Assessing the Impact of Information Technology on Firm Performance Considering the Role of Intervening Variables Organizational Infrastructures and Business Processes Re Engineering

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Assessing the impact of information technology on firmperformance considering the role of interveningvariables: organizational infrastructures and businessprocesses reengineering

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007 International Journal of Production Research,

Vol. 45, No. 12, 15 June 2007, 2697–2734

Assessing the impact of information technology on firm performance

considering the role of intervening variables: organizational

infrastructures and business processes reengineering

A. ALBADVIy, A. KERAMATI*z and J. RAZMIz

yIndustrial Engineering Department, Faculty of Engineering,

Tarbiat-Modares University, Tehran, Iran

zIndustrial Engineering Department, Faculty of Engineering,

University of Tehran, Tehran, Iran

(Revision received April 2006)

The relationship between the use of information technology (IT) and firmperformance has been widely researched over recent years. However, there hasbeen no well-founded empirical research on the role of intervening variables onsuch a relationship. The current paper aims to present an instrument to be usedin such research and to study the role of two intervening variables includingorganizational infrastructures and business processes reengineering in such arelationship. Data from 200 car part manufacturers were gathered in a fieldsurvey. The empirical work indicated that constructed measures demonstrate thekey psychometric properties including reliability and validity. The findings alsodemonstrate moderating effects of organizational infrastructures and mediatingrole of business processes reengineering on the relationship between the use ofinformation technology and firm performance.

Keywords: Information technology; Firm performance; Organizationalinfrastructures; Business process reengineering; Empirical study; Questionnaire

1. Introduction

There have now been many studies on the relevancy between the application ofinformation technology (IT) and organizational efficiency or firm performance.The results have shown a significant and positive correlation between IT and firmperformance (Alpar and Kim 1990, Harris and Katz 1991, Rai et al. 1997, Newmanand Kozar 1994, Mukhopadhyay et al. 1995). Meanwhile the other researches havenot been able to find such a relationship (Brynjolfsson and Hitt 1998, Davern andKaffman 2000). This is called productivity paradox in the literature of IT andproductivity. One suggested way to explain the paradox is to consider interveningvariables such as total quality management, reengineering of processes andorganizational infrastructures, on the relationship between IT and performance(Brynjolfsson 2003, Davern and Kauffman 2000). Here, we considered theintervening variables to understand the indirect relationship between IT and

*Corresponding author. Email: [email protected]

International Journal of Production Research

ISSN 0020–7543 print/ISSN 1366–588X online � 2007 Taylor & Francis

http://www.tandf.co.uk/journals

DOI: 10.1080/00207540600767780

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organizational performance. Not much previous research has been done on thisaspect before; also little empirical research has been done on the impact ofintervening variables on the relationship between IT and performance.

There are important challenges for firms in the IT era. Do business processreengineering (BPR) and organizational factors mediate the effect of IT adoption ona company’s performance? In this research we will investigate these importantchallenges. We will show the organizational infrastructures in which firms shouldinvest in order to realize the IT capabilities. Also the effects of process changes onIT productivity will be examined in this research.

We have found organizational infrastructures and business process changes moresignificant than the other intervening variables that have been suggested in therelated literature. This is attributed to the following.

1. According to Boyer et al. (1997), researchers have often diagnosed theproductivity paradox as a failure to balance investments in IT withinvestments in the infrastructure to support it (Brynjolfsson 2000, Meredith1987, Ettlie 1988, Zuboff 1988). Although IT provides powerful newcapabilities for firms, these capabilities can only be fully realized whencompanies also invest in organizational infrastructures, such as providingquality leadership, empowering workers, decentralization, team workingand process management provide one of the keys for unlocking the vastpotential of IT.

2. BPR involves rethinking and redesigning the organizations to create morevalues. As Attaran (2003) mentioned, the rapid evolution of informationtechnologies and its declining costs are creating opportunities for organiza-tions to change dramatically and improve the way they conduct business.IT provides strategic value to an organization by giving support to thebusiness processes. It is used for cost reduction, product differentiation,quality improvement, integration with customers and suppliers, organiza-tional learning, and creating new business opportunities. IT is the mosteffective enabling technology for BPR (Attaran 2003).

3. We believe that a combination of organizational infrastructures and businessprocess changes will provide an integrated organization perspective, involvingeveryone, everything and everybody associated with the company, includingits customers and suppliers.

In section 2 a brief review of literature and theoretical framework of therelationship between IT and performance considering the role of intervening variables(organizational infrastructures and reengineering of processes) will be demonstrated.In section 3 the research methodology is explained. Moderating effects oforganizational infrastructures and mediating effect of business process reengineeringin relation with IT and performance will be empirically analysed in section 4.Limitations, conclusions and discussions will be mentioned in sections 5 and 6.

2. Literature review

With a careful scan of the published work at corporate level IT productivity, we findthat researchers have developed two different approaches in assessing the correlation

2698 A. Albadvi et al.

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between IT implementation and productivity. Broadly speaking, the first approach

focuses on the effects of IT investment on direct and intermediary and financial and

non-financial measures of productivity. This approach could positively prove either

a direct correlation or lack of such a relation. The second approach considers the IT

implementation but emphasizes the role of intervening investments that enhance and

complement the IT implementation. Our research on IT and firm performance is in

accordance with the second approach of IT productivity studies, which considered

the role of intervening variables.A summary of our review is shown in table 1. In the remainder of this section we

will discuss some of the important works that support the idea of the role of

intervening variables.Organizations can achieve more production from their IT investment if IT

investments are coordinated with organizational redesign and other managerial

decisions (Hunter and Lafkas 2003), business strategy and the nature of managerial

work (Pinsonneault and Rivard 1998, Pinsonneault and Kreamer 1997, Belleflamme

2001). Also investment on management skills, user training, application of standards

and the way people work and how their performance is measured and controlled

are critical to realizing more productivity from IT investment (Brynjolfsson 2003,

Davern and Kauffman 2000).Recent research focuses on the impact of IT on organizational structure, culture,

productivity, efficiency and quality. For example, Lau et al. (2001) have investigated

the effect of complexity, formalization, decentralization, span of control,

outsourcing and lateral communication as the factors of structure, and team

working and learning as organization culture. They find that IT investment has

significant impacts on organizational structure and culture.Decentralization and investment on human capital are considered by Brenham

et al. (2001) as IT complementary investments. They conclude that greater levels of

IT are associated with increased delegation authority, greater levels of skill and

education in the work force.Lucas et al. (1993) found that introduction of financial imaging system resulted

in improvements to customer service, control of certificates, higher-quality images,

improved search speed, and cost, time and staff reduction.In summary, the first approach of IT productivity studies is based on the belief

that IT investment leads to cost reduction and improves quality, variety, innovation,

etc. But paradoxical results and a huge variation across organizations (some have

spent vast sums on IT with little benefit, while others have spent similar amounts

with tremendous success) change the critical question facing IT managers

and researchers from ‘Does IT increase productivity?’ to ‘How can we invest in IT

to increase productivity?’ The results of this approach show that investment in

IT does not automatically increase productivity, but is part of a broader system of

organizational investment for changes that do increase productivity (Brynjolfsson

and Hitt 1998).Most importantly, the highest productivity of IT will be realized when IT

investment is integrated with complementary investments (Brynjolfsson and Hitt

1998); new strategies, new business processes, new working practices and new

organizations all appear to be important in realizing the maximum benefit of IT

(Brynjolfsson and Hitt 1998). These changes will require a time-consuming period of

Assessing the impact of information technology on firm performance 2699

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Table

1.

SelectedworksonIT

productivity.

Researcher(s)

Measures

Findings

StudiesthatfoundIT

does

notim

prove

productivity

AlparandKim

(1990)

Multifactor(loansanddem

anddeposits)

ITresultsin

decrease

incostsandincrease

intime

deposits.

HarrisandKatz

(1991)

Operatingexpense

asapercentageof

premium

income

Firmsthatare

profitable

havehigher

growth

onIT

expense

ratiosandlower

growth

onoperatingexpense

ratios.

New

manandKozar(1994)

Positiveidentificationofjewellery

System

resulted

in:Betterasset

managem

entand

financialcontrol

Availabilityofdecisionsupport

forgem

ol-

ogistthroughoutevaluationprocess

Increasedproductivity

Reducedcostsandincreasedrevenue

Betterquality

Merchandise

Mukhopadhyayet

al.(1995)

Inventory

turnover

EDI

resulted

incost

reductions

($100

savings

per

vehicle,annualsavingsof$220million)

Obsolete

inventory

Premium

freight

Annualproductionvolume

Partsvariety

New

partsintroduction

Raiet

al.(1997)

Labourandrelatedexpenses

Allmeasuresof1Tinvestm

entare

positivelyassociated

withfirm

output.IT

capitalandclient/server

expendi-

turesare

positivelyassociatedwithreturn

onassets.

Most

expenditure

exceptsoftware

andtelecom

are

associatedwithincreasedlaborproductivity.

Totalproperty,plant,andequipment

Totalnumber

ofem

ployeescompanysector

sales

Return

onassetsreturn

onequityLabour

productivity

Administrativeproductivity

ISstaff,hardware,software,andtelecom

expenditures

are

negativelyrelatedwithadministrative

productivity.

StudiesthatfoundIT

improvesproductivity

MahmoodandMann(1993)

Return

oninvestm

ent,return

onsales,

growth

inrevenue,

salesbytotalassets,

salesbyem

ployee,market

valueto

book

value.

IndividualIT

investm

entvariableswerefoundto

be

weakly

relatedto

organizationalstrategiesand

economic

perform

ance.


