College of Business Administration

University of Rhode Island

2004/2005 No. 3

This working paper series is intended tofacilitate discussion and encourage the

exchange of ideas. Inclusion here does notpreclude publication elsewhere.

It is the original work of the author(s) andsubject to copyright regulations.

WORKING PAPER SERIESencouraging creative research

Office of the DeanCollege of Business AdministrationBallentine Hall7 Lippitt RoadKingston, RI 02881401-874-2337www.cba.uri.edu

William A. Orme

Miao Zhao and Ruby Roy Dholakia

Effects of Online Store Attributes on Customer Satisfaction & Loyalty

Effects of Online Store Attributes on

Customer Satisfaction & Loyalty

Miao Zhao, Ph.D*. Assistant Professor of Marketing

Roger Williams University One Old Ferry Road

Bristol, RI 02809 Ph: 401-253-5351

Email: mzhao@rwu.edu

And

Ruby Roy Dholakia, Ph.D. Professor of Marketing & Electronic Commerce

The University of Rhode Island College of Business Administration

Ballentine Hall, 7 Lippitt Road Kingston, RI 02881-0802

Ph: 401-874-4390; Fax: 401-874-4312 Email: ruby@uri.edu

* Contact Author

Effects of Online Store Attributes on

Customer Satisfaction & Loyalty

Abstract

With a choice of many online store attributes, retail managers are concerned about which

attributes to include and how to operationalize them as well as their impact on customer

satisfaction and loyalty. There is a lack of consensus regarding online store attributes and their

categories, and there is very limited empirical research regarding their effects. This paper uses a

widely cited secondary source (Bizrate.com) that generates ratings on a wide variety of retail

sites to look at the relationships. Based on the selected attributes included in the Bizrate data set,

the analyses find varying relationships between specific online store attributes and site design,

satisfaction and loyalty. The discussion, taking the methodological limitations in mind, draws

certain conclusions between the online store attributes and the key dependent variables.

2

Introduction

With the emergence of computer-mediated communication (CMC), online stores are

experimenting with attributes that are unique to the new media. There is a choice of many

attributes (such as search engine, ordering system, order status tracking, customer survey,

personalization, and virtual reality display, etc.), each performing a specific function and distinct

from other attributes within the website. In addition to deciding which attributes to include and

how to specifically operationalize the selected attributes, online store managers are also

concerned about the impact of an attribute or a set of attributes on customer satisfaction and

loyalty. While there is an established body of literature and decades of experience regarding the

design of physical stores, the new world of online stores and website attributes are now

beginning to receive attention (e.g. Burke 2002; Eroglu, Machleit and Davis 2003; Park and Kim

2003).

In this paper we examine online retail store attributes and their effects on customer

satisfaction and loyalty. Unlike previous research, we do not create an online store with specific

attributes rated by student respondents; instead, we utilize secondary data on customer ratings of

real-world online stores. Characteristics of the secondary data allow us to separate the

contribution of website attributes at the time of placing an order as well as after receiving

delivery of the ordered merchandise.

The paper is organized as follows. First, the paper briefly reviews the literature on

website attributes and their relationship to customer satisfaction and loyalty. Given the growing

literature on interactivity, which is one of the important website attributes, selected literature on

interactivity and its effects on customer satisfaction and loyalty is also reviewed. Next, the

3

design of the empirical study, specifically the secondary data source, is described. The paper

concludes with presentation and discussion of the analysis of the secondary data.

Literature Review

A website consists of multiple attributes. Emerick (1995) identified “end users group”,

“end user information gathering”, “product/service utilization service”, “product/service

explanation and problem solving”, and “ordering” as attributes adopted by Internet Presence

Sites (IPSs). Marrelli (1996) focused on operational attributes to analyze the Zima website

(www.zima.com) and indicated that attributes such as “email feedback loop”, “multimedia

presentation”, “web questionnaire”, “affinity groups”, and “software downloading” play

important roles in building a highly interactive Internet Presence Site (IPS). Lii, Lim and Tseng

(2004) listed 8 attributes, termed operational factors, including content, attractiveness, ease of

use, personalization, interactivity, online community involvement, security, and maintenance

level. Burke (202) organized 31 features into four groups and used consumer surveys to list“must

have” and “should have” attributes. In addition to dimensions important in offline shopping,

Zeithaml, Parasuraman and Malhotra (2000) discovered several other attributes critical in the

online environment. Ghose and Dou (1998), listed 23 attributes used by IPSs and further

classified them into five groups: “customer support”, “marketing research”, “personal-choice

helper”, “advertising/ promotion/ publicity” and “entertainment”.

