information technology as an enabler of supply chain collaboration: a dynamic-capabilities...

INFORMATION TECHNOLOGY AS AN ENABLEROF SUPPLY CHAIN COLLABORATION:

A DYNAMIC-CAPABILITIES PERSPECTIVE

STANLEY E. FAWCETTGeorgia Southern University

CYNTHIA WALLIN AND CHAD ALLREDBrigham Young University

AMYDEE M. FAWCETTUniversity of Arkansas

GREGORY M. MAGNANSeattle University

Despite substantial information technology (IT) investments, many organi-zations have failed to obtain hoped-for improvements in supply chain (SC)performance. Therefore, we investigate the mechanisms through which ITinfluences SC performance. Specifically, we use the resource-based view (RBV)of the firm to ascertain how IT can be exploited to obtain a distinctive SCadvantage. We do this via a multimethod (survey and case-study) approach attwo periods of time. We use a nested structural equation model (SEM) to testsix hypotheses. Likewise, we content analyze interviews to contextualize theSEM findings. Importantly, we find that investments in IT make their greatestcompetitive contribution when they enable a dynamic SC collaborationcapability. The findings provide valuable insight to guide IT investmentsdesigned to improve SC performance.

Keywords: electronic commerce; strategy development; supply chain management; supplychain performance; survey methods; structural equation modeling; structured interviewing

INTRODUCTIONOver the past two decades, advances in information

technology (IT) enabled the emergence of modern sup-ply chain management (SCM) (Mabert and Venkatara-

man 1998; Hult, Ketchen and Slater 2004). MichaelHammer (1990) popularized IT’s transformative role,arguing that IT made possible the reengineering of work.Unfortunately, many companies failed to grasp his em-

phasis on how IT is used rather than on IT itself. Over adecade after he introduced process reengineering, Ham-mer lamented that fewer than 10 percent of all majorcorporations had used IT to transform their value-cre-ation processes (Hammer 2004). Companies had adop-

ted an IT focus rather than a process focus (Fawcett,

Osterhaus, Magnan, Brau and McCarter 2007). As a re-

sult, they had failed to use IT to leverage complementarycompetencies that reside across the supply chain (SC)to gain competitive advantage (Dyer and Singh 1998;Frohlich 2002).

Theoretically, IT allows various members of an SC toshare information and coordinate competitive initiatives(Frohlich 2002; Wu, Yeniyurt, Kim and Cavusgil 2006).For example, SC leaders like Amazon, Dell, Honda,

Procter and Gamble and Wal-Mart use IT to share real-time information regarding inventory levels and flowthrough rates with key suppliers (Lee 2004; Chopraand Meindl 2009). Better information sharing should

lead to stronger supplier performance and better SC

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relationships, which promote the ideation and exploita-tion of unique forms of SC collaboration (Tippins andSohi 2003; Lee 2004; Liker and Choi 2004). Research has

shown that SC leaders are using IT to enable SC businessmodels that deliver significant performance improve-ments, including lower costs, faster new product devel-opment, shorter order fulfilment lead times and greater

supply flexibility and SC agility (Clark and Hammond1997; Cachon and Fisher 2000; Frohlich 2002; Radjou2003; Hult et al. 2004; Fawcett, Magnan and McCarter2008a; Lao, Hong and Rao 2010).

However, despite considerable IT investments targetedat improving operational performance, many companieshave been unable to replicate the performance resultsobtained by SC exemplars. Because they have focused on

the technology itself rather than how it can be exploitedto transform SC operations and relationships (Wu et al.2006), these companies have been ‘‘disappointed withthe returns from these investments’’ (Jap and Mohr2002). Ultimately, these companies have bought ad-

vanced technologies, but not used them to build non-imitable SC competencies that deliver unique customervalue (Barney 2001; Ketchen, Tomas, Hult and Slater2007). They have failed to understand two critical points:

1. IT is a valuable-but-no-longer-rare resource. IT andsupportive implementation services are now availableto any company with the money to acquire them. Bythemselves, technology investments can be replicatedby competitors and thus provide only a temporarycompetitive advantage (Taylor 2003; Fawcett, Magnanand Ogden 2007).

2. Although IT is almost ubiquitous, the way IT is usedcan enable competitive differentiation. Inimitabilityemerges as IT enables unique value-creation opportu-nities — such as those found in coordinated and col-laborative SC strategies (Brynjolfsson 1993; Clemonsand Row 1993; Powell and Dent-Micallef 1997; Bar-ney, Wright and Ketchen 2001; Wu et al. 2006).

To summarize, although exemplar companies haveleveraged IT to change SC practice and obtain dramaticSC performance improvements, experience shows thatinvestments in IT per se do not necessarily lead to com-

petitive gains (Richey, Chen, Upreti, Fawcett and Adams2009). Our research objective is to evaluate the howsunderlying the effective deployment of IT in winning SCbusiness models and thus explain why some companies’IT and SCM investments are more successful than those

of their counterparts.The remainder of this article is organized as follows. In

the following section, we use the resource-based view(RBV) of the firm to evaluate how IT can theoretically be

used to create SC capabilities and thereby competitiveadvantage. We then describe our multimethod researchapproach. Next our findings reveal that IT investmentscan help create differential returns when they enable the

creation of specific dynamic capabilities. Specifically,

targeted IT investments that promote a dynamic SC col-laboration capability engender distinctive operationaland firm performance. We conclude with theoretical and

managerial implications as well as limitations and futureresearch directions.

AN RBV OF IT’S ROLE IN SCMAlthough enabled by IT, contemporary SC strategies

emerged as a response to intensifying global competi-tion. In particular, the success in the early 1980s of Jap-anese keiretsu-based firms in the consumer electronicsand automobile industries forced managers to reevaluate

their approach to organizing for competitive advantage(Schonberger 1982, 1986; Hayes and Wheelwright1984; Womack, Jones and Roos 1990). Managers beganto realize that more intense and collaborative relation-

ships among members of an SC could create differentialadvantage and confer supernormal rents to well-executedSC strategies (Bowersox, Calantone, Clinton, Closs,Cooper, Droge, Fawcett, Frankel, Frayer, Morash, Rine-hart and Schmitz 1995; Dyer and Singh 1998). As the

RBV of the firm provides insight into how companiesorganize and deploy resources to achieve advantage, webriefly review RBV’s basic tenets and evolution in thefollowing paragraphs.

The essence of the RBV is that a firm consists of ‘‘acollection of productive resources’’ that can be exploitedto create value and advantage (Penrose 1959; Rubin1973; Wernerfelt 1984). The more valuable and rare the

resources, the greater the advantage the firm may obtain(Dierickx and Cool 1989). Barney (1991) argued thatbecause resources are heterogeneously distributed amongfirms and imperfectly mobile, a firm’s distinctive resource

endowments may lead to persistently superior perfor-mance. This formalization of RBV led to an intense focuson acquiring a ‘‘unique’’ resource base.

Over time, this static view of the RBV — that a firm’s

resource base is the antecedent to competitive advantage— has expanded to focus on a firm’s approach to re-source utilization (Mahoney and Pandain 1992; Priemand Butler 2001). Resource possession is a necessary but

not sufficient condition for competitive advantage. Thesufficient condition is communicated by the word how —how is the firm organized to use or exploit its resources tocreate unique capabilities and value (Barney 1997; Teece,Pisano and Shuen 1997)? The RBV thus argues that

as important as a firm’s unique resource base is, it ismore important to develop and configure these resourcesin a way that maximizes their competitive potential(Eisenhardt and Martin 2000).

Even as the RBV has ‘‘evolved into a dynamic recipeexplaining the process by which these ingredients [afirm’s resources] must be utilized’’ to deliver competitiveadvantage, most research continues to evaluate the

competitive impact of a firm’s valuable, rare, inimitable

Information Technology as an Enabler of Supply Chain Collaboration: A Dynamic-Capabilities Perspective

January 2011 39

and nonsubstitutable resources (Newbert 2007). Dyerand Singh (1998), however, pointed out that valuableresources and routines often reside among diverse

members of an SC. They argued that superior perfor-mance depended more on how firms organize and de-ploy these interorganizational resources than on a firm’suse of its own constrained resource base.

