Impact of E-procurement on Procurement Practices and Performance

Benchmarking: An International JournalEmerald Article: Impact of e-procurement on procurement practices and performanceGioconda Quesada, Marvin E. González, James Mueller, Rene Mueller

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To cite this document: Gioconda Quesada, Marvin E. González, James Mueller, Rene Mueller, (2010),"Impact of e-procurement on procurement practices and performance", Benchmarking: An International Journal, Vol. 17 Iss: 4 pp. 516 - 538

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Impact of e-procurementon procurement practices

and performanceGioconda Quesada, Marvin E. Gonzalez,

James Mueller and Rene MuellerDepartment of Marketing and Supply Chain Management, School of Business,

College of Charleston, Charleston, South Carolina, USA

Abstract

Purpose – The purpose of this paper is to investigate the impact of electronic procurementtechnologies on procurement practices (PPR) and procurement performance (PP).

Design/methodology/approach – This paper posits a model of the relationships betweene-procurement technology (EPT) usage, PPR, and PP. This model was tested and validated using asample of 368 procurement specialists in the USA.

Findings – The findings suggest that EPT usage positively affects managers’ perceptions of bothPPR and PP.

Research limitations/implications – The findings of this paper primarily pertain to theoperational level of the organization. Future research could also attempt to isolate the impact ofindividual EPTs on firm performance.

Practical implications – The contribution for practitioners is to provide guidelines for the use ofEPTs, and to report its impact on PP. The measurement instruments developed in this paper can beused to evaluate and benchmark current PPR.

Originality/value – This paper contributes to the literature by providing an empirical test of theimpact of EPTs on perceptions of PPR and performance.

Keywords Procurement, Sourcing, Electronic commerce, Information systems,Supply chain management, United States of America

Paper type Research paper

IntroductionSupply chain management (SCM) involves all the approaches used to efficientlyintegrate the supply-side participants of a firm’s value chain (Porter, 1980) so thatproducts/services are delivered to the customer in the right quantities, to the rightlocation, at the right time, and at optimal cost. The application of information systems(IS) technology to facilitate this integration process is a phenomenon that continues toreceive managerial attention and, consequently, academic interest. Research on theapplication of IS technology to support SCM is abundant, results clearly show that theuse of new SCM technologies increase the efficiency of the supply chain as well asimprove overall firm performance (Lindskog and Wennberg, 2002). While electronicdata interchange (EDI), inter-organizational systems, e-commerce, e-sourcing,e-procurement, and e-auctions are all applications of IS that support SCM(Kameshwaran et al., 2007; Lee and Whang, 2000; Presutti, 2003; Puschmann and Alt,2005; Dedrick et al., 2008). According to Novack and Simco (1991), e-procurement studiesare particular important due to the fact that procurement is one of the most criticalfunctions of the supply chain. In terms of e-commerce, e-procurement is usually the

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Benchmarking: An InternationalJournalVol. 17 No. 4, 2010pp. 516-538q Emerald Group Publishing Limited1463-5771DOI 10.1108/14635771011060576

starting point for many companies’ overall e-commerce strategy (Chang et al., 2004). Onestudy shows organizations spending at least one-third of their overall budget onprocurement products and services (Killen and Kamauff, 1995). More recent research(Moozakis, 2001) finds that investments in procurement technologies account for thegreatest percentage (53 percent) of business investment in enterprise applicationssoftware, followed by customer relationship management (41 percent), SCM (31 percent),and electronic resource planning (8 percent).

Although overall adoption rates of e-procurement technology (EPT) are still a relativeunknown (Pearcy et al., 2008), most researchers agree that the full impact ofe-procurement has not yet been realized and that the adoption and integration of EPTsinto the business mainstream is occurring at a much slower pace than expected(Davila et al., 2003). Indeed, studies have shown that while over 70 percent of Americanbuyers use internet technologies at work (Caridi et al., 2004), the percentage of businessprocurement conducted electronically is relatively low – ranging from 10 percent(Qualyle, 2005) to 20 percent (Kulp et al., 2006). This disconnect is evident in a recentstudy by Gunasekaran and Ngai (2008). In this study, 80 percent of industry respondentsagreed that the use of the internet was important in procurement; however, only20 percent had actually adopted EPTs. According to Talluri et al. (2006), managersrecognize benefits of e-procurement such as: better coordination with suppliers, quickertransaction times, higher flexibility, better supplier integration, and lower costs(Fang et al., 2007).

