Determinants of e-satisfaction in pre-order and post-order phases.

ABSTRACT

Even though customer satisfaction is crucial for the success of e-tailing businesses, very little is known about the key factors that make online consumers satisfied with their e-retailing experiences. This paper investigated the key factors of e-satisfaction from customer evaluations on reputation systems. Customer ratings on e-tailers were taken as a measure of customer e-satisfaction. Through content analysis, nine key determinants affecting e-satisfaction were identified in the pre-order and post-order shopping phases. A conceptual model of e-satisfaction was proposed. As hypothesized, e-satisfaction increased as the customer perception of an e-taller became more positive and decreased as the customer perception of an e-taller became negative in both the pre-order and post-order phases. The findings showed that the primary determinant in customers' positive reviews was on-time delivery and the primary determinant in the customers' negative reviews was customer support services. These two primary determinants in postorder phase appear to be the most important factors that influence customers' purchase decisions and adoption of e-tailers on the Internet.

1. INTRODUCTION

Bringing customers back is a key to success for online businesses. A very low rate of returning customers has contributed to many dotcom failures. A one-time customer is too costly for a merchant to attract (Oz, 2002). Customer satisfaction is associated strongly and positively with repeat intentions (Patterson, 1997). Future purchase intentions have been found to be a function of prior intentions (La Barbera and Mazursky, 1983) and of customer satisfaction and dissatisfaction (Olive 1980). Consumer comments can be a powerful influence on the purchasing decision of others (McGaughey and Mason, 1998). To encourage repeat purchases, build customer trust and deliver high quality e-service, e-tailers must understand how customers perceive and evaluate e-service quality.

This study investigated key factors in the pre-order and post-order phases that influence the online shopper's e-satisfaction and which lead shoppers to purchase from one site rather than another. Content analysis was conducted on customer online evaluations on a shopping agent Web site. Each customer evaluation included ratings and text comments in terms of two phases: pre-order and post-order. We first developed an e-satisfaction conceptual model from the customer online text comments of our preliminary study. We then empirically tested hypotheses across a broader group of customer online evaluations and identified key determinants affecting customer satisfaction in the pre-order and post-order shopping phases. We conclude the paper by discussing the findings, implications, and directions for future study.

Our study was based solely on the customer online comments and ratings. The findings of the research will add value to strategies designed to argument e-satisfaction and guarantee that e-customers will be satisfied. The results of the research will help e-tailers to understand customer's purchase behavior and to improve service quality and customer retention. In addition, it will provide valuable suggestions for the design and implementation of online evaluation in the agent-oriented e-commerce Web environment. The key determinants identified in the e-satisfaction conceptual model will be especially helpful for the design of online evaluation questionnaires.

2. REVIEW OF LITERATURE

Researchers in the marketing field have studied consumer buying behavior and patterns for years. Preorder and post-order purchase behaviors are generally recognized as important factors in the consumer buying decision process. During the pre-order phase, the consumer will ask, "Will I like this?", or "Is this a good deal?". After the purchase, these questions become "Do I like this?" or "Did I get a good deal?". During these purchase phases, consumers experience some level of satisfaction or dissatisfaction (Spreng et al., 1996). According to La Barbera and Mazursky (1983), satisfaction is a function of the extent to which the customer's perception of the product's performance meets his/her expectation. When product performance meets his/her expectation, he/she will be satisfied with the purchase and will be more likely to purchase the product from the same store again. Therefore, it is very important to satisfy consumers by enhancing the store's relationship with them. A satisfied customer will tell three people about their experience, but a dissatisfied customer will complain to thirty people. The level of perceived satisfaction and dissatisfaction is presumed to have influence on the consumer's attitude, intention and complaint behavior (Bearden and Teel, 1983). Taylor (1974) also indicated that consumers tend to regard information obtained by "word of mouth" as more objective and possibly more accurate.

