Behavioral Economics in Information Systems Research:

A Persuasion Context Analysis

Michael Oduor and Harri Oinas-Kukkonen

Oulu Advanced Research on Services and Information Systems,

Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 8000, 90014 Oulu, Finland

Keywords: Behavioral Economics, Information Systems, Persuasive Systems Design.

Abstract: In recent years, there has been growth in information systems (IS) research applying psychological theories

focusing on peoples’ perception towards use of technology and how technology can motivate positive

change. Behavioral economics–grounded in cognitive and psychological principles–on the other hand

studies irrationalities in peoples’ behavior from an economics perspective and is a field that has lately been

starting to gain credence in IS literature. This study’s aim is to establish the depth of behavioral economics

studies in IS research by reviewing the basket of eight journals using the persuasive systems design model

as an analytical tool. From this extant literature, similarities and complementary properties with other

disciplines can be integrated, and improved methods of understanding users and their actions can be used

for better prevention and intervention techniques especially in the domains of health IS and sustainability or

Green IS.

1 INTRODUCTION

To economists and management scientists,

consumers want to maximize utility and if they are

presented with clear and simple choices that they

understand, they will do so. Whereas to behavioral

scientists, the real world is so complicated that the

theory of utility maximization has little relevance to

real choices and even in relatively simple situations,

people do not behave in the way predicted by direct

application of the utility theory (Simon 1959). This

stream of research formed the beginnings of

behavioral decision research. The 1970s heralded the

emergence of behavioral economics with works

from (Tversky and Kahneman, 1974) and

(Kahneman and Tversky, 1979) prospect theory

dealing with decision-making under risk and how

differences in formulating a choice of problems

cause significant changes in people’s preferences.

For more details on the emergence of behavioral

economics see (Angner and Loewenstein, 2007), for

example.

Behavioral economics examines conditions that

influence the consumption of commodities and

combines psychology and economics to investigate

how individuals actually behave as opposed to how

they behave if they were being perfectly rational (as

in the sense of maximizing their utility) (Prince et al.

2013; Thorgeirsson and Kawachi 2013). Behavioral

economics is organized around experimental

findings that suggest inadequacies of standard

economic theory and is focused on individual

choice, the motives underlying that choice and also

knowing more about a subject’s situation at the time

of making a choice (Pesendorfer 2006).

The present study focuses on analyzing

behavioral economics in information systems (IS)

research by utilizing persuasion context analysis as

described in (Oinas-Kukkonen and Harjumaa, 2009).

Applying behavioral economics in studying

technology use and adoption provides a potential

strategy for better understanding users and the

factors that lead to the non-adoption or intended use

of IS. Behavioral economic principles can also aid in

developing techniques for improved presentation,

delivery, and organization of information or

services. Therefore, behavioral economics methods

have enormous potential to inform and complement

information systems (IS) research and as noted in

Goes (2013) there has not been extensive research in

the IS field utilizing behavioral economics methods.

The objective of the present review is, thus, to

examine by applying context analysis (Oinas-

Kukkonen and Harjumaa, 2009), behavioral

economics research in IS that address how cognitive

Oduor, M. and Oinas-kukkonen, H.

Behavioral Economics in Information Systems Research: A Persuasion Context Analysis.

DOI: 10.5220/0006277700170028

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 3, pages 17-28

ISBN: 978-989-758-249-3

limitations influence decision-making. The rest of

the paper is structured as follows: the following

section introduces the theoretical background on

behavioral economics and persuasive systems

design. Section 3 describes the review process,

which is followed by the results. The paper then

concludes with a discussion summarizing the results,

limitations, directions for future work, and

conclusions.

2 THEORETICAL

BACKGROUND

2.1 Persuasive Systems Design

Computers were initially created to perform simple

tasks like calculating, storing and data retrieval, but

they have now adopted increasingly persuasive roles

as they have shifted to our everyday lives (Fogg,

2003). Computers are now viewed as interactive

persuasive systems that motivate and influence users

and can facilitate behavior change (Fogg, 2003;

Oinas-Kukkonen and Harjumaa, 2009).

The aim of persuasive communication is to

voluntarily change one’s attitude and/or behavior

without deception or coercion. The PSD model

presents seven postulates to be taken into account

when developing persuasive systems. These

postulates address accessibility and reach, ease of

use, making and enforcing of commitments,

attitudes and persuasion strategies, sequential nature

of persuasion, the ideal moments for initiating

persuasive features and openness (Oinas-Kukkonen

and Harjumaa, 2009).

