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Propensity to Innovate: Driving in a Professional Services Firm

Associate Professor Desmond Klass* Curtin of , GSB, 78 Murray Street, Perth, Western Australia, Australia, 6053

E- [email protected]

Dr Margot Wood Curtin University of Technology, GSB, 78 Murray Street, Perth, Western Australia, Australia, 6053

E-mail: [email protected]

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Propensity to Innovate: Driving Innovation in a professional Services

ABSTRACT : Innovation has been widely recognised as a means of achieving a sustainable, ompetitive advantage and an important determinant of business performance. Less clear is an understanding of the drivers of the propensity to innovate and their inter-relationships. This paper develops and examines a structural framework that accommodates several constructs concerning the potential impact they have on a professional services firm’s propensity to innovate. A Structural Equation modelling approach was undertaken to inform the and development of a program aimed at enhancing innovation within a professional services organisation. Keywords: Innovation; Change process; Propensity to Innovate; Leadership; Culture; Values; Program development

Innovation has been widely recognised as an important means of achieving a sustainable, competitive advantage (Barsh et al., 2008, Hult et al., 2004), with innovative organisations out- competing rivals through greater value creation (Dobni, 2006). Less clear is an understanding of the drivers of the propensity to innovate and their inter-relationships. This is especially true in the context of law firms which are generally seen as conservative, cynical and somewhat averse to thinking differently (Maister, 2006, Mankin, 2006).

This paper describes the collaborative between the Centre for Innovation in Decision

Quality (CIDQ) and the Perth branch of an international Law firm in developing a research project to better understand the constructs that drive the propensity to innovate in a Professional Services

Organisation. The insights gleaned from the research would then be used to inform the development of an Innovation program woven around the central theme of Thinking Differently – Our Journey

Continues . The project involved co-creation of internal processes, development of support systems, tools and processes, an organisation wide experiential Retreat and development of follow up systems to encourage the generation of ideas and potential development of both incremental and breakthrough .

A three phase Innovation Research Program was developed in parallel with this project in order to gather quantitative empirical evidence regarding the firm’s propensity to innovate, to identify key drivers and to objectively gauge the value of the various interventions. This paper explores the

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outcomes of the first stage of this research and involves the use of structural equation modelling

(SEM) to determine key drivers for innovation propensity. The more descriptive, nuanced story

regarding the design, delivery and enjoyment of the overall project including the Retreat and Phase 2

of the Research Program, is discussed in Vitasovic and Wood (Vitasovic and Wood, 2009). Data

collection for Phase 3 (Innovation Outcomes) was conducted in June 2009 six months after the Retreat

and used to access progress.

CONSTRUCT DEVELOPMENT

It has been claimed that without a propensity to innovate, innovation will not occur (Dobni, 2006).

Therefore, as a critical precursor for innovation, this became the central concept in this first phase.

Propensity to Innovate encompasses the motivation to think differently; a heightened risk propensity

(Dobni, 2006, Sloan, 2008); the ability to copy and adapt ideas from outside (Cannon, 1985, Hansen

and Birkinshaw, 2007, Sloan, 2008) and a willingness to accept new ideas (Morris, 2005, Sloan,

2008).

Thus Propensity to Innovate is defined here as a willingness to explore, accept and adopt external

ideas, to take risks without fear in areas sometimes outside the organisation’s immediate field, to value

the ability to think differently and be willing to support and invest in sometimes quite radical ideas.

This definition is similar to Hurley and Hults’ (Hurley and Hult, 1998) notion of innovativeness,

defined as the organisation’s capacity to engage in innovation, and encompassing the generation of

new processes and products. However, as it is important to distinguish between a firm’s willingness

or ‘readiness’ to innovate and the outcomes which result from this, we have split this notion into

separate constructs - the Propensity to Innovate and the resulting Innovation. This very issue is

discussed and the need to differentiate supported in a paper by Hurley, Hult and Knight (Hurley et al.,

2005) in their response to a similar conceptual challenge raised by Woodside (Woodside, 2004).

A review of the literature, expert opinion and input from executives involved with promoting

innovation within the firm, surfaced perceived antecedents to developing a propensity to innovate.

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This included the following constructs: competitive environment, market orientation, development and implementation of an innovation process, an innovative culture (incorporating leadership, organisational learning and people development), provision of adequate resources and a clear innovation strategy. A brief description highlighting the spirit underlying each of the construct is provided below. Table 1 contains the references used to support the development of these constructs.

