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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

[email protected]

University of Basel

Department of Psychology

Centre for Cognitive Psychology and Methodology

Thesis Supervisors:

Elisa D. Mekler, M.Sc.

Prof. Dr. K. Opwis


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


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.


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,


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).


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


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


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-


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.


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


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.


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


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


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


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


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


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


“…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


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


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.


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


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.


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).


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.


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


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


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.


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


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).


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.


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.


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


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.


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


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.


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