Although the antecedents to customer satisfaction are well documented in a traditional context (Oliver, 1980; Szymanski and Henard, 2001; Yi, 1990), customer e-satisfaction in e-tailing has not been subjected to conceptual or empirical scrutiny. General levels of e-satisfaction have been reported (e.g. Buskin, 1998; Ernst and Young, 1999), but very little systematic research into the determinants of e-satisfaction has been conducted. Szymanski and Hise (2000) conducted an initial study of e-satisfaction based on four groups of interviews of online customers. Their findings indicated that convenience, site design, product offerings/information and financial security were dominant factors in consumer assessments of e-satisfaction. Shim, Shin and Nottingham (2002) conducted two phases of research on four types of e-tailers whose products were purchased by the respondents they had interviews with. Their findings suggested that two key elements in "positive" retail Web sites were convenience of site use and simplicity of site design for the customer's access to information about product/service characteristics and customer-service policies.

3. CONCEPTUAL MODEL DEVELOPMENT

The conceptual model of e-satisfaction was built with the customer perceptions of online shopping identified in the pre-order and the post-order phases using content analysis. According to Krippendroff (1980), the term content analysis is about 70 years old. Researchers have used this approach to the analysis of documents and texts in order to quantify content in terms of predetermined categories and in a systematic and replicable manner. To categorize customers' comments, it is suggested to use previously created categories as often as possible if they fit the framework of the study (Easwar, 1993). However, since research for model formulation of e-satisfaction in content analysis is relative new and has not been done before, a model for assessing customer e-satisfaction was developed.

On the selected agent based Web site, customers are invited to evaluate the e-taller they patronized. Each customer's evaluation had two parts: text comments and overall e-satisfaction rating. The comments were in text format with a limitation of 500 characters. Overall ratings were displayed by giving the number of stars. A pretest was conducted by the authors prior to this study to develop determinants (categories) for content analysis. Thirty customer online text comments from thirty e-tail stores were randomly selected from the shopping agent Web site.

The content analysis was conducted on the base of the coding scheme. The coding scheme consists of the nine distinctive perceptions captured from individual customers' online comments. The unit of analysis is word and theme. The word is the smallest element or unit used in content analysis. Its use generally results in a frequency distribution of specified words or terms. The theme is a more useful unit to count. In its simplest form, a theme is a simple sentence. The theme is used as unit of analysis if the specified word is not found. The combination of both word and theme is used as a content unit. Nine customer perceptions of their online shopping were identified from analysis of the thirty e-tail stores' customer comments. The conceptual model was the first outcome of the research, in which e-satisfaction is depicted as nine distinct consumer perceptions--Web design, price, promotion, merchandise availability, on-time delivery, met expectations, return policy, customer service and order tracking. These nine perceptions occur in two purchase phases: pre-order and post-order. The conceptual model of e-satisfaction in Figure 1 was built on these nine distinctive perceptions in pre-order and post-order phases.

[FIGURE 1 OMITTED]

The four perceptions that occur in the pre-order phase are Web design, price, promotion and product availability. They are defined as follows.

1. Web design indicates how effective the Web site is. This refers to the ability of customers to get to the web site, find their desired product and check it out with minimal efforts.

2. Competitive price indicates how customers feel about the price they pay for the selected merchandise. On the agent-based Web site, customers can see different prices listed from different e-tailers on a single product.

3. Promotion refers to the feedback of a customer to promotion activities on the e-tailer's Web site (e.g. Web banner, coupons and free shipping).

4. Product availability indicates whether the selected product is in stock and ready for shipping.

The relationships between these four pre-order perceptions and e-satisfaction evidenced in customer online ratings are captured in the following hypotheses:

H1.1. Overall e-satisfaction with e-tailers increases as the four pre-order perceptions become more positive.

H1.2. Overall e-satisfaction with e-tailers decreases as perceptions of the four pre-order perceptions become more negative.

The other five perceptions might occur after customers have made an order and especially when they run into problems. These post-order perceptions are order tracking, on-time delivery, met expectations, return policy and customer service.

1. On-time delivery indicates that customers received merchandise on time or sooner.

2. Met expectations indicate that customers received merchandise in the condition they expected.

3. Return policy refers to customers' feedback to the return or exchange policies of e-tailers.

4. Customer service refers to the quality of service provided by e-tail stores' employees via either phone or e-mail.

5. Order tracking refers to on-line tracking confirmation via e-mail and third party Web sites such as UPS and Federal Express.