Given the large number of possible attributes as well as the changing nature of

technology that makes new attributes increasingly possible, it is not surprising that there is a lack

of consensus regarding “must have” and “optional” attributes. Using Ghose and Dou’s (1998)

classification of website attributes, Table 1 lists potential attributes across four different types of

websites – communication, entertainment, information and transaction (online store). A “X”

4

indicates that a specific attribute is more likely to be included in corresponding type(s) of

websites; however, a blank does not mean that the attribute cannot be included in a specific type

of website. For example, the attribute “games” is more likely to be included in an entertainment

website; however, some information websites such as www.electrolux.com also offer several

games that can be related to the firm’s products, even though “games” is not a typical attribute

for an information website.

Table 1: Types of Websites and Typical Website Attributes Website Type

Attribute

Informa-tion

Entertain-ment

Transac-tion (Online retail)

Commu- nication

Keyword Search X X X X Recommendation X My Account/File X X X X Dealer Location X

Personal Choice Helper

Virtual Reality Display X X X Software Downloading X X Online Problem Diagnostics X E-form Inquiry X X X X Order Status Tracking X Comment X X X X

Customer Support

Feedback X X X X Site Survey X X X X Product Survey X

Marketing Research

New-product Proposal X Electronic Coupon X Online Order X Bulletin Board X X Chat Room X Short Message X Email X Sweepstakes/prize X

Advertising, Promotion and Publicity

Banner ads. X X X X Games X Surfer Postings X X

Entertainment E-card X

5

With so many website attributes to choose from, Table 1 suggests that transaction (or

online store) sites are more likely to include certain types of attributes. It also suggests that

online stores are “attribute rich” - potentially containing the maximum number (16) of the 25

specific attributes. It is also very likely that online stores will differ not only on the number of

specific attributes incorporated within specific websites, but also how a specific attribute such as

“customer support” is operationalized. These variations create important challenges for research

on the effects of individual attributes on customer satisfaction and loyalty.

Interactivity as Key Attribute

Interactivity, identified as one of the key dimension of websites, has received perhaps the

most attention among website attributes. While it is recognized as a multi-dimensional construct

(Dholakia et. al. 2001), agreement is lacking regarding the number and specifics of interactivity

dimensions. Liu and Shrum (2002) examined several on-line marketing tools along three

dimensions of interactivity – active control, synchronicity and two-way communication – and

found online stores to be second only to “web community” on interactivity. Lii, Lim and Tseng

(2004) listed interactivity as one attribute and while they failed to determine the underlying

factor structure, they identified three factors - labeled “reliability”, “accessibility” and “feature

enhancement” - that had an impact on operational effectiveness as well as online marketing

performance.

Attempts to specify interactivity beyond its conceptual definitions (Liu and Shrum 2002;

Zhao 2003) have led to measurement of interactivity effects in a variety of contexts. One of the

early pioneers was Fortin (1997) who examined interactivity within the context of banner

advertisements as did Cho and Leckenby (1999).. At the website level, Wu (1999)focused on

interactivity of entertainment websites; Ha and James (1998) chose interactivity of business

6

websites; and Ghose and Dou (1998) studied interactivity of Internet Presence Sites (IPSs).

Raney et. al (2003) compared interactive websites with other forms of entertaining websites on

attitudes toward auto brands, auto websites and purchase intentions. Similarly, Coyle and

Thorson (2001) and Wu (1999) empirically tested the influence of website interactivity on users’

attitude toward the website.

Relationship between Store Attributes, Satisfaction, and Loyalty

Support for a positive satisfaction-loyalty relationship (Rust and Zahorik 1993) is also

evident for retail stores. For example, Bloemer and Ruyter (1998) have shown that customer

satisfaction is an antecedent to store loyalty while Baker-Prewitt and Sivadas (2000) found that

satisfaction positively affects store loyalty through repurchase intentions.

