the effects of continuous improvement and … · the effects of continuous improvement and...

THE EFFECTS OF CONTINUOUS IMPROVEMENT AND INNOVATION MANAGEMENT PRACTICE ON SMALL TO MEDIUM

ENTERPRISE (SME) PERFORMANCE

Dr Milé Terziovski, PhD Director and Deputy Chair, European Australian Cooperation Centre, Faculty of Economics and Commerce,

The University of Melbourne, Parkville, Victoria, Australia Tel: 61 3 93447868, Fax: 61 3 8344 3714, Email: [email protected]

ABSTRACT This paper presents the results of a mail survey used to investigate the relationship between continuous improvement/innovation management practices and SME performance in Australia. Multi-item scales were developed and used to measure key components of continuous improvement and innovation management. Nine dimensions of SME performance were measured, for example, speed to market, success rate of new products, improved product innovation, reduction in waste, etc.,

Hypotheses, relating practice with performance outcomes, were developed and tested within a Continuous Improvement and Innovation Management (CIAIM) framework, using response data from 115 Australian SMEs from the manufacturing sector. A survey response rate of 21 per cent was obtained. The following results were obtained using multivariate analysis techniques: The CIAIM model was found to be a valid and reliable framework for measuring and predicting the relationship between continuous improvement/innovation management practice and SME performance. The most significant predictors of high SME performance were found to be:

• The adoption of a continuous improvement and innovation management strategy. This was found to be a critical factor for high performing SMEs to achieve their strategic goals and objectives.

• The use of core technologies and organizational objectives as a guide for evaluating new ideas and information as part of the continuous improvement and innovation management system.

The paper concludes that a continuous improvement and innovation management strategy and system are significant predictors of SME performance. The implication for managers is that these practices are imperative in order to avoid SME failure. The findings are consistent with the literature.

Key Words: continuous improvement, innovation, performance

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INTRODUCTION According to the Australian Bureau of Statistics (ABS, 1999) there are approximately 900,000 businesses currently operating in Australia of which 94-96 per cent are considered SMEs. These enterprises have generated more than half of Australia’s employment growth and are seedbed for innovation and the formation of large corporations (Australian Bureau of Statistics, 1999).

Despite the overall contribution of SMEs, however, every year thousands of SMEs fail. According to the US Small Business Administration 24 per cent of all new businesses fail within two years and 63 per cent fail within six years (Wheelen and Hunger, 1999, p.284). Similar failure rates occur in Australia, UK, The Netherlands, Japan, Taiwan, and Hong Kong.

This was confirmed by a longitudinal study conducted by Dun & Bradstreet (in Wheelen & Hunger ,1999) of 800,000 small US firms from 1985 to 1994. Seventy per cent of these firms were still in business in March 1994. Contrary to other studies, this study only counted firms as failures if they owed money at the time of their demise. The main reasons for SME failure range from inadequate accounting systems to inability to cope with growth. Two underlying predictors of SME failure emerge from the literature:

• An overall lack of strategic management, with an inability to plan a strategy to reach the customer.

• Failure to develop a system of controls to keep track of SME performance.

Purpose of the Study and Research Questions

The purpose of this paper is to present the results of a mail survey used to investigate the relationship between continuous improvement/innovation management practices and SME performance. A continuous improvement and innovation management framework is developed within which hypotheses are tested using Multiple Regression Analysis. Hence, the following research questions are addressed:

• Is a continuous improvement and innovation management (CIAIM) model a reliable and valid tool for predicting the relationship between SME practice and performance?

• Which management practices are significant and positive predictors of high SME

Performance?

LITERATURE REVIEW For the purpose of this study the term SME incorporates two primary classifications, namely small business and medium business. Behrendorff et al (1996) defines a small business as “being independently owned and managed, being closely controlled by owners/managers who also contribute most, if not all, of the operating capital: having the principle decision-making functions resting with the owner/managers. Three size categories defined by the Australian Bureau of Statistics (1999) are adopted in this paper: ‘small’ (20-49 employees), medium (50-99 employees) and large (100 or more employees).

Continuous Improvement and Innovation Management Innovation is widely accepted as a crucial competitive weapon in today's global market place. Innovation is defined as “the adoption of an idea or behaviour, whether a system, policy,

3 program, device, process, product or service, that is new to the adopting organisation” (Damanpour, 1992). The Innovation Study Commission’s (1993) defines innovation as “something new or improved, which is done by the enterprise to significantly add value either directly or indirectly for the enterprise or its customers.” This definition has been adopted on the survey instrument, instructions to respondents.

Jha et al., (1996) define continuous improvement (CI) as a collection of activities that constitute a process intended to achieve performance improvement. In manufacturing, these activities primarily involve simplification of production processes, chiefly through the elimination of waste. In service industries and the public sector, the focus is on simplification and improved customer service through greater empowerment of individual employees and correspondingly less bureaucracy (McLaughlin,1990).

