect is to design indicators for the concepts used in the
research.
3.8 Descriptive statistics
Descriptive statistics was used to grasp the basic results of the collected data. With the use of
descriptive statistics information such as the demographic of the respondents and the amount of
current users can be presented. Also, thanks to the demographic data, the criteria that the
respondents are of the required age of 18+ can be met.
3.9 Inferential statistics
In order to analyze the results statistical significance inferential statistics was used, specifically a
two sample t-test. These actions were conducted in a statistical software, in this case Minitab. The
sample mean of the differently framed advertising stimuli was compared to examine any
differences and their statistical significance. In order to view the effect the variables product
attitude and purchase intention were chosen. As previously mentioned, each variable had data
collected by using three questions on each subject. The individual's response on each topic was
summarized to create two variables which were purchase intention and product attitude. The mean
of each of these variables were then compared between questionnaires to measure whether or not
there was an effect of the framing. For the testing the confidence interval (90%) was used and the
significance level 10% was chosen, seeing as it is common within social sciences (Black, 1999).
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3.10 Manipulation checks
In accordance with the manipulation check performed by Burböck et al. (2019) where the success
of the framing was measured using a paired t-test, the same testing was used in this thesis. The
aim of the manipulation check is to examine the success of the framing, so for instance, when
examining the perception of the negatively framed experiment the expected outcome is that it is
perceived as negative.
3.11 Sample selection
In order to make generalizable statements about a group of individuals there are certain limitations
that need to be taken into consideration. For instance, the larger the group that is to be generalized
the larger the study needs to be. Seeing as the timeframe of this study does not allow for a study of
large magnitude where the entire population of a country is part of the study, since that would be
too costly and require too large of a data collection, a suitable sample selection has to be
constructed. Eliasson (2010) states that in order to make a statement about a population, the use of
a sampling is needed. According to Eliasson (2010) a sampling is a complete list of a population,
which in the case of this thesis could be the students at LTU.
From the sampling a sample is then drawn. When constructing a complete list of a population in
the sampling there are criteria that can be taken into consideration in order to make the sampling
as complete as possible (Eliasson, 2010). If the example of the students at LTU is used, imagine
that the goal is to construct a complete list of students at LTU, a criteria can be added that needs to
be met in order to make the list. One important criteria here would be that they are still students at
the university, another important criteria is that they meet the age restriction of 18 that is set on
tobacco products. By making sure that there are different criteria’s that can vastly improve the
sampling, the sample will in turn also be vastly improved. (Eliasson, 2010).
Another aspect that needs to be taken into consideration is the probability of the sampling, where
one takes into account the probability of each individual from the sampling to be chosen for the
study (Eliasson, 2010). According to Eliasson (2010) the reason for using probability sampling is
that the sample is given the ability to speak for the population. Eliasson (2010) describes four
different kinds of probability sampling. Firstly there is the simple random selection in which every
individual has the same probability to be a part of the study. If the aim is to speak for the entirety
of the population then the simple random sample will result in the highest credibility and reliability,
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the downside is that if there are smaller groups within the sampling then they risk being left out of
the sample. Secondly there is the stratified sample where the population is stratified into groups
before the sample is drawn from each of them. This is especially useful when one wants to take
into consideration the difference in size of the different groups in the population in order to make
sure that they are equally represented in the sample. Thirdly there is the cluster sampling, where
the population is divided into clusters from which a sample is then drawn from some of the clusters.
The benefit of cluster sampling according to Eliasson (2010) is if there is a difficulty to create a
complete sampling, the disadvantage is that there is no way to accurately project the probability of
each individual in the sample and therefore the ability for the sample to speak for the population is
lost.
Since the aim of the study is to achieve a result which can speak for a population the chosen
sampling method is a probability sampling. Seeing as it is possible to create a complete sampling
with the resources available in the form of a mailing list of students at LTU and making sure that
that list meets the different set criteria’s, a simple random selection will be used. The sample size
of each experiment group will be 35 which gives a total sample size of 105 seeing as there will be
three experiment groups. Stutely (2003) refers to a sample size of 30 as suitable for hypothesis
testing.
