Download H4: Unpleasant affect performs stronger impact on user`s perceived

Experimental Research Proposal
Research Question
Delivering a user-friendly experience has become an important strategy in information
technology development. Designers and developers make every effort to achieve
customer satisfaction physically, cognitively and affectively. The relationship between
users and system design factors are researched. Some of the previous studies examined in
a key-driver analysis way (Shim, Shin, & Nottingham, 2002), which only shows the
determinants and their direct relationship symmetrically and linearly. This means the
impact of positive and negative factors are equal to users. However, recent research finds
that the relationship is more complex than originally proposed (Aderson & Sullivan,
1993). The impact may not be equal to users. This theory is actually not a new one in
some other fields. Psychologists have already proved that one unit of loss outweighs a
corresponding unit of gain (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001).
Negative performance has a greater impact than positive performance. For example,
people react more to bad events than to good events; bad characteristics exert more
influence over the human relationship than good characteristics. Such effect has already
been explored in Information Science area, too. There are already some pioneers proving
the positive-negative asymmetry effect in the IS related fields, such as cognition, website
attributes, etc. However, few studies in affect have examined the positive-negative
asymmetry effect.
Affect is considered to be an important factor in information system design, but it is still
understudied compared to other human computer interaction concerns. Until recent years,
its significant impact has been further explored by several studies in IS domain. And
more and more system vendors begin to pay attention to the role of affect factors in
information technology development. As the positive-negative asymmetry effect is
proven to be existing in many other areas related to information science, positive and
negative affect is conceived to be asymmetrical, too. The purpose of this paper is to
review the previous literature in both psychology and information science that is about
the positive-negative asymmetry effect and then reach to hypothesis that the effect of
negative affect and positive affect, generated during use, is asymmetrical in user
satisfaction of information system. The change in affect will be mainly caused by the
affective qualities of information system because this is more controllable to information
system vendors. Since there is no empirical study about the asymmetric effect of affect in
system usage, we hope to support the hypothesis through an empirical approach.
Literature Review
Previous literature in psychology has explored the asymmetric effect in a broad range of
phenomena. Research across domains of impression formation, emotion, learning,
memory, satisfaction, feedback, constantly found that psychological effects of bad ones
outweigh those of the good ones (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001)
and a general negativity bias of human judgment and behaviors (Rozin & Royzman,
2001). Baumeister et al (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001) defined
good as “desirable, beneficial, or pleasant outcomes”, and defined bas as the opposite.
They reviewed prior literature across areas in which “bad is stronger than good” are
recognized and those that are not linked yet to reach a conclusion that negatively
valenced things will create “stronger, more consistent, more lasting” effects than
positively valenced things.
Positive-negative asymmetry effect:
Psychologists suggest that one unit of loss is outweighing a corresponding unit of gain
(Colgate & Danaher, 2000). In some circumstances, negative attributes perform a
stronger impact than an equivalent unit of positive attributes. Thus we assume such
attributes have an asymmetric relationship with the stuff being impacted.
The reasons of the asymmetrical effect have been studies for years. Baumeister et al
explain from an evolutionary perspective that because organisms that were better
adaptive to bad things in the history of evolution would have been more likely to survive
dangers and threats and increased the possibility to pass along their genes, generally
human are psychologically designed to respond more to bad than to good. Those who
ignore bad events may be more likely to end up maimed or dead. On the other hand,
human and animals show high awareness of negative events because these events prompt
a need for change on self and lessons learned from these bad events remain more
permanently to ensure that they won’t happen repeatedly. Meanwhile, since people are
always motivated to searching for better results, so they spend more efforts to reduce the
chance of bad events. Another explanation is the prospect theory by Kahneman and
Tversky. The theory includes an argument from an economic behavior perspective that
the hurt people experience in losing a sum of money is greater than the pleasure in
gaining the same amount. Based on the nonlinear value function, the value function for
losses is steeper than gains and is concave for gains and convex for losses. So losses
loom larger than gains. In addition, studies on psychophysiology also demonstrate that
response in the brain is more powerful to negative information than positive.
