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Performance parameters interrelations from a balanced scorecard perspective An analysis of Greek companies
Interrelations from a BSc perspective 485
Sandra Cohen, Dimitris Thiraios and Myrto Kandilorou Athens University of Economics and Business, Athens, Greece Abstract Purpose – The proponents of balanced scorecard (BSc) claim that lead factors interrelate and their improvement ultimately leads to increased financial performance. The purpose of this paper is to use the underlying hypotheses of BSc in order to assess whether improvements that relate to learning and growth, internal processes and customers actually contribute to alterations of reported financial performance. Design/methodology/approach – A structured questionnaire was used and data were gathered from 90 leading Greek companies in relation to the progress they have experienced during a three-year period regarding various activities that can be broadly classified as aspects of the three qualitative perspectives of BSc (i.e. the learning and growth perspective, the internal business and production process perspective, and the customer perspective). Published financial data were used in order to calculate several financial ratios for all sample firms for the same time period. Findings – The empirical data verified the underlying theoretical hypothesis of BSc that lead BSc perspectives are positively correlated with one an: other at a statistically significant level in a sequential way. However, within a given perspective not all measures exhibit homogeneous behaviour in terms of statistical significance. Supportive evidence was also found that the companies that have improved their return on equity (ROE) and return on assets (ROA) during the analysis period have increased their efforts towards aspects that characterize the learning and growth perspective more than the companies whose ROE and ROA values decreased. Originality/value – The innovative dimension of this research work relies mainly on the fact that the BSc framework was used as a general structured model in order to assess the relationships between non-financial parameters and financial performance. Thus, conclusions are not restricted only to companies that actually apply BSc. Keywords Balanced scorecard, Financial performance, Operations and production management, Business performance, Greece Paper type Research paper
Introduction Balanced scorecard (BSc), introduced as a superior combination of financial and non-financial measures of performance (Kaplan and Norton, 1992, 1993, 1996a), has gained increasing popularity and attention (Ax and Bjørnenak, 2005; Lipe and Salterio, 2002). It has been suggested that the use of BSc leads to improved financial performance compared to traditional financial performance measures (Davis and Albright, 2004). The authors would like to thank the two anonymous reviewers and the participants at the 5th Annual Conference of the Hellenic Finance and Accounting Association in Thessalonika for their helpful remarks on an earlier version of the paper.
Managerial Auditing Journal Vol. 23 No. 5, 2008 pp. 485-503 q Emerald Group Publishing Limited 0268-6902 DOI 10.1108/02686900810875307
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However, it has been strongly criticized and questioned for its novelty and efficiency (Chenhall, 2005; Nørreklit, 2000). BSc bases its success on the hypothesis that all four perspectives (learning and growth, internal business and production process, customer and financial) are linked to each other in a cause-and-effect relationship (Aidemark, 2001). In fact, the cause-and-effect logic has been described as the “essence” of the BSc approach, which distinguishes it from other approaches (Kaplan and Atkinson, 1998). According to Kaplan and Norton (1996a), a properly constructed BSc should include measures that correlate with each other. The development and description of these interrelations in the context of a company’s strategy (a procedure called “strategic mapping”) constitutes one of the main features of BSc (Kaplan and Norton, 2001). The clear statement of these connections provides the opportunity for managers to realize how an action classified in one perspective will influence, through chain effects, other dimensions ultimately leading to improved financial results. The scope of this study is two-fold. The first aim is to investigate whether the theoretically grounded interrelationships among the non-financial performance dimensions of BSc can be supported by empirical evidence. In other words, to assess the magnitude of the correlations that exists among the variables that correspond to the learning and growth perspective, the internal business and production process perspective and the customer perspective in a real business setting. The second goal of the study is to assess the influence of efforts towards improvements on the non-financial BSc lead factors on financial performance. More specifically, we study whether improvements in several internal and external company parameters that can be broadly classified within the three qualitative dimensions of BSc have eventually an effect on its financial status. Therefore, in this paper we use the BSc framework in order to analyse in a structured and systematic manner whether a relationship between non-financial and financial parameters is valid. The importance of this survey relies on the fact that it focuses on the analysis of the basic inherent hypotheses of BSc in order to assess whether improvements that relate to customers, learning and growth and internal processes actually contribute to alterations of reported financial performance and not on BSc itself. Thus, we do not analyse companies that actually implement a BSc system. As Greece usually shows a delay in adopting management accounting innovations (Cohen et al., 2005), restricting our analysis only in BSc implementing companies would be problematic mainly due to the fact that it could considerably decrease the sample of our study. Thus, our analysis has a broader scope and its conclusions are expected to make a contribution to the stream of the BSc literature. The paper is constructed as follows: the first section consists of the literature review in relation to BSc; in the second section we present the methodology followed by the development of the research hypotheses; the third section includes the survey results; and finally, in the fourth section we discuss the conclusions. Literature review BSc philosophy has spread rapidly throughout the worldwide business community (Shneiderman, 1999). Over the past decade, hundreds of organizations have implemented the BSc concept in one-way or another. Aidemark (2001) observes that Swedish healthcare organizations have appreciated the BSc philosophy after many
