Information & Management 49 (2012) 135–141

Assimilation of Internet-based purchasing applications within medical practices

Richard Klein *

Department of Management, College of Business and Behavioral Science, Clemson University, 101 Sirrine Hall, Clemson, SC 29634-1305, USA

A R T I C L E I N F O

Article history:

Available online 14 February 2012

Keywords:

Assimilation

Diffusion

Healthcare

IT performance

Supply chain management

A B S T R A C T

The changing shape of the traditional buyer–supplier relationship between independent medical

practices, or office-based physicians, and medical industry suppliers has prompted the proliferation of

Internet-based purchasing applications. Healthcare informatics researchers believe that such systems

hold great promise for improving the efficiency of medical organizations. We surveyed 216 medical

practice adopters of electronic purchasing, to assess the post-adoption benefits following system

assimilation. Results showed that Internet-based purchasing applications had a positive impact on both

claims management and operational performance outcomes. Moreover, we found that compatibility,

facilitating conditions, the IT infrastructure, and preference item purchasing were necessary antecedents

to effective Internet-based purchasing application assimilation.

� 2012 Elsevier B.V. All rights reserved.

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1. Introduction

The U.S. Department of Health and Human Service’s Centersfor Medicare and Medicaid Services reported that the total U.S.expenditure on healthcare was $2.5 trillion and that thisrepresented 17.6% of gross domestic product, which increasedby 4% (more than twice the rate of inflation) in 2009 [14].Consumer and government pressure has forced providers toincrease service quality while strengthening operating efficien-cies in pursuit of cost reductions [9]. Deployment of IT representsa large opportunity for providers to improve both quality of careand system efficiency. However, the National Center for HealthStatistics of the U.S. Center for Disease Control and Preventionreported in their 2010 Survey of Office-based Physicians that justunder a quarter of all U.S. medical practices have a basicElectronic Medical Record (EMR) system, and that only one-tenthhave a fully functional system that includes all requisite featuresand capabilities [8]. This is consistent with Schoen et al.’s [20]2006 survey of primary care physicians in Australia, Canada,Germany, New Zealand, the Netherlands, the U.K., and the U.S.They reported that North American countries consistently fellwell behind other developed nations in their use of such caredirected technology.

Changes in business models aimed at developing bothhorizontal and vertical integration in supplier operations at theprovider practice level lagged even farther behind [6]. Shortliffe

* Tel.: +1 864 656 0591; fax: +1 864 656 2015.

E-mail address: rklein@CLEMSON.edu.

0378-7206/$ – see front matter � 2012 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2012.02.001

[21] observed that the healthcare industry still uses paper, phone,and/or facsimile in more than half of all its transactions.

Little empirical research has been directed at understanding thehealthcare industry’s use of non-care directed IT. Hence, we have alimited understanding of healthcare’s use of IT. Moreover, recenttrends have been to reduce costs; individual provider organiza-tions in the U.S. face pressure on their bottom lines due to cappedfees and managed care plans as well as reduced governmentreimbursements through Medicare and Medicaid.

Some initiatives have been introduced to encourage thediffusion of technology at the medical practice level. For example,with more than 700,000 physicians participating in the Medicareprogram, Rosenfeld et al. [17] reported that the overseeing agencyhas offered incentives to providers who adopted health informa-tion technology (HIT). Further, the American Recovery andReinvestment Act of 2009 provided office-based physicianspayments of up to $18,000 if they became early adopters ofEMR systems, with nominal penalties against Medicare reim-bursement in later years [22].

Saeed et al. [19] noted that Internet-based e-businessapplications, including electronic purchasing, had proliferatedacross all industries. Traditionally, medical suppliers interactedwith individual providers through a direct outside sales forceengaged in recurring on-sight contacts. Medical suppliers in aneffort to reduce cost have begun deploying Internet-basedpurchasing and order management applications to replace theoutside sales force. For suppliers, these traditional sales orga-nizations have propagated a vendor managed inventory (VMI)environment with supplier representatives initiating purchaseorders on behalf of the individual providers. Medical practices,

R. Klein / Information & Management 49 (2012) 135–141136

however, have been slow in changing their inter-firm businesspractices, often opting for less automated and non-computermediated business processes [16].

