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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005

The Asymmetric Effect of Website Attribute Performance on Satisfaction: An Empirical Study Christy M.K. Cheung Department of Information Systems City University of Hong Kong Tel: (852) 2784-4745 Fax: (852) 2788-8694 Email: [email protected] Abstract The purpose of this study is to examine the asymmetrical effects of negative and positive website attribute performance on satisfaction. An online survey on satisfaction with an e-portal was conducted, and a total of 515 usable questionnaires were collected. Psychometric properties of the measures were examined, and ordinary least squares were used to estimate the regression model. Results show that the importance of asymmetrical effect is different for different attributes, where the negative performance on information reliability, system access and usability had a stronger impact than their positive performance. On the other hand, the positive performance on information understandability and usefulness, and system navigation had a stronger impact than their negative performance. This difference of asymmetrical effect is an important area calling for future investigation. Keywords: Asymmetry, Satisfaction, Information Quality, System Quality, Understandability, Reliability, Usefulness, Access, Usability, Navigation

1. Introduction Given the changing dynamics of the global marketplace and the increasingly intense competition, delivering world-class customer online experience becomes a differentiating strategy. Satisfaction is one of the most important consumer reactions in B2C online environment. Recent statistics showed that 80 percent of the highly satisfied online consumers would shop again within two months, and 90 percent would recommend the Internet retailers to others [16]. On the other hand, 87 percent dissatisfied consumers would permanently leave their Internet retailers without any complaints [46]. Theoretical research also demonstrated that satisfaction helps build customer loyalty [4], enhances favorable word of mouth [7], leads to repeat purchases [41], and improves company market share and profitability [42].

Matthew K.O. Lee Department of Information Systems City University of Hong Kong Tel: (852) 2788-7348 Fax: (852) 2788-8694 Email: [email protected] Previous literature has generally identified key attributes of online customer satisfaction [24][43]. These studies primarily assumed either positive or negative website attribute performance would have similar impact on customer satisfaction. In marketing, some scholars [10][22][32] however have urged that the links in the satisfaction model are more complex than originally proposed. Their studies have empirically demonstrated that negative performance will have a greater impact on satisfaction than positive performance, and their asymmetric models also provided better explanation for customer satisfaction. Similarly, we believe that negative website performance will affect the Internet retailer more than the positive performance. Indeed, the Internet has empowered consumers. Consumers can switch brand or leave an Internet retailer with a single “click”. In addition, the power of word of “mouse” is higher in the online environment. Varadarajan and Yadav [45] conceptualized the electronic marketplace as “a networked information system that serves as an enabling infrastructure for buyers and sellers to exchange information, transact, and perform other activities related to the transaction before, during, and after the transaction (p.297)”. The connectivity nature of the Internet allows one-to-many and many-to-many communications among consumers that makes information spreads much faster and broader. In particular, negative word of mouth travels farther and faster than positive word of mouth travels [8]. Despite the dramatic impact of negative website performance on user satisfaction, there is still very little known about the asymmetric nature of links involved in the satisfaction judgment in consumer-based online environment. To fill this gap in the literature, the objectives of this study are (1) to review literature on positive-negative asymmetry and user satisfaction, and (3) to examine the asymmetrical effects of bad and good website attribute performance on satisfaction.

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Proceedings of the 38th Hawaii International Conference on System Sciences - 2005

2. Theoretical Background

2.2. User Satisfaction

2.1. Positive-Negative Asymmetry Effect

In the area of Information systems, a rich body of literature exists in the field of end user computing (EUC) satisfaction, which examines the nature of user satisfaction in the context of using computer application packages. IS researchers continuing to examine user satisfaction in part because it has been widely adopted as an important determinant of IS success [13][14][40]. In the EUC environment, users consume information through direct interaction with application systems. The phenomenon of EUC is characterized by both information quality and system quality, representing semantic level and technical level respectively.