The relationships between these five post-order perceptions and e-satisfaction evidenced in customer online ratings are captured in the following hypotheses:

H2.1. Overall e-satisfaction with e-tailers increases as the five post-order perceptions become more positive.

H2.2. Overall e-satisfaction with e-tailers decreases as perceptions of the five post-order perceptions become more negative.

4. METHODOLOGY AND DATA COLLECTION

With the qualitative findings as a foundation, the quantitative phase of research was conducted to test this e-satisfaction model. Overall ratings were used as a measure of e-satisfaction: the degree to which the customer is satisfied/dissatisfied (Oliver, 1980; Zeithaml, Berry, and Parasuraman, 1996). The e-satisfaction is measured by the number of stars on a scale of 1 to 5, with 1 star being the worst and 5 stars being the best. The perceptions captured in the conceptual model were used in our qualitative data. The first step was to investigate how each of the nine perceptions affect customer overall ratings in preorder and post-order phases. The second step was to identify key determinants affecting customer satisfaction in pre-order and post-order shopping phases. The following research questions were asked in this study:

1. What are the primary determinants of customers' overall e-satisfaction ratings in the pre-order phase?

2. What are the primary determinants of customers' overall e-satisfaction ratings in the post-order phase?

4.1 Data Collection

From the same software agent Web site, a sample pool of 419 online stores was identified. Each of them had various numbers of evaluations ranging up to 364. Stores with less than 30 customer evaluations were eliminated from the study. Based on this criterion, 53 e-tail stores were selected for our study. Each of our two data sets consisted of the same 106 customer online evaluations with 2 customers from each of the 53 e-tail stores. Data Set One contains overall ratings and nine positive/negative perceptions of e-satisfaction in the two shopping phases from 106 customers. Data Set Two contains overall ratings and the number of occurrences for each of the nine perceptions of e-satisfaction in two shopping phases from 106 customers. Nine perceptions of e-satisfaction in two phases were grounded from our conceptual model. The data was collected by one of the authors and reviewed by another author.

5. DATA ANALYSIS AND FINDINGS

The data analysis was based on the data recorded in the coding scheme. The results of data analysis were reported separately and in tabulated forms and figures.

Table I shows nonparametric Spearman's correlation between each of the nine captured perceptions and corresponding overall satisfaction ratings from 106 customers for 53 e-tailers. The result represents the correlation between negative perceptions and negative ratings and positive perceptions and positive ratings in the two different shopping phases. Very dissatisfied/somewhat dissatisfied overall ratings were defined as negative ratings. Very satisfied/somewhat satisfied overall ratings were defined as positive ratings.

The first part of Table I indicates that relationships between three out of four pre-order perceptions are significantly correlated to overall e-satisfaction. The overall positive rating with e-tailers increases as the three pre-order perceptions become more positive. The overall negative rating with e-tailers decreases as perceptions of the three pre-order perceptions become more negative. Since the relationship between the perceptions of promotion activities is not significantly correlated to overall e-satisfaction, H1.1 and H1.2 are not fully supported by Data Set One.

The second part of Table I indicates that the relationships between five post-order perceptions are significantly correlated to overall e-satisfaction. The overall positive rating with e-tailers increases as the five post-order perceptions become more positive. The overall negative rating with e-tailers decreases as perceptions of the five pre-order perceptions become more negative. Therefore, H2.1 and H2.2 are fully supported by Data Set One.

Data Set Two was used for the rest of the following study. Table II shows the frequency of distribution of the number of times each perception has occurred in the corresponding positive rating for each customer. Relative frequency is computed as the number of occurrences of each perception divided by 62 positive ratings. In the post-order phase, eighty-four percent of satisfied/satisfied customers showed that their primary concern was to receive products on time. In pre-order phase fifty-five percent of satisfied/satisfied customers showed their primary concern was to purchase a merchandize with a competitive price without any hidden charge.