Hence, we investigated individual medical practice assimilationof Internet-based purchasing applications. Specifically inquiring:Do medical practices realize improvements in upstream supply chain

management and downstream claims management functions through

greater Internet-based purchasing application assimilation? We alsoexamined internal resource and process factors that facilitate orhinder post-adoption diffusion by asking: What processes and

resources within medical practices yield greater assimilation of

Internet-based purchasing applications?

2. Theoretical background

2.1. Diffusion of innovations

The two dominant perspectives for observing innovations areadoption (focusing on characteristics and/or conditions that makean organization willing to accept an innovation) and diffusion(understanding what leads to widespread acceptance within anorganization). Fichman [3] contended that existing work hasfocused on the adoption decision through an examination of thedifferences between adopters and non-adopters. However, hebelieves that research should also focus on better understandingpost-adoption use. Hence, our research examined the degree ofpost-adoption diffusion in conjunction with specific performanceoutcomes.

We examine assimilation within the latter stages of diffusion,which includes the degree of technology diffusion throughorganizational work processes and thus becomes routinized inthese activities. Greater levels of assimilation should then resultin medical practices maximizing the use of electronic purchasingin all business routines.

Prior efforts have found a positive relationship betweentechnology diffusion and performance. Fichman, however, notedlimitations in the existing focus as performance results ignoredconsideration of how, when, and where the technology improvedoperations. Moreover, Kohli and Devaraj [11] contended that afocus on investments in IT capital and labor, rather than actualusage behavior, failed to capture performance adequately. From apractical perspective, managers need to understand how use of aninnovation provides performance benefits. Thus we focused on theability of electronic purchasing application assimilation to impactperformance positively within firms. Rather than merely demon-strating that basic use and benefits exist, our work considered ISassimilation levels as well as impacts on upstream and down-stream value chain activities – i.e., on operational and claimsmanagement performance respectively.

H5 (-)

H2 (+)

H4 (+)

H3 (+)

Compatibility

Facilitating Conditions

ITInfrastructure

Preference Item Sourcing

InternePurchasing

Assim

Fig. 1. Researc

2.2. Organizational inertia

As firms cannot pursue organic change when faced withsignificant external threats and/or opportunities, external envi-ronmental changes often require internal adaptation. Firms mustovercome organizational inertia in adopting and diffusing IT.Accordingly, we drew upon the organizational inertia perspectiveto suggest antecedent factors that may influence firm-levelInternet-based purchasing application assimilation.

Gilbert [5] categorized organizational inertia as resource and/orroutine rigidity. These occur when firms fail to change resourceinvestment patterns or organizational processes while investing inresources; it often affects a firm’s allocation of resources. In sum,rigidity results in a failure to alter resource investment patternsand/or organizational processes and workflows in response toexternal threats or opportunities. Accordingly, we posit thatresource-based factors serve to overcome (or capitalize on)rigidities hindering (or enabling) system assimilation.

3. Research hypotheses

From a practical perspective, the Healthcare Financial Manage-ment Association’s 2008 Supply Chain Benchmarking Surveystated that supply chain integration and IT investments yieldedthe greatest improvement in administrative and operationalefficiency [7]. Order management functions topped its list as thebest. Accordingly, healthcare IT initiatives must move beyond care-focused innovation deployment and include broader organiza-tional value chain components.

Healthcare is unique because providers must focus onmanaging both consumers (patients) and payers (insuranceproviders and government agencies). Successful management ofcustomers can be assessed through quality of care, doctor–patientrelationship tenure, and patient satisfaction. Fig. 1 depicts ourmodel, which focused on operational and claims managementperformance as well as resource and/or process based antecedentsthat exist due to Internet-based purchasing application assimila-tion.

