cusotmer focused manufacturing strategy and the use of operations based non financial performance...

Pergamon Accounting, Organizations and Society, Vol. 22, No 6, pp. 557-572, 1997

CfJ 1997 Elsevier Science Ltd AU rights reserved. Printed in Great Britain

0361.3682/97 $17.00 + 0.00

PII: SO361-3682(96)00048-7

CUSTOMER-FOCUSED MANUFACTURING STRATEGY AND THE USE OF OPERATIONS-BASED NON-FINANCIAL PERFORMANCE MEASURES:

A RESEARCH NOTE

S. PERERA, CT. HARRISON Macquarie University

and

M. POOLE IBM Australia Ltd

Abstract

This note follows and extends Abemethy and Lillis (Accounting, Organizations and Society, 1995) by

examining: (i) whether firms which maintain a customer-focused manufacturing strategy also maintain an

emphasis on non-financial (operations-based) measures in their performance measurement systems; and

(ii) whether such an emphasis is associated with enhanced performance for those firms. Data are obtained

via questionnaire survey of a random sample of manufacturing firms in Sydney, Australia. Support is found

for the hypothesized association between customer-focus strategy and the use of non-financial perfor-

mance measures (and hence for Abemethy and Liilis) but not for the link to organizational performance.

$3 1997 Elsevier Science Ltd

It is well accepted that the competitive and technological environments of manufacturing have undergone massive change over the past quarter century (Howell & Saucy, 1987b; John- son, 1990b; Samson et al., 1991). Increasing globalization, advances in manufacturing technology and new approaches to manufacturing management (Just-in-Time and Total Quality Control, for example) mean that manufacturing firms no longer operate in an environment of “stable demand for products, (and) high utilisa- tion of factory capacity” (Samson et al., 1991, p. SO), nor one in which competitive strategy can be focused solely on cost leadership (Aber- nethy & Lillis, 1991, p. 227). Rather, the competitive and technological changes have meant that firms must now maintain a manufacturing strategy of customer focus, and concentrate on those factors which provide value to customers,

including not only low cost, but also high quality, flexibility of product characteristics and dependability of supply (Johnson, 1990a).

With these changes has come the question of whether traditional cost and financially-oriented performance measurement systems remain appropriate contemporarily. Authors such as Kaplan (1990) Howell and Saucy (1987b), Vollman (1990) and Dent (1990) for example, have argued that such systems lack relevance in the new manufacturing environment in that they do not reflect, and are inconsistent with, the customer-focus factors of quality, flexibility and dependability which have now become critical to firm success. Worse, continued emphasis on cost and financial-based measures, which are essentially short-term, have been argued to be counter-productive in that they provide little incentive to improve customer-focus factors in

557

558 S. PERERA et al.

manufacturing strategies (Samson et al., 1991, p. 36) and may even prejudice the pursuit of those factors (Dent, 1990 p. 3).

Consequently, the literature has generally argued that the changed manufacturing environment requires not only the adoption of customer-focused manufacturing strategies, but also a concomitant shift from cost and efficiency-based performance measures to ones which capture and reflect those strategies (Macintosh, 1985; Samson et al., 1991, p. 37; Banker et al., 1993). These latter measures are seen increasingly to comprise non-financial (or operations-based) indicators given their ability to measure factors such as delivery schedule maintenance (dependability), product characteristic variation (flexibility) and product quality (Banker et al., 1993, p. 34; Samson et al., 1991, p. 37; Smith, 1995, pp. 43-46; Abernethy & Lillis, 1995, p. 243).

Despite these arguments, few studies have examined empirically the links between customer-focused manufacturing strategies, the use of non-financial (operations-based) performance measures and performance in manufacturing organizations. Indeed, Abernethy and Lillis (1995, p. 241) note that “there has been little sys- tematic study of the link between manufacturing strategy and control system design” generally.

Abernethy and Lillis (1995) (hereafter A&L) examined the link between one component of a customer-focused manufacturing strategy, flexibility, and two aspects of control systems design, specifically the structural arrangements co-ordinating production activities and the performance measurement system. With respect to the latter, they asked the questions: (i) do firms committed to manufacturing flexibility use performance measures which de-emphasize accounting and other efficiency measures; and (ii) is the performance of the firm enhanced when the performance measurement system is adapted to facilitate the implementation of manufacturing flexibility?

A&L hypothesized and found that as firms move towards a strategy of manufacturing flexibility, they rely less on cost efficiency-based performance measures. A&L also found some evidence that reliance on cost efficiency-based measures was positively associated with performance for firms following a non-flexible manufacturing strategy, and negatively associated with performance for firms following a flexible strategy. The A&L study and results, however, also leave an element of equivocality about the performance implications of non-cost (non- financial) efficiency measures for firms pursuing customer-focused strategies. While A&L found positive (negative) associations between cost efficiency measures and performance for firms following non-flexible (flexible) strategies, they did not examine directly the performance implications of non-cost efficiency measures, thus leaving open the question of whether an emphasis on non-cost-based measures is associated with enhanced performance in firms pursuing flexible strategies.