Related Findings in Other Fields:
In everyday event, people react more strongly to bad events than good as bad events
generate more emotion and effects on reactions. In marketing, negative disconfirmation
has a stronger impact on customer satisfaction than positive disconfirmation (Aderson &
Sullivan, 1993). In learning, our brains respond more strongly to bad than good things.
Punishment is relatively more effective than reward across all levels of learning (Penney
& Lupton, 1961). In impression formation, bad information about a person or a new
acquaintance carries more weight on forming impression than good information (Ikegami,
1993). For example, negative trait shows a greater impact in decision speed; bad
behaviors create a powerful effect on people’s overall judgment while good ones do
slightly; words with negative emotion impact significantly on impressions rating. In
feedback, bad feedbacks are more potent than good ones (Coleman, Jussim, & Abraham,
1987). For example, people are more concerned about avoiding bad feedback than about
enhance good feedback, which indicates a greater motivational power of bad feedbacks.
So this is suggested that bad feedbacks produce stronger motivations for people to protect
themselves against bad outcomes. Also people regard bad feedback as more indicative
and more accurate for evaluation. Generally, the principle that bad is stronger than good
is consistently supported across a wide range of phenomena, based on the past research
conducted in different areas.
IS Related Findings
Besides psychology and other social science studies on positive-negative asymmetry
effect, enough attention is also paid to the IS area. Prior literature shows that the theory
bad is stronger than good is also applied to some IS topics. For instance, Cheung and Lee
(Cheung & Lee, 2004) believe that negative website performance will prompt a greater
impact on overall user satisfaction than positive one. There is an asymmetric effect in the
link between customer perception and website attributes performance. Attributes like
reliability and usefulness have more significant impact on customer satisfaction when
they show a negative performance. This has been proved through an online survey with a
total of 515 participants involved in using an e-portal. In Cheung and Lee’s later research
(Cheung & Lee, 2009), they additionally suggest that customer satisfaction appears to
display diminishing sensitivity to information quality when facing the negative perceived
website performance. This means that though negative performance of websites quality
shows a greater impact on customer satisfaction than positive one, customers feel less
influenced as the negative performance is increasing. Suppose there is an extreme point
of the bad website performance, user satisfaction will no longer be affected by it at this
point. So these findings imply that website designers should pay attention to
improvement of bad performance of website reliability and usefulness. Although other
factors are working well, if website reliability and usefulness fail to perform, they will
lose customers.
Yin et al (Yin, Bond, & Zhang, 2010) propose that bad reviews and good reviews are not
created equally in the formation of inline consumer trust. Negative reviews have a
stronger impact than positive reviews on online consumer trust formation and decisionmaking processes. Furthermore, while consumers are supposed to exhibit a negativity
bias generally, its magnitude depends on the dimension of seller behavior involved. For
example, compared to seller’s competence, reviews about seller’s integrity involve
stronger bad-is-stronger-than-good effect in consumer’s trust and intention to purchase.
This may suggest that sellers can lose even more online consumers if there are more bad
reviews about their integrity than about their competence. So perhaps sellers are
encouraged to pay more attention to the improvement of their overall integrity.
In a study of the relationship between emotion and technology usage, based on a test of
Technology Acceptance Model, Cenfetelli (Cenfetelli, 2004) concludes that emotion is
regarded as a significant antecedent to people’s behavior of technology. Moreover,
negative emotion has a significantly stronger influence on beliefs and ultimate intentions.
Impact of feelings like frustration and annoyance toward information system is stronger
on actual usage than feelings like enjoyment and happiness. So for information system
designers, they may need to focus on avoiding the factors that cause bad feelings.
