Positive-Negative Asymmetry of Disconfirmations on
User Satisfaction Judgment
Christy M.K. Cheung
Department of Information Systems
City University of Hong Kong
Tel: (852) 27844745
Fax: (852) 27888694
Email: iscc@cityu.edu.hk
Matthew K.O. Lee
Department of Information Systems
City University of Hong Kong
Tel: (852) 27887348
Fax: (852) 27888694
Email: ismatlee@cityu.edu.hk
1
Positive-Negative Asymmetry of Disconfirmations on
User Satisfaction Judgment
Abstract
Past research in the area of user satisfaction has primarily adopted the conventional “key-driver
analysis” approach under the implicit assumption that either positively or negatively valenced events
would have similar impact on user satisfaction. Researchers in other disciplines have already found
that the links in satisfaction models are more complex than originally proposed. By modeling the link
between satisfaction and its antecedents as symmetric and linear, researchers run the risk of
systematically misestimating the impact of the antecedent variables on user satisfaction. Thus, this
study is a step forward towards incorporating the concept of positive-negative asymmetry into user
satisfaction research. Specifically, the purpose of this study is to explore both the impacts of positive
and negative disconfirmations on user satisfaction. Building upon previous work, we empirically
tested an end-user satisfaction model in the context of e-portal usage. Consistent with the findings of
other disciplines, our results support the argument that negative disconfirmation has a stronger impact
on satisfaction than positive disconfirmation. Through recognizing the asymmetric cognitive
responses underlying satisfaction, we believe that this study has further advanced cognitive research
in general and theories in user satisfaction research in particular, and provided new insights to
practitioners on design priorities. Implications and future research are addressed.
Keywords:
Disconfirmation, Expectation confirmation theory, IS Continuance, Positive-negative asymmetry,
User satisfaction
2
Positive-Negative Asymmetry of Disconfirmations on
User Satisfaction Judgment
1. Introduction
Satisfaction has been a core research topic of numerous studies from diverse theoretical perspectives.
In the area of Information systems, researchers defined and studied user satisfaction broadly in two
different ways. Some studies (e.g. Bailey and Pearson 1983, Doll and Torkzadeh 1988) construed
satisfaction as an outcome resulting from the emotional response to the information/system attributes.
These studies focused primarily on the factors affecting the formation of user satisfaction. Other
studies (e.g. Bhattacherjee 2001, McKinney et al. 2002, Susarla et al. 2003), on the other hand,
delineated user satisfaction based on the perceptual, evaluative, and psychological processes. These
studies incorporating the expectation confirmation theory provided insights to user psychology and
explained the processes of user satisfaction formation.
In the last few decades, the two lines of user satisfaction studies have been receiving considerable
attention in IS. These studies, however, only examined user satisfaction using a conventional “keydriver analysis” approach, where either positively or negatively valenced event would have similar
impact on user satisfaction. Researchers in other disciplines, including marketing, social sciences, and
economics, have already found that the links in the satisfaction model are more complex than
originally proposed. Some of these studies (Anderson and Sullivan 1993, Mittal et al. 1998) have
empirically demonstrated that negative effect has a greater impact on overall satisfaction than an
equivalent unit of positive effect. As argued by Mittal et al. (1998), by modeling the link between
satisfaction and its antecedents as symmetric and linear, researchers might incorrectly estimate the
weights and miss the mark in prioritizing efforts to maintain and improve satisfaction.
Broadly, the purpose of this paper is to examine the asymmetric nature of links involved in the
satisfaction judgment in user satisfaction. More specifically, we
• Synthesize prior research on positive-negative asymmetry and integrate the principle into the
current work on user satisfaction, and
• Empirically test the resulting research model on user satisfaction.
The paper begins with a review of the literature on user satisfaction and its theoretical background. It
moves on to review and discuss prior work on positive-negative asymmetry. We then describe our
research model and present the design of the study and the research methodology. After discussing
the findings, the paper highlights implications for both research and practice and points towards
promising areas for future research.
2. Theoretical Background
In this section, we first provide an overview of research on user satisfaction. We then introduce the
expectation confirmation theory to explain user satisfaction judgment. After that, we summarize
previous studies on positive-negative asymmetry and addressed the importance of this principle in
cognitive research.
3
2.1 User Satisfaction
There is a wealth of literature pertinent to user satisfaction and user satisfaction models. The concept
of user satisfaction as a surrogate of system success can be traced to the work of Cyert and March
(1963), who posited that the ability of an information system to meet the needs of its users would
reinforce satisfaction. Bailey and Pearson (1983) defined user satisfaction as the sum of a user’s
attitudes toward a variety of factors of management information systems and identified 39 factors as
comprising the domain of user satisfaction. The D&M IS Success Model (DeLone and McLean 1992)
has served as a dominant framework for studying user satisfaction. There were over 300 articles in
referred journals have referred to and made use of it, since it was first introduced and published in
1992 (DeLone and McLean 2003). The D&M IS Success Model suggested that information quality
and system quality are two key factors determining user satisfaction. This is consistent with the enduser computing environment, where the phenomenon is characterized by both information
consumption and direct user interaction. The quality of information is typically evaluated by
measuring information attributes. For example, Doll and Torkzadeh (1988) developed a measure that
includes content, accuracy, format and timeliness of system output. System quality is mostly
represented in prior research by ease of use (Rai et al. 2002). User satisfaction remains as an
important research area in current IS research where IS researchers are continuing to examine the
concept inordinately (DeLone and McLean 2003, Rai et al. 2002, Wixom and Todd 2005, Zviran and
Erlich 2003). Recently, the proliferation of electronic commerce has further provoked IS researchers’
interest in the study of satisfaction in the online environment (Devaraj et al. 2002, McKinney et al.
2002, Shim et al. 2002).