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3.12 Methodology summary
The first step of this research was to construct a testable hypothesis. The second step was to craft
an experiment that tests the hypothesis. In the experiments a suitable sample size of participants
was chosen according to the theory of what suits this particular study. The participants were then
randomly assigned into three groups. The first group was given negatively framed advertising
stimuli which constitutes the first independent variable. The negative goal-framing sentence was
phrased as: ”By not using “Pure” you risk getting discolored teeth and bad breath, compared to
traditional oral tobacco”. The second group was given positively framed advertising stimuli which
constitutes the second independent variable. The positive goal-framing sentence was phrased as:
”By using “Pure” you avoid the risk of getting discolored teeth and bad breath, compared to
traditional oral tobacco”. The third group is the control group who will be given a neutral
advertising stimuli in order to rule out any interference and to be able to safely say that the answers
of the two other groups are because of the stimuli. The phrasing of the goal-framing in stimuli was
adapted from a similar study conducted by Burböck et al., (2019). In figure 6 below the
experimental design is visualized.
Figure 6: Experimental design
Source: Authors own construct
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The different groups will be given a questionnaire that measures their attitudes towards the product
and purchase intention of the specific product which constitutes the dependent variable of the
thesis. The questionnaire will be assembled in accordance with theory in a way that attempts to
ensure as good of a response as possible on the measured attitudes and purchase intention. The
questionnaires will then be collected and the data will be gathered and analyzed using descriptive
and inferential statistics.
Sampling will be people over 18 since the products are age restricted. Seeing as in the current
situation the most available participants who are able to act as respondents in this study will be
students this is also for reasons of account research cost. They will be reached by email. The
mailing lists will be collected from LTU's administrative personnel.
In this experiment a fictitious brand of tobacco free snus will be created in order to rule out already
established attitudes towards an existing brand. Also there will be no images of the product
included in order to further diminishing preexisting attitudes towards existing brands. The stimuli
in the experiment will be assembled to resemble an original ad.
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4. Results
4.1 Factor analysis
In table 1 below, the factor analysis conducted in order to test the validity of the constructs is
presented
Table 1: Factor analysis
Rotated Factor Loadings and Communalities Equimax Rotation
Variable Factor1 Factor2 Factor3 Communality
Recoded 0IBUY 0,185 0,951 -0,012 0,938
Recoded 0Ubuy 0,388 0,694 0,104 0,643
Recoded 0buymore 0,357 0,812 -0,045 0,789
Recoded 0HQ 0,848 0,263 -0,292 0,874
Recoded 0valuable 0,895 0,376 -0,137 0,962
Recoded 0attractive 0,856 0,370 -0,281 0,949
Recoded 0positive 0,171 0,014 -0,952 0,936
Recoded 0negative -0,177 0,056 0,939 0,917
Variance 2,6275 2,3948 1,9851 7,0074
% Var 0,328 0,299 0,248 0,876
The factor analysis presents the rotated factor loading. The factor analysis was rotated using an
equimax rotation in order to make it easier to discern patterns between the variables, the choice of
the equimax rotation was subjective, seeing as it was deemed most suitable (Minitab, 2019). There
are a few ways to interpret the data of the factor analysis. The ones used in this thesis are;
communality, variance, % var and explanatory power of the factors.
Firstly looking at the explanatory power of the factors, the unrotated data showed that the variance
of factor three was below 1. This is below the recommended threshold and therefore factor three
was deemed to be poor as a factor that explains the variability in the data. However after performing
the equimax rotation the variance is altered, although the variance of factor three is still lower than
the other two factors. (Minitab, 2019).
Secondly when viewing the % var, which is described as the factor's ability to explain the
proportion of variability in the data, the lowest %var can be seen in factor 3. (Minitab, 2019)
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Lastly the communality presented in table 1 is considered to be good, the closer the communality
is to 1, the better the variable is explained by the factor (Minitab, 2019).
In conclusion the factor analysis shows as intended, that the first three variables are explained by
factor 2, which is then considered to be the purchase intention factor. In turn factor 1 explains the
other three variables, which is considered to be the attitude towards the product. The presence of a
factor 3 is thereby ruled out since the different thresholds were not met for a third factor to be
present.
