Information & Management 49 (2012) 58–67

Information sharing and business systems leveraging in supply chains:An empirical investigation of one web-based application

InduShobha Chengalur-Smith a,*, Peter Duchessi a,1, J. Ramon Gil-Garcia b,2

a School of Business, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USAb Centro de Investigacion y Docencia Economicas, Carretera Mexico-Toluca No. 3655, Col. Lomas de Santa Fe, Mexico, D.F. 01210, Mexico

A R T I C L E I N F O

Article history:

Received 1 October 2008

Received in revised form 1 April 2011

Accepted 1 December 2011

Available online 16 December 2011

Keywords:

Web-based supply chain applications

Supply chains

Resource based view of the firm

Information sharing

Business systems leveraging

Relational concurrence

Business benefits

A B S T R A C T

Web-based supply chain applications promise to provide information sharing capabilities that will

enhance the participating organizations’ information capabilities and business benefits. We performed

an empirical study of a sophisticated Web-based supply chain application to determine the effect of such

information sharing and business systems leveraging on business benefits. We also examined the

importance of relational concurrence (i.e., shared business interests among supply chain partners), as an

antecedent to both information sharing and business systems leveraging. Our work showed that both

information sharing and business system leveraging provided important business benefits and that

relational concurrence was only marginally related to information sharing and not at all related to

business systems leveraging, limiting the significance attributed to this factor in prior research on inter-

organizational systems.

Published by Elsevier B.V.

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Information & Management

jo u rn al h om ep ag e: ww w.els evier .c o m/lo c ate / im

1. Introduction

As use of the Internet and the Web increases in sophistication,companies are deploying Web-based supply chain applications toenhance their operations, improve business performance, reduceinventory costs, etc. Web-based electronic data interchange, supplychain applications, and private and public exchanges are examplesof such systems. Wal-Mart, Dell, and Procter & Gamble are just a fewcompanies with Web-based supply chain applications that state thatthey have improved their planning, forecasting, and replenishmentprocesses [12]. Generally, the improvements are predicated onsharing information and leveraging information systems, therebybroadening information capabilities and increasing the degree ofdigitization of previously manual business activities.

Researchers have examined information sharing and systemsintegration in supply chains from several perspectives, includinginformation sharing requirements [16], hardware and softwareintegration [17], transaction costs [10], and organizationalreadiness. For them, information sharing and systems integrationare either an end in themselves, or they serve as antecedents to

* Corresponding author. Tel.: +1 518 442 4028; fax: +1 518 442 4975.

E-mail addresses: shobha@albany.edu (I. Chengalur-Smith),

PDuchessi@uamail.albany.edu (P. Duchessi), joseramon.gil@cide.edu

(J.R. Gil-Garcia).1 Tel.: +1 518 442 4945.2 Tel.: +52 55 5727 9800.

0378-7206/$ – see front matter . Published by Elsevier B.V.

doi:10.1016/j.im.2011.12.001

other improvements that enhance business performance, includ-ing increased sales, improved business processes, and reducedsupply chain costs [14].

Some supply chain applications, collect and store data about allthe supply chain participants and their activities in a centralizeddatabase and make it readily available to the participants through aWeb browser. This imparts integrative qualities and consequentlyimproves mutual knowledge, including instant sharing of demand,inventory, and shipping information. Because data about supplychain participants and their activities are centralized and readilyavailable through these integrative applications, companies areless likely to experience problems in integrating their systems inorder to transfer and share data throughout the supply chain.

There has been little empirical research on Web-based supplychain applications with such integrative qualities and theirassociated business benefits. For these supply chain applications,we hypothesized that two information capabilities, informationsharing and business systems leveraging, with relational concur-rence acting as an antecedent to them, are essential in order toattain important business benefits. In our model, informationsharing is primarily the degree to which supply chain participantsshare supply chain information via the Web-based supply chainapplication; business systems leveraging is the degree to whichcompanies combine their business systems, including the supplychain, and use them to execute orders; and relational concurrenceis the degree of shared business interests. Companies that combine– or conjoin – business systems, either partially or totally, broaden

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their information capabilities, allowing them to support morebusiness functions and processes than is possible with disjointedapplications. Thus, business systems leveraging is more thansystems integration, the integration of hardware, software, andnegotiated standards that permit data and information access andexchange. Systems integration may result in a short-termcompetitive advantage, while business systems leveraging is likelyto lead to a sustainable competitive advantage due to its limitedsubstitutability, imitability, and mobility, but the commercialavailability of applications does not prevent businesses frommaking their information capabilities and accrued benefits readilyavailable to other companies. Consequently, companies may haveto take other actions, such as leveraging their business systems tobroaden their information capabilities and solidify their perfor-mance gains.

Our work focused on a model that relates, as an antecedent, arelational concurrence construct to two information capabilitiesconstructs: information sharing and business systems leveraging,and each of these two constructs to a business benefits construct.Our work is different from previous efforts in three respects. First,the Web-based supply chain application under investigation, GEOperations Global VMI (vendor managed inventory), known asGEOPS, stores data about multiple supply chain participants andtheir activities in a centralized database, which gives theapplication integrative qualities, allowing it to provide instantinformation sharing and connectivity with little or no systemsintegration. Consequently, we model information sharing per se,without the need to include a system integration construct thatencompasses extensive modifications to hardware and softwarecomponents, computing standards, and organizational roles andresponsibilities, as other researchers have done in the past, andrelate information sharing to business benefits. Second, withGEOPS, we consider the value added by combining – or conjoining– other business systems. Companies may gain considerablebusiness benefits from using multiple business systems inconjunction with one another [18]. Consequently, we relatebusiness systems leveraging to business benefits. Third, weexamine the relevance of relational concurrence – the degree ofshared business interests – as an antecedent to both informationsharing and business systems leveraging. Relational concurrencemay be a necessary condition for both information sharing andbusiness systems leveraging.

