Information & Management 47 (2010) 60–67

How does the application of an IT service innovation affect firm performance?A theoretical framework and empirical analysis on e-commerce

Andrea Ordanini a,*, Gaia Rubera b

a Management Department, Bocconi University, Via Roentgen, 20136 Milano, Italyb Marketing Department, Michigan State University, East Lansing, MI 48824, USA

A R T I C L E I N F O

Article history:

Received 26 May 2008

Received in revised form 13 September 2009

Accepted 10 October 2009

Available online 31 October 2009

Keywords:

IT innovation

IT services

Firms’ performance

E-commerce

A B S T R A C T

Understanding the effects of IT-related innovations on firm performance is crucial for businesses. Extant

research has investigated the implications of IT innovations and provided some important findings, but

the varied theoretical approaches have produced results that are often ambiguous: thus there is a need to

examine the process further. We attempted to provide a systematic, theoretically informed framework

for understanding the conditions that may enhance (or hinder) the potential of IT innovations in a

sample of firms. Our model included business and IT resources, both internal and external, that may

influence the performance of firms which have applied a pervasive IT service innovation: e-commerce.

Our empirical test of the model used a research design that takes into account time-lag effects. The model

explained more than half of the variance in the performance of IT innovators and offered several

explanations for why some firms succeeded in implementing IT service innovations while others did not.

Several theoretical and managerial implications result from these findings.

� 2009 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Information & Management

journa l homepage: www.e lsev ier .com/ locate / im

1. Introduction

The IS literature has used various ways of investigating theperformance implications of IT innovations [8]. However, thevaried approaches often result in different and ambiguous findings,thus our knowledge of this crucial topic remains unsystematic [9].A meta-analysis of the most important empirical studies thatinvestigated IT payoff further revealed that though IT innovationhad a positive impact on firm performance in 64% of the cases, 24%of the time the impact was negligible, and in the remaining 12%, itseffect was negative. Moreover, the vast majority of empiricalinvestigations employed cross-sectional analyses, making itdifficult to detect true causal effects over time [7].

A crucial issue thus becomes a recognition of which conditionsmight enhance (or hinder) the impact of IT innovation by the firm,as well as understanding firm performance after the application ofthe innovation. Efforts to address this void include propositionsmostly inspired by the resource-based view (RBV) of the firm[11,13], that suggest the value of IT innovations results from acomplex interplay of several resources and capabilities that reflectdifferent natures (business versus IT) and sources (internal versus

external), which accumulate over time.We decided to perform a robust, empirical test of previous

theoretical propositions; in this we investigated the performance

* Corresponding author. Tel.: +39 02 58363623; fax: +39 02 58362634.

E-mail address: andrea.ordanini@unibocconi.it (A. Ordanini).

0378-7206/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2009.10.003

effects of a broad set of resources and capabilities in a sample offirms who had applied an IT service innovation to their businessarea: e-commerce. We drew on RBV literature, to provide a modelin which firm performance of IT innovators depended on six sets ofresources and capabilities:

� Slack resources, innovative orientation, and external ties arebusiness resources that may be enhanced through the use ofIT service innovations.� IT capabilities, partners’ readiness, and service providers’ IT

capabilities are categories that can lead to successful ITinnovation.

We estimated the impact of these six by employing a time-lagged research design.

2. Theoretical background

There are three broad categories of IT innovation: base

innovation (e.g., new software or hardware architecture); system

development innovation (e.g., open source development or dataadministration); and service innovation (e.g., computer-integratedmanufacturing). We focused on analyzing the effect of applied ITservice innovation.

Recent research has proposed some theoretical frameworksthat attempt to integrate existing literature according to the RBV,which implies that the performance of IT innovators depends on aninterplay among the assets and capabilities of a firm that allow it to

Fig. 1. The research model.

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–67 61

detect and respond to market opportunities or threats. ISresearchers have used the RBV to investigate how IT might helpfirms deploy technological, organizational, and environmentalresources to create business value, though their results have beendifferent and contrasting [3].

One approach, the strategic necessity hypothesis, proposes thatIT cannot have a direct effect on performance but can enhance thevalue of other firm resources. This stream maintains a commodityview of IT, arguing that technology investment alone has little to dowith a firm’s competitive success, because it can easily beduplicated and imitated by competitors. The main performanceeffect therefore must be ascribed to the quantity and quality ofbusiness resources that can be leveraged purposefully by IT. Theimplicit assumption was that IT innovations provided morebenefits to strong players; i.e., those that possessed superiorfinancial, relational, and cultural resources.