Additionally, A&L examined just one component of a customer-focused manufacturing strategy (flexibility), and did so using interviews with managers in preselected industries. There is motivation, therefore, for extending the research to examine customer-focused manufacturing strategies more generally (including the factors of cost, quality and dependability), and doing so with a different (survey) methodology involving a random sample of compa- nies drawn from manufacturing industries.

This is the purpose of the study reported in this note. Following and extending A&L, the study examines: (i) whether firms which maintain a customer-focused manufacturing strategy also maintain an emphasis on non-financial (non-cost efficiency or operational) measures of performance; and (ii) whether such an emphasis is associated with enhanced performance for firms pursuing a customer-focused strategy.’ The paper is organized as follows. The next

‘As the focus of the study is on manufacturing strategies, the non-financial performance measures covered are correspond-

ingly restricted to operations-based measures and do not cover the broader range of non-financial measures applicable to

organizations more generally.

CUSTOMER-FOCUSED MANUFACTURING STRATEGY 559

section outlines the theory giving rise to the hypotheses to be tested, after first elaborating on the concept of a customer-focused manufacturing strategy, and how such a strategy may be attained. Following sections detail the method and variable measurement, results and discussion.

THEORY DEVELOPMENT AND HYPOTHESES FORMULATION

Customer-focus As noted earlier, a customer-focused manu-

facturing strategy may be seen as comprising the dimensions of cost, quality, flexibility and dependability of supply.* It has been argued (Buffa, 1984; De Meyer et al, 1989) that manu- facturers cannot pursue all four dimensions simultaneously because there are seen to be trade-offs between cost and quality, and cost and flexibility, and must therefore prioritise the dimensions in line with more restrictive strategy choices, such as Porter’s (Porter, 1980) three generic strategies of cost leadership, dif- ferentiation and focus.

However, the environmental changes noted earlier mean that manufactuting firms both need to, and can, seek to attain all four dimensions simultaneously. De Meyer et al. (1989, p. 139) recognized this by noting the Japanese approach to attaining a customer-focused manufacturing strategy through the sequential and progressive building of the four dimensions. Once achieved, the dimensions then hold equal priority, require constant maintenance and form the competitive yardstick.

A way in which equal emphasis on all four dimensions may be attained is implicit in McNair et al. (1988) who argue that manufacturing excellence may be approached through combining processes geared towards (i) the elimination of non-value added costs and (ii) automation. The first of these may be regarded

as relating to the management process and the second to the manufacturing process. Meredith (1987) offers a similar duality, referring to “business techniques” and “manufacturing techniques”. In this study, we use the terms Advanced Management Practices (AMP) and Advanced Manufacturing Technology (AMT) to parallel these dual elements.

Advanced management practices AMP is drawn from Chenhall (1993) (who

termed it advanced manufacturing practices) and is regarded as comprising management philosophies embodied in practices and pro grams used to enhance the manufacturing process with respect to customer-focus. In particular, these programs are directed towards, and reflect, three of the four dimensions of a customer-focused manufacturing strategy: (i) cost, through programs (such as Just-in-Time Manufacturing) aimed at reducing or eliminating inventories, waste and non-value adding activities; (ii) quality, through programs such as Total Quality Management and Employee Involvement; and (iii) dependability of supply (Chenhall, 1993).

Advanced manufacturing technology Advanced Manufacturing Technology (AMT)

relates to the physical hardware of the manufacturing process and is defined here as con- sisting of technological advancements in automation able to be used in the production process. Howell and Saucy (1987~) state that automation, ranging from stand-alone pieces of equipment to fully integrated factories, allows an organization to attain the four dimensions of a customer-focused manufacturing strategy by reducing costs while, at the same time, enhan- cing quality (through increasing the accuracy with which products are made), flexibility (through greater control over set-up times and product runs) and delivery dependability

‘In a broader sense, customer-focus may also entail pre-manufacturing and post-manufacturing activities (such as product

design and after-sales service/warranty). The emphasis of this paper is restricted to customer focus in manufacturing

strategy. The paper therefore relies on the four dimensions’ seen to constitute such strategy by, among others, Bu#a (1984)

and Nemetz and Fry (1988).


(through lower defect rates and flexibility for delivery schedule adherence). With respect to the perceived trade-off between flexibility and cost, Schroeder et al. (1990) contend that computer-controlled process technologies are able to reduce this trade-off, and Yamauchi (1988) demonstrated that robots were important in a Japanese motor vehicle manufacturing firm in allowing the simultaneous satisfaction of several objectives including quality, flexibility and cost.

