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Customer expectations in online auction environments:An exploratory study of customer feedback and risk

Byron J. Finch * Miami University, Department of Management, Oxford, OH 45056, United States

Available online 1 December 2006

AbstractThe importance of understanding the needs of the customer is a widely-accepted pre-requisite to providing quality products and

services. For product purchases through traditional channels, customers are known to have expectations for the product they arebuying as well as for the services associated with its purchase. Online transactions, in which the buyer has no prior knowledge of theseller, are becoming increasingly common and are fraught with risks not present in traditional channels. Feedback about sellers inthese risky markets contains a mix of product-related and service-related comments. This exploratory study identies preliminaryrelationships between customers’ emphases on product or service dimensions of quality in their feedback and the risks to which thecustomer is exposed.# 2006 Elsevier B.V. All rights reserved.

Keywords: Quality management; e-Commerce; Service operations

1. Introduction

In recent decades, the management of product andservice quality has become an integral component of good business practice. While distinctions betweenproduct and service quality have been made byresearchers ( Yang et al., 2003; Parasuraman et al.,1988; Fitzsimmons and Fitzsimmons, 2000 ), customersmust often blend the two because products and servicesaccompany each other. Sellers of products oftendifferentiate their products from those of a competitorby bundling services to enhance thepurchase process andbenet the customer after the sale. Examples of businesses involved in such bundling behaviors includecomputer producers (in-home service, warranties),automobileproducers (freemaintenance, free emergency

service, satellite radio), and catalog retailers (freeshipping, shipping alternatives). Retailers have longknown that customers dene quality in terms of bothproduct and service attributes ( Mehta et al., 2000 ).

Researchers have argued that quality should beviewed multi-dimensionally, and that the denition of quality depends on the context ( Reeves and Bednar,1994). Customers are concerned about a variety of issues in their evaluation of quality and the relativeimportance of each depends on the situation. Asbusinesses devote resources to enhancing product andservice attributes in hopes of attracting customers, themulti-dimensional and contextual nature of quality begsthe questions: ‘‘What is most important to thecustomer? Does service-or product-related qualitymatter most?’’ and ‘‘What characteristics of thetransaction context make a difference?’’

Internet retailing is a rapidly-growing channel forbusiness-to-consumer (B2C) transactions. As the

www.elsevier.com/locate/jomJournal of Operations Management 25 (2007) 985–997

* Tel.: +1 513 529 3159; fax: +1 513 529 2342.E-mail address: [email protected] .

0272-6963/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2006.10.007

mailto:[email protected]

http://dx.doi.org/10.1016/j.jom.2006.10.007

http://dx.doi.org/10.1016/j.jom.2006.10.007

mailto:[email protected]

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Internet’s popularity as a business channel hasincreased, customers have demonstrated their penchantfor quality goods and services there, as well. Despite thefact that Internet transactions have become common, aremaining characteristic of the Internet retail environ-ment is the risk perceived by customers. This is true forwell-known Internet retailers, but as one moves fromwell-known retailers to completely unfamiliar mer-chants, the perceived risks for buyers increase. Theseonline markets, in which the buyer and seller have noprevious relationship, differ signicantly from tradi-tional retail markets. Despite the perceived risks, onlinemarkets with these characteristics have become quitecommonplace. For example, customers increasingly useshopping robots to seek the lowest prices, which areoften offered by a merchant the customer has neverheard of. Third party merchants sell books, CDs, and

other products through Amazon.com’s storefront.Online markets in which the buyer has no previousrelationship with the seller are probably best exempli-ed by online auctions, which involve millions of buyerand seller pairs who are unfamiliar with each other.