In emotion study, negative affect has a stronger impact on people than positive affect
(Diener, et al, 1985). This is because bad emotions require more cognitive processing and
more efforts to avoid. Also, people are more influenced by bad moods and remember
them better.
Theoretical Context: what might be predicted and why
Based on the literature review, prior research has found positive-negative asymmetry
effect in many fields. And some literatures have even explored the affective areas.
However, they just cover some aspects of affect, such as emotion. There is a need to
involve a broader concept of affect with the positive-negative asymmetry effect. So we
can predict that the effect of negative affect and positive affect, generated during use, is
asymmetrical in user satisfaction of information system.
Hypotheses and Established Theories to Use
Our hypotheses motivated by the problem is that the effect of negative affect and positive
affect, generated during use, is asymmetrical in user satisfaction of information system.
Although negative affect and positive affect are formed due to different internal and
external reasons, in this research, they are mainly caused by quality of the information
system itself. To support the hypotheses, we need to prove the existing relationships
between affect and actual system, affect and user acceptance and the measurement of
positive affect and negative affect. So we use the Russell’s core affect circle to
distinguish the positive and negative affect. The relationship between user’s affect and
system affective qualities is explained in previous studies and we will use that to support
the relation between affect and system in our research. We will also use Technology
Acceptance Model and the model of individual interaction with IT to support relationship
between affect and usage. This section includes both hypotheses and the proposed
theories that can be helpful to explain Our hypotheses.
H1: Deactivated (unintensive) affect performs stronger impact on user’s perceived
usefulness than activated affect.
H2: Unpleasant affect performs stronger impact on perceived user’s usefulness than
pleasant affect.
H3: Deactivated (unintensive) affect performs stronger impact on user’s perceived ease
of use than activated affect.
H4: Unpleasant affect performs stronger impact on user’s perceived ease of use than
pleasant affect.
H5: Deactivated affect performs a stronger impact on user’s attitude than activated affect.
H6: Unpleasant affect performs a stronger impact on user’s attitude than pleasant affect.
Previous literatures have identified the relationships among affective qualities, core affect,
affect reaction, cognition, intension and actual behavior of information system. The key
word here, affect, has been widely studied across various disciplines. The idea of affect
covers a set of psychological processes and states including emotions, mood, affective
impressions, and attitudes. Russell’s conceptual framework (Russell, 2003) describes that
the core affect, which is the consciously raw feeling, is similar to affect. It is a single
integral blend of two dimensions in a circular structure: pleasure-displeasure, activation
(arousal)-deactivation (sleepiness). These two dimensions can illustrate both positive and
negative affect of people.
The core affect circle by Russell is to be used to measure the positive and negative affect
in this research. Positive affect refers to what people feel as pleasant, active and alert
(Watson & Clark, 1988), locating in the top right part in the core affect circle. Negative
affect is on the contrast, unpleasant, calm and serene, locating in the bottom left part of
the circle.
Besides the circle, there are other measurable scales that are tested to be useful to
describe positive and negative affect. For example, in Watson and Clark’s Positive and
Negative Affect Schedule (PANAS) (Watson & Clark, 1988), they define positive and
negative affect in 20 items of mood scale to briefly and validly measure affect, such as
interested, distressed, upset, guilty, alert, inspired, etc. Some of them are same with
Russell’s framework.
The affective qualities in the system interface determine how users perceive these
qualities and thereby influence user’s affect (Te'eni, Carey, & Zhang, 2007). In Russell’s
conceptual framework, object affective qualities are the cause of change in core affect.
When people interact with object and events, they perceive affective qualities as stimuli
and then get a reaction to these qualities. The reaction can be called affective impression.
In IS domain, affective qualities of a system (interactivity, vividness, beauty and structure)
can be modified by changing design factors, such as shape, texture, color, and match.
These key design factors are found to have significant impact on users’ emotion (Kim,
Lee, & Choi, 2003). In our research, we will use some of the key design factors to
influence user’s affect.
