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Assessing the impact of information technology on firmperformance considering the role of interveningvariables: organizational infrastructures and businessprocesses reengineering
To cite this Article: , 'Assessing the impact of information technology on firmperformance considering the role of intervening variables: organizationalinfrastructures and business processes reengineering', International Journal ofProduction Research, 45:12, 2697 - 2734To link to this article: DOI: 10.1080/00207540600767780
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© Taylor and Francis 2007
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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
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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
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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
andfactoranalysisofvariable
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
andfactoranalysisofvariable
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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