Acquisition and use of skills for process analysis and problem solving are seen as fundamental to CI in the private and public sectors. CI, innovation management and quality management are closely connected in the literature. For example, Imai(1986) defines Total Quality Control (TQC) as "organized kaizen improvement activities involving everyone in a company - managers and workers - in a totally integrated effort toward improving performance at every level".

The underpinning principle of KAIZEN (Japanese word for continuous improvement) is the use of various problem-solving tools for the identification and solution of work-based problems. The aim is for improvement to reach new ‘benchmarks’ with every problem that is solved. To consolidate the new benchmark, the improvement must be standardised. In many Australian SMEs this standardisation has been attempted via the ISO 9000 quality systems certification process (Terziovski et al., 1997).

KAIZEN generates process-oriented thinking (P criteria) since processes must be improved before improved results (R criteria) can be obtained. Improvement can be broken down between continuous improvement and innovation. KAIZEN signifies small improvements made in the status quo as a result of ongoing efforts. On the other hand Innovation involves a step-change improvement in the status quo as a result of a large investment in new technology and/or equipment or a radical change in process design using the Business Process Reengineering concept (Hammer et al., 1993).

There is one significant difference between KAIZEN and Innovation according to Imai (1986). KAIZEN does not necessarily call for a large investment in capital or a radical redesign of processes to implement the strategy. However, the KAIZEN strategy does call for continuous effort and commitment from all levels of management. Thus KAIZEN calls for a substantial management commitment of time and effort. Investing in KAIZEN means investing in people.

According to Harrington (1995) “..all organisations need both continuous and breakthrough improvement.When breakthrough improvement and continuous process improvement are combined, the result is a 60 per cent improvement per year over continuous improvement alone.” However, Harrington concludes, based on empirical evidence, that continuous improvement is the major driving force behind any improvement effort. Breakthrough improvement serves to ‘jump-start’ a few of the critical processes.

Several other research studies have pointed out to the need for continuous improvement and innovation as a key source of competitive advantage for organizations. In today's competitive environment, the challenges for all businesses (including SMES) is not only to innovate in

4 existing markets to survive and remain profitable, but also to innovate in new markets in order to stay in front of competitors.

A major study commissioned by the Australian Manufacturing Council, Leading the Way (1995) identified “size and complexity” as the main issue for SME survival; the point at witch SMEs switch from informal to more formal and structured planning systems in order to survive. The study confirmed on the basis of a 1300 response data base, that continuous improvement and innovation management has a positive impact on the business performance of individual firms.

A more specific study by Soderquist et al. (1997) investigated continuous improvement and innovation practices in French SMEs. The study was an extension of Birchall et al.'s (1996) study in which a macro-level comparison of factors affecting managing of innovation in SMEs in the UK, France, and Portugal was presented. In their study, Soderquist et al. (1997) examine the drivers for change and the short-and long-term goals, the sources of innovation and the nature of innovation management in French SMEs. Respondents were asked to consider a recent and successful innovation in product or service and then to indicate just how important a number of items were as a source of particular innovation. The top nine sources of innovation were found to be:

• The introduction of new products and/or services. • Continuous improvement of work processes. • Radical change, e.g. through Business Process Reengineering. • Increased focus in marketing/sales efforts. • Reduction in indirect staff numbers. • Improvement on staff competence. • Improved quality of products and services. • Improving the quality of management. • Efforts to improve supplier performance.

The study identified two groups of SMEs. The first group reported satisfaction with their organization's performance in product and service innovation and also reported that their organizations had a strategic approach to innovation. The downside for these companies is that innovation might come at the cost of short-term profitability and innovation in working processes and procedures.

The second group comprised SMEs who were satisfied with current actions for improving short-term performances. Further analysis showed that this group is more likely to report a stronger emphasis on effective performance management approach. The downside for these companies is that such a focus is less likely to result in satisfactory product and service innovation.

The following conclusions can be drawn from the Soderquist et al. (1997) study:

• Continuous improvement of work processes was ranked the most important action for improving the short-term profitability, while increasing customer focus was ranked the most important action for improving long-term well-being of the company.

• The demands placed on business by customers/clients, close working relationship with key customer, and input from their own R&D department were considered as the most relevant sources for successful innovation in product/service.

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• Suggestions from internal quality improvement groups was ranked as the most important source of innovation for work processes and procedures. The respondents placed the highest emphasis on pressures for cost cutting and technology management.

Whilst the findings of this study are relevant and useful to managers and practitioners, the analysis is based on simple statistics, and it does not rigorously test the strength of the relationship between SME practice and performance. More rigorous studies have been conducted by (Gibb and Davies, 1990; Rizzoni, 1991; Sebora et al., 1994). These studies have identified and highlighted the critical success factors for continuous improvement and innovative strategy in SMEs and the importance of a marketing orientation and effective strategic formulation in successful SMEs. The critical success factors highlighted in these studies include:

• Promoting a corporate culture.

• Creating an effective structure.

• Analysing competitors.

• Developing co-operations and partnerships.