Even though the importance of customers’ length of store visit has been recognized in

both academia and practice, retailer loyalty is primarily measured through repeat patronage (e.g.

Sivadas and Baker-Prewitt 2000; Corstjens and Lal 2000). Morrison (2001), for example,

pointed out the role of store atmosphere in customer visit lengths; however, measurement

difficulties prevent its operational use.

Loyalty to a website is similar to loyalty to a retail store in the offline environment. In the

online environment, loyalty is frequently measured as repeat visits to websites. It is feasible,

however, to measure behavioral stickiness in terms of length of the time a user stays within a

website. Gillespie, et. al. (1999), focusing on the cognitive component of loyalty, defined loyalty

to a website as the degree to which it attracts users to stay at the site and to get users to return for

repeat visits. Crockett (2000) focused on the behavioral component of loyalty and argued that

the two dimensions of online loyalty are (1) the number of minutes visitors stay at a site and (2)

the frequency with which they return.

7

Empirical research on relationships between online store attributes and satisfaction and

loyalty is limited. While Baker (1986) and Bitner (1992) proposed categories of attributes that

impact consumer responses to retailer cues, Eroglu, Machleit and Davis (2003) argue that these

typologies do not easily translate into the online world. Instead, they separate cues into high-task

and low-task relevant and attempt to relate their influence on satisfaction mediated through

responses such as pleasure and arousal. Similarly, Park and Kim (2003) propose a specific

concept of “information satisfaction” conceptualized as “an emotional reaction to the experience

provided by the overall information service” (p. 18) which is impacted by online attributes such

as “user interface quality”, “product information quality” etc. Limited research exists to support

a positive relationship between interactivity and satisfaction (Rafaeli 1988; Rafaeli and

Sudweeks 1997; Liu 2002; Liu and Shrum 2002; Dholakia et. al. 2001). For example, Liu and

Shrum (2002) argued that all three dimensions of interactivity - active control, two-way

communication, and synchronicity - are positively related to user satisfaction since

controllability leads to general psychological well-being, and two-way communication and

synchronicity decrease the frustration related to waiting and feeling of ignored. Liu (2002)

empirically proved the positive impact of interactivity on satisfaction in an online environment.

The literature review above suggests that our knowledge regarding online store attributes

and their effects is still evolving. There is no consensus regarding store attributes or the process

by which they impact satisfaction and loyalty. Some of the research have focused on mediating

variables such as pleasure and arousal (Eroglu et. al 2002); others have used specific definitions

of satisfaction such as information satisfaction which is distinct from “overall satisfaction that

refers to the consumers’ overall evaluation.” (Park and Kim 2003). The following study is an

8

attempt to shed additional insights into the relationships between website attributes and their

effects on customer satisfaction and loyalty using secondary data.

An Empirical Investigation of Website Attributes and Their Effects on Customer Satisfaction and Loyalty

An empirical study was designed to test whether retail store site attributes affect users’

satisfaction with and loyalty to the website. Secondary data, collected at the online store level,

were compiled directly from www.bizrate.com in August 2003. Customer ratings on 1079

individual online stores were collected.

Secondary Data source

Bizrate (www.bizrate.com) collects, analyzes, and reports real customers’ online store

ratings. It combines consumer feedback from two different customer groups to create

comprehensive store ratings: a) directly from online customers as they make purchases; b) as

well as from a panel of Bizrate members who have volunteered to rate online stores.

After purchasing at a Bizrate affiliated website, each customer is asked to rate the website twice.

First, immediately after completing the online transaction, Bizrate asks a store's customer to

evaluate his/her attribute-level shopping experience (after check-out). Then, as an "after

delivery" follow-up, the customer is asked to rate the remaining shopping experience attributes,

overall shopping experience and revisit intention. An online store’s ratings are publicly reported

only when at least 30 customers have rated the store. Each rating is the weighted average of

evaluations from both customers and Bizrate members during the past 90 days.

Measures

Rating attributes and their explanations from www.bizrate.com are listed in Table 2.