3.1. Outcomes

An organization’s ability to access timely, if not real time,information constitutes a key strategic initiative in resolvingpersistent supply chain problems, such as the bullwhip effect orpoor customer service. Saeed et al. contended that Internet-basedpurchasing applications give managers the ability to accesssupplier information, including current pricing, available stocks,and delivery status. Thus IS assimilation can be used not merely asa mechanism for facilitating purchase orders, but also as a way of

H1a (+)

H1b (+)

ClaimsManagement Performance

Operational Performance

t-based Application ilation

h model.

R. Klein / Information & Management 49 (2012) 135–141 137

dealing with other organizational and operational needs. Consideras an example that Frohlich [4] found upstream and downstreamsupply chain integration through the Internet led to increasedoperational performance.

Within medical practices, IS can be used to track and managemore reimbursable supplies through the claims process. Moreover,integrating with suppliers affords buyers the ability to access anduse information held by partners. Such information can be used tostrengthen buyer relationships with their clients and businesspartners. Thus, the following hypotheses were made:

H1a/b. The greater assimilation of electronic purchasing applica-tions within medical practices, the greater (a) claims managementand (b) operational performance outcomes.

3.2. Antecedents

IOS adoption and diffusion literature supports our contentionthat resource and process antecedents foster rigidity. Moreover,compatibility, facilitating conditions, and IT infrastructure haveemerged as potential resources driving diffusion of Internet-basedpurchasing applications within medical practices. We also believedthat ‘‘provider preference item’’ purchasing, prevalent within thehealthcare industry, was a potential antecedent. Such practices areseen as a potential hindrance to electronic purchasing applicationassimilation, and thus we considered them among other factors.

3.2.1. Compatibility

Compatibility involves the degree to which an innovationcoincides with existing practices and/or processes. Bhattacherjeeand Hikmet [2] found compatibility a highly significant predictorof healthcare provider perceptions of a computerized physicianorder entry (CPOE) system within a large acute-care hospital. Tuluet al. [24] further examined specific dimensions of compatibilityand demonstrated that compatibility, and specifically workflowcompatibility, impacted healthcare provider intentions to use anInternet-based medical evaluation application. The easier it is foruse of Internet-based purchasing applications to complementexisting processes and workflows, the more likely the medicalpractice will try to absorb all aspects of the system. Hence, wehypothesized:

H2. The greater the compatibility of Internet-based purchasingapplications within medical practices, the greater the application’sassimilation.

3.2.2. Facilitating conditions

Facilitating conditions, such as documentation and usersupport, play an important role in enabling actions and thusreduce or eliminate potential barriers to use. Sabherwal et al.’s [18]meta-analysis of IS success determinants found that facilitatingconditions enhanced the user experience, increased the occurrenceof training, and resulted in a positive user attitude. For medicalpractices, greater access to IT and network support requires suchresources to allow the integration of Internet-based purchasingapplications within the organization. Therefore, we hypothesized:

H3. The greater the facilitating conditions within medical prac-tices, the greater the assimilation of an Internet-based purchasingapplication.

3.2.3. IT infrastructure

Kilo [10] and other healthcare informatics researchers notedthe challenge posed by IT connectivity due to infrastructurelimitations in small business level medical practices, hindering

both adoption and diffusion of HIT. Pflugheoft et al. [15]demonstrated that IT sophistication within small businessespositively correlated with e-commerce infusion, while Zhu andKraemer [25] found that technical competence positively influ-enced e-business use. The absence of a good IT infrastructurestopped medical practices from diffusing technology in their workprocesses. Accordingly, the following hypothesis was made:

H4. The greater the IT infrastructure within medical practices, thegreater the Internet-based purchasing application assimilation.

3.2.4. Preference item purchasing

The healthcare industry generally lacks effective IT linkagesbetween administrative functions and clinical decision makers’actions. One such disconnect is for ‘‘provider preference item’’purchasing. Ballard [1] noted that these purchases includedmedical supplies specifically requested by individual providers(doctors, physicians assistants, midwives, and nurse practitioners).These items often require interacting with non-contract vendorsand fall outside the standard supply chain. Within hospitals theseitems can include expensive medical devices such as implants,stents, and pacemakers; at the medical practice level, disposablesurgical and procedure kits are common among such purchases.Given the rapid pace of care-directed technological innovationsand growing number of patented items, manufacturers aggres-sively market these items outside the context of the buyer–supplier relationship. Ballard estimated that physician preferenceitem purchases were about 40% of the total for care related suppliesin a typical medical facility.

