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
2
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 (5099 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.
5
•
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 cooperation 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
Secretary
1
Manager
19
Operations Manager
8
Financial Controller/Accountant
8
Managing Director or CEO
60
Director
12
Chairman
1
Other
3
TABLE 1 - Position in Company
Percent
0.9
16.8
7.1
7.1
53.1
10.6
0.9
2.7
Number of Employees
Frequency
<5
2
6-20
8
21-50
39
51-100
35
101-499
28
500+
1
TABLE 2: Relevant size of the company
Percent
1.8
7.1
34.5
31.0
24.8
0.9
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
F1:
INNOVATION SYSTEM & STRUCTURE
REFERS
MONITOR
CONSIDER
The vision/mission includes a reference to innovation
Formally monitors developments in new technologies
Considers the use of technology as a driver of business
growth
Allocates resources to sharing technology across the
organisation
0.477
0.584
0.556
Allocates resources to the use of cross-functional teams
Organisation’s culture encourages formal meetings and
interactions
Employees search for information, new ideas and
technologies as part of continuous improvement and
innovation management
Employees search for and incorporate diverse points of
view as part of continuous improvement and
innovation management
Employees facilitate and encourage informal
relationships as part of continuous improvement and
innovation management
O.537
0.373
TECCNOL
TEAMS
MEETINGS
INFORMAT
DIVERSE
INFORMAL
FACTOR
LOADING
0.596
0.715
0.687
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
Employees use failures as opportunities to learn as part
of
continuous improvement and innovation
management
Employees work towards specific technological
goals/objectives as part of continuos improvement and
innovation management
Employees let core technologies/organisational
objectives guide the evaluation of new ideas and
information as part of continuous improvement and
innovation management
0.672
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
Innovation strategy has helped the organization to
achieve its strategic goals and objectives
0.609
FAILURES
SPECTECHT
TECHGUID
ACTIONPL
OBJECTIV
F2:
CONTINUOUS IMPROVEMENT&
INNOVATION MANAGEMENT
CONSTRUCT
RELIABILITY
0.596
0.751
0.652
0.524
α = 0.816
9
STRATEGY
PRODN
in
0.597
in
0.808
in
0.751
in
0.780
in
0.859
in
0.693
in
0.749
F3:
CUSTOMER AND SUPPLIER
RELATIONSHIPS
REPUTAT
Degree to which the firm’s reputation is important to
the firm’s competitive advantage
Degree to which product/service supply is important to
the firm’s competitive advantage
Degree to which customer satisfaction is important to
the firm’s competitive advantage
0.714
SKILLS
CUSTSAT
QUALITY
MORALE
ADMIN
COOPERAT
SUPPLY
CSATIS
Relative importance of innovation strategy
increasing production volume
Relative importance of innovation strategy
increasing employee skills
Relative importance of innovation strategy
contributing to customer satisfaction
Relative importance of innovation strategy
improving product/service quality
Relative importance of innovation strategy
improving employee commitment/morale
Relative importance of innovation strategy
improving administrative routines
Relative importance of innovation strategy
improving internal communication/cooperation
0.820
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
The organisation’s culture encourages managers to
closely monitor work time
The organisation’s culture focuses on short term
performance
The organisation’s culture encourages employees to
interact with insiders only
0.522
SHORT
INSIDERS
F5:
FIRM’S TECHNOLOGICAL
COMPATABILITIES
CUSTTECH
The extent to which customers have the same or similar
technologies to the organization's
The extent to which suppliers have the same or similar
technologies to the organisation’s
The extent to which competitors have the same or
similar technologies to the organisation’s
SUPTECH
COMPTECH
α =0.670
0.792
F4:
WORKTIME
α = 0.872
0.577
0.710
α = 0.627
0.711
0.764
0.857
α = 0.452
0.400
TABLE 3 CONFIRMATORY FACTOR ANALYSIS: INDEPENDENT VARIABLE CONSTRUCTS
VARIABLES
F6:
DESCRIPTION OF VARIABLES
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
FACTOR
LOADING
FIRM PERFORMANCE
0.653
CONSTRUCT
RELIABILITY
10
NEWPROD
PRCONFIG
PRINNOV
METHODS
REDWASTE
MARKOPP
INQUALIT
DIFOT
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
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
As a result of the firm’s product and process innovation
strategy, to what extent did Improved Product InnovationNew Parts and Processes occur based on perception of actual
performance
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
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
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
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
As a result of the firm’s product and process innovation
strategy, to what extent did Increased Delivery-In-Full-onTime occur based on perception of actual performance
0.565
0.545
0.705
0.708
0.653
0.683
0.788
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.
11
F1:Innovati F2:
F3: Customer F4:
Continuous
on System
Organisation
Improvement
and
Supplier
& Structure
al Culture
and Innovation Relationships
Management
Strategy
F6:
F5:
Technologi Firm
cal
Perf.
compatabili
ties
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
1.00
0
** 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
Regression
5
38.758
Residual
107
F = 13.115
Variables
63.242
Beta
Mean Square
7.752
0.591
Signif F =0.000
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
12
F3
F4
F5
Customer
and Supplier
Relationships
Organisational Culture
Technological
compatibilities
-0.011
-0.127
0.899
0.048
0.597
0.552
-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.
13
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 lowperformance 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.
VARIA
BLES
IV1
OBJEC
(IV1)
1.00
ADMIN(
IV2)
.132
TRAD
(IV3)
.004
TECH
(IV4)
.321
**
IV2
IV3
IV4
1.00
.098
1.