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Lovem

an(1994)

Perform

ance

ratios(R

OI)

Contributionofinvestm

entonIT

wasabout0duringa

periodof5years

study

HittandBrynjolfsson(1996)

Productionfunction

ITincreasedproductivityandconsumer

value,

butdid

notresultin

supernorm

albusinessprofitability.

Businessprofitability

Consumer

surplus

Thereisnoinherentcontradictionbetweenincreased

productivity,increasedconsumer

valueand

unchanged

businessprofitability.

Tam

(1998)

Totalshareholder

return

ITinvestm

entisnotcorrelatedwithshareholder

return.

Return

onequity,assets,sales

Level

ofcomputerization

isnotvalued

by

thestock

market

indeveloped

andnew

lydeveloped

countries.

Bookvalueofassets

Market

value

ThereisnoconsistentmeasurementofIT

investm

ent.

Andersonet

al.(2003)

1.Market

value

1.IT

productivityparadoxremainsin

theirdata

andit

presents

anew

ITproductivityparadox.

2.Intangible

assetsvalue(innovation)

3.Effects

ofinvestm

entin

complementary

assetssuch

asgreateruse

ofteams,

broader

decision-m

akingauthority,and

worker

training

2.Twoparallel

explanationsfortheparadox:

Complementary

investm

entin

organizationalassets

accompanyingim

plementationofERPandrelated

system

sincreasedintangible

asset

value.

Andthe

interw

eavingofIT

linksthroughoutthesupply

chain

createdvaluebyenablingeach

mem

ber

ofthesupply

chain

toidentify

andrespondto

dynamic

customer

needs.

Studiesshowstheeffectsofinterveningvariablesonrelationship

betweenIT

andproductivity

Lucaset

al.(1996)

Changes

inorganizationalstructure,work-

flowsandfunctions,interface

operations,

technology

Introductionoffinancialim

agingsystem

resulted

inim

provem

ents

tocustomer

service,

controlofcertifi-

cates,higher-quality

images,im

proved

searchspeed,

cost

reduction,researchtimereduction,staff

reduction.

HendersonandLentz

(1995–96)

Organizationallearning

Thebenefitsanticipatedfrom

ITinvestm

ents

(e.g.innovation)are

marginalunless

integrated,

dynamicprocesses

existto

activelymanageandadapt

theseinvestm

ents.

New

productsandservices

(continued

)


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Table

1.

Continued.

Researcher(s)

Measures

Findings

BrynjolfssonandHitt(1998)

Productivity

Investm

entin

computers

does

notautomatically

increase

productivity,butispart

ofabroader

system

oforganizationalchanges

thatdoes

increase

productivity.

Decentralization

ITspending

Bresnahanet

al.(2000)

Decentralizationandinvestm

entonhuman

capital

1.GreaterlevelsofIT

are

associatedwithincreased

delegationofauthority,greaterlevelsofskilland

educationin

theworkforce,

andthegreaterem

pha-

sizesonpre-employee

screeningforeducationand

training.

2.Thesework

practices

are

correlatedwitheach

other

DevarajandKohli(2002)

Organizationalchange

ITinvestm

entcombined

withbusinessprocess

reengi-

neeringpositivelyandsignificantlyinfluences

perform

ance.

Brynjolfsson(2003)

Humanandorganizationalcapital

Thegreatest

ITbenefitsare

realizedwhen

anIT

investm

entiscoupledwithaspecific

setof

complementary

businessinvestm

ents.

Work

practices

Decisionmakingprocess

Sherer

etal.(2003)

Investm

entin

changemanagem

ent

Planned

communicationsandchangemanagem

ent

strategiesledto

thesm

ooth

implementationofthe

upgradeprocess

andcontributedto

thepayoff

from

theIT

investm

ent.


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reengineering and redesign of organization in order to best utilize their ITinvestment.

In this research we have considered the role of two important variables includingorganizational infrastructures and business process redesign in the relationshipbetween IT and performance. These two variables cover many factors examined inprevious research.

In the next section a theoretical framework has been developed to study theeffects of IT on firm performance by considering the role of two intervening variablesincluding organizational infrastructures and business process change.

2.1 Theoretical framework of assessing the impact of IT on performance

In figure 1, a theoretical framework of the role of organizational infrastructures andbusiness process reengineering in relation with IT and organizational performance ispresented. This framework is an interpretation and synthesis of two previous models.The first one, developed by Grover et al. (1998), studied the relationship betweenIT and performance through the mediation of BPR. The second model, presentedby Boyer et al. (1997), studies the relationship between IT and performance inorganizations considering the role of organizational infrastructures.

Studies of Boyer et al. (1997), Hitt and Brynjolfsson (2000) and Lau et al. (2001)show that in order to benefit from IT potentials and to improve organizationalperformance, proper organizational infrastructures are essential. Boyer et al. (1997)consider quality strategy, soft integration and worker empowerment as necessaryinfrastructures to unlock IT potentials. The results of several case studies by Hitt and

H1

H1

H1

H2

H2

H2

The influence of IT on businessprocesses

• Order flow • Strategic processes • Product • Marketing and sales • Services• Accounting• Personnel• Technology

IT application • IT in communications • IT in planning • IT in operations • IT in quality control • IT as a support for decision making• IT in administrative or office work • IT in financialaffairs

Organisation infrastructures • Delegation of power (reducing hierarchy)• Decentralization • Training • Group work • Process management • Relationship with customers and suppliers

Interactions betweentechnology and organisational

infrastructures

Performance upgrading• Customer results • People results • Operational results• Growth

Figure 1. Theoretical framework of the impact of IT on firm performance considering therole of intervening variables.


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Brynjolfsson (2000) indicate that creation of necessary IT infrastructures is anindispensable element for gaining higher IT performance. They have organized theseinfrastructures into three general categories including inter-organizational transfor-mation, interactions with customers and interactions with suppliers. Lau et al. (2001)have investigated the effects of IT on working conditions including organizationalstructure and culture. They conclude that IT needs its own specific structure andculture. They succeeded in showing the effects of factors such as education, groupwork, control domain and decentralization in the workplace.

In this research we have selected and investigated the most noticeable structuralelements. Based on the results of studies by Boyer et al. (1997), Hitt and Brynjolfsson(2000) and Lau et al. (2001) we consider the effects of organizational infrastructuresas a moderator role. First research hypothesis, in relation to this association, is:

Hypothesis 1: The relationship between IT and firm performance will be moderatedby the extent of practical diligence to organizational infrastructures.

Figure 1 also shows the role of business processes redesign in the relationshipbetween IT and performance. The result of the studies on mediating and moderatingeffects of BPR on the relation between IT and performance by Grover et al. (1998)indicates that organizational reengineering has a mediating role in the relationbetween IT and performance. Gunasekaran and Nath (1997) and Attaran (2003)have also shown the mediating role of BPR. These studies show that inorganizational process reengineering, IT is one of the fundamental factors thatmust be considered as the enabler.

Noticing IT potentials and its proper application is a critical factor for successin BPR programs (Hammer 1990). Executing successful BPR programs and properIT application makes the organizations expect that substantial improvements beproperly made, and these improvements in turn improve performance measures ofthe organization. BPR mediating effects in the relation between IT and performanceare shown in figure 1.

This figure shows that IT investments can improve business processes andthrough which improve organizational performance. This relationship, stated in theform of the second research hypothesis, is:

Hypothesis 2: The relationship between IT and firm performance will be mediated bythe extent of BPR associated with the IT.

Gunasekaran and Nath (1997, pp. 96–97) have shown the effect of IT on thereengineering of processes of order flow, strategic planning, product, marketingand sales, services, accounting, human resources and have indicated the key role ofIT in their reengineering program. The same processes are considered in theframework of this research.

3. Methodology

3.1 Data collection and sampling

In a study of IT performance, Froza (1995) studied sample automotive industriesand electronic industries in Italy. He asserts that the main reason behind choosing


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the automotive industry is that it is one of the most competitive industries in whichinnovation and change play a crucial role. With Iranian automotive industryentering the international competitive arena (this is a strategy favoured byautomakers, government and policy makers), competition, creativity and innovationin the Iranian automotive industry will achieve higher status. As Froza (1995) states,creativity and innovation require the application of modern technologies andreengineering program. We have investigated a sample of companies relating to theautomotive industry in Iran including part manufacturers.

In Iran 560 companies are involved in car part and component manufacturing.Noticing that our sampling was purposive sampling, we have selected the top 200suppliers companies respecting their yearly turnover. Because yearly turnover ofthese companies is significant as those firm’s can invest in IT and reengineeringprograms. 112 of these companies participated in the survey. Therefore, the responserate came to be 56%, a feasible rate for such research (Ang et al. 2001). Thequestionnaires were completed by people in organizational positions such as chiefdirector, factory manager, quality control manager, computer and systems manager,production manager and management advisor or expert.

Noting a variety of respondents, it was essential to look into the probableinfluence that their views might have on research findings. In order to do that, usingone-way ANOVA (analysis of variance), we analysed the differences in answers inrelation to the respondent’s organizational position (table 2). Table 2 showssignificant difference in responses by people in different positions (p50.05) in only6 out of 89 measures. In other measures there is no significant difference betweenresponses in different positions. As shown in table 6, t-test results indicate thatadvisors, compared with other positions, had more pessimistic views. Table 2 alsoshows that quality experts held more pessimistic views about improvement intechnology and service processes.

3.2 Measurement instrument

Figure 1 depicts one independent variable ‘the extent to use IT’, two latent variables‘organizational infrastructure and BPR’ and one independent variable ‘company’sperformance’.