years of exclusively focusing on financial measures. Similarly, a European survey showed that firms in Germany, the UK and Italy are familiar with the BSc concept at rates of 98, 83 and 72 per cent, respectively, (Gehrke and Horvath, 2002). Another recent survey of Nordic companies indicates that 27 per cent of those included in the analysis had already implemented BSc while another 61 per cent was expected to use it within a two-year period (Kald and Nilsson, 2000). According to Silk (1998), 60 per cent of Fortune 1000 companies in USA have experimented with BSc. On the contrary, Bourguignon et al. (2004) report limited adoption of BSc in France because of the extensive use of the French Tableau de Bord. Also, Speckbacher et al. (2003) estimate that only a minority of 26 per cent of the most important publicly traded firms of Germany, Austria and Switzerland use BSc while most of them appear to use only a limited or incomplete version of it. Despite its global success, the BSc approach has been strongly criticized for the lack of evidence that proves its association with improvements in accounting measures. Ittner et al. (2003) provide evidence that the BSc process exhibits almost no association with economic performance. According to Gro¨jer and Johanson (1998), the actual usage of the BSc framework has not yet been a subject of scientific investigation. Moreover, limited empirical research has taken place concerning the reliability of the basic hypotheses of BSc. However, some researchers accept that financial measures are the result of controlling other more important measures of non-financial nature (DeBusk et al., 2003; Davis and Albright, 2004). Based on a sample of 66 Australian companies, Hoque and James (2000) found that the use of BSc is linked to improved performance. On the other hand, Nørreklit (2003) questioned BSc’s hypothesis by arguing that Kaplan and Norton (1996a) do not provide a sufficient description of the assumed causal relationships. Furthermore, according to the same researcher, this relationship cannot be characterized as “causal” but only as “logical”. Moreover, several other aspects are considered problematic in the context of a chain-effects concept. One of them is the time lag, i.e. the period within which an action in the context of one dimension will have an effect on another (Pandey, 2005). This period is not defined by Kaplan and Norton (1992, 1993, 1996a, b, 2001) and in fact, time dimension is not a part of BSc. Another issue is that even though the definition of cause-and-effect relationships is the basis of the BSc success, many organizations seem to use BSc as an aggregation of independent performance measures (Aidemark, 2001). Ittner et al. (2003) found that 76.9 per cent of the companies using BSc give little or no attention to causal models. In their research, Speckbacher et al. (2003) found that only half of their sample companies using BSc were actually able to formulate cause-and-effect relationships among the different objectives and measures. Finally, a study conducted in Finnish companies showed that most companies appear to have scorecards in which the resulting measures and perspectives are fairly independent, lacking the claimed cause-and-effect interconnections (Malmi, 2001). Methodology The four perspectives of BSc refer to the learning and growth perspective, the internal business and production process perspective, the customer perspective and the financial perspective. As our hypotheses are developed on the basis of the BSc framework, we briefly present the characteristics of each one of them:
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(1) Learning and growth perspective. This perspective gives emphasis on innovation, creativity, competence and capability. It refers to the intangible assets that are most important to strategy. The objectives of this perspective are to identify the jobs (human capital), the systems (information capital) and the kind of organizational climate (organization capital) required to support the internal processes. It also focuses on people and their attitude, knowledge, development and ability to learn and improve. (2) Internal business and production process perspective. This perspective identifies the critical processes that create and deliver the customer a value proposition. Internal business processes should ensure that the firm’s products and services are meeting customer needs. This perspective is thought to be the most critical for the success of an organization. Some key performance indicators are process improvement and co-operation with suppliers. An important aspect of this perspective for a company is to be able to capitalize on operational achievements. (3) Customer perspective. This perspective defines the value proposition used to generate sales and loyalty from targeted customers. The customer perspective requires managers to identify the type of customers in the targeted segments that are desirable and consequently, choose the value parameters they should deliver to them. Poor performance in customer satisfaction is considered to be a leading indicator of future financial decline. (4) Financial perspective. This perspective describes the tangible outcomes of the strategy in traditional financial terms. In other words, financial objectives are retained as company goals but they represent the long-term aims of the organization; the outcomes of the non-financial factors. In this way, BSc “helps companies to look and move forward instead of backward” (Kaplan and Norton, 1992). Financial measures are considered “lagging” indicators in the sense that they are the results of other former actions mostly of qualitative nature. Hypotheses formulation According to the model of Kaplan and Norton (1996b), a cause-and-effect relationship exists among the perspectives of BSc in a sequential manner. More specifically, improved performance in the learning and growth perspective will result in ameliorated performance in the internal business and production process perspective that will positively affect company’s performance in relation to customers that will eventually influence financial performance (financial perspective). This relationship is shown in Figure 1. We conduct our research in two steps that coincide with our research questions. Firstly, we try to analyse the relationships among the non-financial perspectives of the BSc construct; secondly, we assess their influence on financial performance. In order to study whether the theoretically grounded interrelationships among the non-financial performance dimensions of BSc can be supported by empirical evidence, which is our first research question, we have selected a number of non-financial
Figure 1. Cause-and-effect concept in BSc
Learning and growth performance
⇒
Internal performance
⇒
Customer performance
⇒
Financial performance