Inherent in these postulates are social

psychological theories on attitude change, influence,

learning and among others that help to explain

human behavior in different circumstances.

Examples of such theories include the theory of

planned behavior (TPB) (Ajzen 1991), which

explains how an individual’s attitude and subjective

norms about a behavior is determined by behavioral

intentions and perception with which the behavior

can be performed. The elaboration likelihood model

(ELM) (Petty and Cacioppo 1986) a theory of

attitude change that describes direct and indirect

routes to information processing and persuasion.

Bandura's (1989) social learning and social cognitive

theories which provide a framework for

understanding, predicting and changing human

behavior. According to theories, people learn new

behaviors by studying, observing and then

replicating the actions of others. Lastly, Cialdini’s

(2007) studies on influence which show how

formulating requests in certain ways can trigger

automatic compliance response from individuals.

Following the postulates, the context for

persuasion is considered. The persuasion context

analysis comprises of recognizing the intent, the

event, and the strategy (Oinas-Kukkonen &

Harjumaa, 2009). The intent includes the initiator for

the development of a system and its purpose. The

event consists of the context of use, the user, and the

technology. The use context refers to characteristics

of the problem domain in question, the user context

includes the differences and characteristics among

users, and the technology context refers to the type

or technical specifications of a system. Finally, the

strategy addresses the analysis of persuasive

message being conveyed and the route, whether

direct or indirect or both (Petty and Cacioppo,

1986), that is used to influence the user (Oinas-

Kukkonen and Harjumaa, 2009).

In most studies on persuasive systems and

success factors for IS, the context and the effect it

can have on a user’s decision-making is rarely

addressed. Success is also usually measured in terms

of changing users’ behaviors in ways predetermined

by developers or providers of the system

(Brynjarsdottir et al. 2012). In persuasive

technology, information is usually provided for

people to better understand either certain problems.

However, there has been research that point to

potential disadvantages of using information-centric

approaches to motivate behavior (Lee et al., 2011).

The emphasis on providing information rests on the

assumption that people are rational actors striving to

enhance activity based on what they know and the

information that is available (Brynjarsdottir et al.,

2012). But, people have been shown to be

predictably irrational with such behavior being

“neither random nor senseless. They are systematic,

and since we repeat them again and again,

predictable” (Ariely 2008).

2.2 Behavioral Economics

Behavioral economics departs from the standard

economic model in acknowledging three human

behavioral traits: 1) bounded rationality – human

beings have limited information processing

capabilities and because of this, they adopt rules of

thumb to aid in problem-solving. 2) Bounded

willpower – accounts for the fact that people do not

always make choices that are in their best long-term

interest, due to a lack of self-control, and 3) bounded

selfishness – relaxes the assumption that people are

motivated by pure self-interest and their actions also

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

include altruistic and spiteful behaviors

(Thorgeirsson & Kawachi, 2013). Within these three

behavioral traits, there are psychological principles

that explain why people act in certain ways. For

example, one’s estimates and judgments being

biased towards some initial anchor value, preference

of the status quo as opposed to changing routines,

interventions that help participants pre-commit to

future healthy behavior and so forth (Thaler &

Sunstein 2008; Thorgeirsson & Kawachi 2013).

Most behavioral economics research mainly

focuses on interventions for healthier living (Prince

et al. 2013; Michie & Williams 2003), strategies for

reducing unwanted behaviors (Lunze & Paasche-

Orlow 2013), environmental sustainability and

improving governmental and institutional policies

that benefit society (Siva 2010; Avineri 2012).

Prince et al. (2013), for example, propose

improvements to better understand the role of

protective behavioral strategies in reducing the use

of alcohol, explain why there have been

inconsistencies in previous studies, and what can be

done to enhance future studies. Michie and Williams

(2003) discuss the factors that lead to work-related

psychological ill health, comparing between

different professions and proposing solutions to

these problems mainly involving training and more

involvement in decision making.

Siva (2010) applies lessons in behavioral

economics to improve pay-for-performance

programs. The reasons why such programs are

flawed and how people respond to incentives are

addressed in the article. Lunze and Paasche-Orlow

(2013) discuss the pros and cons and ethical

concerns on the use of incentives in behavioral

economics to promote healthy behavior and reduce

health costs. Lunze & Paasche-Orlow (2013),

acknowledge the need for safeguards in the

programs to monitor their associated risks and

promoting fairness in offering the incentives for

them to be beneficial. Avineri (2012), links travel

behavior to psychological theories and shows how

individuals’ choices in different contexts deviate

from the predictions of rational behavior.