In this section we will operationally define every one of the research variables andthen introduce their measuring instruments. It is important to note that reuse ofinstrument from previous studies ensures content validity of the current study. Whennecessary, we have defined some first time instruments that are validated at the end.

3.2.1 The extent of IT usage (ITU). A list of information technology use incompanies based on literature by Boyer et al. (1997), Swamidass and Kotha (1998),Martinez-Lorente et al. (2004) is drawn out. Since variables are directly immeasurable, their measurement requires scale definition. Therefore, 35 measures havebeen defined to evaluate IT in organizations (Appendix 1). Then, they have beenclassified into four criteria in terms of their application objectives consisting of ITin communications, IT in decision-making support, IT in production and operation,and IT in administration (see table 3). Respondents were asked to indicate theapplication rate of each technology on a Likert scale from 1 (not used) to 7


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Table

2.

ANOVA

analysisofthedifference

betweenrespondentviewsin

differentpositions.

Organizationalpositionoftherespondent

Variance

analysis

Questioncode

AB

CD

EF

GH

F-value

Sig.

T-test

ITPO

1.3

5.30

4.33

4.76

5.50

2.33

8.00

6.50

5.53

2.229

0.040

A4E,H4F

ITPO

2.7

4.05

2.33

3.00

4.86

5.33

1.00

4.50

2.47

2.720

0.014

A4H,E4C,D4

H,E4H

ITAD

1.1

4.59

6.33

4.12

6.29

4.33

1.00

5.50

4.95

2.325

0.032

D4A,A4

F,D4C,D4F,D4H,H4F

ITAD

1.6

5.03

5.00

5.35

3.71

5.33

1.00

5.00

5.15

2.156

0.046

D4A,A4

F,C4D,C4F,H4D,E4F,H4F

BPSE1

5.03

4.00

4.79

5.67

5.00

�2.00

5.42

2.354

0.039

A4G,C4G,D4G,E4

G,H4

GBPTE2

4.93

2.33

4.71

4.14

6.33

3.00

3.00

4.60

2.230

0.039

A4G,C4B,D4

B,E4B,H4B,C4G,E4

G,H4

G

(A)Chiefdirector,(B)factory

manager,(C

)quality

controlmanager,(D

)computerandsystem

smanager,(E)productionmanager,(F)advisor,(G

)quality

unitexpert,

(H)other.

*P5

0.05.


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Table

3.

ITU

variable.

Measurementcriteria

Source

Definition

ITin

communications

Grover

etal.(1998),Pinnesealt

(1997),Martinez-Lorenze

(2003)

ITin

communicationsrefers

tothose

directlyinvolved

intransactionofinform

ation.Thiscriterionincludes

the

followingapplications:em

ail,fax,cellphone,Internet

access,localaccessnetworks(LAN)fortechnical

data

within

thecompany,LAN

forcompanies,

internalnetworksofthecompany,company’swebsite

foradvertisement,intranet,data

interactionwith

suppliersandcustomers.

ITin

decisionmaking

Swamidass

andKotha(1998),

Boyer

etal.(1997)

Thisdecision-m

akingsupport

criterionindicatesthe

applicationofIT

insupportingmanagem

entof

processes.So,itincludes

ITapplicationssuch

as

decisionsupport

system

s(D

SS),data

analysis

techniques

andprognostic

software.

ITin

manufacturingandoperation

Turbanet

al.(2002),Boyer

etal.(1997),Froza

(1995)

Thiscriterionworksasanumbrellato

delineate

arange

ofcomputer-assistedtechnologiesfordirector

indirectsupport,control,detectingandmonitoring

ofmanufacturingactivities.

ITin

administrativeorofficework

Turbanet

al.(2002),Martinez-

Lorenze

(2003)

Thiscriterionrefers

totheuse

ofIT

tohelpadminis-

trativeorofficework

likeorganizingdocuments

organizingandstoringdata

etc.


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(very frequently used). In IT and performance literature, measuring IT in

organizations using subjective criteria is mainly carried out by researchers such as

Grover et al. (1998), Pinsonneault (1998) and Martinez-Lorenze (2003). In these

researches reliability and validity of such criteria are shown.

3.2.2 Performance measurement (PER). Researchers who have conducted the samestudies as ours have reported that the number of people inclined to answer objective

questions about performance is usually 100% smaller than those who are motivated

to respond to subjective questions (Porter 1979, Vickery et al. 1993, Ward et al.

1994). Thus we have used Likert scale questions from subjective measures to evaluate

performance.To assess organizational performance we have defined measures in relation with

customer results, people results, operational results and growth using different

sources. We have used four different criteria for measuring performance (see table 4).

The first two questions concerning customer satisfaction and relationship are taken

from Froza (1995) and organizational elevation model from the European

Foundation for Quality Management (EFQM) (1999). The mean value for thesetwo questions is termed ‘customer results’. The second criterion for measuring

performance consists of two questions that have been used to evaluate worker

satisfaction and performance. The mean value for these two is named ‘people

results’. These two questions are also taken from EFQM (1999). In the third

criterion, six questions for measuring improvement in flexibility, delivery, quality,cost, defective rates and cycle time have been taken from Froza (1995) and

Swamidass and Kotha (1998). The mean value for these questions is named

‘operational results’. The last criterion consists of two questions, which evaluate the

growth of the company in sales and return of investment (ROI). The respondents are

required to specify the condition of their company in comparison with four years

ago. The response is indicated through a seven-point Likert scale of 1 (significantlylower) to 7 (significantly higher).

Table 4. PER variable.

Measurement criteria Source Definition

Customer results Froza (1995), EFQM(1999)

Customer satisfaction of productquality and better customer rela-tionship are measured with thiscriterion.

People results EFQM (1999),Martinez-Lorenze(2003)

This criterion is used to measureworker satisfaction andperformance.

Operational results Swamidass (1998), Froza91995), Martinez-Lorenze (2003), Boyeret al. (1997)

It is used to measure improvementrate of flexibility, delivery, quality,cost, defectives, and time cycle.

Growth Martinez-Lorenze(2003), Boyer et al.(1997)

With this criterion, the growth rate insales and return of investment(ROI) is evaluated.


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3.2.3 Organizational infrastructure measures (OIS). This part of the questionnaireis also designed to measure the degree to which the company is involved in creatingIT organizational infrastructures. The measures of this part are taken from works ofBoyer (1997), Froza (1995), Lau et al. (2001), Ward et al. (1994), Pinsonnseault andKramer (1997), Flynn et al. (1994), Brynjolfsson and Hitt (2000), EFQM (1999). Theextent of involvement in creating seven organizational infrastructures including workempowerment, decentralization, training, team work, process management andcustomer relationship, changes in supplier relationship and leadership have beenmeasured using 7-point Likert-type scale from ‘no involvement’ to ‘completeinvolvement. In table 5 a summary of measurement criteria for research variable,‘organizational infrastructure’, is presented.

3.2.4 Business process reengineering measures (BPRM). The ranges of transforma-tions in eight business processes have been measured using 7-point Likert-type scalefrom ‘no change’ to ‘basic changes’. These business processes have been taken fromGunasekaran and Nath (1997, pp. 96–97). They have classified the most importantprocesses in service and manufacturing companies. These include the followingprocesses: order flow, strategic planning, product, marketing and sales, services,accounting, personnel and technology. In Appendix 1, assessment method oftransformations of every one of the processes is given. In table 6 a brief accountof measuring criteria of the mediator variable of this research (BPR) is presented.

In Appendix 1, the questionnaire used as data collection instrument in this studyis presented. Measurement instrument in this questionnaire are developed basedon the above definitions.

3.3 Pre-testing

To improve the validity and reliability of research data; pre-testing was conductedbefore sending questionnaires to respondents. In order to control elements such asunderstanding, number, order, sensitiveness, and required time of questions, initialpersonal interviews with eight experts (including academic and industrial experts)were held. First, we asked two experts for any modifications. After applying theirviews, the test was administered for the second time. When the last two experts didnot have any significant points to add, we stopped the modification process.

3.4 Pilot-testing

After pre-testing, the questionnaire was sent to a group of 12 respondents inpositions similar to those of final respondents. They were asked to answer thequestions and suggest any modifying views concerning our questions. We thenapplied slight modifications and prepared the final draft.

3.5 Reliability and validity analysis

The reliability analysis of a questionnaire determines its ability to yield the sameresults on different occasions and validity refers to the measurement of what thequestionnaire is supposed to measure (Cooper and Schindler 2003).


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Table

5.

‘OIS’variable.

Measurementcriteria

Source

Definition

Delegationofpower

Ward

etal.(1994)

Inhumanresourcemanagem

entdiscussions,delegationofpower

isdefined

asgrantingwidespreadresponsibilitiesforexecution

andcontrolofactivitiesrelatingto

workers’life.

Decentralization

Pinsonneaultet

al.(1997)

Decentralizationrelatesto

retentionordelegationofdecision

makingororder-issuingin

theorganization.It

createsmore

flexibilitythroughwhichorganizationaldepartments

andunits

canbetterinteract

withinternalandexternalperiphery.

Training

Lauet

al.(2001)

Inorder

toensure

thatworkerspossessenoughtheoretical

knowledgeandnecessary

instruments

toefficientlytaketheir

responsibilities,they

should

begiven

essentialtrainingLeu

etal.(2001),Lauet

al.(2001)havestressed

thatworking

culture

incooperationwithtechnology,in

whichopen

relationship

withco-w

orkers,im

proved

cooperationand

constanttrainingare

ofgreatim

portance.

Team

work

Pinonnseaultet

al.(1997)

Work

sharingin

work

teamsandtheexistence

ofmatrix

structure

form

asignificantapproach

intheorganizationcanleadto

improved

perform

ance.

