Effect of business characteristicsand ERP implementationon business outcomesAn exploratory study of Korean

manufacturing firms

Pairin KaterattanakulDepartment of Business Information Systems, Western Michigan University,

Kalamazoo, Michigan, USA

James J. LeeDepartment of Management, Seattle University, Seattle,

Washington, USA, and

Soongoo HongDepartment of Management Information Systems,

Dong-A University, Pusan, Korea

Abstract

Purpose This study is an exploratory study aiming to explore whether different groups ofmanufacturing firms with similar business characteristics and enterprise resource planning (ERP)implementation approaches would experience different business outcomes from ERP implementation.The paper aims to discuss these issues.

Design/methodology/approach Cluster analysis with data collected from 256 Koreanmanufacturing firms was employed to identify groups of manufacturing companies having similarbusiness characteristics and adopting similar ERP implementation approaches. Then, the differences inbusiness outcomes from implementing ERP systems among these groups of companies were examined.

Findings Company size and production approaches are useful variables for groupingmanufacturing firms into clusters of companies with similar characteristics. Additionally, largemanufacturing firms with make-to-order production approach have significantly higher perceivedbenefits from implementing ERP systems regarding external coordination and competitive impactthan other firms do.

Research limitations/implications This study was conducted in only one industry of onecountry and used the data collected by self-reporting instrument. Thus, further studies conducted inother industries and/or other countries and using more objective measures would allow moregeneralizability of the findings of this study. It would also be interesting to investigate the effects ofthe logistics practices adopted by small manufacturing firms even though these practices may be moresuitable for large manufacturing firms.

Originality/value This study contributes to the literatures on benefits obtained fromimplementing ERP systems as none of the previous studies has focused on the relationship amongbusiness characteristics, ERP implementation approaches, and business outcomes from ERPimplementation.

Keywords Cluster analysis, Organizational performance, ERP, Manufacturing company

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/2040-8269.htm

Management Research ReviewVol. 37 No. 2, 2014pp. 186-206q Emerald Group Publishing Limited2040-8269DOI 10.1108/MRR-10-2012-0218

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IntroductionEnterprise resource planning (ERP) systems have been touted to streamlineorganizational functions and processes by integrating enterprise-wide data andbusiness processes. However, ERP implementation is risky and requires a substantialamount of resources (Cliffe, 1999). Thus, much academic research has been conducted ondifferent ERP implementation issues; for example, interactions between contingency,organizational IT factors, and ERP success (Ifinedo and Nahar, 2009), expectation andreality in ERP implementation (Helo et al., 2008), perceptions of the ERP systemimplementation project (Maguire et al., 2010), impact of organizational support on ERPimplementation (Lee et al., 2010), critical success factors of ERP implementation (Dezdarand Sulaiman, 2009), and difficulties experienced during ERP adoption (Soja andPaliwoda-Pekosz, 2009). Additionally, there are a few studies focusing on the benefitsobtained from ERP systems, including effects of ERP systems on organizationalperformance (Velcu, 2007), process efficiency and profitability of ERP (Huang et al.,2009), categories of ERP benefits (Gattiker and Goodhue, 2005), and competitiveadvantages of using ERP systems (Beard and Sumner, 2004; Lengnick-Hall et al., 2004).

Factors related to organization and ERP projects are critical for ERP implementationsuccess (Dezdar and Sulaiman, 2009). However, there are no previous studies focusingon the relationship among business characteristics, ERP implementation approaches,and the business outcomes from ERP implementation. Thus, this study is an exploratorystudy mainly aiming to explore whether different groups of manufacturing firms withsimilar business characteristics and ERP implementation approaches would experiencedifferent business outcomes from ERP implementation.

Exploratory research is appropriate for this study as there have been virtually noprevious findings about the effect of the combination between business characteristicsand ERP implementation approaches on the business outcomes from ERPimplementation. Exploratory studies are loosely structured studies that are suitablewhen the areas of investigation are so new or so vague that researchers need to dosome explorations to learn something about the dilemma, to explore some importantvariables, or to develop hypotheses or questions for future research (Cooper andSchindler, 2000).

To accomplish the objective of this exploratory study, we employed cluster analysisto identify groups of manufacturing firms having similar business characteristics andadopting similar ERP implementation approaches; then, we examined the differencesin the business outcomes from implementing ERP systems among these differentgroups of manufacturing firms.

The current study contributes to the existing literature in two ways. First, thisstudy identifies significant factors related to ERP implementation approaches andbusiness characteristics for grouping manufacturing firms. Second, this studyempirically tests whether different groups of manufacturing firms with similarbusiness characteristics and ERP implementation approaches would experiencedifferent business outcomes from ERP implementation. The findings of this study canserve as the starting point for future studies that investigate the relationship amongbusiness characteristics, ERP implementation approaches, and business outcomesfrom ERP implementation.

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ERP implementation approaches, business characteristics, and businessoutcomesERP implementation approachesSome previous studies found the dilemma between software customization and usingstandardized ERP packages (Helo et al., 2008). That is, it may be convenient for theadopting company to just adopt the best practice business processes embedded in ERPsystem; however, process re-configuration could be costly. On the other hand,customization of ERP software to better fit the companys existing business processesis also costly.

In this study, we used two terms to conceptualize these choices: processre-configuration and software customization. Process re-configuration was defined asthe adoption of the best practice business processes embedded in ERP systems withoutmodifying the ERP software, but re-configuring the existing business processesinstead. On the other hand, software customization occurs when the adopting companydo not want to (or cannot) change its existing business processes, and instead modifiesERP software to meet its business requirements.