4.2 Cronbach’s alpha
In table 2 below, the testing results for the Cronbach’s alpha measuring internal consistency is
presented. Table 2: Cronbach’s alpha
In order to measure the internal consistency Cronbach’s coefficient was used. The testing
grouped the questions according to variables. The first three variables are questions regarding
purchase intention (PI). The Cronbach alpha rating for all questions regarding PI are all above the
threshold of 0,68 and are therefore deemed to have very strong internal consistency. The
remaining three variables are questions regarding product attitude (PA). Also these questions are
above the threshold of 0,68 and are deemed to have very strong internal consistency. For all the
questions tested the results ranged from 0,85 to 0,95 which indicates very strong internal
consistency and a highly reliable test.
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4.3 Descriptive statistics
In Table 3, the descriptive statistics is presented. The descriptive statistics shows that the total
respondents was 63. The distribution between the genders saw a slight majority of male
respondents. The amount of users are roughly even between the surveys except for the neutral
survey where the non-users were a clear majority.
Table 3: Descriptive statics
According to the means and standard deviations of the respondent’s age, the mean age was
approximately 25 and according to the data set none of the respondents were below the age of 18
which was a predetermined required criteria.
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4.4 Inferential statistics
In accordance with the frame reference where attitudes are portrayed as an antecedent to
intentions, the results will be presented starting with product attitude followed by purchase
intention.
The following section will compare the differences of the mean (X) between surveys in order to
examine the statistical significance of the difference of the product attitude and purchase intention.
In order to measure the statistical significance of the differences between the sample’s product
attitude and purchase intention variable three two sample t-tests were conducted.
Equal variance was assumed seeing as the samples were of the equal size, by assuming equal
variance the power of the two sample t-test is increased. The chosen confidence interval for the test
was 90% seeing as the sample size was not large enough for a 95% confidence interval where the
limit is a sample size of approximately n=30. Seeing as the confidence interval is lower, the chances
of the population Mean to be contained in the interval is lowered. Chosen significance level (⍺) for
this study was 10% which is common in social sciences. When executing the test the statistical
significance of the difference will be measured using a p-value. If the p-value is lower than the
significance level (⍺) 10% the null hypothesis is rejected. This is commonly
formatted as p-value ≤0,1. All of the tests were conducted as one-
tailed tests seeing as the aim was to review which of the framing
samples showed the greatest effect.
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4.4.1 Product attitude
4.4.1.1 Product attitude: Negative framing vs Neutral stimuli
In table 4 below the two sample t-test examining the statistical significance of the difference
between negative framing and the neutral stimuli is presented
Table 4: Two sample t-test Product attitude: Negative vs Neutral
When comparing the mean (X) of the negative sample 4,079X with that of the neutral sample
3,587X the difference was 0,492 with a p-value of 0,026. The p-value is below the ⍺ of 0,1, the
null hypothesis H₀: μ₁ - µ₂ = 0 is rejected in consideration for the alternative hypothesis H₁: μ₁ - µ₂
> 0. This test result suggests that the difference between negatively framed advertising and the
neutral stimuli is statistically significant, which suggests that negative framing has an effect on
product attitude.
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4.4.1.2 Product attitude: Positive framing vs Neutral Stimuli
Table 5 below contains the two sample t-test that examines the statistical significance of the
difference between the means of the positive framing and the neutral stimuli.
Table 5: Two sample t-test Product attitude: Positive vs Neutral
The next comparison was between the positive sample and the neutral sample. In the test the
difference of the mean of the positive sample 4,000X and the neutral sample 3,587 X was 0,4127.
The significance of this difference was 0,006 which is below the ⍺ of 0,1 which enables a rejection
of the null hypothesis H₀: μ₁ - µ₂ = 0 in consideration for the alternative hypothesis H₁: μ₁ - µ₂ > 0.
This test result suggests that the difference between positively framed advertising and the neutral
stimuli is statistically significant, which suggests that positive framing has an effect on product
attitude.
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4.4.1.3 Product attitude: Negative framing vs positive framing
In table 6 below the two sample t-test which examines the statistical significance of the difference
between the means of the negative framing sample and the positive framing sample is presented.
