The relationship of strategic business alignment and enterprise information management in achieving better business performance

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  • This article was downloaded by: [Dalhousie University]On: 03 October 2014, At: 06:04Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

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    The relationship of strategic businessalignment and enterprise informationmanagement in achieving betterbusiness performanceHonggeng Zhou a , David A. Collier b & Darryl D. Wilson ca Department of Decision Sciences , Whittemore School ofBusiness and Economics, University of New Hampshire , 15 CollegeRoad, Durham, New Hampshire, USAb Lutgert College of Business, Florida Gulf Coast University , FortMyers, Floridac Department of ITDS , College of Business and PublicAdministration, Old Dominion University , Norfolk, VirginiaPublished online: 03 Jul 2008.

    To cite this article: Honggeng Zhou , David A. Collier & Darryl D. Wilson (2008) The relationship ofstrategic business alignment and enterprise information management in achieving better businessperformance, Enterprise Information Systems, 2:2, 201-220, DOI: 10.1080/17517570802095226

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  • Enterprise Information SystemsVol. 2, No. 2, May 2008, 219237

    The relationship of strategic business alignment and enterpriseinformation management in achieving better business performance

    Honggeng Zhoua*, David A. Collierb and Darryl D. Wilsonc

    aDepartment of Decision Sciences, Whittemore School of Business and Economics, University ofNew Hampshire, 15 College Road, Durham, New Hampshire, USA; bLutgert College of

    Business, Florida Gulf Coast University, Fort Myers, Florida; cDepartment of ITDS, College ofBusiness and Public Administration, Old Dominion University, Norfolk, Virginia

    (Received 1 October 2007; final version received 10 February 2008)

    Four constructs are developed and five research hypotheses are tested in astructural equation model focused on the role of strategic business alignment andinformation management in achieving business performance. The data to developthe constructs and test the model are based on a survey of 226 manufacturingfirms in the US automobile components industry. The research is interdisciplinaryin nature with a focus on building theory in an under-researched area of study bytesting a causal model. The structural equation model analysis supports thegeneral theory that the degree of business strategy alignment affects enterpriseinformation management and time-related operating performance, and throughthese two intermediate constructs, improves business performance. Enterpriseinformation management is the key mediating variable in the causal model. Otherinsights based on statistical evidence are presented such as strategic businessalignment, which do not directly improve time-related operating performance butmust act indirectly through enterprise information management (the mediationconstruct) to improve performance.

    Keywords: strategic business alignment; strategic enterprise management; strate-gic information management; enterprise information management; time-basedperformance; structural equation models; interdisciplinary

    1. Introduction

    Aligning the organisations business and information strategy is an important determinantfor market and financial success (Woolfe 1993, Chan et al. 1997). A time-based orientationalso plays a major role in determining a firms business performance (Blackburn 1991,Fairhurst 1992, Bruce 1998). In addition, one of the key characteristics of the MalcolmBaldrige National Quality Award (MBNQA) Criteria (NIST 2002, p. 7) is alignment asnoted by the following MBNQA statement: The Criteria support a systems perspectivemaintaining organisation-wide goal alignment. Alignment in the Criteria is built aroundconnecting (cause-effect linkages) and reinforcing measures derived from your organisa-tions processes and strategy. These measures tie directly to stakeholder value and to

    *Corresponding author. Email: Honggeng.zhou@unh.edu

    ISSN 17517575 print/ISSN 17517583 online

    2008 Taylor & FrancisDOI: 10.1080/17517570802095226

    http://www.informaworld.com

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  • overall performance. Such alignment ensures consistency of purpose while also supportingagility, innovation, and decentralised decision-making.

    We investigate the performance relationships between strategic business alignment(SBA) and business performance (BP) through two intermediate endogenous variables,enterprise information management (IM), and time-related operating performance (TOP)as shown in Figure 1. Structural equation modelling techniques are used to examine theseperformance relationships (Hair et al., 1998). SBA is the exogenous latent variable andIM, TOP, and BP are endogenous latent variables. Also note in Figure 1 that enterpriseinformation management (to be defined shortly) has two ways it can influence businessperformance a direct performance relationship (IM!BP) and an indirect relationshipthrough time-related operating performance (IM!TOP!BP).