Additionally, recent research suggested that several system factors could be criticalin determining the success of ERP rollout approach (Dezdar and Sulaiman, 2009).These factors include number of sites and users, number of ERP modules implemented,complexity of business processes, level of ERP software customization, and existenceof legacy systems. These factors can lead to varied ERP rollout approaches (Parr andShanks, 2000), including:

. The big bang approach this approach has a single go-live date for all selectedERP modules. It refers to a total effort to implement all selected ERP modulestogether at once.

. The mini big bang approach this approach has several go-live dates fordifferent subsets of ERP modules.

. The phase implementation approach this approach involves incrementallyimplementing ERP either module-by-module or site-by-site in a phased manner.

Finally, when implementing ERP systems, companies can employ different ERPselection approaches including a single ERP package, best-of-breed from several ERPpackages, developing ERP systems in-house, or pursuing a hybrid approach thatincludes in-house development and some specialized package functionality(Katerattanakul et al., 2006).

Business characteristicsFor manufacturing companies, the production approach is typically categorized intotwo continuums: make-to-order (MTO) and make-to-stock (MTS) (Gupta andBenjaafar, 2004). Under the MTO approach, a production order is released to themanufacturing facility only after a purchase order has been received from a customer,while under the MTS approach, products are manufactured in anticipation of futureorders and stored in the finished goods inventory (Youssef et al., 2004).

The MTO approach is good for customization and volume flexibilities (Yen andSheu, 2004); that is, when products are low in volume, but high in variety. While theMTO approach eliminates finished goods inventory and reduces a firms exposure tofinancial risk, it usually spells long customer lead times and large order backlogs

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(Gupta and Benjaafar, 2004). On the other hand, when there are requests for highproduction volume, long set-up times, stable production schedules, a relatively smallnumber of suppliers, and lower labor skills, it is better for manufacturing firms toimplement MTS approach to obtain immediate reactivity to external demands at thecost of inventory holding expenses (Yen and Sheu, 2004; Youssef et al., 2004).

Another business characteristic investigated in this exploratory study is companysize due to it being a possible cause explaining why firms have different ERPimplementation experiences (Mabert et al., 2001). This study adopted the definitionprovided by the Small & Medium Business Administration of Korea (eng.smba.go.kr).This definition categorizes companies in Korea, based on the number of employees,into small enterprises (#50 employees), medium enterprises (.50 and # 300employees), and large enterprises (.300 employees).

Business outcomesHaving implemented ERP systems, companies experience some improved performancemainly from the information perspective; that is, information is more easily accessibleand the quality of information is also improved (Mabert et al., 2000; Olhager andSelldin, 2003). Additionally, issues such as coordination with customers and suppliersimprove as well (Mabert et al., 2000; Olhager and Selldin, 2003). Thus, businessoutcomes related to coordination with business partners and improved businessinformation were investigated in this study.

Furthermore, when this study was conducted, Koreas outward-looking policy wasaimed at responding to the era of global competition and catching opportunities createdby a rapidly changing and globalizing marketplace. Therefore, business outcomesrelated to the companys ability to gain strategic advantages and to capture globalopportunities were also investigated in this study.

Research frameworkIn manufacturing firms, different production approaches need different manufacturinginfrastructure including resource allocation systems and distinctive communicationsystems to align the downstream, midstream, and upstream processes (Prasad et al.,2005). Additionally, without proper manufacturing infrastructure, a firm may not beable to achieve the full competitive advantage that the ERP system can provide(Hayes et al., 2005). Furthermore, there is a positive relationship between the success ofERP system and the alignment of ERP implementation and business strategy (Dezdarand Sulaiman, 2009; Velcu, 2007). The findings of these previous empirical studiessuggest that a relationship exists among business characteristics, ERP implementationapproaches, and business outcomes from implementing ERP system.

The organizational configuration theory posits that identifying groups differentfrom others but similar within the group allows better understanding of therelationship between organizational characteristics and performance (Ketchen et al.,1993). This organizational configuration is referred to as commonly occurring clustersof attributes of organizational strategies, structures, and processes (Ketchen et al.,1993, p. 1278).

The research framework of this study is based on the premise of the organizationalconfiguration theory and the findings of some previous studies suggesting that arelationship exists among business characteristics, ERP implementation approaches,

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and business outcomes from implementing ERP system. That is, the groups ofmanufacturing firms with similar business characteristics and similar ERPimplementation approaches employed could have significant effects on businessoutcomes from implementing ERP system.

Unfortunately, there have been virtually no previous studies investigating whetherdifferent groups of companies with similar characteristics experience differentbusiness outcomes from ERP implementation. We argue that the reason for the lack ofstudies investigating this relationship may be from the fact that there is no cleartheoretical development or hypotheses relevant to this relationship. As exploratorystudies help to provide initial results for further theoretical development (Cooper andSchindler, 2000), an exploratory research would be suitable for this study.

In this study, we defined the clusters of manufacturing companies based on two mainattributes: business characteristics (i.e. production approach, company size) and ERPimplementation approaches (i.e. process re-configuration vs software customization,ERP rollout approach, ERP selection approach). Then, we investigated whether makingclusters of manufacturing companies that have similar business characteristics andpursue similar ERP implementation approaches could explain the differential businessoutcomes from implementing ERP systems. Figure 1 shows the research framework ofthis study.