Recently, Newbert analyzed the RBV literature andidentified three RBV approaches that are applicable tothis study’s focus on the role of IT in enabling competi-tive SC strategies:

1. The resource-heterogeneity approach focuses on relation-ships between unique resources/capabilities and asustained competitive advantage, which then leadsto sustained performance (Prahalad and Hamel1990; Barney 1991).

2. The organizing approach argues that certain firm-levelconditions enable the firm to more fully exploit keyresources. To achieve a competitive advantage, valu-able resources must be properly organized and leve-raged (Peteraf 1993; Henderson and Cockburn 1994;Wiklund and Shepherd 2003).

3. The dynamic-capabilities approach emphasizes the needto transform (integrate, build and reconfigure) re-sources into a dynamic capability such as an e-com-merce capability to achieve superior performance in arapidly changing environment (Zhu and Kraemer2002). Business models that create a valued dynamiccapability achieve high levels of competitive advan-tage. Newbert noted that few studies have evaluatedthe dynamic-capabilities approach.

In this study, we evaluate how IT can be deployed in SCstrategy formulation and execution to achieve outstand-ing performance and ultimately competitive advantage.

Thus, we answer Newbert’s (2007) call for further re-search into the dynamics behind the RBV by explicitlydeveloping and testing hypotheses related to the resourceheterogeneity, organizing and dynamic-capabilities per-spectives of the RBV.

Resource Heterogeneity and SC ConnectivitySC connectivity refers to a company’s ability to use IT to

collect, analyze and disseminate information needed tosynchronize decision making across value-added activi-ties. When diverse functions and SC partners are con-nected, better decision making and higher levels ofcoordination and collaboration are possible (Sprague

and Watson 1979). Specifically, adopting the IT neededto share information on customer and supplier needs,capacities and capabilities enables decision makers toinclude the right members of the SC in appropriate

value-creation processes. As this information is ex-changed, each participant can also better be assigned toproper roles and responsibilities to maximize value cre-ation. Although SC connectivity helps managers align

strategy and structure with the environment to improve

performance, relatively few companies have been able toturn their connectivity-related IT investments into astrong SC connectivity capability (Fawcett et al. 2007).

The benefits of higher levels of SC connectivity beginwith reduced environmental uncertainty, lower transac-tion costs and a more rapid response to environmentalchanges (Rindfleisch and Heide 1997; David and Han

2004). For example, bar codes, RFID, data warehousesand data mining technologies allow managers to detectenvironmental trends and monitor real-time customerbehavior. IT connections facilitate quick information

sharing so that needed adjustments to SC composition orto the roles performed by each member of the SC can bemade (McGee 2004). A rapid response capability helpsassure high levels of customer satisfaction. More tangible

benefits from SC connectivity include shorter lead times,reduced inventory levels, faster new product design,shorter order fulfilment cycles and improved purchasing(Cachon and Fisher 2000; Lee, So and Tang 2000; Fro-hlich 2002; Hongtao 2002; Fiala 2005; Robinson, Sahin

and Gao 2005). Our first hypothesis thus states:

H1: The ability to use IT investments to establish an SCconnectivity capability is positively correlated withimproved operational performance and customersatisfaction.

An Information-Sharing Culture as an OrganizingContext

Although IT enables connectivity, it does not guaranteeproactive information sharing. Many managers perceiveinformation to be a proprietary resource and are reluc-tant to share information that, if used opportunistically,

could disadvantage their firm (Williamson 1985). Anunwillingness to share information can thus negate thebenefits of investments in connectivity technologies.Specifically, when critical information — e.g., sales, in-

ventory levels, forecasts, technology roadmaps or marketentry plans — is withheld, a firm’s SC connectivity in-vestments yield minimal improvements in decision-making quality, coordination and performance.

By contrast, a culturally embedded willingness to shareinformation should magnify the value of IT linkages byincreasing the amount, quality and timeliness of infor-mation that is shared (Lawrence and Lorsch 1967; Leeet al. 2000; Mendelson 2000). However, developing an

information-sharing culture as an organizing context isnot easy. To achieve high levels of cultural willingness, afirm must commit sufficient resources to specific orga-nizational mechanisms — e.g., senior manager interac-

tion and interorganizational teams — that increase thelevel of information shared (Hammer 2004; Wagner andBuko 2005; Patnayakuni, Arun and Seth 2006). Fawcettet al. (2009) found that firms are increasingly willing

to share forecast and sales information, but remain

Journal of Supply Chain Management


reluctant to make more sensitive, strategic informationavailable — which may be opportunistically used byother SC members. Sharing such sensitive information

raises vulnerability and safeguarding.Because proactive information sharing can improve (1)

relationship strength among SC members, (2) the abilityto coordinate value-added activities and (3) exploitation

of unique collaboration opportunities, companies with astrong information-sharing culture should outperformtheir counterparts. For instance, Cachon and Fisher(2000) found that SC costs were 2.2 percent lower at

companies with a full information policy than with atraditional information policy. Therefore, we expect tofind evidence of additional performance gains when SCconnectivity and information-sharing culture are present

together. Our second hypothesis states:

H2: An information-sharing culture is positively corre-lated with improved operational performance andcustomer satisfaction.

SC Collaboration as a Dynamic CapabilityAs IT investments are evaluated, managers should

consider how specific investments contribute to com-petitiveness (Fawcett, Osterhaus, Magnan and Fawcett

2008). For example, it has been shown that informationcan substitute for inventory and time in an SC (Lee andWhang 2000; Li, Lin, Wang and Yan 2006; Lee 2010;Sheffi 2010; Sprague and Callarman 2010). Hammer

(1990, 2004) argued that IT can be used to dramaticallyrethink and redesign the core SC processes responsiblefor value creation. Specifically, IT investments are animportant enabler of a unique collaboration capability

that allows companies to share resources and coordinateefforts in the quest for superior performance (Fawcett etal. 2008a). In theory, a dynamic collaboration capabilityshould help a company access, shift and leverage SC re-

sources to rapidly respond to a changing competitiveenvironment (Teece et al. 1997). Firms that possess adynamic collaborative capability should therefore be ableto sustain high performance levels over time.

Establishing a dynamic SC collaboration capability,however, is not easy. Therein lies its value from an RBVperspective: a collaboration capability is rare, valuableand hard to replicate (Fawcett, Ogden, Magnan andCooper 2006; Richey, Roath, Whipple and Fawcett 2010).

Experience demonstrates that SC relationships that arecharacterized by shared resources, joint decision-making,aligned goals and the principle of risk sharing havestrong collaborative potential (Lee 2004; Liker and Choi

2004; Wu et al. 2006; Fawcett, Magnan and McCarter2008b). However, because SC collaboration relies heavilyon sharing constrained resources, a company must focuscollaborative efforts to build productive SC relationships

that are able to create customer-oriented value. SC rela-

tionship strength is enhanced when SC connectivity andan information-sharing culture exist simultaneously(Frohlich and Westbrook 2001; Grzeskowiak, Blut and

Kenning 2007; Min, Mentzer and Ladd 2007).As conceptualized, an IT-enabled collaboration capa-

bility is one mechanism for improving a firm’s perfor-mance. Specific collaboration-driven benefits include

better quality, lower inventory levels, faster new productdevelopment cycles, higher productivity, lower materialsand manufacturing costs and shorter delivery lead times(Ferdows, Lewis and Machuca 2004; Hult et al. 2004; Lee

2004; Ireland and Webb 2007). Effective collaborationalso leads to higher levels of customer satisfaction (Fro-hlich and Westbrook 2001; Fawcett et al. 2007). Threehypotheses emerge from this discussion:

H3: SC connectivity and an information-sharing cultureare antecedents of a dynamic SC collaboration ca-pability.

H4: A dynamic SC collaboration capability is positivelycorrelated with operational performance and cus-tomer satisfaction.

H5: A dynamic SC collaboration capability mediates theinfluence of SC connectivity and information-shar-ing culture on operational performance and cus-tomer satisfaction.