A second applied the RBV principles differently by focusing onthe role of certain IT capabilities rather than enhancements ofexisting business resources. This proposed that the organization’sability to assemble, integrate, and deploy IT resources influencedperformance after it satisfied the conditions proposed by the RBVin terms of value, rareness, and inimitability. In particular, theseconditions would be met when the IT infrastructure related tohuman resources skills (both technical and managerial) and otherintangible capabilities: ‘‘relationship assets’’. Therefore, the maineffect of IT should be due to the quantity and quality of humanresources and intangible capabilities developed before andduring the implementation phase, rather than to complementarybusiness resources.

In the context of pervasive effects of IT service innovations,literature on IT readiness [15] and IT outsourcing [6] suggest thatsuccessful implementations demand more than the IT capabilitiesof the firm: they also require capable external players, such ascommercial partners and IT service providers; i.e. IT innovationcannot be executed in isolation.

From an empirical point of view, prior research consideredthese approaches separately, focusing on a single sets of resources.Integrative frameworks have been proposed only recently andwithout empirical testing. Therefore, we developed a set ofhypotheses related to the role of a broad set of resources andcapabilities of a different nature in an attempt to understandhow firms’ performance may be affected by the application of ITservice innovation.

3. Research model and hypotheses

Fig. 1 shows our research model. Its was used to investigate, inan integrated fashion, how business and IT resources andcapabilities can contribute to the performance of firms that haveadopted e-commerce, which is a complex type of IT serviceinnovation: It has a pervasive impact at the firm level, requiringimplementation within the firm, and needing external players. Wedetermined the time since the implementation of e-commerce andcontrolled for the level of initial performance of IT innovators toavoid potential halo effects.

3.1. Business resources

The strategic necessity hypothesis assumes that businessresources, including all assets, capabilities, etc., matter most inexplaining the performance of IT innovators. We focused on keybusiness resources that e-commerce could enhance: slackresources, which are a potential source of e-commerce success,(cultural) orientation to change, and established external ties.

Slack resources offer a potential advantage because a firm withmore financial resources can afford the investments required toprovide IT innovations.

H1. Slack resources are positively associated with the perfor-mance of IT innovators after the application of e-commerce.

Cultural openness to innovation should help exploit competitivepotential during organizational changes due to e-commerceimplementation. A proactive posture by managers can help inpredicting business needs and facilitate the changes in organiza-tional structure, workplace practice, and external relationships [5].Skills evolve over time, subject to learning [16], which happens atboth the individual and group level, because routines can be refinedand improved.

H2. The firm’s innovative orientation is positively associatedwith the performance of IT innovators after the application ofe-commerce.

Finally, superior capabilities for accessing the supply marketand distribution channels may leverage the potential of IT serviceinnovations and influence interorganizational activities. Because ofthe relational nature of B2B e-commerce, a firm with relational

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–6762

capabilities has knowledge about how upstream and downstreammarkets work, which makes it easier to implement appropriatee-commerce choices.

H3. Interorganizational ties are positively associated with theperformance of IT innovators after the application of e-commerce.

3.2. IT capabilities

Although technological solutions are generally available in themarketplace, capabilities for managing the application of tech-nologies are developed internally [4]. Human resource skillsprovide meaningful leadership in the IS function, because peoplewith these skills can effectively manage complex IT projects,evaluate technology options, manage change, and envisioncreative and feasible technical solutions to business problems.Similarly, IT capabilities reflect the unique history of each firms;often become part of the taken-for-granted routines in anorganization were based on socially complex relationshipsbetween the IT function and business functions in the firm, andbetween the IT function and a firm’s suppliers or customers.

H4. Firm IT capabilities are positively associated with the perfor-mance of IT innovators after the application of e-commerce.

When IT innovations have consequences at the interorganiza-tional level, the IT capabilities of the commercial counterpartsmatter affect the online platform. By facilitating information flowsand reducing information asymmetry and uncertainty, readinessto change enhances coordination benefits, increasing and speedingefforts toward the implementation of a strategy [14]. Partnerreadiness also improves the strategic alignment of the parties,which helps ensure that the behavior of the counterpart isconsistent and compatible with that of the focal firm. In thepresence of high readiness to change, the application of e-commerce should evolve along a compatible and faster imple-mentation path than that when the commercial partner continuesto pursue strategies that do not match the new context.

H5. The IT readiness of the commercial partners is positivelyassociated with the performance of IT innovators after the applica-tion of e-commerce.