variables that are found in the literature (Aidemark, 2001; Banker et al., 2004; Canibano et al., 1999; Chenhall, 2005; DeBusk et al., 2003; Evans, 2004; Ittner et al., 2003; Johnson et al., 2005; Kaplan and Atkinson, 1998; Kaplan and Norton, 1996a; Laudon and Laudon, 2004; Lipe and Salterio, 2002; Malina and Selto, 2001; Pandey, 2005) as relevant to the three non-financial perspectives of BSc. These variables as well as the perspective we believe that they relate to are presented in Table I. A company that invests in learning and growth dimensions, such as investments in new technology and innovative products, fosters intra-company communication, encourages exchange of information with other companies and promotes common business plans. As a result, this company is expected to achieve higher levels of internal business and production process quality. The aforementioned actions are expected to have a positive effect on the dispatching of orders, the cooperation with suppliers and distribution channels as well as the on the speed of innovations adoption. Thus, improved performance in the learning and growth perspective is expected to result in ameliorated performance in the internal business and production process perspective. Within a prosperous internal business and production process environment that is characterised by effective handling of customer orders, good relations with suppliers and distribution channels and quick response to innovation adaptation it is very likely that the end product of the company will satisfy customer needs and prospects. Thus, the company will attract more customers and increase its market share. The actions of satisfied customers would increase brand awareness and image and would be indicative of perceived high levels of service, product quality, value for money and trust towards products. Also, when production process is efficient after-sales service is confronted with less significant problems, which contributes towards customers perceiving positively its effectiveness. Moreover, satisfied customers are less likely to stop cooperating with the company or to make complaints. In order to empirically assess, the sequential cause-and-effect relationship among the non-financial perspectives, we develop factors by categorising into groups the independent variables with similar characteristics that fall into each perspective. Then we test the existence of correlations between these factors. Thus, our hypotheses that deal with the interrelation of the non-financial BSc perspectives on the basis of the claimed sequence on Figure 1 could be stated as following: H1.1. Learning and growth perspective factors positively correlate with internal business and production process perspective factors. H1.2. Internal business and production process perspective factors positively correlate with customer perspective factors. However, as we would like to test whether non-financial perspectives that are not supposed to have a sequential relationship exhibit positive correlations, we also developed H1.3: H1.3. Learning and growth perspective factors positively correlate with customer perspective factors. In order to analyse whether improvements in several internal and external company parameters that can be broadly classified within the three qualitative dimensions of
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Qualitative variables
Abbreviations
Literature
Learning and growth perspective Investments in new technology Innovative products and services
InvTech IPS
Kaplan and Norton (1996a) Evans (2004), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Aidemark (2001) and Kaplan and Norton (1996a) Johnson et al. (2005) and Kaplan and Norton (1996a) Ittner et al. (2003) and Kaplan and Norton (1996a) Malina and Selto (2001) and Kaplan and Norton (1996a)
Collaboration and information exchange in the organization Promotion of common business plans with co-operating companies Exchange of information with co-operating companies Cooperative companies monitoring
FCoEx FPCBP FExCo CCM
Internal business and production process perspective EDO Effective dispatching of orders (in terms of price, specifications and delivery time) Degree of cooperation with suppliers DoCS
Table I. Non-financial variables included in the questionnaire
Degree of cooperation with distribution channels Speed of adopting innovations already introduced in the market Speed of adopting innovations not yet introduced in the market Customer perspective Market share
DoCDC
MSh
Brand awareness Brand image
BA BI
Perceived level of service
PLoS
Perceived level of quality Perceived value of money
PLoQ PVoM
Perceived level of trust to the products
PLoT
After-sales service
AfSS
Percentage of lost clients
PLC
Percentage of customers’ complaints
PCC
SoAIaI SoAInI
Evans (2004) Johnson et al. (2005), Laudon and Laudon (2004), Chenhall (2005), Aidemark (2001) and Lipe and Salterio (2002) Johnson et al. (2005), Aidemark (2001) and Kaplan and Norton (1996a) Pandey (2005), Evans (2004) and Kaplan and Norton (1996a) Evans (2004), DeBusk et al. (2003) and Canibano et al. (1999) Evans (2004), Banker et al. (2004), Malina and Selto (2001), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Kaplan and Norton (1996a) Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Malina and Selto (2001), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Evans (2004) Malina and Selto (2001), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Lipe and Salterio (2002) and Kaplan and Norton (1996a) Evans (2004) and Kaplan and Norton (1996a) DeBusk et al. (2003), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Lipe and Salterio (2002), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a)
BSc have eventually an effect on its financial status, which is our second research aim, we have selected a number of financial ratios that are usually found in relevant studies (Banker et al., 2004; Evans, 2004; Ittner et al., 2003; Kaplan and Atkinson, 1998; Kaplan and Norton, 1996a; Libby et al., 2004; Lipe and Salterio, 2002) as measures of the financial performance of a company. These ratios are commonly encountered as financial perspective measures. The financial ratio variables are depicted in Table II. According to the BSc framework, improvements in the lead factors that are incorporated into the non-financial perspectives will eventually positively affect financial performance. Thus, our second set of hypotheses is that improvements in the non-financial BSc perspective variables have as a result improved financial performance. Financial performance is usually assessed through the use of financial ratios. However, the values of financial ratios per se for a given company are highly influenced by the characteristics of the industry the company operates within, its life-cycle phase, its size, the level of competitive pressures, the influence of economic environment, etc. Thus, in order to deal with the fact that we would analyse companies that are heterogeneous in various aspects and as a result they could exhibit different financial ratio values due to their idiosyncratic differences, we decided to compare each company only to itself. In other words, we define that a company has achieved improved financial performance in any given financial ratio during the period of analysis, if its financial ratio value at the end of the period was ameliorated compared to the ratio value at the beginning of the period. Otherwise, the company is defined as not having improved its financial performance. We then compare companies that have improved financial performance with companies that have not improved their financial performance in terms of their scores in the non-financial BSc perspectives’ factors. The goal of such a comparison is to assess the existence of statistically significant differences in the scores of the non-financial BSc perspectives’ factors between companies that exhibit improved and not improved financial performance. Statistically, significant differences in these scores could be an indication of the influence of efforts towards lead factor achievements on financial performance. Thus, our hypotheses that deal with the effect of the non-financial BSc perspectives on financial performance could be stated as following:
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H2.1. Firms that have improved their financial performance had improved their learning and growth perspective factors more than the firms that have not improved their financial performance.
