E-reverse auction design: criticalvariables in a B2B context

Davide Aloini, Riccardo Dulmin and Valeria MininnoDepartment of Electric Systems and Automation,

University of Pisa, Pisa, Italy

Abstract

Purpose – This paper attempts to provide an empirical cross-industrial study on critical successfactors impacting on “price” and “process” performance in business-to-business (B2B) e-reverseauction design.

Design/methodology/approach – Based on an online survey to a panel of academic experts andpractitioners, the paper presents the empirical validation of a previous conceptual model using aconfirmatory factor analysis (CFA) approach.

Findings – Results demonstrate that a multi facet construct consisting of six main dimensionsimpacts on e-auction performance. Moreover, these dimensions differently impact on price and processperformance.

Research limitations/implications – Because of the complexity of the framework, the sample sizeand the qualitative nature of experts’ observations, results should be seen as more indicative thanconclusive and therefore generalization should be additionally tested.

Practical implications – Findings provide useful information for the formulation of managerialdecisions in designing the auction event/process and supporting the definition of different negotiationstrategies.

Originality/value – This article is a first attempt to test a conceptual framework on critical factorsimpacting on e-reverse auction performance in a B2B context. A lot of conceptual papers try tosystematize the numerous variables affecting e-auction success and their complex relationships into asingle comprehensive framework; nevertheless there is a lack of empirical evidence supporting thesemodels especially in the B2B context.

Keywords E-reverse auction, Performance determinants, Conceptual framework, Validation,Confirmatory analysis, Pricing, Auctions

Paper type Research paper

1. IntroductionAn e-reverse auction (e-RA) is:

[. . .] an online, real-time dynamic auction between a buying organization and a group ofpre-qualified suppliers who compete against each other to win the business to supply goodsor services that have clearly defined specifications for design, quantity, quality, delivery, andrelated terms and conditions. These suppliers compete by bidding against each other onlineover the Internet using specialized software by submitting successively lower priced bidsduring a scheduled time period (Beall et al., 2003).

Compared to traditional negotiation mechanisms in purchasing processes, thesignificant benefits and savings which e-RAs have delivered to many companies inrecent years, both in terms of transaction cost reduction and impact on businessprofitability (Klein, 1997; van Heck, 1998), have led many consulting firms to hype theadvantages of reverse auctions and powered their popularity (Brunelli, 2000). Therising number of business service providers addressing eSourcing and eAuctioning

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Business Process ManagementJournal

Vol. 18 No. 2, 2012pp. 219-249

q Emerald Group Publishing Limited1463-7154

DOI 10.1108/14637151211225180

services to industrial companies is today an evident proof of firms’ expectation andinterest in the e-auction phenomena.

The business-to-business (B2B) context, in particular, is a very interestingenvironment in which e-RA can take place which is usually more complex anddynamic than traditional ones. In that context, in fact, several conditions must exist inorder to obtain an effective execution of an e-auction process and its appropriateintegration with the company’s purchasing process (e.g. selecting the right product,assessing the appropriateness of the supply market). Such conditions for successfule-RAs are spread throughout the literature and almost all authors provide suggestionsbased on their experience, interviews or case studies (Wagner and Schwab, 2004). Atthe best of our knowledge, however, most of the studies face the problem from a priceperspective and neglect the process dimension which can instead assume a strategicvalue in B2B transactions. Emiliani and Stec (2002), argued that some savings wouldbe lost due to various factors associated with the award decision, data integrity andadditional business expenses.

This evidence underscores the need to further analyze the mechanism of the onlineauction and to investigate what really matters in B2B e-RA design (Klemperer, 2002).In fact, even if auction theorists have made important progress on these topics forexample in economic theory, auction theory, finance and law, most of thesecontributions are difficult to apply for actually designing auctions.

The main purpose of this research is to identify and examine factors explainingprice and process performance in B2B e-RAs and to expand the scope of information inthis field in order to study e-RA feasibility, supporting the formulation of managementactions during the e-auction design phase. With this aim, given the literature reviewed,a performance model is proposed. Here, based on an online survey to academicsexperts and practitioners from four European Business Providers, the model has beenvalidated by a confirmatory factor analysis (CFA).

The value of this research is first academic since its will to classify, systematize andempirically test the numerous variables affecting e-auction suitability and finalperformance determination. Moreover, from a managerial perspective, it wouldsupport decisions in the e-auction design process differentiating, in particular, mostrelevant determinants according to a price and process view. For these reasons bothacademicians and practitioners (e.g. auction managers/designer from business serviceproviders; buyers and sellers from firms participating to e-auctions) can get value fromthe potential theoretical and practical contributions of this work.

The paper is structured as follows. First, an overview of the theoretical foundationsof e-RA, B2B context and potential critical factors is presented. Next, after presentingthe research framework, objective and methodological approach, the empirical modelvalidation is performed. Finally, the results are reported and the paper concludes witha discussion of the theoretical and managerial implications of performance drivers inB2B e-auction design, limitations and further researches.

2. Theoretical background2.1 E-auction in the B2B contextIndustry practice in designing and running online auctions presents some relevantdifference with other market (B2C, consumer-to-consumer (C2C)) context. A properly

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designed and executed online auction constitutes more than collecting a few suppliers,proving their bids in an open auction and picking the lowest price (Vogt, 2005).

According to Chen and Wilson (2002), in fact, three sub-groups of online auctionmarket can be distinguished according to buyers and sellers characteristics:consumer-to-consumer (C2C), business-to-consumer (B2C) and B2B. In the C2Ccontext, public marketplaces enable person-to-person exchange, helping buyers andsellers to deal with transaction, billing, shipping and payment. In the B2C context,business players use digital market place to sell directly to final customers.

B2B auctions, instead, refers to exchange between business players which executeauctions for resale items or for their own procurement; these are in general reverseauctions and take part to a more complex sourcing process. Here, auctions supplierrelationships, market and item knowledge, quality and delivery performances assumea relevant meaning in winner determination. Buyers theoretically operate according tothe total cost of ownership evaluation, they usually consider suppliers diversity underdifferent aspects and attributes. Auctions are part of a more complex and extendede-sourcing process. According to a buyer perspective, typical expected benefits are forexample lower prices and a more efficient purchasing process (short negotiation time,improvement in buyers’ productivity through the elimination of low-value activities).

In this perspective, main phases of an e-auction event are:

(1) Pre-auction:. Information exchange. This phase consists of one or two RFI round and

should allow buyers to assess total cost of purchasing related to a supplieroffer. Within RFI, information about product characteristics, logisticsconditions, value added activities, as well as other information to prepareboundless for the auction event are exchanged.

. Auction design. After supplier identification and qualification, the auctionformat is defined and parameters are designed for that specific event. Finally,the information policy is chosen and boundless are eventually prepared.

. Supplier training. Training is provided to each supplier in order toparticipate to the competition.

(2) Auction. During the auction event, suppliers present their bids and the winner isdetermined according to a selected winner algorithm, which may differaccording to auction type.

(3) Post-auction. Some auctions might conclude with more than a single winner. Inthese cases, a post auction negotiation process is necessary to select the finalsupplier. In other cases, the buyer cannot re-negotiate the final auction price butother strategic terms of the contract.

Auction is a low-cost mechanism, but its integration in the whole sourcing process maybe very expensive depending on the market and item features, item descriptioncomplexity, etc. These features significantly differ B2B auctions from B2C or C2C ones.E-auctions are not standard processes fitting any firm, as a consequence managers haveto adapt rules, assumptions and format to the specific requirements, organizationalstructure and supply characteristics (as for example, suppliers relationships, contractualpower, business impact, supply/market complexity, leverage opportunities and otherconstraints).

