Firm characteristics andbalanced scorecard usage inSingaporean manufacturing

firmsLing Liu

Department of Accounting and Finance, University of Wisconsin-EauClaire, Eau Claire, Wisconsin, USA

Janek RatnatungaSchool of Commerce, University of South Australia, Adelaide, Australia, and

Lee J. YaoDepartment of Accounting, Loyola University New Orleans, New Orleans,

Louisiana, USA

AbstractPurpose – This study aims to examine the effects of balanced scorecard (BSC) usage on performancesin the context of four contingent variables in Singaporean manufacturing firms. The results show thatfirms are more likely to adopt BSC if they are large in size, have products at an early product lifecycle(PLC) stage, operate in highly uncertain environments or adopt differentiation strategies. However, theadoption of BSC improves performance only in firms that are large or have products at an early PLCstage. The results suggest that contingent factors in which a firm operates can exert significant effectson the results of adopting BSC.Design/methodology/approach – Survey data are collected, then the authors check datacorrelations, principle components analysis, run regression analysis and ANOVA.Findings – BSC use is positively and significantly correlated with PLC stage, external environmentand performance. Size and strategy are positively correlated with BSC usage, but are not statisticallysignificant. Higher BSC use is found in large firms with products at an early PLC stage or operating ina highly uncertain environment. Companies with a cost leadership strategy are significantly associatedwith BSC use.Research limitations/implications – This study has small sample size and uses survey researchmethod. The measurements to capture all aspects of BSC usage are non-exhausting. Future research canuse different methodologies, such as field studies, case studies and lab experiments, to examine otherindustries than manufacturing.Practical implications – The authors results show the positive association between BSC use andcertain firm characteristics. Firms with those characteristics should get insights about the benefit ofusing BSC and extract the maximum benefit from their investment on the BSC use and those firmswhich don’t have BSC in implication may think of implementing the BSC use.Social implications – Firms’ contingent factors affect the value of adopting BSC. With the authorsresearch result, firms will be aware of how to extract the most value out of BSC and improve the socialwealth of the manufacturing industries.Originality/value – The authors paper is the first paper to use survey method to examine theassociation between BSC and firms’ contingent factors in the Singaporean manufacturing firms setting.

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1834-7649.htm

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Received 21 May 2013Revised 26 July 2013

Accepted 20 August 2013

International Journal of Accounting& Information Management

Vol. 22 No. 3, 2014pp. 209-222

© Emerald Group Publishing Limited1834-7649

DOI 10.1108/IJAIM-05-2013-0038

Keywords Balanced scorecard, Organizational performance, Environmental uncertainty, Productlifecycle, Singaporean manufacturing firms

Paper type Research paper

IntroductionBalanced scorecard (BSC) has been widely adopted by businesses since the introductionof the concept by Kaplan and Norton (1992). A worldwide survey conducted by Bain(2006) shows approximately 70 per cent of the firms responding to the survey adoptedBSC[1]. These firms differ in size, organizational structure, strategy, operatingenvironments, country, tasks and operating results from adopting BSC. Contingencytheory suggests an organization needs to match the BSC with its contingency factors tobe successful (Donaldson, 1995). Sisaye and Birnberg (2010) suggest that managementaccounting researchers pay particular attention to an organization’s approach todiffusion and adoption strategies of innovation, particularly, the extent and scopedimensions when designing and implementing process innovation programs.Fleischman et al. (2010) provided evidence to support the contention that there aresignificant perceptual differences about management accounting system servicesbetween providers and users. Hoque and James (2000) examined the impact of anorganization’s size, product lifecycle (PLC) and market position on BSC usage. Theyfound that an organization’s operating environment plays an important role in BSCadoption. Firms operating in different countries are subject to vastly different cultures.The literature has so far focused on the BSC adoption experience of US companies. Onlya few studies have investigated BSC in non-US firms (Hoque and James, 2000;Speckbacher et al., 2003; Sandhu et al., 2008).