This study explores the online auction marketenvironment to determine if customers’ quality-relatedexpectations – more particularly their emphasis onservice- or product-oriented quality dimensions – varyas the context of Internet auction transactions vary. Thecontextual focus is on risk, the characteristic of the

online auction market that most signicantly distin-guishes it from more traditional markets. The study alsoidenties preliminary relationships between the custo-mer’s denition of quality (as expressed by theiremphasis on product- or service-oriented dimensions)and the context (the level of risk to which the customeris exposed). The source of the data is the feedback frombuyers on eBay, the largest Internet auction house. It isthat source of data that makes this work particularlyappropriate for this special issue on Innovative DataSources for Empirically Building and ValidatingTheories in OM.

2. Literature review

2.1. Theory building

Exploratory research is an important component of the theory development process. Handeld and Melnyk (2003) describe the theory-building process as begin-ning with discovery and description , proceeding tomapping , and then to relationship building, theoryvalidation and theory extension/renement . Exploratory

research is intended to expand the map of existing

theory and identify new variables, which form theempirical foundation that links theory to reality(Eisenhardt, 1989; Flynn et al., 1990 ) and guide futureresearch. The contribution of this study is one of mapping and relationship building. It denes quality inthe online auction context through the identication of ambiguity and price as variables that inuencecustomers’ quality focus. This literature reviewexamines previous theoretical developments in threepertinent areas: dening quality, seller reputation andfeedback, and risk.

2.2. Dening quality

Quality has been widely accepted as an importantcomponent of value ( Cravens et al., 1988; Zeithaml,1988 ), but scholars have not converged on a single

denition ( Sousa and Voss, 2002 ). They have, however,distinguished between dimensions of product qualityand dimensions of service quality. Garvin’s (1984)product quality dimensions of performance, features,etc. provide a foundation for understanding the scope of product quality. Parasuraman et al. (1988) andFitzsimmons and Fitzsimmons (2000) include suchattributes as reliability, responsiveness, assurance, etc.to form the basis for understanding service quality. Thelist of service quality dimensions has been modied byGronroos (1988) , Silvestro and Johnston (1990) , Finn

and Lamb (1991) , Collier (1991) , Cronin and Taylor(1992) , and others. Recognizing that some dimensionsof service quality may not be relevant in the onlinecontext, Zeithaml et al. (2001) , Burke (2002) , Madu andMadu (2002) , Parasuraman and Zinkhan (2002) andothers have proposed additional dimensions of onlineservice quality that address website design andperformance issues. These dimensions include suchthings as the availability of product prices, productspecications, access to customer service, etc. ( Burke,2002 ).

In an agenda for future quality research, Sousa andVoss (2002) recommended two points for consideration.First, they recommend the use of multi-dimensionalmeasures of quality, stressing (from Garvin, 1984 ) thatcompetitive advantage results from a match between theimportance a market assigns to a particular qualitydimension and the organization’s performance on thatdimension. Second, and of greater importance for thepurposes of this study, Sousa and Voss (2002)recommended the use of different denitions of qualityin different contexts, encouraging researchers to focuson dening quality in a way that encompasses the

relevant dimensions for the organization’s output.

B.J. Finch/ Journal of Operations Management 25 (2007) 985–997 986

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in frequency of product-oriented and service-oriented feedback across the four contexts.

Second, it attempts to identify preliminary relation-ships between the context and whether customersemphasize product-or service-oriented dimensions of quality. Beyond simply observing differences voiced inseller feedback, if relationships between the context andfeedback content exist, one would expect to see patternsthat indicate relationships between feedback contentand the customer’s risk exposure.Proposition 2. When examined in aggregate, relation-ships between the risk context and the frequency of product-oriented and service-oriented feedback will beapparent.

Proposition 2a. The importance of service-oriented dimensions of quality will be greatest at low levels of risk.

Proposition 2b. The importance of product-oriented dimensions of quality will be greatest at high levels of risk.