Process

managem

entand

customer

relationship

Flynnet

al.(1994),

BrynjolfssonandHitt

(2000),EFQM

(1999)

Process

managem

entfocusesondirectingbusinessprocesses

basedoncurrentandfuture

needsofcustomers.

Changes

intransactionwithsuppliers

BrynjolfssonandHitt(2000)

Technologiessuch

aselectronicdata

interaction(EDI),andother

intraorganizationalinform

ationsystem

shavesignificantly

reducedcost,timeandother

problemsofinteractionwith

suppliers,Ordering,invoiceissuing,andstock

controlare

amongfactors

thatchangewithinform

ationtechnologies

Leadership

Pinsonneaultet

al.(1997),

Flynnet

al.(1994)

Inorder

tosuccessfullyexecute

improvem

entplans,top

managem

entissupposedto

takeleadership

responsibilitieslike

relationship

withworkers,encouragem

entandpromotion.

ThereisgreatamountofsynergybetweenIT

andim

provem

ent

planssuch

asTQM

andBPR.


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3.5.1 Reliability. In order to assess the reliability of instrument, we have calculatedCronbach’s alpha for criteria of research variables [IT application (ITU) includingfour criteria; the influence of IT in process reengineering (BPRM) including eightcriteria; practical diligence in applying organizational infrastructures (OIS) includingseven criteria; and finally improvement of performance (PER) including fourcriteria]. The strategic planning criterion (INSE) from OIS variable is the onlycriterion with an alpha of 60% that does not in fact possess acceptable reliability.Eliminating a measure, reliability index will increase to an acceptable level over 70%[see table 7(a)]. It is now time to assess the validity of instrument.

3.5.2 Validity analysis. Construct validity, content validity and predictivevalidity were analysed to ensure the validity of the instruments (Nunnally andBernstein 1994).

Table 6. ‘BPRM’ variable.

Measurement criteria Source Definition

Order flow Gunase Karan,Nath (1997, pp. 96–97)

Order flow includes activities such assupply, product assembly, manufac-turing production, ordering, placeand installation of the product.Notice that the role of IT in thisprocess is defined in terms of basicactivities, objectives or customerneeds.

Strategic planning Strategic planning process is a blend offormulating strategy and planning oforganizational structure. In this pro-cess we need not only external analy-sis but also analyses of the data within the organization.

Product Product process includes planningactivities, engineering and design.

Marketing and sales This process includes customer satisfac-tion, market survey anticipation anddecision-making about productmakeup.

Services This includes maintenance and repair ofproducts, after sales services andquality control.

Accounting Accounting process includes productpricing, budgeting, and making deci-sions for purchase or manufacturing.

Personnel This process involves various units suchas employment, selection, promotionsystems, and performance upgrading.

Technology Technology process includes selection,installation, establishment and dispo-sal of the factory or its equipment.There are many uses for decision-making support systems and multi-media systems.


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Table

7(a).

Validityindex

andfactoranalysisofvariable

BPRM.

Variable

Measurement

#of

measures

#ofelim

inated

measures

NMean

Std.

deviation

Alpha

Eigenvalue

%from

total

variance

Theim

pact

of

ITapplicationon

businessprocess

reengineering

(BPRM)

Businessprocess

oforder

flow:BPOF

50

97

5.22

1.05

0.7194

2.549

50.978

Businessprocess

ofstrategy:BPST

20

96

4.86

1.57

0.8246

1.703

85.152

Businessprocess

ofproduct:BPPR

30

97

5.57

1.08

0.7082

1.954

65.135

Businessprocess

ofmarketing

andsales:BPMS

40

97

4.92

1.37

0.8773

2.935

73.384

Businessprocess

ofservices:BPSE

30

97

5.30

1.19

0.7191

2.001

66.706

Businessprocess

ofaccounting:BPAC

30

97

5.25

1.29

0.8126

2.187

72.901

Businessprocess

ofpersonnel:BPPE

40

97

4.78

1.25

0.8488

2.784

89.603

Businessprocess

oftechnology:BPTE

20

97

4.83

1.57

0.8631

1.760

87.979

TotalBPRM

93

5.09

1.30

Analphaofbelow

0.7

andover

0.6

fornew

instruments

isacceptable

(Nunnly

1987).

Analphaofbelow

0.6

isnotacceptable.


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Construct validity shows the extent to which measures of a criterion are

indicative of the direction and size of that criterion (Flynn et al. 1994). It also shows

that the measures do not interfere with measures of the other criteria (Flynn et al.

1994). Construct validity of measurement instrument is analysed through factor

analysis. In this study each measurement criterion is considered as a distinct

construct. The most common decision-making technique to obtain factors is to

consider factors with eigenvalue of over one as significant (Olson et al. 2005, Hair

et al. 1998).Factor analysis shows that ITPO, ITDS and ITAD possess more than one factor

with eigenvalue of over one. Eliminating ITDS3 solved the problem with ITDS.

Concerning ITPO and ITAD factor analysis indicates three and two factors for each

of these measures respectively [see tables 7(b) and 7(c)].The type and definition of questions show that ITPO has three latent variables

including IT in planning, IT in operation and IT in quality control [see table 7(b)

below]. ITAD also has two latent variables including IT in administrative affairs and

IT in financial affairs [see table 7(c)].To ensure instrument reliability we have calculated reliability indexes for all

final criteria again.Table 7(d) shows that all criteria of variable ITU except criteria of IT in

administrative and financial affairs have an alpha of over 0.7. An alpha of 0.6 was

Table 7(c). Factor loadings for ITPO.

ITPO criterion Factor 1 Factor 2 Factor 3

Measures of ITPO New code IT in planning IT in operations IT ion quality control

ITPO4 ITPO1.4 0.864394 0.100607 0.000795ITPO5 ITPO1.5 0.768977 0.297352 �0.00291ITPO11 ITPO1.11 0.744054 0.17913 0.171894ITPO3 ITPO1.3 0.543544 0.178223 0.113729ITPO8 ITPO2.8 0.319612 0.803941 0.083888ITPO7 ITPO2.7 0.261655 0.799537 0.019016ITPO9 ITPO2.9 0.090871 0.723657 �0.10322ITPO13 ITPO3.13 0.057749 0.027447 0.935693

ITPO12 ITPO3.12 0.144478 �0.06436 0.923877

Table 7(b). Factor loadings for ITAD.

ITAD criterion Factor 1 Factor 2

Measures of ITAD New code IT in financial pecuniary affairs IT in administrative affair

ITAD10 ITAD2.10 0.841349 0.098666ITAD8 ITAD2.8 0.782807 0.22032ITAD9 ITAD2.9 0.728648 0.087321ITAD7 ITAD1.7 0.066869 0.752603

ITAD6 ITAD1.6 0.040672 0.740113ITAD2 ITAD1.2 0.114139 0.632419

ITAD1 ITAD1.1 0.389275 0.496301

ITAD5 ITAD1.5 0.188359 0.463711


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Table

7(d).

Validityindex

andfactoranalysisforIT

Uvariable.

Variable

Measurementcriterion

#ofmeasures#ofelim

inated

measures

NMean

Std.

deviation

Alpha

Eigenvalue%

from

total

variance

Inform

ation

technology

use

(ITU)

ITin

communicationsIT

CO

82

96

4.37

1.32

0.7673

2.945

49.086

ITin

productionand

operation:IT

PO

ITin

planning

13

497

4.43

1.40

0.7521

2.314

57.853

ITin

operation

96

3.92

1.63

0.7106

1.929

64.306

ITin

quality

control

97

5.89

1.48

0.8621

1.760

88.001

ITin

decisionmakingandsupport:IT

DS

41

97

3.05

1.51

0.7749

2.102

70.067

ITin

administration:

ITAD

ITin

administration

10

297

4.52

1.02

0.6364*

2.089

41.775

ITin

pecuniary

affairs

97

5.98

1.04

0.6936*

1.961

65.354

TotalIT

U97

4.59

0.85

Analphaofbelow

0.7

andover

0.6

fornew

instruments

isacceptable

(Nunnly

1987).

Analphaofbelow

0.6

isnotacceptable.


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obtained in criteria of IT in administrative and financial affairs. This is acceptablewith regard to the fact that the criterion is new. This table also shows that somemeasures have been eliminated because they do not focus on one specific factor.The column for the percentage from total variance shows what percentage of totalvariance is covered by the relating factor. Except for IT in administrative affairs,all other criteria indicate a considerable percentage of the total variance. Thisproduces a rather good construct validity for this variable.

A summary of calculations relating to reliability and validity indices of variableOIS is shown in table 7(e). Except for criterion INEM, other criteria have an alpha ofover 0.7 and INEM also has an alpha of over 0.6. Therefore, all criteria possess anacceptable reliability index. Factor analysis also shows that all criteria lie on a factorwith eigenvalue of over one. The only factor for each one of the criteria indicates ahigh percentage from the total variance (except for INEM). This table also showsthat a measure from strategy criteria is eliminated owing to results from factoranalysis.

In table 7(a) validity factor analysis results for BPRM variable are shown. Theseresults indicate that all criteria of BPRM have an alpha of over 0.7, and all criteriaonly have a factor with an eigenvalue of over one. The column for the percentagefrom total variance also shows rather good construct validity for this variable.

The results of examining validity and reliability of variable PER are shown intable 7(f). As it is seen in this table, all criteria have an alpha of over 0.7 except forcompany’s growth rate (PEGR) that also has an alpha of near 0.7. One of themeasures relating to PEOP is eliminated to place all instruments on one factor.