Early user satisfaction research tended to focus primarily on the operationalization of satisfaction
construct and ignored the theoretical bases. In addition, these studies construed satisfaction as an
outcome resulting from the emotional response to the information/system attributes. According to
Melone (1990), “This lack of agreement on the conceptual definition of the user-satisfaction construct
has lead to a situation in which there are many operationalizations and an equal number of conceptual
definitions, for the most part lacking theoretical foundation (p.80).” In response to the call for a
rigorous theoretical support in the study of user satisfaction, recent studies are more grounded with
theories. For instances, Devaraj et al. (2002) examined consumer-based channel satisfaction using
technology acceptance model, transaction cost analysis, and service quality. Bhattacherjee (2001),
McKinney et al. (2002), and Susarla et al. (2003) adopted the expectation confirmation theory to
examine satisfaction. Among diverse theoretical frameworks, expectation confirmation theory has
been receiving a great deal of attention in recent IS research. These studies provided more insights to
user psychology and explained user satisfaction formation processes.
2.2 Expectation Confirmation Theory
The expectation-confirmation theory has been the most widely adopted approach in research and
managerial practice for understanding consumer satisfaction. Oliver (1976) was the pioneer to bring
the adaptation-level theory into the consumer satisfaction research and explained the satisfaction
formation in terms of expectation, performance, and disconfirmation. The underlying satisfaction
formation process is demonstrated in Figure 1. Expectations create a frame of reference as a
comparative judgment, where a cognitive comparison of prepurchase expectation level with product
or service performance is then executed. If performance exceeds expectation (a positive
disconfirmation), the consumer becomes satisfied. On the other hand, if performance falls below
expectation (a negative disconfirmation), the consumer becomes dissatisfied.
4
Disconfirmation
under Perceived
Performance with
Indifference
Expectation
Level
Negative
Disconfirmation
Zero
Positive
Zone of
Indifference
Disconfirmation
under Performance
without Indifference
Low
Performance
Performance
Matching
Expectations
High
Performance
Figure 1: Satisfaction Formation Process (Adopted from Oliver 1997)
In recent years, we witnessed an increasing amount of IS research using expectation confirmation
theory to explain satisfaction. Building upon the expectation confirmation theory, Bhattacherjee
(2001) proposed an IS continuance model that relates satisfaction and perceived usefulness to the
degree in which users’ expectations about an information system are confirmed. Expectation provides
a baseline level to evaluate the actual performance of an IS and confirmation (disconfirmation) in turn
determines satisfaction.
2.3 Positive-Negative Asymmetry
Over the years, the positive-negative asymmetry has been extensively studied in psychology.
Baumeister et al. (2001) found that the principle of bad is stronger than good is consistent across a
broad range of phenomena, including information processing (e.g., Abele 1985, Graziano et al. 1980,
Klinger et al. 1980, Pratto and John 1991, Taylor 1991), emotion (e.g. Diener et al. 1985), marital
relationship (e.g. Gottman 1994, Gottman and Krokoff 1989, Huston et al. 2001, McCarthy 1999),
impression formation (e.g. Hamilton and Zanna 1972, Ikegami 1993, Skowronski and Carlson 1992),
and. In recent years, researchers in marketing started to adopt the principle of positive-negative
asymmetry to investigate consumer satisfaction. For instances, Anderson and Sullivan (1993) showed
that negative disconfirmation affects consumer satisfaction more than positive disconfirmation.
Halstead (2002) also found that dissatisfied consumers engaged in significantly more word of mouth
behavior than satisfied consumers. Dissatisfied consumers tend to tell more people about their
dissatisfactory experiences. Table 1 presents a summary of prior studies indicating positive-negative
asymmetry effect.
5
Table 1: Selected Studies on Asymmetrical Effects
Author
Abele 1985, Graziano et
al. 1980, Pratto and John
1991, Taylor 1991
Anderson and Sullivan,
1993
Bless et al., 1992;
Skowronski and
Carlston, 1987
Coleman, Jussim and
Abraham, 1987
Conhen and Herbert,
1996; Kiecolt-Glaser et
al. 1984
David et al., 1997
Diary study
Diener et al., 1985
Emotion
Gottman, 1979;
Gottman, 1994
Marital
relationship
Halstead 2002
Word of mouth
Hamilton and Zanna
1972, Ikegami
1993,Kanouse and
Hanson, 1972; Peeters
and Czapinski, 1990
Kahneman and Tversky,
1984
Mittal et al., 1998
Impression
formation
Penney and Lupton,
1961; Penney, 1968
Wells et al., 1999
Area
Information
Processing
Customer
satisfaction
Memory
Feedback
Health
Choice, values,
and frames
Customer
satisfaction
Learning
Psychological
distress
Findings
People engaged in more thinking and reasoning (quantity of
cognition in response to various interpersonal events) about bad
than good events.
Negative disconfirmation had a stronger impact on satisfaction
than positive disconfirmation.
Participants remembered bad behaviors better than good ones.
Bad behaviors were recalled better than good ones, for both
extreme and moderate levels.
Bad feedback had a stronger effect on the students’ perceptions
of their own performance than good feedback.
Bad events have greater impact on health than good ones. One
reported high levels of loneliness had weaker immune
functioning than one did not report high levels of loneliness.
Undesirable events had larger effects on subsequent mood than
desirable events.
Negative affect and emotional distress had stronger impacts than
positive affect and pleasant emotions.
The presence or absence of negative behaviors had greater
power to the quality of couples’ relationships than the presence
or absence of positive behaviors.
Dissatisfied consumers engaged in significantly more word of
mouth behavior than satisfied consumers.
Bad information about a stimulus person or new acquaintance
carries more weight and has a large impact on impressions than
good information.