    The general theory being tested is that the degree of business strategy alignment affectsenterprise information management and time-related operating performance, and throughthese intermediate constructs, improves business performance. The theory is grounded inthe literature to be examined in detail later in this article. The literature review alsohighlights that little statistical evidence exists on the impact of information managementon time-based operation and business performance (Davenport 1992, Brynjolfsson andHitt 1996). Although some case studies such as McAfee (2002) explore the benefits ofinformation management, no empirical research tests the interdisciplinary hypotheses

    IM

    TOP1

    5 6 7

    8 9 10 11 12

    13

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    1 (1)

    2 (2)

    1

    2

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    IM3 IM2 IM4

    TOP2 TOP3 TOP4 TOP5

    BP

    15

    2 (4)

    1 (3)

    3 (5)

    3

    SBA

    TOP

    IM1

    BP1

    BP2

    BP3

    SBA1

    SBA2

    SBA3

    Figure 1. Hypothesised covariance structure model.

    220 H. Zhou et al.

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  • tested here. This study contributes to theory building, theory testing, and tointerdisciplinary research by subjecting the hypothesised causal model shown inFigure 1 to the rigorous statistical methods of structural equation modelling (Bollen1989, Bollen and Long 1993, Hair et al. 1998).

    Also note in Figure 1 that a direct performance linkage between SBA and BP is nothypothesised because SBA alone accomplishes very little. That is, business strategyand alignment do not directly achieve business results but require execution of thestrategy through information management and time-related operating performance.As John Trani, head of General Electrics medical systems business (Schneier et al.1991) states, At best the plan and strategy is 20 percent of the game. Execution is80 percent of it.

    This article is organised in the following way. Section 2 presents a review of theliterature that establishes the content validity for the four constructs and provides supportfor the theory, research hypotheses, and the structural model. Section 3 describes thesample data and research methodology. Section 4 discusses the results of a structuralequation model analysis of the hypothesised model in Figure 1. The paper concludes witha summary and directions for future research.

    2. Literature review and hypotheses

    This research uses structural equation modelling to test the relationships among four keyconstructs (SBA, IM, TOP, and BP). Each construct has three to five indicators as shownin Figure 1 and the Appendix. The five research hypotheses shown in Figure 1 are stated asfollows:

    H1: Strategic business alignment (SBA) has a positive statistically significant relationshipwith enterprise information management (IM). [140]

    H2: Strategic business alignment (SBA) has a positive statistically significant relationshipwith time-related operating performance (TOP). [240]

    H3: Enterprise information management (IM) has a positive statistically significantrelationship with time-related operating performance (TOP). [140]

    H4: Enterprise information management (IM) has a positive statistically significantrelationship with business performance (BP). [240]

    H5: Time-related operating performance (TOP) has a positive statistically significantrelationship with business performance (BP). [340]

    The literature review helps establish the content validity of each of the four constructs andprovides support for the five research hypotheses tested.

    2.1. Strategic business alignment

    The importance of alignment is firmly established in the literature. In addition, there ismuch evidence to support SBA being a key exogenous construct. For example, in an articletitled The Age of Alignment, Berger et al. (1991) conclude that the Chief ExecutiveOfficers (CEOs) job should be restructured and re-labelled as the Chief AlignmentOfficer. Gale and Wood (1994) argue that the primary reason total quality management(TQM) initiatives fail is due to lack of alignment. They cite 14 reasons why TQMinitiatives fail and then ask: What do these reasons have in common? The answer is,

    Enterprise Information Systems 221

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  • People and processes in organisations that fail simply are not aligned to pursue the goal ofserving customers in a unified way.

    Several studies support the hypothesis that only when business strategies are alignedare information capabilities fully utilised (i.e. SBA! IM in Figure 1) (Li et al. 2008). Forexample, strategic information alignment, as described by Woolfe (1993), is the state inwhich the goals and activities of the business are in harmony with the information systemsthat support them. Henderson and Venkatraman (1993) propose a strategic alignmentmodel, which suggests that the choices made in business strategies will impact theinformation systems strategy. Chan et al. (1997) find that alignment between businessstrategic orientation and information system strategic orientation positively affects theinformation system effectiveness. Gupta et al. (1997) identify the need for establishing alink between a firms information management strategy and business strategy. They find adirect linkage between business strategy and information management.