2. The research model

Supply chain management research has focused on solvingspecific operational problems and on operational improvementsthat incorporate specific technologies, including EDI [6], e-procurement systems, and vendor managed inventory systems.Recent work has focused on the development of supply chaincapabilities that link a company with its suppliers and customersto create value for all involved companies [7]. Generally, thisliterature incorporates the resource based view (RBV) of the firmfor studying companies’ ability to deploy IT to obtain businessbenefits.

According to RBV theory, resources are assets and capabilitiesthat allow companies to respond to external opportunities andthreats. Assets, both tangible and intangible, are anything thatimproves business processes, while capabilities are repeatablepatterns of actions in the use of assets [20]. The assets can betechnological (e.g., IS), organizational (e.g., organizationalarrangements), and/or environmental (e.g., supplier–customerrelationships) in nature. Capabilities tend to be company-specificand embedded in the company’s organizational structures andbusiness processes. Companies may use IT assets alone orcombine them to develop information capabilities, including

improved information sharing, process execution, and demandsensing, which, in turn, provide operational, financial, and otherbusiness benefits. Some researchers have referred to these asonline information capabilities [1]; however, we consider them asjust information capabilities, recognizing they can also be offline(e.g., analysis of customer data). Thus, there are two approachesthat companies may use to provide information capabilities: (1)implement business systems alone, or (2) combine – or conjoin –business system, either partially or totally. The first providescompanies with information capabilities that are related to theapplication, while the second may permit companies to broadentheir information capabilities and thus provide more extensivesupport for business processes and functions. Our study consid-ered just information sharing as an information capability,recognizing that there were other information capabilities.

The application, or business system, GEOPS, stores data aboutsupply chain participants and their activities in a centralizeddatabase and makes it readily available to participants through aWeb browser. As a result, all suppliers and customers in a supplychain may instantly share demand, inventory, and other datawithout having to deal with systems integration issues. After acompany acquires GEOPS, it decides with whom to share data.Thus, the GEOPS information sharing construct regulates thedegree to which supply chain participants have access to and shareimportant supply chain information. Thus, systems integration isnot an antecedent to information sharing.

Concerning the second approach, the business systemsleveraging construct represents the degree to which companiescombine (conjoin) multiple business systems to develop broaderinformation capabilities and the degree to which companies usethose systems to execute business processes. Companies thatconjoin business systems and broaden their capabilities are ableto support their business processes, including supply chainprocesses, and business functions. According to RBV theory, bothapproaches should provide operational, financial, and otherbusiness benefits. Thus, the business benefits construct repre-sents the degree to which companies realize these businessbenefits through improved information sharing and businesssystems leveraging.

In our study we also included a relational concurrenceconstruct, as an antecedent to both information sharing andbusiness systems leveraging. This was a new construct andrepresented the degree of shared business interests. Althoughrelational concurrence did not arise from RBV theory, research onvarious facets of relational concurrence (e.g., relationshipsbetween supply chain partners, joint planning, and joint investing)have suggested that relational concurrence may foster informationsharing and business systems integration. Thus, we included it asan antecedent to information sharing and business systemsleveraging. Because we model relational concurrence as anantecedent to both information sharing and business systemsleveraging, we did not have to consider information sharing andbusiness systems leveraging as mediators for relational concur-rence, though we did test for a direct effect between relationalconcurrence and business benefits.

Finally, to account for the potential of rival hypotheses, ourresearch model included several control constructs that couldaffect business benefits. These constructs accounted for differencesin industry practice and company size.

As we have overviewed the underlying theory for each of themodel’s primary constructs, information sharing, businesssystems leveraging, business benefits, and relational concur-rence we can now develop each construct, discuss theirrelationships (e.g., between information sharing and businessbenefits), and list the associated hypotheses for our researchmodel (see Fig. 1).

BusinessBenefits

IndustryPrac tice

Relational Concurrence

BusinessSystems

Leveraging

Information Sharing Size

Fig. 1. Research model.

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2.1. Business benefits

Business benefits are the degree of operational (e.g., moreefficient planning and replenishment and increased availability ofmaterial resources), financial (e.g., reduced supply chain andinventory costs), and other advantages that companies realizethrough improved information sharing and business systemsleveraging. Operational and financial benefits arise from acompany’s ability to share information and leverage businesssystems, both within the company and across multiple companies.Companies that share information have increased visibility of theiractivities and thus are able to coordinate replenishment moreeffectively and streamline the flow of goods and services [9].Companies that leverage their e-business applications broadentheir information capabilities and are able to implement moresophisticated supply chain practices that result in lower invento-ries, fewer stock outs, and lower costs. The supply chain practicesinclude the integration of both physical and financial flows.Information sharing and business systems leveraging may provideimportant operational and financial benefits, including improvedon-time delivery and productivity.

2.2. Information sharing

Information sharing is the degree of access to and sharing ofimportant supply chain information between a company and itssupply chain partners. This information includes forecasts;manufacturing schedules, as reflected in inventory drawdownrates; and inventory data at upstream locations. Companies thatshare manufacturing and replenishment schedules can improve thecoordination of supply chain activities, material requirements, andplanning roles and responsibilities. Similarly, companies that shareinventory information can reduce inventories across the supplychain. By sharing information, companies are substituting informa-tion for inventory and other resources. Companies that shareimportant information, such as forecasts, manufacturing schedules,and on-hand inventory balances, increase the visibility of presentand future material and manufacturing requirements. With bettervisibility, companies can manipulate their manufacturing opera-tions to achieve economies of scale, coordinate inventory replen-ishment, and optimize deliveries. Increased visibility of materialusage reduces the distortion of actual demand, allowing companiesto run more efficiently without jeopardizing customer serviceobjectives. Then companies can produce product based on customerneed. Moreover, companies do not have to hold more raw materialand finished goods inventory than required to protect themselvesagainst uncertainties in supply and demand. Increased informationsharing also improves utilization of facilities and cash flow andimproves customer service.