More distress of losing money than the joy of gaining the same
amount of money.
Negative performance on an attribute had a greater impact on
overall satisfaction than positive performance.
The punishment of incorrect responses is more effective than the
reward of correct reward. Punishment led to faster learning than
reward.
Gains in resources did not have any significant effects, but
losses produced significant effects on postpartum anger.
Prior literature in psychology suggested that events that are negatively valenced will have longer
lasting and more intense consequences than positively valenced events of the same type. The greater
power of negative than positive performance in customer satisfaction has also been well-documented
and recognized in marketing (Colgate and Danaher 2000; Mittal and Bladasare 1996, Mittal et al.
1998). This positive-negative asymmetric effect is closely aligned with the loss aversion described in
prospect theory (Kahneman and Tversky, 1979). Prospect theory argues that losses loom larger than
gains. Kahneman and Tversky (1984) conducted an experiment in which participants either gained or
lost the same amount of money. They found that participants were more upset about losing money
than happy about gaining the same amount of money.
6
However, much of what we known about the formation of satisfaction in the IS literature comes from
studies in which key attributes are identified and examined in a conventional “key-driver analysis”
approach. This line of research assumes that either positive or negative event would have a similar
impact on user satisfaction (see Figure 2a). However, as mentioned in previous section, researchers in
other disciplines have already found that one unit of loss is weighted more than a corresponding unit
of gain. This suggests that the links in user satisfaction models may follow the pattern as shown in
Figure 2b.
Satisfaction
Satisfaction
Negative
Disconfirmation
Positive
Disconfirmation
Figure 2a: Symmetric Relationship
Negative
Disconfirmation
Positive
Disconfirmation
Figure 2b: Asymmetric Relationship
We believe that negatively valenced event has a greater impact on overall satisfaction than an
equivalent unit of positively valenced event. In such cases, there is a negative asymmetry in the
satisfaction model. Therefore, if we assume an attribute (or disconfirmation) that has an asymmetric
relationship with satisfaction to be symmetric, we will systematically misestimate the impact of that
attribute on satisfaction. This can explain why in some cases, improving the performance on “key
driver” does not have a corresponding increase in overall satisfaction.
3. Integrating the Positive-Negative Asymmetry to Explain User Satisfaction Judgment
In this study, we examined the positive-negative asymmetry in the context of university students’ eportal use. Building upon McKinney et al’s (2002) recent work on web satisfaction, we incorporated
the concept of positive-negative asymmetry and investigated the impacts of positive and negative
disconfirmations on user satisfaction judgment.
As shown in Figure 3, McKinney et al. (2002) proposed a theoretical model of user satisfaction with
the web environment. Their theoretical model was incorporated with the expectation confirmation
theory to explain web satisfaction in terms of both disconfirmation and performance. The
disconfirmations are in turn based on the evaluations of the expectation and perceived performance
on the quality constructs.
McKinney et al. (2002) further suggested that web satisfaction should consist of two levels, web
information quality satisfaction and web system quality satisfaction. To some extent, user experiences
with a particular website are heavily relied on the information published on the website, as well as the
quality of the system (Janda et al. 2002). Similar to the EUC satisfaction model, their model also
7
urged that web satisfaction should be analyzed at two levels, information level and system level.
Using both exploratory and confirmatory approaches, McKinney et al. (2002) identified
understandability, reliability, and usefulness of information as the three key dimensions of
information quality, whereas access, usability, and navigation are the key dimensions of system
quality. Table 2 summarizes the definition of the antecedent variables of web satisfaction.
IQ
Expectation
Information Quality
IQ
Disconfirmation
IQ Perceived
Performance
Web-IQ
Satisfaction
Web
Satisfaction
SQ Perceived
Performance
SQ
Disconfirmation
SQ
Expectation
Web-SQ
Satisfaction
System Quality
Note: IQ- Information Quality
SQ- System Quality
Figure 3: McKinney et al.’s Web Satisfaction Model
Table 2: Definition of the antecedents of web satisfaction
Antecedent
Understandability
Reliability
Usefulness
Access
Usability
Navigation
Definition
Concerned with such issues as clearness and goodness of the information
Concerned with the degree of accuracy, dependability, and consistency of the
information
Users’ assessment of the likelihood that the information will enhance their decision
Refers to the speed of access and availability of the web site at all times
Concerned with the extent to which the web site is visually appealing, consistent, fun
and easy to use
Evaluates the links to needed information
McKinney et al.’s (2002) work provides us with a good starting point for the current study, as their
model has a very strong theoretical base, and the measures are developed and empirically validated
using both exploratory and confirmatory approaches. However, like most other studies in this area,
McKinney et al.’s model relies uniquely on the “key-driver” analysis approach. To improve the
explanatory value of the model further, there is a need to extend the model by incorporating the
“positive-negative” asymmetry approach in our analysis. In this study, we focus primarily on the
impact of disconfirmation on user satisfaction, in particular, the positive-negative asymmetric
disconfirmations.
8
4. Study Design and Method
We studied the positive-negative asymmetry in the context of university undergraduate students’ eportal use. The sections below describe in detail the data collection procedure, the measures, common
method variance, and method of analysis.
4.1 Data Collection
Data for this study were obtained from an online survey of first-year undergraduate students of a local
university. An e-portal was introduced to them at the beginning of the semester, and after their usage
for a six-week time period, an online survey assessing their satisfaction with the usage was
conducted. Online survey design has the advantages of speeding up large amount of data collection
and allowing for electronic data entry (Parasuraman and Zinkhan, 2002). Participation in this study
was voluntary. To encourage participation, incentives of three USB memory drives were offered as
lucky draw prizes. A total of 515 usable questionnaires were collected. Among the respondents,
45.2% are male and 54.8% are female.