Research methodologyData collectionThe data set used in this study was from an extensive survey conducted on ERPimplementation in Korean manufacturing firms. This survey was administeredby Pollever Research Center, a leading market research company in Korea(www.pollever.com). The questionnaire used in this survey was adapted from theinstruments used in the similar studies conducted on US and Swedish manufacturing

Figure 1.Research framework

Business Characteristics: Production approach Company size

ERP Implementation Approaches: Process re-configuration vs. Software customization ERP rollout approach ERP selection approach

Manufacturing Firms

Combination of Business characteristics and ERP ImplementationApproaches

Outcomes fromimplementing ERP

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firms (Mabert et al., 2000; Olhager and Selldin, 2003). The original version of thequestionnaire was tested in a group interview with five Korean managers in charge ofERP in their companies. Based on the feedback from this group interview, we modifiedthe original version of the questionnaire. The final version of the questionnaire waswritten in English and included more than 60 questions. In this final version of thequestionnaire, we minimized the threats of common method bias by measuring eachof the constructs via several different questions. The questions measuring thesame construct were placed in random order in the questionnaire. Additionally,some questions measuring the same construct employed reverse scale to createcounterbalance.

The Pollever Research Center sent e-mail invitations to the managers in charge ofERP systems in Korean manufacturing firms. This e-mail invitation included the URLaddress of the web site hosting the questionnaire. This web site was developed to alloweach Korean manufacturing company participating in this study to submit its responseonly once. This would eliminate any bias that may occur due to multiple responsesfrom the same participant.

Before sending the e-mail invitation, the project team at the Pollever ResearchCenter contacted the managers in charge of ERP systems in Korean manufacturingcompanies and invited them to participate in the study. After one month, the researchcenter made follow-up phone calls to these managers regarding the study. Aftertwo months, a total of 366 responses were collected. However, 110 of those responsesincluded incomplete data; thus, only 256 responses were included in this study.

Business characteristics and ERP implementation approaches. From the256 responding firms, 71.9 percent of them had less than 300 employees (Table I);thus, according to the Small & Medium Business Administration of Korea(eng.smba.go.kr), these firms are considered small or medium enterprises. Regardingthe production approach, the percentages shown in Table I indicate the portion of itemsbeing produced in MTS or MTO fashion. Majority of the responding firms (69.9 percent)are dominated by MTO approach; whereas only 10.2 percent of them are dominated byMTS approach. The remaining 19.9 percent of them have a more or less equal splitbetween MTS and MTO. Almost half of the responding firms (43.8 percent) pursue asingle ERP package; while 27.7 percent of them pursue a more multifaceted approach byselecting best-of-breed from several ERP packages. Interestingly, 28.5 percent of themindicate that all or parts of their ERP systems were developed in-house (Table I).

When implementing their ERP systems, most of the responding firms (75.0 percent)follow either the Big bang or the Mini big bang implementation approaches.Only 25.0 percent of them report following either Phase-in by module or the Phase-inby site.

Three particular ERP modules distribution/logistics (DLModule), materialmanagement (MMModule), and production planning (PPModule), are directly related tomanufacturing process and widely implemented among Korean manufacturing firms.Thus, these three ERP modules were included and investigated in this current study. Onaverage, across the implementations of these three ERP modules among the respondingfirms, 47.3 percent of the implementations involve some ERP software customization;whereas 28.5 percent of the implementations involve some re-configuration of theexisting business processes. Approximately 24.2 percent of the implementations do notneed to conduct either ERP software customization or process re-configuration (Table I).

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Outcomes from implementing ERP systems. This study measured six businessoutcomes from implementing ERP systems. These six outcomes were also reported inthe previous studies on US and Swedish manufacturing firms (Mabert et al., 2000;Olhager and Selldin, 2003). Each of these outcomes was administered by using thefive-point Likert scale where the 5-scale represents a great amount of benefit and the

%

Number of employees (SIZE)1. #50 (small businesses) 26.62. .50 and # 300 (medium enterprises) 45.33. .300 (large enterprises) 28.1Production approach (MTSMTO)1. Portions of items produced by MTO

are $ 65% 69.92. Portions of items produced by MTO and

by MTS are approximately equal 19.9Portions of items produced by MTSare $ 65% 10.2ERP selection approach (SELECT):1. A single ERP package 43.82. Best-of-breed from several ERP packages 27.73. In-house development for all or parts of

ERP system 28.5ERP vendorsBizentro (a subsidiary of SamsungCorporation) 46.1SAP 26.6Oracle 16.4Other domestic vendors 10.9ERP rollout approach (ROLLOUT):1. Big bang: a single go-live date for all

ERP modules 47.72. Mini big bang: several go-live dates for

different subsets of ERP modules 27.33. Phased-in by module or by site:

incrementally implement the ERP system 25.0Process re-configuration vs softwarecustomization (MODIFY)

DLMODULE(%)

MMMODULE(%)

PPMODULE(%)

Average(%)

1. Significant numbers of changes are madeto the ERP module to fit the existingprocesses 15.6 12.5 12.9 47.3

2. Some changes are made to the ERP moduleto fit the existing processes 32.8 33.6 34.4

3. The ERP module and the existingprocesses are fit to each other without anychange 25.4 24.6 22.7 24.2

4. Some changes are made to the existingprocesses to fit the ERP module 20.3 23.8 20.7 28.5

5. Significant numbers of changes are madeto the existing processes to fit the ERPmodule 5.9 5.5 9.4

Table I.Business orientation andERP implementationapproach

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1-scale represents not at all. Table II shows the percentage of the responses for eachoutcome. Majority of the responding firms perceive at least some benefit fromimplementing ERP systems. The responding firms experience improved performance interms of quality and availability of information, coordination with both suppliers andcustomers, and competitive impact (i.e. linking to global activities and gaining strategicadvantage).

ProcedureCluster analysis is an analysis tool applied to the data that exhibit natural groupings.Its objective is to sort through cases or observations (e.g. people, things, events) andclassify them into groups or clusters so that the degree of association is strong betweenmembers of the same cluster and weak between members of different clusters. That is,a cluster is a group of relatively homogeneous cases or observations. Each clusterdescribes the group to which its members belong. Members in a cluster are similar toeach other. They are also dissimilar to members in other clusters.

