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What are the most effective user data for persuasion profiling? 1
Personalised persuasion –
What are the most effective user data
for persuasion profiling?
Bachelor’s Thesis
October 2013
Clemens Steiner
Schwandenstrasse 6
6382 Büren, NW
University of Basel
Department of Psychology
Centre for Cognitive Psychology and Methodology
Thesis Supervisors:
Elisa D. Mekler, M.Sc.
Prof. Dr. K. Opwis
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Contents
Abstract ..................................................................................................................................3
Introduction ............................................................................................................................4
Short history of persuasion ............................................................................................4
Computers as persuasive technologies ...........................................................................5
Terms and definitions ....................................................................................................6
Why personalised persuasion? .......................................................................................6
How to gain user data? ..................................................................................................9
Theory .................................................................................................................................. 10
Basic theories about persuasion: Attitude and behaviour change .................................. 10
Persuasive technologies and persuasion in the web ...................................................... 17
Why personalised persuasion ....................................................................................... 20
Personalisation on different user information ............................................................... 21
Persuasion Profiles, Answers to persuasion principles and persuasibility. ........... 21
Personality traits. ................................................................................................ 23
Gathering user data ...................................................................................................... 26
Discussion ............................................................................................................................ 30
Promising user data that could increase the effectiveness of persuasion profiles .......... 30
Data gathering ............................................................................................................. 31
Further thoughts .......................................................................................................... 32
Criticism, further research, and future. ......................................................................... 32
Conclusion .................................................................................................................. 34
References ............................................................................................................................ 36
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Abstract
Already Aristotle knew that it is important to know his audience. But only recently it became
possible to address every individual of larger groups and provide them with a personalised
persuasion. Persuasion profiles include measured individual susceptibilities to particular
influence or sales strategies. This thesis aims to investigate which additional user data, has
potential to increase the effectiveness of a persuasion profile. The literature suggests that with
additional user data like personality traits, behaviour in social networks, browser histories and
demographic data, the effectiveness of these profiles could possibly be increased. Future
empirical research has to be done to verify the effects of including the suggested user data in
persuasion profiles. Due to ethical implications, the topic of personalised persuasion has to be
addressed carefully.
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Introduction
Short history of persuasion
Manipulating other’s opinions has been an issue long before the first electronic computers
were invented. In ancient Greek mythology, there was a goddess named Peitho, who
personified persuasion (Smith, 2003). Aristotle stated in his work (350 BC), that there are
three modes of persuasion, which were important to be proficient in, especially as a Greek
politician, but also as a Greek citizen in everyday life. A successful speaker should be able to
apply these modes to a speech; ethos describes persuasion through the personal character of
the speaker, where for example credibility is important. Another way to persuade others is
through the speech itself, appealing to logic reasoning and the truth, which is called logos.
Persuasion can also be achieved through the audience, specifically through the emotions, that
the speaker triggers in them. This is what Aristotle meant with pathos, which is also today a
very important aspect when it comes to persuasion. With today’s technology knowing the
audience and thus triggering desired emotions, reactions and behaviours can be achieved more
easily than in ancient Greek. Nowadays it is even possible to track and profile individual
internet users’ every move (Atterer, Wnuk & Schmidt, 2006) and also their interests, habits
and more (Gauch, Speretta, Chandramouli & Micarelli, 2007).
In more recent history, the work of Bernays (1928) is worth mentioning. In an article,
he describes how to use the means and insights of introspective psychology to change
attitudes of larger groups of people in an intended direction. The main techniques used in the
psychology of public persuasion are first collecting facts and opinions of the public and the
object of interest, the second important ability is to apply diagnostic procedures and statistics
to interpret the data. Here once again, the audience and their interests and habits are
important. According to Bernays it is also important for a specialist in swaying public
opinion, to take insights in group behaviour, group leaders and group habits as a “…part of
the technical background…” (Bernays, 1928, p. 961) into account. With these techniques,
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such a specialist can get to know his target group well. By also applying various techniques of
persuasion, he is able to educate or persuade the public to new ideas, overcome stereotypes,
change behaviours and other goals. As Aristotle before, also Bernays points out the
importance of knowing his audience. The goal of this bachelor’s thesis is to find out how well
a persuader has to know his audience or his target group on an individual level and which
individual characteristics have to be addressed to increase the effectiveness of personalised
persuasion.
Computers as persuasive technologies
In the age of modern computers there are completely new ways to influence, manipulate or
persuade people, which were not imaginable to achieve without these inventions. The
question is why persuasive technologies and persuasive design are emerging and growing?
The reason is not only that it is possible today but also because of changing unique selling
propositions in the information age. According to Schaffer (2008), there have been four waves
of the information age until now. During the first wave, a unique selling proposition was good
hardware. In the second wave, good hardware was the new normal, and the right software
became the new differentiator. Having become also an ordinary feature, usability was the
most important thing during the third wave. The fourth wave is now all about persuasion,
emotion and trust.
Given the fast spreading of these technologies it is important to introduce frameworks
and descriptions as a common ground to research these new possibilities with computers as
persuasive technologies. One of the first review about the perspectives and research directions
in the field of persuasive computers, was written by Fogg (1998). The study of Computers As
Persuasive Technologies is agreed by a special interest group of a CHI conference to be
called captology (B.J. Fogg, 1997).
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Terms and definitions
Fogg (1998) introduced five different perspectives on computing technology and persuasion
in his article about captology. The perspectives range from a definition of what persuasive
computers are, how they function, describing different levels of analysis for captology,
defining the design space for persuasive technologies and finally to the important issue of
ethics of computers that persuade.
A central aspect of this bachelor’s thesis focuses on persuasion profiles and which user
data are most promising to increase their effectiveness. Regarding the origins of the term
persuasion profiling, Kaptein and Eckles (2010) mentioned that Fogg has used it since 2004 in
lectures. Later, Fogg mentioned and explained persuasion profiles in the 2006 U.S. Federal
Trade Commission hearing on the subject of protecting consumers in the next tech-ade. These
public hearings addressed new emerging technologies and their potential effects on consumers
and how to protect them. Kaptein and Eckles (2010) describe these profiles as collections of
measured susceptibilities of individuals to a particular influence or sales strategy. They are
based on the previous interactions between the users of an interactive web system. The system
captures, how vulnerable a user is to certain persuasion strategies and will be using those
which work best, the next time the system is used again. The clue is, this profile does not
track simple interests, instead it tracks by which means a user is most likely to be persuaded
into doing something desired from the persuasion agent.
Why personalised persuasion?