If managers and workers understand the benefits of EPTs, why are they not used?Gilbert (2000) has partially answered this question by arguing that companies jump ontothe e-procurement bandwagon without fully understanding the inter-organizationalcollaboration and network effects underlying these technology models, the investmentrequired to move the right information from suppliers to employees, and thecomplexities of integrating these technologies with existing enterprise resourceplanning systems. Recognizing the managerial challenges, operational risks, anddifficulty measuring incremental increases to profit inherent in implementing new(and relatively expensive) supply chain technologies, this research seeks to explore theeffect of EPT usage on procurement practices (PPR) and PP). Through a large-scaleempirical study investigating how emerging EPTs affect the procurement function,a theoretical model is developed and resulting hypotheses are empirically tested. First,a literature review is presented.

Literature review and hypotheses developmentLeenders et al. (2002) briefly summarize the history of procurement since the late 1800s.Initially, procurement (purchasing) was considered a clerical function. By the 1970s,purchasing/procurement began to receive academic attention as its importance as anadministrative function became recognized (Ammer, 1974). It was Porter’s (1980)seminal work, however, that prompted firms to think of procurement as a strategicfunction rather than simply and administrative one (his five forces model includessupplier and buyer power as two critical forces for competitiveness). Since the 1980s,procurement has evolved from being viewed as merely a process for buying goods andservices for a firm, to being more comprehensively defined as all the activities necessaryto acquire goods and services needed to achieve user requirements (Tassabehji andMoorhouse, 2008).

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The strategic importance of procurement has been reiterated frequently, and is stillone of the critical themes found in the literature (Drake and Lee, 2009; Ordanini andRubera, 2008; Rajagopal and Bernard, 1993; Ellram and Carr, 1994; Rink and Fox, 1999;Kocabasoglu, 2002). Soares-Agular and Palma-Dos-Reis (2008) and Drake and Lee (2009)argue the importance of giving procurement a strategic role in the organization andagree that achieving world-class status in procurement requires leadership andalignment of purchasing strategy with business strategy.

While relatively fewer studies have analyzed procurement and its impact on differentfunctional, firm or supply chain performance objectives (Croom and Johnson, 2003;Gebauer et al., 1998; Frohlich and Westbrook, 2002), components of the basice-procurement model can be gleaned from contributions in the literature at both thestrategic and operational levels. The benchmarking process can provide a critical link inunderstanding the relationship between the components of this model, as shown inFigure 1, and explained in the next sections.

EPT usageEPT usage has been defined as the extended usage of electronic network technologiesand practices that facilitate electronic communication, information exchange andtransaction support through either public or private networks (Min and Galle, 1999).In this context, it becomes critical to understand the effects of changing informationtechnologies on EPT usage, business performance, and the achievement of businessgoals.

Previous literature has used the term e-procurement to describe the use of the interneton procurement tasks (Davila et al., 2003; Presutti, 2003). The mistaken emphasis on theinternet only could lead academicians and practitioners to understand too narrowly thecapabilities, benefits and limitations of e-procurement; however, this is not internetprocurement, but electronic procurement (Neef, 2001). Clearly, the internet provides alow-cost solution for those firms wanting to start e-procurement but not having theresources necessary for adopting more expensive information technologies such as EDI.Despite the emphasis on the internet, EPT is not synonymous withinternet-procurement. Indeed, Ordanini and Rubera (2008) found that the internet isuseful primarily when used as a complementary tool used in conjunction with otherEPTs. Other researchers have cleared this misunderstanding by naming web-based B2Bprocurement as specific procurement activities done through the internet (Candrasekarand Shaw, 2002; Lindskog and Wennberg, 2002).