4.2 Measures
The measures of this research were borrowed from McKinney et al.’s study with modifications to fit
the specific context of e-portal. A series of statements for Satisfaction (SAT) was asked, from very
dissatisfied to very satisfied, very displeased to very pleased, frustrated to contended, and
disappointed to delighted. Measures of satisfaction were measured on a seven-point scale with the
following anchors: 1=very dissatisfied and 7=very satisfied. Disconfirmation measures were available
on six independent variables, including understandability, reliability, usefulness, access, usability, and
navigation, that were identified in McKinney et al.’s (2002) study on web satisfaction. A seven-point
scale was used to evaluate the disconfirmation, varying from +1 to +3 for “better than what you
expected” (positive disconfirmation) and from –1 to –3 for “worse than what you expected” (negative
disconfirmation), with zero as a neutral point (confirmation). Appendix A lists all sample items in this
study.
Measures with high degree of reliability and validity are prerequisites to cumulate IS knowledge
(Bailey and Pearson 1983, Ives et al., 1983). Before we conducted the data analysis, the measures of
this study were first examined. Convergent validity indicates the extent to which the items of a scale
that are theoretically related should be related in reality. As shown in Table 3, all the values of
Cronbach alpha, composite reliability and average variance extracted were considered very
satisfactory, with cronbach alpha at 0.87 or above, composite reliability at 0.92 or above and average
variance extracted at 0.79 or above. All constructs were well in excess of the recommended 0.70 for
Cronbach alpha (Nunnally 1994), 0.70 and 0.50 for composite reliability and average variance
extracted (Fornell and Larcker 1987).
Indeed, Fornell and Larcker (1987) further suggested that average variance extracted can be used to
evaluate discriminant validity. To demonstrate the discriminant validity of the constructs in this study,
the square root of average variance extracted for each construct should be greater than the correlations
between that construct and all other constructs. Table 4 shows the correlation matrix of the constructs.
In this study, the assessment of discriminant validity does not reveal any problem.
9
Table 3: Psychometric Properties of the Measures
Construct
Item
Understandability
CA=0.96
CR=0.97
AVE=0.89
Reliability
CA=0.95
CR=0.96
AVE=0.86
Usefulness
CA=0.92
CR=0.95
AVE=0.86
Access
CA=0.87
CR=0.92
AVE=0.79
Usability
CA=0.95
CR=0.96
AVE=0.86
Navigation
CA=0.88
CR=0.93
AVE=0.81
Satisfaction
CA=0.94
CR=0.96
AVE=0.85
Item Loading
T-statistic
Mean
Standard
Deviation
DUN1
DUN2
DUN3
DUN4
0.94
0.95
0.94
0.93
116.69
143.70
147.89
104.41
0.23
0.29
0.33
0.31
0.93
0.94
0.94
0.92
DRE1
DRE2
DRE3
DRE4
0.91
0.94
0.93
0.93
98.85
146.01
104.15
119.11
0.30
0.39
0.35
0.43
1.11
1.06
1.09
1.10
DUSE1
DUSE2
DUSE3
0.92
0.93
0.92
95.87
124.44
97.80
0.33
0.34
0.44
1.07
1.06
1.06
DACC1
DACC2
DACC3
0.86
0.89
0.91
44.23
81.26
91.34
0.18
0.31
0.34
1.10
1.21
1.16
DUSA1
DUSA2
DUSA3
DUSA4
0.92
0.94
0.94
0.92
103.81
141.49
148.61
79.66
0.29
0.39
0.33
0.37
1.12
1.06
1.09
1.08
DNAV1
DNAV2
DNAV3
0.90
0.90
0.90
89.75
49.38
81.75
0.48
0.44
0.49
1.06
1.00
0.98
SAT1
SAT2
SAT3
SAT4
0.91
0.93
0.92
0.92
82.96
108.77
94.70
90.32
4.90
4.84
4.83
4.85
1.04
1.03
1.06
1.05
Note: CA = Cronbach Alpha, CR = Composite Reliability, AVE = Average Variance Extracted
Table 4: Correlation Matrix of the Constructs
(Note: Diagonal Elements are square roots of Average Variance Extracted)
DUN
DRE
DUSE
DACC
DUSA
DNAV
SAT
DUN
0.94
0.63
0.59
0.61
0.66
0.66
0.51
DRE
DUSE
DACC
DUSA
DNAV
SAT
0.93
0.66
0.68
0.65
0.65
0.61
0.93
0.70
0.68
0.70
0.63
0.89
0.75
0.74
0.65
0.93
0.76
0.64
0.90
0.60
0.92
10
Overall, these results provide strong empirical support for the reliability and convergent validity of
the scales of our research model.
4.3 Common Method Variance
Since the data was collected from a single source (e.g. self-report questionnaire), there is the potential
for the occurrence of method variance (Podsakoff et al., 2003). A Harman single factor test was
therefore conducted to determine the extent to the method variance in the current data. All 21
variables in the instrument were subjected to an exploratory factor analysis. Results suggested that no
single factor explained most of the variance, indicating the common method effects are not a likely
contaminant of the results observed in this investigation. In addition, the different scale endpoints
and formats for the dependent variable (satisfaction) and the independent variables (understandability,
reliability, usefulness, access, usability, and navigation) help diminish method biases.
4.4 Method of Analysis
The analytical strategy of this study was adapted from Mittal et al. (1998). First, a single item for each
variable was generated through averaging their original measures. Though a single item for each
attribute was used in this study, we found that there is considerable precedent for using single-item
measures in the satisfaction studies (Iacobucci et al. 1996, Kahn and Meyer 1991, and Yi 1990). Next,
each independent variable was decomposed into positive and negative disconfirmation based on each
respondent’s answer. Note that in this analysis plan, two coefficients are estimated for each
independent variable for a total of twelve coefficients. For instances, if understandability received an
average rating of +3 (positive disconfirmation), then POS_DUN is equal to +3 and NEG_DUN is
equal to zero. On the other hand, if understandability received an average rating of -3 (negative
disconfirmation), then NEG_DUN is equal to -3, and POS_DUN becomes zero.