The implementation of major IT service innovations requiresthe application of different technologies and solutions to businessissues, yet firms rarely can develop the necessary assets, resources,and capabilities alone. Innovators often rely on consultants ortechnology specialists to whom they outsource development ofpart of the system; this is particularly true for small firms.

For the realization of innovations, such as e-commerce, theservice innovations are usually accompanied by other IT base andsystem development innovations which have broad technologicalscope and generally are developed by specialized serviceproviders. The decision to implement an e-commerce innovationthus occurs at the end of series of IT investments, which areseldom completely endogenous to the firm. Thus, the capabilitiesof service providers play a crucial role in the exploitation of an ITservice innovation.

H6. Capabilities of the IT partners are positively associated with theperformance of IT innovators after the application of e-commerce.

4. Key constructs and measures

4.1. Business resources

We measured slack resources with a three-item scale thatindicates perceptions of the market share, cash flow, and workingcapital levels compared with the firm’s competitors.

Innovative orientation is a broad cultural measure that reflectsthe extent to which the firm is forward-looking in formulating itsstrategy. It consists of a prospective market orientation, ‘‘seeking toexploit new opportunities’’ [1], develop products, and createmarkets, and a learning orientation, the ‘‘set of values thatencourage, or even require, employees to constantly questionthe organizational norms’’ [2]. We measure innovative orientationas a higher-order variable consisting of these two as first-orderfactors. The prospective orientation measure used a three-itemscale that reflected the extent to which top management relied onfirst-mover strategies to alter the market equilibrium, tried toanticipate competitors through sunk costs, and acted as an earlyadopter of new technologies [12]. Our measure of learningorientation also employed a three-item scale that measuredhow much a firm engaged in systematic research efforts, endorseda systematic approach to learning, and fostered a generativelearning approach.

To measure external ties, we used a two-item scale whichindicated the extent to which a firm has easy and establishedaccess to both supply markets and distribution channels.

4.2. IT capabilities

The Firm’s IT capabilities help firms manage their IT: they includehuman resource skills and relationship assets that togetherprovide a competitive barrier. Obviously, as has been stated formore than 30 years, the relationship assets occur when there ismutual respect and trusting rapport between the IS and businessdepartments. Thus, we measured the firm IT capabilities as ahigher-order variable consisting of human resource skills (invol-ving a three-item scale that captures the level of technical skills,management skills, and training activity of the IT employees) andrelationship assets, a four-item scale, adapted from Wade andHulland, measuring the levels of interfunctional cooperation, ITplanning and strategy alignment, coordination of IT-drivenchanges, and IT innovation risk sharing.

Because partner readiness determines how much their com-mercial partners are likely to adapt their structures and activitiesto execute e-commerce innovations, we measured it with a four-item scale that reflected the firm’s perception of the motivations oftheir commercial partners of the relevance of online exchange, theavailability of adequate Web-based systems, and willingness toimprove coordination and information sharing.

Service providers’ IT capabilities are the contribution of externalvendors. This contribution can be measured on a two-item scalethat captures the perception that technology partners provideadequate IT interfaces and are flexible and rapid in adjusting them.

4.3. Dependent variable and controls

Firm performance. We used a composite of observed perfor-mance measures, consisting of return on assets (ROA), return onsales (ROS), and operating income as a fraction of net assets (OI/A).This captures different traits of firm performance and avoidsconsideration of atypical or extraordinary sources of income. Wecollected each measure for the current and previous years andcalculate their average, which reduced the potential variance ofexceptional events. We also normalized all performance measureswith average industry levels to provide easy comparison of themeasures.

Control variables. First, we included the performance measuresjust before the firms started to employ e-commerce, whichreduced any potential halo effect. Second, because innovationsneed time to recover investment costs and reach a sufficient degreeof activity before they have a positive effect on performance, weincluded time in our analysis, as a single item that measured the

Table 1The full measurement instrument.