Shoppers and panel members rated website-related attributes – eight, including “overall look and

9

design of site” at “checkout” and seven, including satisfaction and loyalty “after delivery”. Each

measure is rated on a 1-10 scale.

Table 2: Attribute Ratings and Their Explanations in Bizrate

Rating Explanation Source A. Attribute Measures Ease of finding what you are looking for

How easily were you able to find the product your were looking for at checkout

Selection of products Types of products available at checkout

Clarity of product information How clear and understandable was the product information at checkout

Prices relative to other online merchants Prices relative to other web sites at checkout

Shipping charges Shipping charges at checkout Variety of shipping options Desired shipping options were available at checkout

Charges stated clearly before order submission

Total purchase amount (including shipping/handling charges) displayed before order submission

at checkout

Availability of product you wanted

Product was in stock at time of expected delivery after delivery

Order tracking Ability to track orders until delivered after delivery

On-time delivery Product arrived when expected after delivery

Product met expectations Correct product was delivered and it worked as described/depicted after delivery

Customer support Any post-purchase activity such as: questions, complaints, replacements, and returns

after delivery

B. Overall Measures

Overall look and design of site Overall look and design of the site at checkout

Would shop here again Likelihood to buy again from this store after delivery

Overall rating Overall experience with this purchase after delivery

Despite several disadvantages, secondary data can be quite useful. The major advantage

of using the Bizrate data is its external validity since it was collected from real customers making

10

purchases at actual merchant sites. Retail sites for a large number of product categories are

included in the database and the ratings take place twice, although not on the same set of

attributes.

Analysis and Results

The primary analytical procedure used was regression analysis. First, multicollinearity

among the thirteen attributes was assessed using the variance inflation factors (VIFs). According

to Belsley, Kuh, and Welsch (1980), a VIF of less than 10 indicates lack of a significant

multicollinearity; all thirteen had a VIF of less than 10 and twelve of thirteen VIFs in our model

were less than 5, indicating a low level of mulitcollinearity. Factor analysis of 12 attributes

(excluding “overall look and design of site”) indicates that the time at which the measures were

taken contributed to the factor loadings; hence the two factors are labeled “at check out” and

“after delivery” (Table 3). The two factors explained 68 percent of the variance.

Table 3: Factor Analysis of Bizrate Store Attribute Ratings

Factor 1 “After Delivery”

Factor 2 “At Check Out”

Ease of finding what you are looking for .770

Selection of products .697

Clarity of product information .641

Prices relative to other online merchants .723

Shipping charges .669

Variety of shipping options .679 Charges stated clearly before order submission .746

Availability of product you wanted .781

Order tracking .901

On-time delivery .927

Product met expectations .773

11

Customer support .893

Variance Explained 36.18% 32.02%

Regression Analysis

The first analysis involved examining the effect of individual attributes on “overall look

and design of site” measured at check out (Table 4). In drawing managerial implications for e-

stores from their study on brick-and-mortar stores, Baker et.al (2002) speculate that “design of e-

stores (e.g. appearance and layout of home pages) may affect e-shoppers’ perceived psychic

costs significantly and thus impact their propensity to shop at those stores” (p. 138).

Table 4: Online Store Attributes and Site Design Overall look and design of site

Ease of finding what you are looking for .48**

Selection of products -.02

Clarity of product information .40**

Prices relative to other online merchants -.16**

Shipping charges .02

Variety of shipping options .03

Charges stated clearly before order submission .09**

Adj. R2 .67

** p < .05

The regression analysis suggests that “ease of finding what you are looking for”, “clarity

of product information” and “charges stated clearly before order submission” significantly

impact evaluation of overall look and design of site. “Prices relative to other online merchants”,

has, however, a negative influence on overall look and design of site. “Selection of products”,

“shipping charges” and “variety of shipping options” do not appear to contribute to overall look

and design of site.

12

To examine the impact of online store attributes on satisfaction and loyalty, the next

regression analysis included all the store attributes, including the ratings collected after delivery.

Several sets of regression analyses are conducted (Table 5).