Content validity indicates meeting the specific range of contents that have beenselected (Nunnally and Bernstein 1994). It also shows that measurement instrumentshave elements that cover all aspects of variables under measurement. Contentvalidity cannot be numerically measured, but we can measure it subjectively andjudgmentally. Basically, content validity depends on the appropriateness ofthe content and the method of rendering (Nunnally and Bernstein 1994). Since theselection of research variables is based on an intensive survey of literature and all theelements are supported by authentic research, the instrument has content validity.Furthermore, academic and industrial experts have examined the content of thequestionnaire during the pre-testing.

Predictive validity is in fact the correlation between measurement instrument andan independent variable taken from relating criteria (Nunnally 1978). This validity isonly possible through correlation between the predictor (independent variable) andcriterion (dependent) variable (Nunnally and Bernstein 1994). In this study, theresults of two-variable and multi-variable correlation between ITU as independentvariables and PER as dependent variable have shown that there is significantcorrelation between intended criteria under measurement in this study.

3.6 Non-response bias test

Two time-dated groups were used to test for non-response bias test (Cooper 2003).First-group returns were received within one month after the survey was sent out (wehad asked respondents to answer no later than one month, after they received thequestionnaire). Subsequent responses, coded as second-group returns, were receivedafter the reminder letters had been sent out to the managers to follow.


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Table

7(e).

Validityindex


OIS.

Variable

Measurement

No.of

measures

No.ofelim

inated

measures

NMean

Std.

deviation

Alpha

Eigenvalue

%from

total

variance

Practicaldiligence

fororganizational

infrastructures(O

IS)

Empowerment:IN

EM

40

97

4.98

0.98

0.6345

1.911

47.773

Decentralization:IN

DE

50

96

4.91

1.02

0.8492

3.167

63.349

Training:IN

TR

30

97

5.55

1.01

0.8473

2.300

76.680

Groupwork:IN

GO

20

97

5.51

1.20

0.7518

1.610

80.480

Process

managem

ent:IN

PC

70

97

6.14

0.79

0.8890

4.256

60.802

Changein

interactionswith

suppliers:IN

SU

30

97

5.93

0.93

0.7945

2.162

72.067

Leadership:IN

LE

41

97

6.15

0.82

0.7186

1.944

64.801

TotalOIS

93

5.60

0.97

Analphaofbelow

0.7

andover

0.6

fornew

instruments

isacceptable

(Nunnly

1987).

Analphaofbelow

0.6

isnotacceptable.


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Table

7(f).

Validityindex


PER.

Variable

Measurementcriteria

No.of

measures

No.ofelim

inated

measures

NMean

Std.

deviation

Alpha

Eigenvalue

Percentagefrom

totalvariance

Perform

ance:

(PER)

Customer

results:PECO

20

97

6.14

0.92

0.7417

1.596

79.784

Employee

results:PEEM

20

97

5.46

0.93

0.7756

1.638

81.877

Organizationalperform

ance

results:PEOP

61

97

5.97

0.81

0.8587

3.203

64.063

Company’sgrowth

rate:PEGR

20

97

5.40

1.08

0.6810*

1.558

77.876

TotalPER

97

5.81

0.76

TotalPER*(PEGR

elim

inated)

97

5.90

0.78

*Analphaofbelow

0.7

andover

0.6

fornew

instruments

isacceptable

(Nunnly

1987).

Analphaofbelow

0.6

isnotacceptable.


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To test the non-response bias, time-dated groups were compared with variables.No t-tests were statistically significant at the 0.05 level. These results show thatfindings can be generalized to the sample.

3.7 Method of analysis

The method of analysis is applied at three levels of study. First, data are examinedand some descriptive statistics obtained in order to obtain an overview of thecharacteristics of the sample and to assess issues such as mean and standarddeviation. This analysis examines the scales as independent entities to determine theextent of use of IT in sample companies, company’s performance, the extent of use ofIT in BPR and the degree to which the company cares for creating IT organizationalinfrastructures. Second, bivariate correlations between variables are analysed withrespect to the correlation between scales of IT use and company performancemeasures, and also two intervening variables. This aspect of the analysis forms abasis to examine the existence of association between the dependent, independentand intervening variables. The final stage of the analysis adopts a regression analysis.The variables are drawn together in the application of regression analysis toinvestigate the relationship between the extent of use of IT and companyperformance with considering the role of intervening variables. In particular, itexamines the research hypothesises.

4. Empirical results

4.1 Univariate analysis

4.1.1 IT usage (ITU). This section highlights the extent of the use of IT in samplecompanies. Table 7(d) shows the total use of IT exceeded from moderate level (4.59).This table shows that the highest amount of IT usage is in the ‘IT in pecuniaryaffairs’ (5.98) closely followed by ‘IT in quality control’ (5.89). IT applications inpecuniary affairs are one of the eldest applications of IT (Turban 2002) andnumerous software applications are developed and used in companies, inexpensively.Also, implementing a quality management system (such as ISO 9000, QS 9000) is oneof the requirements of car part suppliers in Iran. These companies use ITapplications for gathering and analysing quality data. Table 7(d) indicated thatonly ‘IT in decision support systems’ is used less than moderate level (3.05).Decision-support systems are more advanced and more expensive than the other typeof IT applications in table 7(d).

4.1.2 Emphasize on organizational infrastructure (OIS). Table 7(e) indicates thatcompanies under study pay considerable attention to organizational infrastructures.The total average of variable OIS is exceeded from the moderate level (5.60).Table 7(a) shows that all of the criteria of OIS variables are above 4.9 on a seven-point Likret scale. The ‘leadership’ has been emphasized at the highest level (6.14)followed closely by the ‘process management’ (6.14) and ‘Change in interactions with


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suppliers’(5.93). Although the decentralization has the lowest mean of emphasis, themean of this criterion is exceeded significantly from the moderate level (4.09).

4.1.3 The impact of IT application on business process reengineering

(BPRM). Table 7(a) indicates respondents’ perspectives of the influence of IT ontheir business process transformation. Table 7(e) shows that IT applications affect allof the business processes more than the moderate level (3.5). IT has the highesteffect on ‘business process of product’ (5.57). The ‘business process of personnel’ hasthe lowest mean of IT effects (4.78). Table 7(e) summarize total effects of IT on alleight-business processes.

4.1.4 Company performance (PER). In this study we asked respondents to ratetheir plant’s position with respect to competitors on a seven-point Likert scale.Table 7(f) shows that most of the respondents recognized themselves ashighly competitive. They recognized the most competitive improvement in‘Customer results’ (6.14), in descending order, followed by ‘organizationalperformance results’(5.97), ‘employee results’(5.46) and ‘Company’s growth rate’(5.40) (table 7(f)).

Consequently, the results of the univariate analysis indicate that four variablesconsiderably exceeded moderate level in the sample companies of this study.

4.2 Bivariate correlation analysis

This section shows the results of testing the correlation between four researchvariables including amounts of use of IT (ITU), company performance (PER),practical diligence to organizational infrastructures (OIS) and the effects of IT onbusiness processes reengineering (BPRM) (table 8(a)). Altogether, all of the bivariatecorrelations in tables 8(a), 8(b) and 8(c) are positive and statistically significantexcept the correlation between ‘growth rate (PEGR)’ and ‘IT in communication(ITCO)’ as well as ‘IT in production and operation (ITPO)’. Consequently, ‘growthrate (PEGR)’ scale has been deleted from the later analysis, because bivariatestatistically significant correlation is essential for the special type of regressionanalysis in this paper. Tables 8(a)–8(c) show the values of the bivariate Pearson’scorrelation coefficients (r) and respective statistical significant levels (p). Followingthese results, it appears logical to pursue regression analysis.

4.3 Findings about moderating effects of OIS

The procedure that is used to test the moderating effect of organizationalinfrastructures on relationship between IT usage and company performance ishierarchical regression analysis. Boyer et al. (1997), Cohen and Cohen (1975), Millerand Droge (1986), Stone and Hollenbeck (1989), Dean and Snell (1991), Baron andKenny (1986) have suggested this procedure for this kind of research. This procedurefacilitates an analysis of the effects of groups of variables in an incremental,controlled manner (Boyer et al. 1997). In order to test the moderating effect ofeach of the seven organizational infrastructure scales, seven regression equationsare applied and analysed. This procedure is used to conduct seven separate


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Table

8(a).

Bivariate

correlationsbetweenIT

Usageandcompanyperform

ance.

Criterion

Customer

results

(PECU)

Employee

results(PEEM)

Perform

ance

(PEOP)

Growth

rate

(PEGR)

TotalPER*

(PEGR

elim

inated)

ITin

communications:

ITCO

r0.302y

0.269y

0.318y

0.144

0.335y

p0.003

0.008

0.002

0.162

0.001

N96

96

96

96

96

ITin

production

andoperation:IT

PO

ITin

planning:IT

POI

r0.424y

0.428y

0.449y

0.103

0.482y

p0.000

0.000

0.000

0.314

0.000

N97

97

97

97

97

ITin

operation:IT

POII

r0.202*

0.377y

0.345y

0.164

0.354y

p0.049

0.000

0.001

0.111

0.000

N96

96

96

96

96

ITin

quality

control:IT

POIII

r0.263y

0.299y

0.272y

0.096

0.293y

p0.009

0.003

0.007

0.352

0.004

N97

97

97

97

97

ITin

decisionsupport:IT

DS

r0.246*

0.336y

0.290y

0.104

0.321y

p0.015

0.001

0.004

0.312

0.001

N97

97

97

97

97

ITin

administration:

ITAD

ITin

administrativeaffair:IT

ADI

r0.428y

0.375y

0.460y

0.223*

0.476y

p0.000

0.000

0.000

0.028

0.000

N97

97

97

97

97

ITin

pecuniary

affair:IT

ADII

r0.351y

0.281y

0.427y

0.214*

0.416y

p0.000

0.005

0.000

0.035

0.000

N97

97

97

97

97

TotalIT

usage:

ITU

r0.481y

0.535y

0.562y

0.228*

0.590y

p0.000

0.000

0.000

0.024

0.000

N97

97

97

97

97

*Correlationissignificantatthe0.05level

(2-tailed).

yCorrelationissignificantatthe0.01level

(2-tailed).