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2.2 Literature reviewMaking a brief excursus of auction research literature, we found that manycontributions have succeeded both in economics and in operation research as such as indata mining and management community. Many economic researches have examinedtraditional auctions from both a theoretical and empirical perspective (McAfee andMcMillan, 1987; Milgrom, 1989; Kagel, 1995) dealing almost exclusively with forwardauctions. These branches of research generally have mainly focused on identifyingoptimal auction design in various settings (Rothkopf et al., 1998; Klemperer, 1999; Kellyand Steinberg, 2000) and on the “winner’s course problem” (Kagel and Levin, 1986).

As auctions have moved to the internet, research has extended modeling studies inthis new changing and dynamic environment in order to explore online auctions. Theimplementation of e-procurement initiatives started to be intensively investigated, anda strong interest in understanding new issues involved in their implementation hasrisen up, especially in web-enabled environment (Angeles and Nath, 2007). This isparticularly the case of B2B e-RAs.

While theory would suggest that the results from the experimental economicsliterature should generalize to e-RAs, this has not yet been demonstrated (Kagel, 1995).Although, in fact, some similarities exist between the online reverse auctions andtraditional auctions, several significant differences (including product type, winnerdetermination, interdependent bidding practices and emphasis on bidder behavior overcontext) make it difficult to generalize from the economic literature to industrialsettings (Jap, 2003). For example, the products in the auctions of the theoreticalliterature tend to be commoditized, with price determining the product’s completevalue. Purchasing price, instead, is only one component of the value of the sourcedproduct in the case of e-RAs. Another difference is that the majority of auctionsexamined in the theoretical literature specify how to determine the winner of the event.On the contrary, e-RAs often do not determine a winner, as subsequent negations arefrequently adopted. Finally, the theoretical literature focuses on the processes by whichindividual actions translate into prices but not on the auction context. This should notlead to ignore past findings from the economics literature, but rather to considerexisting literature as a building block, in conjunction with exploratory field-work, fordeveloping theory as it applies to e-RAs (Carter et al., 2004).

We next briefly review the most relevant e-RA research literature which mainlyconsists of three streams:

(1) The first focuses on the e-RAs process features and their impacts on purchasingprocess efficiency (Emiliani, 2000; Mabert and Skeels, 2002; Meier et al., 2002;Gattiker et al., 2007), including comparisons with traditional negotiationprocesses and potential integration strategies in the overall purchasing process(Smeltzer and Ruzicka, 2000; Hur et al., 2006).

(2) A second stream analyzes the auction feasibility, i.e. conditions andimpediments to the adoption and implementation of e-RAs (Hur et al., 2006,2007; Mabert and Schoenherr, 2001; Hartley et al., 2004). It deals withinfluencing factors, prerequisites, critical success factors and auction designand format related characteristics ( Jap, 2002; Beall et al., 2003; Skjøtt-Larsenet al., 2003).

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(3) A third stream of research addresses the impact of e-RAs (from a post adoptionview) on the market context and in particular on the buyer-supplier relationshipand trust (Beall et al., 2003; Smeltzer and Carr, 2003; Bartezzaghi and Ronchi,2005; Jap, 2007; Amelinckx et al., 2008).

In synthesis, we found that in spite of the great conceptual interest in auction theory,due to poor data availability and expensive data collecting processes, as highlighted byLucking-Reiley (1999), only a few empirical studies have been developed up to now,especially in B2B context. However, a drastic growth in the number of contributionshas been made since real online data were available on internet.

In the “e-auction feasibility” field, in particular, the majority of research is conceptual,prescriptive and descriptive in nature. Parente et al. (2004) suggest a broad conceptualmodel addressing the basis for future research in online auctions; Amelinckx et al. (2008),based on an extensive literature review and multiple case study research, develop aconceptual model on auction outcomes including positing organizational and projectantecedents. Empirical studies are relatively young (Lucking-Reiley, 1999; Wagner andSchwab, 2004) and mainly based on qualitative interviews and case studies (Wagner andSchwab, 2004):

. a principal research branch focuses on bidder/seller behavior dynamics(Bajari and Hortacsu, 2003; Bapna et al., 2004);

. a second field of research concerns impact of auction features on processperformances (Carter et al., 2004; Kaufmann and Carter, 2003; Millet et al., 2004;Lucking-Reiley et al., 2007); and

. several case studies (Handfield et al., 2002; Emiliani, 2004) have examined factorsrelated to e-auction success or failure.

Some empirical-quantitative contributions about the impact of auction features on finalprice or about auction price prediction were deployed just in the B2C and C2C marketsby the artificial intelligence community (Wellman et al., 2002, 2004; Etzion et al., 2003).

In the B2B field, Jap (2003) conducted a small-sample quasi-experiment to comparethe open-bid format and sealed-bid format and argued that cost savings are commoditycategory specific and not systematically related to the bid format. Carter and Stevens(2007) conducted a laboratory experiment to investigate how different reverse auctionconfigurations jointly influence bid price and suppliers’ perceptions of buyeropportunism. However, to our best knowledge, only a few empirical-quantitativecontributions directly investigate which factors affect e-auction performance andmoreover none of them explicitly distinguish between the performance (savings)related to the price optimization and to the improvement in process efficiency due tothe adoption of online auctions in a B2B environment.

3. Research frameworkThe research framework shown in Figure 1 is drawn from the interpretative review ofthe literature and is composed of seven key conceptual constructs (groups):

(1) market context;

(2) buyer characteristic;

(3) supplier characteristic;

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(4) buyer-supplier relationship;

(5) product characteristic;

(6) auction process; and

(7) price/process performance (Parente et al., 2004; Aloini, 2007).

We have re-analyzed the general framework presented by Parente et al. (2004) andaccomplished an extended literature analysis in order to identify and systematizepotentially interesting success factors of e-RAs. First, the problem of “auction success”definition was faced and a set of appropriate (output) performance dimensions andrelated indicators were hypothesized. Then input variables were identified andclassified according to six other conceptual constructs.

3.1 Auction outputsAs for outputs, in our opinion an effective e-auction success evaluation, especially inB2B procurement context, should embrace a broad total cost of ownership philosophyand consider all costs associated with acquisition, possession, use and disposition ofpurchased goods. In this view, the “price” dimension is not the only one in whichauction success should be considered, purchasing price reduction is just one of the

Figure 1.Research framework

MarketContext

BuyerCharacteristic

AuctionParameters

Price/ProcessPerformance

a

c

f

SupplierCharacteristic

ProductCharacteristic

Buyer/SupplierRelationship

e

d

b

SC1

..

..

SC6

BC1

..

BC6

..

..

BS1

..

BS5

..

MC1

..

MC4

PC1

..

PC6

..

AP1

..

AP11

..

PROCESS

PRICE

MarketContext

ProductFeatures

ContextualDimension

ProcessDimension

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primary benefits of e-auctions but it is also important to consider process cost savingsin assessing e-auction profitability. Here we report the two dimensions of the analysis:

(1) The first performance dimension is price efficiency, which directly analyzessavings on final price obtained for goods auctioned. A possible indicator ofprice efficiency is the close-of-auction index which is defined as the differencebetween the historical price paid and the lowest bid price in a given bid event(Emiliani and Stec, 2002).

(2) The second is process efficiency, which refers to internal process cost savingsdue to e-auction adoption. e-RAs, in fact, force buyers to structure the bid priorto the event, to standardize the procurement process and to develop a strategyfor groups of items; for repeated auctions it means a shorter cycle time andincreased productivity (Carter et al., 2004). The process efficiency dimensionincludes reductions in some components of typical pre-/post-transactional costssuch as negotiation and monitoring costs, due to improvement in operationalactivities:. total negotiation cycle time reduction (pre-auction cycle time þ auction cycle

time);. reduction of man-hours spent in non-value added activities such as

comparison of different bid frameworks received in different times;. reduction of errors from paper based activities and manual data entry; and. reduction of post transactional activities due to controversies.

However, many of these process improvements are difficult to quantify or estimate ascost reductions and the relationship with the specific adopted negotiation tool is hardto isolate; this makes the process performance dimension much harder to explore.