Even Aristotle knew that a speaker can be successful when he knows the audience and knows
how to trigger the right emotions in them. Later Bernays (1928) proposed to include insights
of introspective psychology, sociology and statistics as a technical background for getting to
know the target group and then forming public opinions. As already mentioned, computers
offer new possibilities in the field of persuasion. With these automated structures, which can
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handle and sort a lot of information, it is nowadays possible to get to know every individual of
a large group and discover and track each user’s interests, beliefs, behaviours and attitudes
(Atterer et al. 2006; Mobasher, 2007; Schafer, Konstan & Riedl, 2001) and thus personalise
the web experience with dynamic content.
Personalisation aims to enhancing the user experience by providing information and
recommendations tailored to every user. For example recommender systems help consumers
find music, magazines and other products they like in an overwhelming amount of products
available that one cannot search through all (Schafer et al. 2001). Mobasher (2007) states that
the end-goal of user-adaptive systems is giving the users what they want before they ask for it
explicitly. Examples are, as already mentioned recommender systems, further, personalisation
involves also advertisements, links or the appearance of a web page. Mobasher distinguishes
between automatic personalisation and customisation. The difference of these two ways is
who is in control for the creation of user profiles and the appearance of the interface of a
website. Customisable webpages let the users make decisions about how for example the
interface should look. For instance iGoogle (www.google.com/ig) lets its users choose a
background picture and order the interface elements with drag and drop. Further one can
install google gadgets which provide news, games, pictures, the weather forecast and many
more. Automatic personalisation can happen through data mining processes which include
amongst others data collection, pattern discovery and applying this knowledge in real-time to
bring a personalised web experience to the user. For example Youtube’s (www.youtube.com)
recommender system for videos which brings recommendations based on previous search
requests or already viewed videos.
According to the elaboration likelihood model (ELM) from Petty and Cacioppo
(1986), persuasive appeals are processed either in a central or a peripheral way. The central
way describes a well-elaborated attitude formation by scrutinizing the merits of message
arguments. The peripheral route is a faster processing route, where the outcoming attitude
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change is not as stable as changes resulted from central processing. This faster route can be
triggered by heuristic cues, such as the framing of message. Which processing way is chosen
depends on several factors like personal involvement, motivation and personality
characteristics and thus differs largely inter-individually.
Berkovsky, Freyne and Oinas-Kukkonen (2012) propose to bring the new possibilities
of personalisation together with persuasive technologies, thus increasing its effectiveness.
Persuasive applications often use a one-size-fits-all method and lack content tailored to each
individual user’s characteristics, habits and preferences. Combining the insights of
personalisation and persuasion can lead to a personalisation of the persuasive intervention in a
way that the messages, interfaces, timing, the persuasive strategies and other factors are
tailored to one specific user. In line with Fogg’s framework on captology (1998), Berkovsky
et al. (2012) suggest to personalise the functional triad of persuasive technologies, which
states that, persuasive technology can serve as a tool, media or as a social actor. An example
for one personalised function is when a computer serves as a tool to achieve goals, a
personalised persuasive technology could monitor the variations of variables which are
important for a specific user.
The peripheral processing path from the ELM gets, as stated before, triggered by
heuristic cues like the amount of arguments or the framing of the message. Influence or sales
strategies exploit this mechanism and trigger peripheral processing which brings faster and
more automatic attitude changes. The most well-known influence principles are the six
influence strategies introduced by Cialdini (2007), namely reciprocity, commitment and
consistency, social proof, liking, authority and the scarcity principle, which will be elaborated
later in this thesis. These strategies exploit heuristics of individuals, which were shown in past
experiences to guide behaviour well and save cognitive resources in similar situations. How
effective these routes can be triggered through peripheral cues differs largely inter-
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individually. One way to address these differences is to personalise persuasion, for example
with persuasion profiles.
Also Kaptein and Eckles (2012) investigated individual differences in answers to
persuasion principles. In two studies they showed also that one size does not fit all. There is a
large enough heterogeneity in responses to these strategies that on average effective influence
strategies could have negative effects for many individuals. They could also replicate the
findings in a second study which lasted over three sessions, providing evidence that the found
heterogeneity is due to stable individual differences.
How to gain user data?
Applications of artificial intelligence are becoming more and more sophisticated and serve
more functions than just forming recommender systems. As Kaptein, Parvinen and Pöyry
(2013) say, they are able to lead to an “…interactive ‘dialogue’ between the buyer and the
selling e-commerce platform” (p. 2763). They describe two different ways of computer based
learning, which can adapt to the recipient and adopt the functions salespeople fulfil in
traditional sales situations. One way is the traditional light-weight theory-driven and, since
computers became fast enough, second the heavy computing needing data-driven approach of
automated computer based learning.
It is also possible to gain user data in social networks. Back et al. (2009) could show
that profiles in social networks reflect the user’s actual personality. Aral and Walker (2012)
described a method for identifying susceptible and influential users in a social network.
Other good sources of user data are the browser history or the search history of
internet users (Grčar, Mladenić & Grobelnik, 2005; Mobasher, 2007). This thesis investigates
if including these data in persuasion profiles could lead to an increased persuasive power.
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Theory
Basic theories about persuasion: Attitude and behaviour change
Persuasion profiles and persuasive communication in general, aim to change attitudes and
behaviours in an intended direction. Intention requires a persuasive agent to know what he
wants to achieve and what types of behaviour change exist. In order to determine how to
define and classify behaviour changes, Fogg and Hreha (2010) created a framework to
classify several types of behaviour change and provide solutions to achieve these. The so-
called Behavior Wizard is an advancement of the earlier method Behavior Grid introduced by
Fogg (2009) and contains fewer behaviour change paths for the sake of convenience and
practice. Classification of behaviour change based on the Behavior Wizard is done on two
axis, where one describes the behaviour flavour ranging from unfamiliar new behaviour over
increasing certain behaviour to stop doing a behaviour. The second axis illustrates the
duration of behaviour change.
Several theories exist about persuasive interaction. Fogg (1998) defines persuasion as
“…an attempt to shape, reinforce, or change behaviors, feelings, or thoughts about an issue,
object, or action” (p. 225), while true persuasion requires intentionality. Behaviour or attitude
change therefore does not always have to be a result of persuasion. For example loud music at
a concert may lead people to wear ear protectors, but it is commonly not intended by the
organisers, so it is not a persuasive event.