Consequently, as an information capability via GEOPS, infor-mation sharing should produce important operational andfinancial business benefits. Hence, we hypothesized:

H1. Information sharing is positively related to business benefits.

2.3. Business systems leveraging

Business systems leveraging is the degree to which companiesconjoin their business systems, including GEOPS, and use them toexecute their supply chains. Companies need to combine data and/or business systems and use the ensuing capabilities to improvesupply chain integration and performance; mere data and systemsintegration are insufficient. In a similar way to Hong and Kim [8],we considered three crucial aspects of leveraging: the capability ofintegrating data within business systems, integrating processesand systems with business partners, and integrating IS outputacross the supply chain. Companies that integrate and use businesssystems to improve supply chain performance are going beyondsimple systems integration and are leveraging their businesssystems improve supply chain performance. Some authors refer tothis as digitization of business activities.

Companies that combine business applications are performingmore business activities in an integrated, electronic environmentthan would be possible through a single application. Withoutleveraging business systems, companies are likely to developislands of automation, limiting the scope and scale of theirinformation capabilities and organizational impacts. Becausecompanies with broadened information capabilities are able tosupport more of their value chains, they can reduce the cost ofperforming primary and secondary value chain activities and thecost of synchronizing those activities. When companies leveragetheir business systems, they are better able to perform andcoordinate these activities, including procurement, manufacturing,and shipping, with the needs of the supply chain. Better internaland external coordination leads to lower inventory, less obsoles-cence, and lower transportation costs, and thus improve its overallproductivity [21]. Companies that use their combined businesssystems to execute their supply chains can coordinate theirinternal and external supply chain activities and derive importantoperational and financial business benefits. Hence,

H2. Business systems leveraging is positively related to businessbenefits.

Although commercially available business systems, like GEOPS,may improve a company’s effectiveness and/or efficiency, theymay not be rare (i.e., they are available to other companies) andthey may be appropriable (i.e., consequent benefits are fully, orpartially, controlled by a third party). Thus to maintain informationcapabilities and the consequent business benefits, companiesshould make it difficult for competitors to imitate the system byensuring it has a unique history, causal ambiguity, and/or socialcomplexity, limited use of equivalent resources, and limitedtransferability. Companies may thus prevent their benefits frombeing replicated or appropriated. Others (e.g., [19]) discuss howexploitative uses of IT that involve supply chain managementsystems enable changes in business processes and subsequentattainment of business benefits. Thus, companies may benefit fromleveraging business systems to enhance their ex ante and ex poststrategic importance and derive business benefits.

2.4. Relational concurrence

Relational concurrence is the degree of shared businessinterests between suppliers and customers, as reflected in theamount of cross investing, satisfaction of mutual needs, and the

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importance placed on the customer–supplier relationship. Theliterature considers the nature and degree of relationshipsbetween supply chain partners to be an important factor inunderstanding supply chain mechanics. Besides transaction costeconomics, sociological factors influence the relationships be-tween supply chain participants. Relational exchange theorysuggests that relational norms (e.g., participation, communication,and trust) promote information sharing in supply chains.Research has shown that relational norms, including coordinationand cooperation, are important for promoting information sharingand communication between supply chain participants, with trustas a particularly important relational norm for e-businessapplications that link companies. Trust fosters the developmentof long-term relationships and reduces the need for othergovernance mechanisms, including long-term contracts. Manifes-tations of trust between a supplier and a customer are a hallmark ofsuccessful relationship management. A long-term relationshipbased on inter-dependence and relational symmetry engenderstrust and confidence in both parties and may result in informationsharing.

Web-based supply chain applications may affect the degree ofsupplier interdependence and power – such applications permitcontinuous visibility of internal data, companies are more apt todeploy such Web-based applications when they value theirpartners, receive mutual benefits, and share investments. More-over, the sharing of supply chain resources promotes shareddecision making, collaboration, and know-how exchange amongsupply chain partners including both explicit and tacit knowledge.Relational concurrence based on shared interests, rather thanformalization, centralization, and opportunism, is more likely tofoster information sharing and systems leveraging.

Consequently, companies with relational concurrence are likelyto share information via a Web-based supply chain application andleverage the Web-based supply application with other businesssystems to expand their information capabilities. Hence,

H3. Relational concurrence is positively related to informationsharing.

H4. Relational concurrence is positively related to business sys-tems leveraging.

2.5. Control variables

Given our set of focused relationships, we incorporated twocontrol variables: industry practice and size. We included industrytype as it has been shown to impact the diffusion and businessvalue created by IT, through differences in adoption rates andcapabilities [15]. We included a construct that measured theenvironmental predisposition to share information and leverage e-business applications. In general, the amount of IT a company usesis dictated by the IT usage of its customers and suppliers and thequality of its IT infrastructure. With regard to industry size, weused a construct that incorporated sales and number of employees.Large companies have more resources to accomplish supply chaininitiatives and more power to influence the outcomes of theirinitiatives than small companies.

3. Research methods

We selected a Web-based supply chain application, GEOPS, asthe setting for testing our research model. We made this choicebecause GEOPS is a Web-based application that links suppliersand customers with no changes to existing hardware andsoftware, existing skill base, and organizational roles andresponsibilities.