    The literature also documents that strategic business alignment contributes to beingfast and directly affecting time-related operating performance (i.e. SBA!TOP inFigure 1). Bruce (1998), for example, suggests that todays hyper-competitive marketsbring pressure for firms to shorten the product life cycles and quickly increase time-basedoperational efficiencies. Droge and Germain (2000) study the influence of informationtechnology on electronic data interchange and time-based operation performance. Li et al.(2008) suggest that when enterprise information systems are integrated properly withmanufacturing infrastructure, they can support a firms market competitiveness, improveproduction performance, and achieve better financial results. Other studies examine theimpact of time-based practices on firm performance, such as Blackburn (1991), Fairhurst(1992), Bruce (1998), Koufterous et al. (1998), and Rondeau et al. (2000). Hence, there issubstantial support in the literature for H1 and H2.

    The strategic business alignment (SBA) construct has three indicators:

    (1) work unit performance measures linked to strategies,(2) short term strategic plans alignment, and(3) long-term strategic plans alignment (see the Appendix).

    Linking work unit performance measures to the strategies of the organisation is animportant step to link business unit strategy to information management and time-relatedoperating performance results. Cross and Lynch (1992), for example, propose a paradigmcalled the performance pyramid, which contains the objectives and measures that link afirms daily operations to its strategic vision. The top level of the pyramid is theorganisations strategic vision. The second level is the business unit and includes keybusiness unit level performance measures such as market and financial performance(Li et al. 2008). The bottom level is the day-to-day operational measures, many of whichfocus on time-related performance. The study finds that the decision to emphasisea particular aspect of performance (time, cost, quality, etc.) depends on business unitstrategies and their alignment.

    In the SBA construct, the second and third questions are about the alignment ofbusiness unit strategies over the short- and long-term, respectively. Among the alignmentof all business unit strategies, the alignment between manufacturing strategy andmarketing strategy is critical. Crittenden (1992) provides insights into the short-termalignment issue when he cites what managers say about the value of integrating theproduction and marketing information systems. For example, one marketing managersaid, When both sides, marketing and production, see the profit implications of our

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  • varying decisions, we begin to work together on a daily basis in a much better fashion.

    Hill (1994) suggests the use of product profiling to measure strategic long-term

    misalignment. Bozarth and Edwards (1997) report the impact of market requirements

    and manufacturing characteristics on plant performance. They find that a lack of focus in

    either market requirements or manufacturing characteristics at the plant level leads to

    poorer plant performance, and the impact of the market focus requirement on

    manufacturing performance is mediated by some of the manufacturing characteristics

    such as process choice and work-force skills.

    2.2. Information management

    The information management (IM) construct has four measures:

    (1) aligning performance measure information for products and services with strategic

    objectives,(2) training employees on data analysis techniques,(3) focusing information on key business drivers, and(4) integrating data from across the business unit to support business planning (see the

    Appendix).

    Here, the concept of information management includes both strategic and tactical criteria.The four IM construct measures highlight the role of alignment, employees, identifying

    key business drivers, and integration across business units in defining information

    management. Chan and Huff (1993) survey senior managers and find that the issue of

    alignment is most important in how to align the companys information systems strategy

    with its business strategy. In addition, Chan et al. (1997) develop a conceptual model,

    examining the relationships between the following five constructs: business strategic

    orientation, information system strategic alignment, information system strategic

    orientation, information system effectiveness, and business performance. The centrepiece

    of their research is the hypothesis that information system strategic alignment leads to

    improved information system effectiveness and business performance. Partial least squares

    regressions and factor analyses are used to analyse the data. Path coefficients and bivariate

    regression model R-squares are reported. Four of the path coefficients are positive and

    statistically significant as hypothesised but the path coefficient from information system

    strategic orientation to information system effectiveness is negative. The results find

    a direct performance relationship from information system strategic alignment

    and information system effectiveness to business performance, and therefore, support

    H3 and H4.The employees ability to use the information systems is another determinant of

    information management capability. No matter how good the strategy or information is, if

    employees cannot use it, the strategy and information have little value. Li and Benton

    (2006) indicate that the role of staff members in implementing technology is an integral

    part of business performance. Crank (1997) finds that in the credit union industry, it is the

    marriage of information technology and training employees that empowers people to

    make the most of good information. Khalil and Elkordy (1999) find empirical evidence

    that training contributes to the information system effectiveness. Training (1996) reveals

    that the average organisation now devotes 25% of its total employee training budget to

    teaching the use of computers and computer systems. Half of all information

    Enterprise Information Systems 223

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  • technology training dollars goes to outside suppliers who design and/or deliver employee

    training.Enterprise information systems that focus on the key business drivers, one measure of

    the IM construct, have been found to be important for information management success.