3. Research methodology

3.1. Data collection

eBay’s feedback forum provided the data used in thisstudy. A brief overview of eBay is important to fullyunderstand this information. eBay is an online auctionhouse established in 1995. It has grown rapidly to over48 million active users and over 12 million items listed

at auction in more than 18,000 categories. eBay

estimates that more than 430,000 people make mostor all of their income through its auctions. Revenues for2004 were projected to be $3.4 billion, on $32 billionworth of merchandise. That makes it the largest onlinemarketplace. If it were a retailer, it would be nearly thesize of Lowe’s ( Siskos and Stevenson, 2003; eBay’sSecret, 2004 ).

eBay takes no ownership of products sold, actingstrictly as the seller’s agent, nor does it provideguarantees related to the quality of products andservices offered or the reliability of the sellers or buyers.eBay’s revenues come from listing fees and commis-sions. Upon completion of an eBay transaction, awinning bidder is allowed to post a feedback message of no more than 80 characters in eBay’s Feedback Forum.Online auction buyers dene their seller feedback aspositive (I’m satised), negative (I’m not satised), or

neutral (I have mixed feelings), followed by a commentof their own, which can often be interpreted as ‘‘andhere’s why . . ..’’ Despite the fact that a product is beingpurchased, feedback frequently mentions serviceaspects of the transaction. The entire feedback recordof a member is translated into a quantitative score(positive = 1, neutral = 0, negative = À1).

In this research, high-price products are dened asthose having prices in the range of $300 to $1000. Low- price products are dened as those having prices lessthan around $25. Low-ambiguity products are dened as

being identical from one alternative to another. They arebrand new and in the original package, and two productswith the same descriptions but being offered by twodifferent sellers can safely be assumed to be identical. High-ambiguity products , on the other hand, are denedhere as products that are not exactly like alternativesthat may be described similarly. They may be used,second-hand, antique, or even one-of-a-kind items.Therefore, two high-ambiguity products describedsimilarly cannot be assumed to be identical. The low-price/low-ambiguity (LPLA) product has the leastamount of inherent buyer’s risk, while the high-price/ high-ambiguity (HPHA) product presents the greatestrisk exposure.

For each of the four risk contexts identied inFig. 1, the researcher selected a representative eBayproduct category. Within each product category,sellers were identied from auctions in progress.The rationale used to identify the four representativeeBay product categories follows. For low-ambiguityproducts, the broad eBay category topic of computersand electronics was selected because eBay is host tomany sellers of new and factory-refurbished products.

Previous research ( Houser and Wooders, 2000; Ba and


Fig. 1. Transaction risk context as a function of product price andambiguity.

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Pavlou, 2002 ) on reputation systems used computer-oriented products with success. The classication of transactions in this category as low-ambiguity isnot to suggest that all computers sold are identical. Itmerely indicates that the buyer, upon reading thedescription of the item, knows precisely what theproduct is. Another advantage of this category is thatit contains a broad range of prices, making it possibleto easily distinguish between low- and high-priceproducts.

For the low-price low-ambiguity (LPLA) product,the eBay category of electronics and computers/ consumer electronics/PDAs was selected. eBay breaksthat category down into subcategories by PDA brandname, such as Palm, Dell, Sony, and Hewlett-Packard.Each PDA brand category includes a subcategory foraccessories . Sellers of LPLA products were identied

from the accessories categories of the various PDAbrands. Sellers offered PDA cases, antennae, chargers,and other accessories that sold for less than $25. Productlistings were required to have the words ‘‘New,’’ ‘‘Mintin Box,’’ ‘‘Factory Refurbished,’’ ‘‘Factory Sealed,’’‘‘New in Box (NIB),’’ ‘‘With Warranty,’’ or otherindication in the product description that the productwas, in fact, not used.

eBay’s electronics and computers/computers and ofce/laptops category was the source for the high-pricelow-ambiguity (HPLA) risk condition. Sellers specia-

lizing in laptop sales across the various brandsubcategories of Apple, IBM, Compaq, Acer, Dell,Gateway, etc. were identied. Indications that theproducts were new or refurbished and not used, werealso required. Prices of laptops ranged from $400 toover $1000.