Theory development and hypotheses formul-

ation

The foregoing arguments suggest that the joint presence of AMP and AMT allows a firm to maintain the four dimensions of cost, quality, flexibility and dependability of supply as simultaneous priorities within a customer-focused manufacturing strategy. As a consequence, the extent of implementation of AMP and AMT is used in this paper to proxy for the extent of customer-focus in a firm’s manufacturing strategy, on the grounds that it both: (i) indicates, and may therefore be used to operationalize, the extent of a firm’s commitment to the four dimensions of a customer-focused manufacturing strategy, and (ii) is the mechanism by which those dimensions may be attained. Two hypotheses are tested as follows.

Hl : Increasing customer-focus in manufacturing strategy, as proxied by the degree of implementation of AMP and AMT, is associated with increasing use of non-financial performance measures.

H2: Increasing use of non-financial performance measures is associated with enhanced performance for firms pursuing customer-focus in manufacturing strategy, as proxied by the implementation of AMP and AMT.

As this study is an extension of A&L, the theory supporting these hypotheses is largely explicated in that paper, and is discussed only briefly here. A&L (pp. 242-243) draw on Govindarajan (1988) Simons (1987), Brownell and Merchant (1990) Macintosh (1985) and Eccles (1991) to argue and demonstrate that

“effective performance measures (for customer- driven manufacturing strategies) will require a shift from measures which focus on manufacturing efficiency to measures which.. .capture the critical success factors related to customer- initiated demands” (Macintosh, 1985) (A&L, p. 243).

Two, of several, reasons for this expectation are drawn on here. First, traditional financial accounting measures are seen as too aggregate, too short-term oriented, and not sufficiently comprehensive or focused to capture the elements critical to customer-driven manufacturing strategies (Macintosh, 1985; Singleton-Green, 1993, p. 52). As such, these measures are seen to be less capable of supporting the achievement of manufacturing strategic priorities associated with customer-focus than are the more direct and specific non-financial (operations- based) measures of quality, flexibility and dependability (Govindarajan, 1988; Simons, 1987).

Second, traditional financial performance measures are argued to rely on manufacturing assumptions of standardization, an ability to specify unproblematic input/output relationships, and “mass production of a mature product with known characteristics...in a well- specified and stable environment” (Kaplan, 1983, p. 688). The typical absence of such characteristics in manufacturing environments involving competitive strategies of customer- focus is seen to render financial measures of manufacturing performance less relevant (Brownell & Merchant, 1990; cited in A&L, p. 242) and non-financial measures more relevant (Chenhall, 1992).

These reasons underpin the expectation in Hypothesis 1 of this study. If traditional financial performance measures are inadequate in assessing achievement of the dimensions of quality, flexibility and dependability, then they will be inadequate in supporting the customer-focused manufacturing strategy of which the dimensions are integral component parts. Thus, a firm following such a strategy will be expected to make use of non-financial performance measures targeting the specific operational aspects of quality, flexibility and dependability as part of a


multiple measures approach.3 Consequently, the customer-focus dimensions, such actions the more a firm seeks to pursue simultaneously and decisions need to be informed by relevant all dimensions of a customer-focused manufac- and specific feedback on those dimensions. turing strategy, proxied by and reflected in the Singleton-Green (1993, p. 52) argues that the extent of implementation of AMP and AMT, the most important reason supporting the power of greater the expected use of non-financial mea- non-financial indicators to enhance performance sures in performance evaluation. in a customer-focused manufacturing environ-

Stated another way, when the extent of ment is that “they (non-financial measures) deal implementation of both AMP and AMT are low, with causes not effects”. reflecting a low commitment on the part of the Profit measures (as an example of financial measures)

organization to both the management processes show the effects of non-financial activities and

(AMP) and technology (AMT) relevant to a cus- achievements; (but) they do not pin down precisely

tomer-focused manufacturing strategy, it is what it is in your business that you are getting right or

expected that the use of non-financial perfor- wrong (Singleton-Green, 1993, p. 52).

mance measures will also be low. This is The motivational premise of Hypothesis 2 is because there will be little perceived purpose that managers have an incentive to concentrate in formally measuring operations-based perfor- on, and will seek to maximize performance mance indicators relevant to a customer- against, those activities on which their perfor- focused manufacturing strategy in the absence mance is measured (Hopwood, 1974, Ch. 5;

of such a strategy. By contrast, when both AMP Hayes et al., 1988, p. 130; Singleton-Green, and AMT are high, reflecting a high commit- 1993). As such, to enhance performance in a ment to both the management processes and firm pursuing a customer-focused manufactur- technology associated with a customer-focused ing strategy, the dimensions of that strategy manufacturing strategy, it is expected that the need to be embedded in the performance mea- use of non-financial performance measures will surement system (Samson et aZ., 1991, p. 36).

also be high for the reasons outlined previously As has been shown, the performance measures in this section. Which of the two (AMP or AMT) relevant to such a strategy involve indicators of is stronger in its association with the use of the dimensions of quality, flexibility and non-financial measures, and the nature of their dependability, as well as cost. Hypothesis 2 fol- interaction, are empirical questions on which lows that increasing use of such measures in this study seeks to shed some light. firms pursuing a customer-focused manufactur-