Regarding the formation of an understanding towards attitude and behaviour change,
in the subsequent section some basic theories about the underlying psychological mechanisms
are elaborated. There are lots of basic psychological theories about attitude change, like the
cognitive dissonance theory from Festinger (1957), which is exploited in the foot-in-the-door
technique (Freedman & Fraser, 1966) by asking someone a favour and once the other person
agreed, he or she is more likely to comply with a larger request too, because people tend to be
consistent with their decisions and commitments; this is also exploited in Cialdini’s
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commitment and consistency influence principle (2007). Another theory of attitude change is
Kelley’s attribution theory (1967) which describes that people interpret behaviour of other
persons either internally or externally meaning dispositional or situational respectively, which
for example can lead us to like people or not. There were many more theories, but explaining
all the different theories of persuasion or the underlying mechanisms of each of the six
influence principles of Cialdini would go beyond the scope of this thesis. When it comes to
persuasive communication, the most relevant basic theories in the last few decades, which are
important for understanding persuasion profiles, can be divided into dual-process models and
unimodels. Dual-process models like the elaboration likelihood model (ELM) from Petty and
Cacioppo (1986) or the heuristic systematic model (HSM) from Chaiken, Liberman and Eagly
(1989) suggest two different routes of procession of persuasive messages.
The ELM is an attempt to outline a “…general framework for organizing,
categorizing, and understanding the basic processes underlying the effectiveness of persuasive
communications” (Petty & Cacioppo, 1986, p. 125). Petty and Cacioppo proposed to
summarise the many different empirical findings in the field of attitude persistence into a
theory, which should sort these findings into “…two relatively distinct routes to persuasion”
(p. 125), as seen in Figure 1. The central route describes well elaborated message processing.
By elaboration, Petty and Cacioppo “…mean the extent to which a person thinks about the
issue-relevant arguments contained in a message” (p. 128). Moderated by one’s ability,
motivation, personal involvement, cognitive style like need for cognition, other personality
characteristics and other factors, this extent is called the elaboration likelihood. Due to the
influence of different individual and contextual factors on elaboration and the processing
route, people differ largely in the processing of persuasive communication. Persuasion
profiles try to address these differences in providing personalised persuasion attempts,
tailored to individuals.
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Figure 1. This schema illustrates the two routes to persuasion according to Petty and Cacioppo. (Petty &
Cacioppo, 1986). The left pathway describes the central route and the right pathway shows the peripheral route
to persuasion.
Two ways of elaboration are possible: One is a relatively objective elaboration, similar
to bottom-up processing. This form of elaboration is strongly data-driven and focuses on the
merits of a message. The second way of elaboration is more theory or schema driven and has
more in common with top-down processing and may thereby be biased. In a situation where
ability, motivation or both are low, attitudes can be formed by simple positive or negative
cues, which are not necessarily contained in the message itself. The peripheral processing can
also be triggered by heuristic cues, such as the framing of the message, which is exploited
within the influence principles of Cialdini (2007). This simpler, faster and cognitive resources
sparing method of processing persuasive arguments is called the peripheral route. Though
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persuasive message elaboration likelihood can be viewed as a continuum, one can distinguish
between primarily well elaborated persuasion and primarily cue resulted attitude change,
especially in prototypically extreme situations. In both routes there are several mediating
context factors that influence the persuasive outcomes, like relevance, motivation, cognitive
resources, distraction and various others, that differ inter-individually.
Akin to the ELM, the heuristic systematic model from Chaiken et al. (1989) defines a
heuristic, fast, top-down process, which gets triggered by heuristic cues, and a systematic
bottom-up process to deal with persuasive messages. Akin to the central route in the ELM,
systematic processing refers to an extensive, analytic information handling. All available data
is scrutinised and all relevant, useful information is integrated in the attitude forming process.
Again alike in the ELM, the degree of systematic processing can oscillate on a continuum,
with the difference, that Petty and Cacioppo (1986) state that one can distinguish between
primarily central or peripheral attitude change, while in the HSM, the concept of a continuum
is more stressed and both ways can also co-occur. The heuristic pathway, which is defined
narrower than the peripheral pathway in the ELM, involves only information processing
triggered by heuristic cues and describes a form of information handling on the other end of
the continuum, where cognitive effort is low and often individual motivation or ability to
process systematically is low or reduced. Again, like in the ELM, people differ largely in
which way they process persuasive attempts due to individual factors that influence whether
information is processed through the heuristic or the systematic pathway. Heuristics “…are
learned knowledge structures…” (Chaiken et al. 1989, p. 213) that are self-consciously or
non-self-consciously used to deal quickly with given situations in an automated manner, often
triggered by heuristic cues. Examples of heuristics are the belief, that experts can always be
trusted or social stereotypes like good looking attractive people are also trustful. Further
examples can also be found in the six influence principles of Cialdini (2007), e.g. that
something scarce has to be valuable and therefore deserves being considered to buy which led
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to the scarcity influence principle. Chaiken et al. also mention, that heuristic processing can in
some cases be well controlled and intended, and in other cases one could assume that it
happens automatically.
The dual process models like the ELM and the HSM connote, that there is a relation
“…between the amount of thinking the recipient engages in and the object(s) on which that
thought is projected…” (Lavine, 1999, p. 141). Effortful, systematic, bottom-up and analytic
thinking happens when processing the merits of message arguments, whereas top-down,
heuristic thinking orientates more to nonmessage factors or circumstances in the persuasion
situation. Systematic thinking, achieved through scrutinising the message arguments leads
often to more stable, stronger attitudes than the parsimonious message processing.
Kruglanski and Thompson (1999) contradict the view that the two routes of persuasion
are qualitatively different and state that “…the two modes of persuasion lack discriminant
validity, or functional independence…” (p. 88). They propose another theory, called the
unimodel. The unimodel assumes the same overall process for both ways of persuasion.
Theoretical background is the lay epistemic theory (LET) of subjective knowledge formation
(Kruglanski, 1989). Kruglanski and Thompson’s view of persuasion is as it follows:
…We view persuasion as integrally related to the general epistemic process of judgment
formation. We believe it to be a motivated process of hypothesis testing and inference
dependent on individuals’cognitive capacity and affected by cognitive availability and
accessibility (Higgins, 1996) of pertinent information.