As the role of information technology (IT) in

people’s daily decision-making and experiences has

increased, new opportunities to assist people in

making self-beneficial choices have arisen (Lee et

al. 2011). The persuasive element in behavioral

economics lies in the presentation of choices in a

way that leverages people’s decision processes; thus,

encouraging them to make self-beneficial choices

(Lee et al. 2011). For example, Crowley et al. (2011)

apply behavioral economics in developing sensor-

based interactive systems to initiate change in

residential energy consumption. They argue that

even though the success of most of the sensor-based

power meters and other related residential

monitoring devices depends on users responding to

the data they generate with appropriate changes in

their consumption behavior, most of these devices

have not been developed with the end-user in mind.

Therefore, a more human-centered process that

integrates behavioral insights to determine the

effectiveness of sensor-based interactive systems

and of interfaces based on cognitive, social and

affective frames is proposed (Crowley et al. 2011).

3 REVIEW PROCESS

A structured literature review is a focused approach

to identify relevant articles. Structured reviews

provide means to identify and categorize most of the

existing literature concerned with the research

question(s). The reasons for conducting a review

include, but are not limited to summarizing the

existing facts about use of technology, creating a

firm foundation for advancing knowledge,

identifying gaps in current works in order to suggest

areas for further analysis, and providing a

framework for suitably positioning research interests

(Webster and Watson 2002; Kitchenham 2004). Our

objective, is to use the PSD model (Oinas-Kukkonen

and Harjumaa 2009) to examine behavioral

economics research in IS. This would enable us to

identify any recurring and emerging themes and

identify gaps in the literature.

3.1 Need for a Review of Behavioral

Economics in IS

Webster and Watson (2002) state that a literature

review process stems from scholars need to report

progress in a particular stream of research and from

those who have completed a review prior to starting

a project and have developed theoretical models

from the review. Additionally, there are reviews on

mature topics and those on emerging issues that

would benefit from exposure to new theoretical

foundations (Webster and Watson 2002). Behavioral

economics has lately been gaining attention in the IS

field, and although it is a relatively new field, it has

been widely studied in economics and mostly health

and environmental conservation-related topics.

Vassileva (2012), in her overview of the growth of

web-based social applications and the approaches

they use to motivate user participation, states that

most of these applications employ simple

Behavioral Economics in Information Systems Research: A Persuasion Context Analysis

approaches that have been successful in engaging

users. Although, these approaches only ensure that

users act accordingly, but are unable to guide the

social system(s) towards a desirable overall

behavior. For this reason, several future trends

related to the application of social psychology,

behavioral economics and their convergence with

other disciplines are suggested in the design of

reward and incentive mechanisms for particular

types of communities, persuasive and other user-

adaptive systems (Vassileva, 2012).

Therefore, there is a great opportunity to

combine behavioral economics and IS as these two

disciplines both seek to enhance the understanding

of the user. Both disciplines emphasize how context

and cognitive effects influence decision-making–the

IS field is mostly about information processing for

decision-making (Goes, 2013). Subsequently, we

have examined articles from the top IS journals and

have not found a comprehensive review that

addresses the research question below regarding the

integration of behavioral economic in IS.

The main research question that guided our research

is:

RQ: How can behavioral economics enhance

understanding of users and their interactions with

information systems?

3.2 Electronic Search

For the present review, a literature search was

conducted for the years between 2006 and 2014. The

keywords used were behavior(u)ral economics,

prospect theory, mental accounting, cognitive bias,

choice architecture, nudge, persuasive systems

design, persuasive technology, behavior(u)r change,

attitudes, and persuasion. This was to ensure that we

got a wide variety of article applying behavioral

economics principles and persuasive techniques.

The above keywords were used to search the

metadata related to the top eight IS journals (MIS

Quarterly, European Journal of Information Systems

(EJIS), Information Systems Journal (ISJ),

Information Systems Research (ISRe), Journal of

Information Technology (JIT), Journal of

Management Information Systems (JMIS), Journal

of Strategic Information Systems (JSIS), Journal of

the Association for Information Systems (JAIS)) in

Wiley, INFORMS PubsOnline, EBSCOhost,

ScienceDirect, Taylor Francis Online, and ProQuest

ABI/INFORM.

The search string resulted in 919 articles and

after excluding editorials, book reviews and

commentaries, and reviewing the abstracts, 63

articles remained, these were further reduced to 15

(Figure 1) based on the eligibility criteria below.