3.2 Input dimensionsIn Table I, input variables and main literature sources are shown, according to sixmain conceptual constructs, in order to simplify their interpretation. Eachconstruct/group can be reliably measured with a multi-item scale; in this aim, eachconstruct/group involves a set of relevant variables selected both from literatureevidence (Parente et al., 2004; Kaufmann and Carter, 2003; but more than 150 articleswere reviewed) and from semi-structured interviews to practitioners.

Determinants (input variables) affecting final auction performance (outputs) can beassociated both with a “contextual dimension” in which we classified exogenousvariables, i.e. context-related variables impacting on the auction process, and with a“process dimension” which groups endogen and controllable process variables.

In the “contextual dimension”, we identified two main classes of variables: the firstclass is “market conditions” which refers to information about trading players, theirrelationships and the market sector; the second concerns “product features” whichinvolves general information about exchanged products.

“Market conditions” class groups: buyer and supplier characteristics,buyer/supplier relationship, market context.

(1) Traditional buying and supplier characteristics continue to strongly influenceprocurement exchanges. These factors are frequently included in thepre-auction phase for supplier screening and often influence winner

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determination algorithms and post-negotiation phases, especially inmulti-attribute competitions. In this group, we focus principally on:. Player reputation (SC1-BC1). As Walley and Fortin (2005) affirm reputation

is a subjective concept that resides in the mind of the buyer; so the inabilityto observe and quantify reputation makes it difficult to estimate its effect onprice, wherever it exists. This variable includes several attributes such as

Group/construct Item Variable/measures Main literature evidences

Market context MC1 Concentration of supply market Parente et al. (2004),Wagner and Schwab (2004),Losch and Lambert (2007)

MC2 Volatility of supply market priceMC3 Presence of a leading firmMC4 Number of potential suppliers

Suppliercharacteristic

SC1 Supplier reputation Min (1994), Choi (1996), Carter et al.(2004), Walley and Fortin (2005),Losch and Lambert (2007), Hur et al.(2007)

SC2 Total number of firm’s employeesSC3 Total turnoverSC4 Relative dimensionSC5 Company IT cultureSC6 E-commerce confidence

Buyercharacteristic

BC1 Buyer reputation Min (1994), Choi (1996), Carter et al.(2004), Walley and Fortin (2005),Hur et al. (2007), Losch and Lambert(2007)

BC2 Total number of firm’s employeesBC3 Total turnoverBC4 Relative dimensionBC5 Company IT cultureBC6 E-commerce confidence

Buyer-supplierrelationship

BS1 Age of relationship Jaworski and Kohli (1993), Parente(1998), Carter et al. (2004), Parenteet al. (2004), Losch and Lambert(2007)

BS2 Relative contractual powerBS3 Dimensional gapBS4 IT/e-commerce gapBS5 Information gap

Productcharacteristic

PC1 Product type Klein (1997), Jap (2002), Levi et al.(2003), Parente et al. (2004), Wagnerand Schwab (2004), Hur et al. (2007),Losch and Lambert (2007)

PC2 Strategic importance of auctionedgood for final product/service

PC3 Product standardization orcustomization degree

PC4 Description complexity of auctioneditem

PC5 Repeatability of auction eventPC6 Total value/size of auctioned lot

Auction process AP1 Auction type Jap (2002), Bajari and Hortacsu(2003), Beall et al. (2003), Pinker,et al. (2003), Carter et al. (2004),Millet et al. (2004), Parente et al.(2004), Wagner and Schwab (2004),Lucking-Reiley et al. (2007)

AP2 Aggregation mechanismAP3 Winner algorithmAP4 Minimum bid settingAP5 Reverse price level settingAP6 Product information disclosureAP7 Process information disclosureAP8 Timing mechanism (e.g. auto-

extension)AP9 Opening bidAP10 Total number of bidderAP11 Bid visibility

Table I.Constructs and variables

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quality and delivery reliability, economic performance, financial stabilityand other criteria traditionally used for supplier selection (Ellram, 1990; Min,1994; Choi, 1996), all of which contribute to define a market supplier profile.

. Company dimension. This could play an important role in the sourcingprocess because of critical buying mass and other benefits such as volumediscount, process cost saving and the contractual power which it coulddetermine. Empirical research showed that lot size and value have asignificant weight on auction players’ motivation and final price reduction.Quantitative indicators in this field could be: total number of firm’s employees(SC2-BC2,) total turnover (SC3-BC3) and relative dimension (SC4-BC$).

. Company information technology (IT) culture (SC5-BC5) and e-commerceconfidence/attitude (SC6-BC6) could have a relevant influence on theadoption and learning rate of new tools and practices, and deeply influenceparticipation level in e-auctions. Carter et al. (2004) affirm that there is noinitial relationship between the level of buyer experience and auctionsuccess, but suppliers tend to be unsuccessful in the initial events in whichthey participate.

(2) Buyer/supplier relationship. As Parente et al. (2004) suggest among the othercharacteristics that are important for auction success, interaction betweenbuyer and seller is the most critical. Factors influencing company relationshipsare numerous and many were identified in marketing research ( Jaworski andKohli, 1993; Parente, 1998). We can model relational characteristics such as the“distant factor” which indicates dimensional, financial and cultural differencesbetween companies. Referring to variables previously explained in buying andselling firm characteristics, we analyze: age of relationship (BS1), relativecontractual power (BS2), dimensional gap (BS3), IT/E-commerce gap (BS4),information gap (BS5).

Long relationships between the buyer and seller as also the gap indimension, IT culture and process information could prejudice auctionapplicability and motivation to competition. Carter et al. (2004) say that reverseauctions are viewed more favorably by buyers than by suppliers; this condition,in some cases, may cause a decrease in the level of trust, collusive behavior andless motivation to competition. The contractual power equilibrium of a tradingtransaction depends on the credibility and effectiveness which a player mayrepresent when dealing with another player. Differences in contractual powercertainly contribute to influencing bidding behavior, motivation to competitionand final auction performances.

(3) Market context groups information related to the market sector. Obviouslymarket information is relevant in good price definition; bidding behavior, as alsothe buyer reserve price depends on the buyers and sellers perception andconfidence and on market uncertainty. Market concentration gives importantinformation on potential motivation to auction competition. In particularmatching supply and demand concentration, it could be possible to betterunderstand the market attitude to auction sourcing and increase suppliermotivation to compete in e-auctions. We think that in a buyer-driven auctionevent, concentration on the supply market could differently impact on motivation

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to competition in relation to the concentration on the demand side. Theconcentration of supply market (MC1), the presence of leading firms (MC3), thenumber of potential suppliers (MC4) as also market price volatility (MC2) areinteresting variables in the market context dimension. A high-price volatility inselected markets, for example, could cause high irregularity in price performanceand this could induce suppliers to adopt a more aggressive/protective behaviorimpacting on motivation and auction competition.

In “product feature”, we classify product attributes which could modifyauction costs, dynamics and player motivation. Klein (1997) suggested that aspecific auction format may be better for certain types of products.

(4) The product characteristic construct involves the following variables:. Product type (PC1) and strategic importance of auctioned goods for final

product/service (PC2). Product class, buying situation (New Buy, Rebuy,Straight Rebuy) (Faris et al., 1967) and strategic importance (Kraljic, 1983) arecorrelated with the relative value perceived by buyers and sellers, managerialcomplexity and business risk perception. This can impact on theircompetition motivation, acceptance thresholds and finally on auction results.

. Product standardization or customization degree (PC3) and descriptioncomplexity of auctioned items (PC4) could severely impact on negotiationcomplexity, negotiation costs and supplier comprehension of the capitulate;so effectiveness, efficiency, dynamics of auction, motivation to competitionand final results could be compromised.

. The repeatability of the auction event (PC5) and the total value/size of theauctioned lot (PC6) could impact on the one hand on amortization of costs forcomplex capitulates during the exchange of high specific products and onsupplier motivation to e-auction tool adoption, and on the other hand it couldinduce supplier adaptation and opportunistic/collusive behavior.