Competitive Environment and Propensity to Innovate

The competitive environment forms a critical backdrop for professional firms. This entails recognition of the need to be innovative in order to respond to competitive threats and grasp market opportunities . The ability to address the competitive environment encompasses the perception that creativity and innovation impact on the success of the organisation and are required to excel in the market place. This led to our first hypothesis:

H1: There is a positive and significant relationship between ‘Competitive Environment’ and ‘Propensity to Innovate’. Market Orientation and Propensity to Innovate

Market Orientation primarily relates to understanding the needs of clients through information acquisition so as to ultimately create value. Formal measurement of client satisfaction is central to this understanding. It is inferred, if one adopts a resource based view of the firm, that there are four capabilities - market orientation, entrepreneurship, innovativeness, and organizational learning – which contribute to the creation of positional advantages. The literature argues that and learning orientations are required to maximise the effectiveness of innovation processes. Following from the above we thus hypothesise:

H2: There is a positive and significant relationship between ‘Market Orientation’ and ‘Propensity to Innovate’. Innovation Processes

An organisation’s Architecture – including processes and the business model – needs to support innovation and opportunities for emergence. Clear processes are also a key factor impacting on the ability to manage innovation. This led to the third hypothesis:

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H3: There is a positive and significant relationship between ‘Innovation Process’ and ‘Propensity to Innovate’. The Role of Culture

Organizational culture is an important determinant of sustained innovativeness and financial

performance. In terms of culture, leadership and innovation, it is imperative that innovation forms a

core part of the leadership’s agenda, followed by modelling the right behaviour and improving

processes for managing innovation risk. An innovative culture is one where leadership attempts to

make it easier to innovate where knowledge and information sharing propels innovation, employees

are valued and believe their has meaning and organisational learning is part of this. A culture of

innovation facilitates the generation of novel ideas that are supported in both tangible and intangible

ways, rather than providing mere lip service.

H4: There is a positive and significant relationship between ‘Innovation Culture’ and ‘Propensity to Innovate’. Innovation Strategy

A clear Innovation Strategy may be a necessary precursor to developing an innovative

organisation, with market orientation also playing a role here. ‘Innovation Strategy’ focuses on the

organisation having a strategy for innovation that is clearly communicated and allows for the

accommodation and evaluation of suggestions. We hypothesise that:

H6: There is a positive and significant relationship between ‘Innovation Strategy’ and ‘Propensity to Innovate’ H8: There is a positive and significant relationship between ‘Market Orientation’ and ‘Innovation Strategy’. H10: ‘Innovation Strategy’ positively and significantly mediates the relationship between ‘Market Orientation’ and ‘Propensity to Innovate’.

Innovation and Resources

It is inferred that the provision of relevant resources clearly contributes to innovation. The resource

construct includes measurement items that account for sufficient funds being directly allocated to

encourage innovation. The construct also factors in the recognition and provision for “time to think”

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about new ideas and the future and includes the monetary and non-monetary rewards for new ideas that are implemented. Thus:

H5: There is a positive and significant relationship between ‘Resources’ and ‘Propensity to Innovate’.

H7: There is a positive and significant relationship between ‘Innovation Culture’ and ‘Resources’.

Given the earlier discussion, it is inferred that the relationship between Innovation Culture and

Propensity to Innovate is possibly mediated by Resources (Figure 1). The implication here is that such a culture is more likely to take ideas seriously and provide budgets sufficient to encourage and fund new developments. We therefore suggest that:

H9: ‘Resources’ positively and significantly mediates the relationship between ‘Innovation Culture’ and ‘Propensity to Innovate’.

The above constructs were included in the conceptual model designed to infer their impact on the

Propensity to Innovate. The relationship between theses constructs and the focal dependent variable,

Propensity to Innovate are illustrated in the structural model presented in Figure 1.

THE RESEARCH MODEL

A research model (Figure 1) was developed based on the above hypotheses. The model has six independent variables and a focal dependent variable Propensity to Innovate. Each of the variables in the model include a number of measurement items i.e. Innovation Culture (16 items), Innovation

Process (8), Resources (12), Competitive Environment (4), Innovation Strategy (4) and Market

Orientation (7). The focal dependent variable, Propensity to Innovate, includes 6 measurement items.

The variables Resources and Innovation Strategy are treated as mediating variables in the model.

The model was analysed using PLS-GRAPH, a Partial Least Square based structural equation modeling approach developed by Chin (Arnold et al., 2004, Chin and Newsted, 1999). For additional information on PLS-GRAPH refer to http://www.plsgraph.com. The model assumes uni- dimensionality of the constructs and defines the relationships of the indicators (measures) as reflective.

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Reflective indicators are believed to reflect the unobserved, underlying construct, with the construct

giving rise to the observed measures.

Source of Empirical Data

Empirical data was obtained electronically, sent via email to the total available population of

employees from the organisation (n =192). The invitation presented an overview of the study and

included the questionnaire as an attachment, which then could be completed electronically or in hard

copy. A note from the Managing Partner encouraging participation was emailed to employees prior to

questionnaire . This was followed up by two reminders outlining progress to date.

A total of 101 valid responses were received, for an effective response rate of 53%. The responses

were in proportion to the organisation’s role classifications ie: Partners, Special Counsel/Senior

Associates/Associates, /Article Clerks, Law Clerks/Settlement Clerks/Casual Clerks,

Executive Associates/Secretaries/WP Operators and Shared Services. Fifty three percent (53%) of the

respondents had been with the organisation for less than 2 years.

Measurement Items

Measurement items were based on a review of the existing literature, expert opinion and input from

the executives involved with promoting innovation within the organisation. Participants were asked to

rate the performance of the organisation on each of the items, using a six-point scale ranging from

strongly disagree (1) to strongly agree (6). To provide additional insight and to secure data for the

second phase of the research program, participants were also asked to indicate perceived importance of

the same items using a six-point scale, ranging from unimportant (1) to extremely important (6).