SC Collaboration’s Influence on BusinessPerformance

It is important to evaluate the competitive impact of anSC collaboration capability on firm performance. Indeed,the fundamental goal of the RBV is to explain why somefirms outperform their counterparts, focusing on the

notion that rare, valued and inimitable competencieslead to better market performance and higher profit-ability (Barney 1991, 2001; Wernerfelt 1995). Improvedcustomer satisfaction coupled with higher operationalperformance levels should lead to better overall organi-

zational performance. Such performance is often mea-sured via two profit-statement effects: top-line growthand bottom-line profitability. Companies that increaserevenues while managing costs so that margins meet

or exceed averages tend to perform well over time.Therefore, we propose a mediated relationship between acollaboration capability and improved overall organiza-tional performance.

H6: Operational performance and customer satisfactionare positively related to profitability and growth.

The Competitive Process as a Driver of CapabilityDevelopment

Resource-advantage (R-A) theory extends the RBV bylooking at it through the lens of heterogeneous-demandtheory (Hunt and Davis 2008). In essence, R-A theory

evaluates how the process of competition contributes to


January 2011 41

improved organizational performance, emphasizing theimportance of learning in the creation of inimitablecompetencies. From this perspective, R-A theory raises

the question of whether or not competitive forces haveled firms to learn how to integrate IT systems, cultivate aninformation-sharing culture, and collaborative moreeffectively.

By collecting data at two different points in time, ourstudy provides a unique opportunity to examine thisquestion. A propensity to rely on technological solutionscoupled with rapid IT innovations should have driven a

connectivity-based competitive response. Likewise, anintense competitive environment should have led to agreater willingness to share information as well as agreater awareness of the need to collaborate effectively.

Thus, we expect to see higher levels of SC connectivity,information-sharing culture, and SC collaboration overtime. However, the dynamics of a more competitive en-vironment may mitigate any performance improve-ments. We therefore approach this aspect of the study

from an exploratory perspective.

METHODOLOGYBecause issues involving IT’s role in SCM are complex,

dynamic and not well understood, an exploratorymultimethod research framework was used as themethod of analysis. As the initial study began in the late

1990s, modern IT — including the Internet — had beenidentified in the literature as the enabler of SC collabo-ration, but the mechanisms through which IT influencesSC capabilities had not been fully explored (Brynjolfsson

1993; Clemons and Row 1993; Bowersox et al. 1995;Fawcett and Clinton 1997; Bowersox, Closs and Stank1999; Lucas and Spitler 1999; Bharadwaj 2000).Similarly, an effort had not been made to empirically

evaluate the evolution of SC-related technology invest-ments — thus, the decision to replicate the study overtime. The data collection for first study concluded in2001 at the height of the Internet boom and following

the massive IT investments associated with the Y2Kphenomenon. During the following years, the NASDAQretrenched, but both IT capabilities and SC visibility ad-vanced significantly. Therefore, it was determined that afollow-up study was merited to identify the extent to

which SC practice had changed. A 6-year interval be-tween Periods 1 and 2 provided sufficient time to eval-uate how firms were implementing and organizing for ITto enable greater SC capabilities. Three preliminary steps

were undertaken to firmly ground the research:

1. A literature search going back to the early 1980s wasconducted to provide the insight needed to design thesurvey and interview guide.

2. A series of preliminary, informal managerialinterviews were conducted to ensure managerial rele-vance.

3. An advisory board consisting of managers and aca-demics was assembled to provide feedback on the re-search content and process.

These efforts provided the context to analyze and in-

terpret the survey and interview findings regarding theinfluence of IT on SC capabilities and firm performance.

Survey Data CollectionGaining an understanding of IT’s enabling role together

with the longitudinal nature of the study required carefuland consistent selection of the survey’s key informants.The presurvey interviews and advisory board discussionssuggested that participants be limited to senior-level

managers (e.g., director, vice-president, CEO) with broadorganizational accountability, collaborative interactionsand access to overall firm-level performance data.Therefore, in each time period, three professional asso-

ciations — the Council for Supply Chain ManagementProfessionals, the Institute for Supply Management andAPICS — helped compile a mailing list consisting of theirsenior-level executives. The random samples in Periods 1and 2 were designed to mirror each other in terms of

geography, industry and management position.In both time periods, the survey process followed

Dillman’s total design method; that is, three mailings of acover letter, an instruction sheet and the survey. To in-

crease the response rate, prenotification phone calls weremade to invite managers to participate. Managers werealso offered a copy of the study findings and the op-portunity to be entered into a drawing for one of several

iPod Nanos. Overall, 702 usable surveys were returnedfor a response rate of 14.69 percent. Table 1 providesdetailed response rates broken down by time period andprofessional organization. The relative sample sizes and

proportions from each of the three professional associ-ations were consistent in each of the two time periods,suggesting sample equivalence. Further, firm size asmeasured by number of employees was used as a control

variable. No significant difference was found.Nonresponse bias was evaluated in both time periods.

Two methods were used. First, a comparison of earlyversus late responses was performed (Armstrong and

Overton 1977). Specifically, responses from the firstmailing were compared with responses from the thirdmailing. T-tests for each of the constructs of interest wereperformed — no significant differences were found,suggesting no response bias.

Second, to more clearly verify that the respondents andnonrespondents were not uniquely different, the demo-graphic profiles of the two groups were compared. InPeriod 1, because responses were anonymous, we called

managers on the mailing list until we had contacted 300nonrespondents who were willing to talk with us (100from each sampling frame; i.e., APICS, ISM, CSCMP). Weasked them to share data on number of employees and

sales so that respondent and nonrespondent profiles



could be compared. T-tests revealed no significant differ-ences across the variables employees or sales. In Period 2,respondents were tracked so that mailing and survey ad-

ministration costs could be minimized. Thus, nonre-spondents could be identified. Demographic profilesfor 300 randomly selected nonrespondents (100 fromeach sampling frame) were developed using Dun andBradstreet databases. These profiles were compared with

those of the respondents. Again, no significant differenceswere found (employees: p50.98; sales: p50.64).

Finally, because respondents from different associationsmay have different functional perspectives based on their

training and experience, we needed to verify that thesurvey responses could be pooled for data analysis.Therefore, invariance of the structural weights across thethree sample frames was tested within a multigroup

structural equation model (SEM). That is, a multigroupSEM was constructed to test for variability in the estimatedmarginal effects associated with each of the three sampleframes for each time period. The baseline model allowed

effects to vary across groups and periods. Each estimatedmarginal effect was then constrained to be equal acrossthe three groups and a chi-square CMIN difference testwas conducted to determine if the fit of each constrained

(nested) model differed significantly from that of theunconstrained (reference) model. The individual tests areconservative because they are not adjusted for multiplecomparisons. Results showed no significant difference

between the various constrained models and the uncon-strained model. Together, the balanced composition of

the overall sample and the results of chi-square differencetests provided sufficient justification to pool survey re-sponses from all three sample frames.

Case Study Data Collection Interview ProcessAlthough survey research is a relatively economical

approach for theory testing, it does not provide depth ofunderstanding regarding multifaceted issues. A case

study methodology is needed to explore the complexwhat, why and how questions associated with organi-zation capabilities and performance outcomes (Yin1981; Meredith, Raturi, Amoako-Gyampah and Kaplan

1989; McCutcheon and Meredith 1993). Therefore, fol-lowing the initial stages of the survey data collection ineach time period, a series of detailed interviews wereconducted to complement and contextualize the survey

findings (Pettigrew 1990). For Period 1, 51 interviewswere conducted. For Period 2, 58 interviews were per-formed. Fifteen companies participated in both roundsof interviews. The typical interview lasted 2–4 h. Table 2shows the overall demographic statistics for the inter-

view companies. By design, the participants in the twointerview panels possess similar characteristics — al-though a conscious effort was made to include somesmaller finished goods manufacturers, suppliers and

service providers in Period 2.Once a company agreed to participate, a brief overview

of the research objectives and a copy of the interviewprotocol were provided (Spradley 1979). A semistruc-

tured interview guide was used to assure comparability of

TABLE 1

Respondent Composition: Sampling Frame and Firm Size

ProfessionalAssociation

Period 1 Period 2

CompletedSurveys

ResponseRate (%)

Percent of TotalP1 Sample

CompletedSurveys

ResponseRate (%)

Percent of TotalP2 Sample

Respondent composition by sampling frameAPICS 168 12.1 36 75 20.0 32CSCMP 161 11.6 35 102 23.0 43ISM 137 10.6 29 59 20.1 25Overall 466 11.4 100 236 21.2 100

ProfessionalAssociation

Period 1 Period 2

Revenue (US$Million) Employees Revenue (US$Million) Employees

Median StandardDeviation




Respondent composition by firm sizeAPICS 75 5,663 371 19,189 110 68,566 425 83,018CSCMP 1,350 59,110 3,000 31,130 1,500 77,637 2,000 32,560ISM 150 2,714 500 9,139 208 15,128 550 9,402Overall 200 35,399 800 22,584 510 64,774 1,000 51,777


January 2011 43

findings while allowing for flexibility in pursuing insight

into unique practices and programs that became evidentduring the interview. During each interview, extensivenotes were made for later reflection. These notes werethen translated into structured case write-ups to avoid

‘‘data asphyxiation’’ (Pettigrew 1990). Importantly, eachcase was viewed as a ‘‘stand-alone entity’’ to help identifyunique patterns and to validate generalized theory incross-case comparisons (Eisenhardt and Graebner 2007).