Ratio
Abbreviation
Literature
Return on assets Return on equity
ROA ROE
Inventory turnover Debtors turnover Sales margin Assets turnover
IT DT SM AT
Evans (2004) and Ittner et al. (2003) Evans (2004), Kaplan and Atkinson (1998) and Kaplan and Norton (1996a) Banker et al. (2004) and Lipe and Salterio (2002) Banker et al. (2004) and Lipe and Salterio (2002) Lipe and Salterio (2002) and Libby et al. (2004) Banker et al. (2004)
Table II. Financial ratios
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H2.2. Firms that have improved their financial performance had improved their internal business and production process perspective factors more than the firms that have not improved their financial performance. H2.3. Firms that have improved their financial performance had improved their customer perspective factors more than the firms that have not improved their financial performance. Data selection Survey data consist of measures of financial and non-financial context. All data concerning performance in non-financial performance perspectives (that can be broadly classified in the three non-financial perspectives of BSc) were collected through responses in a short questionnaire sent to corporations between June and July 2004. The sample surveyed included leading Greek companies in the manufacturing, retail and service sectors. The selection of companies was made on the basis of their net income and sales for the year 2003. The three-page questionnaire was divided into three broad thematic units, each of which contained one or more sub-units using a five-degree Likert scale. Managers where asked to define the level of change in relation to a set of non-financial variables during the period of 2001-2003. As the time lag effect is well documented in the literature (e.g. the time period needed for a lead factor change to influence a lag factor), the data were selected for a three-year time period. The scale used was 1 – substantially decreased to 5 – substantially increased. The variables of the three BSc perspectives were shuffled in the questionnaire so that the respondents would not usually encounter successive variables of the same BSc perspective. Data regarding the calculation of financial measures were gathered by using the ICAP database. The questionnaire was sent to 181 companies. A total of 90 completed questionnaires were received. The response rate was approximately 50 per cent (49.72 per cent), which is quite satisfactory. Descriptive statistics The descriptive statistics (mean values and standard deviations) of all non-financial variables that were included in the questionnaire are presented in Table III. The variables of the three BSc perspectives were grouped into factors. Tables IV-VI present the results of the factor analysis. The six variables that were included in the learning and growth perspective were grouped into two factors. The first factor was named “External environment orientation – IGL1” and accounts for the 32.59 per cent of the variance explained (mean value ¼ 3.670 and SD ¼ 0.540). The second factor was named “Internal environment orientation – IGL2” and accounts for the 27.55 per cent of the variance explained (mean value ¼ 4.000 and SD ¼ 0.514). The five variables that fall broadly according to literature and our perceptions into the internal business and production process perspective were also grouped into two factors. The first factor was named “New process efficiency and effectiveness – BP1” and accounts for the 35.04 per cent of the variance explained (mean value ¼ 3.629 and SD ¼ 0.667). The second factor was named “Process efficiency and effectiveness – BP2” and accounts for the 33.10 per cent of the variance explained (mean value ¼ 3.780 and SD ¼ 0.512). Finally, the ten variables categorized as dimensions of the customer perspective were also grouped into two factors. The first factor was named “Customer satisfaction – C1” and accounts for the 41.86 per cent of the variance
Qualitative variables
Mean value
SD
4.1111 3.7303 4.1600 3.4444 3.7333 3.8315
0.710 0.670 0.690 0.736 0.683 0.643
3.7556 3.8222 3.7614 3.8427 3.4157
0.675 0.646 0.711 0.736 0.765
3.8444 3.9101 3.9667 3.8778 3.8667 3.7444 3.9444 3.6782 3.5730 3.3977
0.763 0.701 0.626 0.632 0.656 0.757 0.676 0.637 0.877 0.687
Learning and growth perspective Investments in new technology (InvTech) Innovative products or services (IPS) Collaborating and exchanging information within the organisation (FcoEx) Apply business plans with cooperating companies (FPCBP) Exchanging information with cooperating companies (FExCo) Cooperative companies monitoring (CCM) Internal business and production process perspective Effective dispatching of orders (EDO)a Degree of cooperation with suppliers (DoCS) Degree of cooperation with distribution channels (DoCDC) Speed of adopting innovations already introduced in the market (SoAIaI) Speed of adopting innovations not yet introduced in the market (SoAlnI) Customer perspective Market share (MSh) Brand awareness (BA) Brand image (BI) Perceived level of service (PLoS) Perceived level of quality (PLoQ) Perceived value of money (PVoM) Perceived level of trust to the products (PLoT) After-sales service (AfSS) Percentage of lost clients (PLC) [revere coded) Percentage of customers’ complaints (PCC) [revere coded)
Notes: aIn terms of prices, specifications and delivery time. The scale used was 1 – substantially decreased to 5 – substantially increased
Learning and growth perspective External environment orientation – IGL1 Frequency of promoting common business plans with co-operating companies Frequency of exchanging information with co-operating companies Cooperative companies monitoring Internal environment orientation – IGL2 Innovative products or services Investments in new technology Frequency of collaboration and information exchange within the organization