Cluster analysis is thus a tool of discovery and may reveal associations and structurein data which, though not previously evident, are sensible and useful once found. Theresults of cluster analysis may contribute to the definition of a formal classificationscheme, such as similar customers in each market segment. Researchers have usedcluster analysis technique to identify distinctive groups of supply chain partners inautomobile industry (Trappey et al., 2010).

This study is an exploratory study aiming to explore whether different groups ofmanufacturing firms with similar business characteristics and ERP implementationapproaches experience different business outcomes from ERP implementation. Thus,in this study, we employed cluster analysis to identify groups of manufacturing firmshaving similar business characteristics and adopting similar ERP implementationapproaches. Then, multivariate analysis of variance (MANOVA) with post-hoc testswere employed to examine whether different groups of manufacturing firms withsimilar business characteristics and ERP implementation approaches experiencedifferent business outcomes from ERP implementation. However, before performingcluster analysis and MANOVA, we conducted construct reliability and validity tests.

Construct reliability. The level of process re-configuration vs software customizationwas measured across three ERP modules DLModule, MMModule, and PPModule.Thus, we needed to assess the reliability of this construct. Additionally, among thesix business outcomes, we explored whether similar outcomes could be grouped to formsome constructs. First, we conducted an exploratory factor analysis (EFA) on these nine

Area benefiting from implementing ERP 5 (%) 4 (%) 3 (%) 2 (%) 1 (%)

Improved coordination with customers (CUST) 8.2 46.5 32.4 9.0 3.9Improved coordination with suppliers (SUPPLY) 5.5 46.5 37.5 9.0 1.6Link to global activities (GLOBAL) 6.6 31.6 42.2 13.7 5.9Gain strategic advantages (STRADV) 4.7 31.3 49.2 9.8 5.1Quality of information (QUAINFO) 14.1 38.7 32.0 12.5 2.7Availability of information (AVAINFO) 15.2 47.3 30.1 6.3 1.2

Notes: 5 a great amount of benefit; 4 significant amount of benefit; 3 some benefit; 2 only alittle benefit; 1 not at all

Table II.Outcomes from

implementing ERP(in percentage)

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measuring items (i.e. level of process re-configuration vs software customization acrossthree ERP modules and six business outcomes). The factor loadings (see Table AI in theAppendix) suggest four constructs: process re-configuration vs software customization(MODIFY), informational impact (INFO), external coordination (EXTCO), andcompetitive impact (COMP). All constructs have their Cronbachs a values (Table AII)either above or very close to the cutoff point of 0.70 (Nunnally, 1978).

Then, based on the EFA result which suggested a measurement model with fourconstructs, we estimated this measurement model by conducting a confirmatory factoranalysis (CFA). The CFA result is presented in Table AIII and the model fit summaryis in Table AIV in the Appendix. These model fit test results suggest that themeasurement model with four constructs fits the sample data fairly well. Additionally,we computed the composite reliability (CR) and the average variance extracted (AVE)values of the four constructs (see Table AII in the Appendix). All CR values, exceptthat of the COMP construct (i.e. 0.676), are above the recommended threshold of 0.70(Fornell and Larcker, 1981; Hair et al., 2006; Segars, 1997). Similarly, all AVE values areabove the suggested threshold of 0.50 (Hair et al., 2006; Segars, 1997), indicating thatthe four constructs have captured a relatively high level of variance. In sum, all theresults of these reliability tests indicate a reasonably high level of instrumentreliability.

Convergent and discriminant validity. All loadings from the CFA results(see Table AIII in the Appendix) are high and the t-values (ranging from 4 to 14) forall loadings are above the 2.54 threshold supporting the statistical significance of theloadings ( p , 0.01). Additionally, all squared multiple correlations (R 2) values are high.These results support the assertion that the measuring items in this study are goodmeasures of the constructs (Gefen et al., 2000).

Based on the factor loadings of the EFA results (see Table AI in the Appendix), thereis no cross loading above 0.40. This suggests the discriminant validity of the fourconstructs (McKnight et al., 2002). Additionally, the square root of the AVE of eachconstruct is greater than any of the constructs correlations with other constructs(see Table AII in the Appendix). This result provides evidence for discriminant validityof the constructs in the model (Fornell and Larcker, 1981; Segars, 1997). We alsocompared the discriminant validity in the original measurement model with fourconstructs against other measurement models with only three constructs, whichincluded every possible combination of collapsing two constructs into one (Gefen et al.,2000). The x 2-value in the original measurement model is significantly better than thex 2-value of every reduced measurement model (see Table AV in the Appendix).

Cluster analysis. A three-cluster solution is suggested by result of the two-stepclustering method to be the most parsimonious grouping of the responding firms andalso the solution that best reflects the meaningful pattern of the relationships betweenbusiness characteristics and ERP implementation approaches. Frequency of theresponses (in percentage) regarding each of the four categorical cluster variables(i.e. SIZE, MTSMTO, SELECT, and ROLLOUT) for each cluster is reported in Table III.Additionally, Table IV shows the mean and standard deviation of all five clustervariables for each of the three clusters. Figure 2 shows these mean scores (or clustercenters) with a snake diagram.