Figure 1.Research framework

E-procurementtechnology usage

(EPT)

Procurementpractices

(PPR)

Procurementperformance

(PP)

H3H2

H1

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Procurement practicesDespite significant academic interest in the subject, it does not appear to be a universallyaccepted delineation of the PPR construct. Based on a review of the literature (Cammishand Keough, 1991; Keough, 1993; Ellram and Siferd, 1993; Laios and Xideas, 1994;Baldwin and Orr, 1997; Cavinato, 1991; Novack and Simco, 1991; Rajagopal andBernard, 1993; Herberling, 1993; Sutton, 1989; Archer and Yuan, 2000; Leenders et al.,2002; Lincoln University, 2001; Gebauer and Segev, 2001; Kong and Li, 2001; Rink andFox, 1999; Segev, 2001; Berger and Gattorna, 2001; Subramaniam and Shaw, 2002; Neef,2001; Alt and Fleisch, 2000; Presutti, 2003; Tracey, 2004; Gonzalez and Medrano, 2002),PPR can be divided into information gathering, supplier contact, contracting,requisitioning, and intelligence/analysis as described below:

(1) Information gathering. Webster and Wind (1996) specify the buying tasks as:. identification of need;. establishment of specifications;. identification of alternatives;. evaluation of alternatives; and. selection of suppliers.

All of these steps are done in the procurement stage of information gathering.As stated by Segev et al. (1998), in information gathering, prospectivebuyers identify their needs and evaluate potential sources to fulfill them. Thisprocess is accomplished by gathering information about market conditions,products and sellers. Novack and Simco (1991) explain the informationgathering process as conducting market analysis, depending upon if it is acompetitive market (many suppliers), an oligopolistic market (a few largesuppliers) or a monopolistic market (one supplier).

(2) Supplier contact. The buyers’ request for quotes, request for proposals (RFP),request for information and bids are all contained in supplier contact. Rink andFox (1999) include supplier contact as part of the procurement activities in anystage of a product-life cycle, from requesting for quotes, to requesting for volumediscounts and bids. Segev et al. (1998) report that the RFP ranked third infrequency-of-use as a negotiation technique, after face-to-face contact and bids.

(3) Contracting. Negotiation is the interaction of partners to determine price,availability and delivery times of goods and services (Segev et al., 1998).Contracting is simply the result of successful negotiations. The contractingprocess varies depending on whether the transaction is a new buy, a modifiedrebuy, or straight rebuy (Anderson et al., 1987).

(4) Requisitioning. In requisitioning, the terms of the contracts are carried out andgoods and services are transferred in exchange for money or other forms ofcompensation. Requisitioning is also referred to as settlement (Segev et al., 1998),or delivery of products and performance of service (Novack and Simco, 1991) andculminates with the generation of performance data used as inputs in thefollowing stage, intelligence, and analysis.

(5) Intelligence and analysis. Berger and Gattorna (2001) define intelligence andanalysis as the identification, collection and use of internal and external data to


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enable procurement to make smart sourcing decisions. Narasimhan and Carter(1998) specified purchasing practices as:. supplier certification;. supplier development;. supplier qualification;. just-in-time procurement; and. supply base rationalization.

All these activities require intelligence and analysis as part of the PPR to makebetter decisions about suppliers. Novack and Simco (1991) argue to includeintelligence and analysis as a post-purchase/make performance evaluation forcontrol purposes; however, Gonzalez et al. (2004) argue that intelligence is morethan just a control of performance; high-quality information is a key tool fordeveloping effective strategies.

Recent literature in SCM shows empirical and theoretical evidence that improvement inPPR; positively affect the procurement function performance. Vaidyanathan andDevaraj (2008) show empirical evidence that support the relationships between PPR ande-procurement satisfaction performance. Vaidyanathan et al. (2008) provided resultsbased on Australian companies showing that higher frequency of PPR positivelyimpacts the effect of e-procurement on procurement performance (PP). Tatsiopoulos(2004) indicate that about 60 percent of global purchasing expenditures are spent onhigh-volume, low-money maintenance, repair, and operating (MRO) purchases (MROsupplies); which typically account for 20 percent of an organization’s purchases but80 percent of its orders. Therefore, by improving the PPR of MRO ordering, a dramaticdecrease in transaction costs is expected. Tavi (2008) emphasizes that organizationscannot ignore the abundant benefits that world-class PPR offer in an increasingly globaleconomy (increased control, cost savings, efficiencies and good corporate citizenship,among others). Based on the literature, the authors claim:

H1. The higher the use of PPR in a firm, the higher the PP.