(p. 89)
Also in the unimodel, individual differences affect the outcome of the processing of a
persuasive attempt, since cognitive capacity, cognitive availability and motivation influence
the hypothesis testing. In other words, according to the unimodel, beliefs and attitudes are
formed based on evidence which emerges from the hypothesis testing. Kruglanski and
Thompson state, according to LET, one performs syllogisms with a major and a minor
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premise, to come to a conclusion, which serves as evidence for a certain belief. For example
Mr Wayne, a well-known jewellery expert states that diamonds contribute to civil wars and
should be prohibited. For someone whose general beliefs or background knowledge contains a
major premise like “if something contributes to civil wars it should be prohibited”, the
statement of Mr Wayne yields persuasive evidence in such a way that the statement serves as
a minor premise, which leads to conclusion or persuasive evidence that diamonds should be
prohibited. Further Kruglanski and Thompson argue, that the idea of evidence brings together
the dual modes of persuasion into one more general mode, where cues and message
arguments only serve as different contents of evidence which leads to a conclusion, instead of
qualitative differing processes. Going back to the previous example one would view this as a
message argument. Assuming the major premise were experts’ opinions are always right and
the minor premise were Mr Wayne is an expert, one can come to the conclusion that Mr
Wayne’s right and diamonds should be prohibited, which is a non-message argument with the
expert as heuristic cue. Kruglanski and Thompson (1999) argue, that both routes of persuasion
“…share a fundamental similarity in that both are mediated by if-then, or syllogistic,
reasoning leading from evidence to a conclusion” (p. 90). Therefore we should not
fundamentally differ between the two ways.
With these theories as background, the working mechanism of current persuasion
profiles, which are “…collections of expected effects of different influence strategies for a
specific individual” (Kaptein & Eckles, 2010, p. 86), becomes clearer. According to the ELM
and the HSM, the message framing with influence strategies serves as a heuristic cue which
shifts elaboration towards the peripheral or the heuristic pole respectively. According to the
unimodel, the heuristic cue of the message framing serves as one out of all the arguments,
which are considered to process a persuasion attempt and come to a persuasive evidence.
Influence or sales strategies vary in numbers with different researchers. Cialdini
(2007) introduced six principles. Namely reciprocity, meaning that people tend to return a
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favour, scarcity, people are attracted by limited products, authority, people are influenced by
authority persons like experts, commitment and consistency, people tend to be consistent in
their attitudes and or behaviour and stick to commitments, consensus or social proof which
means that people often do things that other people also do and liking which means that
people are easier persuaded by people they like. These principles are learned heuristics that
were shown by experience to guide behaviour in most situations well. Instead of elaborating
all possibilities every time such a situation occurs, these heuristics prevent extensive cognitive
efforts and activate something like automatic behavioural schemas. Using these principles
leads to individually differing effects, which lead to the assumption, that one size does not fit
all and persuasion could profit from individually tailored approaches (Berkovsky et al., 2012;
Kaptein & Eckles, 2012).
In order to get closer to a completion of the theory of persuasion, it is also important to
look at how people develop, use and change persuasion knowledge (Friestad & Wright, 1994)
and how this influences the outcomes of persuasion attempts, especially in a marketing or
advertising situation. In a persuasive situation there are three relevant factors according to
Friestad and Wright. Namely the targets whom the persuasion attempt is aimed to, further the
agent who is in the eyes of a target responsible for creating a persuasion attempt and in the
end there is the persuasion attempt, which describes a target’s view of the agent’s means for
influencing its behaviour or attitudes that includes not only message related features but also
all other perceptions of non-message factors that contribute to the persuasion attempt. After
the observable part of a persuasion attempt, called the persuasion episode, a target tries to
cope with it by selecting response tactics so as to “…maintain control over the outcome(s) and
thereby achieve whatever mix of goals is salient to them” (Friestad & Wright, 1994, p. 3).
Thus knowledge-based expectations about and memories of tokens of persuasion attempts are
relevant capabilities for targets. Most important, three kinds of knowledge structures influence
the outcomes of persuasion attempts: First knowledge about persuasion in general, the second
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structure is called agent knowledge by Friestad and Wright and includes “…beliefs about the
traits, competencies, and goals of the persuasion agent…” (p. 3) and the third knowledge
structure is about features and beliefs about the topic which the persuasion attempt deals with.
Persuasive technologies and persuasion in the web
As today computers play a big role in everyday life it is important to investigate how
persuasion changes with this technology and what the new possibilities are. Nass and Moon
(2000) investigated human computer interaction (HCI) in a social science view and came to
the conclusion that people apply social rules and expectations also in a HCI context and
sometimes unconsciously treat computers as if they were a social interaction partner with its
own personality. Participants of an experiment from Nass and Moon for example were polite
to computers, applied reciprocity rules, used gender-stereotypes and other interesting effects
could be observed. They were asked how they see the computer and no subject said it is like a
person or has its personality. Social rules were often applied mindlessly and unconsciously.
One explanation the researches provide is that “…individuals frame interactions with
computers as interactions with imagined programmers…” (Nass & Moon, 2000, p. 94). These
findings suggest that research in social behaviour could contribute to understand and
investigate human computer interaction and that also findings in the traditional persuasion
field may possibly be transferred to HCI to a certain degree, leading to persuasive
technologies.
In 1998, Fogg proposed a framework in response to the introduction of the study of
persuasive technology, called captology, with the aim to support future discussion with
proposing definitions and introducing a framework to understand the basics for the field of
captology. To achieve that, Fogg offers five perspectives on captology. In the first perspective
he defines persuasive computers as “…an interactive technology that attempts to change
attitudes or behaviors in some way” (Fogg, 1998, p. 225), with stressing the fact that
What are the most effective user data for persuasion profiling? 18
“…persuasion implies an intent to change attitudes or behaviours…” (Fogg, 1998, p. 226). In
order to take into account that computers do not have intentions, Fogg proposes to classify a
computer as a persuasive computer, when the designer or creator (endogenous intent), some
distributors (exogenous intent) or users (autogenous intent) of this technology intend to alter
human behaviour or attitudes. Perspective two describes the functions of persuasive
computers. Today’s computers’ functions can be described with a functional triad, so that they
can be used as helping tools, as media delivering symbolic or sensory content, and they can
also function as social actors (see also Nass & Moon 2000). The third perspective treats levels
of analysis for captology because “…different levels of analysis cause different variables to
be salient, which generates new insights in research or design” (Fogg, 1998, p. 228). He
proposes four levels which are as it follows: an individual, a family, an organizational and a
community level. Perspective four tries to conceptualise the design space. Fogg proposes to
identify domains and issues first and then use the previous perspectives for generating new
insights. The fifth perspective is also very important to always keep in mind, which is ethics.
Designers of persuasive technologies have to be careful and sensitive and always consider the
ethical implications of their created technologies. Berdichevsky and Neuenschwander (1999)
introduced eight ethical principles of persuasive design with the golden rule of persuasion
which reads as it follows: “The creators of a persuasive technology should never seek to
persuade a person or persons of something they themselves would not consent to be
persuaded to do” (p. 52).
Using the framework could be done by taking these steps suggested by Fogg (2008):
First selecting an issue or domain then decide at which level of analysis to tackle the problem.
The next step could be to question the functions regarding the functional triad and the last step
is to investigate the possible intentions.