Table III shows the frequency of distribution of the number of times each perception has occurred in the corresponding forty-four negative ratings. Relative frequency is computed as the number of occurrences of each perception divided by 44 negative ratings. In the post-order phase, eighty-two percent of very/dissatisfied customers showed their primary concern was customer service via phone or e-mail. This reflects that most dissatisfied customers might be irritated by either unfriendly or insufficient customer services. In the pre-order phase, fifty percent of very/dissatisfied customers showed their primary concern was availability of merchandise in stock for delivery.

Interestingly, there was a difference between the primary concerns of customers with positive ratings and that of customers with negative ratings. Figure 2 visually illustrates the comparison of the frequency occurrences of each perception in both positive comments and negative comments in pre-order and post-order shopping phases. Figure 2 shows a frequency of distribution of the number of times each perception has occurred in sixty-two positive comments and in forty-four negative comments. Relative frequency is computed as the number of occurrences of each factor divided by sixty-two positive comments and divided by forty-four negative comments.

[FIGURE 2 OMITTED]

Table IV presents a frequency distribution of 106 overall star ratings classified by the customers overall e-satisfaction level. This exhibit lists the frequency of occurrence of each classification of overall rate. The total number of comments was 106. The percent of total was computed by dividing the number in each level by the total number of overall star ratings and multiplying by 100. There were more positive than negative overall ratings indicating most customers were satisfied with the Internet stores listed on the agent Web site. 58.5% of the total comments were either very satisfied or somewhat satisfied. 41.5% of the total were either very dissatisfied or somewhat dissatisfied. There was no presentation of neutral evaluation. The customers who remained neutral might not be motivated to rate the online store.

6. CONCLUSION

The study concluded that customer post-order perceptions affect e-satisfaction ratings more than customer pre-order perceptions. The primary key factor in negative comments was customer support services in the post-order phase, which was present in 36 out of 44 comments. The primary determinant in positive comments was on-time delivery in the post-order phase, which was present in 52 out of 62 comments.

The results of correlation analysis indicated on-time delivery ([r.sub.s] = .881, p < 0.01) and customer service ([r.sub.s] = .895, p < 0.01) in the post-order phase were significantly correlated to overall e-satisfaction. However, correlation analysis results indicated that only competitive price ([r.sub.s] = .887, p < 0.01) in the pre-order phase was significantly related to overall satisfaction ratings.

Although Zeithaml (2002) proposed an e-service quality conceptual model incorporating a pre-order and a post-order shopping phase aspect, e-satisfaction research has not been done on the pre-order and the post-order shopping phase. The significance of this study is in three aspects: 1) identified the key factors of e-satisfaction in the pre-order and post-order shopping phases, 2) proposed a e-satisfaction model, 3) investigated the correlation between the factors in the pre-order shopping phase and the e-satisfaction rating, and the correlation between factors in the post-order shopping phase and e-satisfaction. No one can observe directly how satisfied a customer is in a certain situation. One can, however, ask this person to verbalize the psychological processes they experience (Schellhese et al., 2000). Customer online text comments describe their shopping experience and reflect their direct feedback to the e-tail stores they patronized. The identified determinants of e-satisfaction should objectively reflect the online customers' insight.

7. DISCUSSION AND IMPLICATION

The study suggested that e-tailers take the identified pre-order and post-order factors into account when they implement their e-stores. It is time for e-tailers to shift their development focus from the pre-order phase to the post-order phase. A tremendous amount of research has been devoted to finding out how to attract customers to an e-commerce Web site (e.g., the use of banner advertisements, referral programs, etc.). Most e-tailers have invested more money in Web design than in any other aspect of the company in order to attract customers to shop in their stores. The most experienced and successful e-tailers are beginning to realize that key determinants of success or failure are not merely Web presence or low price, but instead customer e-satisfaction for the entire purchase experience (Zeithaml 2002). Price and promotion are no longer sufficient to draw customers into making a buying decision. More and more sophisticated online customers would rather pay a higher price to e-tailers who provide high quality service.