As the interview process continued, the researchers met

to compare notes. This iterative discussion-based processwas used to dissect the results, derive a consensus re-garding their meaning, and improve the process for up-coming interviews (Eisenhardt 1989a, b; Seidel 1998).

After all of the interviews were conducted, a rigorouscontent analysis was performed. Because of the varied andnuanced answers as well as a broad array of language andterms used by the interview managers, we determined that

a careful manual coding and evaluation process wouldprovide the best contextualization of the interview find-ings. This analysis consisted of the following three steps:

1. Based on the literature and the iterative discussionsdescribed above, an initial categorization schema wasdeveloped.

2. Each interview was carefully read to identify keywords, phrases and interrelationships. Each surveywas coded and the results entered into a spreadsheet.

3. The findings from the coding process were tabulatedand frequency diagrams created.

The results of this qualitative interview analysis are usedto add depth and breadth of understanding to the SEM

analysis and findings discussed below.

ANALYSIS: CONSTRUCT EVALUATION ANDMODEL FIT

To test the hypothesized relationships, survey ques-tions, and subsequently, constructs were developed basedon the literature review and feedback from the advisory

board. Specifically, we followed the scale-developmentprocedures suggested by Churchill (1979). First, the lit-erature was searched to (1) determine how previous re-searchers had defined the domain of each construct and

(2) locate existing, relevant scales. Bowersox et al.’s(1995) approach to measuring the role of IT in an SCsetting was adopted. Likewise, Dess and Robinson’s(1984) scale for measuring firm performance was used.

The advisory board provided valuable feedback to helpmodify constructs to fit the study objectives.

Table 3 reports descriptive statistics for the purified con-structs as well as the individual measures that comprise

them. The Bonferroni adjusted p-values for multiple inde-pendent-sample t-tests of mean differences across samplesreveal that companies are making significant improve-ments in the areas of SC connectivity, information-sharing

culture, SC collaboration, customer satisfaction, operationalperformance and profitability (see Table 3). No significantchange was found in the construct score for firm growth.

Construct EvaluationBecause SEM is sensitive to nonnormality, we tested each

of the constructs for normality following the proceduresoutlined by Tabachnick and Fidell (2007). None of themeasures exceeded the recommended thresholds for

skewness or kurtosis. Construct acceptability was further

TABLE 2

Qualitative Sample: Channel Sales, Profits and Employee Levels

Number

Period 1 Period 2

Channel positionRetailer 14 15Finished-goods assembler 13 19Direct-materials supplier 15 13Service provider 9 11

Public vs. privatePublic company 34 36Private company 17 22

DescriptiveStatistics

Annual Sales(US$ Million)

Annual Profits(US$Million)

Employees Annual Sales(US$Million)

Annual Profits(US$Million)

Employees

Mean 28,751 1,704 124,706 24,077 2,168 94,408Median 9,045 589 44,750 4,954 679 16,300Minimum 103 � 705 2,701 3 � 4,183 35Maximum 285,222 10,267 1,700,000 378,799 12,731 2,100,000



TABLE 3

Descriptive Statistics: Means (Standard Deviation) and p-Values of Multiple Independent T-testsa

Questions/Measures P1 [n5466]Mean (Standard

Deviation)

P2 [n5236]Mean (Standard

Deviation)

SignificanceDifferencea

Information-sharing culture 4.29 (1.25) 5.07 (0.92) 0.00Frequent, open information sharing

among supply chain members4.60 (1.57) 5.42 (1.11) 0.00

Use of cross-functional and supply chainteams

3.83 (1.46) 4.90 (1.32) 0.00

Senior-level managerial interactionamong supply chain members

4.21 (1.64) 4.86 (1.39) 0.00

Sharing of technical expertise withcustomers and suppliers

4.24 (1.41) 4.87 (1.17) 0.00

A willingness to share information amongsupply chain members

4.56 (1.56) 5.29 (1.16) 0.00

Supply chain connectivityb 3.46 (1.24) 3.81 (1.37) 0.00Current information systems satisfy supply

chain communication requirements3.30 (1.56) 3.86 (1.63) 0.00

Information applications are highlyintegrated w/in the firm and the supplychain

3.44 (1.4) 3.82 (1.51) 0.01

Adequate information systems linkagesexist with supplier and customers

3.64 (1.41) 3.75 (1.48) 0.37

Supply chain collaborationb 4.00 (1.08) 4.25 (1.10) 0.00My firm aggressively shares resources to

help suppliers improve their capabilities3.67 (1.47) 4.25 (1.46) 0.00

Strategic objectives are jointly developedby supply chain partners

4.00 (1.43) 4.04 (1.38) 0.71

Supplier performance is closely monitoredand is the basis for future business

4.62 (1.39) 4.67 (1.44) 0.65

The principle of shared rewards and risksgoverns supply chain relationships

3.74 (1.34) 4.11 (1.38) 0.00

Value-added resources are shared amongsupply chain members

3.96 (1.44) 4.17 (1.26) 0.06

Customer satisfactionc 4.66 (1.24) 4.91 (1.02) 0.01Responsiveness to customer requests or

unexpected challenges4.68 (1.37) 4.89 (1.19) 0.05

On-time delivery/Due-date performance 4.66 (1.45) 4.93 (1.20) 0.02Overall customer satisfaction 4.64 (1.36) 4.92 (1.11) 0.01

Operational performance 4.16 (1.09) 4.38 (0.95) 0.01Cost of purchased items 4.56 (1.47) 4.60 (1.32) 0.70Inventory performance (e.g., cost, levels,

turns)4.47 (1.51) 4.65 (1.20) 0.11

Overall product and supply chain costs(productivity)

4.33 (1.21) 4.64 (1.16) 0.00

Overall product quality 4.15 (1.47) 4.34 (1.31) 0.09New product development capability

(e.g., cost, time, uniqueness)3.61 (1.36) 3.98 (1.40) 0.00

Transportation costs 3.86 (1.56) 4.10 (1.34) 0.05


January 2011 45

evaluated using reliability/validity tests proposed by For-nell and Larcker (1981). Table 4 reports the key statistics.

First, the composite reliability r was calculated to assessconstruct reliability. Reliability scores for all of the con-structs substantially exceeded the recommended thresholdof 0.70 in both periods (Fornell and Larcker 1981).

Second, confirmatory factor analysis (CFA) was used toevaluate construct validity. The standardized loadings wereall significant and relatively large, exceeding the 0.50threshold (Fornell and Larcker 1981). Likewise, the average

variance extracted exceeds 0.50 for the six constructs inPeriod 1, dropping slightly for two of the constructs inPeriod 2 (information-sharing culture at 0.49 and operationalperformance at 0.45). However, the average shared variance

scores were all relatively small and none exceeded the av-erage variance extracted for any of the modeled constructs.Moreover, the overall CFA model statistics (CFI50.94,IFI50.94, NFI50.91 and RSMEA50.045) suggest that theproposed construct structure fits the data well.