The foregoing citations and evidence also ing strategy (where that focus comprises these support the expectation in Hypothesis 2. Both four dimensions and is proxied by the extent of implicit and explicit in the citations is the pre- implementation of AMP and AMT, which toge- mise that aligning performance measures with ther capture and facilitate those dimensions) strategic priorities is important in generating will be associated with enhanced performance. and directing manager actions towards the attainment of those priorities. The theory METHOD AND VARIABLE MEASUREMENT underlying this argument is both instrumental and motivational. With respect to the former, Data were gathered by a questionnaire Chenhall (1995, p. S), Nanni et al. (1992, p. 6) mailed to 200 managers of manufacturing firms/ and Singleton-Green (1993) among others, divisions randomly selected from the Sydney, argue that for manager actions and decisions to NSW area using Riddell’s Business Who’s Who

be effective in achieving performance against Australia (1994). Only firms/divisions with

‘This argument is not meant to deny the role of financial performance measures. Indeed, cost, as one of the four dimensions

of a customer-focused manufacturing strategy, is captured by such measures Rather, the argument seeks to demonstrate the

limitations of financial measures in this strategic context, and the need for them to be supported by non-financial measures.


more than 50 employees were included. Senior level managers were chosen as respondents needed to be knowledgeable about their firm’s manufacturing strategy, production process, performance measurement systems and performance. Riddell’s directory provides names for senior managers and questionnaires were per- sonally addressed. Telephone calls were made to confirm that these names were current or to obtain names of replacements. The questionnaire contained questions designed to measure the variables of AMP, AMT, emphasis on non- financial performance measures and organizational performance.*

Advanced management practices (AMP)

AMP was measured with Chenhall’s (Chen- hall, 1993, p. 13) instrument designed “to establish the extent to which divisions had progressed in the development of AMP”. The instrument comprises seven items (shown in the Appendix) assessing the extent to which an organization has implemented programs involving reduction of waste, quality improvement, cycle time improvement, etc. Respondents were asked to indicate the extent to which their organization had implemented these (AMP) programs on a five-point Likert-type scale, anchored on “To very little extent” and “To a great extent”. Chenhall’s application of the instrument showed high construct validity with all items loaded onto a single factor with a criterion of 0.40 (Chenhall, 1993, p. 13).

Advanced manufacturing technology (AMlJ

AMT was measured using an adaptation of the instrument developed by Inkson et al.

(1970). The instrument measures the degree of workhow integration (automation) as charac- terised by six categories (shown in the Appen- dix) ranging from hand tools to computer control.5 Respondents were asked to indicate, on a five-point Likert-type scale, anchored on “Not used at all” and “Used to a great extent”, the extent to which the degree of automation represented by each category was used in their production process. Responses to four of the six categories were used to score the extent of automation. Two categories (“hand tools and manual machines” and “powered machines and tools”) were excluded as they essentially reflect the absence of automation. The four which were included (“single cycle automatics and self-feeding machines”, “automatic- repeats cycle”, “self-measuring and adjust- ment-feedback” and “computer control- automatic cognition”) all involve automation and increasing degrees of automation.

A weighted measure of automation was used based on the argument that the intervals between the categories are not equal but increasing as the category moves from low to high. The more sophisticated forms of automation are more expensive, and involve committing the firm to a specific level of technology for a relatively long time. This makes it difficult for firms that are already highly automated to become relatively more so, while it is comparatively easier for firms with low degrees of automation to move to higher ones. A simple weighting system was applied, multiplying the score for the first (lowest) auto mation level category by 1, and the scores for the second, third and fourth categories by 2, 3 and 4, respectively.”

*A copy of the questionnaire may be obtained from either of the university authors. We acknowledge the permission of

Professor Robert Chenhall to use the AMP scale he developed in our study.

%VhiIe this is a relatively old instrument, it allows capture of the extent of implementation of automation, which is the

characteristic of relevance to the study. Later instruments (such as Hendricks, 1988, for example) capture the type of

automation rather than the degree or extent of its implementation.

“It is acknowledged that these weights are arbitrary, and that the decision to use four categories is subjective A second

measure of AMT using all six categories in unweighted form was also used as a sensitivity test. Results with the second

measure were not substantively different and are reported where appropriate later in the paper.


Non@zancial performance measures The extent of use of non-financial perfor-

mance measures was measured by adapting the A&L instrument, which comprised 18 performance measures. Ten were selected from this list based on how well they represented the four dimensions of a customer-focused strategy, and five new measures were added. The full instrument, containing 11 non-financial and four financial measures, is given in the Appen- dix. Examples of the non-financial measures are “product defects” and “number of product returns” relating to quality, “ability to vary pro duct characteristics” (flexibility), and “on time delivery” (dependability). The financial measures are “profit/net income”, “return on investment”, “sales” and “material price variance”.