“Process dimension”, instead, refers directly to the auction event and tointernal process control variables that may impact on final performance(i.e. auction mechanism and settings). In literature, a variety of auctionformats is available and each allows personalization through numerousparameter settings, and thus thousands of different combinations areeffectively configurable.

Many authors have studied how parameters impact on final auction price,adopting different analysis approaches especially in B2C and C2C context(Bajari and Hortacsu, 2003; Pinker et al., 2003; Lucking-Reiley et al., 2007).Carter et al. (2004), for example suggest that auctions that utilize multiple lotsare more likely to be successful and that the size of auction in terms ofmonetary volume is in general significantly greater for “successful” auctions.

Online auction designers should pay attention to how their project decisioninfluences the participative behavior of consumers over time. Pinker et al.(2003) divided the global set of rules for online auctions in two sub-sets,referring one to the design mechanism and the second to bid constraints.

. Auction process fits this dimension. Here we present a list of principalvariables (parameters/settings) that we have considered in our analysis:auction type (AP1), aggregation mechanism (AP2), winner algorithm (AP3),

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minimum bid setting (AP4), reserve price level setting (AP5), productinformation disclosure (AP6), process information disclosure (reserve price,bids, etc.) (AP7), timing mechanism (auto-extension or rigid deadline) (AP8),opening bid (AP9), total number of bidder (AP10), bid visibility (AP11).

4. Research objectiveThis study investigates data gathered in an online survey in order to validate thepresented conceptual framework and to identify most of the critical factors which drivethe e-auction success.

Assessing the suitability on a B2B e-RAs and investigating critical success variablesin order to provide the conditions for the event to be successful, is an ambitious andrecurring task in literature. e-RAs are complex processes and the literature in this field ispopulated by contributions which analyze different features, in terms of auction classand parameters, market conditions, characteristics of purchasing; moreover, variablesare often described and organized differently (Walley and Fortin, 2005).

However, to our best knowledge existing research in this field has severallimitations:

. Many conceptual papers try to systematize the numerous variables affectinge-auction success and their complex relationship into a single comprehensiveframework; nevertheless there is a lack of empirical evidence supporting thesemodels.

. Several empirical works focus from different perspectives (both in B2B and mostnumerous in B2C and C2C field) on the impact of specific variables on auctionsuccess but only a few globally investigate these variables and their relationships.

. Despite a clear distinction between auction “price” and “process” performance asassessed in conceptual literature, a lack of empirical contributions focusing onhow different variables impact on them, still exists.

This research aims to fill the gap in literature providing an empirical cross-sectorialstudy which directly investigates critical success factors impacting on “price” and“process” performance in B2B e-RA design. In particular, a broad conceptual modelwas here first presented and discussed in the light of literature evidence and thentested by a CFA.

Main research hypotheses were:

H1. A multi-faced construct, related to the impact on auction price and processperformance, exists and consists of market context, buyer characteristic,supplier characteristic, buyer-supplier relationship, product characteristic,auction process (Figure 1).

H2. Market context, buyer characteristic, supplier characteristic, buyer-supplierrelationship, product characteristic, auction process differently impact onauction price and process performance (Figure 1, arrows a, b, c, d, e, f).

5. Research methodologyThe presented conceptual framework (both for price and process dimension) wasvalidated by a confirmatory approach which consists of three main phases.

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First, a preliminary analysis of data was attempted in order to define a first classificationof variables according to expert judgment and to filter most critical variables. Then astructural equation model-based CFA was assessed (by AMOS 7.0 Software). A set ofthree different tests was performed within the groups to test the construct dimensionality,evaluate their reliability and drop the less significant variables. Finally, retained variablesand constructs were mapped in the conceptual model and it was analyzed by a CFA.

As opposed to a theory-generating method such as the exploratory factor analysis(EFA), CFA is a theory-testing model. A model, or hypothesis, based on a strongtheoretical and/or empirical foundation, specifies which variables will be correlatedwith which factors and which factors are correlated. CFA analysis offers the researchera viable method for model testing and evaluating construct validity.

Here CFA, as a particular analysis of structural equation modeling, was used tocheck the goodness of fit of the measurement scales, providing also the correlationsbetween factors and constructs. It includes the establishment of scale dimensionality,reliability and validity (Gerbing and Anderson, 1988). Scale validity includes contentvalidity (which is assumed to be correct when the scale has been constructed accordingto literature), convergent validity and discriminant validity. More details about thesetests will be given in the sections below.

5.1 Sample and data collectionIn order to provide a relevant and significant feedback relative to principal factorsaffecting price and process performance in e-auction based negotiations, aheterogeneous panel of experts – representative of a highly qualified population,directly involved in the analyzed problem – was assembled from the industrial andacademic world. A reasoned sample (i.e. survey universe) of academic auction expertsand professional users was selected (it included 255 different academic authors from allover the world and 100 senior consultants from four European Business Providersoperating on multi-sector business both from horizontal/maintenance, repair oroperation (MRO) and heterogeneous vertical markets).

Data for the analysis were collected by an on-line survey through the use of aquestionnaire. The respondents were asked to separately indicate their perceptionabout the level of importance (on a seven-point Likert scale) of the selected variablesaffecting the online auction performance: the “price” gain and the efficiency of thenegotiation “process”, as perceived (Appendix 1).

The total number of usable responses was 78; the overall response rate for thisstudy was 22.6 per cent (16.3 per cent for academics and 38 per cent for industry’ssample). Respondents are quite equally distributed between academics andpractitioners, all of them have a long experience in the auction field (more thanseven years) and they are mostly from Europe and North America.

5.2 Steps of the analysisMain steps of the analysis were:

(1) Preliminary analysis. First, variables were classified according to theirm averageand s standard deviation (SD), respectively, used as indicators of the perceived“variable impact” on performance and of the expert agreement degree “answerreliability”. A matrix (Figure 2) was filled in order to discriminate between: (a)high (s # 1:53Þ or low (s . 1:53Þ significance of experts opinions and (b) high

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(m $ 5Þ, medium (3 , m , 5) or low (m # 3Þ impact of variables (thresholdswere computed depending on the whole range of a sample’s SD). Variables werethen classified as “critical”, “not critical”, “moderate” and “uncertain”. Criticalvariables were directly included in the final model to be analyzed by CFA (Step 3)since they were considered important in the model definition.

(2) Scale dimensionality and reliability test. Dimensionality and reliability of the sixmain constructs were assessed using the following steps in order todemonstrate that they significantly measure the related construct and thatthe selected variables were correctly interpreted. The planned steps of thisanalysis were:. Corrected item total correlation (CITC). CITC was used for purification

purposes because unimportant items may confound the interpretation of theFA. It refers to a correlation of an item or indicator with the composite scoreof all the items forming the same set (Koufteros, 1999). If the correlation islow, it means the item is not really measuring what the rest of the test istrying to measure. The CITC analyses were performed for each construct(variable reliability). Using the cut-off value 0.40 for evaluating CITC, somevariables were eliminated from subsequent analyses.

. FA. FA was performed within construct blocks in order to confirm that onefactor can be identified in a given block of items and in essence addresses theconstruct-dimensionality. A cut-off value 0.60 was used for evaluatingprincipal FA loadings.

. Cronbach’s alpha was computed for each construct. Cronbach’s alpha is oneof the most widely used measures for evaluating group reliability (Koufteros,1999); the reliability threshold for each construct was set to 0.5. Reliabilityvalue for each construct was above the value of 0.5.