Constructs Measurement items

Firm performance

Post-application performance Observed measures from firms’ balance sheets, normalized with relative industry measures (three digits)

PAP1 – Operating income on assets: OI/A 05/06

PAP2 – Return on sales: ROS 05/06

PAP3 – Return on assets: ROA 05/06

Initial level of performance Observed measures from firms’ balance sheets, normalized with relative industry measures

(three digits) (‘‘t’’ is the year of e-commerce application)

AAP1 – Operating income on assets: OI/A t-2/t-1

AAP2 – Return on sales: ROS t-2/t-1

AAP3 – Return on assets: ROA t-2/t-1

Business resources

Slack resources Compared to your relevant competitors, please indicate the level of agreement (1 = completely disagree;

7 = completely agree) about the following propositions:

SR1 – We have a stronger capability to generate cash flow

SR2 – We have more working capital available to invest

SR3 – We have a higher share in our market segment

Innovative orientation

Prospective orientation Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following proposition

regarding the strategic inclination of your firm:

PO1 – We generally increase capacity before our competitors do the same

PO2 – We are usually the first ones to introduce various products and/or services in the market

PO3 – We adopt innovations early

Learning orientation Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following proposition

regarding the strategic inclination of your firm:

LO1 – The engagement in a systematic research effort is the norm in my unit

LO2 – A systematic approach to learning is constantly endorsed by top managers

LO3 – A generative learning approach is expected as an outcome of my unit

External ties Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following proposition

regarding the vertical ties of your firm:

ET1 – We have open and trusting relationships with key suppliers

ET2 – We have open and trusting relationships with key customers

IT resources

Firm IT capabilities

Human resource skills Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following

proposition regarding HR skills:

HR1 – Our technical staff possesses the know-how needed to manage IT applications

HR2 – Our functional managers are capable to coordinate IT applications in their processes

HR3 – We regularly train our technical staff and functional managers on IT

Relationship assets Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following proposition

regarding the relationship between IS and other department of your firms:

RA1 – We have cross functional teams

RA2 – We constantly align IT planning and business strategy

RA3 – We coordinate IT innovations and related business changes

RA4 – Risks and responsibilities of IT innovation are shared

Partners readiness Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following

propositions regarding the perception about the IT readiness of your commercial counterparts:

PR1 – Our commercial partners consider it important to engage in electronic commerce

PR2 – Our partners have adequate IT systems to engage in e-commerce

PR3 – Our partners feel comfortable regarding security and privacy in e-commerce with us

PR4 – Our partners are willing and capable to share information electronically

Service providers IT capabilities Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following

proposition regarding the capabilities of your IT external partners:

SPC1 – Our IT partners provide us with adequate IT solutions and services

SPC2 – Our IT partners are quick and flexible in satisfying our needs in terms of IT solutions and services

Other control variables

Time Please indicate the number of years elapsed from e-commerce application _____

Environmental uncertainty Please indicate the level of agreement (1 = completely disagree; 7 = completely agree) about the following

proposition regarding the environment where your firm play:

EU1 – Actions of competitors are unpredictable

EU2 – Technology and production processes changes often in major ways

EU3 – Demands and tastes are almost unpredictable

Size Please indicate the number of employees in your firm _____

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–67 63

number of years the company had been operating their e-commerce effectively [10]. Third, firm-level performance may beaffected by conditions that industry normalization only partiallycaptures, so we controlled for the degree of uncertainty in the firmenvironment: its measure was a three-item, scale. Fourth, weincluded firm size as a proxy of the overall set of resourcesavailable to the firm. Table 1 shows the measurement instrument,with its constructs, measures, and the source of the measure.

5. Methodology

5.1. Sampling

We extracted a stratified random sample of 2000 firms withmore than 20 employees from the most recent Italian CensusInformation Database. The questionnaire survey relied on thecomputer-aided telephony inquiry (CATI) technique; when the

Table 2Key features of the sample.

Sample Population

No. of firms

Size

20–49 employees 118 50.4% 62.1%

50–249 employees 99 42.3% 32.7%

>250 employees 17 7.3% 5.2%

Total 234 100% 100%

Average size 105.6

Industry

Textile and other traditional 29 12.4% 16.6%

Metals 39 16.7% 17.5%

Mechanicals 26 11.1% 8.8%

Electronics and other science-based 14 6.0% 5.0%

Retail 69 29.5% 23.2%

Other services 57 24.4% 29%

Total 234 1

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–6764

survey is short and clear, CATI can significantly reduce collectionbias and data entry errors. The contacts were managed by trainedprofessionals, and the answers were recorded when collected.Furthermore, the detailed procedure that the operator used tomanage each question and interact with the respondent during thetelephonic interview allowed for double-checking intended toreduce the inconsistent answers and refusals.

Among the 962 firms that agreed to participate in the survey,we checked for bias by comparing early versus late respondents.The average firm size of the earliest 150 respondents was 79.4employees and that of the last 150 respondents was 91.2; thisdifference is not significant (t = 1.01). Furthermore, 53.6% of theearly respondents were in service sectors, and 46.4% were frommanufacturing; among late respondents, the breakdown was52.2% service and 47.8% manufacturing. In terms of average sizeand industry distribution, we thus could ignore the potential fornon-response bias at this stage.