    The third measure for the IM construct highlights key business drivers. For example,

    Rockart (1979) proposes the critical success method for building information systems. The

    critical success method focuses only on the key business drivers (and performance metrics)

    that are critical to the success of the business.Information integration can enhance information capabilities, as captured by the

    fourth survey question on IM. Under traditional information systems, different

    functions in an organisation usually have their own information systems.

    The information in different functional systems is frequently not consistent. The

    inconsistency causes major problems when collaboration and speed across functions are

    needed. Recognising that existing information systems in organisations are usually not

    integrated, theoretical approaches for integrating functional databases have been

    developed (Batini et al. 1986). Enterprise information integration results in improved

    communication across functions (Galbraith 1973, Li et al. 2008). Lacking sufficient

    data integration could cause delays, slow communications and process performance,

    and could distort management decision-making. Linking separate information systems

    into a networked information system can help to speed up product delivery and

    improve customer satisfaction (Sweat 1999, Chopra and Meindl 2001, Xu et al. 2005,

    Li and Zhao 2006, Xu 2006, Xu et al. 2006, Hou et al. 2007, Hsu and Wallace 2007,Staley and Warfield 2007, Warfield 2007, Xu 2007, Xu et al. 2007) which support H3.

    In summary, the literature supports the content of the IM construct and the

    formulation of H3 and H4.

    2.3. Time-related operating performance

    In the 1990s, time became more and more important in business competition. Theeffect of time-related operating performance on business performance is well

    established in the literature (i.e. TOP!BP in Figure 1). Time-based competition isfocused on reducing time in every facet of the value chain from customer needs

    analysis and final product development to post-sale maintenance and repair

    (Abegglen and Stalk 1985). Blackburn (1991) describes many situations in

    manufacturing firms to reduce time-related operating performance and thereby

    improve overall business performance. The literature strongly supports H5 or

    TOP!BP in Figure 1.Enterprise information management (IM) is the enabler that makes the value chain

    and its processes able to achieve dramatic improvements in time-related operating

    performance, and ultimately, in business performance (i.e. H3 or IM!TOP in Figure 1).Quick response capability is based on reducing development, production, order process,

    and vendor delivery cycle time, and requires the synchronisation of information flow

    (Forrest 1994). Schmitz (1999) finds that the use of product information managementtechnology cuts product development time. Co et al. (1998) provide a list of benefits from

    implementing information system, including reduced manufacturing lead-time and time-

    to-market. Burhanuddin and Randhawa (1992) reveal that a varied set of information and

    analysis tools could shorten product development time and time-to-market, and reduce

    224 H. Zhou et al.

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  • the costs. Sussams (1992) finds that improvement in information management means

    improvement in performance in terms of both speed and accuracy.In this study, the time-related operating performance (TOP) construct measures the

    time-related aspects of the operating system. The TOP construct includes five

    measurements as shown in Figure 1 and the Appendix as follows:

    (1) delivery speed,(2) production lead-time,(3) new product introduction frequency,(4) product development cycle time, and(5) vendor on time delivery.

    These measurements are important aspects of time-related operating performance. They

    highlight the importance of developing products faster and more frequently than

    competitors, speeding products through the production process and delivering them to

    customers, and the reliability of supplier deliveries. Speed and reliability not only increase

    sales, but can also increase profits through lower costs. All of the above strongly supports

    H5 or TOP!BP in Figure 1.

    2.4. Business performance

    One study by Powell (1992) examines two US industries; the wood upholstered furniture

    industry and the womens dress industry. Using hierarchical regression analysis on a total

    of 113 usable surveys, he concludes that: The results support the notion that some

    organisation alignments generate supernormal profits to the firm, and constitute an

    important source of competitive advantage. To the extent that the alignments resulted

    not from luck, but from administrative skill, alignment skills stand alongside industry

    and strategic positioning as a key source of competitive advantage (p. 128). Li et al.

    (2008) suggest that enterprise information systems have significant positive effects on

    customer-focused performance, production/operations performance, and financial

    performance.The business performance (BP) construct includes three indicators:

    (1) market share,(2) return on investment, and(3) return on sales (see the Appendix).

    These measures have been widely used as the indicators of business performance. Droge

    and Germain (2000), for example, test the impact of electronic data interchange (EDI) on

    financial performance, which include profits, return on sales, and return on investment.