00
.06
.058
1.
00
IV5
IV6
IV7
DV1
DV2
DV3
DV4
DV5
DV6
14
WORK
(IV5)
-.008
QUALI
(IV6)
.42
**
ACTIO
(IV7)
.368**
NEWP
(DV1)
.463
**
.134
.176*
-.064
1.
00
.323**
.015
.1822*
.0
5
1.00
.003
.003
.319**
.1
4
.258
-.097
.26
.34
**
**
.22
**
.24
.22*
-.231
211*
**
METH
(DV2)
.22*
.27**
-.039
.373**
.141
1.00
**
1.0
0
0.2
2*
1.00
.22
3*
.489
**
REDW
(DV3)
.246**
.32**
.059
.316**
.108
.40
**
.32
**
MARK
(DV4)
.492**
.285
-.142
.374**
.014
.29
**
1.00
**
.39
**
.40
5
**
.340
.407
**
**
.554
.497
**
**
.382
.368
.475
.559
**
**
**
**
1.00
**
INQUA
(DV5
.396**
.293
-.099
.304*
.051
.36
**
.30
**
.42
2
**
.422
1.00
**
**
DIFOT
(DV6)
.325
**
.337
-.039
.126*
.066
.30
**
.30
**
.34
3
**
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
R Square
Adjusted R Square
0.691
0.478
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
Independent
Variables
Designation
OBJECTIV
ADMIN
TRADIT
F =2.099
Independent
Variable Description
FACTOR
Signif F =0.004
Beta
T
Sig
T
Innovation strategy has
helped the organization to
achieve its strategic goals
and objectives
Relative importance of
innovation strategy in
improving administrative
routines
F1: Innovation
System& Structure
0.322
2.618
0.011
F2: Continuous
Improvement and
Innovation
Management Strategy
0.321
2.186
0.32
The organisation’s culture
reinforces behaviours that
uphold traditions
F4: Organisational
Culture
-2.219
0.029
-0.234
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:
R Square
Adjusted R Square
Standard Error
0.653
0.427
0.177
0.660
Analysis of Variance (ANOVA):
DF
Sum of Squares
Mean Square
Regression
34
25.248
0.743
Residual
78
33.941
0.435
Independent
Variable
Designation
TECHGUID
WORKTIME
F =1.707
Independent
Variable Description
Employees let core
technologies/organisational
objectives guide the evaluation
of new ideas and information as
part of continuous improvement
and innovation management
The organisation’s culture
encourages managers to closely
Signif F = 0.027
FACTOR
Beta
T
Sig T
F1: Innovation
System& Structure
0.375
2.734
0.008
F4: Organisational
Culture
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:
R Square
Adjusted R Square
Standard Error
0.649
0.421
0.169
0.670
Analysis of Variance (ANOVA):
DF
Sum of Squares
Mean Square
Regression
34
25.803
0.759
Residual
78
35.441
0.454
Independent
Variables
Designation
QUALITY
F =1.670
Independent
Variable Description
Relative importance of
innovation strategy in
improving product/service
quality
Signif F = 0.032
FACTOR
Beta
T
F2: Continuous
Improvement and
Innovation
Management
Strategy
0.354
Sig T
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
Independent
Variables
Designation
OBJECTIV
ADMIN
F =2.781
Independent
Variable Description
Innovation strategy has helped
the organization to achieve its
strategic goals and objectives
Relative importance of
innovation strategy in improving
Signif F = 0.000
FACTOR
Beta
T
Sig T
F1: Innovation
System& Structure
0.273
2.387
0.019
F2: Continuous
Improvement and
0.370
2.703
0.008
17
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:
R Square
Adjusted R Square
Standard Error
0.685
0.469
0.238
0.600
Analysis of Variance (ANOVA):
DF
Sum of Squares
Mean Square
Regression
34
24.916
0.733
Residual
78
28.192
0.361
Independent
Variables
Designation
TECHGUID
F =2.028
Independent
Variable Description
Employees let core
technologies/organisational
objectives guide the evaluation
of new ideas and information as
part of continuous improvement
and innovation management
Signif F = 0.005
FACTOR
Beta
T
F1: Innovation
System& Structure
0.276
2.095
Sig T
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:
R Square
Adjusted R Square
Standard Error
0.608
0.370
0.096
0.750
Analysis of Variance (ANOVA):
DF
Sum of Squares
Mean Square
Regression
34
25.451
0.749
Residual
78
43.302
0.555
Independent
Variables
Designation
ACTIONPL
F =1.348
Independent
Variable Description
Employees actively monitor
progress by using action
plans/timetables to ensure that
goals are met as part of the
Signif F = 0.140
FACTOR
Beta
T
F1: Innovation
System& Structure
0.292
2.324
Sig T
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
Employees let core
technologies/organisational
objectives guide the
evaluation of new ideas and
information as part of
continuous improvement
and innovation management
process
Beta
Effect on Dependent Variable
T
R
Squared
Sig.
T
0.375
0.427
2.734
0.008
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
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
Innovation strategy has
helped the organization to
achieve its strategic goals
and objectives
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
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
0.354
0.421
2.072
0.042
0.322
0.478
2.618
0.011
DIFOT As a result of the firm’s product
0.292
0.370
2.324
0.023
0.276
0.469
2.095
0.039
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
Employees let core
technologies/organisational
objectives guide the
evaluation of new ideas and
information as part of
continuous improvement
and innovation management
and process innovation strategy, to what
extent did Increased Delivery-In-Full-onTime occur based on perception of actual
performance
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
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.
20
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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