(3) Scale validity (SEM-based CFA). CFA approach was applied to the entire set ofconstructs simultaneously (the whole model was tested by the Software AMOS7.0) in order to globally assess convergent and discriminant validity. Factorloadings and correlation among the six key constructs (Koufteros, 1999) werecomputed and analyzed. Convergent validity is verified when factor loadings ofeach construct are significantly high (.0.4), discriminant validity, on the other

Figure 2.Preliminary analysis table

LO

W NOT CRITICAL MODERATE CRITICAL

HIG

H

UNCERTAIN UNCERTAIN UNCERTAIN

LOW MEDIUM HIGHData

Average m

Stan

dard

dev

iatio

n s

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hand assesses the fact that two constructs measure different aspects(correlation significantly different from 1) (McEvily and Zaheer, 1999).Finally, the normal x 2 index (x 2/df) was used to check the goodness-of-fit of themeasurement scale with data.

(4) Performance impact matrix construction. A performance impact matrix wasbuilt starting from global average (mÞ and SD (sÞ computed for each constructby AMOS. So constructs were finally classified according to the perception oftheir main impact and reliability of the judgment.

6. Data analysisIn this section, we describe the three steps of the analysis of the presented framework.As we distinguished “price performance” (price efficiency) from “process performance”(price efficiency), the answering section of each question was explicitly divided in twoparts according to these criteria. Also the data analysis follows this scheme: price andprocess data will be independently analyzed and results will be presented separately.Finally, a global comparison between the two dimensions is assessed in the discussionsection.

6.1 Preliminary variables analysisAs shown in Figures 3 and 4, we classified variables according to the measured valuesof their mean and SD (Appendix 2). Most critical variables were excluded from othertexts and confirmed for the final AMOS-based CFA since their impact on final auctionperformance was considered reliably significant.

Figure 3.Preliminary analysisof price

LOW MEDIUM

HIG

HL

OW

Stan

dard

dev

iatio

n s

HIGH

Average m

SC3 SC4 BC2

SC2

SC5BS1AC2AC4AC6AC9

MC2MC3SC1SC6BC1BC3BC4BC5

BC6BS3 BS4 BS5PC1PC4PC5PC2AC7

MC1MC4AC1AC3AC8AC10AC11

BS2PC3PC6AC5

CriticalNotCritical

Moderate

UncertainUncertainUncertain

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For the price dimension “critical” variables are: concentration of supply market (MC1),number of potential suppliers (MC4), auction type (AC1), winner algorithm (AC3),timing mechanism (AC8), total number of bidders (AC10), bid visibility (AC11).

For the process dimension they are: e-commerce confidence (BC6), descriptioncomplexity of auctioned item (PC4), repeatability of auction event (PC5), productinformation disclosure (AC6), process information disclosure (AC7).

6.2 Scale dimensionality and reliability testAs specified, before running the CFA on the whole model, the CITC test, an EFA andthe Cronbach’s alpha test were performed on data (Appendices 3 and 4). Variables,which were not previously classified as “critical” and not within the selected threshold,were dropped from the model. 13 variables were definitively excluded from the pricemodel and nine from the process model.

6.3 Scale validity (SEM-based CFA)The factor structure of the framework is shown in Figures 5 and 6. In order to confirmthe framework validity, a SEM-based CFA on the whole model was run by AMOS 7.0.

The factor loadings of the variables (measurement items) on the constructs (factors)were all significant at p , 0.05 level. With the exception of variables which were forcedin the analysis such as MC4, AC3, AC10, AC11 (and some others such as BC6 and SC6in the price performance model), all the other variables presented quite high-factorloadings, ranging form 0.4 to 0.95. This underscores a good fit of the remainingvariables with the underlining constructs.

Figure 4.Preliminary analysis

of process

LOW MEDIUM

HIG

HL

OW

Stan

dard

dev

iatio

n s

HIGH

Average m

SC2SC3 SC4

CriticalNotCritical

Moderate

UncertainUncertainUncertain

AC1--

BC6PC4 PC5 AC6 AC7

MC1SC6BC1BC2BS1BC3 BC4 BC5

BS2BS3 BS4 PC1 PC2 PC3 PC6AC2 AC9

MC2 MC3 MC4 SC1SC5BS5

AC3AC4AC5 AC8AC9 AC10

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Correlations among the constructs were computed and shown in Figures 5 and 6 inorder to test discriminate validity and to achieve a better interpretation of theconceptual structure of the model.

Finally, in order to assess the fit and uni-dimensionality of the global model,AMOS provides absolute goodness-of-fit measures. The normed x 2 (x 2/degreefreedom) value of the two models, after modification suggested by the modificationindex, are:

Figure 5.Factor structure for price

MarketContext

BuyerCharacteristic

AuctionProcess

–0.14

–0.10

SupplierCharacteristic

ProductCharacteristic

Buyer/SupplierRelationship

0.33

0.07

–0.19

0.37

0.37

0.45

0.87

SC2

SC3

SC4

BS3

BS1

BS5

MC1

MC4

PC1

PC3

PC5

PC4

AP1

AC3

AP7

AC6

0.47

0.32

0.01

0.35

0.03

0.54

SC5

SC6

0.95

0.29

0.32

0.41

0.92

0.90

0.75

BC2

BC3

BC4

BC5

BC60.35

0.540.84

0.78

0.75

0.56

0.60

0.64

PC60.62

0.610.75

0.66

0.63

AC8

AP11

AC100.24

0.530.88

0.87

0.20

0.36

0.43

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Price fit index :x 2

dfprice¼ 426:874=300 ¼ 1:423

Process fit index :x 2

dfprocess¼ 783:467=407 ¼ 1:925

Figure 6.Factor structure for

process

MarketContext

BuyerCharacteristic

AuctionProcess

0.15

0.63

SupplierCharacteristic

ProductCharacteristic

Buyer/SupplierRelationship

0.49

0.63

0.41

0.60

0.82

0.53

0.75

SC2

SC3

SC4

BS2

BS1

BS3

MC3

MC4

PC2

PC3

PC5

PC4

AP1

AC3

AP5

AC4

0.38

0.17

031

0.07

0.26

0.36

0.58

0.58

0.880.94

0.74

BC2

BC3

BC4

BC5

BC60.21

0.42

0.86

0.79

0.79

0.71

0.74

0.68

PC6

0.68

0.65

0.35

0.60

0.57

AC6

AP8

AC7047

0.50

0.64

0.62

0.73

0.78

0.65

MC1

MC2

0.71

0.60

BS50.42

PC1 0.55

AP11

AC100.70

0.76

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These fall well within the recommended range (1 , x 2/df , 2) for conditional supportto be given for model parsimony (Lu et al., 2007). In summary, these indexes of overallgoodness-of-fit for the model provide sufficient support for the results to be deemed anacceptable representation of the hypothesized constructs.

6.4 Performance impact matrix constructionA total impact index and construct SD were computed for each construct both for priceand process dimension. By the use of AMOS, in fact, it was possible to elaborate aglobal mean and SD value for each construct taking into account respondents data,factor loading and construct correlations. Values are given in Table II.

In Figures 7 and 8, we classified constructs by their global mean and SD as indicatorsof the perceived impact on performance and of the global reliability of responses.

Figure 7.Constructs classificationfor price

LOW MEDIUM

HIG

HL

OW

Stan

dard

dev

iatio

n s

HIGH

Average m

CriticalNotCritical

Moderate

UncertainUncertainUncertain

--

* Market Context

* Supplier Characteristic* Buyer Characteristic

* Supplier/Buyer Relationship

* Product Characteristic

* Auction Process

1.54

0.5

53

-- --

Total construct impactPrice dimension Process dimension

Groups/constructsImpact index

(corrected – average) SDImpact index

(corrected – average) SD

Market context 5.55 1.54 4.23 1.20Supplier characteristic 2.70 1.11 2.87 1.19Buyer characteristic 2.98 1.21 3.27 1.25Supplier/buyer relationship 3.90 0.89 4.17 1.13Product characteristic 4.85 1.07 4.23 0.89Auction process 4.98 0.64 5.01 1.10

Table II.Global construct impacton price and processperformance

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7. DiscussionThe empirical results point to some important findings that can contribute to a betterunderstanding of critical success factors impacting on “price” and “process”performance in B2B e-RA design.