When it comes to persuasion in a web context, there are also new possibilities as never
seen before. A new form of persuasion is described in Fogg’s article about Mass Interpersonal
What are the most effective user data for persuasion profiling? 19
Persuasion (MIP) (2008). The launch of the Facebook Platform made it possible for the first
time that interpersonal persuasion is combined with the reach of mass media. Everyone can
build apps which can connect masses of people with similar interests and persuade them into
revealing personal information which can be used for further marketing campaigns. For
example the app of a company called iLike gained a massive amount of users in a short time,
who then shared personal information about music. Later some of them could be persuaded
into buying concert tickets with their friends. Mass Interpersonal Persuasion consists of six
components, which occur for the first time altogether in one place, the Facebook Platform.
This new form of persuasion is important to be investigated because “…this new
phenomenon gives ordinary individuals the ability to reach and influence millions of people”
(Fogg, 2008, p. 33).
Other new forms of personalised persuasion in the field of captology are recommender
systems, for example used by amazon (Schafer et al., 2001). Recommender systems track the
interests of individuals in a certain field, like books, from the browse history of a user, and
recommend similar books.
The next step of personalised persuasion is that persuasion attempts are tailored to an
individual user’s characteristics, like his personality, behaviour or susceptibility to different
means of persuasion (Berkovsky et al. 2012). Persuasion fitted to a user’s vulnerabilities to
various types of persuasion strategies is an emerging field, called persuasion profiling, which
was mentioned by Fogg already in a 2006 Commission Hearing about protecting customers in
the next tech-ade. Fogg says “Whenever we go to a Web site and use an interactive system, it
is likely they will be capturing what persuasion strategies work on us and will be using those
when we use the service again” (pp. 167/168). These captures can then be put into a profile
mapped to a specific user. Since the tracked information is about how someone reacts to a
certain presentation of a product or an ad and not about what product or ad is clicked on, this
profile is end-independent and can be used for selling insurances or car sales as well as for
What are the most effective user data for persuasion profiling? 20
political campaigns. Kaptein and Eckles (2010) describe persuasion profiles as “…collections
of expected effects of different influence strategies for a specific individual” (p. 86). Amongst
previous online behaviour, a persuasion profile could also contain other available demo- and
psychographic information.
A lot of research in the field of persuasive technologies comes from public health or
health education field, because these technologies often serve as tools to improve healthy
behaviours like to reduce snacking (Kaptein, de Ruyter, Markopoulos & Aarts 2012),
smoking cessation (Dijkstra, 2006), increase activity motivation (Berkovsky, Freyne, Coombe
& Bhandari, 2010, Kaptein & Halteren, 2012) and others.
Why personalised persuasion
As already mentioned before, knowing the audience has ever been an issue when it comes to
persuasion. Berkovsky et al. (2012) take it a step further and investigate how persuasion
effects increase, when not only the target group, but also every individual is paid attention to.
In their review, they compared different studies that fused personalisation and persuasion,
“…where the type of intervention itself is adapted to a user’s personality, behavior, and
susceptibility to various forms of persuasion” (Berkovsky et al., 2012, p. 9:3). The question is,
which user data provides best results in successful persuasion attempts.
Several studies, lots of them in the field of health improvement, showed increased
successful persuasion effects, when the persuasion attempt was combined with personalisation
(Dijkstra, 2006; Kaptein, Lacroix & Saini, 2010; Kaptein et al. 2012). Dijkstra (2006) for
example studied the influence of personalisation and feedback on several smoker’s quitting
activities. After four months of personalised feedback, quitting activities significantly
increased compared to the standard messages.
What are the most effective user data for persuasion profiling? 21
Personalisation on different user information
Berkovsky et al. (2012) propose three main fields for personalisation in persuasive systems.
One of these are personalised assistive features, which help users to achieve goals of personal
importance. Another field would be personalised messages where many characteristics of a
message, like the layout or the language, could be personalised for each user. A very
interesting and under-investigated field is that of personalised persuasive strategies, where the
means of a persuasion attempt are tailored to a user’s beliefs, behaviours, personality or his
susceptibility to certain persuasion strategies. One promising form of this third field is that of
persuasion profiles, where the vulnerability to various persuasion strategies is measured and
mapped to an individual user.
The subsequent section focuses on the different user characteristics being used for
personalisation.
Persuasion Profiles, Answers to persuasion principles and persuasibility.
A very promising approach of adaptive personalised persuasion is to measure and track the
individual differences in responses to influence or sales strategies which is called a persuasion
profile. In an empirical study Kaptein (2011) investigated how a possible implementation of
such a persuasion profile on an e-commerce website could be done. He and his team tested the
use of two out of the six persuasion principles of Cialdini (2007), namely the consensus and
the scarcity strategy; an additional neutral strategy was also used as a control group. The
website they used for the research offers several products of two different vendors and aims to
attracting traffic and click-through to these vendors. The products on the website were
accompanied by either a text which incorporates one of the two strategies, or no special
strategy at all. Then a tracker measured whether the product was clicked or not and saved the
outcome of the persuasion attempt. Through a Bayesian learning algorithm the system
What are the most effective user data for persuasion profiling? 22
adapted the shown texts to the answers of an individual user and presented him incrementally
more appropriate or effective framed texts.
Figure 2. This figure shows the overall average effects of the conditions over time. The blue line indicates the
neutral strategy, the green line represents the consensus strategy and the pink line shows the scores of the
scarcity influence strategy (Kaptein, 2011).
After four weeks of testing the percentage of users who clicked on a product and were token
to the vendor’s website increased, as in Figure 2, from about 14% in the baseline period to
about 18% in the adaptive test period, which is statistically significant. Also the average
earnings generated by the users increased from 0.037 Euros to 0.046 Euros, which is an
increase but not statistically significant. The results should be interpreted carefully because
there is a large variability in click-through rates. The researchers state that actual effect sizes
should be estimated with a larger sample. Besides, the realisation of the system design is a
very parsimonious one because only two strategies were investigated and there are not
different implementations of the same strategies neither.
What are the most effective user data for persuasion profiling? 23
Personality traits.
According to Corr, DeYoung and McNaughton (2013), a personality trait can be defined as an
inter-individually varying constant that predicts “…the frequency and intensity with which
individuals exhibit various motivational states, as well as the behavioural, emotional, and
cognitive states that accompany these motivational states…” (p. 160).