While the positive-negative asymmetry effect has found only little attention in the IS literature, over the years it has been extensively studied in other disciplines [6][10][29]. 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. This positive-negative asymmetric effect is closely aligned with the loss aversion described in prospect theory [29]. Kahneman and Tversky [30] conducted an experiment that 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. The greater power of negative than positive performance in customer satisfaction has been well-documented and recognized in marketing [10][22][33]. Anderson and Sullivan [2] found that negative disconfirmation affects the firm more than positive disconfirmation. Table 1 presents a summary of prior studies indicating positivenegative asymmetry effect.

Table 1: Selected studies on asymmetrical effects Author Anderson & Sullivan [3] David et al. [12]

Area Customer satisfaction

Diener et al. [15]

Emotion

Gottman, [20][21]

Martial relationship

Kahneman & Tversky [30] Mittal et al. [33]

Choice, values, and frames

Wells, et al. [47]

Diary study

Customer satisfaction

Psychological distress

Findings Negative disconfirmation had a stronger impact on satisfaction than positive disconfirmation. 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 More distress of losing money than the joy of gaining the same amount of money. Negative performance on an attribute has a greater impact on overall satisfaction than positive performance. Gains in resources did not have any significant effects, but losses produced significant effects on postpartum anger.

To a certain extent, a website may be regarded as a computer application involving interactions with a computer environment. User experiences are heavily relied on the information published on the website, as well as the quality of the systems [14][31]. In marketing, consumer satisfaction is one of the most investigated constructs. However, consumer satisfaction dimensions developed for offline setting may not directly correspond to, or fully capture, the elements that should be considered in the online environment. Analysts of Patricia B. Seybold have already highlighted the quality of online shopping experiences depends on several different facets. Some of them are very specific to the online environment. “The quality of a customer’s experience with your ebusiness is dependent on thoughtful design, streamlined business processes, carefully respected policies, good customer service, and excellent execution [2].” As a result, this study built upon the prior work specific to the online environment. McKinney et al. [31] specified web satisfaction as impacted by information quality and system quality. Their work provides us with a good starting point for the current study. They identified the dimensions for information quality (understandability, reliability, and usefulness) and system quality (access, usability, and navigation), and empirically validated the measures using both exploratory and confirmatory approaches.

3. Research Model Integrating the principal of bad is stronger than good into McKinney et al.’s work, this study attempts to examine the asymmetrical effects of bad and good website attribute performance on web satisfaction. Figure 1 depicts the research model. This is consistent with the findings in other disciplines. Generally, individuals tend to prevent and rectify bad things, and thus they are more

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sensitive to negative information. Taylor [44] suggested that bad information is processed more thoroughly than the good. The more extensive processing may lead to enhanced memory for bad material. Therefore, we believe that one unit of negative performance on an attribute will have a greater effect on overall satisfaction than the same unit of positive performance. System Quality

Information Quality Understandability (+ve)

Access (+ve)

Understandability (-ve)

Access (-ve)

Reliability (+ve)

Web Satisfaction Reliability (-ve)

Usability (+ve)

Usability (-ve)

Usefulness (+ve)

Navigation (+ve)

Usefulness (-ve)

Navigation (-ve)

Note: +ve – positive, -ve - negative

Figure 1: Research Model

Understandability is concerned with the clearness and goodness of the information. Huang et al. [25] suggested that an information system with high information quality need to provide concise presentations of the information that is interpretable and easy to understand. Palmer [37] contended that ease of reading of the information on the web can generate a desirable perception of its use and an intention to use the site. Reliability incorporates the degree of accuracy, dependability, and consistency of the information. This is consistent with the media richness theory [11] that emphasizes the importance of quality, accuracy, and reliability of information exchange across a medium. The reliability of e-commerce website content facilitates consumers to perceive lower risks, better justifications for their decisions and ease in reaching the optimal decisions, and in turn, affects satisfaction and purchasing intention. Usefulness refers to users’ assessment of the likelihood that the information will enhance their decision. Gehrke and Turban [19] urged that the usefulness of the web content determines whether potential customers will be attracted to or driven away from the website.