The responding firms in Cluster 1 are small businesses focusing more on MTS small businesses with MTS-oriented. Majority of the responding firms in this cluster

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Categorical cluster variables Cluster 1 (%) Cluster 2 (%) Cluster 3 (%)

SIZE1. Small businesses 100.0 7.52. Medium enterprises 92.5 29.43. Large enterprises 70.6MTSMTO1. MTO-oriented 84.9 98.02. Equal MTS/MTO focuses 57.4 15.1 2.03. MTS-oriented 42.6SELECT1. A single ERP package 52.5 61.3 22.52. Best-of-breed from several ERP packages 34.4 49.03. Some in-house development 13.1 38.7 28.4ROLLOUT1. Big bang 41.0 77.4 24.52. Mini big bang 31.1 5.4 45.13. Phased-in 27.9 17.2 30.4

Table III.Frequency of each

categorical variablefor each cluster

Cluster variables Cluster 1 Cluster 2 Cluster 3 Total

SIZE 1.0000 1.9247 2.7059 2.0156(0.000) (0.265) (0.458) (0.741)

MTSMTO 2.4262 1.1505 1.0196 1.4023(0.499) (0.360) (0.139) (0.667)

SELECT 1.6066 1.7742 2.0588 1.8477(0.714) (0.979) (0.715) (0.838)

ROLLOUT 1.8689 1.3978 2.0588 1.7734(0.826) (0.768) (0.742) (0.823)

MODIFY 2.7978 2.7312 2.7255 2.7448(1.008) (1.083) (0.982) (1.022)

Cases 61 93 102 256

Table IV.Mean and standarddeviation of cluster

variables for each cluster

Figure 2.Snake diagram of the

cluster centers

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select a single ERP package (52.5 percent) and employ big bang implementationapproach (41.0 percent). Virtually all of the responding firms in Cluster 2 are mediumenterprises focusing on MTO medium enterprise with MTO-oriented. More thanhalf (61.3 percent) of the responding firms in this cluster select a single ERP packageand majority of the responding firms in this cluster (77.4 percent) employ big bangimplementation approach. Finally, majority of the responding firms in Cluster 3(70.6 percent) are large enterprises focusing on MTO large enterprises withMTO-oriented. Approximately half (49.0 percent) of the responding firms in thiscluster select best-of-breed from several ERP packages. The responding firms in thiscluster mainly employ mini big bang or phased-in implementation approaches.

Multivariate analysis of variance. The MANOVA results in the following Table Vindicate that the three clusters are significantly different based on the five clustervariables (i.e. SIZE, MTSMTO, SELECT, ROLLOUT, MODIFY). Then, we conducted apost-hoc test to check the equality of all five cluster variables across the three clusters.Researchers suggest that the follow-up analysis in this post-hoc test could employ eitheranalysis of variance (ANOVA) or discriminant analysis (Bray and Maxwell, 1985).

Results of the post-hoc test employing ANOVA are presented in the followingTable VI (we also conducted the post-hoc test employing discriminant analysis and theresults of this discriminant analysis are similar to the ANOVA results). The threeclusters are significantly different in only four cluster variables SIZE, MTSMTO,SELECT, and ROLLOUT.

Furthermore, we conducted pairwise comparisons using Bonferroni test. Bonferronitest is the optimal test for pairwise comparisons because of its reasonable power andease of application (Bird, 1975; Ramsey, 1980). From these pairwise comparisons, allthree clusters are significantly different from each other in only two cluster variables SIZE and MTSMTO. Results of these pairwise comparisons are presented in thefollowing Table VII.

Based on the mean scores of the three business outcomes across the three clusters(Table VIII), the responding firms in all three clusters report that they experienced atleast some benefits in all three business outcomes.

Then, we followed similar steps (i.e. using MANOVA followed by post-hoc ANOVAtest including pairwise comparisons with Bonferroni test) to test the difference in thebusiness outcomes from implementing ERP system across the responding firms in thethree clusters. The MANOVA results (Table IX) suggest that the three clusters are

Effect Value F Hypothesis df Error df Sig.

InterceptPillais trace 0.983 2,826.911 5 249 ,0.001Wilks lambda 0.017 2,826.911 5 249 ,0.001Hotellings trace 56.765 2,826.911 5 249 ,0.001Roys largest root 56.765 2,826.911 5 249 ,0.001ClusterPillais trace 1.164 69.572 10 500 ,0.001Wilks lambda 0.081 125.338 10 498 ,0.001Hotellings trace 8.344 206.922 10 496 ,0.001Roys largest root 7.964 398.192 5 250 ,0.001

Table V.MANOVA testingdifference across clustersbased on all five clustervariables

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significantly different because of the three business outcomes from implementing ERPsystem. Then, results of the post-hoc test using ANOVA in Table X indicate thatmanufacturing firms in the three clusters experienced significant differences in onlyEXTCO and COMP outcomes (we also conducted this post-hoc test by usingdiscriminant analysis and results from this discriminant analysis are similar to theresults from ANOVA). Finally, results of the pairwise comparisons (Table XI) showthat the manufacturing firms in Cluster 3 (large enterprises with MTO-oriented) reportsignificantly higher benefits in both EXTCO and COMP outcomes than the

Variables Sum of squares df Mean square F Sig.

SIZEBetween groups 112.288 2 56.144 513.730 ,0.001Within groups 27.650 253 0.109Total 139.938 255MTSMTOBetween groups 84.787 2 42.394 372.788 ,0.001Within groups 28.771 253 0.114Total 113.559 255SELECTBetween groups 8.596 2 4.298 6.379 0.002Within groups 170.463 253 0.674Total 179.059 255ROLLOUTBetween groups 21.982 2 10.991 18.430 ,0.001Within groups 150.877 253 0.596Total 172.859 255MODIFYBetween groups 0.227 2 0.113 0.108 0.898Within groups 266.211 253 1.052Total 266.438 255

Table VI.Post-hoc ANOVA testing

equality of all fivecluster variables

I J Mean difference (I J) SE Sig.