In order to test and examine the positive-negative asymmetry, we further followed the approach that
is commonly used in the marketing literature (Anderson and Sullivan 1993, Mittal et al. 1998). First,
we constrained the coefficients for negative and positive disconfirmation to be equal (e.g.,
βpositive=βnegative), then we compared the performance of the constrained model to that of the
unconstrained model and determined whether the constraint can or cannot be rejected. The
asymmetry is supported if the constraint is rejected and the absolute size of the coefficient for
negative disconfirmation is greater than the coefficient of positive disconfirmation (e.g., βnegative >
βpositive).
5. Analysis and Results
The hypotheses with respect to satisfaction were tested in a regression model, where coefficients were
estimated using ordinary least squares (OLS).
SAT = Intercept + β1POS_DUN + β2NEG_DUN + β3 POS_DRE + β4 NEG_DRE + β5 POS_DUSE
+ β6 NEG_DUSE + β7 POS_DACC + β8 NEG_DACC + β9 POS_DUSA + β10 NEG_DUSA + β11
POS_DNAV + β12 NEG_DNAV
11
SAT = Satisfaction
POS_DUN= Positive Disconfirmation of Understandability
POS_DRE= Positive Disconfirmation of Reliability
POS_DUSE= Positive Disconfirmation of Usefulness
POS_DACC= Positive Disconfirmation of Access
POS_DUSA= Positive Disconfirmation of Usability
POS_DNAV= Positive Disconfirmation of Navigation
NEG_DUN= Negative Disconfirmation of Understandability
NEG_ DRE= Negative Disconfirmation of Reliability
NEG_ DUSE= Negative Disconfirmation of Usefulness
NEG_ DACC= Negative Disconfirmation of Access
NEG_ DUSA= Negative Disconfirmation of Usability
NEG_ DNAV= Negative Disconfirmation of Navigation
The results of the regression analysis (OLS) are shown in Table 5. Ten out of the twelve explanatory
variables are found statistically significant and explains 51 percent of the variance of the satisfaction
model (F-value = 43.91, p=0.000). Except the disconfirmation of “navigation” (Indeed, both its
positive and negative disconfirmations are not significant to user satisfaction), the constraint for each
of the attribute is rejected and the absolute size of the coefficient for negative disconfirmation is
greater than the coefficient for positive disconfirmation. Basically, the negative-positive asymmetry is
supported in this study. Hence, we believe that user satisfaction is significantly more sensitive to
negative disconfirmation than positive disconfirmation.
Table 5: Results of the Regression Analysis of Web Satisfaction
Attribute
Regression Coefficients for
Positive Disconfirmation
0.12* [2.05]
Understandability
0.18** [2.46]
Reliability
0.15** [2.40]
Usefulness
0.16* [3.06]
Access
0.14* [3.04]
Usability
0.10 [4.02]
Navigation
R2= 0.51; Adjusted R2= 0.50; F-value = 43.91
Note: *** significant at 99% significant level
** significant at 95% significant level
* significant at 90% significant level
Regression Coefficients for
Negative Disconfirmation
-0.19** [1.91]
-0.23*** [1.95]
-0.23** [2.50]
-0.28*** [2.85]
-0.21** [2.69]
-0.17 [2.29]
Reject
Constraint#?
(F-statistics)
5.93***
13.53***
24.19***
11.94***
7.88***
2.80**
# Wald Tests are performed to test the equality constraints in
the model (Details are found in Appendix B)
[ ] – the value in the blanket is the VIF
To check if multicollinearity problems occur in the research model, we examined the significance of
the variance inflation factor (VIF). Neter et al. (1996) argued that these factors measure how much
the variances of the estimated regression coefficients are inflated as compared to when the predictor
variables are not linearly related, and they suggested that VIF value in excess of 10 is taken as an
indication of the occurrence of multicollinearity problems. As shown in Table 5, all the independent
variable of the regression model in this study have VIF values lower than 10, indicating that our
research model does not suffer from multicollinearity problems.
6. Discussion and Conclusions
This study introduces the concept of positive-negative asymmetry into IS research. It thus represents
one of the very first studies in IS that attempts to incorporate the concept into existing work on user
satisfaction. Departing from the conventional “key-driver analysis” approach, we have closely
examined the role positive-negative asymmetry in the context of user satisfaction. Incorporating the
12
expectation confirmation theory in explaining user satisfaction, we postulated that negative
disconfirmation has a stronger effect on user satisfaction than positive disconfirmation. As shown in
Section 5 our results supported the positive-negative asymmetry in explaining user satisfaction. In
this section, we will first address the limitations of the study, we will then provide several
implications for research and practice, and we finally present the future research directions.
6.1 Limitations
Before discussing the implications for research and practice, we would like to address the limitations
of the current study. First, we examined the asymmetric effects on user satisfaction with an e-portal,
we cannot claim that the results obtained here will hold equally well in the context of other
information technologies. Second, the data were collected from university students. More research is
needed to permit the generalizability of the results to other types of organizational settings. Another
potential limitation of this study is related to the fact that this study focused on the post-adoption of
new technologies, where our data was collected in a cross-sectional setting. Therefore, we could only
investigate the direct impact of positive and negative disconfirmations on web satisfaction. To keep
the model parsimonious, the baseline of each factor was not included in the investigation.
6.2 Implications for Theory and Research
After a study is completed and new insights have been generated, it is always an interesting exercise
to ask what these new insights imply for past, present and future research in the area. To answer this
question, we would like to raise the research communities’ awareness with respect to the following
issues.