As the practice of preference item purchasing becomesembedded in organizational processes, the ability to use Inter-net-based purchasing applications to facilitate purchases declines.Accordingly, we posited that:

H5. The greater the purchasing of preference items within medicalpractices, the lower the Internet-based purchasing applicationassimilation.

4. Research methodology

4.1. Sample

Our field study surveyed 1285 organizations registered asindependent medical practices in a southeastern U.S. medicalassociation. A qualitative analysis of information obtained duringan earlier case-based phase, examining an industry supplier andthree practices, revealed managerial differences in medicalpractices wholly owned by managed care and hospital networks.Such organizations faced different decision making procedures andoften lacked autonomy at the practice level in purchasing, internalIT deployment and use, and claims management functions. Hence,informing decision-making at the practice-level necessitatedexamining independent organizations with direct responsibilityfor management of these functions.

We identified business/office/practice managers as the mostknowledgeable informants of the overall purchasing function. Ourstrategy was to collect claims management and operationalperformance data separately from two senior-level respondentswithin each organization. The survey effort, conducted in thewinter months of 2007–2008, resulted in 268 adopters of Internet-based purchasing applications providing data with respect toantecedents and system diffusion. Responses specific to outcomevariables yielded usable responses from 216 different medicalpractices for a response rate of 17%. In 39 instances a singlerespondent was identified as the individual most knowledgeableabout both antecedent and assimilation as well as performance

Table 1Respondent organization demographics.

Demographic Category Frequency Percent

Practice type Anesthesiology 8 4

Cardiology 9 4

Dermatology 20 9

Otorhinolaryngology 13 6

Family Practice 32 15

Gastroenterology 4 2

Internal Medicine 34 16

Obstetrics/Gynecology 30 14

Oncology 6 3

Pediatrics 20 9

Podiatry 17 8

Proctology 3 1

Rheumatology 3 1

Radiology 5 2

Surgery 5 2

Urology 6 3

Other 1 0.5

# of employees 50 plus 7 3

40–50 38 18

30–40 60 28

20–30 72 33

10–20 28 13

0–10 11 5

# of providers 10 plus 15 7

6–9 48 22

4–7 62 29

1–3 91 42

Table 2Measurement items.

Claims management performance

a. Timeliness of filing

b. Accuracy of filing

c. Ability to capture all reimbursable medical supplies used

d. Decreased rejected/returned filings

Operational performance

a. Faster delivery times

b. Reduced transaction costs

c. Enhanced inventory turnover

d. Improvements in purchasing contracts

Assimilation

a. Volume: Percentage of purchase orders initiated through an Internet-based

application: ___.

b. Breadth: Percentage of suppliers employed by our organization, for whom

use of Internet-based procurement applications has been established: ___.

c. Depth: Level at which Internet-based purchasing applications are used

for management of organization’s procurement functions.

Compatibility

a. Using the Internet-based purchasing application is compatible with all

aspects of purchasing within our practice.

b. Using the Internet-based purchasing application completely compatible

with our current situation.

c. I think that using the Internet-based purchasing application fits well with

the way our practice handles purchasing.

d. Using the Internet-based purchasing application fits into the work style

within our practice.

Facilitating conditions

a. Available guidance on the selection of hardware and software

b. Available for assistance with application difficulties

c. Available specialized instruction concerning the system

d. Available assistance with hardware difficulties

e. Available assistance with Internet access difficulties

IT infrastructure

a. Availability of telecommunications infrastructure (i.e., local area network)

b. Availability of high-speed Internet

c. Availability of practice management applications

d. Use of database oriented applications regularly in daily operations

Preference item purchasing

a. Purchase of non-contract items at individual providers’ (i.e., doctor,

physician’s assistant, or nurse practitioner) request

b. Purchase non-stocked items at individual providers’ (i.e., doctor,

physician’s assistant, or nurse practitioner) request

c. Purchase newly marketed items at individual providers’ (i.e., doctor,

physician’s assistant, or nurse practitioner) request

R. Klein / Information & Management 49 (2012) 135–141138

data. These respondents represent smaller medical practices withan average of only 1.3 providers.