SIZECluster 1 vs Cluster 2 2 0.9247 0.0545 ,0.001Cluster 1 vs Cluster 3 2 1.7059 0.0535 ,0.001Cluster 2 vs Cluster 3 20.7811 0.0474 ,0.001MTSMTOCluster 1 vs Cluster 2 1.2757 0.0556 ,0.001Cluster 1 vs Cluster 3 1.4066 0.0546 ,0.001Cluster 2 vs Cluster 3 0.1309 0.0484 0.022SELECTCluster 1 vs Cluster 2 20.1676 0.1352 0.649Cluster 1 vs Cluster 3 20.4523 0.1329 0.002Cluster 2 vs Cluster 3 20.2846 0.1177 0.049ROLLOUTCluster 1 vs Cluster 2 0.4710 0.1272 0.001Cluster 1 vs Cluster 3 20.1900 0.1250 0.389Cluster 2 vs Cluster 3 20.6610 0.1107 ,0.001

Table VII.Pairwise comparisons

testing equality of fourcluster variables across

three clusters

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manufacturing firms in Cluster 1 (small businesses with MTS-oriented) and Cluster 2(medium enterprises with MTO-oriented) do.

DiscussionWhen implementing ERP systems, the responding firms in all three clusters seemed tohave approximately the same amount of process re-configuration vs softwarecustomization. The responding firms reported that, on average, they made only somechanges to the ERP modules; that is, the average score for MODIFY was approximately2.75 (Table IV) from the five-point dichotomous scale (Table I). This may be the result

Variables Cluster 1 Cluster 2 Cluster 3 Total

EXTCO 3.1885 3.2419 3.5245 3.3418(0.690) (0.686) (0.772) (0.735)

INFO 3.2541 3.4570 3.4167 3.3926(0.789) (0.765) (0.731) (0.759)

COMP 3.1148 3.0000 3.4363 3.2012(0.733) (0.818) (0.757) (0.796)

Cases 61 93 102 256

Table VIII.Mean and standarddeviation of the outcomesfor each cluster

Effect Value F Hypothesis df Error df Sig.

InterceptPillais trace 0.973 3,033.355 3 251 ,0.001Wilks lambda 0.027 3,033.355 3 251 ,0.001Hotellings trace 36.255 3,033.355 3 251 ,0.001Roys largest root 36.255 3,033.355 3 251 ,0.001ClusterPillais trace 0.115 5.124 6 504 ,0.001Wilks lambda 0.887 5.188 6 502 ,0.001Hotellings trace 0.126 5.250 6 500 ,0.001Roys largest root 0.109 9.163 3 252 ,0.001

Table IX.MANOVA testingdifference across clustersbased on all threebusiness outcomes

ERP outcomes Sum of squares df Mean square F Sig.

EXTCOBetween groups 5.766 2 2.883 5.522 0.004Within groups 132.077 253 0.522Total 137.843 255INFOBetween groups 1.615 2 0.807 1.407 0.247Within groups 145.181 253 0.574Total 146.796 255COMPBetween groups 9.857 2 4.929 8.229 ,0.001Within groups 151.533 253Total 161.390 255 0.599

Table X.Post-hoc ANOVA testingequality of all threebusiness outcomes

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from the finding that the major ERP vendors among the responding firms were domesticcompanies (Table I). Similarly, the domestic ERP vendors dominated the Chinese ERPmarket as well (Liang et al., 2004).

Foreign ERP systems may not meet the requirements of the domestic companies interms of language, reporting format and content, and system flexibility (Liang et al.,2004). On the other hand, the ERP systems from domestic vendors may better meet therequirements of the domestic companies in Asia; thus, these domestic companies do notneed to extensively modify either their existing business processes or the ERP software.However, a firm explanation can only be supported with additional investigations.

For ERP selection approach, large enterprises tend to select best-of-breed fromseveral ERP packages; on the other hand, small businesses and medium enterprisestend to select a single ERP package (Table III). Unlike large enterprises, small andmedium businesses usually have scarce financial resources and do not have the sameease of hiring qualified IS/IT experts (Caldeira and Ward, 2003). Thus, it is morefeasible for large enterprises to adopt several different ERP packages that best meettheir business requirements and then integrate these packages together. In contrast, forsmall businesses and medium enterprises, this approach is less feasible because oftheir lack of IS/IT expertise; thus, it is more reasonable for small businesses andmedium enterprises to adopt a single ERP package and avoid the integrationchallenges of implementing several different ERP packages.

For small businesses and medium enterprises, the most common ROLLOUT approachemployed is the big bang approach; in contrast, large enterprises tend to adopt eithermini big bang or phase-in approaches (Table III). For small businesses, their businessfunctions and processes are simple and less complicated, implying somewhat easy ERPimplementation (Shin, 2006). Thus, it is feasible to rollout the whole ERP project in smallbusinesses by using the big bang approach. However, for the bigger ERP project in largeenterprises, it is necessary to employ either mini big bang or phase-in approaches.

Results of the analysis suggest that none of the three cluster variables regarding ERPimplementation approaches MODIFY, SELECT, and ROLLOUT, are significantlydifferent across the responding firms in all three clusters. On the other hand, the two clustervariables related to business characteristics SIZE and MTSMTO, are significantlydifferent among the manufacturing firms across the three clusters. These findings suggestthat business characteristics, not ERP implementation approaches, are significant factorsfor grouping manufacturing firms into clusters of companies with similar characteristics.

The findings also showed that small businesses tend to be more MTS-oriented;while medium and large enterprises tend to focus more on MTO orientation.

I J Mean difference (I J) SE Sig.