Carr et al. (2000) found that higher firm performance is associated with functional-levelpurchasing expertise, purchasing risk-taking, and strategic purchasing activities.Likewise, Bayraktar et al. (2009) report a positive correlation between the adoption of IStechnology and the level of SCM practices and firm performance by using a sample ofmetal fabrication industry in Turkey. The transformational effect of e-procurement hasbeen empirically validated by Croom and Brandon-Jones (2007); however, it is withinthe narrowly constrained domain of nine UK public sector organizations. Finally, whileGarrido et al. (2008) investigated the impact of internet intensity-of-use in PPR on theorganizational processes and structure, it was only done with Spanish industrial firms.Segev et al. (1997) investigated the impact of the internet on PPR; however, the findings donot report validity and reliability of the PPR construct, only descriptive statistics related toprocurement implementation via internet applications. This study seeks to overcomesome of the constraints presented in previous literature by using a large andrepresentative random sample selected from the Institute for Supply Chain Management,the world’s largest SCM association. Based on previous literature, the authors suggest:

H2. The higher the EPT usage of the firm, the higher the PPR.

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Procurement performanceAlthough the need for performance measurement in procurement has long beenrecognized, for a variety of reasons, many organizations fail to measure it adequately(Cammish and Keough, 1991; Brun et al., 2004). Easton et al. (2002) review the history ofPP measurement in the literature through the 1980s and early 1990s and conclude that ageneral weakness of “traditional” measures is that they recognize and reward mainlyshort-term gains, rather than long-term ones. Laudon and Laudon (2010) argued thatmeasuring long-term impact is notoriously difficult.

Another problem with traditional metrics is that they often work to improve the PP atthe expense of other departments’ performance; however, the concept of improving onlyone unit’s performance (a traditional way of measuring PP) has been heavily criticized inthe literature (Bourne et al., 2002; Ghalayini and Noble, 1996, 1997) and is counter to thetotal quality management philosophy. Other criticisms of traditional measures of PPinclude: being based too much on financial performance; one-dimensional or incomplete;contradictory to continuous improvement; inflexible; no strategic focus; and eveninvalid (Easton et al., 2002).

The literature on e-PP is divided in terms of its impact at the operational or strategiclevel of the organization. At the operational level, there have been several studiesinvestigating the impact of EPTs on PPR and PP including Mishra et al. (2007),Vaidyanathan and Devaraj (2008) and Teo et al. (2009). It is argued that by utilizing newprocurement technologies, firms can increase the efficiency of their entire procurementprocess and, thereby, can achieve higher firm performance (Lindskog and Wennberg,2002). Research by Gebauer et al. (1998) has also described PPR and how these positivelyimpact PP in terms of cost, time, satisfaction, quality, stock, and value.

Several studies are particularly useful for helping define and understand e-PP andhow it can be measured. Croom and Johnston (2003), for example, focus one-procurement when they address the impact of e-business on internal customer service.Frohlich and Westbrook (2002) measure the impact of web-based procurement inoperational performance (delivery time, transaction cost, profitability, and inventoryturnover) while Gebauer et al. (1998) analyze the effect of the internet on strategicprocurement planning practices and how these practices influence PP. Relatively fewstudies, however, have analyzed this phenomenon and its impact on different functional,firm or supply chain performance objectives.

The potential benefits of e-procurement have been described extensively in bothpractitioner and academic journals (Kocabasoglu, 2002; Lindskog and Wennberg, 2002;Gebauer et al., 1998). There is general agreement that e-PPR positively impact PP interms of cost, time, satisfaction, quality, stock, and value; however, estimates of theimpact of investments vary (Ordanini and Rubera, 2008; Gunasekaran and Ngai, 2008)and empirically derived figures are difficult to unearth. The Aberdeen Group (2006)reports e-procurement benefits including: 64 percent reduction in off-contract(“maverick”) spending, 7.3 percent reduction in prices for spend brought back ontocontract, 66 percent reduction in requisition-to-order cycles and 58 percent reduction inrequisition-to-order costs; accordingly, the report concludes that e-procurement “reallyworks”. A.T. Kearney, a global management consulting firm, similarly argues thatcompanies can save more than 13 times their investment in EPTs and claims further thatthe top 500 global companies could realize $330 billion in annual savings through the useof e-procurement (Plano, 2002). Hackett Benchmarking & Research likewise argue that


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that e-procurement can save a company 2 percent annually (Roth, 2001). Rai et al. (2009)provide evidence of the positive impact of e-procurement on procurement productivity.Hence, the authors suggest:

H3. The higher the EPT usage of the firm, the higher the PP.