Therefore, the constructs are adequately normal, theo-retically unique and possess good reliability as well asacceptable convergent and discriminant validity. We canbe reasonably confident that the measured items reflect

the theoretical constructs they are designed to measure.Further, because use of rigorous tests to establish con-vergent and discriminant validity have shown the factorsto be distinct and unique, we conclude that common

methods bias does not unduly affect the interpretabilityof the findings (Podsakoff, MacKenzie, Lee and Podsak-off 2003).

Model Fit and EstimatesTo test the hypothesized relationships across the two

time periods, we estimated a composite multigroup pathand latent structural model. The measurement modelincludes both reflective constructs and formative indices(Diamantopoulos and Winklhofer 2001; Jarvis, Mack-

enzie and Podsakoff 2003). Information-sharing culture,SC connectivity and SC collaboration are introduced aslatent (reflective) constructs indicative of orientationspredicting the observed phenomena. Customer satisfac-

tion, operational performance, profitability and growthare introduced as summated (formative) indices that arecomposites of the observed measures. For example, anattitude of willingness leads to open information sharing,

managerial interactions and the sharing of technical ex-pertise, while responsiveness to customer requests, on-time delivery, and satisfaction judgments are direct in-dicators of satisfaction. The structural model is repre-sented in Figure 1 (panel A–C). Finally, we compared the

default model containing firm size with a nested modelomitting firm size. Based on this test, the two models areshown not to differ significantly. Thus, we can concludethat the model without firm size is the better, more

parsimonious model.Interactions among latent constructs were modeled

following the recommendations of Schumacker (2002).First, imputed scores were obtained for each of the latent

constructs for the unmoderated model. An interactionterm was then computed from the imputed scores.Models represented in panels A and B of Figure 1 are

TABLE 3 Continued

Questions/Measures P1 [n5466]Mean (Standard

Deviation)

P2 [n5236]Mean (Standard

Deviation)

SignificanceDifferencea

Growthd 4.86 (1.14) 4.94 (1.01) 0.40Sales growth in the last 3 years 4.97 (1.31) 5.08 (1.13) 0.31Market share growth in the last 3 years 4.85 (1.31) 4.92 (1.18) 0.48Growth in return on assets (ROA) in the

last 3 years4.76 (1.29) 4.81 (1.17) 0.63

Profitabilitye 4.50 (1.37) 4.69 (1.17) 0.06Firm profitability 4.50 (1.37) 4.69 (1.17) 0.06

aT-tests assume equal variance. All measures have 700 df. Two-tailed significance.bIndicate the extent to which you agree with each of the following statements as they relate to your firm’ssupply chain: (15strongly disagree; 75strongly agree).cTo what extent do the following improve collaboration between your firm and other supply chain members?(15does not improve, 75greatly improves).dHow does your firm’s performance compare to leading rivals in your primary industry for the following?(15much less, 75much greater).eTo what extent has SC collaboration improved your firm’s performance in the following areas? (15notimproved, 75greatly improved).



TABLE 4

Factor Loadings and Measurement Properties of Predictive Constructs

Construct/Item CFA StandardLoadings

AverageVarianceExtracted

Reliabilityq

AverageVariance Shared

c2

P1 P2 P1 P2 P1 P2 P1 P2

Information-sharing culture 0.62 0.49 0.89 0.83 0.30 0.16Frequent, open information sharing

among supply chain members0.80 0.66

Use of cross-functional and supplychain teams

0.80 0.79

Senior level managerial interactionamong supply chain members

0.76 0.66

Sharing of technical expertise withcustomers and suppliers

0.75 0.66

A willingness to share informationamong supply chain members

0.84 0.72

Supply chain connectivity 0.60 0.66 0.82 0.85 0.29 0.24Current information systems satisfy

SC communication requirements0.78 0.81

IS applications are highly integratedw/in the firm & the supply chain

0.83 0.86

Adequate IS linkages exist withsupplier and customers

0.71 0.77

Supply chain collaboration 0.50 0.52 0.83 0.84 0.41 0.35My firm shares resources to help

suppliers improve capabilities0.62 0.66

Strategic objectives are jointlydeveloped by supply chain partners

0.77 0.76

Supplier performance is monitored & isthe basis for future business

0.57 0.66

The principle of shared rewards & risksgoverns SC relationships

0.80 0.80

Value-added resources are sharedamong supply chain members

0.76 0.70

Customer satisfaction 0.71 0.61 0.88 0.82 0.34 0.33Responsiveness to customer requests

or unexpected challenges0.82 0.76

On-time delivery/Due-dateperformance

0.84 0.77

Overall customer satisfaction 0.87 0.81

Operational performance 0.53 0.45 0.90 0.86 0.37 0.36Cost of purchased items 0.68 0.67Inventory performance (e.g., cost,

levels, turns)0.69 0.58

Overall product and supply chain costs(productivity)

0.88 0.87

Overall product quality 0.74 0.63


January 2011 47

nested within the full model represented in panel C ofFigure 1, allowing comparison between iterations of

the model (Bentler 2000; Mulaik and Millsap 2000).Chi-square difference tests show the nested models todiffer significantly from the full model and each otherat p< 0.001. Differences in hypothesized relationships

(as measured by the b-coefficient) were also testedacross periods using chi-square difference tests. Resultsof these tests are reported in Table 5. In the full model,five of the reported effects differ significantly from

Period 1 to Period 2. All other effects are consistentacross periods.

DISCUSSION OF HYPOTHESES

SC Connectivity’s Heterogeneous-Resource EffectHypothesis 1 examined the relationship between a

firm’s connectivity level and its value-creation ability. Themodel depicted in panel A of Figure 1 shows that SCconnectivity is positively and significantly related to oper-ational performance and customer satisfaction in both time

periods. Based on the b-coefficients, SC connectivity’s rel-ative influence on operational performance (b50.44 inPeriod 1 p< 0.01; b50.37 in Period 2 p<0.01) is ap-proximately three times stronger than on customer satis-

faction (b50.15 in Period 1 p<0.01; b50.12 in Period 2p50.01). This finding suggests that companies are moreadept at using IT to share information to drive efficiencythan to promote improved customer satisfaction. Inter-

estingly, the strength of IT’s influence on value creation

appears to be decreasing marginally over time (futureresearch is needed to confirm this trend). This apparent

decrease is occurring despite significantly higher levels ofsystems connectivity over time (Period 153.46; Period253.81 p< 0.01). The analysis thus suggests that al-though it may not be as rare or nonimitable as in the

past, SC connectivity remains a valued capability.The qualitative analysis supported these SEM find-

ings. Interview managers in both time periods identi-fied IT as a key enabler of value creation (P1: 86

percent; P2: 56 percent). However, the distinctive en-abling role of IT was mentioned significantly less oftenin Period 2. By Period 2, IT investments had becomedefensive. That is, managers expressed the sentiment

that, ‘‘We can’t afford to be caught fighting tomorrow’scompetitive battles with yesterday’s technology.’’ Thereality is that as competitors are aggressively imple-menting the latest technology, IT investments are nee-ded to ‘‘stay in the game.’’ But managers acknowledged

that IT itself seldom delivered an advantage. Managersnoted that investments in Internet interfaces, enterprisesystems and data capturing and analysis tools were usedprimarily to share forecast, inventory and production

schedule information, which enabled them to performtheir jobs more efficiently. Two in five lamented thatmore strategic information was seldom shared. Thisinsight helps explain the finding regarding the greater

influence of SC connectivity on operational performancethan on customer satisfaction. The interviews also con-firmed that companies are better able to connect across

TABLE 4 Continued

Construct/Item CFA StandardLoadings

AverageVarianceExtracted

Reliabilityq

AverageVariance Shared

c2

P1 P2 P1 P2 P1 P2 P1 P2

New product development capability(e.g., cost, time, uniqueness)

0.72 0.71

Transportation costs 0.63 0.50

Growth 0.70 0.66 0.87 0.84 0.03 0.06Sales growth in the last 3 years 0.94 0.92Market share growth in the last 3 years 0.93 0.91Growth in Return on Assets (ROA) in

the last 3 years0.60 0.55

Profitability (single measure construct)

w2 (df) 51,400.028 (570); CFI50.91; IFI50.91.N Period 1 (P1)5466, N Period 2 (P2)5236.NCP (90% CI)5830.028 (723.978–943.747.)RSMEA (90% CI)50.046 (0.043–0.049).All loadings significant at p< 0.001.



functional and organizational boundaries. Managersreporting a lack of connectivity as a serious hindranceto value creation decreased significantly over time (P1:

58 percent; P2: 25 percent). They noted that continuedIT investments had greatly improved their ability toaccurately manage real-time information.