    Barua et al. (1995) test the impact of information management on business performance,

    which include market share and returns on assets. Therefore, this study uses the three

    indicators cited as the measures for the business performance construct (BP). Moreover,

    this literature supports H4.In general, the literature supports the content validity of the four constructs used

    here and the hypothesised model shown in Figure 1. The theory of this causal

    model hypothesises direct and indirect effects on business performance with

    information management and time-related operating performance being key intermediate

    constructs.

    Enterprise Information Systems 225

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  • 3. Research methodology

    We begin this section by describing the sample population and final sample characteristics.Then we discuss the development of the four constructs SBA, IM, TOP, and BP including factor analysis results.

    3.1. Data characteristics

    The population for this study is manufacturing companies in the US automotivecomponents industry that were surveyed by mail. The target respondents for the surveywere plant managers. A list of 800 respondents was developed using the American Societyfor Quality (ASQ) Automotive Division membership directory. A total of 226 usablesurveys were received out of the 800 respondents. Of the remaining 574 potentialrespondents, 71 were no longer at the address indicated and nine were with companies thatprovided a service and had no manufacturing functions. The response rate for themain survey was 31.4% (226/(800-71-9)), which met the required response rate of 20%. Apilot survey was also mailed out to 128 Ohio manufacturing companies, 38 of which werecompleted and returned at a response rate of 29.7%. Improvements were made in the mainsurvey based on the results of the pilot study.

    The average company size was 398 employees. The sample includes many industriessuch as non-metallic mineral, textile mill production, furniture and fixture, chemicals andallied products, rubber and plastic products, glass products, primary metal industries,fabricated metal products, electronic equipment, and instruments and related products.Eighteen percent of the respondents classified their manufacturing function as job shop,37% classified their processes as batch, 39% classified their processes as mass production,and 6% as continuous flow process. The automobile components industry is a diverseindustry as the previous characteristics document and their aggressive implement ofinformation management systems all along the value chain makes them a good samplingframe to test the causal model in Figure 1.

    To check for inter-rater reliability, 51-second rater responses were obtained. Inter-raterreliability deals with the degree to which different respondents from the same companyagree on scale scores. Using scores from two raters, we tested the hypothesis that there wasno difference between the responses of the paired raters from each company. Conducting at test for the null hypothesis: diff 0 for each of the items, we found no significantdifference between any of the items used in this research. Based on the power table inCohen (1988), the minimum power of the t tests is larger than 0.90.

    3.2. Construct development

    Four constructs are developed from the 15 survey questions in order to examine theresearch hypotheses. Each construct is evaluated for content validity to ensure that theconstruct measures what it is intended to measure. In this study, the survey questions andconstructs are grounded in the literature. The content validity of the constructs used in thisstudy is persuasive. Three to five indicators as shown in Figure 1 and in the Appendixdefine each latent construct. Three to five indicators per construct are desirable forstructural equation modelling (Hair et al. 1998). Too few indicators may not represent theconstruct adequately, while too many indicators may result in difficulty in achievingadequate model fit (MacCallum et al. 1996). All survey questions use a 17 Likert scale,

    226 H. Zhou et al.

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  • all the scales are standardised, and the directions of the scales if reverse coded are adjusted

    if necessary. In addition, the survey questions are randomised in order to reduce the

    danger that the respondents are framed by previous questions. The scales were checked for

    departures from normality, kurtosis, and skewness. All scales were found within

    acceptable norms.Reliability measures the consistency among the scales used in the measurement of a

    latent variable. Flynn et al. (1990) recommend using internal consistency as a measure of

    reliability. A high degree of internal consistency implies a high degree of correlation

    among the items in a construct. Cronbachs alpha is a widely accepted measure for internal

    consistency. A Cronbachs alpha of 0.70 is suggested to be an acceptable minimum value

    for an existing construct. A Cronbachs alpha of 0.60 is suggested to be an acceptable

    minimum value for a new construct that has not been empirically tested (Flynn et al. 1990).

    In this study, SBA, IM, TOP, and BP are new constructs. The factor loadings and

    Cronbachs alpha values for the four constructs used in this paper are provided in Table 1

    and the eigenvalues per construct are reported in Table 2.The reason for using factor analysis is twofold. One reason is to check whether the

    scale items are sufficiently loaded on the corresponding constructs. The results of the

    factor loadings are shown in Table 1. According to Carmines and Zeller (1979), one

    guideline is that factor loadings need to be at least 0.3, while the factor loadings in Table 1

    range from 0.515 to 0.926. The factor analysis is also used to check the unidimensionality

    of each construct. Carmines and Zeller (1979) provide several guidelines for testing the

    scales unidimensionality. One criterion is that only one item in each scale can have an

    eigenvalue that is larger than 1. The factor analysis results in Tables 1 and 2 show that

    Table 1. Latent variables: factor loadings and alpha coefficients.