Among the interviewed firms, 376 (39%) said they had adoptedat least one e-commerce activity in recent years. We then asked ifthese firms had applied e-commerce to innovate their businessprocesses. We thus discarded the 51 firms that adopted e-commerce merely as a tentative solution, without really imple-menting or applying it to improve their exchange processes.Another 71 firms were discarded because they only applied e-commerce in the previous year, so they had no post-applicationperformance measures. Finally, 20 firms lacked data pertaining toperformance before the application, so we removed them. The finalsample thus consisted of 234 firms that had applied e-commerce tochange their business processes.

In terms of other IT applications, we found that 147 (62%) ofe-commerce adopters also possessed supply-chain integrationsystems and 141 (60%) had CRM software. Compared with therelevant population, our sample contained slightly larger firms(105.6 versus 80.4 employees), though this difference was notstatistically significant (t = 1.57). The distribution across indus-tries indicated no significant differences. On average, firms inour sample earned s37.1 million. Table 2 shows somestructural features of the firms in our sample.

5.2. Data collection

We assessed content validity by using a discovery approach.Most perceptual measures are based on scales that were previouslyvalidated. When we created adaptations or new scales, we basedthem on insights from in-depth interviews with a conveniencesample of 15 CEOs. Next, we pilot tested the full measurement

instrument to make sure that the wording was understandableand the length was appropriate, modifying the final versionaccordingly.

The questionnaires were completed by the CEOs of the firms.Prior work [8] and our interviews confirmed that, given thestrategic nature of the questions, the type of data collectionprocedure employed and the type of firms in our sample, the CEOrepresented the best respondent (the average age of the CEOs inour sample was 43.1 years, and 30.4% had tertiary education).Because some of our data came from a self-reported survey, wepaid careful attention to potential biases in the data collectionphase: the interviewers were trained on the content of the survey,and we conducted meetings to discuss potential concerns beforeand after the pilot test. To increase the response rate, we limitedthe length of the questionnaire to about 10 min. Third, wepromised participants: an executive summary of the findings andan invitation to participate in a university workshop during whichthe outcomes would be discussed.

We collected secondary data (performance measures) from theAIDA Bureau Van Djick database, which contains balance sheetsand financial data pertaining to Italian firms with more than 10employees. We avoided the use of financial market indicators toassess firm performance because there are few Italian firmscurrently listed on the stock exchange. We collected measures atboth the firm and industry levels, at the time of the e-commerceapplication and currently.

5.3. Analytical model

Because we performed a multiple regression analysis withlatent variables, we used a SEM approach, specifically, PLS usingthe SmartPLS 2.0 software. Given the causal-predictive nature ofour analysis, PLS was more suitable than other covariance-matrixtechniques. Also, PLS requires fewer assumptions about the datadistribution, so its findings may be less sensitive to data skew andkurtosis: our data set violated the conditions of multivariatenormality (Mardia’s K = 20.6; MK critical value = 3).

6. Findings

6.1. Measurement model assessment

We assessed the measurement instruments using a confirma-tory factor analysis, which allowed us to examine its convergentvalidity, construct reliability, and discriminant validity of theconstructs. We show the measurement properties in Table 3.

The t-statistic for each factor loading verified convergentvalidity. All factor loadings were statistically significant andgreater than the cut-off point of 0.50. We also find evidence ofconstruct reliability, which measured the stability of the scale onthe basis of an assessment of the internal consistency of the itemsthat measured the construct; all the values of the constructs weregreater than 0.7. In addition, the proportion of total variance in aconstruct extracted by the component set of indicator variableswas greater than 0.50.

In Table 4, the diagonal elements represent the square root ofthe average variance extracted (AVE), which measured thevariance shared between a construct and its indicators. The squareroot of the AVE must be larger than the correlations betweenconstructs (i.e., off-diagonal elements) to confirm discriminantvalidity. All the constructs met this criterion.

We modeled innovative orientation and firm IT capabilities assecond-order constructs, following the hierarchical componentapproach, which allows for the simultaneous estimation of eachmeasurement step. We confirmed the validity of the second-orderconstructs in the lower part of the table: the reliability and validity

Table 3PLS measurement model assessment.