Classification of the measures into financial and non-financial followed A&L and Horngren et al. (1994, pp. 890892). To be classified as financial, an item was financially aggregate, not specifically or directly reflective of customer- focus factors of quality, flexibility and dependability, and could potentially be enhanced by a tradeoff against, and even to the detriment of, customer-focus factors. These criteria were those underpinning the arguments for the relevance or irrelevance of traditional financially oriented performance measurement systems (Kaplan, 1990; Howell & Saucy, 1987b; Voll- man, 1990; Dent, 1990) and which therefore support the theory drawn on in the study.’

Respondents were asked to indicate on a five- point Likert-type scale, anchored on “Of little or no importance” and “Of utmost importance”,

the importance of each measure with respect to the extent of its use in their performance measurement systems. The emphasis placed on non-financial measures was determined by summing the scores for the 11 non-financial measures. This summation is consistent with the absolute (rather than relative) method of scoring used by Brownell (1985) to score emphasis on accounting performance measures8

Performance A self-rating instrument used by Chenhall

(1993) and adapted from Swamidass and New- ell (1987) was used to measure performance. On a five-point Likert-type scale, anchored on “Well below (above) industry”, respondents were asked to rate performance against industry average on each of the three dimensions of annual rate of growth in sales, profitability and return on assets over the past three years. Although self-rating measures have sometimes been criticised for a potential leniency bias, this is less a concern where such bias is generic and where the ratings are needed for relative rather than absolute analysis, as is the case in the pre- sent study. Following Chenhall (1993) performance was scored as the mean of the responses to the three questions.

RESULTS

Of the 200 questionnaires mailed, 109 were returned for a response rate of 54.5%. Four responses were excluded because of incom- plete data.” Descriptive statistics, including

‘It was pointed out by a reviewer that, depending on how it is used, “sales” might be a component of a non-financial

measure. If used, for example, as the percentage of revenue derived from new products, it could be a measure of future

positioning rather than current performance. In this study, the generic term “sales” was used and included as a financial

measure, following Homgren et al. (1994, p. 891).

‘A relative measure, relating the summed scores on the non-financial measures to those on the financial measures, was also

used to test the robustness of the results. Results with the relative measure were not substantively different and are repor-

ted where appropriate later in the paper.

9No follow up mailing was done because of time constraints. However, the presence of nonresponse bias was tested using

the analysis of early versus late responses suggested by Oppenheim (1966) p. 34). This test, plus the comparatively full

range of observed responses, suggest that nonresponse bias is not a problem in this instance.

564 S. PEREIU et al.

TABLE 1. Descriptive statistics

Variable Mean

Non-financial 40.28

Financial 16.58

Performance 3.64

AMP 25.36

AMT 29.81

S.D.

Possible range

Minimum Maximum

Observed range

Minimum Maximum Cronbach alpha

6.93 11 55 14 52 0.82

2.42 4 20 11 20 0.56

0.73 1 5 1 5 0.78

4.51 7 35 11 35 0.77 10.41 10 50 10 50 0.69

TABLE 2. Correlation matrix for independent and dependent variables

Non-financial Financial Performance AMP

Financial 0.553 (0.000)

Performance 0.147 0.068

(0.135) (0.487) AMP 0.606 0.236 0.251

(0.000) (0.015) (0.010) AMT 0.280 0.158 0.140

(0.004) (0.107) (0.153)

0.344

(0.000)

alpha reliability statistics (Cronbach, 195 l), for the variables measured are given in Table 1 and a correlation matrix in Table 2.”

The first hypothesis was that increasing customer-focus in manufacturing strategy, as proxied by the degree of implementation of AMP and AMT, would be associated with increasing use of non-financial measures. With respect to this hypothesis, Table 2 shows that the correlations between each of the two components of customer-focus (AMP and AMT) and the use of non-financial performance measures are positive and significant, with that between AMP and non-financial measures being higher at ~0.606, p=O.OOO, than between AMT and non-financial measures at r-=0.280, p=O.O04. Additionally, the

(1)

following regression model was fitted to the data.

y =bO + b,AMP + bp4MT

+bglMPxAMT+e

where:

y = Use of non-financial performance measures

AMP = Advanced management practices AMT = Advanced manufacturing technology

Table 3 presents the results of fitting equation (1). Table 3 shows that the model has an adjusted R square of 39%, and that b, is positive and significant (t=2.504, p=O.O07), indicating an interactive effect of AMP and AMT on the

‘oAhhough the AMP measure attained a Cronbach alpha of 0.77, it is a relatively new measure and, hence, a factor analysis

was also conducted. The analysis produced two factors. However, the first of these had an eigenvalue of 3.07 and explained

44% of the variance, while the second just attained the criterion eigenvalue of 1.02 and explained 14% of the variance.

Additionally, six of the seven items in the measure loaded on the first factor with loadings greater than 0.62, while the

seventh loaded at 0.40. The seventh was hem 6 in the Appendix.