• Developing flexibility and speed of response.

In a recent study by (Boer, et al, 2000) describe and explain how companies can gain a competitive advantage by extending their innovation efforts to the various phases of the product life cycle and by facilitating knowledge transfer and learning both within the company and with other partner organizations. The authors develop a methodology based on collaborative research by the authors and their involvement in the Euro-Australian co-operation project CIMA (Euro-Australian Co-operation Centre for Continuous Improvement and Innovation Management). This project was established as a joint venture between the European Commission and the Australian Government in 1997.

The methodology provides a structured, step-by-step approach to mapping the user company’s current level of learning within product innovation, identifying strengths and weaknesses and then suggesting enabling mechanisms which can be implemented by the company to stimulate continuous improvement and learning, depending on specific contingencies. This process is supported by a behavioural model, explaining relationships between learning behaviours and outcomes, capacities enabling these behaviours, levers that managers can use to change existing or promote new behaviours, and contingencies affecting this whole set of relationship.

Literature Synthesis

Despite the large size of the literature on CI and innovation management, there is little empirical research with the desired focus or rigour according to Jha et al., (1996). Less than 2 per cent of the 1,002 bibliographic references on CI analysed by Michela et al.(1996), were explicitly identified in the subject fields of the references as "studies" by the database developers compared with the 24 per cent identified as "case studies".

Although one could criticize the practitioner literature for being, at times, repetitive, some of the repetition is justified when the same principles, tools and success factors are described to

6 different audiences (e.g. accountants, executives, human resource specialists, manufacturers) as reached by publishing in targeted journals.

The Soderquist et al. (1997) study has shed some new light on the critical success factors in managing continuous improvement and innovation management in French SMEs. However, there are some limitations with the study which need to be taken into account. As this is a single region study, generalizations are limited. Furthermore, the study does not comply with generally-accepted standards of methodological rigour. The analysis is based on simple statistics, and it does not rigorously test the strength of the relationship between SME practice and performance.

Therefore, our study aims to address the above issue by intergrating CI and innovation management under five key areas: strategy, structure, culture, systems, and performance in a similar fashion as the CIMA methodology (Boer, 2000). Multivariate analysis is used in order to test the validity and relaibility of the CIAIM model and the strength of the relationship between SME practice and performance. The following hypotheses were formulated for testing:

Hypotheses to be Tested H1: “The CIAIM model is a reliable and valid instrument for measuring and predicting the relationship between continuous improvement/innovation management practice and firm performance.” H2: “Continuous improvement and innovation management strategy and systems have a positive and significant effect on SME performance”

RESEARCH DESIGN

In order to test the formulated hypotheses, quantitative data was gathered from a large random sample in a mail survey of SME manufacturing firms. Twelve industry codes based on the Australian Standards Industry Classification (ASIC) system were. A total of approximately 550 SME manufacturing sites were sent a questionnaire. Responses were received from 115 sites yielding a response rate of 21 percent.

Variables /Survey Instrument A total of 19 questions were included in the questionnaire. A variety of sources were used in developing the questions, including the Continuous Improvement and Innovation Management (CIMA) Methodology (Boer, et al, 2000) developed by the Euro-Australian Cooperation Centre for Continuous Improvement and Global Innovation Management. The questionnaire for our study was pilot tested on 12 sites in Australia, and subsequently revised. SME Performance is used throughout this paper to represent innovation performance (eg. higher success rate of new products launched, faster speed to market) and operational performance (eg.reduction in waste, increased quality, delivery-in-full-on-time). (Venkatraman & Ramanujam ,1986, p.801).

Data Preparation

A total of 57 independent variables and 12 dependent variables were used in the analysis. Confirmatory Factor Analysis was conducted where 5 usable independent factors were extracted and one dependent factor. Factor Analysis and Multiple Regression Analysis require all cells in the data set to be complete. The variable mean was substituted for missing cells.

7 Profile of respondent firms Section 1 of the survey instrument asked 5 demographic questions. Two of these questions addressed the characteristics of the respondent firms. With reference to Tables 1and 2, more than 50 per cent of the total respondents were Managing Director or CEO with the balance of respondents were Manager level or higher. The size of the companies that responded range from less than 5 employees to 1 firm being greater than 500 employees, with 90 per cent of firms falling between 21 and 500 employees. These figures correspond with the Australian Bureau of Statistics (1999) classification of firms discussed in the introduction section of the paper.

Position Frequency Percent Secretary 1 0.9 Manager 19 16.8

Operations Manager 8 7.1 Financial Controller/Accountant 8 7.1

Managing Director or CEO 60 53.1 Director 12 10.6

Chairman 1 0.9 Other 3 2.7

TABLE 1 - Position in Company

Number of Employees Frequency Percent <5 2 1.8

6-20 8 7.1 21-50 39 34.5 51-100 35 31.0 101-499 28 24.8

500+ 1 0.9 TABLE 2: Relevant size of the company

QUANTITATIVE DATA ANALYSIS The survey instrument developed for the study was used as a basis for the development of the CIAIM model. The variables assigned to each of the six constructs were subjected to Confirmatory Factor Analysis to ensure that they were reliable indicators of those constructs (Nunnally, 1978). A cutoff loading of 0.30 was used to screen out variables which were weak indicators of the constructs.