This paper examines the impact of culture and other organizational contingentfactors on the effectiveness of BSC adopted by Singaporean manufacturing firms.Singapore has limited natural resources, a small labor force and a small domestic marketwith manufacturing and service as two major economic sectors (Quazi et al., 1998).Singaporean companies depend heavily on the export market to compete globally.Rapid economic developments in other countries have greatly intensified competitionfor Singapore’s manufacturing sector (Ghosh et al., 2001). Many companies in Singaporehave adopted innovative management accounting practices such as total qualitymanagement (TQM) (Quazi et al., 1998), business process reengineering (BPR)(Ranganathan and Dhaliwal, 2001) and BSC to improve their competitive effectiveness.Mandatory BSC usage by all government agencies further increases the awareness andpopularity of BSC among Singaporean businesses (Chia and Hum, 2000).

This study examines how contingency factors lead to differences in BSC usage andoperating results among Singaporean manufacturing firms. Contingency theory’sgeneral presumption is that there is no one best organizational structure and the mostappropriate organizational structure should match the various contingency factors suchas size, strategy and firm environment (Donaldson, 1995). Hoque and James (2000)examined Australian manufacturing companies regarding the relationships betweenBSC and companies’ size, PLC and market position. Our study extends theirs byincorporating four contingent variables of Singaporean manufacturing firms: size; PLCstage; environmental uncertainty; and strategy.

Although Kaplan and Norton (1992) view BSC as a generic system applicable tovarious settings, Sandhu et al. (2008) conclude from their case study that the BSC is a

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“messy object” with different connotations depending on how the BSC is translated intolocal settings. The inclusion of environmental uncertainty in our study provides moreinsights about its effect on BSC usage and results[2].

The next section discusses the literature and hypotheses development, followed bythe discussion of findings, the limitations of this study and future research suggestions.

Literature review and hypotheses developmentContingency theory has been a major research paradigm in management accounting(Donaldson, 1995) since the 1960s. The theory posits that there is no one bestorganizational structure for all firms; the best organizational structure adapts to itscontingent factors such as size, local environment, business strategies and technologies(Donaldson, 2001). Hoque and James (2000) surveyed Australian manufacturingcompanies to find that large firms with new products are more likely to use BSC, marketposition is not related to BSC usage and adoption of BSC improved firm performancewhich did not depend on organization size, lifecycle stage of the firm’s products andmarket position. Chang et al. (2003) tested relationships between contingency factorssuch as task uncertainty and decentralization in the context of accounting informationsystems (AIS) and found that user’s satisfaction improved when AIS provided a broadscope of timely and aggregated information in situations with high task variabilityand/or in a highly decentralized organization. These studies suggest that contingencyfactors influence usage of management controls and its success in an organization.

Management accounting research on firms in SingaporeSingaporean manufacturing firms are deemed to be among the most cost effective andglobally competitive manufacturers[3]. Harrison et al. (1994) compared organizationaldesigns and management planning and control systems between Anglo–Americancompanies (Australia and the USA) and East Asian companies (Singapore and HongKong). They found significant association between national cultural differences and thedifferences in organizational design and management planning and control. Chiaand Hum (2000) reported significant associations between the characteristics ofmanagement accounting system information and decentralization and managerialperformance of Singaporean firms. Ghosh et al. (2001) identified key success factors andstrategic choices of top small manufacturing entities in Singapore. Quazi et al. (1998)examined the critical factors for success in TQM practices of Singaporean firms andfound these factors to be consistent with those of firms in the USA, United ArabEmirates and India. Ranganathan and Dhaliwal (2001) found a high percentage andincreasing number of Singaporean firms engaged in BPR practices. The lack of humanand financial resources and information technology expertise are major factors thathindered the adoption of BPR by Singaporean firms. Williams and Seaman (2001) foundthat national culture-based power distance, organization capacity and industrycategory affect changes in the management accounting systems of Singaporean firms.These prior studies suggest that contingent factors and the local setting of a firm drivethe success of BSC implementation (Sandhu et al., 2008) and emphasize the importanceof understanding the drivers.