The Big Five or Five Factor Model describes five different traits that represent
different dimensions of personality which reflects differences in thinking, feeling and
behaving. The Big Five dimensions thus reflect relatively stable inter-individual differences in
experience and behaviour but does not immediately provide causal sources of personality
traits, i.e. why people think, feel and behave the way they do (Corr et al., 2013). Corr et al.
investigated this question and came to the conclusion that one possibility could be that the Big
Five also reflect individual differences in the motivational systems. Scores on Extraversion
seem to indicate individual sensitivity to positive affect and reward, while scales of measuring
neuroticism also indicate scores on negative affect and punishment. Openness and Intellect
are linked with “…cognitive exploration and sensitivity to the reward value of information…”
(Corr et al. 2013, p. 169). Conscientiousness has a very complex relation to motivation but is
amongst others linked to impulse-control, avoidance of distraction, complying with norms and
following rules and pursuing long-term goals. This trait is also linked to a motivation towards
achievement and success. The score in Agreeableness also indicates a constraint of impulses,
mostly of a social nature. It correlates with activation in brain areas related with emotion
regulation and “…might be described as a general motivation toward altruism” (Corr et al,
2013, p. 171). Other authors formulate the relation of agreeableness with its underlying
motivational system as a tendency to cooperate in resource conflicts (Denissen & Penke,
2008).
What are the most effective user data for persuasion profiling? 24
Andrews (2012) showed in a study about system personality that the persuasiveness of
an interactive system correlates significantly with the user’s preferred personality and the
user’s own personality trait. This leads to the assumption, that personality tailoring could
influence the persuasiveness of interactive systems.
In another study, Hirsch, Kang and Bodenhausen (2012) investigated the effectiveness
of persuasive appeals where the message framing was tailored to recipient’s personality
profiles. Participants of the study evaluated five advertisements in terms of persuasiveness,
effectiveness, purchase intention, interest and liking. The advertisements consisted of a
picture of a cell phone and a text which was manipulated to address one of the Big Five
personality dimensions and their underlying motivational aspects. Then their personality was
estimated with the Big Five Aspect Scales. The results showed an increasing effectiveness of
advertisements the more the framed texts met the participants’ personality dimensions. In a
secondary analysis Hirsch et al. could show that correlations with matched traits were
significantly larger than the correlations with non-matched ones. All correlations were
significant except for the advertisement which targeted neuroticism. The authors also state,
that there are several circumstances where congruence of the message framing with the
personality traits can also lead to a negative outcomes. That could be because it is possible
that the increased effectiveness of congruent framing could be due to an increased attention. If
that is the case, this attention could also lead to a more negative evaluation of the message if
its argument or overall quality is low.
The problem regarding the assessment of the scores on the different personality
dimensions could be addressed in gathering different data about the users’ Facebook profile,
e-mail address, language use and purchase or site-visit histories; Hirsch et al. (2012) mention
various authors which could show that reliable assumptions about the personality can be
educed from these variables.
What are the most effective user data for persuasion profiling? 25
Another promising approach is paying attention to the cognitive style of recipients.
Differences in the need for cognition (NFC) personality trait, introduced by Petty and
Cacioppo in 1982, “…represent differences in peoples’ chronic tendencies to engage in and
enjoy effortful thinking” (Haugtvedt, Petty & Cacioppo, 1992, p. 242) and “…has the
potential to serve as an operationalization of the motivational component of the ELM…”
(Haugtvedt et al. 1992, p. 241). In their paper, Haugtvedt et al. (1992) conducted three studies
to investigate the effects of the scores on the need for cognition trait on perceptions of
advertisements, which all showed differences in perceptions of advertisements between those
who scored high and those who scored low in NFC, as seen in Figure 3 for example, those
participants with both, a high and a low NFC had a positive attitude towards the product, that
was advertised with strong arguments, but those scoring high in NFC had a more negative
attitude towards the weak advertised product, than those scoring low in NFC.
Figure 3. This figure shows the interaction effect for Need for Cognition × Argument Quality on attitudes
towards the advertised product, meaning the higher individuals scored in need for cognition, the greater the
quality (strong vs. weak) of the arguments influenced the rating of the product (Haugtvedt et al. 1992).
With higher motivation to scrutinise and elaborate persuasive messages, also the likelihood of
processing via the central route increases, meaning that argument quality and accuracy are
more taken into account than the framing, which can serve as a peripheral cue, of the
What are the most effective user data for persuasion profiling? 26
message. Also Kaptein (2011) mentions that individuals scoring high in need for cognition are
less susceptible to implementations of influence strategies. Also in another work, Kaptein et
al. (2010) assessed the susceptibility of individuals to influence strategies, and found that for
those who were low susceptible, an implementation of an influence strategy could even have
negative effects. Because of these reasons, it could be beneficial to implement an adaptation
to scores in need for cognition in future persuasion profiles.
Gathering user data
When it comes to adaptive systems that behave like salespeople in the real world, who adapt
their sales strategies to their customers, artificial intelligence comes into the play and creates
an “…interactive ‘dialogue’ between the buyer and the selling e-commerce platform”
(Kaptein et al. 2013, p. 2763). This leads to the differentiation between e-commerce and
actual e-selling. There are three central quests, which salespeople usually have to complete.
Firstly, they have to determine the right product for the customer, secondly finding the right
product pitch. The third task is often to set the right price. When all the quests that salespeople
fulfil in real life is done by an interactive system it is called e-selling. In today’s western
countries prices are often standardized and are not always a point of discussion in a sales
situation. With data-driven and theory-driven approaches there are two possible ways of
machine learning. The latter is widespread at the moment, because theoretical thinking is
assumed to be powerful in creating an understanding of customer’s reactions to commercial
offerings and furthermore theory-driven algorithms are often lightweight and do not need
heavy computations. This approach starts by a theoretical division of customers into
categories and then shaping offerings that meet the customer’s needs according to theoretical
assumptions about the client category. As an example customers are divided theoretically into
utilitarian and hedonistic shoppers, each with its theoretical assumed characteristics. Then the
evaluation of the results is used to update and adjust the strategies to come up with better
What are the most effective user data for persuasion profiling? 27
solutions in a heuristic fashion. Data-driven learning algorithms start by gathering and
aggregating data about the customers and adapt in real-time to for example buying histories or
current purchasing actions. The collected data is then used to compute and predict clients’
reactions in a given situation “…without the involvement of theoretical assumptions on
customer decision-making” (Kaptein et al., 2013, p. 2764). There is a trade-off between
theory-driven and data-driven approaches in terms of generalisability. While theory-driven
methods are less context dependent, fully data-driven methods are almost too specific to use
the gained knowledge in other areas but still outperform theory-driven algorithms in specific
contexts. Kaptein et al. (2013) propose to use the middle way and use theories as a starting
point of data-driven learning to use the benefits of both approaches, like the within-context
accuracy of data-driven methods and the generalisability of theory-driven algorithms.