3.3 Eligibility Criteria

Inclusion and exclusion criteria were used to select

articles from the original search to be used in

answering our research question. Studies were

selected, if they: had behavioral economics in the

abstract, b) were full research papers (and not

editorials, commentaries), c) described the

persuasive/cognitive stimuli applied, d) investigated

the relation between the stimuli and (behavioral)

outcome. Articles were excluded, if they: a) only

discussed system implementation; b) were about

either general systems development or systems

development to meet organizational/individual needs

without a behavioral outcome; c) only discussed

systems benefit(s) to an organization; or d) were

purely on research methodology or systematic

reviews not related to the topic.

3.4 Data Extraction and Synthesis

The first author coded all the articles using

predefined criteria (devised by both authors) and any

uncertainty about a particular article was discussed

prior to its inclusion or exclusion based on the

eligibility criteria.

Each selected publication was examined for the

following elements: Objective of the study and

corresponding research question(s); study

environment and participants; themes emerging from

the study; and, the relevance of the studies’ results.

This was then followed by a synthesis of the

emergent themes and categorization of the articles

according to the cognitive/persuasive stimuli

studied. To integrate the search results and our

conceptualization of behavioral economic studies in

IS we applied context analysis as defined by Oinas-

Kukkonen and Harjumaa (2009) to categorize

articles (Tables 1-3) according to the objectives, the

cognitive principle(s) studied, the user and

technology contexts, and contribution of the study.

This categorization is suitable because of the level of

abstraction it enables in identifying the effects of the

measures used in the reviewed studies.

4 RESULTS

Analysis of these papers was based on the

aforementioned objectives and the results reveal a

difference in coverage of behavioral economics in

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

the major IS journals. A majority of the articles were

from EJIS and ISRe. All but one of the journals

produced original results with data, and only two of

the articles (Goh and Bockstedt 2012; Adomavicius

et al. 2013) from ISRe contained behavioral

economics as a keyword in their abstracts. While the

following analysis is based on the 15 articles that we

have labeled–according to the persuasion context

analysis–as investigating a behavioral economic

principle, it is important to note that certain articles

(Tsai et al. 2010; Goh and Bockstedt 2012;

Adomavicius et al. 2013; Wu and Gaytán 2013; Park

et al. 2013; Chiu et al. 2014; Legoux et al. 2014)

much more strongly considered decision-making and

the valuation of presented choices than others (e.g.,

Blanco et al. 2010; Lee and Benbasat 2011).

Some articles, while explicitly focused on

investigating users’ valuation of choices,

additionally engaged with goals and design issues

that may not fall under the realm of behavioral

economics (Lankton and Luft, 2008; Angst and

Agarwal 2009; Blanco et al. 2010)

Figure 1: Literature search and selection process.

Accordingly, our classification of each of these

articles as investigating a behavioral economics

principle should be considered with this caution in

mind. As further noted in (Avineri, 2012), it is

important to acknowledge that behavioral economics

is not a homogenous field that can straightforwardly

be demarcated and there are opposing views as to

what counts as behavioral economics. A related

discussion can be found in a Q & A with R. Thaler

on what it really means to be a “Nudge” (Ubel

2015). The characteristics of the studies based on the

principle examined are presented in Tables 1-3.

Analysis of the persuasion context requires an

understanding of the occurrences in information

processing as the context assists in learning and

better understanding user behavior.

4.1 The Intent

Determining the intent involves defining the purpose

of an interactive system. Intentions can arise from

the creators of interactive systems, those who give

access to the system, and the individuals using the

systems (Fogg, 2003; Oinas-Kukkonen & Harjumaa,

2009). In the case of the current study, the intent is

derived from both the objectives of the reviewed

studies, the systems being studied, and their purpose.

Most of the studies were either Web or mobile-based

and involved either experimentation (Goh and

Bockstedt 2012; Wu and Gaytán 2013) or surveys

(Tsai et al. 2010; Park et al. 2013) Six of the studies

investigated various aspects of user behavior in

online stores. Chiu, Wang et al. (2014) use prospect

theory (Kahneman and Tversky, 1979) to highlight

decision-making under risk and why people continue

to buy from an online store. Prospect theory is used

to explain decision-making from a value maximizing

perspective and how, when one makes a decision,

s/he does not take into account the decision’s effect

on their consumption (Chiu et al. 2014).