The completed surveys showed reasonable demographics ofrespondents (e.g., their gender and work experience). The vastmajority of antecedents and assimilation respondents identifiedthemselves as business/office/practice managers; of these, inexcess of 98% indicated having primary responsibility for practicepurchasing activities. Performance data respondents includedaccountants, business managers, and partners. The respondingorganizations represented a cross section of major medical practicetypes of varying sizes. Table 1 details the descriptive information ofour respondent organizations.

4.2. Measures

Table 2 presents the construct items. We adapted measures ofcompatibility used by Bhattacherjee and Hikmet, facilitatingconditions from Sabherwal et al., and measures of IT infrastructureconsistent of Pflugheoft et al. Other construct measures weredeveloped based on qualitative data generated during the earliercase study phase, which also served to establish the contentvalidity of measures within the final instrument. Preference itempurchasing assesses requests of individual providers withinpractices for non-contract, non-stocked, and/or newly marketedsupplies. Claims management performance items developed heremeasure timeliness and accuracy of filings, ability to capturereimbursable supplies, and rejected or returned filings. Operation-al performance items capture speed of delivery, reductions intransaction costs, enhancements to inventory turnovers, andimprovements in purchasing contracts.

Liang et al. [13] provide a basis for assessing IOS assimilation.Our study adopted their measures (which drew on a multi-dimensional scale of EDI usage) and included usage volume,breadth, and depth. Volume measures the percentage of purchasesinitiated through Internet-based applications. Breadth capturesthe overall percentage of supplier firms connected through IOSwith the adopter. Finally, depth measures the vertical impact of

systems on decision-making in purchasing functions within theadopting organization. An additional dimension, diversity,assessed the number of different functional areas using thesystem. Given our focus on IT innovation at the small business leveland a single functional activity, we felt that diversity had limitedapplicability in our study.

We operationalized assimilation, facilitating conditions, ITinfrastructure, claims management, and operational performancemeasures as formative, while compatibility and preference itempurchasing are reflective. Additionally, practice age and number ofproviders, a proxy for size often studied in IT adoption [12], areincluded as controls. A pilot study of practice managers providedfor an initial assessment of convergent and discriminant validity inaddition to further validation of content validity.

4.3. Non-response bias analysis

In assessing non-response bias, limited demographic informa-tion was collected from 65 randomly selected non-respondentorganizations. An ANOVA examined practice age, type, and numberof providers; no significant differences were detected (p < 0.01). Awave analysis compared initial respondents with those whoresponded during the final weeks of the data collection effort. This

R. Klein / Information & Management 49 (2012) 135–141 139

technique treats late respondents as a proxy for non-respondents.Exactly 42 of the 216, or 19%, of the total responding organizationscompleted the survey during the latter period. Differences acrossthe respondent waves were assessed with respect to practice ageand type; number of providers; individual respondent’s gender,tenure, and overall work experience; in addition to study variables.The ANOVAs again revealed no significant differences (p < 0.01).

Given that 52 practices responded to only the antecedent andpurchasing activities portion of the survey (i.e., no performancedata), we also compared these respondents to the full sample withrespect to practice age and type; number of providers; individualrespondent’s gender, tenure, and overall work experience; as wellas antecedent and assimilation variables. The analyses againrevealed no significant differences (p < 0.01).

4.4. Common method bias assessment

In an effort to safeguard against common method bias, ourstudy sought to survey the two performance outcome variablesthrough a second informant within each medical practice.Additionally, different and some reverse scale types wereemployed across measures. Our data set also included 39organizations with the same respondent completing both portionsof the survey. Differences across these two groups were assessedwith respect to practice age and type; number of providers;individual respondent’s gender, tenure, and overall work experi-ence; and study variables. Comparing construct means through anANOVA found no significant differences (p < 0.01) with theexception of practice size (i.e., number of providers). As noted,the 39 organizations were small relative to the complete sample.