3.1. Research setting

The GEOPS business system stores data about supply chainparticipants and their activities in a centralized database andmakes it available through a Web browser; it is a service thatsuppliers and customers purchase from GE Operations. In itssupply chains, GE Operations is just a service provider and, unlikean intermediary or a broker, GE Operations has no special status,role, or authority to influence the adoption decision. Once theapplication has been bought by a company, it decides on thesuppliers and/or customers with whom to share data. GEOPSemploys electronic sensors at various points in a supply chain tocollect and store data about supply chain participants and theiractivities, stores the data in the database, and provides instantaccess to it. Companies require just a Web browser to gain access tothe data and the supply chain information, including inventorylevels, demand forecasts, and shipments. The sensors are able tomeasure raw material inventories at Six Sigma quality levels andsensor installation and commissioning is a relatively straightfor-ward process. Because data and information about supply chainparticipants and their activities are centralized and readilyavailable, systems integration is not a major concern.

Order fulfillment users at supplier locations and purchasingusers at customer locations can view actual demand, or drawdown, at all silos and inventory locations; consumption forecastsand on-hand balances; and shipments in progress (see Fig. 2).Users can receive alerts if inventory exceeds predetermined levelsand download all data into their ERP systems. Because of itsintegrated architecture, GEOPS permits, if authorized by suppliers/customers, access to inventory data and information at upstreamand downstream locations. For example, a supplier at thebeginning of a supply chain can view its customers’ inventorydata and information, its customers’ customers’ inventory data andinformation, etc. GEOPS’s functionality provides connectivity andsupply chain visibility. In addition, GEOPS using companies canconsider instituting vendor managed inventory programs wherethe suppliers – not the customers – decide when and how muchstock to replenish, given their on-hand balances and draw downdata.

GEOPS is also different from ERP and CPFR applications. ERPapplications provide multiple templates for defining and imple-menting formal business processes within one company. Addi-tionally, ERP applications generally contain internal data and limittheir functionality to activities primarily within the company. CPFRis a multi-step, best-practice framework for a supplier and acustomer to develop formal policies and business processes thatcan build consensus sales forecasts, establish replenishment plans,and manage structured workflows. Together, a supplier andcustomer create a cooperative replenishment strategy that alignsobjectives and processes and define technical standards for Web-based communication; it requires numerous periodic – even daily– interactions between several teams in each company, includingforecasting, purchasing, and materials management teams.

3.2. Questionnaire design

We developed an initial draft questionnaire based on aliterature review, our experience, and discussions with businessleaders from GE Operations and GE Global Research, who hadsignificant supply chain and IT applications experience. Thequestionnaire contained both validated items from previousresearch and items specific to Web-based supply chain applica-tions. Through a long series of discussions, we pared the items to amanageable number and created two questionnaires: one for buy-side (i.e., customer) and one for supply-side (i.e., supplier)respondents. The questionnaires contained the same items with

Fig. 2. Screen Shot of silo summary showing upper and lower limits and inventory drawdown.

I.S. Chengalur-Smith et al. / Information & Management 49 (2012) 58–6762

only minor modifications to customize the questions for eachgroup of respondents.

As a pretest, we asked over a dozen buy- and supply-side usersto review the questionnaire, add items, delete items, rephrasequestions, arrange the items into natural groups, and record theamount of time to complete the questionnaire. The pretestprovided item-by-item insight into critical issues, includingimportant steps, activities, and relationships, overall organizationof the questionnaire, and correct wording of items. We then furthermodified the questionnaire, reducing its length, and rephrasingitems. We establish content validity via the literature review, ourcollective expertise, and the formal pretest process, whichprimarily gleaned the questionnaires’ items from both GEOperations Business Leaders and clients. This process of fine-tuning the questionnaires resulted in a parsimonious list ofquestions that could be completed in about 10–15 min.

3.3. Construct operationalization and measurement

Our model required the determination of the constructs, theirassociated items, and the hypothesized relationships between theconstructs and items. We modified some previously validateditems and also created some new items that were related to GEOPS’specific information capabilities and business benefits. Forexample, based on our definition of information sharing, weadded the following information sharing items to the question-naire: ‘‘We share long-term forecasts with our supplier’’, ‘‘Weshare our manufacturing schedules with our supplier’’, and ‘‘Wehave access to inventory data at locations that supply oursupplier’’. Also, based on our definition of business systemsleveraging, we added the following business systems leveragingitems to the questionnaire: ‘‘We integrated GEOPS Global VMIinventory data with our facility’s business systems’’, ‘‘Our facility’sbusiness systems are highly integrated with our supplier’ssystems, and ‘‘We use GEOPS Global VMI for real time integrationof our supply chain’’. Thus, the constructs determined thequestionnaire’s items. Subsequent factor analysis determined

the exact number of items for each construct. Concerning thehypothesized relationships between the constructs, partial leastsquares (PLS) analysis via path strengths was used to determinethe nature of the hypothesized relationships between constructs.

We used a seven point Likert scale for items that representedBusiness Benefits. The anchors for these items were 1 = very mucha benefit and 7 = not any benefit. We use three Likert-scaled itemseach to measure the constructs for information sharing, businesssystem leveraging, and relational concurrence. Industry practicewas also measured by three items on a Likert scale. The ordinalscale for these Likert items was Strongly Disagree, Disagree,Slightly Disagree, Neither Agree nor Disagree, Slightly Agree, Agree,and Strongly Agree. By using different scales, we attempted todecrease item characteristic effects that could arise from using thesame scale formats. We included several other items to character-ize the sample, including the facility’s position in the supply chain(e.g., middle), the respondent’s functional area, and the amount ofexperience (in months) with GEOPS. In order to reduce methodbias, although we organized the items into groups, we did notarrange the items in any particular order in the groups.