Blanco et al. (2010) develop mock-ups based on

e-commerce practices to investigate the ideal

combination of presenting visual and textual

information and how various combinations of these

affect consumers’ cognitive states. Wu and Gaytán

(2013) discuss a risk-based conceptual framework to

help understand the role of seller reviews and

product prices on buyers’ willingness to pay.

Adomavicius et al. (2013) investigate the influence

of recommender systems’ ratings on consumers’

preferences by exploring anchoring, timing, system

reliability and granularity issues that are related to

their impact. Goh and Bockstedt (2013) apply

behavioral economic principles to examine seller’s

design choices and how these influence consumer

behavior.

Lankton and Luft (2008) apply behavioral

economic theories to study IT investment valuation

and predict the differences between intuitive

judgment and real options prescriptions. Ma et al.'s

(2014) study integrate gambling theory, the

Journal search

919 articles

identified

231 editorials and book

rev iews

excluded

688 articles

screened

625 excluded

by abstract

63 articles

considered

48 excluded for

not meeting criteria

15 articles

for final review

Behavioral Economics in Information Systems Research: A Persuasion Context Analysis

availability heuristic, and repeated behavior into a

framework that explains online gambling over time.

The rest of the studies examine various aspects of

human behavior in different environments. These

include, the role price and context play in mobile

service adoption (Blechar, Constantiou et al. 2006)

and the role consumer trust in online merchants

plays in purchase decisions (Liu and Goodhue

2012).

4.2 The Event

The event consists of the use, user and technology

contexts. These are the issues arising from the

problem domain, individual differences in people

that influence their information processing, and the

technologies or strategies employed in computer-

human and computer-mediated interaction (Oinas-

Kukkonen and Harjumaa, 2009). The use context

was not discussed in detail in any of the studies. This

is because they primarily concentrated on

investigating some aspect of user behavior related to

valuation of choices and/or presentation of

information, and proposing solutions and directions

for future research without going into details on the

actual use of the systems and/or features

investigated. As noted in Lehto and Oinas-

Kukkonen (2011) a high abstraction level in systems

descriptions makes it difficult to understand the

actual interactions taking place through the system

and the extent to which any potential outcome(s) are

due to the system’s intent.

The tested hypotheses and conducted

experiments in the studies provided a clearer picture

of the results’ relevance and their practical and

theoretical implications. For example, Chiu et al.’s

(2014) study extended prospect theory and provided

additional theoretical reasons why consumers

become more risk-seeking or less risk averse in

different circumstances. One of the practical

implications of their study was a suggestion of how

online sellers could attract potential buyers and turn

infrequent buyers into frequent ones. This was

through delivering guarantees on issues such as

security, inspiring customers and keepings

customers informed (Chiu et al., 2014).

The studies analyzed were about, 1) Web-based

environments which analyzed aspects of user

behavior (e.g. Chiu et al., 2014; Adomavicius et al.,

2013; Ma et al., 2014), 2) ways of improving user

interactions or understanding user actions online

(Legoux et al. 2014), 3) mobile-based services

(Blechar et al. 2006), and 4) the decision to use

certain systems (Constantiou et al. 2014). Although

these studies reported the technology context, as

their focus was mostly on studying users’ actions,

they did not provide a detailed description of the

technologies investigated.

4.3 The Strategy

Analysis of the strategy involves identifying the

route of message delivery–the underlying theories

applied in the studies to reach an intended audience,

the medium used, and the persuasion elements that

are conveyed in a message. A majority of the studies

investigated were not inherently persuasive,

therefore, the route used was either not described or

clearly discernable. Only in Angst and Agarwal

(2009) is there a mention of the route as the study,

investigating privacy concerns, is explicitly about

direct and indirect routes to persuasion. The study

highlights under which circumstances either or both

routes could be used. Unlike previous studies on the

ELM where the main focus was on attitude/opinion

change, Angst and Agarwal (2009) investigate a

choice process that could be cognitively taxing.

The message refers to the techniques used to

influence or alter users’ actions and in our study,

these are the principles (see Tables 1-3) applied in or

emerging from the reviewed articles. These included

how people react to information and/or choices

depending on how they are presented (framing)

(e.g., Goh and Bockstedt 2012), relying only on

information that confirms an initial assumption

while discounting opposing information

(confirmation bias) (e.g., Park et al., 2013), how loss

is more significant than an equivalent gain (loss

aversion) (e.g., Chiu et al. 2014), relying on one

piece of information (an anchor value) when making

decisions (anchoring) (e.g., Adomavicius et al.