Formally testing for common method bias, the Harmon one-factor test resulted in seven extracted factors. These in totalaccounted for 53% of the variance with the first factor accountingfor 17%. No single factor accounted for the bulk of the covariance.An additional test employing a marker for a theoretically unrelatedvariable, such as the antecedent and assimilation variablesrespondent age, similarly found no support for common methodbias.

5. Analysis and results

The quantitative analysis included measurement validation andhypothesis testing. Although we adopt some existing, previouslyvalidated measures, all were further validated within the currentnomological network. Our research model included multipleinterdependent relationships and formative measures, specificallyassimilation, facilitating conditions, IT infrastructure, and theoutcome variables. Employing SEM techniques permitted anexamination of interrelated constructs. PLS allowed us to examine

Table 3Descriptive statistics, reliability, inter-correlations and AVEs.

Mean S.D. Cronbach alphas Matrix of i

(1)

Claims mgt. perform. (1) 3.34 1.74 Formative 0.91

Operational perform. (2) 4.1 1.11 Formative 0.25*

Assimilation (3) 4.63 1.31 Formative 0.43**

Compatibility (4) 4.11 1.12 0.86 0.13

Facilitating cond. (5) 3.36 1.22 Formative 0.12

IT infrastructure (6) 3.76 2 Formative 0.07

Pref. item purchase. (7) 3.62 1.79 0.78 0.11

Practice age (8) 7.71 2.2 1 0.13

No. of providers/size (9) 3.56 1.98 1 0.11

n = 216.* p < 0.05.** p < 0.01.

measurement and structural models while making no distribu-tional assumptions. PLS ensured that a solution existed for allparameters within the structural model. In the measurementmodel, item weights and loadings indicated the strengths ofrelationships between indicators and constructs, while in thestructural model the estimated path coefficients reflected thestrength and direction of the relationships.

5.1. Reliability and validity assessment

Table 3 shows our reliability and validity assessment findings.All measures exceeded the 0.7 threshold for Cronbach’s alphas,which were not reported for formative measures as theseindicators did not need to exhibit internal consistency. To assessdiscriminant validity we compared inter-construct correlationswith the AVE, measuring the percentage of overall variance inindicators captured by a latent construct. When the square root ofthe AVE of a measure exceeds the correlations between therespective measure and all other measures adequate discriminantvalidity exists. These results also showed convergent validity. Thesquare roots of the AVEs are reported along the diagonal withinter-correlations in the rest of the matrix. The inter-correlationsand square roots of AVEs show that we experienced no problemswith discriminant or convergent validity.

5.2. Formative measure assessment

We employed different procedures to assess the properties offormative measures. The AVE analysis presumes that constructsexhibit convergent validity, a condition not required for formativemeasures, hence, item-to-item and item-to-construct correlationsfor these variables were examined. PLS provides item weights thatreflect the influence of individual formative indicators used tocompute weighted item scores and weighted composite constructscores. These, in turn, serve as the basis for calculating item-to-item and item-to-construct correlations for evaluating discrimi-nant validity. Our analysis found that intra-construct item-to-itemcorrelations were greater than inter-construct item-to-itemcorrelations. Also, items exhibited stronger correlations with theircomposite construct scores than with the composite scores ofother constructs. Thus the results suggest sufficient constructvalidity of the formative measures.

With PLS, weights provide insight into the meaningfulness ofthe set of formative indicators and their relative importance for aconstruct in the overall nomology. When n orthogonal formativeindicators are specified, the ceiling on their average weight is thesquare root of 1/n. This average standardized weight results whenindicators explain all of the variance in a formative measure. Givenfour indicators for IT infrastructure and both outcome measures,

nter-correlations and square root of AVEs

(2) (3) (4) (5) (6) (7) (8) (9)

0.89

0.37** 0.85

0.12 0.26* 0.91

0.02 0.19 0.06 0.93

0.17 0.3 0.16 0.26* 0.83

0.23 �0.29* 0.1 0.12 �0.14 0.89

0.2 0.01 0.23 0.01 0.12 0.09 1

0.09 0.07 0.19 0.01 0.09 0.01 0.21 1

Table 4PLS results for formative construct weights.