Table 5: Relationship of Online Store Attributes And Customer Satisfaction and Loyalty

Overall satisfaction Loyalty

Overall Site Design .01 .03*

At Checkout factor .06** .03*

After Delivery factor .93** .91**

Overall Satisfaction .96**

Adj. R2 .93 .92 .88

* p < .05, ** p < .01

First, it is quite clear that overall satisfaction is the most important explanation of

behavioral intention (loyalty); the simple regression alone offers the strongest explanation of

consumers’ intentions to revisit a retail site. Second, the “after delivery” factor is very strongly

related to satisfaction as well as behavioral intention. In both these cases, the main reason is

likely the time of measurement – since these attributes were measured at the same time as the

two dependent variables and so there is a very strong temporal association. However, the

attributes measured at check out, so not temporally associated, are also significant although their

contributions are much smaller. It is also interesting to note that “overall site design” measured at

check out appears to make no difference on the satisfaction rating, but does impact behavioral

intention.

When we attempt to disaggregate the specific attributes and their contributions (Table 6)

we find that satisfaction is impacted most by “on-time delivery”; “product meeting expectations”

and “customer support” are also very important. For loyalty, “customer support” is most

13

important followed by “product meeting expectations” and “on-time delivery”. “Availability of

product wanted” and “order tracking” are both significant but not as important as other items

rated also after delivery. Among the at-checkout attribute ratings, “charges stated clearly before

order submission” is significant for both dependent variables. “Clarity of product information”

and “variety of shipping options” are not significant for either of the two dependent variables.

Table 6: Disaggregating the Effects of Online Store Attributes On Customer Satisfaction and Loyalty

Satisfaction Loyalty

At Checkout Attributes

Ease of finding what you are looking for -.01 -.076* -.076** .014

Selection of products -.01 .086** .049** .027

Clarity of product information .013 .277** .018 .268**

Prices relative to other online merchants .019* -.04 .008 -.022

Shipping charges .035** -.001 .005 .023

Variety of shipping options -.014 .143** -.013 .147**

Charges stated clearly before order submission

.032** .260** .049** .242**

Overall look and design of site .024* .035** .073

After delivery attributes

Availability of product you wanted .105** .048**

Order tracking .038** .061**

Ontime delivery .398** .266**

Product met expectations .241** .286**

Customer support .245** .338***

Adj. R2 .94 .90

*p < .10, ** p < .05

There are other interesting differences. “Shipping charges” has a significant influence on

satisfaction, but not on loyalty; similarly, “prices relative to other online merchants”. However,

14

the relationships are reversed for “ease of finding what you are looking for” and “selection of

products” which are significant for loyalty but not for satisfaction.

In order to determine how memory might have influenced the relationship between online

store attributes and satisfaction and loyalty, since the customer ratings occurred at two time

points, a second set of regression analysis was conducted with only the attributes rated at check

out. “Charges stated clearly before submission” remain important for both satisfaction and

loyalty; “clarity of product information” and “variety of shipping options” become significant

when only this set of attributes is considered. “Ease of finding what you are looking for” and

“selection of products” emerge significant for satisfaction and lose their significance for loyalty.

Discussion and Conclusion

From the analysis of the customer ratings, some conclusions emerge regarding the

influence of online store attributes on site design, satisfaction and repeat purchase intentions.

First of all, “ease of finding what you are looking for” and “clarity of product information” are

the two most important attributes for generating positive ratings of overall look and design of the

site. This is consistent with other research that have focused on the content and presentation of

product and service information (e.g. Park and Kim 2003). As Baker et al (2002) conclude,

design cues are the most significant and consistent influence on shopping experience costs and e-

stores have to similarly pay attention to design factors such as appearance and layout of home

pages.

The analysis also suggests that how specific attributes are operationalized are as

important as whether or not a specific attribute is included. Clearly, “charges stated clearly

before order submission” is very important – it influences overall look and design of site as well

as satisfaction and loyalty. However, another cost-related information, “prices relative to other

15

online merchants” is not at all important in the customer ratings of retail sites. Similarly, it

appears providing a “variety of shipping options” does not influence overall look and design of

site, satisfaction or loyalty ratings; however, specifying “shipping charges” does impact

satisfaction.