Coins were selected as the high-ambiguity productbecause they are sold in various conditions that play acritical role in determining value. Coins have also beenused successfully in previous research ( Melnik andAlm, 2002 ) on reputation systems. Another advantageof coins is that coin sellers specialize in ways thatreduce price variability for individual sellers. Notsurprisingly, the coins sold by sellers specializing inLincoln cents sell for lower prices than the coins of sellers specializing in gold coins.

The eBay category of coins/coins:US/Lincoln Wheat 1909–1958 and coins/coins:US/Lincoln Memorial1959-now provided the source for low-price high-ambiguity (LPHA) product seller feedback. Thesesellers were high-volume collectable penny merchants.Coins sold by these sellers were typically less than $25.

The eBay category of coins/coins:US/gold was used

for high-price high-ambiguity products. Sellers of coins

for collector value, rather than for bullion value, wereidentied. Products sold by these sellers typically soldfor amounts between $300 and $1000.

To ensure that the seller was an actual business,rather than someone just cleaning out a closet, small-volume sellers (those with low feedback ratings) werescreened out. eBay calculates the feedback rating bysumming the unique positive feedbacks (+1) and theunique negative feedbacks ( À1). For three of the fourproducts it was possible to only use sellers with afeedback rating of greater than 1000. A rating of 1000means that the seller had a minimum 1000 sales, butgiven the typical feedback response rate of approxi-mately 50–70%, probably had more than 1000. Sellersof laptops did not deal in the high volumes of the otherproducts, so they were included if they had a minimumfeedback rating of 600.

For each product, twenty sellers with adequatefeedback ratings were identied, and the most recent 50positive feedback posts for each were gathered. Theselection of twenty sellers for each of the four risk contexts and the most recent 50 positive feedbacks foreach of the sellers ensured that each risk category hadthe same total number of feedback posts (1000) and thatthey were for high-volume sellers. Going to a greaternumber of sellers would have meant using sellers thathad smaller sales volumes.

3.2. Describing and measuring feedback content

Each feedback post was classied on one of fourfeedback variables:

- service content only- product content only- both service and product content (if both were present)- nonspecic.

Service content-only feedback contained commentsthat were associated with the service received andincluded such issues as shipment or delivery speed,packaging, communication, and problem resolution, aswell as comments that specically praised the transac-tion or service. Product content-only feedback con-tained comments addressing issues such as how goodthe product was, whether the product was exactly asdescribed, or that mentioned the product’s condition.Both service and product content feedback containedcomments relating to both the service and the product.Nonspecic feedback content provided no specicproduct-or service-oriented comments but praised the

seller or just gave the seller a grade. Examples of


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classied feedback are presented in Table 1 . For the rsttwo feedback posts in Table 1 , since service-oriented

and product-oriented comments were present, it wasclassied as both service and product-oriented. Becausethe general content of the third feedback post did notspecify product or service, it was classied as non-specic. The fourth and fth feedback posts wereproduct-only.

Feedback classication was performed indepen-dently by two-trained classiers. Both were providedan explanation of the four feedback variables and theclassication process and were provided writteninstructions for classication. Sample key wordswere provided for service feedback, product feed-back, and nonspecic feedback. Examples of actualfeedback that had been correctly classied were usedin the training process as well. Each classiercompleted all 4000 feedback items. While it iscommon to examine inter-rater reliability whenmultiple raters are used for scaled or numerical data,the use of multiple raters for categorical data does notlend itself to the same treatment. Percent agreement isoften used as an indicator of consensus for categoricaldata classication. The two classiers agreed on theclassication of 84.4% (3496 out of 4000) feedback

posts. After comparison of the two sets of feedback,

and the identication of inconsistencies, the classi-ers were asked to re-visit each item that they

disagreed on and come to consensus on theclassication of each of those items. The tests of propositions were then performed on the completeset of classied data. The actual frequencies for eachof the four feedback variables are presented inTable 2 .