    Strategic business alignment (SBA)* (Alpha 0.8623)1. Work unit performance measures are linked to strategies 0.9082. Short term strategic plans are well aligned 0.9263. Long term strategic plans are well aligned 0.915

    Information management (IM)* (Alpha 0.8046)1. Aligning performance measures for products andservices with strategic objectives

    0.846

    2. Training employees on data analysis techniques 0.7013. Information systems focus on key business drivers 0.8244. Integrating data from across the business unit tosupport business planning

    0.846

    Time-related operating performance (TOP)* (Alpha 0.6003)1. Delivery speed 0.6542. Production lead time 0.5003. New product introduction frequency 0.6624. Product development cycle time 0.5915. Vendor on time delivery 0.515

    Business performance (BP)* (Alpha 0.8060)1. Market share 0.7272. Return on investment 0.9273. Return on sales. 0.922

    Note: *Text of questions appears in Appendix A.

    Enterprise Information Systems 227

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  • all constructs satisfy the unidimensionality requirement. Only for the TOP construct is the

    second largest eigenvalue equal to 1.052; which is slightly larger than 1. However, after

    checking the scree plot for the TOP construct, it is decided that one factor is best for the

    five indicators in the TOP construct. In general, the four constructs defined in this research

    are grounded in the literature, statistically significant, of practical significance, and

    represent a sound measurement model.

    4. Results and discussion for the hypothesised model

    Structural equation modelling is used to model the performance relationships hypothesised

    in Figure 1. All indicators of SBA, IM, TOP, and BP are statistically significant at least at

    the 5% level (p50.05) as reported in Table 3. That is, all t-values for the path coefficients

    Table 3. Measurement model parameters.

    Effect indicatorParameter

    estimate (t-value)

    Strategic businessalignment (SBA)

    Work unit performance measure arelinked to strategies

    0.85 (36.91)

    Short term strategic plans are well aligned 0.86 (38.80)Long term strategic plans are well aligned 0.87 (41.24)

    Informationmanagement (IM)

    Aligning performance measure informationwith strategic objectives

    0.79 (25.25)

    Training employees on data analysistechniques

    0.57 (11.43)

    Information systems focus on keybusiness drivers

    0.73 (20.02)

    Integrating data from across the business unit 0.77 (23.37)

    Time-related operatingperformance (TOP)

    Delivery speed 0.59 (8.76)Production lead time 0.32 (4.15)New product introduction frequency 0.47 (6.61)Product development cycle time 0.19 (2.44)Vendor on time delivery 0.43 (6.05)

    Business performance (BP) Market share 0.50 (9.48)Return on investment 0.92 (34.66)Return on sales 0.89 (32.73)

    Note: All path coefficients estimates are statistically significant at least at p 0.05 level.

    Table 2. Scale unidimensionality.

    Construct(latent variable)

    Largesteigenvalue

    Second largesteigenvalue

    SBA 2.518 0.268IM 2.601 0.647TOP 1.731 1.052BP 2.237 0.617

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  • from the indicators to their corresponding latent constructs are statistically significant at

    0.05. Other evidence of a statistically significant measurement model is that the lowerbounds of the 90% confidence intervals for all the path coefficients from indicators to their

    corresponding latent variables are larger than 0 (the 90% confidence intervals are not

    reported).A variety of overall model fit statistics are reported in Table 4 such as the sample

    discrepancy function value, root mean square error of approximation (RMSEA), p-value

    of the close fit test (H0: RMSEA 0.05), the Chi-square test statistic, non-normed fit index(NNFI), normed fit index (NFI), and comparative fit index (CFI). These fit statistics are

    defined and recommended in several leading references (Bollen 1989, Bollen and Long

    1993, MacCallum et al. 1996, Hair et al. 1998, Hu and Bentler 1998) and will not be

    individually defined here but some will be discussed as we review the statistical results.For the structural model in Figure 1 and Table 4, the RMSEA is 0.042. The 90%

    confidence interval for RMSEA is (0.022, 0.059). Browne and Cudeck (1993) recommend

    that an absolute Root Mean Square Error of Approximation (RMSEA) value of less than

    0.05 suggests a good fit, and a RMSEA value between 0.05 and 0.08 indicates a reasonable

    fit. The RMSEA measures the sample discrepancy function value per degree of freedom.