AVE CR Items Loading SE t-Value

First-order variables

Post-application performance 0.81 0.93 PAP1 0.94 0.02 53.6

PAP2 0.85 0.05 15.7

PAP3 0.92 0.01 66.9

Initial level of performance 0.72 0.88 ILP1 0.93 0.02 58.4

ILP2 0.91 0.02 38.9

ILP3 0.73 0.11 6.40

Prospective orientation 0.63 0.84 PO1 0.77 0.04 21.9

PO2 0.83 0.02 36.1

PO3 0.78 0.03 25.3

Learning orientation 0.65 0.85 LO1 0.82 0.03 32.6

LO2 0.82 0.02 36.2

LO3 0.77 0.03 26.2

Slack resources 0.66 0.85 SR1 0.84 0.02 36.2

SR2 0.84 0.03 32.9

SR3 0.74 0.04 17.8

External ties 0.77 0.87 ET1 0.73 0.42 2.04

ET2 0.80 0.38 2.37

HR skills 0.65 0.85 HR1 0.85 0.02 46.4

HR2 0.85 0.02 46.2

HR3 0.71 0.04 19.9

Relationship assets 0.57 0.84 RA1 0.79 0.03 31.5

RA2 0.72 0.04 18.5

RA3 0.80 0.03 30.3

RA4 0.71 0.03 20.5

Partners readiness 0.56 0.83 PR1 0.75 0.04 17.4

PR2 0.65 0.05 13.9

PR3 0.77 0.05 14.4

PR4 0.79 0.04 21.6

Service providers IT capabilities 0.77 0.87 SPC1 0.91 0.02 49.4

SPC2 0.84 0.04 23.5

Environmental uncertainty 0.63 0.83 EU1 0.86 0.10 8.47

EU2 0.85 0.12 7.52

EU3 0.54 0.20 2.96

2nd order reflective variables

Innovative orientation 0.59 0.80 Prospective orientation 0.79 0.03 23.2

Learning orientation 0.81 0.03 30.0

Firm IT capabilities 0.58 0.87 HR skills 0.87 0.02 46.7

Relationship assets 0.91 0.01 79.7

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–67 65

of the first-order factors were satisfactory, and the paths from thesecond-order to the first-order factors were significant and of highmagnitude. Thus, the analyses indicated strong validity of thelatent constructs.

6.2. Hypotheses testing

We assessed the significance of each path in the model by usinga nonparametric technique: a bootstrapping procedure with 1000

Table 4Convergent validity: correlations between reflective latent variables (off diagonal) and

1 2 3 4

1st order

1. Service providers IT capabilities 0.882. Environmental uncertainty 0.04 0.803. Established ties 0.19 0.07 0.884. HR skills 0.20 0.03 0.09 0.815. Learning orientation 0.20 0.07 0.19 0.20

6. Partners readiness 0.20 0.12 0.13 0.08

7. Initial level of performance 0.06 0.01 0.01 0.18

8. Performance post-application 0.40 �0.16 0.13 0.34

9. Prospective orientation 0.27 0.06 0.19 0.15

10. Relationship assets 0.24 0.03 0.12 0.58

11. Slack resources 0.18 �0.10 0.08 0.10

2nd order 12 13

12. Firm IT capabilities 0.6913. Innovative orientation 0.31 0.64

samples of the same size (n = 234), which confirmed thehypotheses on the basis of the t-tests associated with each path.The threshold level of 0.20 provided a critical value for detecting‘‘practical’’ significance (Table 5).

The control variables acted mostly as expected. The initial levelof performance was positively associated with the level after theapplication of e-commerce, which suggested the presence of a haloeffect. However, its magnitude, though significant, was marginal(explaining less than 6% of the variance). Therefore, the effect on

square root of AVEs (diagonal in bold).

5 6 7 8 9 10 11

0.800.17 0.750.14 0.10 0.850.35 0.35 0.34 0.900.27 0.24 0.08 0.32 0.790.26 0.13 0.09 0.38 0.25 0.760.16 0.17 0.04 0.23 0.21 0.20 0.81

Table 5PLS structural models: hypotheses testing.

Paths SE t-Value

Initial level of performance 0.24 0.07 3.65^

Size �0.01 0.06 0.32

Environmental uncertainty �0.20 0.06 3.35^

Years of application 0.12 0.05 2.68*

Years of application (squared) 0.10 0.05 1.97*

H1 Slack resources 0.05 0.05 0.77

H2 Innovative orientation 0.24 0.05 4.33^

H3 Existing business resources 0.03 0.05 0.50

H4 Firm IT capabilities 0.17 0.05 3.99^

H5 Partners’ readiness 0.21 0.07 3.13^

H6 Service providers IT capabilities 0.24 0.05 4.99^

R2 0.50

F 12.7^

^p< .01.* p< .05.