As clarified in the previous sections a CFA was adopted in order to validate thepresented conceptual framework. First, a preliminary analysis on data, based on theexperimental means and SD, was assessed. Some variables (about which expertsreliably agree assessing a high impact on auction performance) were identified andlabeled as critical in order to avoid them being prematurely excluded from the FA. In anext step, a standard CFA was performed on survey data by AMOS 7.0. Following astandard approach, before running the CFA on the whole model, first the CITC test,then an internal FA within the identified construct and finally the Cronbach’s alphatest were performed on data (Appendices 3) in order to drop from the model non“Critical” variables which were not within the acceptance threshold. 13 variables weredefinitively excluded from the price model and nine from the process-model since theydid not reliably fit with the constructs. After this phase, the (AMOS based) CFA wasapplied to the whole model with the aim of testing the model validity. Factor loadings,with the exception of critical variables which were forced in the analysis, range from0.4 to 0.95 and are all significant at the p , 0.05 level, moreover construct correlationswere computed to achieve a better interpretation of the model and identify highlycorrelated constructs (as input to the model refinement). The normalized x 2 (x 2/degreefreedom) values were calculated for the two models as absolute goodness-of-fitmeasures. Since they fit well within the recommended acceptance threshold, we canconclude that models are a good representation of the experimental data.

Figure 8.Constructs classification

for process

LOW MEDIUM

HIG

HL

OW

Stan

dard

dev

iatio

n s

HIGH

Average m

CriticalNotCritical

Moderate

UncertainUncertainUncertain

--

* Market Context

* Supplier Characteristic* Buyer Characteristic

* Supplier/Buyer Relationship

* Product Characteristic

* Auction Process

1.54

0.5

53

-- --

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In the last step of the analysis, information provided by the CFA was also used inAMOS to assess a global mean and SD of each construct. Constructs were finallyclassified according to their global means and SD (impact index).

7.1 Contributions to research theoryThe validated framework mostly confirms by empirical findings the investigatedhypothesis and also supports previous conceptual models or background on e-RAperformance determinants in a B2B context as stated also by Parente et al. (2004) orAmelinckx et al. (2008). Moreover, the study assesses the relevance of Market Context,Product Characteristic and Auction Process variables impact on e-RA performance andthe need for a more in depth quantitative research in this field.

H1. Outcomes from the analysis demonstrate that a multi-facet construct consistingof six main dimensions impacts on e-auction price and process performance. Factorloadings of almost all the variables in each group (exception made for “forced”variables) are sufficiently high, showing that variables have a good matching with theunderlining construct. Correlations among constructs significantly differ from 1 withfew exceptions which, however, represent a good input for model refinement. Finally,the normalized x 2 index related to the factor structure of the model, in fact, fit well inthe acceptance threshold and support the evidence of a good matching with theempirical survey data.

H2. Using respondents data, factor loadings and construct correlations provided byAMOS, a total impact index and construct SD were computed for each construct bothfor the price and process model. Construct means and SDs were interpreted,respectively, as measures of the relative impact of the construct on performance and asreliability of the measure. The six constructs were finally classified according to thesevalues and an interpretative impact matrix was filled in. It is evident that theconstructs do not have the same weights in affecting a specific auction performanceand, moreover, they differently influence the “price” and “process” model.

Looking at the “price” dimension, for example, it seems that Market Context(number of potential suppliers (MC1) and concentration of supply market (MC4)) werethe most critical constructs for an e-RA success. These constructs show the highestimpact on expected final price performance (Table II), and also present the highest SDand this could affect the reliability of the measure, suggesting that experts’ thinkingabout this construct is not univocal. Evidence from the analysis underlines the directdependence between the price performance, which we interpreted as the price gain ofthe auctioned items on the historical purchasing price, and the market context.

In the “Process” dimension, on the other hand, the most relevant construct is theauction process. The construct groups several variables such as Auction type (AP1),Winner algorithm (AP3), Minimum bid setting (AP4), Reserve price level setting (AP5),Product information disclosure (AP6), Process information disclosure (reserve price,bids, etc) (AP7), Timing mechanism (auto-extension or rigid deadline) (AP8), Totalnumber of bidders (AP10), Bid visibility (AP11). Results seem to suggest that theauction process design (parameter setting, information process disclosure, etc.) is thefirst factor affecting auction process performance (increase in purchasing processefficiency and/or productivity).

Other relevant constructs which present a moderate impact on “price” or “process”performance are: auction process, buyer/supplier relationship and product

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characteristics for “process” dimension and buyer/supplier relationship, marketcontext and product characteristics for “process”.

As for the less impacting factors, in both price and process performance dimensions,we identified buyer and supplier characteristics. Outcomes suggest that the companyprofile in terms of size/dimension, IT culture and players’ reputation does not reallyaffect performance. But, the strong inter-correlation with the buyer/supplierrelationship construct also advises that this construct cannot be independent andshould be included in a more general construct; this result provides a potential inputfor model refinement or future developments of this research and agrees withParente et al. (2004) model.

7.2 Managerial implicationsFindings from this research conceptualize and clarify different dimensions impactingon price and process performance of e-RAs and provide a highly qualified feedbackfor managers with respect to most critical variables in e-auction design. In thisperspective, the research has had two main implications. First, it emphasizes the needfor decisional support in e-RA design (feed-forward control in the pre-auction phase)and second, it provides an improvement in the knowledge of the “system” workingdynamics and performance (feed-back control in the post auction phase).

From an operative point of view, the two analyzed performance dimensions can beinterpreted according to the following quantitative expression, which explains the totalsavings actually achieved and in particular to the specific component PS and PCS(respectively “price” and “process” performance).

TS ¼ PS 2 SW 2 AC þ PCS ð1Þ

where:

TS total savings achieved by the e-RA adoption.

PS price savings is computed as historic price – lowest bid (Emiliani and Stec,2002; Hur et al., 2007).

SW switching costs are all costs due to the changing of supply sources such as thebuyer’s resource deployment for supplier qualification, personnel training andother similar costs (Emiliani and Stec, 2002; Hur et al., 2007).

AC auction costs which are fees eventually paid to the e-auction service companyor linked to the auction process setting (Emiliani and Stec, 2002).

PCS process cost savings are due to the increased productivity of the purchasingprocess.

Interpreting results from a practical perspective and comparing outcomes from theprice and process dimension, it appears that price performance is strictly affected byexogenous variables related to the market condition and in particular by the marketcontext. This is in line with literature evidence, which underlines the fact that the moresuccessful e-RAs are characterized by higher levels of competition among suppliers(Beall et al., 2003; Carter et al., 2004; Amelinckx et al., 2008).

Example. This circumstance is typical in the purchasing process of MRO goods orleverage items in the Kraljic model (paper, office, packaging supplies, etc.) for which

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performance is often measured as reduction on the purchasing price. In this case, infact, market conditions are very relevant in good price definition (Parente et al., 2004);as also the characteristics of market demand, market capacity, elasticity (Smeltzer andCarr, 2003) and price volatility may heavily influence the price definition process sincethey could induce suppliers to take a more aggressive/protective behavior duringnegotiations.

These considerations support the importance conferred by literature in choosing theright negotiation strategy in relation to the market context, in assessing auctionfeasibility and in the effort recommended during the pre-auction phase both byacademics and practitioners. Appropriate market conditions in terms of marketfragmentation, excess supply capacity and market price elasticity, are necessary forreverse auctions to be successful (Smeltzer and Carr, 2003).

On the other hand, auction process, the most significant variable in the processperformance dimension, is clearly an endogenous variable and can be controlled by thepurchasing manager or auction designer during the auction design.