3.1. Web Satisfaction Consistent with the definition by Chin and Lee [9], we define satisfaction as the overall affective evaluation a user has regarding his or her experience related with the website.

Building upon prior studies, we believe the impact of negative website attribute performance (Information quality) on satisfaction will be greater than the impact of positive attribute performance. we have the following hypotheses: H1: Negative performance on information understandability of the e-portal would have a greater impact on satisfaction than the positive performance.

3.2. Information Quality High information quality has long been found associated with system use, user satisfaction, and net benefits [13][14][40]. In the online environment, the importance of information quality is further elevated. McKinney et al. [31] suggested that understandability, reliability, and usefulness of information as the three key dimensions related to information quality (See Table 2).

H2: Negative performance on information reliability of the e-portal would have a greater impact on satisfaction than the positive performance. H3: Negative performance on information usefulness of the e-portal would have a greater impact on satisfaction than the positive performance.

Table 2: Dimensions of Information Quality Dimensions Understandability

Reliability

Usefulness

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

Manifest Variables Clear in meaning Easy to understanding Easy to read Trustworthy Accurate Credible

Informative Valuable

3.3. System Quality System quality is a measure of the information processing system itself, and focuses on the outcome of the interaction between the user and the system. The key capability of the Internet supports greater interactivity for consumers, and thus system quality is largely characterized by the interaction between consumers and the website. McKinney et al. [31] empirically demonstrated that access, usability, and navigation are the three dimensions of system quality (See Table 3).

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Table 3: Dimensions of System Quality Dimensions

Definition

Access

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

Usability

Navigation

Manifest Variables Responsive Quick loads Simple layout Easy to use Well organized Easy to go back and forth A few clicks

Access refers to the speed of access and availability of the web site at all times. Consistent with the end user computing literature, the speed with which a computer system responds has been argued to be an important factor influencing the usability and emotional responses from users [9]. In the e-commerce environment, the response time of the e-commerce system affects web user satisfaction. Gehrke and Turban [19] also found that pageloading speed was rated as the most important determinant of successful website design. Usability is concerned with the extent to which the web site is visually appealing, consistent, fun and easy to use. In end user computing literature, system quality has been represented by ease of use [40], which is defined as the degree to which a system is “user-friendly” [17]. In the e-commerce environment, consumers may assess the websites based on how easy they are to use and how effective they are in helping them accomplish their tasks [52]. Navigation deals with the evaluation of the links to needed information. Nah and Davis [34] highlighted that navigation is an important design element, and emphasized the challenge in building a usable website with good links and navigation mechanisms. They categorized two challenges for navigation: Disorientation (losing the sense of location and direction in a nonlinear document), and cognitive overhead (the challenge of maintaining several tasks or hyperlink trails simultaneously). Building upon the principal that bad is stronger than good, we believe the impact of negative website attribute performance (system quality) on satisfaction will be greater than the impact of positive attribute performance. Here, the hypotheses of the current study are: H4: Negative performance on access of the e-portal would have a greater impact on satisfaction than the positive performance. H5: Negative performance on usability of the e-portal would have a greater impact on satisfaction than the positive performance.

H6: Negative performance on navigation of the e-portal would have a greater impact on satisfaction than the positive performance.

4. Method The following sections below describe in detail the data collection procedure employed, the measurement used, and the type of data analysis performed.