Balanced scorecardShortcomings of the traditional finance-based performance measurement systems ledresearchers to recommend inclusion of both financial and non-financial measurements

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in performance evaluations to provide a balanced and complete picture (Kaplan andNorton, 2000, 2004, 2006). Designed initially as a multi-dimensional measurementsystem for management control and evaluation purposes, the BSC has evolved intoa strategic management system (Malina and Selto, 2001; Banker et al., 2004).Schneiderman (2001) documented that BSC adoptions have led to dramaticimprovements in customer satisfaction and operating efficiency and contributed in partto a hundred-fold increase in stockholder value. A 2007 survey conducted by themanagement consulting firm Bain[4] shows that in 2006, 66 per cent of firms worldwideand 71 per cent of companies in Asia used the BSC. It is evident that BSC has gainedprominence for integrating financial and non-financial performance measures (Ruhl,1997; Simons, 2000).

Contingent factorsThe literature above has reported size, PLC stage, environmental uncertainty andstrategy as significant contingent factors affecting BSC adoption and its success (Chowet al., 1997; Hayes, 1977; Hoque and James, 2000.) This study addresses whether the fourabovementioned contextual variables are systematically related to BSC usage and itattempts to postulate a fit between the four contextual variables and BSC usage. Theframework for the research is illustrated in Figure 1. Let’s now discuss these fourcontextual variables.

SizeAn organization’s increase in size is likely to lead to more dispersed and complicatedmanagement processes. Large firms are likely to be more highly decentralized, thus thetop management often faces information overload, which demands a more formal,specialized and sophisticated management control system (Speckbacher et al., 2003).Chenhall and Langfield-Smith (1998) found a majority of large Australian firms haveadopted a range of management accounting tools that emphasize non-financialinformation with a strategic focus. Speckbacher et al. (2003) found a strong positiveassociation between organizational size and BSC usage among German companies. Incontrast, small or local companies often find the use of elaborate management toolseither unnecessary or ineffective (Chow et al., 1997). A small business owner, who is alsolikely both the strategy setter and strategy implementer, does not need to rely on

Organizational Size

External Environment

Organizational Strategy

Product Life Cycle Stage Balanced

ScorecardUse

OrganizationalPerformance

Figure 1.Research framework

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sophisticated management tools for effective communication and decision making(Brady, 1995). More often than not, the management at all levels of small firms is close tothe market situation and is highly responsive to the needs of the customers without anyformal monitoring devices. This leads to the first hypothesis, stated in alternative form:

H1. Large firms are more likely than small firms to use the BSC performancemeasurement system.

PLC stageProducts pass through sales lifecycle stages and a successful firm often sets strategiesand goals tailored to a product’s lifecycle stages. Identifying a product’s lifecycle stageallows firms to gain insights into a product’s competitive dynamics (Kotler, 2001).

The five common PLC stages are development, introduction, growth, maturity anddecline. Hoque and James (2000) found a positive association between BSC and the earlyPLC stages, but their analyses of four BSC perspectives indicate that firms are morelikely to use non-financial measures for new products and an organization with productsat an early PLC stage may find the BSC useful only because of its inclusion of bothfinancial and non-financial measures. One reason is that financial outcomes are lesscertain for products at early stages and financial successes often are not confirmed untillater stages. This leads to the second hypothesis:

H2. Firms in the early stages of PLC are more likely to use a BSC performancemeasurement system than firms at later stages of the cycle.

Environmental uncertaintyFacing an uncertain environment, firms are likely to seek information to reduce theperceived uncertainty and increase comfort levels in decision-making. By linkinglagging and leading measures of financial and non-financial performance, the BSC canhelp with decision-making towards achieving better operating results. Thus, facingheightened environmental uncertainty, firms are likely to increase the use of the BSC,which leads to the third hypothesis:

H3. Firms that operate in a highly uncertain environment are more likely to use aBSC performance measurement system than firms that operate in a lessuncertain environment.

StrategyAn organization with “defender” strategy competing on low product prices adopts a“cost leadership” strategy. An organization with a “prospector” strategy competing onunique product features adopts a “differentiation” strategy (Porter, 1985). Regardless ofthe strategy a firm chooses to compete with, the BSC can align the goals of a departmentor an individual with the overall organizational strategy (Kaplan and Norton, 1992).