When there is a time gap between interacting with a site and evaluating the experience or

indicating revisit intentions, not all dimensions of the interactions persist in their impact.

“Charges stated clearly before order submission” is clearly very important – it is significant

when we look at its impact on site design (measured concurrently), on satisfaction and loyalty

(measured later). “Clarity of product information” and “variety of shipping options” are

significant for both satisfaction and loyalty only when the set of attributes measured at check out

is considered; this suggests that their salience gets drowned by attributes measured concurrently

with satisfaction and loyalty. “Ease of finding what you are looking for” and “selection of

products” (site design factors) have different relationships to satisfaction and loyalty suggesting

that recall of these attributes follow a different path than the other attributes.

Methodological issues need to be considered in the interpretation of the results. While the

customer ratings were acquired twice, the temporal association of the second set of attribute

ratings with the dependent variables seems to be the primary reason for the observed

relationship. It would have been useful to obtain measures of satisfaction and loyalty (via

intentions) also at the time of check out; this would have allowed capturing customer evaluations

immediately after interacting with the site.

Once the customer rated the online store after delivery, it is not surprising that

“fulfillment” variables such as “on time delivery”, “product met expectations” became the

dominant attributes influencing online store ratings. These fulfillment variables are biggest

16

challenges to all non-store retailing, including internet retailing. In the physical stores, customers

can touch and examine products before they buy, and can take possession of them once they

make a purchase. However, buyers cannot see, touch and take immediate possession online;

Therefore, it is very important to receive delivery of the expected products at the expected time.

To satisfy online buyers and attract them to repurchase, a website should control out-of-stock

conditions, enable customers to track orders online, deliver on time, ensure that the products

actually match the descriptions on the site, and offer timely support to customers with questions

and problems. This suggests that the most creative, interactive, vivid online site won’t

compensate for weak fulfillment and customer support capabilities.

Limitations and Future Directions

Using secondary data from Bizrate had several limitations, but the following are most

relevant for this analysis. There are more attributes in a transactional website than the attributes

selected by Bizrate; therefore, the analysis could focus on only those selected attributes. Despite

some literature on interactivity as a key attribute, the Bizrate set of attributes can only be linked

to this concept in a limited way. This restricts our ability to relate the findings with the available

literature.

Second, the two dependent variables - satisfaction and loyalty - were both measured with

a one-item scale. Multiple-item scales would have provided more reliability. Eroglu et. al

(2003), for instance, used 4 items to measure satisfaction (alpha=.81) but their research design

involved a hypothetical site and 48% of the respondents had never purchased online. Given the

attractiveness of Bizrate data involving real customers and real retail sites, a longer scale with

multiple items for constructs and additional attributes, would have probably depressed

completion rates.

17

The study design did not include any variables that may have acted as potential

mediators; Eroglu et.al (2002) measured pleasure and arousal which can be more directly related

to site design variables. Similarly, Park and Kim (2003) used “information satisfaction” to

mediate the relationship between specific site related variables and “site commitment”. Future

research would have to make the trade-offs between internal and external validity.

18

Reference:

Baker, J. (1986). The Role of Environment in Marketing Services: The Consumer Perspective. In J. A. Czepiel, C. Congram &J. Shanahan (Eds.) The Services Marketing Challenge: Integrated for Competitive Advantage. Chicago: American Marketing Association, 79-84. Baker, J. A., Parasuraman, A., Grewal, D. & Voss, G.B. (2002). The Influence of Multiple Store Environment Cues on Perceived Merchandise Value and Patronage Intentions,

Journal of Marketing, 66, 2, 120-141. Baker-Prewitt, J., & Sivadas, E. (2000). An Examination of the Relationship

Between Service Quality, Customer Satisfaction, and Store Loyalty. International Journal of Retail & Distribution Management, 128(2), 73-82.

Berthon, P., Pitt L. F., & Watson, R. T. (1996). The World Wide Web as an

Advertising Medium: Toward an Understanding of Conversion Efficiency. Journal of Advertising Research. 36(1): 43-54.