Panel A: SC Connectivity’s Heterogeneous-Resource Effect

Panel B: Information-Sharing Culture’s Organizing-Context Effect

Panel C: SC Collaboration’s Dynamic-Capability Effect

FIGURE 1Estimated Model of Information Technology as a Competitive Enabler


January 2011 49

TABLE 5

Coefficients for Nomological Relationships

Variables Period 1 Period 2 w2

Difference

R2

Estimate Significance Estimate Significance P1 vs. P2 P1 P2

SC connectivity’s heterogeneous-resource effect

SC Connectivity Operational

Performance

0.44 p� 0.01 0.37 p� 0.01 0.31 0.24 0.27

SC Connectivity Customer

Satisfaction

0.15 p� 0.01 0.12 p50.01 0.65 0.57 0.41

Operational Performance

Customer Satisfaction

0.76 p� 0.01 0.59 p� 0.01 0.03


Profitability

0.83 p� 0.01 0.84 p� 0.01 0.99 0.54 0.53


Profitability

0.10 p50.05 0.10 NS 0.94


Growth

0.04 NS 0.19 p50.02 0.18 0.04 0.14

Customer Satisfaction Growth 0.16 p50.01 0.23 p� 0.01 0.48

Estimated as independent model Compared with full model

N Period 15466, N Period 25236 CMIN (df)5989.4758 (22), p50.000

w2(df)583.802 (22) Where full model

CFI50.97, IFI50.97, NFI50.96 w2(df)–591.969 (246)

RSMEA (90% CI)50.063 (0.049–0.078) CFI50.94, IFI50.94, NFI50.91

RMSEA (90% CI)50.045 (0.040–0.049)

Information-sharing culture’s organizing-context effect


Performance

0.28 p� 0.01 0.33 p� .01 0.54 0.34 0.29

Info-Sharing Culture


0.29 p� 0.01 0.19 p50.03 0.37

Info-Sharing Culture � SC

Connectivity Operational

Performance

� 0.10 p� 0.01 0.01 NS 0.00


Satisfaction

0.12 p� 0.01 0.07 NS 0.48 0.57 0.46

Info-Sharing Culture Customer

Satisfaction

0.13 p� 0.01 0.32 p� 0.01 0.07


Connectivity Customer

Satisfaction

� 0.06 p50.02 � 0.04 NS 0.78



0.68 p� 0.01 0.54 p� 0.01 0.08


Profitability

0.83 p� 0.01 0.84 p� 0.01 0.99 0.53 0.53


Profitability

0.10 p50.05 0.10 NS 0.94

Operational Performance Growth 0.04 NS 0.19 p50.02 0.18 0.04 0.14




TABLE 5 Continued

Variables Period 1 Period 2 w2

Difference

R2

Estimate Significance Estimate Significance P1 vs. P2 P1 P2

Estimated as independent model Compared with full model

N Period 15466, N Period 25236 CMIN (df)5672.5739 (10), p50.000

w2(df)5351.8378 (116) Where full model

CFI50.94, IFI50.94, NFI50.91 w2(df) – 591.969 (246)

RSMEA (90% CI)50.054 (0.048–0.060) CFI50.94, IFI50.94, NFI50.91

RMSEA (90% CI)50.045 (0.040–0.049)

SC collaboration’s dynamic-capability effect


Performance

0.01 NS 0.06 NS 0.61 0.41 0.53

Info-Sharing Culture Operational

Performance

0.04 p� 0.01 � 0.02 NS 0.64


Connectivity Operational

Performance

� 0.10 p� 0.01 0.10 p50.03 0.00

SC Collaboration Operational

Performance

0.58 p� 0.01 0.72 p� 0.01 0.41


Satisfaction

0.20 p� 0.01 0.03 NS 0.07 0.57 0.47

Info-Sharing Culture Customer

Satisfaction

0.21 p� 0.01 0.28 p� 0.01 0.60


Connectivity Customer

Satisfaction

� 0.05 p50.03 � 0.01 NS 0.48

SC Collaboration Customer

Satisfaction

� 0.20 NS 0.20 p50.08 0.02



0.71 p� 0.01 0.45 p� 0.01 0.01

SC Connectivity SC Collaboration 0.48 p� 0.01 0.38 p� 0.01 0.17 0.80 0.48

Info-Sharing Culture SC

Collaboration

0.44 p� 0.01 0.29 p� 0.01 0.18


Connectivity SC Collaboration

� 0.01 NS � 0.14 p� 0.01 0.04


Profitability

0.83 p� 0.01 0.84 p� 0.01 0.99 0.53 0.53

Satisfaction Profitability 0.10 p50.05 0.10 NS 0.94

Operational Performance Growth 0.04 NS 0.19 p50.02 0.18 0.04 0.14


Full model

N Period 15466, N Period 25236

Default model

w2(df)5591.969 (246)

CFI50.94, IFI50.94, NFI50.91

RMSEA (90% CI)50.045

(0.040–0.049)


January 2011 51

An Information-Sharing Culture’s Organizing-Context Effect

Hypothesis 2 assessed the influence of a firm’s will-ingness to share information on its value-creation capa-

bility. Information-sharing culture’s direct effect and itsinteraction effect with SC connectivity were evaluated. Themodel shown in panel B of Figure 1 shows that infor-mation-sharing culture’s relationship to value creation isgenerally positive, but nuanced over time.

Specifically, in Period 1, information-sharing culture has astrong positive influence on both operational performance(b50.29, p<0.01) and customer satisfaction (b50.13,p< 0.01). In the presence of information-sharing culture,

SC connectivity’s influence on operational performance(b50.29, p<0.01) and customer satisfaction (b50.12,p< 0.01) decreases somewhat. Further, a significantnegative information-sharing culture � SC connectivity in-

teraction effect is evidenced in Period 1 (see panel B,Figure 1). In effect, adding the interaction term allowsinformation-sharing culture and SC connectivity to havenonlinear effects such that the marginal impact of one

diminishes as the other increases. Examining the relevantscatter plots shows that the direct effects of information-sharing culture and SC connectivity dominate and that thetwo constructs independently complement each other.

Thus, investments in either improve value creation, butno amplification effect exists between the two. Informa-tion-sharing culture and SC connectivity appear to be twodistinct components of a higher-order information-

sharing capability.The Period 2 findings tell a somewhat different story.

Information-sharing culture continues to have a significantpositive direct effect on both operational performance(b50.19, p50.03) and customer satisfaction (b50.32,

p< 0.01). SC connectivity’s direct effect on operationalperformance (b50.33, p< 0.01) remains consistent.However, its influence on customer satisfaction (b50.07,p5NS) declines and becomes nonsignificant. The inter-

action effect also disappears (operational performanceb50.01, customer satisfaction b5�0.04). Of note,managers report an increase in the willingness to shareinformation over time (P154.29; P255.07; p<0.01).

Overall, the analysis shows that information-sharing cul-ture is a valued capability and that it may be beginning toplay a new role in competitive differentiation strategies.That is, the magnitude of information-sharing culture’s in-

fluence on customer satisfaction increases at a marginallysignificant level (p50.07). This finding suggests thatproactive information sharing is beginning to enablenon-productivity-driven competitive initiatives — an

emerging opportunity for SC information sharing.The interviews findings help explain these SEM results.

Interview managers in both time periods identified thewillingness to share information across functions and

among SC partners as critical to good decision-makingand high levels of value creation (P1: 69 percent; P2: 68

percent). Two themes emerged from the discussions re-garding the willingness to share information.