3.4. Survey administration

Our sample was taken from the population of GEOPS businesscustomers – a database of 543 managers and employees in anumber of connected facilities. Although our sample includedmany respondents from each company, they belonged to differentfacilities. Thus, the unit of analysis was actually the facility.According to GE Operations Business Leaders, these managers andemployees were in charge of GEOPS and, as a result, were the mostinformed and knowledgeable about GEOPS in their facilities. Wealso targeted users from both the buy-side and sell-side of abusiness, to ensure that our sample was not biased by taking theviewpoint of a single stakeholder. We assured all respondents ofcomplete confidentiality of their responses.

Initially, we emailed a notification of our study and forthcomingquestionnaire. Two weeks after this, we sent another email that

Table 1Distribution of respondents.

Categories Percentages

Functional areas

Sales/customer service/order fulfillment 27

Purchasing/materials management 26

Manufacturing 19

Logistics/supply chain 7

Unknown 21

Organizational levels

Managers 33

Non-managers 67

Sales

Less than $500 million 34

$500 million to less than $1 billion 18

$1 billion to less than $5 billion 12

$5 billion and over 9

Unknown 27

Number of employees

Less than 5000 48

5000–50,000 28

50,000 and over 7

Unknown 17

Position in the supply chain

Beginning 54

Middle 34

End 12

Number of suppliers/customers linked via GEOPS

1 36

2 17

3 7

4 4

5 3

6 or more 11

Unknown 22

Percentage of raw materials managed by GEOPS

Less than 1% 6

1–5% 14

5–10% 6

10–20% 14

20–50% 18

50% or more 11

Unknown 31

Length of experience with VMI 6 months or less 11

Over 6 months and up to a year 37

Over a year and up to 18 months 19

Over 18 months and up to 2 years 27

Over 2 years 6

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invited users to complete the questionnaire; it contained theembedded URL and password for accessing the questionnaire onour website. After another 2 weeks, we sent a third email thatinvited those who had not replied to complete the questionnaire.Overall, we received 89 usable responses to our solicitations,yielding a 16.3% response rate. This response rate is modest, but itis just below the recommended level of 20% for organizationalsurveys and similar to the response rates of other IT-focusedsurveys.

Changes in the ownership of GEOPS business prevented us fromfurther attempts to increase the response rate. It also preventedtelephone follow-up interviews to assess reasons for non-responsethat may be specific to our study (e.g., poor questionnaire design).In lieu of polling non-respondents, to assess the degree of non-response bias, we compared the first and second response setsusing a number of critical items. We examined some keybackground variables across the two sets: the proportion ofbuyers and sellers (no significant differences, p-value = 0.2), theposition of the responding organization in the supply chain, i.e.,beginning, middle, and end (no significant differences, p-value = 0.51), managerial status of the respondent (no significantdifferences, p-value = 0.485), and size indicators: number ofemployees and total sales (no significant differences, p-val-ue = 0.234 and 0.278, respectively). Thus we found no systematicdifference between the first and second response sets andconcluded that non-response bias was not significant.

We compared respondents from the sell- and buy-side of abusiness, on all the Likert-scaled items. We found marginaldifferences on some items. For example, buyers rated reducedcoordination and inventory costs higher (p-value of 0.035 and0.001, respectively) than suppliers, on average. In addition,suppliers were more likely to have access to customers’ inventorydata than buyers to have access to suppliers’ inventory data (p-value = 0.0001). Suppliers were also slightly more likely to agreethat their relationship with their customers was important (p-value = 0.0276), and that their business systems were highlyintegrated with their customer’s business systems (p-val-ue = 0.0681). Overall, however, no systematic differencesemerged; consequently, we combined them into one sample.

3.5. Respondents

The majority of our 89 GEOPS respondents were from sales andorder fulfillment (27%), purchasing and materials management(26%), and manufacturing (19%) (see Table 1).

A little over a third (36%) of the facilities are connected to justone supplier/customer via GEOPS, while 11% of the facilities areconnected to six or more suppliers/customers. Interestingly,although GEOPS is capable of connecting companies in anyarrangement, most companies prefer to implement just a dyadicconnection, limiting visibility to just their immediate suppliers/customers. A large majority of the facilities are managing at least10% of their materials through GEOPS. About 90% of all respondentshave been using GEOPS for at least 6 months. Thus, our samplecontains a variety of GEOPS users from small to large facilities thatare mostly at the start and middle of the chemical and plasticssupply chain.

4. Data analysis

We performed an exploratory factor analysis followed by PLSanalysis, using PLS-Graph, to analyze the validity of the model’sconstructs and the relationships between the constructs. PLS iswell suited for analyzing highly complex predictive models with,multiple-item constructs and both direct and indirect paths. PLSperforms a measurement (outer) model analysis to ascertain the

overall psychometric properties of the scales used to measure themodel’s variables and a structural (inner) model analysis toascertain the important relationships among the variables. PLS canhandle small sample sizes and does not impose multivariatehomogeneity and normality requirements on the data [5]. Theseaspects of PLS are especially important because our measures relyon ordinal data, which may not meet the homogeneity andnormality requirements.

4.1. PLS measurement model results

We ran a factor analysis with principal axis factoring andoblimin rotation [2]. In order to determine whether the factoranalysis was appropriate for our data set, we checked the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’stest of sphericity. The KMO statistic of 0.620 was above 0.500,suggesting that the data was suitable for factor analysis. Moreover,Bartlett’s test resulted in a highly significant chi-square statistic(x2 = 927, p-value = 0.000), indicating adequate correlation amongthe items. Thus factor analysis was appropriate for the existingdata set. The factor analysis showed that six constructs: business

Table 2Item loadings for constructs.