As already mentioned, it is possible to gain valuable information from social networks,
for example about the personality of its users. Back et al. (2009) investigated in a sample with
236 users of on-line social networking (OSN) sites how their profiles reflect their actual
personality. With the so-called extended real-life hypothesis, which assumes that OSN
profiles are used to communicate real personalities, they tested a contrary hypothesis to the
widely held assumption that people use OSNs to communicate and create idealised selves. In
order to get indices of how profile owners actually are, the researchers aggregated multiple
personality reports which measured the Big Five personality dimensions. The reports
consisted of self-reports, reports of friends and the outcomes of several personality
questionnaires. Additionally, ideal-self perceptions were estimated by rephrasing personality
questionnaires. These data was then compared to observer ratings, who looked through the
OSN profiles and rated their impressions using observer-report forms of personality
questionnaires. The results were consistent with the extended real-life hypothesis and, even
when controlled for self-idealisation, the correlation between actual personalities and OSN
profile impressions were significant for all Big Five personality traits, except for neuroticism.
What are the most effective user data for persuasion profiling? 28
Computational Social Science is a new and emerging field, which is a data-driven
approach in social sciences that tries to collect and analyse all sorts of data available in the
internet and searches for patterns of individual and group behaviours to reach a new
understanding of individuals and society (Lazer et al., 2009). Aral and Walker (2012) for
example, investigated in study, how influential and susceptible individuals could be identified
in social networks. They conducted an in vivo randomised experiment with a representative
sample of 1.3 million Facebook users. Dyadic and non-dyadic analyses showed amongst
others, that susceptibility decreases with age, men are more influential than women, women
influence men more than other women, single and married individuals are the most
influential, and the engaged and those who have “it’s complicated” as their relationship status,
are the most susceptible individuals to influence. There seems to be a trade-off between
influence and susceptibility, since influential individuals are less susceptible and vice versa.
Further they found that “…influential individuals connected to other influential peers are
approximately twice as influential as baseline users” (Aral & Walker, 2012, p. 340). The
offered product was a commercial Facebook app, that lets its users rate and comment on
movies, actors, directors, and the film industry. The presented method avoids the biases
inherent in traditional estimates of social contagion by controlling for several confounding
variables by a randomised experiment setting and analyses of susceptibility and influence
together with network structure, using the statistical approach of hazard modelling, that is also
being used for social contagion studies in economics, marketing and sociology. According to
a press release from New York University Stern School of Business (2012), Aral states, that
the method can be used for developing effective strategies for the spreading of products and
behaviours in society, like targeted advertising, viral marketing and they are even working on
possible implementations to promote HIV testing in Africa.
Atterer et al. (2006) propose another way of obtaining user data. Their idea consists of
implementing an HTML proxy between the client and the server. The proxy alters HTML
What are the most effective user data for persuasion profiling? 29
pages by adding JavaScript code, which allows to track user’s every move, even if they are
hesitating to fill in a form or mouse movements and more. This approach can ease usability
evaluation of websites in the wild and enables tracking of implicit interaction, for example the
accuracy of mouse pointing or typing speed, which can be used to profile users. For example
with typing speed computer experience could be assessed.
Further, it is also possible to create user profiles through analysis of the interest-
focused browsing history of internet users, collaborative filtering methods that use rule-based
algorithms for recommender systems, and other techniques from the field of recommender
systems (Grčar, Mladenić & Grobelnik, 2005; Mobasher, 2007; Sugiyama, Hatano &
Yoshikawa, 2004). The work from Gauch et al. (2007) shows how the massive amount of
information available online can be used to create different kinds of user profiles. They
differentiate between explicitly gathered data, which is also called explicit user feedback,
where users fill in forms or questionnaires about mostly demographic data. Implicit data
collection does not require any action from the user. Through this kind of collection data can
be obtained about the browsing history through the browser cache, browsing activity through
proxy servers, browser agents or web logs, further all user activity can be collected through
desktop agents and finally search history from search logs. Some of these methods require
users to install a software, but after that it collects data without any user actions. Three kinds
of profiles can then be made with this data. Keyword profiles, which consists of keywords
with numerical representations of users’ interests. Further, semantic network profiles address
the problem of ambiguity inherent in keyword profiles by representing a “…weighted
semantic network in which each node represents a concept” (Gauch et al. 2007, p. 66).
Probably the most sophisticated type are concept profiles, which are similar to semantic
network profiles, with the difference, that nodes do represent “…abstract topics considered
interesting to the user…” (Gauch et al. 2007, p. 77).
What are the most effective user data for persuasion profiling? 30
Discussion
Promising user data that could increase the effectiveness of persuasion profiles
A first interesting factor that could enhance persuasion profiles is tailoring persuasive
attempts to individual scores on the need for cognition trait (Haugtvedt et al., 1992). Given,
that their work about the need for cognition shows that higher scores in this trait increases the
probability of processing persuasive attempts via the central route in the ELM or the
systematic pathway in the HSM, where peripheral cues and also message framing becomes
less relevant, it could be a good start of increasing the effectiveness of a persuasion profile by
filtering users, that score high in the need for cognition and instead of just framing messages
and adapting influence strategies leaving these individuals either out for personalisation, or
provide them a higher number of superior qualitative arguments. This argument is also in line
with the statement in Kaptein and Eckles (2012) work, where they stress the consequences of
disclosure on means-based adaptive persuasive systems. Disclosing that a system adapts to
individuals may lead to an increase in elaboration of the presented influence strategy, which
decreases their effectiveness, given that the strategy builds primarily on heuristic processing.
The work of Corr et al. (2013) and Denissen and Penke (2008), which investigated the
Big Five traits and the differences in its underlying motivational systems can be used for the
assumption, that persuasion profiling can also profit when addressing these differences.
Hirsch et al. (2012) showed in an empirical study that tailoring persuasive messages to the
Big Five traits “…can be an effective communication strategy” (p. 580) and significantly
increases the effectiveness of advertisements. Caution is advised with the interpretation or
assumption of causal reasons for the increased effectiveness. Tailoring to personality traits,
could lead to a higher attention, which could also increase the probability of processing
information via the central or the systematic pathway, which could lead to an increased
importance of strong arguments according to the ELM or the HSM.
What are the most effective user data for persuasion profiling? 31
Another interesting area of promising user data would be social networks, which
provide valuable information about its users and their relations to other people (Aral &
Walker, 2012; Baker, 2009; Lazer et al. 2009). Then the next step of increasing the
effectiveness of persuasion profiles is to implement all sorts of data regarding internet search
history, browser behaviour and more of which user profiles could be made about user’s
interests, skills and more (Gauch et al. 2007).