The composite reliabilities of four of the six constructs meet Nunnally’s recommended standard (Cronbach Alpha ≥ 0.70) for early stage research (Nunnally, 1978). The reliabilities of the remaining two constructs: ‘organisational culture’ and ‘technological compatibility’ both fell short of this standard (0.63 and 0.45 respectively).

However, further culling of variables did not improve this situation, as the reduction in the number of indicators outweighs the benefits of shedding the less reliable indicators. Once the confirmatory factor analysis was complete, factor scores were calculated from the remaining variables to provide estimates for each of the six constructs.

The factor scores for the first five constructs and the items that factored on these constructs were used as independent variables in a multiple regression analysis. Correlation matrices

8 were produced for the factor scores of the six constructs and the respective items that factored on each construct. The factor scores for the sixth construct, and the individual items that factored on this construct were used as the dependent variables in the regression analysis. The results of the factor analysis are presented in Tables 3 and 4.

Results - Confirmatory Factor Analysis and Reliability Analysis Table 3 shows the construct ‘strengths’ for the five independent variable categories of the CIAIM Model. VARIABLES DESCRIPTION OF VARIABLE FACTOR

LOADING CONSTRUCT RELIABILITY

F1: INNOVATION SYSTEM & STRUCTURE REFERS The vision/mission includes a reference to innovation 0.477 MONITOR Formally monitors developments in new technologies 0.584 CONSIDER Considers the use of technology as a driver of business

growth 0.556

TECCNOL Allocates resources to sharing technology across the organisation

0.596

TEAMS Allocates resources to the use of cross-functional teams O.537 MEETINGS Organisation’s culture encourages formal meetings and

interactions 0.373

INFORMAT Employees search for information, new ideas and technologies as part of continuous improvement and innovation management

0.715

DIVERSE Employees search for and incorporate diverse points of view as part of continuous improvement and innovation management

0.687

INFORMAL Employees facilitate and encourage informal relationships as part of continuous improvement and innovation management

0.604

EXPERIM Employees take reasonable risks by continuously experimenting with new ways of doing things

0.636

CHALLENG Employees challenge the status quo, thereby encouraging constructive conflict as part of continuous improvement and innovation

0.672

FAILURES Employees use failures as opportunities to learn as part of continuous improvement and innovation management

0.596

SPECTECHT Employees work towards specific technological goals/objectives as part of continuos improvement and innovation management

0.751

TECHGUID Employees let core technologies/organisational objectives guide the evaluation of new ideas and information as part of continuous improvement and innovation management

0.652

ACTIONPL Employees actively monitor progress by using action plans/timetables to ensure that goals are met as part of the continuous improvement and innovation management process

0.609

OBJECTIV Innovation strategy has helped the organization to achieve its strategic goals and objectives

0.524 α = 0.816

F2: CONTINUOUS IMPROVEMENT& INNOVATION MANAGEMENT

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STRATEGY PRODN Relative importance of innovation strategy in

increasing production volume 0.597

SKILLS Relative importance of innovation strategy in increasing employee skills

0.808

CUSTSAT Relative importance of innovation strategy in contributing to customer satisfaction

0.751

QUALITY Relative importance of innovation strategy in improving product/service quality

0.780

MORALE Relative importance of innovation strategy in improving employee commitment/morale

0.859

ADMIN Relative importance of innovation strategy in improving administrative routines

0.693

COOPERAT Relative importance of innovation strategy in improving internal communication/cooperation

0.749 α = 0.872

F3: CUSTOMER AND SUPPLIER RELATIONSHIPS

REPUTAT Degree to which the firm’s reputation is important to the firm’s competitive advantage

0.714

SUPPLY Degree to which product/service supply is important to the firm’s competitive advantage

0.820

CSATIS Degree to which customer satisfaction is important to the firm’s competitive advantage

0.792 α =0.670

F4: ORGANISATIONAL CULTURE

KNOWLEDGE

The organisation’s culture encourages employees to hold knowledge closely

0.640

TRADIT The organisation’s culture reinforces behaviours that uphold traditions

0.522

WORKTIME The organisation’s culture encourages managers to closely monitor work time

0.577

SHORT The organisation’s culture focuses on short term performance

0.710

INSIDERS The organisation’s culture encourages employees to interact with insiders only

0.711 α = 0.627

F5: FIRM’S TECHNOLOGICAL COMPATABILITIES

CUSTTECH The extent to which customers have the same or similar technologies to the organization's

0.764

SUPTECH The extent to which suppliers have the same or similar technologies to the organisation’s

0.857

COMPTECH The extent to which competitors have the same or similar technologies to the organisation’s