Belsley, D.A., Kuh, E., & Welsch, R.E. (1980). Regression Diagnostics:

Identifying Influential Data and Sources of Collinearity, New York: Wiley. Bitner, M.J. (1992). Servicescapes: The Impact of Physical Surroundings on Customers and Employees. Journal of Marketing. 56, 57-71. Bloemer, J. & Ruyter, K. (1998). On the Relationship between Store Image,

Store Satisfaction, and Store Loyalty. European Journal of Marketing, 32(5/6), 499-513. Burke, R. R. (2002). Technology and the Customer Interface: What Consumers Want in the Physical and Virtual Store. Journal of the Academy of Markting Science. 30, 4, 411-432. Cho, C. H., & Leckenby, J. D. (1999). Interactivity As a Measure of Advertising

Effectiveness: Antecedents and Consequences of Interactivity in Web Advertising. in M. S. Roberts (edt.) Proceedings of the 1999 Conference of the American Academy of Advertising (pp. 162-179). Gainesville, FL: University of Florida.

Corstjens, M., & Lal, R. (2000). Building Store Loyalty Through Store Brands.

Journal of Marketing Research, XXXVII (August), 281-291. Coyle, J. R., & Thorson, E. (2001). The Effects of Progressive Levels of

Interactivity and Vividness in Web Marketing Sites. Journal of Advertising, 30 (Fall), 65-77.

Crockett, R. (2000). Keep ‘em Coming Back. Business week, 3681, May 15;

Industrial/technology edition; pg. EB20. Dholakia, R., Zhao, M, Dholakia N.,, & Fortin, D (2001). Interactivity and

19

Revisits to Websites: A Theoretical Framework. Proceeding of 2001 AMA Winter Conference, vol. 12, 108-115.

Emerick, T. (1995). Media and Marketing Strategies for the Internet: A Step-by-

Step Guide. In E. Forrest & R. Mizerski (eds.), Interactive Marketing. Lincolnwood, IL: NTC Business Books.

Eroglu, S. A., Machleit, K.A. & Davis, L. M. (2003) Empirical Testing of a Model of Online Store Atmospherics and Shopper Responses. Psychology & Marketing. 20, 2, 139-150. Fortin, D. R. (1997). The Impact of Interactivity on Advertising Effectiveness in

the New Media. Unpublished doctoral dissertation., The University of Rhode Island, Kingston.

Ghose, S., & Dou, W. (1998). Interactive Functions and Their Impacts on the Appeal of Internet Presence Sites. Journal of Advertising Research, March/April, 29-43.

Gillespie, A., Krishna, M., Oliver C., Olsen, K., & Thiel, M. (1999). Using Stickiness to Build and Maximize Website Value. Retrieve January 30, 2001, from http://www2000.ogsm.vanderbilt.edu/Student.Projects/stickiness.build.maxmize.site.valu e/stickiness.htm. Gronholdt, L., Martensen, A., & Kristersen, K. (2000). The Relationship Between Customer Satisfaction and Loyalty: Cross-Industry Differences. Total Quality Management, 11, 509-14. Ha, L., & Lincoln, J. E. (1998). Interactivity Reexamined: A Baseline Analysis of

Early Business Web Sites. Journal of Broadcasting and Electronic Media, 42(4), 457-474.

Lii, Y-S, Lim, H.J. and Tseng, L.P. D. (2004). The Effects of Web Operational Factors on Marketing Performance. Journal of American Academy of Business, 5 (Sep), 486- 494. Liu, Y. (2001). Interactivity and Its Measurement. Experiential E-Commerce Conference, East Lansing, MI.

Liu, Y., & Shrum, L. J. (2002). What is Interactivity and is it Always Such a Good Thing? Implications of Definition, Person, and Situation for the Influence of Interactivity on Advertising Effectiveness. Journal of Advertising, 31(4), 53-64.

Marrelli, C. (1996). Anatomy of Web Advertisement. In E. Forrest and R. Mizerski, (eds.) Interactive Marketing. Lincolnwood, IL: NTC Business Books.

McMillan, S. J. & Hwang J. (2002). Measures of Perceived Interactivity: An Exploration of the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of Interactivity. Journal of Advertising, XXXI(3), 29-42.