    The p-value of the close fit test (H0: RMSEA 0.05) is 0.752. NNFI, NFI, and CFI are0.90. All fit statistics in Table 4 are statistically significant and support the causal modelin Figure 1.

    Four of the five hypotheses are statistically significant at least at p50.05, asdocumented in Table 5. Hypotheses H1 (SBA! IM), H3 (IM!TOP), H4 (IM!BP) andH5 (TOP!BP) are supported. The path coefficients are positive and t-values arestatistically significant at least at the 5% level (p 0.05). Hypothesis H2 (SBA!TOP) isnot supported. This result shows that better business strategy alignment has a slightly

    negative effect on the time-related operating performance. The path coefficient is negative

    (0.13) and the t-value (0.54) of the path coefficient is not significant at the 10% level(p40.10). Based on the literature review and our hypothesis, better business strategyalignment should lead to better time-related operating performance. Further analysis

    reveals that SBA has an indirect effect on TOP through IM (SBA! IM!TOP). Thetotal effect from SBA to TOP is calculated by summing the indirect and direct effects.

    The total effect of SBA on TOP is 0.566 (0.87 * 0.80 0.13 0.566), which is positive andstatistically significant according to the approximate t-value of 3.56 in the footnote of

    Table 5 (Venkatraman 1989, p. 430).

    Table 4. Overall model fit statistics.

    Sample discrepancy function value 0.531Root mean square error of approximation(RMSEA) point estimate

    0.042

    RMSEA 90% confidence interval (0.022, 0.059)p-value of close fit test (H0: RMSEA 0.05) 0.752Chi-square test statistic 119.5Degrees of freedom (df) 85Non-normed fit index (NNFI) 0.90Normed fit index (NFI) 0.92Comparative fit index (CFI) 0.98

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  • The results from the statistical analysis are summarised and discussed as follows:

    (1) This research develops two useful new constructs. The enterprise information

    management (IM) construct has four measurements: (i) aligning performance

    measure information for products and services with strategic objectives,

    (ii) focusing information on key business drivers, (iii) training employees on data

    analysis techniques, and (iv) integrating data from across the business unit to

    support business planning. The time-related operating performance (TOP)

    construct has five measurements: (i) delivery speed, (ii) production lead-time,

    (iii) new product introduction frequency, (iv) product development cycle time, and

    (v) vendor on time delivery. The two constructs prove to be valid and reliable

    through this study. Future research in time-based competition and in information

    management can use these two constructs and further validate the two constructs

    in other industries or applications.(2) Improved strategic business alignment results in improved information manage-

    ment. Authors such as Zemke and Schaaf (1989) based upon field research and

    consulting experience find well-aligned strategies and performance measures the

    key for business success. Their discussion and insights provide anecdotal evidence

    and company case studies of the importance of strategic business alignment and

    the role of communication and information systems to achieve superior market

    and financial results. Our study contributes to theory development in this area by

    providing statistical evidence (based on a large scale survey) to support their

    anecdotal claims and H1 (SBA! IM, 140).(3) Improved information management fosters time-related operating performance

    (H3: IM!TOP). To improve time-related operating performance (TOP) requiresthat firms excel at information management which supports physical flows andother information dependent areas of the firm. For example, with timely

    information, materials and parts can be delivered to the manufacturing area

    directly rather than from supplier to warehouse to manufacturer. Many

    practitioner articles and some academic studies have emphasised the importance

    of information management in achieving time-based operational capability

    Table 5. Estimated path coefficient statistics (see Figure 1).

    Causal path Hypothesis

    Path coefficientpoint

    estimateStandarderror t-value

    Statistically significant,valid and hypothesis

    supported

    SBA! IM H1 0.87 0.029 29.69* YesSBA!TOP H2 0.13 0.243 0.54 NoIM!TOP H3 0.80 0.242 3.31* YesIM!BP H4 0.31 0.122 2.49* YesTOP!BP H5 0.36 0.138 2.56* Yes

    Notes: *Path coefficient estimates are statistically significant at least at p 0.05 level. The total effectof SBA on TOP is 0.566 (0.87 * 0.80 0.13 0.566). The approximate t-value is

    0:87 0:80ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

    0:872 0:0292 0:802 0:2422p 3:56

    (Venkatraman 1989, p. 430).