EXTCOCluster 1 vs Cluster 2 2 0.0534 0.1190 1.000Cluster 1 vs Cluster 3 2 0.3360 0.1170 0.013Cluster 2 vs Cluster 3 2 0.2826 0.1036 0.020COMPCluster 1 vs Cluster 2 0.1148 0.1275 1.000Cluster 1 vs Cluster 3 20.3215 0.1253 0.033Cluster 2 vs Cluster 3 2 0.4363 0.1110 ,0.001

Table XI.Pairwise comparisons

testing equality of twobusiness outcomes across

three clusters

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These results may be understood based on the relationship between the number ofproduct offerings and the production approach. That is, firm size affects the productvarieties at which the firm can produce internally (Ono and Stango, 2005) and theincrease in number of product offerings goes hand-in-hand with a shift from MTSapproach to MTO approach (Gupta and Benjaafar, 2004).

Regarding business outcomes from implementing ERP systems, the analysis resultssuggest that the manufacturing firms in all three groups experience similar benefits fromimplementing ERP systems regarding the quality and availability of information. However,the manufacturing firms in the large enterprises with MTO-oriented group perceivesignificantly higher benefits in coordination with their business partners and in competitiveimpact than the manufacturing firms in the other two clusters do. This may be explained bythe previous findings that small firms are subject to contradictory pressures forcing them toprovide better logistics contributions and to develop and maintain closer relationships withtheir trading partners despite their limited resources (Bagchi and Virum, 1998).

To be successful in the current competitive environment, it is important for bothlarge and small manufacturing companies to coordinate with their business partnersand adapt to the demand of logistics chain integration. Large manufacturingenterprises usually find themselves at the top of large networks of suppliers which aremostly small manufacturing firms. It is normal for large manufacturing enterprises toplace strong pressure on small manufacturing firms (i.e. their suppliers) to adopt themanagement practices of large manufacturing enterprises (De Toni et al., 1995). Thesemanagement practices include controls, procedures and systems, etc. which areestablished by the ERP systems adopted by the large manufacturing enterprises.

On the other hand, small manufacturing firms are often conditioned to behaveaccording to large manufacturing enterprises expectations and management practices.The logistics and operational systems put in place in small manufacturing firms are verydependent on their large manufacturing customers. Similarly, how the ERP is planned isnot the sole decision of these small manufacturing firms (Huin et al., 2002).

Unfortunately, the ERP systems adopted by large manufacturing enterprises mayimplement the logistics chain integration that is not compatible with the features andthe intrinsic characteristics of small manufacturing firms. As a result, these smallmanufacturing firms often face risk of quasi-integration (Gelinas and Bigras, 2004).

Additionally, the ongoing unilateral relationship between a large manufacturingenterprise and its small suppliers (i.e. small manufacturing firms) may inhibit the smallmanufacturing firms relationships with other partners as well (Kasouf and Celuch,1997). It was found that only 61 percent of small manufacturing firms said they wereinvolved in at least one partnership, compared to nearly 88 percent of largemanufacturing enterprises (Gelinas and Bigras, 2004).

Thus, when compared to large manufacturing enterprises, small manufacturing firmsmay perceive that they gain limited benefits from implementing ERP systems to participateand coordinate in logistics chain activities, and eventually limited competitive impact.

ConclusionIn this study, we employed cluster analysis to identify groups of manufacturing firmshaving similar business characteristics and adopting similar ERP implementationapproaches. Results of the cluster analysis suggest three clusters or groups ofmanufacturing firms. However, none of the three ERP implementation approaches

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investigated in this study (i.e. MODIFY, SELECT, and ROLLOUT) are significantfactors for grouping manufacturing firms. On the other hand, the two businesscharacteristics included in this study (i.e. SIZE and MTSMTO) are significant criteriafor grouping manufacturing companies. This result is consistent with the notion thatlarge enterprises offer higher product variety than small businesses do (Ono andStango, 2005) and the increase in number of product offerings goes hand-in-hand witha shift from MTS approach to MTO approach (Gupta and Benjaafar, 2004).

One of the major implications of this study lies in these findings. The findingssuggest that company size and production approaches are useful variables forgrouping manufacturing firms into clusters of companies. Thus, any future studiescould refer to these findings and use company size and production approaches ingrouping manufacturing companies.

Then, we examined the differences in three business outcomes from implementing ERPsystems among the three groups of manufacturing firms. The manufacturing companiesin all three clusters report that they experience at least some benefits in all of the threebusiness outcomes. However, the results of MANOVA and post-hoc tests suggest that themanufacturing companies in the large firms with MTO-oriented group havesignificantly higher perceived benefits in coordination with their business partners andin competitive impact than the smaller manufacturing firms in the other two clusters do.

Small manufacturing firms often are forced by their customers (which are usually largemanufacturing firms at the top of the networks of suppliers) to adopt the logistics practicesestablished by ERP systems that may be more suitable for large manufacturing firms.This leads to some disadvantages for small manufacturing firms and eventually thesesmall manufacturing firms gain only limited benefits from adopting ERP systems,participating in logistics integration, and coordinating with large manufacturing firms.

The findings of this study suggest several other implications for future research.Large manufacturing firms may tend to select best-of-breed from several ERPpackages because these large firms have IS/IT capability to integrate the ERPpackages together. However, additional investigations would be necessary to confirmthis argument. It would also be interesting to investigate the effects of the logisticspractices established by ERP systems and adopted by small manufacturing firms eventhough these logistics practices may be more suitable for large manufacturing firms.

Additionally, the practical implication of this study is for the managers of smallmanufacturing firms. A small manufacturing firm may need to adopt an ERP system inresponse to the request from its business partners who are large manufacturingcompanies. The manager of this small manufacturing company needs to be aware that theERP system suggested or requested by his business partners (i.e. large manufacturingfirms) may not be compatible with the features and the intrinsic characteristics of his smallmanufacturing firm. In this case, the manager should identify the requirements of hisbusiness partners and the incompatibilities that the suggested ERP system could cause tohis small manufacturing firm. Then, the manager may negotiate to implement anotherERP system that could fulfill the same requirements and cause the least incompatibilities.Alternatively, the manager may need to prepare for a project to rework his companysexisting incompatibilities or to modify the suggested ERP system.