0.400 α = 0.452

TABLE 3 CONFIRMATORY FACTOR ANALYSIS: INDEPENDENT VARIABLE CONSTRUCTS

VARIABLES DESCRIPTION OF VARIABLES FACTOR LOADING

CONSTRUCT RELIABILITY

F6: FIRM PERFORMANCE

SPEED As a result of the firm’s product and process innovation strategy, to what extent did Faster Speed to Market occur based on perception of actual performance

0.653

10 NEWPROD As a result of the firm’s product and process innovation

strategy, to what extent did Higher Success of New Products Launched occur based on perception of actual performance

0.565

PRCONFIG As a result of the firm’s product and process innovation strategy, to what extent did Greater Number of Product Configurations occur based on perception of actual performance

0.545

PRINNOV As a result of the firm’s product and process innovation strategy, to what extent did Improved Product Innovation-New Parts and Processes occur based on perception of actual performance

0.705

METHODS As a result of the firm’s product and process innovation strategy, to what extent did Improved Work Methods and Processes occur based on perception of actual performance

0.708

REDWASTE As a result of the firm’s product and process innovation strategy, to what extent did Reduction in Waste of Resources occur based on perception of actual performance

0.653

MARKOPP As a result of the firm’s product and process innovation strategy, to what extent did Increased Market Opportunities occur based on perception of actual performance

0.683

INQUALIT As a result of the firm’s product and process innovation strategy, to what extent did Increased Quality occur based on perception of actual performance

0.788

DIFOT As a result of the firm’s product and process innovation strategy, to what extent did Increased Delivery-In-Full-on-Time occur based on perception of actual performance

0.687 α = 0.839

TABLE 4 CONFIRMATORY FACTOR ANALYSIS: DEPENDENT VARIABLE Constructs as Predictor Variables in the Regression Model Table 3 shows the bi-variate correlation coefficients of factors of the CIAIM model and their relationship with the SME performance construct developed in Table 3. From scanning and cutting the data set and from the literature it can be ascertained that firms often specialise or focus to excel in only a subset of these performance dimensions.

As would be expected, firms which are advanced in their practices on some factors tend generally to be more advanced on others. For example, F1: Innovation System& Structure, F2: Continuous Improvement and Innovation Management Strategy, and F3: Customer and Supplier Relationships all have a significant and positive relationship with SME Performance.

On the other hand it is interesting to note that F4: Organisational Culture and F5: Technological Compatibilities had negative correlations, but not significant. From these correlations, F4 and F5 did not seem to be closely related to SME Performance and the rest of the group, whereas all the other CIAIM model categories have significant positive correlations, albeit of varying strengths. Table 5 shows the multiple regression of the five factors of the CIAIM model regressed on the dependent variable F6: SME Performance. From these analyses, our intent was to test the first hypothesis, H1.

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F1:Innovation System & Structure

F2: Continuous Improvement and Innovation Management Strategy

F3: Customer

and Supplier Relationships

F4: Organisational Culture

F5: Technological compatabilities

F6:

Firm

Perf.

F1 1.000

F2 0.241** 1.000

F3 0.163* 0.344** 1.000

F4 -0.259** -0.070 0.124 1.000

F5 0.021 0.101 -0.041 0.042 1.000

F6 0.566** 0.477** 0.201* -0.100 -0.064 1.000

** Correlation is significant at the 0.01 level (1-tailed) * Correlation is significant at the 0.05 level (1-tailed)

TABLE 5 CORRELATION MATRIX OF INDEPENDENT VARIABLES (CONSTRUCTS)

Testing of Hypotheses H1 “The CIAIM model is a reliable and valid instrument for measuring and predicting the relationship between continuous improvement / innovation management practice and firm performance.”

Dependent Variable: F6: Firm Performance Construct Multiple R 0.616 R Square 0.380 Adjusted R Square 0.351 Standard Error 0.7687 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 5 38.758 7.752

Residual 107 63.242 0.591 F = 13.115 Signif F =0.000

Variables Beta T Sig T

F1 Innovation System& Structure

0.423 5.226 0.000

F2 Continuous Improvement and Innovation Management Strategy

0.379 4.554 0.000

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F3 Customer and Supplier Relationships