CIJSTOMER-FOCUSED hIANUFACTURING STRATEGY 565

TABLE 3. Results of estimating a model for the two-way interaction between AMP and AMT for the dependent variable of

use of non-financial performance measures

Variable Coefficient Value SD. t-statistic p-value (one-tailed)

Intercept bc, 35.074 8.175 4.290 0.000

AMP bl 0.115 0.334 0.345 0 365

AMT bz -0.617 0.273 -2.256 0.013

AMPxAMT b? 0.027 0.011 2.504 0.007

R’=0.41, adjusted RZ=0.39, n=105, F 3,,0,=23.40, significance 0.000

use of non-financial performance measures. * ’ The extent of use of non-financial performance measures is dependent on the levels of both AMP and AMT. The data, therefore, support the argument that there is a greater emphasis on non-financial performance measures for firms in which both AMP and AMT are high; i.e. for firms which are pursuing a strategy of customer-focus through the employment of both advanced management practices and advanced manufacturing technology.

To examine the nature of the interaction, the procedure suggested by Schoonhoven (1981) was used.12 First, the partial derivative of equation (1) for the use of non-financial performance measures with respect to AMP was taken as equation (2) below.

dy/dAMP = bl + b.glMT (2)

Figure 1 plots equation (2) using the regression coefficients from Table 3 and the observed range (of 10 to 50) for AMT scores. The vertical axis represents dy/dAMP and the horizontal axis indicates the level of AMT. The graph shows that the effect of changes in AMP on the

use of non-financial measures is monotonic over the empirically observed range of AMT. The effect is positive with increasing implementation of AMP being accompanied by increasing use of non-financial performance measures, irrespective of the level of AMT, and with the rate of increase of such measures becoming greater as the level of AMT increases.13

Second, the partial derivative of equation (1) for the use of non-financial performance measures with respect to AMT was taken as equation (3) below.

dy/dAMT = b2 + bgikfP (3)

Figure 2 plots equation (3), again using the regression coefficients from Table 3, and the observed range for AMP scores (of 11 to 35). The vertical axis represents dy/dAMT and the horizontal axis the level of AMP. The graph shows that the effect of changes in AMT on the use of non-financial measures is non-monotonic over the observed range of AMP, with the point of inflexion at a value of AMP of 22.8 (determined by setting the partial derivative dy/dAMT

equal to zero) lying within the observed range of AMP. This indicates that for low levels of

“Results using the relative measure of emphasis on non-financial performance measures were also positive and significant

(k2.098, p=O.O19), although with a smaller 1 value than in the model using the absolute measure.

“This procedure has been used previously by Mia (1989) and Harrison (1993) to explore the nature of interactive effects.

“The point of inflexion determined by setting the partial derivative dy/aAMP equal to zero lies at a value of AMT of -4.26.

This lies outside the observed range of AMT scores of 10 to 50. The Schoonhoven procedure requires the use of point

estimates rather than confidence intervals and this should be borne in mind in interpreting the procedures and plots.


a.5 l .

-1 l . __:. : i

_* ~_________.________ i .._................. 1 -..- : -. -..-. _,....... :.. ..:...

0 10 20 30 40 50 60

AMT

Fig. 1. The effect of AMT on the relation between the use of non-financial performance measures and AMP.

2 v- ....-.-.....--..--I --.-.. :. ...--..-- ----- : .;... -.

dYldAMT 0 l

-1.5 . .-... .......-..... .....‘.... ...... ... .... .. .. .. .. .. .’ I .. ..

-2 _______ I ..:. .i I

0 10 20 30 40 50 60

AMP

Fig. 2. The effect of AMP on the relation between the use of non-financial performance measures and AMT.

AMP (below 22 and, therefore, reflecting “very An alternative method of visually represen- little” to “some” degree of implementation of ting the Schoonhoven analysis is to dicho AMP programs), increasing levels of AMT are tomize AMP at its point of inflexion and accompanied by decreasing use of non-financial re-estimate the regression model in equation (1) performance measures. However, once firms with a dummy variable for AMP. Figure 3 plots begin to implement AMP to a greater extent the results of this regression showing separate (above 22) this is accompanied by increasing lines for high and low AMP (AMP greater than movement towards non-financial performance 22 and less than or equal to 22, respectively). measures. These lines are iocated, again, by using the


30

Non-Financial Performance

Meaeures 2.

20 *MT 30

High AMP

Low AMP

Fig. 3. The relation between AMT and the use of non-financial performance measures for high and low AMP.

observed range (of 10 to 50) for AhIT scores, but using the coefficients generated by the regression model with AMP as a dummy variable.