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hierarchical regressions: one for each of of the seven organizational infrastructuremeasurement scales (INEM, INDE, INTR, INGO, INPC, INSU, INLE). For eachof the seven organizational infrastructure scales, this analysis is conducted inthree steps:

1. In each equation, one of the organizational infrastructure scales is enteredinto the equation [for example INEM is entered in equation (1)].

2. Total mean of the ITU is entered into the equation.3. Finally, the interaction between respective organizational infrastructure and

ITU are entered into the regression equation.

When these interaction terms account for a significant amount of incrementalvariance in the dependent variable, as measured by the t-tests for each interaction orby significance tests for the incremental F-statistic, then there is evidence to supportresearch Hypothesis 1, that there is a moderating effect of infrastructure on the useof IT. Total mean of company performance scales (PER*) is considered as thedependent variable in each of the regression equation. Results of hierarchicalregression are demonstrated in the following section.

Table 9 shows the results of a hierarchical regression with PER* as the dependentvariable, the organizational infrastructure scales (for example INEM in first part ofthe table 9), ITU, and their interactions entered in the sequential manner describedabove. Results of hierarchical regression with, for example, INEM as the moderatingvariable will be discussed in the following paragraph. The discussion of the othermoderating variables is the same as INEM.

Table 8(c). Bivariate correlations between PER, ITU and BPRM.

BPOF BPST BPPR BPMS BPSE BPAC BPPE BPTE

Total PER*(PEGR eliminated)

r 0.436y 0.351y 0.407y 0.411y 0.349y 0.433y 0.387y 0.481y

p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000N 97 96 97 97 97 97 97 97

IT Usage: ITU r 0.354y 0.269y 0.406y 0.325y 0.333y 0.326y 0.327y 0.324y

p 0.000 0.008 0.000 0.001 0.001 0.001 0.001 0.001N 97 96 97 97 97 97 97 97

*Correlation is significant at the 0.05 level (2-tailed).yCorrelation is significant at the 0.01 level (2-tailed).

Table 8(b). Bivariate correlations between PER, ITU and OIS.

INEM INDE INTR INGO INPC INSU INLE

Total PER* (PEGR eliminated) r 0.491y 0.410y 0.314y 0.221y 0.518y 0.395y 0.592y

p 0.000 0.000 0.002 0.030 0.000 0.000 0.000N 97 96 97 97 97 97 97

IT Usage: ITU r 0.513y 0.512y 0.381y 0.262y 0.467y 0.437y 0.502y

p 0.000 0.000 0.000 0.009 0.000 0.000 0.000N 97 96 97 97 97 97 97

*Correlation is significant at the 0.05 level (2-tailed).yCorrelation is significant at the 0.01 level (2-tailed).


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The organizational infrastructure scale, which is entered into the model in thefirst step, accounts for a significant amount of variance (an R2 of 0.241, P50.000).The inclusion of ITU in the second step provides a significant improvement(an incremental R2 of 0.154, P50.000), and also the interaction terms of step 3 resultin an incremental R2 of 0.054, which is significant (P50.01). The overall effect of themodel is the explanation of 39.6% of the variance in PER*, which the associated Ftest indicates is significant at P50.000. Note that the interaction between ITU andINEM has a negative coefficient (�0.155). While at first glance this appears toprovide evidence contradicting Hypothesis 1, an examination of table 8(b) showsthat the negative coefficient is likely a result of multi-collinearity. Table 8(b) showsthat the interaction between ITU and INEM has a significant, positive correlation

Table 9. Results of hierarchical regression analysis: testing moderating effects of OIS.

t-test F-test

Step Measurement scale B Statistics Sig. R2 �R2 Statistics Sig.

Empowerment: INEM1 INEM 0.393 5.498 0.000 0.241 0.241 30.229 0.0002 ITU 0.419 4.903 0.000 0.396 0.154 24.038 0.0003 ITU*INEM �0.155 �3.026 0.003 0.450 0.054 9.155 0.003

Decentralization: INDE1 INDE 0.315 4.354 0.000 0.168 0.168 18.953 0.0002 ITU 0.463 5.239 0.000 0.357 0.190 27.452 0.0003 ITU*INDE �0.147 �2.687 0.009 0.404 0.047 7.220 0.009

Overall 20.806 0.000

Training: INTR1 INTR 0.242 3.218 0.002 0.098 0.098 10.358 0.0022 ITU 0.504 6.147 0.000 0.357 0.259 37.782 0.0003 ITU*INTR �0.157 �2.978 0.004 0.413 0.056 8.870 0.004

Overall 21.796 0.000

Group work: INGO1 INGO 0.144 2.208 0.030 0.049 0.049 4.876 0.0302 ITU 0.523 6.636 0.000 0.352 0.303 44.042 0.0003 ITU*INGO �0.135 �2.747 0.007 0.401 0.049 7.545 0.007

Overall 20.744 0.000

Process management: INPC1 INPC 0.510 5.897 0.000 0.268 0.268 34.778 0.0002 ITU 0.407 5.021 0.000 0.423 0.155 25.209 0.0003 ITU*INPC �0.164 �2.856 0.005 0.469 0.047 8.156 0.005

Overall 27.416 0.000

Change in interactions with suppliers: INSU1 INSU 0.331 4.193 0.000 0.156 0.156 17.582 0.0002 ITU 0.472 5.664 0.000 0.371 0.215 32.078 0.0003 ITU*INSU �0.191 �3.629 0.000 0.449 0.078 13.168 0.000

Overall 25.251 0.000

Leadership: INLE1 INLE 0.565 7.158 0.000 0.350 0.350 51.231 0.0002 ITU 0.358 4.481 0.000 0.465 0.114 20.077 0.0003 ITU*INLE �0.103 �1.821 0.072 0.483 0.018 3.317 0.072

Overall 28.975 0.000


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with PER* when taken by itself. However, in the hierarchical regression oftable 9 the inclusion of multiple variables in the model causes some of the variablesto take on negative correlations, a common occurrence in regression analysis(Cooper 2003).

In short, as implied in table 9, moderating effects are observed for all of theorganizational infrastructure scales (as indicated by the significance level of theinteraction effect with p50.05, except for INLEA which is significant at p51).

The regression model shown in table 9 has two major implications. First,the overall result indicates that organizational infrastructure and the interactionsbetween information technology usage and organizational infrastructure havepositive associations with company performance. This outcome is important becauseit provides support for the proposition that practical diligence to the organizationalinfrastructure and the IT usage in a plant is positively associated with performance.Second, the significant incremental improvement in the model upon the addition ofthe interactions between ITU and OIS supports the premise that organizationalinfrastructures have a positive moderating effect on the relationship between ITUand performance. The results therefore support Hypothesis 1.

4.4 Findings about mediating effects of BPRM

Judd and Kenny (1981) noted that a series of regression models provides the best testof a mediating effect. To establish mediation, the following conditions must hold:

1. The independent variable must affect the mediator [equation (1)].2. The independent variable must affect the dependent variable [equation (2)].3. The mediator must affect the dependent variable equation (3).4. If these conditions hold, then the effect of the independent variable on the

dependent variable must be less in equation (3) than in equation (2).

Table 10 contains the results of the regression equations estimated for a mediatingeffects model. As shown in the first row of table 10, the regression equationsPER¼ f(ITU) suggests that higher IT usage is associated with higher levels ofcompany performance improvement. Regression equations No. 1 in table 9 suggeststhat for total average of ITU company performance improvement are stronglyassociated with business process change. Table 10 shows that in descending order,the strongest associations are observed for business process of order flow (BPOF)(b¼ 0.324, p50.000), business process of product (BPPR) (b¼ 0.296, p50.000),business process of personnel (BPPE) (b¼ 0.242, p50.000), business process oftechnology (BPTE) (b¼ 0.239, p50.000), business process of marketing and sales(BPMS) and business process of accounting (BPAC) (b¼ 0.236, p50.000), businessprocess of services (BPSE) (b¼ 0.230, p50.000) and business process of strategy(BPST) (b¼ 0.175, p50.000). The regression results seem to suggest that, to varyingdegrees, ITU is a positive contributor to performance. The results also suggest thatprocess redesign with respect to ITU is directly associated with companyperformance improvement, a first indication of mediating effects between ITU andcompany performance improvement.

Where IT usage significantly affects process change [rows No. 2 in table 10:BPxx¼ f(ITU)), assessment of mediation can be made through comparison of the


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regression coefficients of PER*¼ f(ITU), rows No. 1 (PER1¼ f(BPxx)), and rowsNo. 3 (Per¼ f(ITU, BPxx).

In the case of BPOF, the beta-coefficient of equation PER*¼ f(ITU) suggeststhat Performance is a function IT Usage (b¼ 0.0.540, p50.000). The coefficient of

Table 10. Results of regression analysis: testing mediating effects of BPRM.

t-test F-test

Measurement scale B Statistics Sig. R2 Adj. R2 SE Statistics Sig.