The relevance of short-term financial measures for manufacturing firms pursuingdifferentiation strategies, however, is controversial. Design and implementation of asystem to accommodate product variations often creates complex and multifunctionalsituations (Abernethy and Lillis, 1995). The inability to define optimal input/outputrelationships renders financial metrics alone inadequate as the sole measure ofmanufacturing performance (Hayes, 1977) and necessitates that firms undergo afundamental shift from financial metrics to a broader set of measures. Therefore:

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H4. Firms with a differentiation strategy are more likely to use a BSC performancemeasurement system than firms with a low cost strategy.

Interaction effectsPrior research shows that performance improved when the BSC fits into the contextualvariables (Hoque and James, 2000). The complexity of large firms makes it necessary forthem to rely on structured communication and coordination systems such as the BSC.With the full potential of its product not yet realized and reflected in financial measures,a firm with one or more important products at an early PLC stage is likely to depend onnon-financial control measures in monitoring operations and gauging the progress ofthe product. In contrast, financial measures can usually capture most, if not all, importantinformation on operating results for firms with mature products. Environmental uncertaintyclouds financial measures and firms facing high environmental uncertainty requirenon-financial information and control systems to gain a clear picture of its operations.Firms adopting a differentiation strategy rely more on sophisticated information andcontrol systems to coordinate the diversified tasks than do firms adopting a low-coststrategy.

Research methodThe manufacturing industry varies in scales of operation, product market, the stages ofthe PLC and the dynamic operating environment, which provides a rich setting fortesting the effect of contingency factors on the BSC usage and the resultant firmperformance.

We identified firms from the member listings of the Singapore Confederation ofIndustries and sent out questionnaires to 300 financial controllers. The questionnairewas pre-tested with a preliminary sample to ensure clarity and understandability. Inaddition, external validation checks were administered two weeks after receiving eachquestionnaire to detect errors and to ensure consistency in the responses. We conductedt-tests for the two independent samples to test response bias. Lack of significantdifferences (at p � 5 per cent) of the mean scores for all four contingency factors betweenthe early and late responses suggests negligible response bias.

We received 32 responses from the two mailings. Five respondents did not completethe questionnaire because of contravening company policy or staffing constraints.Another 10 responses were not usable because the firms were not BSC users. To increaseour sample size, we emailed our questionnaire to 50 financial controllers and receivedfive completed questionnaires. The adjusted usable response rate was about 9.09 percent.

Non-response biasWe conducted telephone callbacks to reduce non-response bias. The t-test for the twoindependent samples of respondents and non-respondents showed no significantdifferences in the mean scores of size, PLC stage, external environment and strategyindices. We, therefore, concluded that the non-response bias is negligible.

Demographics of the respondentsTable I shows the average experience of the staff responding to the questionnaire was7.6 years, ranging from 3 to 12 years. About 50 per cent of the firms were multinational

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companies, 27.3 per cent were private firms, 9.1 per cent were family-owned and 13.6 percent were listed on the Singapore Stock Exchange.

Data analysisSizeTable II presents the descriptive statistics of all variables in the survey. We used threealternative measures for size: annual revenue, total assets and total number ofemployees. The Pearson correlations between pairs of these three measures show highcorrelations (p � 0.001). Furthermore, the results of statistical analyses are consistentwhen each of these three measures serves as the proxy for size. Consequently, we reportbelow only the results using the total number of employees as size proxy. The squareroot of this variable is used in subsequent regression analyses because of thenon-normality of the variable.

PLC stageFollowing Hoque and James (2000), we asked respondents to assess the percentage ofeach of their firm’s product offerings at each of the PLC stages. We used the differencebetween the total percentage of products at the development and growth stages and

Table I.Profile of responding

companies (N � 22)

Number ofemployees N Principal business activity

0-99 11 Chemical products manufacturingElectrical machinery and apparatus manufacturingElectronic products and components manufacturingFurniture manufacturingPaper-related products manufacturing

100-199 6 Basic metal products manufacturingMachinery and equipment manufacturingPaper-related products manufacturing

200-299 2 Electronic products and components manufacturing� 300 3 Basic metal products manufacturing