20

21

Morrison, M., (2001). The Power of Music and Its Influence on International Retail Brands and Shopper Behavior: A Multi Case Study Approach. Retrieved Dec 10, 2002, from http://130.195.95.71:8081/www/ANZMAC2001/AUTHORS/pdfs/Morrison.pdf . Park, C-H & Kim, Y-G (2003). Identifying Key Factors Affecting Consumer Purchase Behavior in an Online Shopping Context. International Journal of Retail & Distribution Management. 31, 1, 16-29. Rafaeli, S. (1988). Interactivity: From new media to communication. In R.

Hawkins, J. Wiemann, & S. Pingree (Eds.), Advancing Communication Science: Merging Mass and Interpersonal Processes (pp. 110-134). Newbury Park, CA: Sage Publications.

Rafaeli, S., & Sudweeks, F. (1997). Networked Interactivity. Journal of Computer

Mediated Communication, 2(4).

Raney, A. A., Arpan, L. M., Pashupati, K. and Brill, D.A. (2003). At the Movies, on the Web: An Investigation of the Effects of Entertaining and Interactive Web Content on Site and Brand Evaluations. Journal of Interactive Marketing, 17 (4), 38-53. Rust, R. T., & Zahorik, A. (1993). Customer Satisfaction, Customer Retention, and Market Share. Journal of Retailing, 69(2), 193-216. Sivadas, E. & Baker-Prewitt, J. L. (2000). An Examination of the Relationship between Service Quality, Customer Satisfaction and Store Loyalty. International Journal of Retail & Distribution Management, 28 (2), 73-82. Wu, G. (1999). Perceived Interactivity and Attitude toward Website. In M. S.

Roberts (eds.), Proceedings of the 1999 Conference of the American Academy of Advertising (pp. 254-262). Gainesville, FL: University of Florida. Retrieved March 6, 2003, from http://www.ciadvertising.org/studies/reports/info_process/perceived_interactivity.html.

Zeithaml, V. A., Parasuraman, A., Malhotra, A. (2002). Service Quality Delivery Through Web Sites: A Critical Review of Extant Knowledge. Journal of Academy of Marketing Science. 30, 4, 362-375. Zhao, Miao (2003). Attribute-Level Interactivity, Satisfaction, and Loyalty in the

Online Environment. Unpublished doctoral dissertation., The University of Rhode Island, Kingston.

Our responsibility is to provide strong academic programs that instill excellence,confidence and strong leadership skills in our graduates. Our aim is to (1)promote critical and independent thinking, (2) foster personal responsibility and(3) develop students whose performance and commitment mark them as leaderscontributing to the business community and society. The College will serve as acenter for business scholarship, creative research and outreach activities to thecitizens and institutions of the State of Rhode Island as well as the regional,national and international communities.

Mission

The creation of this working paper serieshas been funded by an endowmentestablished by William A. Orme, URICollege of Business Administration,Class of 1949 and former head of theGeneral Electric Foundation. This workingpaper series is intended to permit facultymembers to obtain feedback on researchactivities before the research is submitted toacademic and professional journals andprofessional associations for presentations.

An award is presented annually for the mostoutstanding paper submitted.

Founded in 1892, the University of Rhode Island is one of eight land, urban, and sea grantuniversities in the United States. The 1,200-acre rural campus is lessthan ten miles from Narragansett Bay and highlights its traditions ofnatural resource, marine and urban related research. There are over14,000 undergraduate and graduate students enrolled in seven degree-granting colleges representing 48 states and the District of Columbia.More than 500 international students represent 59 different countries.Eighteen percent of the freshman class graduated in the top ten percentof their high school classes. The teaching and research faculty numbersover 600 and the University offers 101 undergraduate programs and 86advanced degree programs. URI students have received Rhodes,

Fulbright, Truman, Goldwater, and Udall scholarships. There are over 80,000 active alumnae.

The University of Rhode Island started to offer undergraduate businessadministration courses in 1923. In 1962, the MBA program was introduced and the PhDprogram began in the mid 1980s. The College of Business Administration is accredited byThe AACSB International - The Association to Advance Collegiate Schools of Business in1969. The College of Business enrolls over 1400 undergraduate students and more than 300graduate students.

Ballentine HallQuadrangle

Univ. of Rhode IslandKingston, Rhode Island