4.1. Data Collection An e-portal was introduced to the first-year undergraduate students at the beginning of the semester. The respondents are believed to be potential online consumers. With reference to ACNielsen [1], the young generation will be the major groups participating in online activities in the future. The data in the current study was collected through an online survey after their usage for six-week time. Online survey design has its advantages of speeding up large amount of data collection and allowing for electronic data entry [38]. Participation in this study was voluntary and 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. Measures of Understandability (UN), Reliability (RE), Usefulness (USE), Access (ACC), Usability (USA), and Navigation (NAV) were phrased as questions on a seven-point Likert scale, from 1=strongly disagree to 7 = strongly agree. 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. Appendix 1 lists all sample items in this study. In fact, 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 [39]. Measures with high degree of reliability and validity are prerequisites to cumulate IS knowledge [5][27]. 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 4, all the values of Cronbach alpha, composite

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reliability and average variance extracted were considered very satisfactory, with cronbach alpha at 0.811 or above, composite reliability at 0.888 or above and average variance extracted at 0.726 or above. All constructs were well in excess of the recommended 0.70 for Cronbach alpha [36], 0.70 and 0.50 for composite reliability and average variance extracted [18].

Table 4: Psychometric Properties of the Measures Construct Understandability CA=0.923 CR=0.946 AVE=0.813 Reliability CA=0.908 CR=0.935 AVE=0.783 Usefulness CA=0.897 CR=0.936 AVE=0.829 Access CA=0.811 CR=0.888 AVE=0.726 Usability CA=0.914 CR=0.940 AVE=0.796 Navigation CA=0.879 CR=0.926 AVE=0.806 Satisfaction CA=0.941 CR=0.958 AVE=0.851

Item

Item Loading

UN1 UN2 UN3 UN4

0.901 0.920 0.906 0.879

RE1 RE2 RE3 RE4

0.877 0.894 0.885 0.883

USE1 USE2 USE3

0.909 0.921 0.901

ACC1 ACC2 ACC3

0.807 0.862 0.885

USA1 USA2 USA3 USA4

0.869 0.907 0.901 0.890

NAV1 NAV2 NAV3

0.898 0.898 0.897

SAT1 0.912 SAT2 0.930 SAT3 0.925 SAT4 0.922 Note: CA = Cronbach Alpha, CR = Composite Reliability, AVE = Average Variance Extracted

Indeed, Fornell and Larcker [18] 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 5 shows the correlation matrix of the constructs. In this study, the assessment of discriminant validity does not reveal any problem.

Table 5: Correlation Matrix of the Constructs (Note: Diagonal Elements are square roots of Average Variance Extracted) UN

RE

USE

ACC

USA

NAV

UN

0.902

RE

0.769

0.885

USE

0.751

0.780

0.910

ACC

0.731

0.723

0.694

0.852

USA

0.704

0.742

0.729

0.787

0.892

NAV

0.747

0.715

0.742

0.773

0.725

0.898

SAT

0.579

0.556

0.563

0.594

0.612

0.599

SAT

0.922

Overall, these results provide strong empirical support for the reliability and convergent validity of the scales of our research model.

4.3. Checking for 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 [39]. 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.

4.4. Methods of Analysis The analytical strategy for this study was adapted from Anderson and Sullivan [3] and Mittal et al. [33]. First, a single item for each attribute was generated through averaging their original measures. Though the measures become single-item scales, we found that there is considerable precedent for using single-item measures in the satisfaction studies [26][28][49]. Table 6 summarizes the descriptive statistics of these constructs.

Table 6: Descriptive Statistics of the Constructs No. of Items 4

Mean

Standard Deviation

4.447

0.881

Reliability

4

4.485

0.889

Usefulness

3

4.449

0.936

Access

3

4.340

0.903

Usability

4

4.537

0.920

Navigation

3

4.471

0.911

Satisfaction

4

4.447

0.881

Constructs Understandability

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Second, the responses were then transformed by centering the original 7-point scale at 0 based on the means value of each attribute and multiplying by a constant to produce a new scale ranging from -7 to +7. The benefit of rescaling the sample based on the mean value is that it allows increasingly negative or positive values of the performance variable [22]. For example, as shown in Table 7, a total of 268 users who have “above average” perception (positive performance) on understandability of the website, and a total of 247 users have “below average” perception (negative performance) on understandability. In addition, the effect of negative or positive perceived performance is relative to an underlying normative perception [50]. Table 7: Descriptive Statistics of the Constructs Constructs