Example. The purchasing process for near-commodity products/services and/orspecialty goods as such as building/office maintenance service has often the target toimprove the purchasing process performance (improvement in the purchasing processefficiency or productivity). The winner algorithm, the auction procedures and methodsused for “product” and “process” information disclosure, could strongly affect theexpected output. E-RAs, in fact, forcing players to structure the bid prior to the event,to standardize the procurement/information process and to develop a strategy forgroups of items, could also influence negotiation cycle times, man-hours, error ratesand post-transactional activities due to controversies, especially for repeated auctions.

According to this perspective, negotiation by e-RAs could be used also to structureand standardize competition among qualified validated suppliers from the vendor list;this is true, in particular, for multi-attribute auctions where competition is not onlyprice based.

8. Conclusion and further researchThis article is a first attempt to test a conceptual framework on critical factorsimpacting on e-RA performance in a B2B context. It contributes both to the academicliterature and managerial practice by an overall testing of the presented conceptualframework. The results demonstrate that a multi-facet construct consisting of six maindimensions impacts on e-auction performance.

Moreover, data indicate that constructs impact with a different weight on price andprocess performance; nevertheless some of them are convergent on a commonconstruct.

Main outcomes are:. Market context and auction process have a “moderate-high” impact, respectively,

on auction price and process performance.. Buyer/supplier relationship, product characteristic, market context (for process

dimension) and auction process (for price dimension) have a “moderate” impacton auction performance.

. Supplier characteristic and buyer characteristic have a “moderate-low” impacton auction performance.

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. Supplier characteristic and buyer characteristic are highly correlated with thebuyer/supplier relationship and it is likely that they belong to a more generaltheoretical construct.

The findings also have clear implications for management, since they provide usefulinformation for the formulation of managerial decisions in designing the auctionevent/process and support the definition of different negotiation strategies. However,these underpinnings should be seen as more indicative than conclusive since based onexperts’ perceptions and, therefore, generalizations should be tested. Presentedoutcomes, driving variables selection, also suggest interesting further directions forresearch and set the stage for further analysis, since they provide a first step needed inorder to develop a more quantitative model with which to perform a sensitivityanalysis on final auction performances.

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Further reading

Elmaghraby, W.J. (2007), “Auctions within e-sourcing events”, Production and OperationsManagement, Vol. 16, pp. 409-22.

Emiliani, M.L. and Stec, D.J. (2005), “Wood pallet suppliers’ reaction to online reverse auctions”,Supply Chain Management: An International Journal., Vol. 10 No. 4, pp. 278-88.

Corresponding authorDavide Aloini can be contacted at: Davide.Aloini@dsea.unipi.it

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Appendix 1. Questionnaire form

SECTION 1 Personal Data Name:Surname:City/Country:Nationality:Telephone:E-mail:Firm Data Firm Name:Job position: Firm Turnover:Firm Country: Number offirm's employees:

Weight Description 1 Extremely Low Impact

Very Low Impact23 Low Impact4 Normal Impact5 High Impact 6 Very High Impact7 Extremely High Impact

**Likert’s Scale for Section 2

IT Data How long do you use InformationTechnology for process support? (years): How long do you use IT in purchasing process? (years):How long do you use online auctions? (years):

SECTION 2 Question - To what extent do you agree with saying that following variables are important for e-auction performance?A - MARKETCONDITION

2.1 Market Context Weight MC1 - The concentration of supply market This variable refers to the numberof firms and their respective market shares in the supply market. MC2 - The volatility of supply market price This variable refers to medium variation ofthe auctioned item price in supply market during a specified timeline.MC3 - The presence of a leading firm This variable refers to the presence of a big player which has a dominant position in the market. MC4 - The number of potential suppliers This variable refers to thetotal number of potential supplierin aparticular supply market.2.2 Supplier CharacteristicsSC1 - Supplier reputationThis variable includes several attributes like image, quality and delivery reliability, financial stability and others criteriausually used for suppliers selection.SC2 - Total number of firm’s employees This variables is linked to firm’s dimension and managerial complexity.SC3 - Total turnoverThis is an economic index of firm’sdimension.SC4 - Relative Dimension(total turnover/total number of employees)Thisis a relative indicator offirm’s dimension and complexity.SC5 - Company IT culture This variable refers to general IT culture of firm in terms of awareness of potential improvements of businessperformance and management of IT introduction process. SC6 - E-commerce confidence This variable refers to the experience using e-commerce tools: how long time the company has introduced it andnumber of reiterations of the specific auction event. 2.3 BuyerCharacteristics BC1 - Buyer reputationThis variable includes several attributes like image, quality and delivery reliability, financial stability and other criteria traditionally used. BC2 - Total number of firm’s employeesThis variables is linked to a firm’s dimension and managerial complexity. BC3 - Total turnover This is an economic index of firm’s dimension.BC4 - Relative Dimension(total turnover/total number of employees)This is a relative indicator offirm’s dimension and complexity.BC5 - Company IT culture This variable refers to general IT culture of firm in terms of awareness of potential improvements of businessperformance and management of IT introductionprocess. BC6 - E-commerce confidence This variable refers to the experience using e-commerce tools: how long the company has been introduced it andnumber of reiterations of the specific auction event.

(continued)

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2.4 Buyer/Supplier RelationshipBS1 - Age of relationship This variable refers to the length of relationship between sellerand buyer (the number of yearsthey dobusiness). BS2 - Relative contractual power The contractual power of a firm is function of the dependency degree that the opposing party it has in its confronts. Itislinked to dimension, concentration both demand-side and offer-side, or other technical dependencies. BS3 - Dimensional gapThe dimensional gap include differences related to total turnover ofthe company “A” regarding the company “B”. BS4 - IT/ E-commerce gap This variableis related to the experience and confidence gap usingIT support andin particular E-commerce tools.BS5 - Information gap This variable resumes the information asymmetry between suppliers and buyers interm of quality and quantity oftechnical and market information.2.5 Product CharacteristicsPC1 - Product Type This variable refers to the class which the auctioned item belong to. That is for example: Manufacturing (Specialty orCommodity) vs MRO good (Maintenance, Repair or Operation). PC2 - Strategic importance of auctioned good for final product/service This variableis related to the perceptual incidence of the total value of auctioned items on total procurement value and/or the contribution to the innovation, image, quality level and value offinal product/service. PC3 - Product standardization or customization degree This variable refers to the specificity and uniqueness of auctioned product/service. PC4 - Description complexity of auctioned item This variable estimates the complexity of auction’s product and costs associated with the communication process ofproduct capitulate PC5 - Repeatability of auction event This variable refers to the repeatability of an auction event so thatyou can reuse general auction settings and capitulate specification more than once. PC6 - Total value/size of auctioned lotThis variable refers to the dimension and value of the auctioned lot.2.6 Auction Process ParametersAP1 - Auction type This variable describes if the auction is a single attribute or multi attribute auctionAP2 - Aggregation mechanismThis variable describes if the auction use: simple aggregation, multi-lot aggregation (yankee e-RA), combinatorial aggregationAP3 - Winner algorithm This variables refers toaward modality (automatic or with award reservoir) and the typology of the award price (first price or secondprice) AP4 - Minimum bid settingThis parameter indicates the existence ofthe minimal offer. AP5 - Reverse price level setting This variable indicates the existence of a minimum price for auction to be successful. AP6 - Product information disclosure This variable indicates the adoption of a structured processfor product information communication (RFx, etc).AP7 - Process information disclosure This variable indicates the adoption of a structured process for auction process information communication (Supplier qualification, invitation, training). AP8 - Timing mechanism and presence of auto-extensionThis parameter indicates the auction timing and the auto-extensions due to a bid on the last minutes of the event (if theauction is hard close or soft close). AP9 - Opening bid This variable refers to the minimum bid to entry in the auction process. AP10 - Total number of bidderThis variables refers tothe total number of participants of the auction event.AP11 - Bid visibility This parameter indicates ifthe auction is: an open auction or saeled bid. It is related to visibility ofthe bidding process.

SECTION 3

Question 1 - Please indicate, from your point ofview, the most important factors (max 5) that you consider strongly impacting onprice auction performance and which you consider in auction setting process.