Owing to the great impact of e-procurement on business performance (as reviewedpreviously), it is the purpose of this research to provide a broad understanding of theimpact of all kinds of electronic technologies that facilitate the PPR among organizations(Berger and Gattorna, 2001). In this study, the researchers include in EPTs (public aswell as private networks) that could be designed for specific firms, e.g. EDI and otherinterorganizational systems.

Research methodsA large-scale survey approach was used to test the hypotheses derived for the researchmodel (Figure 1). The constructs for this research were developed with a strongtheoretical foundation based on a review of available literature. The literature reviewincluded theoretical models as well as reliable and valid measures that have been used inpast research on PPR and performance. Items were found in the literature and wereaugmented by open-ended interviews with procurement managers. A five-point Likertscale where 1 – not at all, 2 – to a small extent, 3 – to a moderate extent, 4 – to aconsiderable extent, and 5 – to a great extent was used. A sixth classification wasprovided for reducing missing values, 6 – do not know.

The first step was to allow experts in the business and academic fields to review theitems for clarity and content. The items were modified, deleted and added as necessaryby incorporating their feedback and analysis. The researchers then used the Q-sortmethodology (Stephenson, 1953) to pre-test the convergent and discriminant validity ofthe scales (Q-sort also ensures content validity and clarification of the items anddimensions of the different constructs).

A large-scale survey was the instrument for data gathering (Appendix 1). The focusof the study is procurement specialists, since they are the most appropriate to answerquestions related to PPR, PP, and EPT usage. The Institute for Supply Management(ISM) was selected as the source for the mailing list. ISM is the largest supplymanagement association in the world with nearly 43,000 members. It is a prestigiousassociation of professionals in the area of procurement from diverse industries aroundthe nation. The mailing list contained 5,000 names randomly selected from the ISM USmembership database. Priorities were given to members in the following SICclassifications: 28 “chemicals and allied products”, 33 “primary metal industries”,34 “fabricated metal products”, 35 “industrial and commercial machinery and computerequipment”, 36 “electrical equipment and components”, and 37 “transportationequipment”. Respondents included procurement/materials/supply chainvice-presidents, directors, and managers. The mailing list was further refined. Sincethe large-scale survey was going to be implemented using online data gathering, thosenames with no email addresses were deleted from the initial contact database. From the5,000 names, a total of 3,532 contained e-mail addresses, and therefore, passed the firstscreening.

A x 2 test was conducted to check for non-response bias. The results showed thatthere is no significant difference between the sample of 2,712 and the respondents of 368

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when considering the percentages in SIC codes at 0.05 level of significance, therefore, thesample showed that the sample was not biased. Another non-respondent bias analysiswas done using the first round of the respondents (those who responded to the firste-mail) as the expected frequency and the second round of the respondents (those whoresponded to the second e-mail) as the observed frequency for the different SIC codes.Both tests exhibit that the respondents represent an unbiased sample.

The first step after collecting the large-scale data is to perform a confirmatory factoranalysis for the measurement models, and then, focus on the structural equation modeldisplaying the hypothesized relationships. The purification of the measurement modelswas done by examining the modification indexes along with the factor loadings anderror terms as first criteria for testing the model fit (Appendix 2). Then, the researchersstudied the logic and theoretical support for deleting items. The researchers deleted oneitem at a time if there was a reason to do so, based on the criteria for model fit. Otherwise,the item remained in the model. The researchers continued the modifications in each ofthe measurement models until an acceptable model fit was obtained.

The researchers used a combination of several fit measures for model testing, asproposed in the literature (Bagozzi and Yi, 1991). Bagozzi and Phillips (1992) propose touse a confirmatory factor analysis to assess the hypothesized model for each construct.Once the hypothesized model has passed the preliminary first criteria for model fit(absence of negative error variances, correlations greater than one and very largestandard errors), the overall model fit indexes were checked. Table I summarizes themodel fit criteria used in the measurement models and in the structural equation model.