Theoretical assumptions and empirical work shows that these data influence the
outcomes of persuasive attempts outside the context of persuasion profiles. The limitations
regarding the central or systematic processing are opposed with the unimodel. Assuming the
unimodel as theoretical basis, it would not matter, whether messages are processed via a
central or peripheral (ELM) or systematic or heuristic (HSM) pathway, as long as the end-
result of the information processing is still persuasive evidence.
Data gathering
As the work from Aral and Walker (2012) showed, with computational social science (Lazer
et al., 2009) that it is possible to extract valuable data from social networks, for example as
they did, about how influential or susceptible individual users in social network are. Also
Baker (2009) wrote in his article that companies can learn a lot about online friendships. The
simplest way of learning consists of knowing the relationships between the users, because
statistically, friends tend to behave similar. Facebook, for example gathers a massive amount
of data of its users and sells this data to advertisement companies and also changes its privacy
settings in a way, that it is difficult for average users to keep their data private (Lyons, 2010).
According to Gauch et al. 2007, it is further possible to gain various sorts of data through for
example individual’s internet search history or browser behaviour.
What are the most effective user data for persuasion profiling? 32
Further thoughts
Ethics is an aspect that has to be taken into account very carefully when it comes to
persuasion in general and especially when it is done on an individual non-disclosed level.
Berdichevsky and Neuenschwander (1999) introduced eight ethical principles of persuasive
design, which should always be considered, when designing persuasive technology. Ethics
should not only address the intention of a designer, but also predictable and unpredictable
outcomes of interactions with persuasive design and persuasive technology. They propose a
rule-based utilitarianism, where rules for ethical standards are only defined, when following
them always results in compelling benefits.
Criticism, further research, and future.
Difficulties occurred in defining the scope of this bachelor’s thesis, since the field is new and
emerging, there are not well-established terms for it. For example Dijkstra (2008) states that
tailoring can be done in three ways: adaptation (or customisation), which means to adapt
persuasive communication to individual characteristics. These characteristics could be
demographics, behaviour or psychological concepts, like the motivation to process.
Personalisation means to include recognisable aspects of a person, for example the name, or
referring explicitly to the reader by statements like “This message is especially for you”.
These items should signal that a message is directed to an individual, in contrast to adaptation,
where texts are more generally written, and users don’t necessarily notice that the messages
are adapted. The last ingredient of Dijkstra is feedback which means to provide feedback
about individual important goals.
Kaptein and Eckles (2010) “…define adaptive persuasive technologies as technologies
that aim to increase the effectiveness of some attitude or behaviour change by responding to
the behaviour of (and other information about) their individual users” (p. 84). In another
What are the most effective user data for persuasion profiling? 33
article of Kaptein et al. (2010) use the term personalisation generally for messages that are
tailored to users in any way.
Hirsch et al. (2012) write in their article about tailoring messages to individual traits
and their underlying motivational systems and describe this as personalisation. When
compared with Dijkstra’s definitions one should rather assume this to be adaptation.
Another author, Mobasher (2007), distinguishes only between personalisation and
customisation. Both refer to tailored content delivery and the difference between them is who
is in control for the creation of profiles and the appearance of interfaces. Customisation is the
process, where the user is in control.
Another unclear point is whether the inclusion of too much personal information may
generally lead to deeper elaboration or processing via the central rout in the ELM paradigm,
due to higher involvement and self-relevance (Dijkstra 2008).
The fact, that persuasion profiles are end-independent, because they do not aim
primarily to the outcomes but the means, through which persuasion takes place, increases
possible fields of usage for highly sophisticated user-profiles tremendously. Therefore it is
also difficult to define the literature search area and makes the selection of research difficult
and or subjective, because any research in any subfield in persuasion could contribute to
answering the thesis which would go beyond the scope of this thesis.
Because the field of persuasion profiles is a new and emerging one, there is not very
much empirical research yet. An unsolved question is still how the implementation of
additional user data in persuasion profiles will affect the outcomes and whether this leads to
more effective persuasion or if there are other effects, like a less effect due to deeper
elaboration. So far, no empirical work investigated empirically how the implementation of
additional user data in persuasion profiles affects its effectiveness or persuasive power.
What are the most effective user data for persuasion profiling? 34
Another unclear point, relates to the durability of a successful attitude or behaviour change
through persuasion profiles and how strong these changes are. Answering these questions
yields further empirical research.
There are lots of user data that could be used to increase the effectiveness and creating
a highly sophisticated persuasion profile from the research of recommender systems. A
persuasion profile could also be enriched with data of user interests, user habits, user skills in
different areas and more. This data cannot be used for end-independent general persuasion
attempts but in some situations it could be beneficial to implement. An extensive review of all
the variations of user data used for recommender systems is unfortunately beyond the scope
of this work but could be very interesting in the future. Kaptein (2011) mentions also in his
paper the future possibility of including user’s background characteristics, based on previous
purchases or behaviour, into a persuasion profile.
In the future of wearable computing (Swan, 2012), ambient intelligence (Kaptein et al.
2009) and ubiquitous computing, there will be a lot more available user data to implement in
persuasion profiles. Broek et al. (2006) for example, conducted a study, where they tailored
persuasion strategies to psychophysiological measures of emotions, like heart rate variability
and the variability of the pitch of the voice.
Conclusion
Already Aristotle’s pathos showed that knowing his target audience is crucial when it comes
to persuasion, which was also stressed in the work about public relations from Bernays
(1928). The literature review, aiming to detect which kind of user data could increase the
effectivenss of persuasion profiles, shows that by implementing user data, like the scores on
need for cognition and the Big Five traits, information from social networks, and using the
persuasion profile what it is today, i.e. estimates of answers to influence strategies, for the
fine-tuning of how to present users at which time which information, the persuasive power of
What are the most effective user data for persuasion profiling? 35
persuasion profiles could be heightened considerably, but needs further empirical
investigation. In advance not all factors promise the same increase in effectiveness. The most
promising factor is possibly the score on the need for cognition, because it represents an
overall cognitive style that influences the enjoyment of thinking and complex tasks and thus
has an effect on the probability whether the central or systematic or the peripheral or heuristic
pathway is taken for the elaboration of persuasive attempts.
Further research is needed to empirically investigate the outcoming effects of
including additional user data in persuasion profiles. Previous work in the persuasion field
showed, that considering user data like personality traits, such as need for cognition or the Big
Five and their underlying motivational systems, and user data from social network have
positive effects on persuasion. Based on the reviewed theoretical and empirical literature
body, the inclusion of the suggested additional user data appears to be a very promising
approach with a high potential for increasing the effectiveness of persuasion profiles.
What are the most effective user data for persuasion profiling? 36
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