-0.011 -0.127 0.899

F4 Organisational Culture 0.048 0.597 0.552

F5 Technological compatibilities

-0.109 -1.414 0.160

TABLE 6. MULTIPLE REGRESSION ANALYSIS

Validity and Reliability Information about validity and reliability was necessary in order to determine whether the six constructs of the CIAIM model were stable and accurate and whether they truly measure what they set out to measure. This provides assurance that the findings reflect an accurate measure of the underlying constructs, Factors 1 to 6 in Tables 3 and 4, and that the results are believable (Hair et al., 1992). Three different types of validity were considered in this study: content, construct and criterion validity. Content Validity A category is considered to have content validity if there is general agreement from the literature that the CIAIM model has measurement items that cover all aspects of the variable being measured. Since selection of the initial measurement items was based on the extensive review of international literature the CIAIM measures were considered to have content validity. Construct Validity A measure has construct validity if it measures the theoretical construct that it was designed to measure. The construct validity of each category was evaluated by using Principal Components Factor Analysis (Hair et al., 1992). The measurement items for each of the categories were factor analysed. The results in Table 3 for the independent constructs, and Table 4 for the dependent variable construct, show that those items which had a factor loading less than 0.30 were eliminated Criterion Validity This is also known as predictive validity or external validity. In this instance, it is concerned with the extent to which the model is related to independent measures of SME performance. For example, criterion-related validity of the CIAIM model to predict future success of an SME is high if the CIAIM model is correlated with SME performance and has a reasonably high Multiple Correlation Coefficient, R. The criterion related validity of the CIAIM model was determined by examining the Multiple Correlation Coefficient computed for the five categories and a measure of SME performance (R=0.616). This indicates that the five categories have a reasonably high degree of criterion-related validity when taken together.

Reliability Internal Consistency for the six categories of the CIAIM model was estimated using Chronbach’s alpha, which ranges between the values 0.00 and 1.00 (Nunnally, 1978). Using the SPSS for Windows reliability test program, an internal consistency analysis was performed separately for each of the constructs. The analysis in Tables 3 and 4 revealed that maximisation of the Chronbach alpha coefficient would require eliminating some items from each category of the CIAIM model.. The reliability values shown in Tables 5 and 6 generally meet or exceed prevailing standards of reliability for survey instruments (Hair et al., 1992) with the exception of the ‘firms technological compatibilities’ construct which had an Chronbach Alpha of 0.452. However, the overall Chronbach Alpha for the model was 0.816.

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Discussion of Findings Hypothesis H1, which stated that the CIAIM model is a reliable and valid instrument for measuring and predicting the relationship between continuous improvement-innovation management and SME performance is supported. Each of the six categories did form a ‘solid’ construct. It is important to note from these results, that we cannot suggest that for a single SME, Organisational Culture, and Technological compatibilities should not be improved because they are not positively and significantly related to SME performance. Nor can we directly say that better technological compatibility leads to worse performance, and that the CIAIM model is ‘weak’ because some of the factors do not contribute positively to explain SME performance variance. The study was cross-sectional and descriptive of a sample at a given point in time.

However, the relative strengths and significance of the regression coefficients in Table 6, coupled with the bi-variate correlations between these factors shown in Table 5, are instructive in understanding the underlying differences between high-performance and low-performance SMEs. Noting the strong correlations in Table 5, it is reasonable to conclude that the negative coefficients (β‘s) in Table 6 are due to the way the least squares algorithm found the “best-fitting” regression equation.

A possible explanation is that in the ‘least squares’ regression scheme, the powerful explanatory variables such as Innovation System& Structure and Continuous Improvement and Innovation Management Strategy caused the solution to be positioned such that the three F3 and F5 achieved a weaker, but insignificant negative position. While, F4 achieved a weaker positive, but also insignificant position.

Testing of Hypothesis H2 - Individual Independent and Dependent Variables

In order to further explore the relationship between continuous improvement/innovation management and SME performance at the individual item level which constitute each of the six factors extracted in Tables 3 and 4, Bi-variate Correlation Analysis and Multiple Regression Analysis was conducted using the SPSS for Windows statistical package.

Table 7 shows the bivariate correlation coefficients of CIAIM model variables that are significant at the 0.05 level of significance and their relationship with individual factors of the SME performance construct. The seven independent variables mostly have a positive and significant relationship with each of the dependent variables, within the range of r= 0.04 and r=0.49 significant at the p< 0.05 and 0.01 levels of statistical significance, therefore regression analysis was feasible.

VARIABLES

IV1 IV2 IV3 IV4 IV5

IV6 IV7 DV1 DV2 DV3 DV4 DV5 DV6

OBJEC (IV1)

1.00

ADMIN(IV2)

.132 1.00

TRAD (IV3)

.004 .098 1.

00

TECH (IV4)

.321 ** .06 .058 1.

00

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WORK (IV5)

-.008 .134 .176* -.064 1.

00

QUALI (IV6)

.42 ** .323** .015 .1822* -

.05

1.00

ACTIO (IV7)

.368** .003 .003 .319** -

.14

.258

**

1.00

NEWP (DV1)

.463 ** .22* -.231

**

211* -.097 .26

**

.34

**

1.00

METH (DV2)

.22* .27** -.039 .373** .141 .22

** .24

**

0.22*

1.00

REDW (DV3)

.246** .32** .059 .316** .108 .40

** .32

**

.223*

.489

**

1.00

MARK (DV4)

.492** .285

**

-.142 .374** .014 .29**

.39 **

.405

**

.340

**

.407

**

1.00

INQUA (DV5

.396** .293

**

-.099 .304* .051 .36**

.30**

.422

**

.554

**

.497

**

.422

**

1.00

DIFOT

(DV6)