Discriminate validity measures the ability of measurement items to differentiateamong constructs being measured (Syamil, 2000). The researchers assesseddiscriminate validity by running a x 2 test of discriminate validity for each pair ofconstructs (Bagozzi and Phillips, 1992). This was done by obtaining the difference in x 2

between a fixed correlation of 1 between the constructs and a freed correlation betweenthe same constructs. Reliability estimation is left for last because in the absence of a validconstruct, reliability may not be relevant (Koufteros, 1999). Composite reliability wasused to measure the reliability of the hypothesized measurement models. The formulaused was:

Composite_Reliability ¼

Pstandardized_loading

� �2

Pstandardized_loading

� �2þP

1j

Model fit measures Class Acceptable value References

x 2/df 1 1 , x 2/df , 3 (at most) Carmines and McIver (1981)p-value 1 .0.05 Joreskog (1969)RMR 1 Smaller better Arbuckle and Wothke (1999)GFI 1 Closest to 1 better Tanaka and Huba (1985)AGFI 2 $0.9 Hair et al. (1998)NFI 2 $0.9 Bentler and Bonett (1980)TLI 2 $0.9 Bentler and Bonett (1980)CFI 2 $0.9 Hair et al. (1998)RMSEA 1 ,0.05 (the most ,0.08) Browne and Cudeck (1993)ECVI 3 Should decrease Hair et al. (1998)

Table I.Model fit criteria


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A commonly used value for acceptable reliability is 0.7 (Hair et al., 1998). More reliablemeasures give greater confidence that the individual indicators are all consistent in theirmeasurements, and therefore, the model is repeatable. The method for structuralequation model is that the researchers state a model based on theoretical foundations.Then, the researchers test its plausibility based on sample data that comprise allobserved variables in the model. If the discrepancy between the theoretical and thedata-oriented model is small, the theoretical model is statistically well fitting, and thus,substantially meaningful (Zhang, 2001).

Of the data, 2 percent was taken out of the analysis since the respondents specifiedthat they have not used e-procurement at all. First, the averaged score of the items loadedfor each dimension of each construct was computed. Second, these scores were used asindicators for the corresponding construct. In the case, there are no dimensions (i.e. EPTusage), all the items are put together in one dimension (it does not mean that EPT ismeasured only by one item). Schumacker and Lomax (1996) indicate that the use of itemsfrom an instrument to measure the latent variables in a structural model increasesthe degrees of freedom in the structural equation model and may cause problems inmodel fit.

Research resultsThe measurement models were validated and tested for reliability before adding them tothe structural equation model. The structural equation model is shown as a pathdiagram in Figure 2. In this model, EPT usage is treated as the exogenous variable (j1).The endogenous variables include PPR (h1) and PP (h2). Exogenous latent variables(i.e. independent variables, X-variables) cause fluctuations in the values of other latentvariables in the model. Changes in the values of exogenous variables are not explainedby the model. Endogenous latent variables (i.e. dependent variables, Y-variables) areaffected by the exogenous variables in the model, either directly or indirectly.

The structural equation model showed a good fit between the theoretical model andthe data. Measures of absolute fit of the model to the data show a high degree to whichthe overall model predicts the observed covariance matrix (x 2/df ¼ 2.52, RMR ¼ 0.04,GFI ¼ 0.93, RMSEA ¼ 0.06). Measures of incremental fit show a good fit (AGFI ¼ 0.90,NFI ¼ 0.90, TLI ¼ 0.92 and CFI ¼ 0.93). Overall, the model is accepted as a good modelfit. The three hypothesized paths were significant at the 0.05 level of significance. Theresults indicate that EPT usage affects both PPR and PP. Presutti (2003) supportstheoretically that EPT usage significantly improves the effectiveness and efficiency ofthe procurement process. Content validity was assessed with literature review andinterviews with practitioners and academicians. Reliability was assessed for eachdimension using the composite reliability. All reliability estimates exceed customaryacceptable levels (higher than 0.75 for all of them). Discriminant validity was assessedby comparing the x 2 and degrees of freedom differences between the constrained modeland the unconstrained model relating two dimensions. This process was done for all pairof constructs, and no significant differences were found at 0.001 level of significance. Allcorrelations among constructs were significant using a two-tail test.

Once the model was tested for reliability and validity, the researchers focused onthe hypotheses testing. Each path with a single-headed arrow was estimated by astructural equation. The path coefficients between each pairs of factors showed

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p-values less than 0.05. This means that the paths in the model shown in Figure 1 arepositive and significant. The results show full support for hypotheses H1-H3.