Factor loading
Percentage of variance explained
Cronbach’s a
32.599
0.685
27.553
0.612
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Table III. Descriptive statistics of non-financial variables per perspective
0.863 0.858 0.545 0.821 0.766 0.571 60.153
Notes: The factors were identified by using the principal component analysis extraction method and the varimax with Kaiser Normalization rotation Method (KMO). The KMO measure of sampling adequacy is 0.681 and the Bartlett’s test of sphericity is 96.010 (sig. 0.000). Also, the conventional recommendation of five observations per parameter is met
Table IV. Factor analysis results of learning and growth perspective variables
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Table V. Factor analysis results of internal business and production process perspective variables
Internal business and production process perspective New process efficiency and effectiveness – BP1 Speed of adopting innovations not yet introduced in the market Speed of adopting innovations already introduced (in the market) Process efficiency and effectiveness – BP2 Effective dispatching of orders (in terms of price, specifications and delivery time) Degree of cooperation with suppliers Degree of cooperation with distribution channels
Factor loading
Percentage of variance explained
Cronbach’s a
35.045
0.734
33.101
0.621
0.892 0.802 0.843 0.653 0.640 68.146
Notes: The factors were identified by using the principal component analysis extraction method and the varimax with KMO. The KMO measure of sampling adequacy is 0.708 and the Bartlett’s test of sphericity is 92.835 (sig. 0.000). Also, the conventional recommendation of five observations per parameter is met
Customer perspective Customer satisfaction – C1 Perceived level of quality Brand awareness Perceived level of trust to the products Perceived value of money Brand image Market share Perceived level of service After-sales service Customer retention – C2 Percentage of lost clients Percentage of customers’ complaints
Factor loading
Percentage of variance explained
Cronbach’s a
41.865
0.874
18.137
0.706
0.802 0.799 0.785 0.783 0.773 0.669 0.614 0.484 0.910 0.807 60.002
Table VI. Factor analysis results of customer perspective variables
Notes: The factors were identified by using the principal component analysis extraction method and the varimax with KMO. The KMO measure of sampling adequacy is 0.810 and the Bartlett’s test of sphericity is 384.835 (sig. 0.000). Also, the conventional recommendation of five observations per parameter is met
explained (mean value ¼ 3.860 and SD ¼ 0.492). The second factor was named “Customer retention – C2” and accounts for the 18.13 per cent of the variance explained (mean value ¼ 3.500 and SD ¼ 0.686). The results of the factor analysis are highly consistent with the BSc literature in relation to the measures that are commonly encountered in the non-financial BSc perspectives. However, the factors identified do not exhaustively cover all BSc parameters as the questions in the questionnaire were deliberately constrained to only some of them, mainly those of a generic nature.
We used the Cronbach a test in order to assess whether the variables accumulated to calculate the six factors were reliably measured. The results of these tests, as shown in Tables IV-VI, indicate that all variables are effectively measured since all six factors’ Cronbach a reliability values are above 0.60 (Bagozzi, 1994). The correlations among the six factors are presented in Table VII. An analysis of Table VII reveals that the first factor of learning and growth perspective that deals with external environment orientation is not statistically related to any of the internal business and production process perspective factors. On the other hand, internal environment orientation factor is positively and statistically significantly related at 1 per cent statistical significance level to both internal business and production process perspective factors. These results indicate that only one dimension of learning and growth has a direct effect on internal process amelioration. Thus, our empirical findings provide supportive evidence that there is a positive and statistical significant relationship between the first two perspectives of BSc but not all dimensions of the learning and growth perspective have a direct positive impact on internal business and production process perspective variables. The two factors of internal business and production process perspective are statistically significantly related at 1 or 5 per cent to both customer perspective factors. However, customer satisfaction factor (r ¼ 0.614) is more positively related to internal business and production process perspective factors than customer retention factor (r ¼ 0.370). This finding indicates that increased performance in internal processes is reflected in improved customer relations. Finally, the analysis of the correlations among the customer perspective factors and the learning and growth perspective factors does not exhibit statistically significant relationships with the exception of the positive at 1 per cent statistically significant correlation between the customer satisfaction factor and the internal environment orientation factor. By analyzing the variables that constitute internal environment orientation factor, it is evident that customers appreciate innovation and technology initiation. It is interesting to note that the internal environment orientation factor was not statistically correlated with any of the internal business and production process perspective factors. However, it has a statistical significant relationship with the customer perspective, which is not the subsequent perspective. This finding is indicative that the theoretical sequential model shown in Figure 1 could exhibit differentiations in practice. In Table VIII, the correlations among the 21 variables are presented. Two main conclusions can be drawn from Table VIII. Firstly, that not all variables are Factors