The first concerns the wide-spread practice in IS research by using the “conventional key-driver
approach” to estimate the strength between user satisfaction and its antecedents. As we have shown,
at least in the case of user satisfaction with an e-portal, this is not necessarily the case. In the current
analysis, negative disconfirmation of the performance of website attribute is found to have a stronger
impact on user satisfaction than the positive disconfirmation. The result supports the perspective as
suggested in marketing. The greater power of negative than positive effects in customer satisfaction
has been well-documented and recognized in marketing. Researchers even urged the need to replace
the “first generation” view of the satisfaction-profit chain as linear and symmetric with a “second
generation” view (Anderson and Mittal 2000). Thus, one important implication of our research is to
urge scholars adopting the asymmetric and nonlinear approach in IS research. For one, this practice
would prevent scholars from underestimating/overestimating the links in the research models.
We would also like to raise here is that our study enriches current IS research in the area of user
satisfaction. Recent research on user satisfaction has been greatly advanced with a stronger theoretical
foundation, and research incorporating expectation confirmation theory suggested that satisfaction is
formed resulting from the comparison between user expectation and his/her actual usage experience.
In this study, we further enrich this line of research and suggest that IS users are more sensitive to the
attribute where its performance is below their expectations. The impact of negative disconfirmation is
stronger than the positive disconfirmation. Through recognizing the asymmetric cognitive responses
underlying satisfaction, we believe our work has advanced the current state of cognitive research in
IS.
13
6.3 Implications for Practice
While this study raises interesting implications for researchers, we also consider it relevant for
practitioners. Understanding IS user satisfaction is important because a high level of satisfaction is
associated with several key outcomes (e.g., continued intention, word of mouth, and so). Our analysis
implies that to maintain user satisfaction, IS practitioners/designers should not just focus on
maximizing the performance of the information systems. They should also pay attention to user
expectation about the information systems, as user satisfaction is more sensitive to the negative
disconfirmation. If the systems fail to meet user expectation, user satisfaction will drop dramatically.
One important guideline to IS practitioners is that they should not overstate the functions of an
information system when the system is first introduced to the users. If a user gets a wrong expectation
about an information system, he/she may get dissatisfied easily.
6.4 Future Research
Our findings show that the link between disconfirmation and user satisfaction is rather complex.
Incorporating the positive-negative asymmetry into the investigation is a first step toward better
understanding of this relationship. Future research should include the asymmetric conceptualization
into studies on the relationship between user satisfaction and its consequent behaviors, such as
continuance intention and word of mouth. The conceptualization of positive-negative asymmetry is
rather new in IS research, the analytical framework used in this study is adopted directly from the
marketing literature. Future research should develop a better analytical plan and adopt different
research methods, for instances using experimental research, survey research, econometric modeling,
and qualitative methodologies to understand the link in user satisfaction judgment.
In conclusion, considering that this study has raised many interesting questions, we hope that it
triggers additional theorizing and empirical investigation aimed at better understanding user
satisfaction.
7. References
Abele, A. (1985) "Thinking about thinking: Causal, evaluative, and finalistic congnitions about social
situations," European Journal of Social Psychology, (15), pp. 315-332.
Anderson, E. W. and Mittal, V. (2000) "Strengthening the Satisfaction-Profit Chain," Journal of Service
Research, (3)2, pp. 107-120.
Anderson, E. W. and Sullivan, A. W. (1993) "The antecedents and consequences of customer satisfaction for
firms," Marketing Science, (12)2, pp. 125-143.
Anderson, R. E. and Srinivasan, S. S. (2003) "E-satisfaction and E-loyalty: A contingency framework,"
Psychology and Marketing, (20), pp. 123-138.
Bailey, J. E. and Pearson, S. W. (1983) "Developing a tool for measuring and analyzing computer satisfaction,"
Communications of the ACM, (26), pp. 785-793.
Baumeister, R. F., Bratslavsky, E., Finkenauer, C. and Vohs, K. D. (2001) "Bad is stronger than good," Review
of general psychology, (5), pp. 323-370.
Bhattacherjee, A. (2001) "Understanding Information Systems Continuance: An Expectation Confirmation
Model," MIS Quarterly, (25)3, pp 351-370.
Bless, H., Hamilton, D. L. and Mackie, D. M. (1992) "Mood effects on the organization of person
information," European Journal of Social Psychology, (22), pp. 497-509.
Cohen, S. and Herbert, T. B. (1996) "Health psychology: Psychological factors and physical disease from the
perspective of human psychoneuroimmunology," Annual Review of Psychology, (47), pp. 113-142.
14
Coleman, L. M., Jussim, L. and Abraham, J. (1987) "Students' reactions to teacher evaluations: The unique
impact of negative feedback," Journal of Applied Social Psychology, (17), pp. 1051-1070.
Colgate, M. R. and Danaher, P. J. (2000) "Implementating a customer relationship strategy: The asymmetric
impact of poor versus excellent execution," Journal of the Academy of Marketing Science, (28), pp.
400-405.
Cyert, R. M. and March, J. G. (1963) "A behavioral theory of the firm," Englewood Cliffs. NJ: Prentice-Hall.
David, J. P., Green, P. J., Martin, R. and Suls, J. (1997) "Differential roles of neuroticism, extraversion, and
event desirability for mood in daily life: An integrative model of top-down and bottom-up influences,"
Journal of Personality and Social Psychology, (73), pp. 149-159.
DeLone, W. H. and McLean, E. R. (1992) "Information systems success: The quest for the dependent
variable," Information Systems Research, (3), pp. 60-95.
DeLone, W. H. and McLean, E. R. (2003) "The DeLone and McLean model of information systems success: A
ten-year update," Journal of Management Information Systems, (19), pp. 9-30.