Constructs Items Loadings

Business benefits More efficient planning and replenishment practices 0.82

Improved on-time delivery performance 0.79

Increased availability of raw materials at our site 0.79

Reduced coordination costs with our supplier 0.77

Increased productivity 0.77

Reduced supply chain costs 0.71

Reduced inventory costs 0.67

Information sharing We share long-term forecasts with our supplier 0.87

We share our manufacturing schedules with our supplier 0.86

We have access to inventory data at locations that supply our supplier 0.43

Business systems leveraging We integrated GEOPS Global VMI inventory data with our facility’s business systems 0.88

Our facility’s business systems are highly integrated with our supplier’s systems 0.77

We use GEOPS Global VMI for real time integration of our supply chain 0.75

Relational concurrence We encourage cross-investing with our supplier for major projects 0.86

We structured the relationship with our supplier to satisfy mutual needs 0.83

Our relationship with our supplier is important to us 0.53

Industry practice In our industry, suppliers and customers are linked through information technology 0.97

Information sharing is common among suppliers and customers in our industry 0.85

Alliances and partnerships are common in our industry 0.41

Size Number of employees 0.93

Company’s total sales 0.93

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benefits, information sharing, business systems leveraging, rela-tional concurrence, industry practice, and size together explained68% of the total variance.

Construct validity, determined through the presence ofconvergent and discriminant validity, demonstrates how wellthe measurement items relate to the constructs. To demonstrateconvergent validity, we use three tests: item reliability, compositereliability, and average variance extracted. We determined itemreliability by examining construct item loadings. In general,loadings at or above 0.5 demonstrate adequate item reliability.All items had loadings above 0.500, with two exceptions, ‘‘We haveaccess to inventory data at locations that supply our supplier’’ fromthe information sharing construct, and ‘‘Alliances and partnershipsare common in our industry’’ from the industry practice construct(see Table 2). As PLS is a non-parametric procedure, we usedbootstrapping to perform significance testing for the loadings. Thet-statistics for the loadings of the measurement items on theirlatent constructs were all significant at the 5% level, except for theitem from the industry practice construct. However, we retained itbecause it is close to the cutoff and we deemed it to be important.Further validity analysis using PLS confirmed this decision.

Cronbach’s alphas also provide evidence of composite reliabili-ty and values above 0.6 demonstrate that it is adequate. All thecomposite reliabilities for our constructs were above 0.7 and all theCronbach’s alphas are above 0.6 (see Table 3). Finally, the averagevariance extracted (AVE) represents the amount of variance aconstruct captures via its items relative to the amount of variationdue to measurement error. We found that each construct’svariance extracted was above the recommended value of 0.5.

Table 3Convergent validity analysis.

Constructs Composite

reliabilities

Cronbach’s

alphas

AVE

Business benefits 0.91 0.88 0.58

Information sharing 0.78 0.62 0.56

Business systems leveraging 0.84 0.72 0.64

Relational concurrence 0.79 0.64 0.57

Industry practice 0.81 0.74 0.60

Size 0.93 0.83 0.86

Thus we concluded that all our constructs had satisfactoryconvergent validity.

We used two tests for discriminant validity: comparison of itemloadings with item cross loadings and comparison of the varianceextracted from the construct with shared variance. Each itemshould load more highly on its intended construct than on otherconstructs [4]. We found that all our items satisfied this condition(see Table 4). Secondly, a construct’s variance extracted, or sharedvariance between the construct and its items, should be greaterthan the shared variance between the construct and otherconstructs; this was measured by comparing the square root ofa construct’s average variance extracted (AVE) to its correlationswith other constructs. For each construct, we observe that thesquare root of the AVE is considerably larger than its correlationswith other constructs (see Table 5). Consequently, our constructsdemonstrate adequate discriminant validity.

4.2. PLS structural model results

We next examined the overall explanatory power of thestructural model, the amount of variance explained by theindependent variables, and the magnitude and strength of itspaths, where each of our hypotheses corresponds to a specificstructural model path.

We used R2 to measure the model’s explanatory power,interpreted in the same way as for regression analysis. The explainedvariation should exceed 10% to qualify for suitable explanatorypower. The analysis revealed that the structural model explainedabout 20% of the variation in Business Benefits, suggesting that thestructural model provided adequate explanatory power. We usedbootstrapping with 200 re-samples to obtain the t-statistics fortesting the statistical significance of the model’s paths/relationships.(Ideally, the paths should be at or above 0.2, have significant t-statistics, and be directionally consistent with expectations.)

The path between information sharing and business benefits washighly significant (t = 2.72, p = 0.007), fully supporting Hypothesis 1(see Fig. 3). The path between business systems leveraging andbusiness benefits was also highly significant (t = 3.05, p = 0.003),confirming Hypothesis 2. Thus, the two information capabilitiesconsidered here positively affected businesses. The path betweenrelational concurrence and information sharing was moderately

Table 4Item loadings and cross loadings.

Items Benefits Information

sharing

System

leveraging

Relational

concurrence

Industry

practice

Size

More efficient planning and replenishment practices 0.82 �0.37 �0.19 �0.29 �0.02 �0.09

Improved on-time delivery performance 0.79 �0.27 �0.18 �0.03 �0.02 0.03

Increased availability of raw materials at our site 0.79 �0.37 �0.15 �0.15 �0.03 �0.03

Reduced coordination costs with our supplier 0.77 �0.15 �0.17 �0.10 �0.05 0.01

Increased productivity 0.77 �0.24 �0.38 �0.10 �0.10 0.04

Reduced supply chain costs 0.71 �0.21 �0.30 �0.18 �0.29 0.00

Reduced inventory costs 0.67 �0.13 �0.24 �0.10 �0.19 0.28

We share long-term forecasts with our supplier �0.29 0.87 0.01 0.29 0.22 0.12

We share our manufacturing schedules with our supplier �0.28 0.86 0.15 0.16 0.21 0.08

We have access to inventory data at locations that supply our supplier �0.17 0.43 0.22 0.03 0.09 �0.10

We integrated GEOPS Global VMI inventory data with our facility’s business systems �0.31 0.09 0.88 0.14 0.30 0.18