4. Results

4.1. Introduction

The analyses were completed in two steps, corre-sponding to the two propositions. The rst addressedProposition 1 , to determine if there were differences infeedback content (serviceversus product-orientation)across the four risk contexts. The second addressed theProposition 2 , that relationships between the risk context and the frequency of product-oriented andservice-oriented feedback content will be apparent.More specically, that the importance of service-oriented dimensions of quality will be greatest at lowlevels of risk ( Proposition 2a ) and the importance of product-oriented dimensions of quality will be greatest

at high levels of risk ( Proposition 2b ).


Table 1Example feedback and classication

Service-only

Product-only

Both service-and product-orientedtransaction

Non-specic

Praise: Nice Laptop! Great customer service! 1 1 1Praise: Excellent Service!! Perfect Product!! Fast shipping!! AAA+++ 1 1 1Praise: Very Pleased! Very happy with purchase 1Praise: GREAT LAPTOP!HONEST SELLER!!! 1Praise: Item in nice shape 1Praise: Product as described, excellent deal, super fast delivery aaa+++ 1 1 1Praise: Good Seller 1Praise: Excellent product for the price 1Praise: Great customer service!! Took care of problems quickly! 1Praise: Great emails and packaging. I’ll buy from him again 1Praise: THE BEST 1

Table 2Frequencies for feedback variables

Non-specic Service-oriented only Product-oriented only Both service-and product-oriented

1000 Low-price low-ambiguity 148 468 69 3151000 High-price low-ambiguity 148 330 113 4091000 Low-price high-ambiguity 143 299 165 3931000 High-price high-ambiguity 179 272 154 395

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4.2. Feedback differences between risk contexts

In the analysis for Proposition 1 , a chi-square testwas performed on the product-only and service-onlyfrequencies across all four risk contexts (shown asLPLA, HPLA, HPHA, and LPHA in Fig. 2). Testingwas limited to the product-only and service-onlyfeedback because those were the only categories inwhich the customer could be shown to have a clearemphasis on one or the other. Feedback that mentionedboth product and service issues demonstrated acustomer’s interest in both, but no emphasis on oneor the other could be deduced.

The results of the chi-square analyses of feedback variable frequencies among the four risk contexts arepresented in Table 3 . The high chi-square value (90.8)relative to the critical value (11.3449) demonstrates

with near certainty ( p < .01) that the relative impor-tance of serviceversus product dimensions, as exhibitedby the proportions of service-only and product-onlyfeedback, differs among the four risk contexts shown inFig. 2. This supports Proposition 1 .

4.3. Preliminary relationships between risk and customer feedback content

The chi-square test performed for Proposition 1indicates that at least one signicant difference exists

among thefrequencies of theproduct only or service onlyfeedback proportions among the four risk contexts. Itdoes not identify which feedback variables were actuallydifferent. For the second phase of this analysis – theidentication of preliminary relationships between theservice-or product-orientation of the feedback contentand risk context suggested in Proposition 2 – theMarascuilo procedure ( Berenson et al., 2002; Maras-cuilo, 1966 ) was used as a follow-up to the chi-square.

The Marascuilo procedure compares all pairs of proportions by comparing the absolute values of theproportion differences for each pair to a distinct criticalrange at the desired level of signicance. A pair of feedback frequencies, each expressed as a proportion of 1000, are deemed signicantly different from each otherif the absolute difference exceeds the critical range forthat pair. Thus, conclusions can be drawn as to which of the four risk contexts have signicantly more or lessproduct-only or service-only feedback out of the 1000

possible for each. For example, if the frequency of product-only feedback (expressed as a proportion of 1000) in the LPLA risk context is less than that of theproduct-only feedback in the HPLA risk context, theMarascuilo procedure determines whether that differ-ence is signicant. The procedurewas performed withinthe product-only and service-only feedback frequenciesseparately, comparing frequencies for each pair of risk contexts at p < .01.