2013), and application of persuasive principles (

explained by information processing-related theories

such as the ELM in investigating choice decisions)

in Websites to influence users (Angst and Agarwal

2009).

5 DISCUSSION

5.1 Contribution and Implications

It is important to understand how different

contextual issues affect users’ decision-making,

decision-making that may not always follow a

systematic pattern. Behavioral economics recognizes

that people are influenced by who they communicate

with, their reactions are shaped by predictable

mental cues (heuristics), people are strongly

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

influenced by others’ actions, relevant innovations

draws our attention, people’s acts are often

subconsciously influenced, people seem to be

consistent with their public promises and often have

an urge to reciprocate acts of kindness (Thaler and

Sunstein, 2008; Ariely, 2008; Dolan et al. 2010;

Thorgeirsson and Kawachi, 2013).

Behavioral economics has the potential to be an

important enabler of sustained behavior change,

especially in technology-mediated environments.

Behavioral economics also offers means to obtain a

deeper understanding of how IS and the design of IS

can influence users. Primarily because effective

persuasive communication is about correctly

interpreting the purpose of an IS–the intent.

This communication can be disrupted the noise

sometimes created by people’s cognitive biases

related to their judgment and decision-making

(Ariely 2008). As such, the educational approach

and the assumption that people are rational actors

prevalent in persuasive systems design and most IS-

related theories such as the technology acceptance

model (TAM) (Markus and Tanis 2000; DiSalvo et

al. 2010; Lee et al. 2011; Brynjarsdottir et al. 2012)

is not the most effective approach to driving

(behavior) change. Rather, an understanding of

people’s regular biases can be more useful for

stimulating change (e.g., Blechar et al., 2006;

Adomavicius et al., 2013; Goh and Bockstedt, 2013;

Ma et al., 2014).

Related to this, we asked one major question in

this paper: “How can behavioral economics enhance

understanding of users and their interactions with

information systems?” To examine behavioral

economics research specific to IS, we turned to the

major IS journals. We were particularly interested in

studies investigating various behavioral economic

principles related to valuation, the effects of

cognitive stimuli on choices, and how these relate to

and can be explained by persuasion context analysis.

The choice of the eight IS journals was because

the major contributions in a particular field are likely

to be in the leading journals (Webster and Watson,

2002) and studies accepted in these fora are usually

concise and comprehensive, detailing all the relevant

aspects of the particular field studied. Although,

Webster and Watson (2002) also suggest searching

for articles elsewhere after the initial search in the

major journals. In this study, we limited the search

to only the basket of eight. We also concentrated on

the major journals because we were interested in

those studies focusing on the IS community and thus

excluded from review behavioral economics studies

meant for other audiences such as psychology and

marketing. Consequently, this review can be

considered as the first attempt in synthesizing

behavioral economics studies in IS research.

Furthermore, we have also discussed the limitations

of IS and looked at ways behavioral economics with

its focus on judgment and decision-making can help

address some of these limitations.

In terms of coverage, the findings suggest that 1)

there is great potential in enhancing research in the

two fields especially as one entails incorporation of

cognitive, emotional and environmental principles in

decision-making and the other is about information

processing for decision-making. 2) There has been

an increase in the studies integrating the application

of behavioral economic principles in IS, the majority

of which have focused on online retail stores and

recommender systems. 3) Considering some of the

limitations of IS, behavioral economics in its

grounding on cognitive theories as shown in the

reviewed studies, offers possibilities to enhance both

IS design and implementation and PSD and 4) the

decision-making process is not consistent. The study

of behavioral economics principles in most cases

should involve field and/or experimental tests to

determine the underlying theoretical relationships in

order to enhance the clarity of the studies and the

principles applied.

In the previous section, we have positioned the

behavioral economics literature based on context

analysis and although there were similarities in

approach as mentioned above, the studies analyzed

were not about behavior change, which is a key

concept in persuasive systems research. Inherently,

they do involve change, but the coverage of the

change and the actual aspect changing is limited.

Additionally, most of the studies do not factor in

differing user characteristics and how these

differences may lead to varied responses to stimuli.

Therefore, in context analysis, as viewed from the

persuasive systems domain, not all factors could be

applied which has implications for the findings.

In behavioral economics, the behavioral

assumption is that people often act irrationally

(Ariely 2008) and not all their actions can be

reasonable and/or according to predefined criteria–

there is always a need to understand the audience

and the context in which information is received.