Chemical products manufacturing

Table II.Descriptive statistics

(N � 22)

Variable Mean SD Theoretical range Actual range

Organizational size (original) 127.05 102.71 NA 8-341(Transformed) 10.31 4.66 NA 2.83-18.47

PLC stage �10% 0.56 �100%-100% �10%-80%Environment 4.07 1.06 1-7 2.00-6.40Strategy 3.50 0.59 1-7 2.42-4.76Financial perspective 5.03 1.12 1-7 3.60-7.00Internal business perspective 4.88 1.21 1-7 3.00-6.71Innovation learning perspective 3.42 1.51 1-7 1.25-6.50Customer perspective 5.36 1.08 1-7 2.57-7.00Overall BSC use 4.68 0.85 1-7 3.24-6.74Organizational performance 4.99 1.09 1-7 2.60-7.00

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those at the mature and declining stages as the measure for PLC stage for the firm. Apositive value indicates that the firm’s products are predominantly at early PLC stages,while a negative value suggests the opposite.

Environmental uncertaintyTo assess environmental variability, we followed Miller and Droge (1986) in includingtwo subscales: unpredictability and dynamism. Each was measured on a 7-point Likertscale. The unpredictability subscale measures a firm’s ability to forecast consumer andcompetitor behavior, while dynamism indicates the rate of change in marketingpractices and product technology. We followed Miller (1988) in focusing on a narrowlydefined spectrum of the environment, rather than the overall industry parameters.Although there are alternative measures for perceived environmental uncertainty,managers’ perceptions of the actual target markets (Dess and Beard, 1984) has beendemonstrated to possess objective validity (Miller, 1988) and is the measure used in thisstudy for perceived environmental uncertainty.

StrategyPorter (1985) shows two common business strategies: differentiation strategy or costleadership strategy. A firm with differentiation strategy can price its products atpremium prices through differentiating its products via customization, productinnovation and product variety. A cost-leading firm earns its profits throughprice-cutting and cost reduction. Rather than using a two-strategy categoricalclassification, this study followed Abernethy and Lillis (1995) in using a 7-pointcontinual scale. The scale places cost leadership and differentiation at the two ends ofthe continuum reflecting the degrees of differentiation and cost leadership in itsstrategy.

Balanced scorecardWe modified the instrument developed by Hoque and James (2000) to measure theuse of the BSC. The adapted instrument comprised 20 items, chosen for the localcontext and incorporated the four dimensions of Kaplan and Norton’s BSC.Respondents were asked to indicate on a scale from 1 (Not at all) to 7 (To a greatextent) the extent to which each item was used in their organizations’ performanceevaluations. The Cronbach alpha coefficient is 0.85, indicating satisfactory internalreliability for the scale.

A principal component analysis resulted in four factors all with eigenvalues of [mtqu]1.Sixteen of the 20 items have factor loadings of � 0.4 on one of three factors (Table III).Identifying the four factors using the factor loadings of these 16 items, the factors followalong the lines of the four BSC perspectives identified by Kaplan and Norton (1992).The following data summarizes items with factor loadings of � 0.4.

(1) internal business perspective;• material waste and scrap loss;• yield;• average cycle time;• plant utilization; and• manufacturing cycle efficiency

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(2) innovation and learning perspective;• number of new patents;• percentage of sales generated from new products;• time-to-market of new products; and• number of employee suggestions

(3) financial perspective; and• return on equity;• cash flow;• return on sales; and• return on investment

(4) customer perspective• market share;• on-time delivery; and• customer satisfaction surveys