IGL1
IGL2
BP1
BP2
C1
C2
External environment orientation (IGL1) Internal environment orientation (IGL2) New process efficiency and effectiveness (BP1) Process efficiency and effectiveness (BP2) Customer satisfaction (C1) Customer retention (C2)
1 0.309 0.085 0.198 0.152 0.051
0.341 * * 1 0.674 * * 0.430 * * 0.433 * * 0.128
0.119 0.639 * * 1 0.413 * * 0.441 * * 0.221 *
0.253 * 0.478 * * 0.437 * * 1 0.614 * * 0.370 * *
0.158 0.445 * * 0.476 * * 0.628 * * 1 0.344 * *
0.085 0.149 0.234 * 0.371 * * 0.342 * * 1
Notes: The correlations above diagonal are Pearson two-tailed correlations and below the diagonal are Spearman two-tailed correlations. Correlation is significant at: *0.05; * *0.01 levels (two-tailed)
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Table VII. Correlations among factors of the three non-financial perspectives
*
*
* *
*
* * * * * * *
* * * * * * * *
* * * * *
* * * * * * * *
* *
* *
MSh
*
0.252 0.242 1 0.003 0.173 0.103 0.095 0.114 0.447 0.485 0.439 0.458 0.407 0.432 0.363 0.321 0.185 0.247 0.416 0.282 0.095
AfSS
0.264 1 0.092 0.021 0.009 0.168 0.249 0.225 0.080 0.172 0.173 0.033 0.261 0.302 0.262 0.214 0.259 0.215 0.147 0.216 0.051 * *
* * * * * * * * *
* *
*
PCC 0.255 0.032 0.004 1 0.529 0.074 0.058 0.018 0.050 0.231 0.323 0.243 0.254 0.252 0.251 0.401 0.106 0.147 0.222 0.212 0.058
PLC 0.088 0.084 0.181 0.545 1 0.036 0.140 2 0.022 0.107 0.262 0.297 0.303 0.290 0.221 0.181 0.176 0.033 0.068 0.184 0.094 0.140 * * * * * * * *
* *
* *
*
* * *
* * * *
*
CCM 0.190 0.257 0.084 0.079 0.034 1 0.321 0.331 0.110 0.121 0.317 0.214 0.094 0.061 0.125 0.049 0.212 0.148 0.091 0.173 0.321 * *
*
*
*
* *
* *
FCoEx 0.198 0.069 0.070 0.038 0.146 0.307 1 0.627 0.116 0.211 0.163 0.124 0.225 0.103 0.150 0.065 0.066 0.208 0.026 0.113 0.627
0.040 20.060 0.084 20.021 0.011 * * 0.304 * * 0.618 1 0.087 0.123 0.120 0.046 0.180 0.104 0.079 20.005 0.135 0.113 20.007 0.073 * * 0.293
FPCBP
* * * * * * * * * * *
* * * * * * * * * * *
* * * * *
BA 0.215 0.324 0.480 0.044 0.086 0.117 0.114 0.079 1 0.639 0.396 0.348 0.594 0.492 0.574 0.409 0.345 0.285 0.304 0.352 0.116
BI 0.326 0.289 0.506 0.216 0.262 0.123 0.215 0.081 0.644 1 0.508 0.448 0.655 0.558 0.564 0.510 0.269 0.266 0.300 0.218 0.211 * * * * * * * * * * * *
*
* * * * * *
* *
*
* * * * * * * * * * * *
*
* * * * * * * * * * *
* * * * *
* * * *
* * * * * *
CwS 0.337 0.240 0.445 0.335 0.294 0.334 0.146 0.097 0.413 0.513 1 0.427 0.440 0.403 0.425 0.479 0.267 0.269 0.429 0.315 0.163
* * * * *
* * * * * * * * *
* *
* *
* * * * * *
* * * * * *
CwDC 0.278 0.338 0.441 0.279 0.314 0.231 0.123 0.034 0.335 0.453 0.435 1 0.388 0.332 0.249 0.318 0.339 0.262 0.435 0.244 0.124
Notes: The correlations above diagonal are Pearson two-tailed correlations and below the diagonal are Spearman two-tailed correlations. Correlation is significant at: Appendix and Table III
* *
* * * * * * * *
* * *
EDO
Table VIII. Correlations among non-financial perspectives’ variables
1 0.048 0.281 0.267 0.117 0.151 0.201 0.080 0.234 0.364 0.350 0.273 0.265 0.304 0.269 0.316 0.033 0.241 0.240 0.101 0.201 *
0.05;
* * * *
* *
* * * * * *
* * * * *
* * *
* *
* * * * *
* * * * * * * *
PVoQ 0.306 0.454 0.429 0.271 0.252 0.080 0.095 0.077 0.489 0.563 0.420 0.353 0.767 1 0.660 0.534 0.209 0.333 0.407 0.362 0.103 * * * * *
* * * * * *
* * * * *
* *
* * *
* * * * * * *
PVoM 0.228 0.308 0.358 0.265 0.224 0.142 0.127 0.045 0.530 0.526 0.411 0.243 0.673 0.676 1 0.577 0.308 0.286 0.310 0.398 0.150
PLoS 0.297 0.268 0.309 0.446 0.210 0.087 0.054 2 0.051 0.382 0.500 0.496 0.338 0.509 0.528 0.567 1 0.191 0.268 0.332 0.289 0.065
* * * * * * *
* * * * *
* * * * * * *
* * * * * * * *
IPS 0.027 0.296 0.200 0.100 2 0.002 0.188 0.087 0.156 0.362 0.247 0.253 0.309 0.149 0.196 0.261 0.197 1 0.362 0.456 0.460 0.066
* * * * * *
*
* * * * * *
* *
*
* * *
* * *
* * * * *
* * * * * * * * *
* * *
InvTech 0.245 0.264 0.240 0.174 0.077 0.163 0.201 0.119 0.293 0.286 0.288 0.243 0.317 0.345 0.283 0.256 0.373 1 0.592 0.350 0.205
* * * * * * * * * * * *
* * * * * * * * * *
* * * * * *
SoAIaI 0.239 0.294 0.420 0.265 0.178 0.111 0.005 0.006 0.347 0.307 0.439 0.443 0.306 0.426 0.313 0.329 0.488 0.574 1 0.546 0.026
* * * * * * * * *
* * *
* * *
*
* *
* * *
SoAInI 0.091 0.101 0.318 0.226 0.070 0.192 0.086 0.108 0.361 0.194 0.335 0.239 0.323 0.352 0.375 0.263 0.487 0.329 0.580 1 0.113
*
*
* * * * * *
FExCo 0.198 0.069 0.070 0.038 0.146 0.307 0.285 0.618 0.114 0.215 0.146 0.123 0.211 0.095 0.127 0.054 0.087 0.201 0.005 0.086 1
0.01 levels (two-tailed). The abbreviations of the variables presented on this table are analyzed both in the
* * * * * * *
* * * * * *
* * * *
*
* * * * *
*
PLoT 0.240 0.207 0.397 0.269 0.302 0.108 0.211 0.163 0.561 0.632 0.414 0.359 1 0.769 0.670 0.537 0.199 0.334 0.331 0.339 0.225
496
EDO AFSS MSh PCC PLC CCM FCoEx FPCBP BA BI DoCS DoCDC PLoT PLoQ PVoM PLoS IPS InvTech SoAIaI SoAInI FExCo