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–6766

the performance of IT innovators largely depended on whathappened after the IT innovation was applied to the businessprocesses. Firm size had no effect on post-application performanceand the uncertainty of the environment had an expected negativeeffect. The years since the first application indicated a positive effect,though when we explore a potential nonlinear effect, we found thatthe link with firm performance better fits a U-shaped pattern(b = 0.10; t = 1.97). Because the mean of the years of application was3.55, we posited that, on average during the first 3 years afterapplication, firms that implemented e-commerce suffered dimin-ished performance, but after roughly 4 years, they probably enjoyeda significant positive payoff (see Fig. 2). This outcome should betreated with some prudence, because our time lag was limited to 6years after the date of application, and thus our data cannot revealanything about potential longer-term effects beyond then.

In H1, we postulate that the level of slack resources positivelyaffected the level of firm performance after e-commerce applica-tion, but the data did not support this proposition (b = 0.05; ns).Conversely, the data supported H2, in which an orientation towardinnovation enhanced post-application performance (b = 0.24;t = 4.3). We do not find support for H3, which considered the roleof external ties (b = 0.03; ns). Therefore, our first set of findingsquestion the role of business resources in influencing firmperformance after the application of an IT service innovationand challenged several of our assumptions associated with thestrategic necessity hypothesis.

The joint presence of human resource skills and relationshipassets were found to provide a positive predictor of performance

Fig. 2. The evolution of firm performance over time after the application of an IT

service innovation (e-commerce).

(b = 0.17; t = 3.99), in support of H4. Moreover, our data supportedH5 on the role of readiness among commercial counterparts(b = 0.21; t = 3.13), as well as H6, dealing with the contributions ofthe IT service providers’ capabilities (b = 0.24; t = 5.0).

7. Discussion, implications, and conclusions

Our research provided a robust empirical test of severalassumptions about the contribution of resources and capabilitieson IT innovation payoff. This effort departed from prior work inthree ways: (1) it was inspired by RBV principles; (2) it wassystematic, integrating different and competing explanations; and(3) it included time-lag effects.

7.1. Limitations

First, our investigation depended on the national context of ourstudy: the structural peculiarities of the Italian system, such as thelarge percentage of small businesses and specialization intraditional industries, must be taken into consideration beforegeneralizing the outcomes. Second, we cannot be sure that somepurposeful effects were missing from our framework. Third, wetested the model at the firm level, though the resources might beembodied in specific processes and activities, so that firm-levelperformance might result from competitive advantages anddisadvantages at the process level—we cannot exclude thepossibility that some spurious correlations may affect ourdependent variable. Fourth, we cannot exclude the presence ofsome reverse causality effects. Common method biases could bepresent; some answers might have been affected by reversecausation.

7.2. Insights and practical implications

Our analysis suggested a hierarchy of performance drivers for ITservice innovators, providing some immediate insights. First, tomaximize the payoff from the application of e-commerce, firmsshould build a strong bundle of internal IT capabilities by hiringqualified people with good IT skills, training their existingworkforce to function in a new IT-intensive context, andreorganizing business processes to increase interactions betweenIS and other departments.

Second, the business (i.e., non-IT) resources have lessinfluence on e-commerce payoff; firms without slack financialresources are not necessarily at a disadvantage in their efforts toexploit the potential of e-commerce. We believe that e-commerceoffers a fruitful opportunity for market followers too. Theonly business issue that matters is the cultural orientation oftop management; firms without an organizational commitmentto change cannot take full advantage of IT innovations. Ifmanagers do not champion the initiative throughout theorganization, the complexity and pervasiveness of changesassociated with IT innovation may be delayed or improperlyexecuted.

Third, success from applying a pervasive IT innovation requiresthe careful selection of external partners, because such innovationscannot be executed in isolation. On the commercial side, clients orsuppliers that are not ready for e-commerce can slow theimplementation of the innovation and hamper the learningprocess. For IT innovators, helping clients or suppliers improvetheir IT capabilities represents a viable option.

7.3. Theoretical implications

Our findings suggest some deeper theoretical reflections onhow firm performance may develop in relation to the application of

A. Ordanini, G. Rubera / Information & Management 47 (2010) 60–67 67

IT service innovations. New ways to explain the payoff of ITinnovations should propose the potential central role of theprocess of specific IT capabilities; it also should revise the strategicnecessity hypothesis to highlight the role of dynamic capabilities(firm’s capacity to reconfigure its resources to match environ-mental changes) instead of the function of static businessresources.