Mean

Sample above Average

Sample below Average

Understandability

4.447

268

247

Reliability

4.485

269

246

Usefulness

4.449

234

281

Access

4.340

216

299

Usability

4.537

244

271

Navigation

4.471

237

278

The results of the regression analysis (OLS) are shown in Table 8. Six out of the twelve explanatory variables are found statistically significant and explains 55.00 percent of the variance of the satisfaction model (F-value = 51.101, p=0.000). Recall that we postulate that negative performance on an attribute will have a higher impact than positive performance on the same attribute. We first compare the absolute magnitude of the positive and negative estimates of each attribute. However, as shown in Table 8, the asymmetry between positive and negative performance varies somewhat. The positive performance on “Understandability of information”, “Usefulness” of information, and “Navigation” of the website affect user satisfaction much larger than the negative performance on the same attribute. On the other hand, the negative performance on “Reliability of information”, “Access” of the website, and “Usability” of the website affect user satisfaction much higher than the positive performance on the same attribute. Among all these factors, negative performance of information reliability and positive performance of system navigation would have the greatest impact on overall satisfaction, with path coefficient -0.214 and 0.187 respectively. Table 8: Results of the Regression Analysis

Note: Total number of users in the sample: 515

ȕi

Each attribute was further decomposed into positive and negative performance based on the new scale. Note that in this analysis plan, two coefficients are estimated for each attribute for a total of 12 coefficients. For instances, if performance on understandability received a rating of +4, then POS_UN is equal to +4, and NEG_UN is equal to zero. On the other hand, if performance on understandability received a rating of -4, then NEG_UN is equal to -4, and POS_UN is equal to zero.

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 + ȕ1POS_UN + ȕ2NEG_UN + ȕ3 POS_RE + ȕ4 NEG_RE + ȕ5 POS_USE + ȕ6 NEG_USE + ȕ7 POS_ACC + ȕ8 NEG_ACC + ȕ9 POS_USA + ȕ10 NEG_USA + ȕ11 POS_NAV + ȕ12 NEG_NAV Note:

SAT = Satisfaction POS_UN= Understandability (+ve) POS_RE= Reliability (+ve) POS_USE= Usefulness (+ve) POS_ACC= Access (+ve) POS_USA= Usability (+ve) POS_NAV= Navigation (+ve)

NEG_UN= Understandability (-ve) NEG_ RE= Reliability (-ve) NEG_ USE= Usefulness (-ve) NEG_ ACC= Access (-ve) NEG_ USA= Usability (-ve) NEG_ NAV= Navigation (-ve)

Path Coefficient 0.104* -0.078 0.000 -0.214*** 0.106** -0.026 0.051 -0.100* 0.038 -0.146** 0.187*** -0.037

ȕ1 Understandability (+ve) ȕ2 Understandability (-ve) ȕ3 Reliability (+ve) ȕ4 Reliability (-ve) ȕ5 Usefulness (+ve) ȕ6 Usefulness (-ve) ȕ7 Access (+ve) ȕ8 Access (-ve) ȕ9 Usability (+ve) ȕ10 Usability (-ve) ȕ11 Navigation (+ve) ȕ12 Navigation (-ve) R2= 0.550; F 12, 502 = 51.101 Note: VIF – Variance Inflation Factor Note: *** significant at 99% significant level ** significant at 95% significant level * significant at 90% significant level

pvalue 0.053 0.177 0.998 0.000 0.042 0.669 0.307 0.061 0.521 0.022 0.001 0.548

VIF 3.198 3.747 3.755 3.111 3.010 3.984 2.808 3.139 3.931 4.519 3.480 4.139

To check if multicollinearity problems occur in the research model, we examined the significance of the variance inflation factor (VIF). Neter et al. [35] 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 8, all the constructs in this study have VIF values lower than 10, indicating that our research model does not suffer from multicollinearity problems.