The Schoonhoven analysis, and the relative magnitudes of the correlations in Table 2,

suggest that the implementation of, and commitment to, advanced management practices is a stronger stimulus to the use of non- financial performance measures than is the level of advanced manufacturing technology. Nonetheless, it should be kept in mind that the results of the interaction regression model (Table 3) indicate that firms’ use of non- financial performance measures is dependent upon both the level of AMP and AMT in inter-

action. ‘*

A complementary analysis was conducted using the responses to the two categories in the Inkson et al. (1970) measure of automation that were excluded from the AMT measure used in the main analysis (“hand tools and manual machines” and “powered machines and tools”). Although excluded from the AMT measure for the reason given earlier, summing the responses to these two items allows the firms in the sample to be distributed along a continuum representing the extent to which they use no (or minimal levels of) automation. The correlation between the summed scores on these two categories and the use of non-financial performance measures was negative and significant (?=-0.19O,p=O.O52). This indicates that the more (less) firms rely on non-automated technology,

‘% footnote 6, it was noted that an unweighted measure of AMT using all six categories in the Inkson et al. instrument was

also used as a sensitivity test. In this measure, responses to the first three (zero or low automation) categories were reverse

scored and summed with responses to the last three categories. The results of the analyses using this measure were not

substantively different from those with the weighted, four category measure. The interaction between AMP and AMT

affecting the use of non-financial performance measures remained significant, although at a lower level (1=1.750, p=O.O42 one-tailed), and the Schoonhoven analysis produced graphs of the same form as those in Figs 1 and 2. This lends consider-

able comfort to the robustness of the results of the study.


the less (more) they use non-financial performance measures. This result is consistent with, and supports, the result of the main analysis.15

Hypothesis 2 The second hypothesis was that increasing

use of non-financial performance measures would be associated with enhanced performance for firms pursuing a customer-focused strategy built upon the joint components of AMP and AMT. To examine this question, the following three-way interaction regression model was estimated.

y =bo + b,AMP + bzAMT + b3NF

+b&PxAMT+b+MTxNF

+bglMPxNF (4)

+b&MPxAMTxNF+e

where:

y = Performance AMP = Advanced management practices AMT = Advanced manufacturing technology

NF = Use of non-financial performance measures

The results of fitting this equation are reported in Table 4 which shows the coefficient b, for the three-way interaction term is insignificant. (This term was also insignificant in the model

using the relative measure of emphasis on non- financial measures.) Hence, no support is found for the argument that increasing use of non- financial performance measures is associated with enhanced performance for firms pursuing a customer-focused strategy. Some potential reasons for this result are given in the Discus- sion section.

Following this finding, further analysis was undertaken by fitting separate two-way interaction regression models for the customer-focus components of AMP and AMT, respectively.16 While the results of fitting the model for AMP yielded an insignificant interaction coefficient, the interaction coefficient for the AMT model was significant (t=2.435, p=O.OOS), indicating that increased use of non-financial performance measures is associated with enhanced performance for those firms implementing increased levels of advanced manufacturing technology. The results should be treated with caution, however, as the R square for the AMT model was only 6%.17

DISCUSSION

This study provides empirical evidence of the increased use of non-financial performance measures by firms pursuing a customer-focused

r5Although not of direct concern to the questions asked, the association between customer-focus (with its components of

AMP and AMT) and the use of financial performance measures was also examined. A regression model of the form specified

in equation (1) with the dependent variable of use of financial performance measures yielded no significant coefficients.

Hence, the data show no evidence of a relation between increased customer-focus and increased use of financial perfor-

mance measures. This result is consistent with that from testing Hypothesis 1 using the relative measure of emphasis on

non-financial performance measures (refer to footnote 11).

‘%e coefficients for the nvoway interactions cannot be estimated from the three-way interaction model given as equation

(4). The data used in the study are based on Likert-type scales and are therefore interval at best. As such, the scales have

arbitrary origins and linear transformations are legitimate and trivial. However, Southwood (1978) and Jaccard et al. (1990)

have shown that although such transformations are trivial theoretically, they can change the regression coefficients, stan-

dard errors and significance tests for all terms below the highest order interaction term, and hence the lower order (here,

the twoway) interaction terms may be neither stable nor interpretable in the higher order (here, the three-way) interaction

model.

“Further caution is added here in that the interaction coefficient did not reach significance in the model using the relative

measure of emphasis on non-financial performance measures.


TABLE 4. Results of estimating a model for the three-way interaction between AMP, AMT and use of non-financial perfor-

mance measures for the dependent variable of performance

Variable Coefficient Value S.D. t-statistic p-value (l-tailed)

Intercept

AMP

AMT

Non-financial

AMPXAMT

AMTx non-financial

AMP x non-financial

AMP x AMT x non-financial

bo 6.265 6.600 0.950 0.172

b, -0.006 0.292 -0.022 0.491

bz -0.168 0.213 -0.789 0.216

bs -0.068 0.167 -0.411 0.341

b4 0.004 0.009 0.399 0.345

bs 0.003 0.005 0.659 0.255

bs 0.000 0.007 0.007 0.497

b, 0.000 0.000 -0.261 0.397

R*=0.12, adjusted Ra=0.06, n=105, F7,s7=1.97, signiticance 0.07

manufacturing strategy. As such, the results support those of A&L (and add to the scarce empirical research) in providing evidence of management beliefs that changes in manufacturing strategies to emphasise quality, flexibility, dependability and low cost should be accompanied by changes in formal performance measurement systems to place greater emphasis on non-financial (operations-based) measures.