In our analysis, the costs (benefits) of e-commerce increase(decrease) as the rate of expansion accelerates. To gain furtherinsights into this phenomenon, we conducted follow-up inter-views with a convenience sample of firms. The phenomenaappeared to depend mainly on the effort required to become acredible partner in the online market, availability of investments toincrease the readiness of commercial partners, and the develop-ment of idiosyncratic IT capabilities.

Apparently, it is not possible to understand the success orfailure of a pervasive IT innovation simply by looking at the focalfirm that applied the new solution; approximately half of theperformance effect is derived from the resources and capabilities ofexternal players. Therefore, the resource dependency theory mightbe extremely useful as a means to advance understanding of the ITinnovation payoff.

References

[1] K. Atuahene-Gima, S.F. Slater, E.M. Olson, The contingent value of responsive andproactive market orientations for new product program performance, Journal ofProduct Innovation Management 22 (46), 2005, pp. 464–482.

[2] W.E. Baker, J.M. Sinkula, Market orientation and the new product paradox, TheJournal of Product Innovation Management 22, 2005, pp. 483–502.

[3] A. Barua, P. Konana, A.B. Whinston, F. Yin, An empirical investigation of net-enabled business value, MISQ 28 (4), 2004, pp. 585–620.

[4] A.S. Bharadwaj, A resource-based perspective on information technology cap-ability and firm performance: an empirical investigation, MISQ 24 (1), 2000, pp.169–196.

[5] D. Chatterjee, R. Grewal, V. Sambamurthy, Shaping up for e-commerce.Institutional enablers of the assimilation of web technologies, MISQ 26 (2),2002, pp. 65–89.

[6] K.M. Gilley, A. Rasheed, Making more by doing less: an analysis of outsourcingand its effects on firm performance, Journal of Management 26 (4), 2000, pp. 763–790.

[7] R. Kohli, S. Devaraj, Measuring information technology payoff: a meta-analysis ofstructural variables in firm-level empirical research, Information SystemsResearch 14 (2), 2003, pp. 127–145.

[8] K. Lyytinen, G.M. Rose, The disruptive nature of IT innovations: the case ofInternet computing in system development organizations, MISQ 27 (4), 2003,pp. 557–595.

[9] N. Melville, K. Kraemer, V. Gurbaxani, Information technology and organizationalperformance: an integrative model of IT business value, MISQ 28 (2), 2004, pp.283–322.

[10] L. Motiwalla, M.R. Khan, S. Xu, An intra- and inter-industry analysis of e-businesseffectiveness, Information & Management 42 (5), 2005, pp. 651–667.

[11] G. Piccoli, B. Ives, IT-dependent strategic initiatives and sustained com-petitive advantage: a review and synthesis of the literature, MISQ 29 (4),2005, pp. 747–776.

[12] R. Sabherwal, Y.E. Chan, Alignment between business and IS strategies: a study ofprospectors, analyzers, and defenders, Information Systems Research 12, 2001,pp. 11–33.

[13] M. Wade, J. Hulland, The resource-based view and information systemsresearch: review, extension, and suggestions for future research, MISQ 28 (1),2004, pp. 107–142.

[14] I. Wu, J. Chen, A hybrid performance measure system for e-business investmentsin high-tech manufacturing: an empirical study, Information & Management 43,2006, pp. 364–377.

[15] K. Zhu, K. Kraemer, E-commerce metrics for net-enhanced organizations: asses-sing the value of e-commerce to firm performance in the manufacturing sector,Information Systems Research 13 (3), 2002, pp. 275–295.

[16] Y. Zhuang, A.L. Lederer, A resource-based view of electronic commerce, Informa-tion & Management 43, 2006, pp. 251–261.

Andrea Ordanini, PhD, is associate professor with the

Department of Management at the Bocconi University

of Milan. He is the co-director of the CSSlab (Customer

and Service Science Lab) at the same University. He was

a visiting professor at the University of California at

Irvine in 2003 and 2006. He is author of many papers on

the impacts of Information Technology on business

processes, and his works have been published on

Decision Sciences, Communications of the ACM, Mar-

keting Letters, and California Management Review. His

current research interests are focused on e-commerce

and service marketing.

Gaia Rubera, PhD, is assistant professor of marketing

with the Department of Marketing at the Eli Broad

College of Business, Michigan State University. She was

a research fellow at the University of Southern

California in 2007. Her works have been published on

Journal of Service Research and Marketing Letters. Her

current research interests are focused on service

marketing and technology-based innovations.