The prevailing environment and one’s emotional

state affects decision-making. Thus, behavioral

economics investigates the scope of decisions

regarding finances, health, and dietary choices that

people make (Ariely 2008). The persuasive element

involves presenting these choices in a way that

leverages people’s decision making and persuades

them to make self-beneficial decisions (Lee et al.

2011). The psychological barriers that prevent

Behavioral Economics in Information Systems Research: A Persuasion Context Analysis

desired behaviors should be understood and this

knowledge incorporated into decision-making and

systems design. As Wu & Du (2012) have stated, in

order to better understand system-use behavior,

especially in behavioral economics research,

researchers need to enhance their conceptualization

and measures of system usage which also factor in

the information quality and complexity of the IS

environment.

5.2 Limitations and Future Research

For this study, we concentrated only on articles from

the major IS journals and as comprehensive as these

are, they may not present all the relevant information

that has been conducted. Especially when one

considers that behavioral economics is a relatively

new field and IS research is itself multidisciplinary

so there may be other relevant studies outside the IS

realm. For example, examining articles from well-

known conference and workshop proceedings.

Secondly, our search was meant to produce a large

number of articles for review. But by including

additional terms in the search string (e.g., known

behavioral economic principles (cognitive biases

prevalent in judgement and decision-making) such

as framing, priming, incentives etc.) and searching

in other online libraries and databases, a highly-

focused pool of potential articles for review could

have been found. Lastly, in persuasion context

analysis since the articles did not prescribe to

persuasive systems design, interpretive

categorization, which may be subjective, was used.

Further research to extend the scope of the search

is planned and specifically to investigate how

behavioral economic principles can be integrated in

both IS research and more concentrated persuasive

systems design. As most of the studies included in

the current review have focused on examining

aspects of human behavior, a potential avenue for

future research would be to implement some of the

principles in actual systems design and study the

subsequent effects. For example, how varying the

allocation and rate of rewards combined with social

support in incentive schemes could influence goal

achievement.

Furthermore, the reviewed studies are very

diverse in nature. Some studies focused on

behavioral IS and others on the economics of IS.

Future research could examine whether there are any

differences in adopting behavioral economics

between different fields of research and the

implication(s) this has for the findings.

Table 1: Characteristics of studies related to framing.

Study Objective User context Technology

context

Contribution

Blanco et

al., 2010

Examine how product

presentation affects recall and

perceptions on quality

(framing)

Graduate and

Postgraduate

students

(N=108)

Mock websites

based on e-

commerce

practices

Confirmation of the importance

of product presentation online,

consumer characteristics, and

how people perceive and process

product information

Goh &

Bockstedt,

2013

Measure whether framing

influences consumers’ value of

customizable bundle offers

from online stores (framing)

Behavioral

experiments

(N=454)

Online

streaming and

movie rentals

The technology-driven context

of a purchase decision can have

significant effects on consumer

choices and economic outcomes.

Tsai et al.,

2011

Investigate whether

prominence of privacy

information influences

incorporation of privacy

considerations in online

purchasing decisions.

(Salience, framing, and

priming)

Online

responses to a

concerns

survey and a

shopping

experiment

(N=238)

Shopping

search engine

interface,

Privacy Finder

New insight into consumers’

valuations of personal data and

evidence that privacy

information affects online

shopping decision-making.

Angst &

Agarwal

2009

Investigate whether persuasion

can change attitudes and opt-in

intentions toward electronic

health records even in the

presence of significant privacy

concerns. (Persuasion and

framing)

Participants

(attendees to

a conference

and online

survey)

(N=366)

Electronic

health records

Even when people have high

concerns for privacy, their

attitudes can be positively

altered with appropriate message

framing. These results as well as

other theoretical and practical

implications are discussed.

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

Table 2: Characteristics of studies related to risk aversion and confirmation bias.

Study Objective User context Technology

context

Contribution

Chiu et al.,

2014

Understand reasons for

customers’ repeat purchase

in online retail stores and

the effect perceived risk

would have. (Risk aversion)

Customers of

Yahoo! Kimo

in Taiwan

(N=782)

Yahoo!

Kimo -

online

shopping

store

The moderating effect of perceived

risk, extends prospect theory and

provides additional theoretical

reasons for risk seeking and risk

averseness in consumers.

Wu &

Gaytan,

2013

Apply the buyers’ risk

perspective to reconcile and

explain seemingly

conflicting results in

previous literature. (Risk

aversion and framing)

Undergraduates

students

(N=78)

eBay auction

site

(empirical

study)

Customers have different risk

preferences and thus select sellers

with different risk profiles to match

their risk appetites.