Table 3

Results of chi-square analysisLow-pricelow-ambiguity

High-pricelow-ambiguity

Low-pricehigh-ambiguity

High-pricehigh-ambiguity

Service only frequencies 468.00 330.00 299.00 272.00Service only expected frequencies 393.13 324.31 339.69 311.87Service only chi-square values 14.26 .10 4.87 5.10

Product only frequencies 69.00 113.00 165.00 154.00Product only expected frequencies 143.87 118.69 124.31 114.13Product only chi-square values 38.96 .27 13.32 13.93

chi-square = 90.8Degrees of freedom = 3 p = .01 critical value = 11.3449

Fig. 2. Percentages associated with feedback frequencies.

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Table 4Summary of marascuilo analysis: pairwise comparisons of frequency of service only feedback across risk contexts

Risk contexts being compared LPLA vs. HPLA LPLA vs. LPHA LPLA vs. HPHA HPLA vs. LPHA HP

Actual frequenciesbeing compared

468 & 330 468 & 299 468 & 272 330 & 299

Critical range calculationsfor p < .01 .073 .072 .071 .070

Absolute values of proportion differences

.138 .169 .196 .031

Signicant difference(abs value of difference> critical range)

p < .01 p < .01 p < .01 -

Interpretation Frequency of serviceonly feedback inLPLA > HPLA

Frequency of serviceonly feedback inLPLA > LPHA

Frequency of serviceonly feedback inLPLA > HPHA

No signicantdifference betweenHPLA and LPHA

Table 5Summary of marascuilo analysis pair wise comparisons of frequency of product only feedback across risk contexts

Risk contexts being compared HPLA vs. LPLA LPHA vs. LPLA HPHA vs. LPLA HPLA vs. LPHA HP

Actual frequenciesbeing compared

113 & 69 165 & 69 154 & 69 113 & 165

Critical rangecalculationsfor p < .01

.043 .048 .047 .052

Absolute values

of proportiondifferences

.044 .096 .085 .052

Signicant difference(abs value of difference> critical range)

P < .01 p < .01 P < .01 -

Interpretation Frequency of productonly feedback inHPLA > LPLA

Frequency of productonly feedback inLPHA > LPLA

Frequency of productonly feedback inHPHA < LPLA

No signicantdifference betweenLPHA and HPLA

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feedback posts by buyers in low-risk contexts does notnecessarily mean that those buyers submitting service-only feedback do not care at all about product quality.Likewise, in high-risk contexts the increased emphasison product quality issues does not mean that servicequality does not matter. It more likely indicates that theyare more attentive to service issues or more attentive toproduct issues. Carman’s (1990) contention that therelative importance of quality attributes in differentsituations is an important component of the qualityevaluation is probably at play here. The work byChowdhary and Prakash (2005) that examines satisersand dissatisers may be relevant. It could be, forexample, that under high risk conditions when positivefeedback is dominated by product-oriented feedback,negative feedback could be dominated by service-oriented feedback.

5.2. Managerial implications

The results of this study provide online auctionmerchants with specic direction and may haveimplications for service system design in this environ-ment. Managers selling predominantly low risk products should be aware that customers will viewservice-oriented quality dimensions as most important.Conversely, managers selling mostly high-risk productsshould be aware that customers will focus on product-

oriented quality dimensions. Those who sell a mix of high-risk and low-risk products may need to adjust theirbusiness processes to align themselves with customerexpectations appropriately, depending on the product.These relationships may also provide ways formerchants to reduce costs by not providing benetswhich are not valued by the customer.

Wemmerlov (1989) distinguishes between uid(exible) and rigid (inexible) service processes.Dynamic customer expectations resulting from varyingproduct price and ambiguity may indicate a need foruid processes among those sellers whose wares vary onrisk dimensions, while rigid processes may sufce forsellers with a very homogeneous product line. Amerchant with a broad product line of computer-relatedproducts, for example, may need to provide fast deliveryfor low-price products in order to meet customerexpectations, but may not need fast delivery for high-price products.

Greater knowledge of customers’ expectationsmakes it easier to meet those expectations. Such serviceattributes as fast shipping, communication, and problemresolution will be important for a low-risk product. A

service design that effectively provides those services

will be viewed positively by buyers, and should beconsidered by those sellers. For high-risk products,however, dollars spent to reduce delivery time orenhance communication may not be viewed bycustomers as important.