Theoretical model Full model

Claims management performance

a. Timeliness of filings 0.43** 0.44**

b. Accuracy of filings 0.45** 0.45**

c. Ability capture reimbursable supplies 0.42** 0.42**

d. Decreased rejected/returned filings 0.41** 0.40**

Average 0.42 0.42

Operational performance

a. Faster delivery times 0.41** 0.41**

b. Reduced transaction costs 0.48** 0.49*

c. Enhanced inventory turnover 0.42** 0.41**

d. Improvements in purchasing contracts 0.45** 0.45**

Average 0.44 0.44

Assimilation

a. Volume 0.57** 0.57**

b. Breadth 0.56** 0.55**

c. Depth 0.49** 0.50**

Average 0.54 0.54

Facilitating conditions

a. Hardware and software selection 0.40** 0.40**

b. Application difficulty assistance 0.41** 0.41**

c. Specialized instruction 0.39** 0.40**

d. Hardware difficulties 0.41** 0.42**

e. Internet access difficulties 0.43** 0.43**

Average 0.41 0.41

IT infrastructure

a. Telecommunications 0.47** 0.48**

b. High-speed Internet 0.42** 0.42**

c. Practice management applications 0.43** 0.44**

d. Database applications 0.48** 0.48**

Average 0.45 0.46

* t < 0.01.** t < 0.001.

R. Klein / Information & Management 49 (2012) 135–141140

the theoretical maximum average weight of all indicators is 0.5. Forfacilitating conditions, an average weight of 0.45 applies given fiveindicators, while for assimilation a weight of 0.58 applies giventhree indicators. Compared to the theoretical maximum, theobserved average weights for indicators associated with eachconstruct, as reported in Table 4, are favorable. These high averageweights, in comparison to the theoretical maximum, provideevidence of their importance.

5.3. Hypothesis testing

PLS employs a series of ordinary least squares regressions toestimate the dependent paths among hypothesized theoreticalrelationships. Path coefficients represent the direct effect of the

Fig. 2. PLS analy

predictor variable on the dependent variable and should be above0.2 to be meaningful. While the product of predictor andintervening variables constitutes the indirect effect, the totaleffect equals the sum of both direct and indirect effects. Ouranalysis supported all hypotheses but the result for H3 was weak.Antecedents accounted for 40% of the total variance explained, orR2, in Internet-based purchasing application assimilation (seeFig. 2). Additionally, our model accounted for 33% of the variance inclaims management performance and 45% in operational perfor-mance.

Further examining mediating effects, we compared the powerof the fully mediated (examining the indirect effects of antecedentvariables on the focal outcome variables and partially mediatingboth direct and indirect) and partially mediated models. Wecomputed separate PLS analyses for the full and partial mediationmodels and examined the contribution of the direct effect for eachantecedent variable toward explaining additional variance in thefocal outcome variables. Cohen’s f 2 and F-statistics validated thesignificance of the direct paths through effect size and varianceexplained with the addition of each mediating path, where effect

size, f 2, for the change in R2 is equal to ðR2partial � R2

fullÞ=ð1 � R2partialÞ.

A small effect size is 0.02; medium approaching 0.15; and largeapproaching 0.35. A pseudo-F test for the change in R2 with 1 and(n � k) degrees of freedom can be calculated by multiplying f 2 by(n � k � 1). Results found no significant direct effects, or subse-quent contribution toward explaining additional variance, for anyantecedents, supporting full mediation through assimilation.

Our hypothesis testing also examined the sub-sample ofmedical practices with different respondents for outcome vari-ables, specifically excluding the 39 organizations with a singlerespondent. Support was again found for all hypotheses with 39%of the variance accounted for in assimilation in addition to 34% and45% in claims management and operational performance respec-tively.