H1: PPR have a direct positive relationship with PPThe structural equation model supports H1. The strength of the relationship betweenPPR and PP is 0.45, which presents a p-value of 0.005. Theoretically, this hypothesisshows that when the dimensions for PPR are high, the dimensions for PP are also high.Information gathering, supplier contact, contracting, and intelligence and analysis areimportant PPR that affect positively the PP (i.e. internal performance, supplier-relatedperformance, and internal customer performance). This result suggests that firms with

Figure 2.Structural equation model

0.40

IG

0.26

SC

0.41

CNF

er1 er2 er3

0.62

Procurementpractices

(PPR)

0.41

IANF

0.46

IP

0.65

SR

er4 er5 er6

0.59


(PP)

0.61

IC

er7

res2 res3

0.810.680.64

0.78

0.45

0.63 0.510.64

eProcurementtechnology

usage (EPT)

0.26

EPT

er14

0.79

0.51

0.36


525

procurement managers investing resources to seriously pursue better PPR achievehigher levels of PP than firms with lower levels of commitment into their PPR.

H2: EPT usage has a direct positive relationship with PPREPT usage affects directly PPR, as supported by the structural equation model(path ¼ 0.79), supporting H2. This means that higher usage of these technologies willimprove the levels of PPR. Therefore, managers should seriously consider the usage ofEPTs as a means for continuously improving their information gathering, suppliercontact, contracting, and intelligence and analysis practices.

H3: EPT usage has a direct positive relationship with PPThe path coefficient between EPT usage, and PP was 0.36 with a p-value of 0.04. Thisresult implies that when procurement departments have higher levels of usage of EPTs,the impact on PP will be positive. Therefore, firms using more EPTs for theirtransactions and communications will receive higher levels of PP results.

Theoretical implications and contributionsThis research provided a theoretical framework that identified positive and significantrelationships between EPT usage and both PPR and PP. The main contribution toresearch is supported by the research framework shown in Figure 1. This frameworkprovides a foundation for future research in the area of procurement.

As stated in the literature review, there is a gap in the literature when analyzinge-procurement. Previous research on e-procurement has limited the literature to studyinternet-based procurement only, which creates a gap for other EPTs. This study goesbeyond that to include in e-procurement those technologies that facilitate PPR includingEDI, FTP, video conferencing, electronic markets, and internet/reverse auctions.

The study contributes to the procurement literature in a number of ways. First, thisresearch identifies measurement models for PPR, EPT usage and PP which werevalidated and tested for reliability thereby providing new ways for evaluatingprocurement measures in the business environment. These measurement models can bereplicated by other researchers or used in the development of new models inprocurement. This research also identified PPR as a construct with a multi-dimensionalnature, and it was defined as a second-order factor with four first-order factors(information gathering, supplier contact, contracting, and intelligence and analysis).

The research also provides insight about usage of EPTs usage, for example,21.4 percent of the companies in the sample do not process any procurement transactionin e-procurement and 37.2 percent use it in less than 10 percent of their procurementtransactions; also, 76 percent of the firms indicated that their experience with EPTs isless than 3 years. Finally, PP also showed to be a multi-dimensional construct and it wasdefined as a second-order factor with three first-order factors (internal performance,supplier-related and internal customer).

Practical implications and contributionsThe empirical results from this study have important implications and contributions forpractitioners. It is important in any kind of research to provide strong theoreticalcontributions; however, empirical contributions are also needed as a start point to

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provide final contributions to practitioners. This study is not an exception and theresearchers summarize the empirical contributions and implications.

This study shows practitioners that e-procurement is not widely used (76 percent ofthe firms indicated that their experience with EPTs is less than three years). This findingis also supported by a survey of 200 multinational firms that showed that only 30 percentof the firms have implemented at least a basic e-procurement system (Presutti, 2003).Also, it shows that EPT usage has a positive impact on PPR, therefore, facilitates thedevelopment of operational tasks in the procurement area. Managers should seriouslyconsider the usage of EPTs as a means for continuously improving their informationgathering, supplier contact, contracting, and intelligence and analysis practices.