Table III.Principal components

analysis of the BSC

Item

Factor loadings after varimax orthogonal rotationFactor 1Internalbusiness

perspective

Factor 2Innovation and

learningperspective

Factor 3Customer

perspective

Factor 4Financial

perspective

Eigenvalues 5.373 3.110 1.678 1.622Percent variance explained 33.581 19.440 10.488 10.137Return on equity 0.111 0.201 �0.111 0.863Cash flow 0.250 �0.126 0.422 0.593Return on sales 0.142 0.162 0.485 0.485Return on investment 0.226 0.134 0.115 0.849Material waste and scrap loss 0.895 �0.247 �0.089 0.169Yield 0.752 0.039 �0.105 0.075Average cycle time 0.732 0.228 0.164 0.177Plant utilization 0.769 0.134 0.411 0.086Manufacturing cycleefficiency 0.790 �0.003 0.494 0.247Number of new patents 0.082 0.836 0.082 0.203Percentage of sales generatedfrom new products �0.262 0.767 0.180 0.479Time-to-market of newproducts �0.028 0.921 0.018 0.027Number of employeesuggestions 0.382 0.661 0.121 �0.057Market share 0.279 �0.097 0.789 0.266On-time delivery 0.019 0.184 0.830 �0.058Customer satisfaction surveys �0.081 0.469 0.585 0.008

Note: Numbers in bold represent factor loadings that were � 0.4

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PerformanceFollowing Abernethy and Lillis (1995), we asked respondents to appraise theirorganization’s performance regarding margin on sales, capacity utilization, customersatisfaction and product quality. The performances were appraised on a scale from 1(below average) to 7 (above average). A factor analysis indicates that all four measuresloaded on one factor, explaining about 69.6 per cent of the total variance. The Cronbachalpha coefficient of 0.89 suggests satisfactory internal reliability.

ResultsTable IV shows Pearson correlation results. As expected, BSC use is positively andsignificantly correlated with PLC stage, external environment and performance.Although size and strategy are positively correlated with BSC usage, they are notstatistically significant.

The following regression is used to test H1-H4:

BBC_USE � �0 � �1 SIZE � �2 PLC � �3 ENV � �4 STRA � e (1)

Where BSC_USE � BSC usage; SIZE � firm size; PLC � product-life-cycle stage,measured in five stages from product introduction to maturity; ENV � externalenvironment, i.e. the managers’ perceptions of the actual target market’s unpredictabilityand dynamism; and STRA � strategy, measured in a continuous scale from low cost todifferentiation.

Table V presents the regression results. Adjusted R2 of the model is 0.568. Pearsoncorrelation coefficients reported in Table III shows that PLC stage, strategy and externalenvironment are all significantly correlated at p � 0.01. The tolerance test (tolerance �0.1) and variance inflation factor (VIF � 10) suggest that multicollinearity is not likelyto have significantly affected the results.

The regression results show �1, �2 and �3are positive and highly significant (p � 0.05),

implying that higher BSC use is found in large firms with products at an early PLC stageor operating in a highly uncertain environment. �4 is negative and significant,suggesting that companies with a cost leadership strategy are significantly associatedwith BSC use. These results support H1, H2 and H3, but not H4.

Table IV.Correlation matrix(pearson correlation)

VariableOrganizational

sizePLCstage Environment Strategy

OverallBSC

usageOrganizationalperformance

Organizational size 1.000 �0.184 0.480 0.142 0.229 0.491*PLC stage 1.000 0.421* 0.559** 0.515** 0.219Externalenvironment 1.000 0.605** 0.543** 0.614**Organizationalstrategy 1.000 0.143 0.307Overall BSC usage 1.000 0.741**Organizationalperformance 1.000

Notes: * Correlation is significant at the 0.05 level; ** Correlation is significant at the 0.01 level

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Table VI reports the ANOVA tests of BSC on performance and the four contingentfactors. None of the interaction effects were significant at a level below p � 0.05. Theeffect of BSC use on performance was significant for large firms (p � 0.01) and for firmswith products at early PLC stages (p � 0.05), which supports H1 and H2. Thenon-significant results for environmental uncertainty and strategy failed to support H3and H4.

DiscussionKidwell et al. (2002) have demonstrated a large number of successful implementations ofthe BSC in the USA with only few exceptions. Such evidence has not yet beendocumented in non-US firms. Our study fills this gap by examining BSC use inSingapore, which possesses a unique operating environment and business practicesdiffering from those of the USA BSC is not merely an amalgamation of financial andnon-financial metrics; it encompasses an integrated system that motivates employees atall levels to achieve goal congruence.