In the high-risk context, the customers’ risk is thatthe product will not be what is expected, and thatexposure increases as the price increases. This risk canbe reduced by the seller in several ways. The productdescription should be complete and detailed, commu-nicating as many important details about the product aspossible. Adding high-quality photos that provideextensive information about product condition wouldalso reduce ambiguity. The seller can also encouragepotential buyers to ask questions if they have them, andoffer to provide more details or photos off-line. In somecases, particularly with some common collectible

products, third-party graders provide services that canbe used to remove product ambiguity.

6. Limitations and future research

6.1. Limitations

The major limitations of this study are tied to twoissues—the data source and the generalizability of theresults. The use of this unobtrusive data collectionmethod has the obvious advantages of data richness and

the data not being affected by the collection methods orexperimental design. It brings limitations, however,when compared to the more traditional approaches likesurveys and experiments. More traditional methodsallow the researcher to establish controls that may makethe data a better t for analysis, but the researcher’sinvolvement with the data creation may also contam-inate it. An unobtrusive measure eliminates thatcontamination, but the data, as exemplied here, areunlikely to be as ‘‘neat’’ as the researchers would like.Unlike a survey, for example, which is designed withthe analysis in mind, this mode of data collectionrequires more effort to convert the raw data to a formthat can actually be analyzed. The researcher must thenmake sure that that conversion is unbiased, requiringmultiple interpreters, etc. Data of this type are also moresuited to broader propositions and research questions,rather than tests of more specic hypotheses possiblewith surveys or experiments designed with a particularhypothesis-testing goal in mind.

A second potential limitation is the generalizabilityof these results beyond eBay. eBay was selected as themarket context, but it may have other characteristics

that affect outcomes. For example, it is possible that an


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auction rather than a xed-price market, may have animpact on expectations. There was no evidence infeedback content, however, that mentioned that auctionpricing as an issue. It would seem likely, however, thatcustomers in an auction market would be more willingto take on risk than customers in a xed-price market.Feedback is archived and accessible in several xed-price markets (Amazon, for example, or eBay’s xed-price Half.com), so the study could easily be replicatedin that environment to see if these results weregeneralizable to xed-price markets.

The current trend is towards more markets with thesecharacteristics rather than fewer. In the early days of theInternet, the characteristics of online shoppers weresignicantly different from the characteristics of traditional shoppers. Those differences have disap-peared. As transaction characteristics associated with

these types of markets become more commonplace, thegeneralizability and importance of this data shouldincrease as well.

Generalizability beyond the business-to-consumer(B2C) auctions to business-to-business (B2B) auctions,is an issue that could have far reaching implications.The characteristics of many B2B auctions, however,differ from B2C auctions like eBay. The two environ-mental characteristics that distinguish the B2C onlineauction – lack of a relationship between the buyers andsellers and product ambiguity – do not exist to the same

degree in B2B online auctions. The desire to avoid therisk inherent with ambiguous products leads B2Bauction users to purchase goods or services which lendthemselves to specications. In fact, B2B reverseauctions have been shown to be a less desirablepurchasing solution when complexity, as dened by thenumber of non-price issues germane to the transaction,increases ( Gattiker et al., 2005; Burt et al., 2003 ).Kaufmann and Carter (2004) contend that ‘‘specicity’’of the product is the most important factor in selectingan online reverse auction as a good purchasing t.

The relationships between buyers and sellers in theB2B auction setting also differ from those in the B2Cauction characterized by eBay. In many B2B auctionscenarios, buys and sellers are familiar with each otherand engage in repeated transactions. The use of reputation systems, like eBay’s feedback archive, iscritical to the eBay market because of the lack of ongoing relationships between buyers and sellers andlow occurrence of return customers. Sellers areconcerned about feedback because the feedback formalizes their reputations to prospective buyers. Ina B2B setting, actual experience is more likely to form

the reputation sellers have with buyers.