Practitioners currently interested in adopting EPTs can see the positive impact it hason PPR, which in turn, presents a positively significant impact on PP. Therefore,organizations that are implementing e-procurement are achieving short-term benefits inPPR; however, it is expected that the adoption of e-procurement will have a positiveimpact at the strategic level. Firms with procurement managers investing resources toseriously pursue better PPR achieve higher levels of PP than firms with lower levels ofcommitment into their PPR. This hypothesis merits further research. The researchpresented in this paper also supports previous publications in practitioners’ journalsthat PPR impact PP. Better PPR will positively affect the outcomes of the procurementfunction.

Limitations of the study and future research guidelinesThis study contributes to the literature by building on past theoretical and empiricalstudies through close collaboration with manufacturing firms. It also opens a windowfor further research in this area. Each of the limitations of this study is an opening area ofnew research in future studies. Therefore, in the following section, the researchersdiscuss limitations and recommendations for future research.

First, the researchers measured only the operational side of PPR considering the earlystages of e-procurement implementation on the organizations. Further research couldextend on measuring the strategic level of PPR and analyze the impact of EPT usage atthe strategic level.

Second, it would be interesting to measure individually the impact of each of thedifferent EPTs usage on the different PPR. For instance, are firms using certain EPTs onpayment processing and others for supplier contact? This would have made thequestionnaire far lengthier and it could have affected response rates; however, designinga research to do this specifically could be more manageable.

Third, the researchers limited the industries to the following SIC classifications:28 “chemicals and allied products”, 33 “primary metal industries”, 34 “fabricated metalproducts”, 35 “industrial and commercial machinery and computer equipment”,36 “electrical equipment and components”, and 37 “transportation equipment”.Therefore, results can only be generalized cautiously to other industries. Future researchshould include other industries.

Finally, future research can expand the current theoretical model by incorporatingconstructs from other fields of study. For instance, it would be interesting to include inthe research model e-commerce measures in general, not limiting the items to thosespecific to e-procurement.


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Appendix 1. Large-scale questionnaire

(continued)


533

(continued )

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(continued)


535

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Appendix 2. Large-scale item purification for PPR, PP and EPT usage – path

diagrams

Figure A1.PPR measurementmodel – Trial 11

0.60

cnf1

0.36

cnf2

0.59

cnf6

cnfe

r2cn

fer1

cnfe

r6

0.63

Con

trac

ting

(CN

F)

0.78

0.77

0.28

ianf

40.

58ia

nf6

ianf

er4

ianf

er6

0.39

Inte

llige

nce/

anal

ysis

(IA

NF)

0.55

ianf

70.

67ia

nf8

ianf

er7

ianf

er8

0.82

res2

res3

0.68

IG4

0.74

IG3

0.45

IG2

iger

3ig

er4

iger

2

0.48

Info

rmat

ion

gath

erin

g (I

G)

res5

0.82

0.67

0.86

0.42

sc4

0.76

sc2

0.56

SC3

scer

4sc

er2

scer

1

0.32

Supp

lier

cont

act (

SC)

res4

0.65

0.75

0.00

Proc

urem

ent

prac

tices

(PPR

)

0.79

res1

0.60

0.53

0.76

0.74

0.63

0.87

0.57

0.69


537

Corresponding authorGioconda Quesada can be contacted at: [email protected]

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

Figure A3.PP measurementmodel – Trial 9

0.62pp1

0.54pp3

0.66pp4

0.51pp5

pper1 pper3 pper4 pper5

0.45

Internalperformance

(IP)

0.79

0.28pp7

0.31pp9

0.42pp10

pper7 pper9 pper10

0.91

Supplier-related(SR)

0.49pp12

0.70pp14

0.46pp15

pper12 pper14 pper15

0.73

Internalcustomer

(IC)

0.49pp17

0.57pp18

pper17 pper18

res2

res3 res4

0.00


(PP)

pper21

0.54pp21

0.730.750.700.680.83

res1

0.670.95

0.85

0.700.650.550.71

0.810.74 0.53

Figure A2.EPT usage measurementmodel – Trial 5

0.44

EPT3

0.45

EPT6

0.41EPT7

0.42EPT8

0.38EPT9

epter3 epter6 epter7 epter8 epter9

0.00

eProcurementtechnology

usage(EPT)

res1

0.610.67 0.67

0.640.65

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Impact of E-procurement on Procurement Practices and Performance

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