This study, like any other studies, is not free of limitations. First, it was conducted inonly one industry of one country (i.e. Korean manufacturing firms). Second, the studyused perception data collected by self-reporting instrument. Thus, further studies

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conducted in other industries and/or other countries and using more objective measureswould allow more generalization of the findings of this study. Third, the data providedby the research center conducting the survey were not sufficient to either assess responserate or analyze any nonresponse bias. Finally, although the results of several reliabilitytests indicate a reasonably high level of instrument reliability, Cronbachs a values ofsome constructs in the measurement model are not higher than the cutoff point of0.70 (Nunnally, 1978) and, for one construct, its CR is not higher than the recommendedthreshold of 0.70 (Fornell and Larcker, 1981; Hair et al., 2006; Segars, 1997).

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Appendix. Construct reliability and validity tests

FactorsEXTCO MODIFY INFO COMP

CUST 0.929SUPPLY 0.927MMMODULE 20.929PPMODULE 20.884DLMODULE 20.863QUAINFO 0.890AVAINFO 0.860GLOBAL 0.844STRADV 0.886

Note: Extraction method principal component analysis; rotation method oblimin with kaisernormalization; rotation converged in six iterations; total variance extracted 79.79 percent; any loadingssmaller than 0.40 are not reported

Table A1.Exploratory factoranalysis

CR AVE 1 2 3 4

EXTCO 0.843 0.729 (0.838)MODIFY 0.875 0.701 0.312 (0.872)INFO 0.701 0.540 0.375 0.149 (0.693)COMP 0.676 0.512 0.504 0.128 0.402 (0.673)

Notes: n 256 cases; / Cronbachs a (reported on the diagonal); CR composite reliability;AVE average variance extracted

Table A2.Construct correlationand reliability

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About the authorsPairin Katerattanakul is a Professor and the Telecommunications and Information ManagementProgram Coordinator at the Department of Business Information Systems, Western MichiganUniversity. He has published his research in several journals such asEuropean Journal of InformationSystems, Journal of theAmericanSociety for InformationScience andTechnology,Communications ofthe ACM, Communications of the AIS, Management Research News, Journal of InformationTechnology Theory and Application. He also has longer than ten years of industry experience as aSystem Engineer and a Product Manager at several multinational companies. Pairin Katerattanakulis the corresponding author and can be contacted at: p.katerattanakul@wmich.edu

Constructs Items Loading t-value R 2

External coordination (EXTCO) Improved coordination withcustomers (CUST)

1.118 9.625 0.710

Improved coordination withsuppliers (SUPPLY)

1.000 0.746

Competitive impact (COMP) Link to global activities (GLOBAL) 1.000 0.559Gain strategic advantages(STRADV)

0.830 5.844 0.465

Informational impact (INFO) Quality of information (QUAINFO) 1.019 4.930 0.474Availability of information(AVAINFO)

1.000 0.605

Process re-configuration vs softwarecustomization (MODIFY)

Distribution/logistics (DLModule) 0.990 13.654 0.658Material management (MMModule) 1.088 14.411 0.828Production planning (PPModule) 1.000 0.618

Notes: Loading is the non-standardized regression weight; R 2 is the squared multiple correlationsTable A3.CFA result

x 2/d.f.x 2 d.f. ratio RMSEA GFI AGFI CFI NFI IFI TLI

Model 24.5 21 1.168 0.026 0.979 0.956 0.996 0.972 0.996 0.993Suggested value NA NA ,3.0 ,0.06 .0.95 .0.95 .0.95 .0.95 .0.95 .0.95

Notes: x 2/d.f. ratio of x 2 to degrees of freedom; RMSEA root mean square error ofapproximation; GFI goodness-of-fit index; AGFI adjusted goodness-of-fit index; CFI comparative fit index; NFI normed fit index; IFI incremental index of fit; TLI Tucker-Lewisindex

Table A4.Model fit summary

Models x 2 d.f.Compared to [1]x2diff , d.f. ( p-value)

[1] Original measurement model 24.5 21 [2] Model with COMP and MODIFY combined 111.9 22 87.4, 1 (,0.001)[3] Model with COMP and EXTCO combined 102.6 22 78.1, 1 (,0.001)[4] Model with INFO and MODIFY combined 123.8 22 99.3, 1 (,0.001)[5] Model with COMP and INFO combined 112.4 22 87.9, 1 (,0.001)[6] Model with INFO and EXTCO combined 135.1 22 110.6, 1 (,0.001)[7] Model with EXTCO and MODIFY combined 105.4 22 80.9, 1 (,0.001)

Table A5.Original vs reduced

measurement models

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James J. Lee is a Genevieve Albers Professor of Albers School of Business and Economics.Prior to that he had served as an Assistant Professor at State University of New York atBinghamton. He teaches primarily in the areas of E-Commerce & Information Systems,E-Business, and Web Applications. His research is in the areas of enterprise integration insocial context, information technology conceptualization, and communication structure.His publications have appeared in such journals as Communications of the ACM, Journal ofInformation Technology, Industrial Management & Data Systems, and many more.

Soongoo Hong is an Associate Professor at the Department of Management Information Systemsin Dong-A University, Korea. He received his PhD in management information systems in 2000 andMaster of arts in management in 1995 from the University of Nebraska Lincoln. He has publishedhis research in several journals such as Communications of the ACM, Communications of the AIS,International Journal of Information and Decision Making. His research interests includeweb accessibility, ERP implementation, data warehousing, and IT impacts on organizations.

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