Park et al.,

2013

Explore the extent investors

are subject to confirmation

bias in the context of

exposure to information on

message boards.

(Confirmation bias)

Investors in

South Korea

(N=502)

Stock

message

boards

Confirmation bias plays a great

role in investment decision-making

in numerous contexts e.g., project

management.

Legoux al.,

2014)

Investigate how experts’

investment decisions are

affected by cognitive biases.

(Confirmation bias)

Participants

from a

financial

institution

(N=100)

N/A Prediction accuracy about market

reactions to IT investments was

hampered by confirmation biases.

Table 3: Characteristics of studies related to other biases.

Study Objective User context Technology

context

Contribution

(Adomaviciu

s et al., 2013)

Explore how preferences at the

time of consumption are

influenced by recommender

systems’ predictions.

(Anchoring effects)

Participants

N = 216

Recommender

systems

Viewers’ preference ratings are

malleable and can be

significantly influenced by the

recommendation received.

(Liu &

Goodhue,

2012)

Explain the role of potential

users’ trust in creating

intention to revisit a website

(Bounded Rationality)

Undergraduat

e MIS

students

(N=314)

Website

which

redirects to

12 other

websites

Consumer trust in e-vendor

plays a major role in purchasing

services

(Blechar et

al., 2006)

Explore the influence of

reference situations and

reference pricing on mobile

service users’ behavior.

(Reference pricing and

Reference situation)

Students and

employees in

the public

sector

(N=74)

Mobile

services

The benefits of approaching

mobile service adoption and use

research in a holistic manner

and the importance of

considering the reference point

on mobile usage behaviors.

(Ma, Kim et

al. 2014)

Develop and test a

model of online gambling that

simultaneously takes into

account cumulative and recent

outcomes, and prior use.

(Availability heuristic)

Actual users

of a gambling

website

(N=22, 304)

Bwin

Interactive

Entertainment

(Internet

gambling)

Integration of gambling theory,

the availability heuristic, and

repeated behavior into a

framework that explains online

gambling over time.

Behavioral Economics in Information Systems Research: A Persuasion Context Analysis

Table 3: Characteristics of studies related to other biases (cont.).

Study Objective User context Technology

context

Contribution

(Constantiou

et al., 2014)

Investigate cognitive processes

involved in the decision to use

location based services (LBS)

and how they influence

information retrieval behaviors.

(Cognitive processes in decision-

making)

Young

smartphone

users

(N=66)

Location-

based services

in the German

telecommunica

tions market

A new conceptual framework to

investigate LBS use and

complement existing models in

user behavior research.

(Lee,

Benbasat

2011)

Extend the effort-accuracy

perspective of understanding

users’ recommendation agents’

(RA) acceptance by including

trade-off difficulty. (Cognitive

aspects of decision-making)

Students at a

large North

American

university

(N= 100)

Web-based

recommendati

on agents

Explains role of preference

elicitation methods (PEMs) in

assisting users with trade-off

difficulty across different

decision contexts Perceived effort

compared to previous research no

longer has a significant influence

in the loss condition.

(Lankton &

Luft 2008)

Provide theory-based

predictions of how consistency

between intuition and

normative real options value

varies for deferral and growth

investment options under

differing conditions. (Intuitive

judgment and regret theory)

MBA students

from a

Midwestern

public

university in

the United

States

(N=70)

N/A Techniques by which

organizations can limit unwanted

effects of regret and

overaggressive competitive

behavior.

6 CONCLUSIONS

The current study attempted to understand the

integration of behavioral economics in IS research

by reviewing the major IS journals. The study chose

a wide perspective in its search for articles that was

intended to capture a broad spectrum of articles that

covered the application of behavioral economics and

integrated persuasive systems design to some extent.

This explains why our eligibility criteria consisted of

issues pertaining to the user and their use (their

actions, intentions) of technology. The research was

also driven by the need for a more profound

understanding of the different principles of

behavioral economics and their application. By

understanding the extant literature in the field,

similarities and complementary properties with other

disciplines can be integrated, and in the case of IS,

improved methods of understanding users and their

actions can be used for better prevention and

intervention techniques especially in the domains of

health and sustainability. Behavioral economics with

its focus on understanding why sub-optimal choices

are made offers great opportunities in IS research by

highlighting these issues and how to counter them,

which can inform systems design.

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