.325 ** .337

**

-.039 .126* .066 .30**

.30**

.343

**

.382

**

.368

**

.475

**

.559

**

1.00

** - significant to 0.01, one-tailed * - significant to 0.05, one-tailed TABLE 7 CORRELATION MATRIX OF INDEPENDENT VARIABLES

Multiple Regression Analysis

Seven statistically significant practices of the regression model that were analysed in the previous section (Table 7) were regressed on individual factors of the SME performance construct in order to identify predictors of high SME performance. Dependent Variable: NEWPROD. As a result of the firm’s product and process innovation strategy, to what extent did Higher Success of New Products Launched occur based on perception of actual performance Multiple R 0.691 R Square 0.478 Adjusted R Square 0.250

15 Standard Error 0.690 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 34 34.006 1.000 Residual 78 37.167 0.476 F =2.099 Signif F =0.004 Independent Variables Designation

Independent Variable Description

FACTOR Beta T Sig T


F1: Innovation System& Structure

0.322 2.618 0.011

ADMIN Relative importance of innovation strategy in improving administrative routines


0.321 2.186 0.32

TRADIT The organisation’s culture reinforces behaviours that uphold traditions


-0.234 -2.219 0.029

TABLE 8 MULTIPLE REGRESSION ANALYSIS

Dependent Variable: METHODS As a result of the firm’s product and process innovation strategy, to what extent did Improved Work Methods and Processes occur based on perception of actual performance Multiple R: 0.653 R Square 0.427 Adjusted R Square 0.177 Standard Error 0.660 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 34 25.248 0.743 Residual 78 33.941 0.435

F =1.707 Signif F = 0.027

Independent Variable Designation


FACTOR Beta T Sig T



0.375 2.734 0.008

WORKTIME The organisation’s culture encourages managers to closely


0.207 1.973 0.052

16

encourages managers to closely monitor work time

Culture

TABLE 9 MULTIPLE REGRESSION ANALYSIS Dependent Variable: REDWASTE As a result of the firm’s product and process innovation strategy, to what extent did Reduction in Waste of Resources occur based on perception of actual performance Multiple R: 0.649 R Square 0.421 Adjusted R Square 0.169 Standard Error 0.670 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 34 25.803 0.759 Residual 78 35.441 0.454 F =1.670 Signif F = 0.032 Independent Variables Designation


FACTOR Beta T Sig T

QUALITY Relative importance of innovation strategy in improving product/service quality


0.354 2.072 0.042

TABLE 10. MULTIPLE REGRESSION ANALYSIS Dependent Variable: MARKOPP As a result of the firm’s product and process innovation strategy, to what extent did Increased Market Opportunities occur based on perception of actual performance Multiple R: 0.740 R Square 0.548 Adjusted R Square 0.351 Standard Error 0.590 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 34 32.886 0.967 Residual 78 27.131 0.348 F =2.781 Signif F = 0.000

Independent Variables Designation


FACTOR Beta T Sig T



0.273 2.387 0.019

ADMIN Relative importance of innovation strategy in improving

F2: Continuous Improvement and

0.370 2.703 0.008

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administrative routines Innovation strategy

TABLE 11 REGRESSION ANALYSIS

Dependent Variable: INQUALIT As a result of the firm’s product and process innovation strategy, to what extent did Increased Quality occur based on perception of actual performance Multiple R: 0.685 R Square 0.469 Adjusted R Square 0.238 Standard Error 0.600 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 34 24.916 0.733 Residual 78 28.192 0.361

F =2.028 Signif F = 0.005



FACTOR Beta T Sig T



0.276 2.095 0.039

TABLE 12 MULTIPLE REGRESSION ANALYSIS Dependent Variable: DIFOT As a result of the firm’s product and process innovation strategy, to what extent did Increased Delivery-In-Full-on-Time occur based on perception of actual performance Multiple R: 0.608 R Square 0.370 Adjusted R Square 0.096 Standard Error 0.750 Analysis of Variance (ANOVA): DF Sum of Squares Mean Square Regression 34 25.451 0.749 Residual 78 43.302 0.555 F =1.348 Signif F = 0.140



FACTOR Beta T Sig T

ACTIONPL Employees actively monitor progress by using action plans/timetables to ensure that goals are met as part of the


0.292 2.324 0.023

18

continuous improvement and innovation management process

TABLE 13 MULTIPLE REGRESSION ANALYSIS

Summary of Results

The regression analysis results in Tables 8 to 13 explain the individual dependent variables in terms of the statistically significant independent variables. The R squared values appear quite strong for this type of research. The table below summarises the findings in order of highest to lowest Beta values for each of the independent variables.