Devaraj, S., Fan, M. and Kohli, R. (2002) “Antecedents of B2C Channel Satisfaction and Preference:
Validating e-Commerce Metrics”, Information Systems Research, 13(3), pp. 316-333.
Diener, E., Larsen, R. J., Levine, S. and Emmons, R. A. (1985) "Intensity and frequency: Dimensions
underlying positive and negative affect," Journal of Personality and Social Psychology, (48), pp.
1253-1265.
Doll, W. J. and Torkzadeh, G. (1988) "The measurement of end-user computing satisfaction," MIS Quarterly,
(12), pp. 259-274.
Fornell, C. and Larcker, D. (1987) "Evaluating structural equation models with unobservable variables and
measurement error," Journal of Marketing Research, vol. 18, pp. 39-50.
Gottman, J. M. (1979) Marital interaction. New York: Academic Press.
Gottman, J. M. (1994) Why marriages succeed or fail. New York: Simon & Schuster.
Gottman, J. M. and Krokoff, L. J. (1989) "Marital Interaction and Satisfaction: A Longitudinal View", Journal
of Consulting and Clinical Psychology, (57), pp.47-52.
Graziano, W. G., Brothen, T. and Berscheid, E. (1980) "Attention, Attraction, and Individual Differences in
Reaction to Criticism," Journal of Personality and Social Psychology, (38), pp.193-202.
Halstead, D. (2002) "Negative Word of Mouth: Substitute for or Supplement to Consumer Complaints,"
Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior, (15), pp.1-12.
Hamilton, D. L. and Zanna, M. P. (1972) "Differential Weighting of Favorable and Unfavorable Attributes in
Impressions of Personality," Journal of Experimental Research in Personality, (6), pp. 204-212.
Huston, T. L., Caughlin, J. P., Houts, R. M., Smith, S. E. and George, L. J. (2001) "The Connubial Crucible:
Newlywed Years as Predictors of Martial Delight, Distress, and Divorce," Journal of Personality and
Social Psychology, (80), pp. 281-293.
Iacobucci, D., Ostrom, A. L., Braig, B. M. and Bezjian-Avery, A. (1996) "A canonical model of consumer
evaluations and theoretical bases of expectations," Advances in Services Marketing and Management,
(5), pp. 1-44.
Ikegami, T. (1993) "Positive-negative Asymmetry of Priming Effects on Impression Formation," European
Journal of Social Psychology, (23), pp.1-16.
Ives, B., Olson, M. H. and Baroudi, J. J. (1983) "The measurement of user information satisfaction,"
Communications of the ACM, (26), pp. 785-793.
Janda, S., Trocchia, P. J. and Gwinner, K. P. (2002) "Consumer perceptions of Internet retail service quality,"
International Journal of Service Industry Management (13)5, pp 412-431.
Kahn, B. E. and Meyer, R. J. (1991) "Consumer multiattribute judgments under attribute weight uncertainty,"
Journal of Consumer Research, (17), pp. 508-522.
Kahneman, D. and Tversky, A. (1979) "Prospect theory: An analysis of decisions under risk," Econometrica,
(47), pp. 263-291.
Kahneman, D. and Tversky, A. (1984) "Choices, values, and frames," American Psychologist, (39), pp. 341350.
15
Kanouse, D. E. and Hanson, L. R. (1972) "Negativity in evaluations," In E. E. Jones, D. E. Kanouse, H. H.
Kelly, R. E. Nisbett, S. Nisbett, S. Valins, and B. Weiner (Eds.) Attribution: Perceiving the causes of
behavior (pp. 47-62). Morristown, NJ: General Learning.
Kiecolt-Glaser, J. K., Glaser, W., Speicher, C. E., Penn, G. M., Holliday, J. and Glaser, R. (1984)
"Psycholosocial modifiers of immunocompetence in medical students," Psychosomatic Medicine, (46),
pp. 7-14.
Klinger, E., Barta, S. G. and Maxeiner, M. E. (1980) "Motivational Correlates of Thought Content Frequency
and Commitment," Journal of Personality and Social Psychology, (39), pp. 1222-1237.
McCarthy, B. W. (1999) "Martial Style and its Effects on Sexual Desire and Functioning," Journal of Family
Psychotherapy, (10), pp. 1-12.
McKinney, V., Yoon, K. and Zahedi, F. M. (2002) "The measurement of web-customer satisfaction: An
expectation and disconfirmation approach," Information Systems Research, (13), pp. 296-315.
Melone, N. P. (1990) "A theoretical assessment of the user-satisfaction construct in information systems
research," Management Science, (36)1, pp 76-91.
Mittal, V. and Bladasare, P. M. (1996) "Eliminate the negative," Journal of Health Care Marketing, (16), pp.
24-31.
Mittal, V., Ross, W. T. and Baldsare, P. M. (1998) "The asymmetric impact of negative and positive attributelevel performance on overall satisfaction and repurchase intentions," Journal of Marketing, (62), pp.
33-47.
Neter, J., Kutner, M. H., Nachtsheim, C. J. and Wasserman, W. (1996) Applied Linear Statistical Models (4th
ed.). IRWIN.
Nunnally, J. (1994) Psychometric theory, (3rd ed.). McGraw-Hill Education.
Oliver, R. L. (1976) "Effect of examination and disconfirmation on post-exposure product evaluations: an
alternative interpretation," Journal of Applied Psychology, (62), pp 490-486.
Oliver, R. L. (1997) Satisfaction: A Behavioral Perspective on the Consumer. New York: McGraw-Hill.
Parasuraman, A. and Zinkhan, G. M. (2002) "Marketing to and serving customers through the Internet: An
overview and research agenda," Journal of the Academy of Marketing Science, (30), pp. 286-295.