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statistically significantly correlated to each other. In other words, the improvements in some non-financial metrics are not necessarily reflected to other qualitative business aspects. The second conclusion is that all statistically significant relations are positive. This means that a business, which improves one aspect that deals with any non-financial variable, should expect, if any, only positive outcomes from such a move. Of course, this conclusion works also the other way around; by neglecting some non-financial aspects several other qualitative variables will be negatively affected. In order to test H2.1-H2.3, the values of the six selected financial variables that were presented in Table II were calculated for both years 2001 and 2003 for all sample companies. The sample companies were then divided into two groups for any given financial ratio; those that have evidenced an increase in the financial ratio (ratio2003 $ ratio2001) and those that have experienced a decrease in the value of the corresponding ratio (ratio2003 , ratio2001) between years 2001 and 2003. An increase in the values of the financial ratios means that the companies are characterized as having improved financial performance. Tables IX-XIV summarize the results of the t-tests[1]. Our empirical data provides supportive evidence that the companies that had increased their return on assets (ROA) from year 2001 to 2003 had improved their internal environment orientation more ( p-value ¼ 0.017) than the companies that had experienced a decrease in the value of the same ratio for the same time period. The same conclusion can be drawn for other two financial ratios; those of return on equity (ROE) ( p-value ¼ 0.027) and inventory turnover ( p-value ¼ 0.092). Moreover, our data revealed that the companies that had increased their ROA between year 2001 and 2003 had improved their new process efficiency and effectiveness ( p-value ¼ 0.030) more than the companies whose ROA for the same time period decreased. Customer perspective factors proved to be a source of differentiation only in relation to the debtors turnover ratio. More specifically, the companies that had improved their debtors turnover ratio have increased the values of customer satisfaction ( p-value ¼ 0.019) and customer retention ( p-value ¼ 0.025) more than the companies that had not done so.
Factors Customer satisfaction Customer retention External environment orientation Internal environment orientation New process efficiency and effectiveness Process efficiency and effectiveness
Return on assets (2001-2003) Group B Group A (ROA2003 $ ROA2001) (ROA2003 , ROA2001)
Diff
497
t-Value ( p-value)
3.950 3.431
3.800 3.549
0.150 1.367 (0.176) 2 0.118 20.788 (0.433)
3.639
3.692
2 0.053 20.453 (0.652)
4.153
3.891
0.262
2.442 (0.017)
3.811
3.500
0.311
2.184 (0.030)
3.865
3.719
0.146
1.327 (0.188)
Notes: The scale used was 1 – substantially decreased to 5 – substantially increased
Interrelations from a BSc perspective
Table IX. Statistical results of t-tests: return on assets (2001-2003)
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Table X. Statistical results of t-tests: return on equity (2001-2003)
Return on equity (2001-2003) Group B Group A (ROE2003 $ ROE2001) (ROE2003 , ROE2001)
Customer satisfaction Customer retention External environment orientation Internal environment orientation New process efficiency and effectiveness Process efficiency and effectiveness
3.905 3.488
3.821 3.511
0.084 0.802 (0.425) 20.023 2 0.156 (0.877)
3.643
3.696
20.053 2 0.455 (0.650)
4.121
3.881
0.240
2.248 (0.027)
3.727
3.533
0.194
1.373 (0.173)
3.773
3.788
20.015 2 0.138 (0.891)
Inventory turnover (2001-2003) Group B Group A Diff (IT2003 $ IT2001) (IT2003 , IT2001)
Customer satisfaction Customer retention External environment orientation Internal environment orientation New process efficiency and effectiveness Process efficiency and effectiveness
3.836 3.438 3.677 4.118
3.875 3.536 3.667 3.927
3.705 3.828
3.582 3.752
t-Value ( p-value)
2 0.039 2 0.356 (0.723) 2 0.098 2 0.654 (0.516) 0.010 0.085 (0.932) 0.191 1.710 (0.092) 0.123 0.076
0.882 (0.381) 0.657 (0.513)
Notes: The scale used was 1 – substantially decreased to 5 – substantially increased
Factors
Table XII. Statistical results of t-tests: debtors’ turnover (2001-2003)
t-Value ( p-value)
Notes: The scale used was 1 – substantially decreased to 5 – substantially increased
Factors
Table XI. Statistical results of t-tests: inventory turnover (2001-2003)
Diff
Customer satisfaction Customer retention External environment orientation Internal environment orientation New process efficiency and effectiveness Process efficiency and effectiveness
Debtors’ turnover (2001-2003) Group B Group A (DT2003 $ DT2001) (DT2003 , DT2001)
Diff
t-Value ( p-value)
4.029 3.789 3.667 4.023
3.772 3.371 3.672 3.989
0.257 2.402 0.418 2.322 2 0.005 2 0.046 0.034 0.322
3.672
3.608
0.064
0.426 (0.671)
3.908
3.718
0.190
1.650 (0.105)
Notes: The scale used was 1 – substantially decreased to 5 – substantially increased
(0.019) (0.025) (0.964) (0.748)
Factors Customer satisfaction Customer retention External environment orientation Internal environment orientation New process efficiency and effectiveness Process efficiency and effectiveness
Sales margin (2001-2003) Group B Group A (SM2003 $ SM2001) (SM2003 , SM2001)
Diff
t-Value ( p-value)
3.898 3.539 3.583 4.067
3.832 3.925 3.741 3.946
0.066 0.635 (0.527) 2 0.386 2 0.484 (0.629) 2 0.158 2 1.394 (0.167) 0.121 1.117 (0.267)
3.738
3.541
0.197
1.406 (0.163)
3.783
3.778
0.005
0.051 (0.959)
Notes: The scale used was 1 – substantially decreased to 5 – substantially increased
Factors Customer satisfaction Customer retention External environment orientation Internal environment orientation New process efficiency and effectiveness Process efficiency and effectiveness
Asset turnover (2001-2003) Group B Group A (AT2003 $ AT2001) (AT2003 , AT2001) 3.869 3.442 3.600 4.074
3.852 3.557 3.742 3.924
3.711
3.545
3.748
3.814
Diff
Interrelations from a BSc perspective 499 Table XIII. Statistical results of t-tests: sales margin (2001-2003)
t-Value ( p-value)
0.017 0.160 (0.874) 20.115 2 0.780 (0.437) 20.142 2 1.246 (0.216) 0.150 1.379 (0.171) 0.166
1.174 (0.244)
20.066 2 0.599 (0.551)
Notes: The scale used was 1 – substantially decreased to 5 – substantially increased