First, managers talked about the types of information

being shared. In Period 1, managers noted that proactivesharing of operational information was a leading-edgepractice — only a few companies made historical salesand future production forecasts openly accessible to all of

their key customers and suppliers. Struggling with the ITdimension of better information exchange, only 34 per-cent of the managers raised the idea that a lack of will-ingness to share was an equal challenge. Most of these

managers emphasized a need for better operational in-formation. Only two managers focused on the lack ofstrategic-level information being shared. By Period 2,only the most resource-constrained companies were not

sharing core operational information via their SC con-nectivity systems. Yet, 40 percent of managers said that alack of willingness to share information was a funda-mental barrier to value creation. Their angst riveted onthe fact that many SC partners were not willing to share

strategic information regarding market entry, productdevelopment and technology roadmaps. In those rela-tionships where such strategic information was beingshared, new forms of collaborative value creation were

emerging.Second, managers in both time periods pointed out

that the willingness to share information — whetheroperational or strategic — depends on relationship trust.

That is, because managers view information as power,they hoard it unless high levels of trust exist and are usedto facilitate the creation of mutual benefit. Of note, 53percent of the managers in Period 2 noted that the in-

ability to mitigate power asymmetries and build trustundermined the ability of SC partners to work produc-tively and proactively together.

Collaboration’s Dynamic-Capability EffectThe RBV has evolved to argue that how a firm organizes

and deploys resources to create a unique capability ismore important than its stock of valued resources. In anSC setting, the ‘‘how’’ extends to the resources of keycustomer and supply partners. Therefore, Hypotheses 3,

4 and 5 evaluate the role of an SC collaboration capa-bility in the value-creation process. Specifically, the dy-namic-capabilities perspective suggests that IT is bestleveraged for advantage when IT investments and a cul-tural inclination toward open information sharing en-

able enhanced collaboration among members of an SC.Hypothesis 3 tested the antecedent role of SC connec-

tivity and information-sharing culture to SC collaboration.The model shown in panel C of Figure 1 demonstrates

that both technological connectivity (b50.48 in Period1 p< 0.01; b50.38 in Period 2 p< 0.01) and a culturalwillingness to share (b50.44 in Period 1 p<0.01;b50.29 in Period 2 p<0.01) are strong precursors

of a collaboration capability. No impact from the



interaction term is seen in Period 1. However, thesignificant negative effect from the interaction term inPeriod 2 reveals that the marginal impact of onediminishes as the other increases. As noted previously,

companies have significantly improved both their con-nectivity and willingness capabilities over time. Theirability to collaborate has also increased during this time(P154.00; P254.25; p<0.01), suggesting companies are

learning how to deploy technology and shape culture toenhance their collaborative capability. Yet, it appearsthat the marginal benefit of investments in connectivityand willingness decrease as the level of collaboration

sophistication increases. Companies are still struggling tolearn how to use information sharing to achieve break-through collaboration.

Hypothesis 4 evaluated the relationship between a firm’s

collaboration capability and its value creation. The analysisdepicted in panel C shows that SC collaboration’s influenceon value creation is positive and increasing over time.However, SC collaboration’s primary and most enduringeffect is on operational performance. This relationship is

highly significant in both time periods (P1: b50.58p< 0.01; P2: b50.72 p< 0.01). By contrast, SC collaborationis not related to customer satisfaction in Period 1 (b5�0.20,p5NS). But, over time, and as firms increase their collab-

orative alliance management skills, a significant positiveeffect on customer satisfaction emerges (b50.20, p< 0.10).

Hypothesis 5 proposed that SC collaboration mediatesSC connectivity’s and information-sharing culture’s influence

on value creation. The findings reported in panel C revealthat SC collaboration’s mediating role is complex andemergent. In both Periods 1 and 2, collaboration fullymediates the influence of SC connectivity and information-

sharing culture on operational performance. However, therelationship with customer satisfaction is quite different. InPeriod 1, SC collaboration does not appear to significantlymediate SC connectivity’s and information-sharing culture’s

relationship with customer satisfaction. By Period 2, SCcollaboration fully mediates SC connectivity’s influence oncustomer satisfaction, but information-sharing culture’s directinfluence on customer satisfaction remains strong and

significant. This finding suggests that the how behind acompany’s IT strategy is increasingly important to a firm’s

competitiveness. Tying this finding together with theprevious discussion implies that as the uniqueness ofboth IT and a willingness to share information dimin-ishes, companies can still use these capabilities to achieve

competitive advantage if they are jointly employed tocreate a dynamic collaboration capability.

The interviews findings provide valuable context re-garding collaboration’s role as a dynamic capability. Per-

haps most importantly, managers noted that inculcating acollaborative culture and establishing appropriate struc-tural facilitators is very difficult. They highlighted a varietyof challenges — 16 of which were identified by at least 20

percent of the interview managers. Table 6 identifies themost frequently mentioned barriers to effective collabo-ration.

Two points merit brief commentary. First, only one

barrier decreased in mention rate over time — poorlyaligned goals and measures. Better connectivity and thesharing of a broader range of information were criticaldrivers of this improvement in cross-functional and SCalignment. Second, the nature of these impediments to

collaboration as well as the fact that companies arestruggling to figure out how to address them suggests thatthe ability to collaborate to develop new products anddesign new processes fits the RBV’s definition of a dis-

tinctive dynamic capability; that is, high-level collabora-tion is rare, valued and hard to replicate.

A final insight to emerge from the interviews relates tocollaboration’s contribution to competitiveness. Four out

of five managers identified lower costs as a primarybenefit of their collaboration initiatives. The next mostfrequently reported benefit, better customer satisfaction,was 20 percentage points less. This outcome supports the

survey finding that collaboration’s foremost contributionto performance is enhanced efficiency. Other oft-identi-fied benefits are reported in Table 7. Among them,managers reported a dramatic increase in the value-

added nature of SC relationships (P1: 22 percent; P2: 55percent). This finding evidences that higher levels of trustare emerging and that more strategic information isbeing exchanged. Both are prerequisites to strategic SC

alliances. Higher value-added relationships also evincethe nature of benefits may be approaching a tipping

TABLE 6

Barriers to Effective Collaboration

Barriers Period 1 (%) Period 2 (%)

Organizational structure and turf conflicts 73 75Resistance to change 53 58Poorly aligned goals and measures 73 55Power asymmetry and a lack of trust 47 54Lack of managerial support 29 42


January 2011 53

point; that is, that collaboration and its precursors willin time drive more creative problem solving, enhanced

innovation and higher levels of customer satisfaction.Future research is required to confirm this outcome.

The Impact on Firm PerformanceOur final hypothesis, Hypothesis 6, explored the over-

all firm performance impact of IT and SC collaboration.Initial analysis showed that SC connectivity, information-

sharing culture and SC collaboration positively influenceprofitability and growth. However, these direct effects werefully mediated by operational performance and customersatisfaction. This finding is consistent with the dynamic-capabilities perspective of the RBV. That is, resources and

capabilities must continually adapt and create uniquevalue to influence firm performance.

Operational performance’s influence on firm performancemanifests itself primarily through a strong, positive and

highly significant relationship with profitability. This re-lationship is consistent across both time periods (P1:b50.83, p< 0.01; P2: b50.84, p< 0.01). Although oper-ational performance is not statistically related to growth in

Period 1, by Period 2 operational performance had begunto drive growth at a moderate level (P1: b50.04, p<NS;P2: b50.19, p< 0.05). This change in relationship maybe the result of a shift in marketplace values. Interview

managers had noted that competition had intensifieddramatically in the interval between the two studies,placing tremendous pressure on cost reduction. Cus-tomers had begun shifting more of their buy to suppliers

with a high-productivity track record. Customer satisfac-tion exerts a consistent positive influence across bothprofitability (P1: b50.10, p<0.05; P2: b50.10, p<NS)and growth (P1: b50.16, p<0.05; P2: b50.23, p<0.01).