Question 2 - Please indicate, from your point ofview, the most important factors (max 5) that you consider strongly impacting on e-auction processefficiency and which you consider inauction setting process.

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Appendix 2. Mean and standard deviation values of observed variables both in price

and process dimension

Price ProcessItem ID Measures/variables Mean SD Mean SD

Market contextMC1 The concentration of supply market 5.54 1.47 4.21 1.71MC2 The volatility of supply market price 4.54 1.58 3.80 1.76MC3 The presence of a leading firm 4.79 1.75 3.94 1.90MC4 The number of potential suppliers 5.79 1.25 4.69 1.74

Supplier characteristicSC1 Supplier reputation 3.92 1.64 4.17 1.96SC2 Total number of firm’s employees 2.63 1.51 2.93 1.64SC3 Total turnover 2.96 1.58 2.91 1.60SC4 Relative dimension 2.85 1.55 2.87 1.48SC5 Company IT culture 3.30 1.49 4.45 1.80SC6 E-commerce confidence 3.61 1.54 4.70 1.56

Buyer characteristicBC1 Buyer reputation 4.65 1.64 4.24 1.71BC2 Total number of firm’s employees 2.93 1.64 3.23 1.64BC3 Total turnover 3.72 1.81 3.33 1.69BC4 Relative dimension 3.12 1.67 3.16 1.55BC5 Company IT culture 3.79 1.74 4.87 1.67BC6 E-commerce confidence 3.97 1.66 5.03 1.56

Buyer-supplier relationshipBS1 Age of relationship 3.90 1.43 4.16 1.72BS2 Relative contractual power 5.18 1.57 4.13 1.64BS3 Dimensional gap 3.82 1.54 3.33 1.53BS4 IT/E-commerce gap 3.16 1.65 3.72 1.72BS5 Information gap 4.73 1.76 4.26 1.77

Product characteristicPC1 Product type 4.84 1.75 4.24 1.70PC2 Strategic importance of auctioned good for final

product/service5.29 1.65 4.87 1.64

PC3 Product standardization or customization degree 4.59 1.62 5.10 1.62PC4 Description complexity of auctioned item 4.28 1.67 5.10 1.62PC5 Repeatability of auction event 5.22 1.57 3.76 1.64PC6 Total value/size of auctioned lot 4.84 1.67 3.96 1.65

Auction processAC1 Auction type 5.13 1.53 5.03 1.74AC2 Aggregation mechanism 4.84 1.47 4.85 1.67AC3 Winner algorithm 5.23 1.52 4.33 1.80AC4 Minimum bid setting 4.19 1.49 3.25 1.77AC5 Reverse price level setting 5.00 1.54 3.67 1.78AC6 Product information disclosure 4.91 1.50 5.04 1.65AC7 Process information disclosure 4.70 1.57 5.29 1.51AC8 Timing mechanism (e.g. autoextension) 5.14 1.41 4.14 1.81AC9 Opening bid 4.13 1.41 3.59 1.59AC10 Total number of bidder 5.93 1.28 4.45 1.76AC11 Bid visibility 5.44 1.28 4.01 1.88 Table AI.

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Appendix 3. Dimensionality and reliability test in price and process dimension

Corrected item –total correlation

Factoranalysis

Cronbach’salpha test

Item ID Measures/variables CITC Loadings a (Group)

Market ContextMC1 Concentration of supply marketa 0.420b 0.828 0.535MC2 Volatility of supply market price 0.351 –MC3 Presence of a leading firm 0.349 –MC4 Number of potential suppliersa 0.160 0.828

Supplier characteristicSC1 Supplier reputation 0.398 – 0.827SC2 Total number of firm’s employees 0.733 0.864SC3 Total turnover 0.635 0.813SC4 Relative dimension 0.646 0.818SC5 Company IT culture 0.580 0.707SC6 E-commerce confidence 0.507 0.637

Buyer characteristicBC1 Buyer reputation 0.390 – 0.838BC2 Total number of firm’s employees 0.599 0.754BC3 Total turnover 0.622 0.764BC4 Relative dimension 0.674 0.838BC5 Company IT culture 0.722 0.836BC6 E-commerce confidence 0.570 0.705

Buyer-supplier relationshipBS1 Age of relationship 0.483 0.777 0.608BS2 Relative contractual power 0.258 –BS3 Dimensional gap 0.505 0.777BS4 IT/e-commerce gap 0.185 –BS5 Information gap 0.454 0.702

Product characteristicPC1 Product type 0.504 0.677 0.784PC2 Strategic importance of auctioned good for final

product/service0.416 0.578

PC3 Product standardization or customizationdegree

0.601 0.745

PC4 Description complexity of auctioned item 0.577 0.739PC5 Repeatability of auction event 0.553 0.711PC6 Total value/size of auctioned lot 0.580 0.729

Auction processAC1 Auction typea 0.536 0.698 0.731AC2 Aggregation mechanism 0.461 0.556AC3 Winner algorithma 0.378 0.424AC4 Minimum bid setting 0.405 0.483AC5 Reverse price level setting 0.469 0.551AC6 Product information disclosure 0.586 0.765AC7 Process information disclosure 0.579 0.752AC8 Timing mechanism (e.g. autoextension)a 0.446 0.607AC9 Opening bid 0.337 –AC10 Total number of biddera 0.198 0.340AC11 Bid visibilitya 0.395 0.538

Notes: aIn bold factors retained by the preliminary analysis (not to be dropped out); bIn bold factorswith CITC .0.4; Loading .0.6; a . 0.5

Table AII.Test on price data

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Corrected item –total correlation

Factoranalysis

Cronbach’salpha test

Item ID Measures/variables CITC Loadings a (Group)

Market contextMC1 Concentration of supply market 0.621a 0.834 0.699MC2 Volatility of supply market price 0.458 0.700MC3 Presence of a leading firm 0.423 0.666MC4 Number of potential suppliers 0.443 0.702

Supplier characteristicSC1 Supplier reputation 0.363 – 0.884SC2 Total number of firm’s employees 0.659 0.845SC3 Total turnover 0.675 0.921SC4 Relative dimension 0.624 0.903SC5 Company IT culture 0.449 0.457SC6 E-commerce confidence 0.391 –

Buyer characteristicBC1 Buyer reputation 0.372 – 0.807BC2 Total number of firm’s employees 0.625 0.791BC3 Total turnover 0.639 0.767BC4 Relative dimension 0.662 0.810BC5 Company IT culture 0.563 0.738BC6 E-commerce confidenceb 0.462 0.649

Buyer-supplier relationshipBS1 Age of relationship 0.567 0.759 0.747BS2 Relative contractual power 0.473 0.820BS3 Dimensional gap 0.610 0.824BS4 IT/e-commerce gap 0.268 –BS5 Information gap 0.483 0.622

Product characteristicPC1 Product type 0.506 0.671 0.774PC2 Strategic importance of auctioned good for final

product/service0.506 0.669

PC3 Product standardization or customization degree 0.557 0.727PC4 Description complexity of auctioned

itemb0.583 0.748

PC5 Repeatability of auction eventb 0.487 0.661PC6 Total value/size of auctioned lot 0.477 0.634

Auction processAC1 Auction type 0.604 0.679 0.874AC2 Aggregation mechanism 0.487 0.571AC3 Winner algorithm 0.697 0.771AC4 Minimum bid setting 0.607 0.701AC5 Reverse price level setting 0.656 0.740AC6 Product information disclosureb 0.502 0.579AC7 Process information disclosureb 0.451 0.525AC8 Timing mechanism (e.g. autoextension) 0.720 0.791AC9 Opening bid 0.501 0.594AC10 Total number of bidder 0.597 0.684AC11 Bid visibility 0.706 0.782

Notes: aIn bold factors with CITC .0.4; Loading .0.6; a .0.5; bIn bold factors retained by thepreliminary analysis (not to be dropped out)

Table AIII.Test on process data

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