While the results suggested that AMP (the management philosophy component of a customer-focus strategy) was a stronger stimulus to the use of non-financial performance measures compared to the AMT component, the study also found that both these components in interaction are important in explaining management choices of performance measures.

component on its own, the results must be seen as failing to support the link to performance. There may be several reasons for this. First, it may reflect the anecdotal evidence noted in A&L that changes to the performance measurement system were considered less important than organizational structural arrangements in their ability to enhance performance under flexible manufacturing strategies. Such structural arrangements were not examined in this study.

The study also provides greater generalizability to A&L in that it found the association between manufacturing strategy and non-financial performance measures, using four components of a customer-focused strategy rather than just one, and with a different (survey) methodology and a broader-based random sample of manufacturing firms.

Second, and consequently, the main benefits of increasing the use of operations-based measures may be motivational rather than instrumental; that is, through aligning the formal performance measurement system (and subse- quent systems of reward and compensation) with those factors that operational managers know they must pay attention to under a customer-focused manufacturing strategy. If so, a match between such a strategy and non- financial performance measures may be reflected in manager-affective outcomes such as increased satisfaction and motivation and reduced stress rather than in direct performance outcomes.

The study was not able to find a consequen- Thud, the absence of results on performance tial link to organizational performance. The might be a consequence of two related limita- only significant finding for performance was tions of the study; the cross-sectional metho- the interaction between the technology com- dology employed and the measure of the ponent of customer-focus and the use of non- performance variable. With respect to the lat- financial measures. However, in the absence of ter, performance is a complex variable with a a finding for customer-focus generally, and with multiplicity of factors contributing to the level no convincing argument for the technology of global performance at any point in time. The


performance measure used here may not have been strong enough or timely enough to capture the effects hypothesized. We did not formally examine whether the performance measures were linked to pay and promotion consequences for the managers in the sample. This may have reduced the strength of the association. Plus, our measure is essentially one of short-term performance and may not, therefore, reflect longer-term performance effects of the hypothesized relationships.

Future research focusing on changes in specific, disaggregated components of performance, and over a longer period of time, might well capture performance effects not discerned in this study. The argument that emphasis on non-financial performance measures in a customer-focused strategic environment should enhance performance remains persuasive, in that information on specific aspects of quality, flexibility and dependability should enable managers to make more informed decisions and take specific control actions with respect to those aspects. More informed decisions and control actions, as well as the motivational effects of non-financial measures in this strategic environment, should translate into

enhanced performance, but not necessarily immediately, or as captured by the global measure used in this study.

A final, but important limitation of the study is its cross-sectional nature. As for all such research, our study is able only to show associations among the variables at issue. While theory and prior literature are available to suggest that manufacturing strategy precedes performance measurement systems change, such a directional implication is entirely theory-driven and cannot be imputed from the cross-sectional survey methodology. As such, the potential for reverse or reciprocal causality cannot be ruled out. In consequence, further research on this question using a longitudinal methodology and examining one or more organizations and their performance measurement systems both before and after adoption of a customer-focused manufacturing strategy would allow empirical testing of the direction of causality, as well as shedding light on the process of systems adaptation. In this way, both associational and longitudinal studies, with their different comparative strengths of generalizability and causality, will be valuable in building conver- gence in this area.

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APPENDIX

Advanced managementpractices (Chenball, 1993)

Programs to improve the quality and reliable delivery of materials and components provided by suppliers.

Programs to reduce waste or non-value added activities throughout the production process.

Programs to redyce time delays in manufacturing and designing products (i e. improve cycle time).

Involvement of employees in quality improvement programs (e.g. training, involvement in improvement teams).

Involvement of functional personnel (manufacturing, marketing, research and development) in strategy formulation.

Developing close contact between manufacturing and customers.

Programs to coordinate quality improvements between parts of the organization.

Advanced manufacturing technology (7nkson et al., 1970)

Hand tools and manual machines.

Powered machines and tools.

Single cycle automatics and self-feeding machines.

Automatic: repeats cycle.

Self-measuring and adjusting: feedback.

Computer control: automatic cognition.

Performance measures (adaptedfrom Abernetby G LilIis, 1995)

On time delivery.

Number of customer complaints.

Iabour utilization/efficiency statistics.

Product defects.

Profit/net income.

Reduction in set-up times.

Reports on whether standard product costs are met.

Rate of introduction of new products.

Return on investment

Number of product returns.

Material purchase price variance

Length of cycle time from order to delivery.

Sales.

Evaluation of the abdity to vary product characteristics. Measurement of machine utilization and down time

cusotmer focused manufacturing strategy and the use of operations based non financial performance...

Documents

manufacturing organizations

manufacturing technology

manufacturing management

financialbased measures

new manufacturing environment

enhanced performance

efficiency measures

cost efficiencybased