CONCLUSIONS AND IMPLICATIONSAs an enabler of collaborative SCM, the IT revolution of

the past 20 years has changed the way companies conductbusiness. Managers have invested billions of dollars toacquire the latest SC-related technologies. In the midst ofall this investment, the question arises, ‘‘How well are

companies using their IT investments to drive better SC

performance?’’ Our study assessed this question in an effortto understand the mechanisms through which IT contrib-

utes to improved SC practice and firm competitiveness.This longitudinal research confirms the fundamental

proposition underlying the dynamic-capabilities ap-proach to the RBV. That is, the possession of a ‘‘valuable,

rare, inimitable, nonsubstitutable’’ resource is a necessarybut not sufficient condition for explaining a firm’scompetitive position (Newbert 2007). Simply stated,possessing a rare resource is less important than how the

company uses it to create distinctive value. The analysisrevealed that SC connectivity and the organizing context ofan information-sharing culture contribute most effectivelyto differential firm performance when they are combinedto build a dynamic collaboration capability. Specifically,

we find that:

1. IT’s ‘‘connecting’’ role remains a valued and some-what rare capability. However, companies with lag-ging IT strategies have made substantial progress inthis arena over the time frame of this study suchthat SC connectivity’s potential to deliver distinctiveadvantage is diminishing.

2. A company’s willingness to share information leads toimproved performance. However, rather than magni-fying the influence of connectivity, an information-sharing culture acts as a strong complement to a firm’sability to connect. That is, when either is close to zero,the other still has a positive competitive effect. Firmsthat possess both outperform their counterparts whohave built only one of these two capabilities. How-ever, the willingness to share more sensitive, strategicinformation and to build better structural mecha-nisms for sharing opens the door to more innovativeSC relationships.

3. In the past, companies have focused their IT capabil-ities on improving efficiency. This reality is true forboth connectivity and information-sharing culture.Thus, IT has had a more pronounced influence onthe productivity side of firm performance. However,the longitudinal aspects of the study show that com-panies are learning how to use information sharingto achieve greater collaboration and higher levels ofsatisfaction.

TABLE 7

Competitive Benefits of Effective Collaboration

Benefits Period 1 (%) Period 2 (%)

Lower costs and higher quality 67 82Improved customer service and satisfaction 53 60Higher value-added relationships 22 55Better inventory performance 47 48Faster responsiveness/velocity 53 45



Importantly, the interview portion of the study not onlycontextualized the SEM findings but also provided valu-

able insight regarding SC-related IT investments. Figure 2demarcates five guidelines to exploiting IT investments fora collaborative advantage. The process begins with a firmunderstanding that IT is an enabler. Managers consistently

noted that too many people see IT investments as a pan-acea to competitive problems and lose sight of the fact thatIT per se does not drive competitiveness. A few managersadded the warning that without some form of capability-

technology map, inappropriate IT investments will bemade. Avoiding these unnecessary investments frees upresources (money and time) to use IT to design moreefficient processes and cultivate more-collaborative SC

relationships. A common theme among the most suc-cessful IT adopters was that unique capabilities emerge astechnological and cultural factors are kept in balance byadopting collaborative measures and building mechanisms

like cross-functional teams and SC advisory boards. Thesecore practices set the stage to use IT and collaboration toideate and innovate — skills that will be increasinglyneeded to fight tomorrow’s competitive battles.

Indeed, in each interview in Period 2, the question wasasked,‘‘How has the competitive environment changed?’’Without exception, managers responded that the world ismore competitive today. Over 50 percent of the interviewmanagers identified globalization and China’s emergence

as two factors intensifying competition. In this environ-ment, the quest for sustainable advantage requires the

development of dynamic capabilities such as collabora-tion that go beyond a firm’s inherent resources. High-level collaboration that brings the resources of diversemembers of an SC together in creative and unique ways

promises a raised level of inimitability and enduringsuccess (Lavie 2006).

Limitations and Future ResearchRespondents from the three different professional as-

sociations included in this study may have different

functional perspectives based on their functional back-ground. Specifically, one might question whether a supplymanagement executive may have the same knowledgeand perspectives as an executive responsible for distribu-tion or manufacturing. Although the high-level focus on

connectivity and cultural willingness mitigate this chal-lenge and we did the pooling analysis to verify compa-rability of the data, future research into function-specificviews may identify some areas where valid differences in

functional perspective have a meaningful influence on therole of technology in SC design and management. Forexample, systems acquisition and implementation issuesmay be best evaluated from a functional perspective.

Likewise, the ideal approach to longitudinal research is to

View Technology as an Enabler

Focus IT Investments on two Capabilities: 1. More efficient processes 2. More collaborative SC Relationships

Avoid IT investments that do not map explicitly to strategic capabilities.

Balance technological and cultural factors by 1. Adopting measures to promote sharing 2. Building mechanisms to promote sharing

Begin to emphasize IT and collaboration’s ideation and innovation opportunities.

FIGURE 2A Process for Exploiting IT Investments for Collaborative Advantage


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track the responses of the same individuals within thesame firms over time. However, the nature and costs oflarge-scale survey research makes this impracticable. Thus,

we collected random samples from the same populationsin both time periods to allow an analysis of the phe-nomena in question over time. Other research ap-proaches, particularly a multiple case study approach,

could be considered for additional longitudinal researchin this field. This approach could be designed to capturedyadic or even SC wide insight. However, a case studymethodology also has limitations, most notably the lack

of generalizability to a large population of firms.As investments in information-sharing technologies,

culture and collaboration continue to evolve, the mostobvious next step in this research stream is to collect

another data set approximately 6 years after the Period 2study was conducted. With another data set we could startto explore possible trends identified in this research. Forexample, is the strength of IT’s influence on value creationcontinuing to decline over time? Are more firms engaging

in collaborative, trust-based relationships where infor-mation can be shared freely, and if so, are those rela-tionships still unique enough to provide a competitiveadvantage? And, how is information, leveraged through a

dynamic collaboration capability, being used to increasecompetitive dimensions (e.g., innovation) beyond pro-ductivity and customer satisfaction?

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Stanley E. Fawcett (Ph.D., Arizona State University) isthe Visiting Distinguished Professor of Logistics and Sup-ply Chain Management at Georgia Southern University inStatesboro, Georgia. His teaching and research interests

currently focus on collaborative business model designand global supply chain strategy. Dr. Fawcett has taught inexecutive development programs in Asia, Europe, andNorth and South America. He is Co-Editor-in-Chief of the

Journal of Business Logistics, and has published over 100articles and six books on supply chain-related topics.

Cynthia Wallin (Ph.D., Arizona State University) isAssistant Professor of Global Supply Chain Managementat Brigham Young University in Provo, Utah. Her

research focuses on buyer-supplier relationships, includ-ing information sharing, collaboration and trust. Dr.Wallin has published in the Journal of Supply ChainManagement, the Journal of Business Logistics, Decision

Sciences, and other academic and managerial publica-tions. Her industry experience includes positions assenior buyer, purchasing manager, stores manager andcommodity manager.

Chad Allred (Ph.D., Purdue University) is Assistant

Professor at Brigham Young University. He began hiscareer as an electrical engineer at the Eyring ResearchInstitute, where he designed digital equipment for 3M,Exxon and Chevron. He earned his MBA and began

nearly a decade of marketing work at Novell. He thendecided to pursue a doctorate; his dissertation researchexamined customer fairness perceptions and satisfaction/loyalty evaluations in high-tech business-to-business

service exchanges. Dr. Allred’s current research interestsinclude business-to-business and services marketing,entrepreneurship/intrapreneurship, and value chains.

Amydee M. Fawcett (MPA, Brigham Young University)is a doctoral student at the University of Arkansas in

Fayetteville, Arkansas. Her research focuses on supplychain collaboration, organizational transformation andhumanitarian disaster recovery. Ms. Fawcett also isManaging Director for Lateral Line Analytics.

Gregory M. Magnan (Ph.D., Michigan State Uni-versity) is Professor of Operations and MBA ProgramDirector in the Albers School of Business and Economicsat Seattle University in Seattle, Washington. He hasreceived several research and teaching awards, including

a Best Paper Award at the 2010 CSCMP Educator’s Con-ference and from the International Journal of PhysicalDistribution and Logistics Management in 2003. Dr. Mag-nan’s research focuses on aspects of strategic supply chain

management, developing collaborative relationships,and the role of leadership in supplier relationship man-agement. He has published his work in numerous jour-nals including the Journal of Business Logistics, Supply

Chain Management: An International Journal, Supply ChainManagement Review, and Business Horizons.


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