Finally, we did not find any statistically significant differences between companies that had improved their sales margin and asset turnover in the three-year period in terms of the values of their non-financial BSc perspectives’ factors. Conclusions The proponents of BSc claim that lead factors interrelate and their improvement ultimately leads to increased financial performance. In our research, we used a structured questionnaire and gathered data from 90 leading Greek companies in relation to the progress they have experienced during a three-year period regarding various activities that can be broadly classified as aspects of the three qualitative perspectives of BSc. We also calculated financial ratios for all the sample firms for the same time period on the basis of published financial statements. Our empirical data verified that most lead BSc perspectives are correlated with each other at a statistically significant level. Our evidence generally supports the theoretical base of BSc that there is a sequential dependency among the non-financial BSc perspectives. However, the relation between customer perspective factors and internal business and production process perspective factors seems to be stronger than the
Table XIV. Statistical results of t-tests: asset turnover (2001-2003)
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relation between learning and growth perspective factors and internal business and production process perspective factors. Moreover, the relation between customer perspective factors and learning and growth perspective factors (i.e. the perspectives that within the BSc framework are not modeled sequentially) exhibit limited statistical significant relationships. We also found supportive evidence that the companies that have improved their financial indicators have increased their efforts towards business activities more than the companies that have not. More specifically, companies that have increased ROA and ROE during the three-year period from 2001 to 2003 have invested more in innovation, new technologies and intra company cooperation and information exchange compared to the companies that had their ROA and ROE values decreased. We believe that our findings are important for several reasons. The first reason is that we used the BSc framework as a general structured model in order to assess the relationships between non-financial parameters and financial performance. Our conclusions that are based on a sample of heterogeneous companies, indicate that a lead-lag relationship hypothesis can be supported by empirical data. Thus, companies should attempt to validate the causal links between lead and lag factors. As Wong-On-Wing et al. (2007) point out weak linkages between the drivers (multiple classes of non-financial measures) and final outcome (financial measures) suggest an ineffective strategy. Moreover, Ittner and Larcker (2003) attribute the reluctance of businesses to establish such casual links partly to laziness or thoughtlessness. The second reason is that we used actual financial data in order to calculate the performance variables so as to obtain as objective financial performance indicators as possible. The use of survey data in this paper coupled with additional secondary sources overcomes the mono-method bias suffered by studies that solely rely on data retrieved by questionnaires. However, we could not overcome the shortcomings that are inherent in every questionnaire survey where answers are based on respondents’ perceptions as far as the measurement of the non-financial variables is concerned. Another limitation of the study relates to the selection of the time period (three-years) that has been used in order to evaluate the influence of non-financial factors on financial performance. The application of an alternative time period could possibly weaken or strengthen our results. This research would serve as a starting point for an analysis of the magnitude of the casual links between non-financial measures and financial performance for companies that operate in specific industries or apply specific business strategies. The study of the influence of strategy on the formulation and the cause-and-effect relations within the BSc framework as well as its subsequent effect on performance could constitute an interesting research area. Note 1. The use of non-parametric tests (Mann Whitney test) presents qualitatively the same results (not presented in the paper).
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Appendix Index Qualitative variables with abbreviations: . Effective dispatching of orders (EDO). . After-sales service (AfSS).
. . . . . . . . . . . . . . . . . . .
Market share (MSh). Percentage of customers’ complaints (PCC). Percentage of lost clients (PLC). Cooperative companies monitoring (CCM). Frequency of collaboration and information exchange within the organization (FCoEx). Frequency of promoting common business plans with co-operating companies (FPCBP). Brand awareness (BA). Brand image (BI). Degree of cooperation with suppliers (DoCS). Degree of cooperation with distribution channels (DoCDC). Perceived level of trust to the products (PLoT). Perceived level of quality (PLoQ). Perceived value for money (PVoM). Perceived level of service (PLoS). Innovative products or services (IPS). Investments in new technology (InvTech). Speed of adopting innovations already introduced (in the market) (SoAIaI). Speed of adopting innovations not yet introduced (in the market) (SoAInI). Exchanging information with cooperating companies (FExCo).
Corresponding author Sandra Cohen can be contacted at:
[email protected]
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Interrelations from a BSc perspective 503