The two models, TAM and IIIT, have been used and tested by many other researches.
They explain the relationships among affect, cognition and behavior. So in proposed
research, they can help to support the existing relationships between affect and IS use,
affect and cognition.
Technology Acceptance Model (Davis, 1989) has been widely studied and used by
researchers and practitioners to predict and explain user acceptance of information
technology since its appearance. The model provides an explanation of the determinants
of user acceptance toward IS that can generally predict system behavior. The two
determinants are perceived usefulness and perceived ease of use. Perceived usefulness is
that people tend to use or not use a system based on their belief that it will help them
achieve a better performance. Perceived ease of use, on the other hand, is people believe
it easy or not to use while it is useful, so that performance benefits outweighs effort of
using. It is the degree to which people feel that using a system would be free of effort.
Both the two determinants have a causal effect on user’s attitude of the system. And
attitude is a major determinant of intension to use. Moreover, Davis found there are
external variables, also called the system design features, which directly influence
perceived ease of use and perceived usefulness, and indirectly influence attitude,
behavioral intention and actual system. In his later research, he found that variables like
experience, computer anxiety, computer playfulness, perceived enjoyment, system
features can influence the system usability and ease of use (Venkatesh & Bala, 2008).
The model of individual interaction with IT (IIIT) (Sun & Zhang, 2006) is based on the
TAM and is constructed as a theoretical model to interpret the role of affect in
information system and predict individual IS behavior. The main idea of IIIT is to posit
that personal traits influence both users’ affective and cognitive reactions toward usage of
information system. Affective reaction and cognitive reaction influence each other and
they jointly determine behavior intension and actual system use. The personal traits and
affective reaction here are all about perceived impact on core affect. Traits, here
including two variables, computer playfulness and personal innovativeness, refer to stable
characteristics of individual that are less vulnerable to situational stimuli. Affective
reactions in the model include 8 variables, all of which are describing different affect of
system usage.
Methodology
To test the hypotheses, we plan to conduct an experiment involving a group of system
users. Participants will get involved into two phases of the experiment. In the first phase,
we are going to find out what kind of design can cause positive or negative affect and to
what degree this positive or negative affect will be. This is to ensure that when
participants are engaged into the second phase, their positive or negative affect is to be
caused mainly by the usage of system instead of other variables. And the units of effects
from positive and negative affect are similar with each other. If they are varying a lot, i.e.
they get a strong positive feeling when viewing red color, but a light negative feeling
when blue, the results will not be what we expected. What we expect is one unit of
negative affect causes stronger impact than one unit of positive affect.
So in the first phase, participants perhaps will be presented with a set of various websites
that are selected from those existing websites with interactive function. These websites
are somehow weak or strong in the following elements: interactivity, vividness, beauty
(shape, texture, color, match,) and structure. After participants use these websites, a
survey will be sent to ask their positive or negative responses in emotion and find out
what causes these responses. Based on the data collected, we can conclude what kind of
design in interactivity, vividness, beauty and structure may directly cause positive or
negative affect. For example, most participants respond that website design that is less
beautiful can make them feel unpleasant. So in the second phase, we can create a website
that is poor in beauty design to elicit negative affect.
The second phase is hoping to recruit the same group of participants because we will give
them a system with website interface designed according to their responses in the last
round. What we expect from the second phase is what we have hypothesized. So the key
is to ensure that the units of positive and negative affect are similar so that we can
measure their impact on PU and PEOU. So perhaps in the second phase, we are going to
develop a sample system that is perfect for our research. And after usage, participants
will be given a survey to ask about how they feel, pleasant or unpleasant, arousal or
sleepiness, and how they think of the usefulness and ease of use, as well as their attitude.
For example, if they feel tired due to the usage, they find the system to be strongly
difficult to use or useless; but if they feel happy, they find it to be averagely useful and
easy to use. This is what we most expect.
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