Recent research has shown that customers rate most e-merchants average or below average in customer support (Schneider 2002). A common weak spot for many e-tail stores is the lack of integration between the companies' customer service centers and their Web sites. As a result, when a customer calls with a complaint or problem with a Web purchase, the customer service representatives do not have information about Web transactions and are unable to resolve the caller's problems. The following suggestions are provided to help e-tail stores to improve their customer satisfaction:

1. Well-trained live customer service representatives are vital to the existence of e-tail stores. One of their major tasks is to solve the problems of miscommunication between the web site and online shoppers.

2. It is important for an e-tailer to keep its promised competitive price on the Web site to customers. In the negative comments, customers pointed out some customer service representatives of e-tail stores didn't recognize the prices displayed on their web site and tried to sell accessories at an additional price, or charged extra for insurance, service or shipping fees. They really irritated and turned away their own customers.

3. The availability of merchandise should be displayed on the store's Web site. Many complaints from the customers indicated that their orders were not received after the charge had been placed on their credit card account for a long time. The cause of the delay might be an out of stock item or poor inventory management by the store.

This study should also have impacts on the development of agent-based E-commerce Web sites. Since the identified determinants of e-satisfaction objectively reflect online customers' insight, the research results are valuable for the design of new questionnaires for agent-based E-commerce Web sites. The eight significant determinants can be used in an online evaluation questionnaire to measure online customer post-shopping satisfaction. The questionnaire survey will save customers time and will possibly generate more comprehensive customer feedbacks though it may take more storage space than text comments.

The data analysis in Table IV indicates that customers with exceptionally positive or negative views of e-tailers are more likely to respond than the general population. This would result in a biased response that would be more likely to identify current quality problems than a controlled survey of equal sample size (Sampson, 1996). However, this kind of data collection is particularly useful in monitoring service quality in day-to-day operations and in identifying ideas for customer satisfaction improvement. However, further research could be conducted with a questionnaire survey on the portion of customers who did not choose to express or share their shopping experience.

Table I. Spearman's Correlation Coefficients
for the E-Satisfaction Model

Perceptions            Positive/negative   Non parametric
Captured               Occurrences         Correlation
In Comments            (N = 106)           Coefficients

Pre-purchase
Web site design        12                  .657 *
Competitive price      49                  .887 **
Product availability   26                  .737 **
Promotion activities   5                   .612

Post-purchase
Customer service       62                  .895 **
On-time delivery       76                  .881 **
Met expectations       32                  .798 **
Return policy          4                   1.00 **
Order tracking         30                  .538 **

* p < .05 (2-tailed)

** p < .01 (2-tailed)

Table II. Occurrences of Perceptions in Positive Comments (N = 62)

Perception in                 Number of Times         Relative
Positive Comments              Of Occurences          Frequency

Pre-purchase
Competitive price                   34                   .55
Web site design                      5                   .08
Product availability                 4                   .06
Promotion activities                 3                   .05

Post-purchase
On-time delivery                    52                   .84
Customer service                    26                   .42
Met expectations                    23                   .37
Order tracking                      16                   .26
Return policy                        3                   .05

Table III. Occurences of Perceptions in Negative Comments (N = 44)

Perception in                 Number of Times         Relative
Negative Comments            Factors Occurred         Frequency

Pre-purchase
Product availability                22                   .50
Competitive price                   15                   .34
Web site design                      7                   .16
Promotion activities                 2                   .05

Post-purchase
Customer service                    36                   .82
On-time delivery                    24                   .55
Met expectations                     9                   .20
Return policy                        1                   .02
Order tracking                      14                   .32

Table IV. Overall Rating Classified by Satisfaction Level

Level of Satisfaction        Number of Overall         Percent
(Overall Rating)        Rates at Satisfaction Level   of Total

Very Satisfied                      53                  50.0
Somewhat Satisfied                   9                   8.5
Neutral                              0                   0.0
Somewhat Dissatisfied                3                   2.8
Very dissatisfied                   41                  38.7

Total Comments                     106                 100.0

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Author Profiles:

Dr. Ming Wang earned her Ph.D. at Southern Illinois University in 1993. Currently she is teaching in the Department of Information Systems at California State University, Los Angeles.

Dr. Adam Huarng earned his Ph.D. at University of Memphis in 1993. Currently he is teaching in the Department of Information Systems at California State University, Los Angeles.