No. PER1¼ f(ITU) 0.540 7.114 0.000 0.348 0.341 0.6358 50.607 0.000

Business process of order flow: BPOF1 PER1¼ f(BPOF) 0.324 4.726 0.000 0.190 0.182 0.70829 22.335 0.0002 BPOF¼ f(ITU) 0.437 3.691 0.000 0.125 0.116 0.99122 13.623 0.0003 PER1¼ f(ITU, BPOF) 0.456 5.856 0.000 0.407 0.394 0.60951 32.224 0.000

0.193 3.062 0.003

Business process of strategy: BPST1 PER1¼ f(BPST) 0.175 3.632 0.000 0.123 0.114 0.73810 13.190 0.0002 BPST¼ f(ITU) 0.495 2.713 0.008 0.073 0.063 1.5173 7.359 0.0083 PER1¼ f(ITU, BPST) 0.486 6.256 0.000 0.383 0.370 0.62252 28.842 0.000

0.104 2.461 0.016

Business process of product: BPPR1 PER1¼ f(BPPR) 0.296 4.345 0.000 0.166 0.157 0.71895 18.879 0.0002 BPPR¼ f(ITU) 0.511 4.333 0.000 0.165 0.156 0.98885 18.775 0.0003 PER1¼ f(ITU, BPPR) 0.465 5.721 0.000 0.381 0.368 0.62248 28.957 0.000

0.146 2.262 0.026

Business process of marketing and sales: BPMS1 PER1¼ f(BPMS) 0.236 4.395 0.000 0.169 0.160 0.71758 19.314 0.0002 BPMS¼ f(ITU) 0.520 3.353 0.001 0.106 0.096 1.2985 11.241 0.0013 PER1¼ f(ITU, BPMS) 0.467 6.040 0.000 0.401 0.389 0.61228 31.507 0.000

0.141 2.906 0.005

Business process of services: BPSE1 PER1¼ f(BPSE) 0.230 3.625 0.000 0.121 0.112 0.73779 13.138 0.0002 BPSE¼ f(ITU) 0.462 3.444 0.001 0.111 0.102 1.1243 11.864 0.0013 PER1¼ f(ITU, BPSE) 0.488 6.151 0.000 0.374 0.360 0.62631 28.031 0.000

0.113 1.976 0.051

Business process of accounting: BPAC1 PER1¼ f(BPAC) 0.263 4.687 0.000 0.188 0.179 0.70940 21.966 0.0002 BPAC¼ f(ITU) 0.492 3.361 0.001 0.106 0.097 1.2252 11.294 0.0013 PER1¼ f(ITU, BPAC) 0.459 5.999 0.000 0.413 0.400 0.60647 33.020 0.000

0.164 0.228 0.002

Business process of personnel: BPPE1 PER1¼ f(BPPE) 0.242 4.096 0.000 0.150 0.141 0.72569 16.775 0.0002 BPPE¼ f(ITU) 0.480 3.369 0.001 0.107 0.097 1.1932 11.353 0.0013 PER1¼ f(ITU, BPPE) 0.475 6.081 0.000 0.390 0.377 0.61803 30.053 0.000

0.136 2.558 0.012

Business process of technology: BPTE1 PER1¼ f(BPTE) 0.239 5.352 0.000 0.232 0.224 0.68998 28.645 0.0002 BPTE¼ f(ITU) 0.596 3.337 0.001 0.105 0.096 1.4972 11.137 0.0013 PER1¼ f(ITU, BPTE) 0.444 5.948 0.000 0.442 0.430 0.59125 37.191 0.000

0.161 3.982 0.000


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row 2 suggests that changes in BPOF is a function of IT Usage (b¼ 0.437, p50.000)

and the coefficients of row 3 suggest that performance improvement is a function

of IT Usage (b¼ 0.456, p50.000) and changes in BPOF (b¼ 0.193, p50.003).

Since the coefficient associated with IT Usage is less in row 3 than in

equation PER*¼ f(ITU), and rows 1 and 2 are both significant, a mediating effect

is implied.This phenomenon is also observed for BPOF, BPST, BPPR, BPMS, BPSE,

BPAC, BPPE, BPTE. In summary, the results suggest that business process change is

a necessary and sufficient condition for improvements in Performance. The results

therefore support Hypothesis 2.

5. Limitations

The most important limitation of this study lies in the study’s sample size. The

study’s sample size is 112 plants (out of 200 plants). This size is considered small for

our statistical analysis. On the other hand, this size is generally used at individual

respondent level of analysis, where measures’ instability is fairly high (Froza 1995,

Hofstede et al. 1990). In the present study, each measure used, has high internal

consistency, in other words, the answers are highly correlated, and this consistency

increases the stability of measure (see table 7). Hofstede et al. (1990) state that a

lower sample size is acceptable when this kind of stable data with high internal

consistency is used.The second potential limitation lies in the process of making the research variable

of PER operational. We used four separate subjective measures to assess the company

performance. Researchers, conducting similar studies, have reported that the number

of people willing to answer objective questions on the company performance is more

than those who want to answer the subjective questions (Boyer et al. 1997, Forza 1995,

Dewhurst 2003 and Ang et al. 2001). This is most likely that the result of being

reluctant to divulge the companies’ confidential performance information somehow

undermine the findings, so we used objective, Likert scale questions to assess

performance.The third limitation of this research is about the stability of performance

measures. We have described four criteria to measure performance: ‘customer

satisfaction and relationship’ were grouped together under a new variable

‘customer results’ based on the mean value; a similar process was done to other

indicators in the questionnaire and related to ‘worker satisfaction and

performance’, labelled ‘people results’ and other six other questions labelled

‘operational results’. Although factor analysis shows that the above measures

cannot be grouped together, according to the previous studies (Froza (1995),

EFQM (1990) and Swamidass and Kotha (1998)), we grouped questions

together based on the mean value and created four above-mentioned criteria

to measure performance. The validity and reliability of the measures are

presented in table 7(f). Only company’s growth rate (PEGR) cannot show

acceptable Alpha (reliability index), consequently, PEGR is eliminated from our

analysis.


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6. Conclusions and discussions

6.1 Measurement instrument

In this study, measurement instruments of the impact of IT on the performanceof manufacturing companies regarding the role of intervening variables includingorganizational infrastructures and business processes reengineering have beendeveloped and their reliability and validity, based on a survey in 200 companies ofcar part manufacturers in Iran, have been assessed. In order to achieve this, fourvariables have been examined: the application of IT as independent variable, firmperformance as dependent variable, the impact of IT on transformation as mediatorand finally organizational infrastructures as moderator variable. We have definedand mentioned all measurement criteria and their applications in the literature. Theirvalidity and reliability have been tested and modified accordingly. Ultimately,we have introduced valid and reliable criteria (seven for ITU variable, three forfirm performance, eight for the impact of IT on reengineering, and eight fororganizational infrastructures). Some criteria were initially defined to be used inmeasuring the application of IT in companies. The defined criteria are: IT incommunications, IT in production and operation, IT in administration and officework, and IT in decision-making.

Although the criteria used in other studies have been proven valid and reliable,using confirmatory factor analysis (CFA) with regard to latent structure amongthese criteria, we found new dimensions in the application of IT in companies understudy. The new criteria resulting from this study are: IT in communications, IT inplanning, IT in operation, IT in quality control, IT in decision-making, IT infinancial affairs and IT in administration and office work.

Another important point about measurement instrument in this study is thatmeasures for measuring the impact of IT on business processes reengineering havebeen created. The impact of IT on business processes and reengineering of processeshas been investigated in many studies, but valid measures for quantitativemeasurement of this impact have not been reported. Only one study (Grover et al.(1998)) examined the impact of IT on transformation in business processes adoptingquantifiable methods. The difference between their criteria and the ones defined inthis study is that in their study the impact of ten ITs including email, electronic datainterchange, the internet, client/server, RDBMS, LAN (local area network). Imagingtechnology has been matured, but in our study we have measured the impact ofIT on processes of order flow, strategy product, marketing and sales, services,accounting, personnel and technology. The variables used in this study are the resultof qualitative research and case studies, but have never been quantitatively used in asurvey. The growth of qualitative research concerning the impact of IT andreengineering of processes has led to the conception of these criteria and paved theway for the employment of such criteria in quantitative research. Validity andreliability of these criteria are proved in this study.

6.2 OIS moderating effect

Results of this study prove the moderating effect of organizational infrastructures inthe relationship between IT and firm performance. In fact, this study shows that


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practical diligence for organizational infrastructures including work empowerment,

decentralization, training, teamwork, process management and customer relation-

ship, changes in supplier relationship and leadership, strengthen the relationship

between IT and firm performance. These results are consistent with the study of

Boyer et al. (1997): the only difference is that they did not consider the role of process

management and customer relationship, changes in supplier relationship in their

study.First, we considered Empowerment as an organizational infrastructure.

Management information systems (MIS) and email simplify communication and

interchange of reports between different organizational levels. Utilization of IT

enables top management to have direct control over different executive organiza-

tional levels and have access on the summarized and graphical reports of their

subordinates. Therefore, organizations can decrease the middle management and

bureaucracy; in return, management should give more authority and power to the

employees in production and operations planning and control. These results

regarding empowerment are consistent with the study of Pinsonneault and Rivard

(1998), which evaluate the effects of IT on managerial nature.Decentralization is the second organizational infrastructure in this study.