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  • (Stalk and Hout 1990, Mason-Jones and Towill 1999). However, we are not awareof any study that shows a statistically significant and positive direct effect ofenterprise information management on time-related operating performance(IM!TOP, 140). These statistical results contribute to theory developmentby complementing past anecdotal evidence.

    (4) Better enterprise information management improves business performance. Thisstudy shows that IM does have a positive and statistically significant direct effecton business performance and supports H4 (IM!BP, 240). Practitioners have tojustify their investment in information technology and know how to leverage it toget market and financial results. Estimating the cost of adopting and usinginformation technology is relatively easier and includes costs like labour, hardware,software and training. However, it is difficult to estimate the economic benefits ofusing information technologies, because many benefits are strategic such asimproved relationship with partners (Iacovou et al. 1994) and gaining competitiveadvantage (Bergeron 1994). Other benefits are intangible such as using informationsystem capabilities to speed up the firms decision-making process and facilitatecommunications within the firm, and across the firms value chain (Huber 1982).In addition, it is difficult to trace the cause and effect performance relationships.

    (5) Improved time-related operating performance leads to better business perfor-mance. The anecdotal evidence has shown that improved time-related operatingperformance is usually associated with better business performance (Stalk andHout 1990, Blackburn 1992, Mason-Jones and Towill 1999). This studycomplements previous research by showing that TOP has a positive andstatistically significant direct effect on BP and the results support H5(TOP!BP, 340). Time-related operating performance affects business perfor-mance in several ways. First, reduced cycle time can increase the asset turnoverrate, which directly increases the return on investment and the profit margin.Second, customers are demanding and getting shorter delivery time. When cycletimes throughout the firm are reduced, both profit margin and asset turnover ratesare likely to increase, which creates increased investment returns.

    (6) Aligning business strategies does not directly improve time-related operatingperformance since H2 (SBA!TOP, 240) was not statistically significant.However, enterprise information management (IM) mediates the impact ofstrategic business alignment (SBA) on time-related operating performance(TOP). The total effect, SBA! IM!TOP, is statistically significant with acoefficient of 0.566 and a t-value of 3.56. The result also supports the conclusionthat the relative importance (i.e. path coefficients i) of path IM!TOP (1 0.80)is greater than path IM!BP (2 0.31). The indirect effect of informationmanagement on business performance (IM!TOP!BP) is 0.288(0.80 * 0.36 0.288). These statistical results confirm John Tranis idea that theexecution of the strategy is much more important than the strategy itself in terms ofthe impact on performance (Schneier et al. 1991). Our results show that properinformation management is an important mediator (enabler) between strategyalignment and performance.

    (7) In general, this study clearly identifies information management as one of theimportant factors needed to gain the economic benefits of business strategyalignment. The statistical results of this research confirm that improvedinformation management enables better time-based operating performance and

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  • business performance (see Figure 1). Previous studies such as Mason-Jones and

    Towill (1999) propose an information enriched supply (value) chain concept andmake a case for why information management is an enabler. Other studies such as

    Srinivasan et al. (1994) have shown the enabling effect of information technologyon business performance. However, we are not aware of any large-scale survey

    research that shows enterprise information managements enabling effect on time-

    based competition. To create superior business results, information managementmakes it happen.

    5. Conclusions

    As information management evolves, practitioners and researchers have increased needs to

    evaluate the value of information in todays competitive environment, and to learn how to

    leverage enterprise information more effectively to improve market and financialperformance. The statistical results reported here provide insight into the direct and

    indirect ways information management helps improve business performance and the roleof strategic alignment and time-based performance. The results of this research also

    support the theory that a cross-functional approach to improvement, driven by strategic

    business alignment, is required to achieve business success.This research uses structural equation modelling to study the complex performance

    relationships between strategic business alignment (SBA), enterprise information manage-

    ment (IM), time-related operating performance (TOP), and business performance (BP).The statistically significant causal model in Figure 1 supports the general theory that,

    the degree of business strategy alignment affects enterprise information management andtime-related operating performance, and through these two intermediate constructs,

    improves business performance. More detailed conclusions have been previouslydiscussed.

    Since the sample size of our study is 226 and the degree of freedom is 85, the power

    of the model fit test is 0.956, which exceeds the suggested cut-off level of 0.80

    (MacCallum et al. 1996). The large sample size and large power of the model fit testsupport the overall findings in this research. One future research opportunity is to test

    whether the causal structure in Figure 1 is supported in other industries and othercountries.

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  • Appendix: Abbreviated survey questionnaire

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