6.2. Future research

This study succeeded in the most important objectivesfor anyexploratory project. It began to mapnew territory,identied variables that seem to make a difference so thatfuture research could better focus, and it exposed morequestions to guide future research.

The rst question resulting from this study deals withmore precise denitions of quality. The relationshipsidentied between risk and the customer’s emphasis onproduct-or service-oriented dimensions point to custo-mers dening quality differently in different contexts.The customer’s denition of quality may, however, gobeyond the product-or service-oriented emphasis andinclude specic dimensions of service or productquality. Now that a link between risk and theproduct-or service-orientation of the customer’s quality

emphasis has been identied, future research can extendthe denitions to specic service or specic productquality dimensions that are most important to thecontexts of the online auction environment. A test of thevalidity of traditionally accepted product and servicequality dimensions, as well as quality dimensions morerecently developed as more relevant to e-markets,would be a good starting point for that project.

Another set of questions that arise from this researchis related to negative feedback. In this study, theevaluation of positive feedback provides a sense of what

customers view as important. Customer complaintscould also add to that knowledge. Negative feedback isalso a component, although for most sellers a small one,of seller reputation. Such parallel questions as: ‘‘What isthe content of negative complaints?’’ ‘‘Is negativefeedback the mirror image of positive feedback?’’ and‘‘Is negative feedback content associated with risk?’’need to be answered to gain a full understanding of customer expectations in these risk-prone transactions.The validity of two-factor theory (satisers anddissatisers) examined by Chowdhary and Prakash(2005) could be tested by including negative andpositive feedback in the feedback analysis.

eBay takes feedback a step further by allowing bothbuyers and sellers to respond to feedback they receive.Sellers rarely respond to positive feedback from buyers,but seller responses to negative feedback from buyers arevery common. Research on traditional quality-relatedfeedback indicatesthat negative feedback canbe themostvaluable feedback for a traditional business because itprovides direction for improvement. A cursory review of sellers’responsesto negativefeedbackfromeBaybuyers,however,shows that negative feedbackisnotviewedasan

opportunity for improvement in this environment. More


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often than not, a seller’s response to negative feedback from a buyer seems to blame the buyer for the problem,rather than to take responsibility for it. Many sellers usethe response as an opportunity to warn other sellers not tosell to that particular buyer, because of the possibility of receivingnegativefeedback.Eventhoughmanysellers inthis market compete with traditional businesses, thenature of the communication exchange between theonline business and dissatised buyers appears to bedifferent. The service recovery practices common intraditional businesses seem absent here. Future researchshould seek to determine how relationships betweenbusinesses and customers differ in these environmentsand the role, if any, service recovery plays.

This research was made possible by the availabilityof a growing source of qualitative data—arguably thebiggest archive of customer feedback on the planet. The

outcomes of this study extend theory relevant to qualitymanagement and service system design. As thesemarkets grow with increasing use of shopping bots andsales of xed-price used goods, the number of buyer’sexposed to these risks increase. Recognizing thatdifferent customers value different aspects of atransaction is not new, but the ability to link customerexpectations to risk associated with particular productsopens up new terrain for managing quality in thisgrowing environment. Understanding the implicationsthose risks have for sellers’ operations will be important

if those markets are to prosper.

Acknowledgements

The author acknowledges the valuable suggestionsfrom editors Diane Parente and Tom Gattiker, as well asthose from the anonymous reviewers.

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Byron J. Finch is a professor of management at Miami University inOxford, Ohio. He received his PhD in operations management fromThe University of Georgia in 1986. He has taught undergraduate andgraduate courses in operations and supply chain management. Dr.Finch’s research interests involve supply chain management, serviceoperations, and the utilization of the Internet to improve product and

service quality. He has published in Journal of Operations Manage-ment , International Journal of Production Research , Academy of Management Journal , Quality Management Journal , International Journal of Quality and Reliability Management , and Production and Inventory Management Journal . He has also authored and co-authoredseveral texts in operations management.


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