Table 6: Two sample t-test Product attitude: Negative vs Positive
The final test on product attitude examined the difference of the negative sample mean 4,079X and
the positive sample mean 4,000X, which was 0,079 at a significance level of 0,343. This p-value
is higher than the accepted significance level and therefore there is a failure to reject the null
hypothesis H₀: μ₁ - µ₂ = 0. Seeing as the null hypothesis could not be rejected, the test was unable
to conclusively show statistical significance of the difference between negative and positive
framing in effect on product attitude.
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4.4.2 Purchase intention
4.4.2.1 Purchase intention: Negative framing vs Neutral stimuli
In Table 7 below the two-sample t-test comparing the difference of Purchase intention mean of the
negative and neutral sample is presented.
Table 7: Two sample t-test Purchase intention: Negative vs Neutral
When comparing the μ₁purchase intention in the negative frame (2,619X) with the µ₂ neutral stimuli
(2,54X) there is a difference of 0,079 although with a p-value of 0,398 therefore the null hypothesis
H₀: μ₁ - µ₂ = 0 cannot be rejected. Seeing as there was a failure to reject the null hypothesis, this
indicates that the testing was unsuccessful at providing statistical significance for the difference
between negative and neutral framing in effect on purchase intention.
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4.4.2.2 Purchase intention: Positive framing vs Neutral stimuli
Table 8 below presents the two sample t-test comparing the positively framed advertisement with
the neutral sample.
Table 8: Two sample t-test Purchase intention: Positive vs Neutral
When comparing the μ₁ purchase intention in the positive frame (3,095X) with that of the µ₂ neutral
stimuli (2,54X) there is a difference of 0,556 at a p-value of 0,021. Since this value is lower than
the significance level 0,1 the null hypothesis H₀: μ₁ - µ₂ = 0 can be rejected in consideration for the
alternative: H₁: μ₁ - µ₂ > 0. This result demonstrates that there is a statistical significance for the
difference between positively framed advertising and the neutral sample in effect on purchase
intention.
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4.4.2.3 Purchase intention: Negative framing vs Positive Framing
In Table 9 below the two sample t-test comparing the statistical significance of the difference
between the purchase intention means of the negative and positive sample is presented.
Table 9: Two sample t-test Purchase intention: Negative vs Positive
The purchase intention of the μ₁ negative frame is as previously mentioned (2,619X) and the
difference when compared to the µ₂ positive frame of (3,095X) is -0,476 at a p-value of 0,894 which
is above the chosen significance level of 0,1 therefore the null hypothesis H₀: μ₁ - µ₂ = 0 cannot be
rejected. The result in table 5 suggests that the test was unable to prove the statistical significance
of the difference between negative framing and positive framing in effect on purchase intention.
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4.4.3 Manipulation checks
The following section will examine whether or not the manipulation of the advertising stimuli was
successful
4.4.3.1 Negative goal-framing paired t-test
In table 10 below, the paired t-test examining the statistical significance of the difference of the
perception of the negatively framed advert is presented
Table 10: Two sample t-test Purchase intention: Negative vs Positive
The results of the paired t-test in the negative goal framing experiment examined the participants'
perception of the advert. The mean for participants who perceived the advert as positive was 5,524
whereas the mean for the participants who perceived the advert as negative was 2,524. The p-value
was <0,001 and the difference between the means were therefore statistically significant. Overall
the negatively framed advert was predominantly perceived as positive.
41
4.4.3.2 Positive goal-framing paired t-test
In table 11 below, the paired t-test examining the statistical significance of the difference of the
perception of the positively framed advert is presented
Table 11: Two sample t-test Purchase intention: Negative vs Positive
The results of the paired t-test in the positive goal framing experiment examined the participant’s
perception of the advert. The mean for participants who perceived the advert as positive was 5,571
whereas the mean for the participants who perceived the advert as negative was 2,190. The p-value
was <0,001 and the difference between the means were therefore statistically significant. Also this
advert was perceived as positive.