In further analysis of the control variables, a comparison of thefull (i.e. all variables and controls) and control variable only modelsrevealed that the full model explained significantly higher variancein all dependent variables. Comparing the theoretical model (i.e.study variables included and control variables excluded) and fullmodel (i.e., including controls), we found that the full modelexplained an equivalent or only slightly higher variance inconstructs, on average less than a single percent.

5.4. Implications

Both internally via operational outcomes and externallythrough claims performance, managerial decisions on the use of

sis results.

R. Klein / Information & Management 49 (2012) 135–141 141

IT within independent medical practices impact healthcare costsand efficiency. Greater assimilation of purchasing activitiespermits automating tracking of materials purchased as well asease and accuracy in monitoring costs versus reimbursements. Inthe early casework for our study, many providers noted an absenceof such supply costs in financial reporting. For claims managementapplications, errors in data entry in a manual environment can beeliminated and automation results in reduced cycle times forclaims processing.

In the wake of the HIPAA implementation, healthcare providersmust utilize standardized coding for care provided, proceduresperformed, in addition to supplies and materials expended.Further, implementation of the International Classification ofDiseases (ICD-10) will require updates to HIPAA standards adding68,000 different codes [23]. These requirements pose an enormouschallenge for healthcare providers. Our work showed thatopportunities exist for better integration of intra-organizationalsystems in support of practice routines and workflows.

Kilo and colleagues within the medical informatics communityhave long contended that individual practices need to invest in ITinfrastructure in an effort to overcome persistent limitationshindering adoption and diffusion of all HIT, not just Internet-basedpurchasing applications. Medical practices resistant to adoptionand assimilation of Internet-based purchasing applications maylack the appropriate infrastructure and resources to supportongoing use of IT solutions. Suppliers need to identify appropriatetarget adopters best suited to implement and realize directbenefits from purchasing applications.

Preferential item purchasing runs counter to IS diffusion; suchactions degrade the positive impact of performance benefits byhindering diffusion. Hospitals have long struggled to controlphysician preference items in an effort to manage their supplychains better.

6. Limitations and conclusions

Due to its healthcare industry focus, generalizabillity of ourresults is limited. Findings may not help in understandinginnovation assimilation outside the industry or even withinmanaged care networks or hospital facilities. Furthermore, non-care versus care directed innovations constitute distinct pursuits.Therefore, focusing on purchasing activities potentially restrictsthe utility of findings to related administrative functions.

Our work also adopted constructs specific to claims manage-ment performance and preference item purchasing. These mayhave narrow application outside the healthcare industry. Ante-cedents were also restricted to focusing on resource and processbased variables under the control of practice management.

Recent shifts in the traditional buyer–supplier relationshipmodel for medical practices and their suppliers have resulted in aproliferation of Internet-based purchasing systems. In an effort tounderstand decision-making that affects potential benefits ofelectronic purchasing assimilation at the medical practice level, wesurveyed 216 office-based healthcare provider practices. Resultssupport a positive relationship between diffusion stages andclaims management as well as operational performance outcomes.Our analysis also showed that compatibility, facilitating condi-tions, IT infrastructure, and preference item purchasing arerelevant resources that affect systems assimilation.

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Richard Klein is an Associate Professor of Management

Information Systems and the Graduate Programs Coor-

dinator for the Department of Management at Clemson

University. He earned his doctorate at Georgia State

University and researches electronic business initiatives

and healthcare information systems. Dr. Klein has been

published in top journals, including Decision Sciences,

the European Journal of Information Systems, the Journal

of Management Information Systems, MIS Quarterly, and

the Journal of Operations Management. Further, he has

presented his work at national and international con-

ferences, including the Annual Meeting of the Academy

of Management, the Americas Conference on Information Systems, the European

Conference on Information Systems, and the Institute for Operations Research and the

Management Sciences Annual Meeting. Dr. Klein also serves as an Associate Editor for

the European Journal of Information Systems and is a past Chair of the Association for

Information Systems Special Interest Group on Healthcare. Additionally, he has over

10 years of industry experience with Automatic Data Processing and First Data.