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Mittal et al. [33] further suggested that we can examine the asymmetry between positive and negative performance by comparing the performance of the constrained model to that of the unconstrained model (The coefficients are free to vary). If the model with the constrained is rejected, it can be concluded that the absolute magnitude of the coefficient for negative (positive) is greater than positive (negative). Table 9: Model Comparison R-squares F statistics df Unconstrained Model

0.550

Incremental F-statistics

12

Constrained Paths ȕ1 ” ȕ2

0.546

55.100

11

3.155***

ȕ3 • ȕ4

0.535

52.655

11

11.713***

ȕ5 ” ȕ6

0.546

55.025

11

3.155***

ȕ7 • ȕ8

0.547

55.149

11

2.369**

54.798

11

3.940***

ȕ11 ” ȕ12 0.540 53.637 Note: *** significant at 99% significant level ** significant at 95% significant level

11

7.845***

ȕ9 • ȕ10

0.545

Table 9 provides a summary of model comparisons, incremental F-statistics were examined. In this study, all incremental F-statistics are statistically significant, when F0.01,6,502 = 2.80 and F0.05,6,502 = 2.10. These results confirm with the findings in our regression analysis.

6. Discussion and Conclusions The intent of this study is to examine the positivenegative asymmetric effects on satisfaction. Through the analysis reported in this paper, the asymmetry between different levels of negative and positive attribute performance are found inconclusive. These results show that the relationship between attribute performance and overall satisfaction judgment is complex. These results have several implications for research and practice that are discussed next.

6.1. Implications for Research Satisfaction has been receiving enormous attention in the studies of consumer-based electronic commerce. This study is one of the very first attempts to integrate the principal that bad is stronger than good into research on online satisfaction. However, our results provide only partial support for the asymmetric effect on online user satisfaction. The asymmetrical effect is found different for different attributes. For instance, the positive performance of understandability, usefulness, and navigation had

greater power than their negative performance, whilst the negative performance of reliability, access, and usability had a greater impact than their positive performance. Among all web attributes, the negative performance of reliability exhibits the greatest impact on overall satisfaction. This is consistent with research on traditional service quality, where reliability is the most important dimension in that context [51]. In marketing, researchers attempted to classify the attributes based on their very nature. Some attributes were categorized as utility-preserving (e.g. safety in air travel), while others were categorized as the utility-enhancing (e.g. entertainment aboard a flight). The rationale behind is similar to the Herzberg’s two-factor theory [23], where attributes can be classified into hygiene and motivation factors, depending on its underlying nature. Indeed, we can also classify our web attributes into utility-preserving (hygiene) factors or utility-enhancing (motivation) factors. Variables where their negative performance would impact overall satisfaction higher than their positive performance are more likely to be the utility-preserving factors. In our case, reliability of information, the accessibility and usability of a website, are the core or basic features that users expect they should be function well. If a website fails in any one of these features, satisfaction will drop dramatically. On the other hand, variables where their positive performance would affect overall satisfaction higher than their negative performance are more likely to be the utility-enhancing factors. Our research suggests that understandability and usefulness of information, and website navigation, are the motivating factors that if they appear in the website, user satisfaction will jump significantly. The finding of this study raises important questions that need more research. First, multi-attribute models should be used to investigate user satisfaction in the online environment. Since the asymmetrical effects are different for different attributes, attribute-based approach provides researchers with a higher specificity and diagnostic usefulness. Second, the conditions under which the negative (positive) performance of an attribute affects overall satisfaction larger than its positive (negative) performance should be outlined. This suggests a need to develop a theoretically driven typology that enables researchers to classify website attributes into different categories. Finally, these results also point toward a careful reexamination of satisfaction and its consequent behaviors, such as retention and word of mouth. Previous studies have simply assumed these relationships to be linear and symmetric [4] [48]. However, the impact of satisfaction on these behaviors could be asymmetric and nonlinear. Future research should incorporate the

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should match content to customer interest (e.g. referring to customer by his/her name, suggestion of products related to previous purchase and etc.)