Independent Variable

Continuous Improvement and Innovation Management Practice

Effect on Dependent Variable

Beta

R Squared

T

Sig. T

Employees let core technologies/organisational objectives guide the evaluation of new ideas and information as part of continuous improvement and innovation management process

METHODS As a result of the firm’s product and process innovation strategy, to what extent did Improved Work Methods and Processes occur based on perception of actual performance

0.375 0.427 2.734 0.008

Relative importance of innovation strategy in improving administrative routines

MARKOPP As a result of the firm’s product and process innovation strategy, to what extent did Increased Market Opportunities occur based on perception of actual performance

0.370 0.548 2.703 0.008

Relative importance of innovation strategy in improving product/service quality

REDWASTE As a result of the firm’s product and process innovation strategy, to what extent did Reduction in Waste of Resources occur based on perception of actual performance

0.354 0.421 2.072 0.042

Innovation strategy has helped the organization to achieve its strategic goals and objectives

NEWPROD As a result of the firm’s product and process innovation strategy, to what extent did Higher Success of New Products Launched occur based on perception of actual performance

0.322 0.478 2.618 0.011

Employees actively monitor progress by using action plans/timetables to ensure that goals are met as part of the continuous improvement and innovation management process

DIFOT As a result of the firm’s product and process innovation strategy, to what extent did Increased Delivery-In-Full-on-Time occur based on perception of actual performance

0.292 0.370 2.324 0.023

Employees let core technologies/organisational objectives guide the evaluation of new ideas and information as part of continuous improvement and innovation management

INQUALIT As a result of the firm’s product and process innovation strategy, to what extent did Increased Quality occur based on perception of actual performance

0.276 0.469 2.095 0.039

19 TABLE 14 SUMMARY OF ‘BEST’ CONTINUOUS IMPROVEMENT AND INNOVATION MANAGEMENT PRACTICES Discussion of Results With reference to Table14, Core technologies and organisational objectives are key drivers of new ideas and information as part of the continuous improvement and innovation management system. This practice has a significant and positive effect on Improved Work Methods and Processes and Increased Quality. An innovation strategy is imperative for achieving strategic goals and objectives, improving product/service quality and administrative systems. This practice has a significant and positive effect on Increased Market Opportunities and Reduction in Waste of Resources. These results are consistent with Soderquist (1996).

Monitoring of SME progress using action plans/timetables as part of the continuous improvement and innovation management system. This practice has a significant and positive effect on Delivery-In-Full-on-Time. Considering the above findings it is reasonable to conclude that Hypothesis 2: “Continuous improvement and innovation management strategy and systems have a positive and significant effect on SME performance” is supported.

Normality of the Error Term Distribution and Individual Variables

The first method used to check normality was a visual check of the histogram of residuals for a distribution approximating the normal distribution. The second method used to check the normality of the error term distribution was the normal probability. The normal distribution makes a straight diagonal line, and the plotted residuals are compared with the diagonal. If a distribution is normal, the residual line closely follows the diagonal. The same procedure was used to compare the dependent and independent variables separately to the normal distribution.

Based on the results obtained from the two methods of checking the error term distribution and individual variables, we can conclude that the independent and dependent variables are normal. Therefore, the normality assumption is not violated.

Multicollinearity The multicollinearity of the independent variables was checked using two methods suggested by Hair et al., (1992, p.48). The first method was a simple examination of the correlation matrices in Tables 5 and 7 for the independent variables. The presence of high correlations, generally above 0.9 and above were the first signs of collinearity. Examining the correlation matrices in Tables 5 and 7, the inter-correlation coefficients were found to be generally well below the recommended correlation coefficient value r=0.9. The second method used was the tolerance value method. Hair et al., (1992) suggest that the tolerance value default in SPSS be set at a higher value than that defaulted by the SPSS program at 0.0001. The tolerance value in our analysis was set at 0.001. Based on the two procedures discussed above, high correlation independent variables were either automatically deleted by the SPSS program default, or were removed manually at the 0.9 correlation coefficient. We conclude that multicollinearity does not appear to be causing any problems with the independent variables in the regression model.

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CONCLUSIONS

The lack of rigorous empirical testing of theories which link continuous improvement/innovation management with SME performance is noted in the literature by Soderquist et al.(1997) and others. The paper adds to the literature by developing a Continuous Improvement and Innovation Management (CIAIM) model which integrates existing theory on CI and innovation management and explains empirically the role of strategy, structure, culture, and systems and their contribution to SME performance.

Considering the results of this study, we conclude that the CIAIM model developed in this paper is a reliable and valid tool for predicting the relationship between SME practice and performance in Australian manufacturing. The paper contributes to the literature by identifying what it is about high performing SMEs that makes them different from other firms and identifies management practices that are critical in achieving high SME performance. These practices are:

• Continuous Improvement and Innovation strategy is imperative for achieving strategic goals and objectives.

• Adoption of core technologies and organisational objectives to drive new ideas • Monitoring of SME progress using action plans/timetables as part of the continuous improvement and innovation management system.

The implication of the research results for managers is that these practices are imperative in order to avoid SME failure. The research results show a strong consistency between the high performing SMEs profile and the type of practices that have been identified by previous research (Dun & Bradstreet, 1994; Soderquist et al.1997).

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