From the results of the two paired-t tests conducted the conclusion that the framing manipulation
was a failure can be drawn. Seeing as the negatively framed advert was not perceived as negative
then perhaps the effect of the framing manipulation might not have been as strong as intended.
Although a point could be made that the positive goal framing manipulation appears to be more
positive than the negative goal framing. Furthermore, the negative goal framing appears to be
perceived as more negative than the positive goal-framing. However further testing is necessary to
be able to make such a conclusion.
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5. Analysis
As previously portrayed in the frame of reference in figure 4, attitudes are antecedents to intention.
Therefore the experiment's success relies on whether or not both attitude and intention was affected
by the framing manipulation. When reviewing the data presented in the results section there were
clear indications with statistical significance, that the neutral advertisement was less effective than
the framed advertising. The testing showed significant results both for positively and negatively
framed goal framing when compared to the neutral sample. However negative goal framing only
had effect on the respondent’s attitude towards the product. The effect on purchase intention by the
negatively framed advertisement had no statistical significance compared to the neutral sample.
Furthermore the testing on the positively framed advertisement had a statistically significant effect
on both product attitude and purchase intention, these findings are deemed highly relevant. The
negative goal framing was therefore deemed less effective than the positively framed goal framing
when compared to the neutral sample. The findings are contradictory to those found by Meyerowitz
& Chaiken (1987), who claimed that laying emphasis on the negative consequences of not
performing an action has the most effect.
The results are in line with the findings of Burböck, Kubli, Maček, and Bobek (2019) who argued
that goal framing was more preferable when framed in a positive way. The findings are also in line
with Tversky & Kahneman (1981) who propose that individual preference is concise
advertisements and with a risk averse attitude when presented with positive frames based on
gaining something. Additionally when reviewing the literature on attitude toward a behavior in the
theory of planned behavior model, as presented previously in figure 3, the findings conform to that
of the model. The theory states that behavior that has desirable consequences is favored while
behavior that is associated with undesirable consequences is unfavored. (Ajzen, 1991). There is a
possibility that the behavior of using all-white snus has been perceived as more desirable than the
behavior of not using all-white snus, and therefore the positively framed advertisement had a
stronger effect than its negative counterpart.
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When reviewing the goal framing samples manipulation checks were included in order to collect
data on whether or not the respondents perceived the sample in a positive or negative light. The
manipulation checks suggested that all the questionnaires were perceived as more positive than
negative. These findings could be interpreted as beneficial to strengthening the significance of the
effect of the positive goal framing sample. However it raises the question of the significance of the
negatively framed sample, seeing as it was perceived as predominantly positive. Perhaps the result
would have been different if the negatively framed goal framing sample was perceived as negative.
When weighing in the distribution of users and non-users as a parameter of the analysis the
descriptive data shows that the group containing the largest number of users is the positive sample.
Seeing as the positive sample also scored the highest purchase intention and attitude towards the
product perhaps the data might be a bit skewed.
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6. Conclusions
As stated previously the aim of the study was to explore attitudes and intentions towards nicotine
products and how they were affected by the goal framing effect. In an effort to meet aims of the
thesis the following research question was stated:
“Research question: Is there a framing effect when advertising tobacco free snus?”
The research question was answered using data collected from a quasi-experiment and later tested
using hypotheses. The hypotheses used in the statistical tests tested the hypothesis stated as:
“H1 Alternative hypothesis: There is a significant difference in effect on attitudes and intention
between using positive or negative goal-framing when advertising tobacco free snus products.”
“H0 Null hypothesis :μ1 – μ2 = 0 There is no significant difference in effect on attitudes and
intention between using positive or negative goal-framing when advertising tobacco free snus
products”
The statistical testing found evidence to reject the null hypothesis in consideration for the
alternative hypothesis: “There is a significant difference in effect on attitudes and intention between
using positive or negative goal-framing when advertising tobacco free snus products”. The results
indicated that both positive and negative goal-framing had a greater effect than the control group.
Furthermore the results of the study suggest that positive goal-framing has an effect on both
purchase intention and product attitude when compared to the neutral sample. The negative goal-
framing had an effect on product attitude when compared to the neutral sample.