Our facility’s business systems are highly integrated with our supplier’s systems �0.20 0.16 0.77 0.05 0.33 0.10

We use GEOPS Global VMI for real time integration of our supply chain �0.22 0.06 0.75 0.17 0.19 0.20

We encourage cross-investing with our supplier for major projects �0.12 0.24 0.17 0.86 0.30 0.24

We structured the relationship with our supplier to satisfy mutual needs �0.26 0.19 0.10 0.83 0.19 0.04

Our relationship with our supplier is important to us 0.01 0.11 0.04 0.53 0.09 0.11

In our industry, suppliers and customers are linked through information technology �0.15 0.15 0.39 0.25 0.97 0.05

Information sharing is common among suppliers and customers in our industry �0.07 0.40 0.16 0.29 0.85 0.00

Alliances and partnerships are common in our industry 0.00 0.13 0.24 0.34 0.41 0.15

Number of employees 0.04 0.10 0.21 0.19 0.05 0.93Company’s total sales 0.04 0.07 0.17 0.15 0.02 0.93

Items in bold are loadings.

Table 5Construct correlations with the square root of AVE along the diagonals.

Business benefits Information sharing Business systems leveraging Relational concurrence Industry practice Size

Business benefits 0.762

Information sharing 0.335 0.75

Systems leveraging 0.309 0.12 0.80

Relational concurrence 0.178 0.25 0.16 0.76

Industry practice 0.131 0.25 0.34 0.28 0.78

Size 0.045 0.09 0.21 0.19 0.04 0.93

I.S. Chengalur-Smith et al. / Information & Management 49 (2012) 58–67 65

significant (t = 2.39, p = 0.018), upholding Hypothesis 3. However,the path between relational concurrence and business systemsleveraging was insignificant (t = 1.43, p = 0.154), rejecting Hypothe-sis 4. Finally, neither of the control variables, namely industrypractice and size, had significant paths, ruling out alternativeexplanations for the results. We also tested a model that includedrelational concurrence as the only predictor of business benefits andit yielded a path coefficient of 0.274 (t-statistic = 1.6 and p-value = 0.11). Thus, we concluded that there was no evidence of adirect path from relational concurrence to business benefits, as theliterature and our model suggested. One of the conditions formediation is that both direct and indirect paths exist. Since there wasno direct path from relation concurrence to business benefits,

0.03

BusinessBenefit s

IndustryPractice

Relational Concurrence

BusinessSystems

Leveraging

Information Sharing Size

0.29*

0.33**

0.32**

0.12

0.17(1.43)

(2.39)

(3.05)

(2.72)

(1.14)

(0.34)

Fig. 3. Results of Structural Model with path coefficients (associated t-statistics are

in parentheses). *Indicates significance at the 0.05 level. **Indicates significance at

the 0.01 level.

information sharing and business systems leveraging were notmediators.

5. Discussion

Our results suggest that integrative Web-based supply chainapplications, specifically GEOPS alone and a combination ofbusiness systems, including GEOPS, provide information capabili-ties that result in valuable business benefits, namely more efficientplanning and replenishment, improved on-time delivery perfor-mance, reduced coordination costs with suppliers and customers,increased availability of raw materials, reduced supply chain costs,reduced inventory costs, and increased productivity. For example,with GEOPS alone, over 66% of the companies in our samplereported more efficient planning and replenishment and improvedon-time delivery performance, and over 50% of the companiesreported reduced coordination costs with suppliers and customersand increased availability of raw materials. Thus, it pays toimplement Web-based supply chain applications.

Concerning information sharing, GEOPS allowed sharing ofimportant supply chain information with suppliers and customers.By sharing information, suppliers and customers increase supplychain visibility, allowing them to perform supply chain activitiesefficiently, effectively satisfy material requirements, and compre-hensively plan manufacturing and replenishment schedules. Yet,only 56% of the respondents invested heavily in IT to enable supplychain improvements, specifically increased visibility. This issurprising, considering the nature and degree of benefits to begained from successfully implementing this and other Web-basedapplications that provide information sharing capabilities.

Only 6% of the respondents reported that they have access toinventory data at upstream locations beyond that of their

I.S. Chengalur-Smith et al. / Information & Management 49 (2012) 58–6766

immediate suppliers. This item loaded – albeit marginally – withinformation sharing, suggesting that companies could be missingsome important business benefits by not using this integrative andexpansive information capability. Others [3] have found thatcompanies first need to develop close partnerships with othercompanies, before they are willing to share template – basedinformation, and eventually escalate to more pro-active informa-tion sharing. Clearly, information sharing is extremely importantin a supply chain. However, though it provides a competitiveadvantage at first, it is not sustainable and thus, companies have tobroaden their information capabilities using other means.

Companies that combine – or conjoin – their business systems,including their Web-based supply chain applications, stand to gainimportant business benefits. With broader information capabilitiesin play, companies can enable more supply chain activities,develop leaner organizational arrangements (e.g., smaller, butequally effective purchasing staff), and realize important businessbenefits, including reduced inventory costs, reduced supply chaincosts, and increased productivity. By increasing the amount ofdigitization, these companies are increasing the extent to whichthey conduct both internal and external supply chain activities in afully electronic environment.

Almost 70% of the respondents report that they modified theirsupply chains during the GEOPS implementation. Apparentlymany companies are using their newly acquired informationcapabilities with other business systems to provide processimprovements. Finally, companies that encourage cross-investingin IT projects, structure their supplier and customer relationshipsto satisfy mutual needs, and consider customer and supplierrelationships as being important are very likely to shareinformation with one another. Commonality of interests isgenerally required to develop meaningful relationships. In fact,98% of the respondents admitted that their relationship with theirpartner was important to them. Although companies may besharing information simply because they have GEOPS, it seemsreasonable to conclude that a good working relationship based oncross-investing, satisfaction of mutual needs, and recognition ofthe importance of supplies and customers to a business areessential for information sharing to flourish.