We adopted the framework by Hoque and James (2000) to test our hypotheses andresearch questions. The inclusion of external environment and organizational strategyin this study adds additional insights. Overall, our results are consistent with thefindings of Hoque and James (2000). In particular, our findings suggest that Singaporeanmanufacturing firms are likely to increase usage of the BSC when they are larger in size,have products at an early PLC stage or operate in a highly uncertain environment. Thefindings show a positive relationship between BSC use and environmental uncertaintybut lack support on differentiation strategy. The two-way interaction effect betweenBSC use and strategy and environmental uncertainty do not have a significant impacton performance. The sample mean of 3.50 on a 7-point Likert scale indicates thatmanufacturing firms in Singapore place equal emphasis on both the cost leadership anddifferentiation strategies. Hoque and James (2000) found firms with an analyzer strategyto use the four perspectives of the BSC to a greater extent than firms following the costleadership or differentiation strategy, while Gibbons (1996) showed that the analyzerstrategy is the most widely adopted strategic approach in Singapore. This may explainthe lack of significant results for increased BSC use by firms with differentiationstrategy and the impact of differentiation strategy and BSC use on performance.

Limitations and suggestions for future researchThis study had a small sample size and used survey research method. Themeasurements to capture all aspects of BSC usage are non-exhaustive. Caution

Table V.Regression results

(H1-H4)BSC_USAGE � �0 �

�1ORG_SIZE � �2PLC ��3ENV �

�4ORG_STRA � e

VariableStandardized beta

coefficients t-statistics p-value Tolerance VIF

Intercept 5.510 6.107 0.000 NA NAORG_SIZE 0.347 2.238 0.039 0.853 1.172PLC 0.701 3.779 0.001 0.598 1.672ENV 0.563 3.056 0.007 0.606 1.651ORG_STRA �0.639 �0.316 0.006 0.502 1.991

Notes: Adjusted R2 � 0.568; F-value � 7.911; p � 0.001; n � 22

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should be exercised in generalizing the findings of this study to other industries andcontexts. Future research can use different methodologies, such as field studies, casestudies and lab experiments, to examine industries other than manufacturing.

Notes1. Check the survey results at www.bain.com/management_tools/tools_balanced.asp?groupCode�2

2. The “local” environment that Singaporean firms face of course is globalised competition.

3. According to a study by KPMG, Singapore is the most cost-competitive business locationamong nine industrialized countries. See www.sedb.com/edb/sg/en_uk/index/why_singapore/singapore_rankings.html

4. The survey can be found at www.bain.com/management_tools/Management_Tools_and_Trends_2007.pdf

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Table VI.ANOVA: two-wayinteraction effects (H5-H8)

Source of variationSum ofsquares

Degree offreedom

Meansquare F

Significanceof F

Panel A: performance by BSC and size(RQ1)BSC usage (a) 6.713 1 6.713 11.213 0.004Organizational size (b) 10.353 1 10.353 17.294 0.001Two-way interaction (a � b) 1.899 1 1.899 3.173 0.092Explained 14.143 3 4.714 7.875 0.001Residual 10.775 18 0.599Panel B: performance by BSC andproduct-life-cycle stage (RQ2)BSC usage (a) 6.443 1 6.443 6.735 0.018PLC stage (c) 1.805 1 1.805 1.887 0.186Two-way interaction (a � c) 4.049 1 4.049 4.232 0.054Explained 7.698 3 7.698 2.682 0.078Residual 17.220 18 17.220Panel C: performance by BSC andexternal environment (RQ3)BSC usage (a) 2.212 1 2.212 2.296 0.147External environment (d) 3.932 1 3.932 4.083 0.058Two-way interaction (a � d) 1.214 1 1.214 1.260 0.276Explained 7.581 3 7.581 2.624 0.082Residual 17.337 18 17.337Panel D: Performance by BSC andstrategy (RQ4)BSC usage (a) 3.115 1 3.115 3.550 0.077Organizational strategy (e) 5.037 1 5.037 2.869 0.084Two-way interaction (a � e) 1.675 1 1.675 1.909 0.185Explained 9.998 4 9.998 2.848 0.056Residual 14.920 17 14.920

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Corresponding authorLing Liu can be contacted at: liul@uwec.edu

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