Peeters, G. and Czapinski, J. (1990) "Positive-negative asymmetry in evaluations: The distinction between
affective and informational negativity effects," In W. Stroebe and M. Hewstone (Eds.) European
review of social psychology, (1), (pp. 30-60). New York: Wiley.
Penney, R. K. (1968) "Effect of reward and punishment on children's orientation and discrimination learning,"
Journal of Experimental Psychology, (1), pp. 140-142.
Penney, R. K. and Lupton, A. A. (1961) "Children's discrimination learning as a function of reward and
punishment," Journal of Comparative and Physiological Psychology, (54), pp. 449-451.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y. and Podsakoff, N. P. (2003) "Common method biases in
behavioral research: A critical review of the literature and recommended remedies," Journal of Applied
Psychology, (88), pp. 879-903.
Pratto, F. and John, O. P. "Automatic Vigilance: The Attention-Grabbing Power of Negative Social
Information," Journal of Personality and Social Psychology, (61), pp. 380-391.
Rai, A., Lang, S. S. and Welker, R. B. (2002) "Assessing the validity of IS success models: An empirical test
and theoretical analysis," Information Systems Research, (13), pp. 50-69.
Shim, J. P., Shin, Y. B. and Nottingham, L. (2002) "Retailer Web Site Influence on Customer Shopping: An
Exploratory Study on Key Factors of Customer Satisfaction," Journal of the Association for
Information Systems, (3), pp 53-76.
Skowronski, J. J. and Carlston, D. E. (1987) "Social judgment and social memory: The role of cue
diagnosticity in negativity, positivity, and extremity biases," Journal of Personality and Social
Psychology, (52), pp. 689-699.
Skowronski, J. J. and Carlston, D. E. (1992) "Caught in the Act: When Impressions based on Highly
Diagnostic Behaviors are Resistant to Contradiction" European Journal of Social Psychology, (22), pp.
435-452.
16
Susarla, A., Barua, A. and Whinston, A. B. (2003) "Understanding the Service Component of Application
Service Provision: An Empirical Analysis of Satisfaction with ASP Services," MIS Quarterly, 27(1),
pp. 91-123.
Taylor, S. E. (1991) "Asymmetrical effects of positive and negative events: The mobilization-minimization
hypothesis," Psychologist, (38), pp. 1161-1173.
Wells, J. D., Hobfoll, S. E. and Lavin, J. (1999) "When it rains, it pours: The greater impact of resource loss
compared to gain on psychological distress," Personality and Social Psychology Bulletin, (25), pp.
1172-1182.
Wixom, B. H. and Todd, P. A. (2005) “A Theoretical Integration of User Satisfaction and Technology
Acceptance,” Information Systems Research, 16(1), pp. 85-102.
Yi, Y. (1990) "A critical review of consumer satisfaction in review of marketing," In V. A. Zeithaml (Ed.)
Review of Marketing (pp. 68-123). Chicago: American Marketing Association.
Zviran, M. and Erlich, Z. (2003) "Measuring IS user satisfaction: Review and Implications," Communications
of the Association for Information Systems, (12), pp. 81-103.
17
Appendix A: The Measuring Items
Variable
Understandability
Measuring Item
DUN1
DUN2
DUN3
DUN4
Reliability
DRE1
DRE2
DRE3
DRE4
Usefulness
DUSE1
DUSE2
DUSE3
Access
DACC1
DACC2
DACC3
Usability
DUSA1
DUSA2
DUSA3
DUSA4
Navigation
DNAV1
DNAV2
DNAV3
Satisfaction
SAT1
SAT2
SAT3
SAT4
The information on e-portal in terms of clear in meaning is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of easy to comprehend is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of easy to read is:
[Better than what you expected to Worse than what you expected]
In general, information on e-portal in terms of understandable for you to use is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of trustworthy is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of accurate is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of credible is:
[Better than what you expected to Worse than what you expected]
In general, information on e-portal in terms of reliable for you to use is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of informative to your usage is:
[Better than what you expected to Worse than what you expected]
The information on e-portal in terms of valuable to your usage is:
[Better than what you expected to Worse than what you expected]
In general, information on e-portal in terms of useful for you to use is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of responsive to your request is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of quickly loading all the text and graphic is:
[Better than what you expected to Worse than what you expected]
In general, The system of e-portal in terms of providing good access for you to use is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of having a simple layout for its contents is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of easy to use is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of well organized is:
[Better than what you expected to Worse than what you expected]
In general, The system of e-portal in terms of user-friendly is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of being easy to go back and forth between pages is:
[Better than what you expected to Worse than what you expected]
The system of e-portal in terms of providing a few clicks to locate information is:
[Better than what you expected to Worse than what you expected]
In general, The system of e-portal in terms of easy to navigate is:
[Better than what you expected to Worse than what you expected]
My overall experience of using e-portal is:
[Very displeased to Very pleased]
My overall experience of using e-portal is:
[Very displeased to Very pleased]
My overall experience of using e-portal is:
[Very displeased to Very pleased]
My overall experience of using e-portal is:
[Very displeased to Very pleased]
18
Appendix B: Wald Test
Wald tests are computed using the estimated coefficients and the variances/covariances of the
estimates from the unconstrained model.
The rationale for this approach is to test βPositive=βNegative
It is equivalent to the test of βPositive - βNegative = 0
This implies that
V(βPositive - βNegative) = V(βPositive) + V(βNegative) – 2COV(βPositive, βNegative)
Hence, an appropriate test statistic for this problem is:
F1, N − K −1
⎛
(bPostive − bNegative )
⎜
=⎜
⎜ sb2Positive + sb2Negative − 2sbPositive ,bNegative
⎝
⎞
⎟
⎟
⎟
⎠
2
19