Yet, some companies can mandate information sharing,especially when a large company is driving an information sharinginitiative. Only 23% of respondents reported that use of GEOPS wasmandated by either a supplier or customer. When sharing andusing information, companies risk losing control over access to andutilization of the data and information.

Curiously, the relational concurrence items that positivelyaffect information sharing do little to determine the degree ofbusiness systems leveraging. Once information sharing begins,companies need very little (e.g., satisfaction of mutual needs) fromtheir suppliers and/or customers to leverage GEOPS with existingbusiness systems. About 60% of the respondents indicated thatthey invested aggressively in IT and in redesigning both theirinternal and external supply chain activities. This proactiveorientation may obviate the need for companies to have strongrelationships with suppliers and customers for leveraging IT.Alternatively, the ease of combining GEOPS with existing businesssystems may mean that companies did not need to forge a strongrelationship with suppliers and customers. Business systems thatautomatically linked companies, with little or no difficulty maymitigate the importance of forging strong relationships amongsuppliers and customers.

6. Limitations

Although we collected a wide range of item and benefit data forour study, our measures were self-reported and based on rating

scales. Clearly, for items and benefits, hard measures are preferableto rating scales. There are multiple sources of common methodbias and we were able to avoid only some of them during thedesign of the survey. In order to reduce method bias, someresearchers (e.g., [11]) have recommended that empirical studiesuse two different sets of respondents, one for capturing responsesto the independent variables and another for capturing responsesfor the dependent variables. The risk of lowering the response rateprevented us from using different target populations for the set ofindependent and dependent items.

In an attempt to reduce method bias, we did not identify therespondents by tracing them back to their facilities. This couldpotentially result in over-representation of certain businessfacilities, particularly the large ones. However, we did controlfor size of the business facility in our analysis, thus mitigating theeffect of any resulting sampling bias. Also an analysis of ourrespondent demographics showed considerable variation in size,suggesting that we were able to capture a significant cross-sectionof the target population. Nevertheless, our approach mayintroduce sampling bias into our findings, especially given themoderate sample size.

Our study did not include a systems integration construct as anantecedent to information sharing because the supply chainapplication stored data about supply chain participants and theiractivities in a centralized database, making it readily availablethrough a Web browser, and thus it could interoperate with legacysystems. Because e-business systems incorporate Internet stan-dards, they generally link with one another regardless of theunderlying technology platforms. Many companies execute legacysystems because of the high cost to redesign and replace them.Consequently, for a new hybrid system (with both Web-based andlegacy systems), companies may still have to deal with systemintegration issues to ensure that the systems work as one.

7. Summary and conclusions

Our empirical study revealed two important conclusions: (1)Web-based supply chain applications provide immediate infor-mation capabilities information sharing which yields importantbusiness benefits and (2) Web-based supply chain applicationsand other business systems, when leveraged, also yield importantbusiness benefits. Consequently, companies should implementWeb-based applications that provide information capabilities andleverage those applications with other business systems tobroaden those information capabilities, enabling more businessactivities than are possible through the Web-based applicationalone.

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InduShobha Chengalur-Smith is the Chair of the

Information Technology Management department at

the School of Business in the University at Albany, State

University of New York. She received her Ph.D. from

Virginia Tech and prior to joining academia she worked

in both the private and the public sectors. Her research

interests are in the areas of Open Source Software,

Technology Adoption and Implementation, Information

Quality, and Security. She serves on the Editorial Boards

of several journals including Information & Management

and the ACM Journal of Data and Information Quality and her research has been

published in academic journals such as Information Systems Research, European

Journal of Information Systems, Journal of the AIS, Journal of Strategic Information

Systems, Communications of the ACM, and multiple IEEE Transactions.

Peter Duchessi is an Associate Professor of Information

Technology Management at the School of Business,

University at Albany, State University of New York. He is

also the former Chair of the Information Technology

Management Department. He is an active member of

the faculty at the Lorange Institute, Switzerland and the

University del Salvadore, Argentina. His areas of

expertise include internal and external business

analyses; development and implementation of new

computer-based technologies; and service manage-

ment. He has provided consulting and management

education services to a number of notable companies

around the world, including Reflexis Systems, Inc. Jet

Aviation AG, Alexanderwerk AG, GE Global Research, Siemens AG, and the World

Bank. He has over 30 peer-reviewed publications in various outlets, including

Journal of Consumer Behavior, Communications of the ACM, Interfaces, European

Journal of Operational Research, IEEE Transactions on Systems, Man, and Cybernetics,

Management Science, and the California Management Review. Additionally, he is the

author of Crafting Customer Value: The Art and Science, which Purdue University

Press publishes in four different languages.

J. Ramon Gil-Garcia is an Associate Professor in the

Department of Public Administration and the Director

of the Data Center for Applied Research in Social

Sciences at Centro de Investigacion y Docencia Econom-

icas (CIDE) in Mexico City. Dr. Gil-Garcia is member of

the National System of Researchers as Researcher Level

II. In 2009, he was considered the most prolific author in

the field of digital government research worldwide.

Currently, he is a Research Fellow at the Center for

Technology in Government, University at Albany, State

University of New York (SUNY) and a Faculty Affiliate at

the National Center for Digital Government, University

of Massachusetts Amherst. Dr. Gil-Garcia is the author

or co-author of articles in The International Public Management Journal, Government

Information Quarterly, Journal of the American Society for Information Science and

Technology, and European Journal of Information Systems, among other internation-

ally recognized academic journals. His research interests include collaborative

electronic government, inter-organizational information integration, adoption and

implementation of emergent technologies, and multi-method research approaches.