Decreasing the middle management levels require the increase in the authority of

reminder of the middle management levels; it means organizations should try to give

decision power in cases of human resource, financial and operations management to

the reminder levels of middle management. Results of decentralization criterion are

in consistent with the study of Boyer et al. (1997).Continuous training of employees improves the utilization of IT and means that

an improvement is expected in their productivity. We considered teamwork as

the next organizational infrastructure in our study. Nowadays technologies such as

group-wares, the internet, intranet, email, and EDI facilitates and improves the

teamwork in organizations. On the other hand, advantages such as synergy and

knowledge sharing in teamwork encourage the teamwork in organizations. This

study shows that group projects and matrix organizational structure are necessary to

realize IT potential. Results of training and teamwork are in consistent with the

study of Lau et al. (2001).Process management is another IT organizational infrastructure. Process

management can be implemented through quality management systems in

accordance with ISO 9000: 2000 or another TQM program. In this approach

business processes are defined according to customer needs. Evaluation criteria are

defined and measured according to processes. IT systems such as process flow

management facilitate process management approach. These systems could be used

to collect data for evaluating the performance and analysis and present the results

of evaluation.Brynjolfsson and Hitt (2000) considered the change in interactions between firm

and customers and also suppliers as one of the requirements in improving the

organizational IT productivity. We considered above-mentioned changes as

organizational infrastructures and proved the moderating effects of those changes

in relationship between IT and firm performance. IT systems such as EFT (electronic

found interchange) and EDI (electronic data interchange) facilitate the processes

of ordering, billing, receipt, and money exchange. Also, the inter-organizational,


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customer relationship management (CRM), and supply chain management (SCM)systems are more applicable in this category.

Leadership is considered as the last organizational infrastructures to realize theIT potentials in our study. The results of this research show that top managementcommitment in continual improvement of processes, training and motivating allemployees in participating in enhancement the quality leads to more IT potential

utilization. These results are in consistent with work of Boyer et al. (1997).

6.3 BPRM mediating effect

One of the most important outcomes of this study is to show the mediating effect ofBPR in the relationship between IT and firm performance. The outcome shows thattransformation in the processes of order flow, strategic planning, product, marketingand sales, services, accounting, personally and technology is the necessaryprecondition for improving the firm performance made by IT usage. The result ofour study is consistent with the outcomes of the research study of Grover et al.

(1998), which showed that the mediating effect of BPR is stronger than themoderating effect of this variable. And also results of Hammer and Champy’s study(1993), which indicated that IT is an important BPR enabler, support our outcome.As Gunasekaran and Nath (1997) mentioned, BPR and IT form an integral system inimproving the performance of manufacturing companies drastically. Basically, ITcan save time and improve accuracy in exchanging information about company goalsand strategies. It removes much of the human error inherent complex and repetitivetasks. IT saves money because it reduces errors, and the time it takes to accomplishtasks. IT provides a competitive advantage by helping a company’s position and

capitalizes on trends so that it should be the first to market a new product.Therefore, it is highly recommended to: (a) use the IT potentials in transforming thebusiness processes, and (b) develop the business processes in alignment with ITpotential for reengineering processes.

7. Future research directions

The strong role of intervening variables such as BPR and OIS to realize IT potentialis outlined in this study. We have considered the role of only two of the above-mentioned important intervening variables in relationship between IT usage andcompany performance: it seems that researchers can study the role of other variables

such as management style and total quality management on such a relation.In addition, the research instrument developed here is useful for further IT andperformance studies.

The second future research direction lies in method of analysis. We usedregressing analysis, which is not based on the examination of simultaneousequations; rather it takes into account separate equations. However, recentdevelopment in the IS field shows a trend in the use of a second-generationsimultaneous equation models (SEMs). We suggest using this approach to furtherknowledge about our model.


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Appendix 1. Questionnaire

Please indicate the extent to which IT has been used by your company by marking

the alternative that best describes your idea, ranging from 1 to 7: (1¼ not at all,

4¼ to some extent, 7¼ strongly)

Code Measures Later code changed to

ITU IT UseITCOM Communication IT

ITCOM1 e-mailITCOM2 Fax Later deletedITCOM3 Mobile Later deletedITCOM4 InternetITCOM5 LAN: Local Area NetworkITCOM6 Web site for advertisementITCOM7 IntranetITCOM8 EDI: Electronic Data

Interchange for interactionswith suppliers

ITPOM Production and operation IT

ITPO1 Barcode Later deletedITPO2 Automatic warehousing Later deletedITPO3 Software for project

managementITPO1.3 (Factor1: IT in planning)

ITPO4 CAPP: Computer AidedProduction Planning

ITPO1.4 (Factor1: IT in planning)

ITPO5 MRP: ManufacturingRequirement Planning

ITPO1.5 (Factor1: IT in planning)

ITPO6 CAD: Computer Aided Design Later deletedITPO7 CAM: Computer Aided

ManufacturingITPO2.7 (Factor2: IT in operation)

ITPO8 CAE: Computer AidedEngineering

ITPO2.8 (Factor2: IT in operation)

ITPO9 CNC: Computer NumericalControl

ITPO2.9 (Factor2: IT in operation)

ITPO10 Robotics Later deletedITPO11 Computer aided production

planningITPO1.11 (Factor1: IT in planning)

ITPO12 Final product quality control ITPO3.12 (Factor3: IT in qualitycontrol)

ITPO13 Process quality control ITPO3.13 (Factor3: IT in qualitycontrol)

ITDS Decision support IT

ITDS1 Data analysisITDS2 Graphical data presentation

toolsLater deleted

ITDS3 DSS: Decision SupportSystems

ITDS4 SIS: Strategic InformationSystems

(continued)


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Please indicate the extent to which information technology (IT) has been changed the

following business processes in your company Likert scale ranging from 1¼ no

effect, to 4¼moderate effects, to 7¼ extreme effects)

Code Measures

BPRM Business process changes

BPOF Order flow

BPOF1 Raw materialBPOF2 Product assemblyBPOF3 Obtaining ordersBPOF4 Delivery of the productBPOF5 Installation of the productBPST Strategic process

BPST1 Formulation of the strategyBPST2 Organizational and behavioural issuesBPPR ProductBPPR1 Design of productBPPR2 EngineeringBPPR3 Process planningBPMS Marketing/sale

BPMS1 Customer satisfactionBPMS2 Market researchBPMS3 ForecastingBPMS4 Product-mix decisionsBPSE Services

BPSE1 Maintenance of the productBPSE2 Quality assuranceBPSE3 After-sale serviceBPAC Accounting

BPAC1 Product costingBPAC2 Make-or-by decisionsBPAC3 BudgetingBPAC4 RecruitmentBPAC5 TrainingBPAC6 MotivationBPAC7 Performance appraisalBPTE Technology

BPTE1 Selection of plant and equipmentBPTE2 Installation of plant and equipment

Continued.

Code Measures Later code changed to

ITAD Administrative IT

ITAD1 Databases ITAD1.1 (Factor1: IT in administration)ITAD2 Spread sheets ITAD1.2 (Factor1: IT in administration)ITAD3 Word possessors Later deletedITAD4 Workflow management system Later deletedITAD5 Internet recruitment ITAD1.5 (Factor1: IT in administration)ITAD6 Training system ITAD1.6 (Factor1: IT in administration)ITAD7 Performance analysis system ITAD1.7 (Factor1: IT in administration)ITAD8 Payroll system ITAD2.8 (Factor2: IT in financial affair)ITAD9 Invoice systems ITAD2.9 (Factor2: IT in financial affair)ITAD10 Financial system ITAD2.10 (Factor2: IT in financial affair)


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Indicate the degree of emphasis that your manufacturing plant places on the

following activities. (Likert scale ranging from 1¼no emphasis, to 4¼moderate

emphasis, to 7¼ extreme emphasis).

Please indicate your level of agreement or disagreement with the following

statements. (Likert scale ranging from 1¼ strongly disagree, to 4¼ neither agree

nor disagree, to 7¼ strongly agree).

Code Measures

OIS Organizational infrastructuresINEM Empowerment

INEM1 Giving authority of scheduling to the workersINEM2 Giving authority of inspection and quality control to the workersINEM3 Changes in managers responsibilitiesINEM4 Giving workers a broader range of tasksINDE Decentralization

INDE1 Giving authority of recruitment to middle managersINDE2 Giving authority of workers assignment to middle managersINDE3 Giving authority of workers control to middle managersINDE4 Giving authority of financial resources assignment to middle managersINDE5 Giving authority of physical assets assignment to middle managersINTR Training

INTR1 Improving supervisors trainingINTR2 Improving workers trainingINTR3 Improving direct workers motivationINTE Teamwork

INTE1 Permanent project teams (with people from different functional areas)INTE2 Matrix organization (people working on a project report functionally

within their department but report to a project manager for projectwork)

INPC Process management and customer relationship

INPC1 Process managementINPC2 Statistical process controlINPC3 Assessment of processesINPC4 Continues improvement of processesINPC5 Customer needs assessmentINPC6 Customer satisfaction measurementINPC7 Customer relationship managementINSU Changes in transaction with suppliersINSU1 Supplier relationship managementINSU2 Improvement of financial exchange with suppliersINSU3 Involvement in supplier quality assurance

INLE Leadership

INLE1 All major department heads within our plant acceptresponsibility for quality

INLE2 Plant management provides personal leadership forquality improvement

Later deleted

INLE3 The top priority in evaluating plant management isquality performance

INLE4 Our top management strongly encourages employeeinvolvement in the production process


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For your major product line, indicate your position with respect to your competitors

on the following dimensions for the last 2 years. (Likert scale ranging from

1¼ significantly lower, to 4¼ equal, to 7¼ significantly higher).

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PER PerformancePEC0 Customer results

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Documents

Assessing the Impact of Information Technology on Firm Performance Considering the Role of Intervening Variables Organizational Infrastructures and Business Processes Re Engineering