asymmetric conceptualization into the studies on the relationship between satisfaction and its consequences. -

6.2. Implications for Practice While this study raises interesting implications for researchers, we also consider it relevant for practitioners. If our findings are successfully replicated in other eportals or websites, they can be of value for practitioners. The analysis implies that to increase user satisfaction in the online environment, Internet retailers need to first understand that different website attributes have different impact on satisfaction. Reliability of information, accessibility and usability of the website are the basic features, if they fail to make them work, user satisfaction will drop dramatically. -

First, reliability of information is the most important feature among all the attributes investigated in this study. Web designers should avoid typographical error in the content, and provide reliable and up-to-dated information.

-

Access of the website includes responsive to user request and page loading speed. Web masters should response to user requests and make sure all text and graphic load quickly.

-

In terms of usability of the website, Web designers should logically organize site-content, e.g. arranging information by alphabetical order or product categories, or providing indicator for page location within the website. In addition, the website should have consistent presentation and well-organized layout of visual elements.

Navigation of the website: Web designers should make sure information can be obtained in the fewest possible steps. Also, hyperlinks should be consistently provided on every webpage, including links to homepage and other main pages on the website. Finally, no broken hyperlink.

We believe the results of this study help Internet retailers to build a more effective strategy that can optimize resource allocation in maximizing user experience in the online environment.

Acknowledgments The work described in this paper was partially supported by a grant from City University of Hong Kong (Project No. 7001160).

7. References [1] [2]

[3]

[4]

For utility-enhancing factors, their positive performance would significantly increase user satisfaction. Among the variables in this investigation, understandability and usefulness of website information, and website navigation would be the utility-enhancing (motivation) factors. Managers/web designers should allocate more resources to maximize the performance of these attributes, here are some suggestions:

[5]

-

Understandability of website information: Web designers should provide information that is easy to read and understand.

[8]

-

Usefulness of website information: Web designers should provide information that is useful to users. For example, a complete detailed product information (e.g. availability or “in-stock” information, time required for delivery, current product price, and etc.). Also, web designers

[6]

[7]

[9]

[10]

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RE 3

The information on e-portal is credible

RE 4

In general, information on e-portal is reliable for you to use

Usefulness (Strongly Agree to Strongly Disagree) USE1 USE2 USE3

The information on e-portal is informative to your usage The information on e-portal is valuable to your usage In general, information on e-portal is useful for you to use

Performance in System Quality of e-portal Access (Strongly Agree to Strongly Disagree) ACC1 e-portal is responsive to your request ACC2 ACC3

e-portal is quickly loading all the text and graphic In general, e-portal is providing good access for you to use

Usability (Strongly Agree to Strongly Disagree) USA1

e-portal is having a simple layout for its contents

USA2

e-portal is easy to use

USA3

e-portal is well organized

USA4

In general, e-portal is user-friendly.

Navigation (Strongly Agree to Strongly Disagree) NAV1 NAV2 NAV3

e-portal is being easy to go back and forth between pages e-portal is providing a few clicks to locate information In general, e-portal is easy to navigate

Appendix 1 Performance in Information Quality of e-portal

Satisfaction SAT1

Understandability (Strongly Agree to Strongly Disagree) UN1

The information on e-portal is clear in meaning

UN2

The information on e-portal is easy comprehend The information on e-portal is easy to read

to

In general, information on understandable for you to use

is

UN3 UN4

e-portal

SAT2 SAT3 SAT4

a. [Semantic differential scale from 1 = very dissatisfied to 7 = Very satisfied] b. [Semantic differential scale from 1 = very displeased to 7 = Very pleased] c. [Semantic differential scale from 1 = frustrated to 7 = Contented] d. [Semantic differential scale from 1 = disappointed to 7 = Delighted]

Reliability (Strongly Agree to Strongly Disagree) RE1

The information on e-portal is trustworthy

RE 2

The information on e-portal is accurate

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