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6.1 Practical implications
In essence this study has shown the presence of the framing effect, specifically the goal-framing
effect in advertising. By revealing its presence in the marketing of nicotine products it brings the
opportunity to suggest recommendations to decision makers in various fields, specifically in
advertising. One such recommendation is the fact that there is a need to be aware of the framing
effect as an advertiser. The need for awareness of the framing effect extends further than to the
situations of deliberate framing. As an advertiser one must be aware that the way in which the
attributes or consequences that are a part of the communication is portrayed, has an effect on the
way in which it is interpreted by the consumer. The framing effect can thus lead to unexpected
pitfalls if used incorrectly or inadvertently. On the other hand the implications of this study show
that the goal framing effect could prove to be a valuable tool in the marketer’s toolbox. With
regards to the advertising of tobacco free snus products, this study has shown to some extent that
it could be preferable to make use of the positive goal framing effect. According to the findings
this should affect the consumers’ purchase intention based on the attitude towards the products.
6.2 Theoretical implications
As previously mentioned various areas have been studied in regards to the framing effect and
advertising. The gap that this thesis is intended to fill is that of the framing effect on advertisements
of nicotine products. By combining the research topic with a well formulated methodology this
thesis has attempted to fill that gap. The main contribution of this thesis is the new perspective of
students' perceptions on goal-framed advertisement of tobacco free snus. This perspective portrays
the existence and effectiveness of the goal-framing effect on attitudes and purchase intentions.
The theoretical implications of this thesis are the building blocks that have been laid for future
research to better understand the framing effect when advertising nicotine products.
This study has confirmed to some extent that framing effect is a factor that is present in the
advertising of tobacco free products. In addition to that, the findings in the experimental measures
conducted in this thesis suggests that the positive goal-framing has an effect on both purchase
intention and product attitude, whereas the negative goal-framing has an effect on product attitude
46
when advertising tobacco free snus products. These findings should contribute to the knowledge
of advertising which could be of interest to both marketers and consumers.
6.3 Limitations
The first error is the fact that the total sample size of the study only reached 63 participants. The
aim of the study was to acquire 105 participants with 35 participants in each of the experiment
groups in order to achieve generalizability. Although the sample size in each experiment group
only reached 21, the testing was still successfully conducted in terms of being able to complete the
two sample t-test, however the ability to generalize the findings is greatly reduced. Under different
circumstances the available participants might have been able to be collected in person but, as
mentioned in the delimitations, physically collecting participants was impossible due to the
negative effects of COVID-19 pandemic which obstructed the efforts.
The second error is attributable to the randomization of the participants. Seeing as participants were
selected from a list of email addresses of LTU students the sample cannot be considered random.
As previously mentioned, the preferred way of portioning and selecting participants was severely
hindered by the COVID-19 pandemic which made it impossible to select participants in the
intended random order. Utmost effort has been put into randomizing the mailing list and thereby
the selection of participants but in essence the selection cannot be considered random.
The third factor that may have affected the results of this study is the way that the statistical testing
has been conducted. It is a common conception that when analyzing three independent groups, in
this case experiment 1, experiment 2 and control that the suggested statistical approach is to
conduct ANOVA tests. However the statistical test approach of this study was two sample t-tests
testing each of the groups against each other. Seeing as there were three groups in this study the
use of multiple two-sample t-tests increased the risk of rejecting the true null hypothesis compared
to ANOVA testing.
Lastly referring to the framing manipulation checks, seeing as the testing of the manipulation
checks suggested that the stimuli in the different experiments was perceived as predominantly
47
positive perhaps the results would have differed if the negative goal-framing sample was perceived
as predominantly negative.
6.4 Suggestions for further research
For future research, find a way to improve the impact of the framing effect in the experiments. Also
use a larger sample size which is randomized. In an effort to delve deeper a study could be done
on users versus non users. Also expanding the study to acquire participants from a larger
international sample in order to generalize the results to a larger population. Moreover another
interesting further research task could be to conduct the same research in other markets other than
the Swedish market. Finally a highly interesting area for further research would be to develop a
standardized measurement in order to be able to compare results between studies.
48
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7.Appendix
7.1 Appendix 1 - Questionnaire
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