Reverse scaling was not employed thus no adjustments were necessary to reflect the dimensionality of

the theorised constructs. Table 2 contains the measurement items retained in the final model.

Data Analysis

In the application of PLS, three general sets of methodological considerations are relevant i.e.:

• assessing the reliability and validity of measures

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• determining the appropriate nature of the relationships between measures and constructs and • interpreting path coefficients, determining model adequacy, and selecting a final model from the available set of alternatives. Consideration of the above was based on the protocol suggested by Barclay, Higgins and

Thompson (1995).

Sample Size

It has been suggested that the minimum number of cases required to run structural equation analysis is about ten times the number of formative constructs in the most complex construct within the model

(Barclay et al., 1995). This study, with a maximum of six formative variables for the dependent variable proved to be more than sufficient for model development given the sample size of 101 cases.

A recent article by Gefen, Straub and Boudreau (2000) demonstrated that the required minimal sample size for partial least square based structural equation modelling was around 100-150 cases. PLS is particularly applicable and appropriate for small sample analysis (Chin and Newsted, 1999).

Measurement Properties

As suggested by Barclay et al (1995) item reliability, internal consistency and discriminant validity are necessary criteria for ensuring that measurement items are reliable and valid for model development. PLS-GRAPH was used on the initial 57 measurement items to test and ensure that the necessary requirements were satisfied.

Item Reliability

To establish the presence of item reliability, all item loadings were examined. A loading of 0.5

(Igbaria et al., 1997) was used as the initial cut-off value to determine reliability. This first step identified items with loadings of less than 0.5 and these were excluded from further analyses in order to achieve the required condition. The initial 57 items loadings are provided in Table 3. Each measurement item is listed under its appropriate construct included in the conceptual model. Six items were removed during this first stage.

Internal Consistency

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When multiple measures are used for an individual construct, it is important to consider not only

the individual measurement item reliability, but also the extent to which the measures demonstrate

convergent validity. Fornell and Larckers’ (1981) approach to determining the achievement of

internal consistency was adopted for this study, with a 0.7 cut-off value used as the threshold. Table 4

presents the measures from PLS-GRAPH, clearly indicating that the latent variables all produced

internal consistency measures of above 0.7, thereby achieving an acceptable level of convergent

validity.

Discriminant Validity

Discriminant validity represents the extent to which measures of a given construct differ from

measures of other constructs in the same model. Discriminant validity was determined following the

procedure of Fornell and Larcker (1981), who suggest comparison of the square root of the Average

Variance Extracted (AVE) for each construct against the correlations among the rest of the constructs.

The leading diagonal of Table 5 (bold type) contains the values of the square root of the AVEs for

each of the constructs. The off-diagonal elements represent the correlations among the constructs. To

fulfil the requirement of discriminant validity it is necessary for the square root of the AVE to be

greater than the off-diagonal elements in the corresponding rows and columns (Barclay et al., 1995).

A total of 28 measurement items was retained in the final measurement model which then formed the

basis for the development of the structural model discussed shortly.

To support the above analysis and strengthen the case for Discriminant Validity, cross loadings for

the model were calculated. Loadings for the constructs against the measurements items included in the

model are presented in Table 6.

The Structural Model and Tests of Hypotheses

The model developed in the previous sections demonstrated the reliability and validity of the

measures and lends itself to the next step of determining and interpreting the relationship between the

constructs. In the significant tests carried out for this exploratory study, hypotheses H1, H2, H3, H4,

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H5, H7, H8 and H9 proved to be significant while H6 and H10 were not supported. The three stage approach suggested by Baron and Kenny (1986) was used to determine the presence of mediation.

The final model explains 72.3% of the variance in Propensity to Innovate and since this figure is more than the 25% claimed by Barclay et al (1995) to be adequate, this model is deemed to have merit.

Table 7 presents the results of the path analysis of the structural model and includes the values for the various paths and associated hypothesis together with an indication of significance. Column 1 in the table provides a description of the hypothesis, with column 2 presenting the short label. Column 3 provides the standardized path coefficient for each of the hypothesized relationships and column 4 indicates whether the hypothesis in this exploratory model is significant at or less than the 10% level.

Given the above analysis the model was recalculated using only the significant paths, with the resultant structural model presented in Figure 2. Here, the values associated with each of the paths represent the path coefficients (original sample estimates) computed using PLS-GRAPH. The values associated with the Resources dependent variable, and the focal dependent variable Propensity to

Innovate, represent the Coefficient of Determination (R squared) for each of these constructs.

Innovation Culture explains 59.3 % of the variance in Resources and the coefficient of determination for the Propensity to Innovate was 72.3%. The values associated with each of the arrows linking the various constructs represent the path coefficients in the structural model.

Discussion

Overview of the Structural Model and Key Relationships

The final structural model clearly demonstrates that within this organisation, Innovation Culture has the highest relative impact on Propensity to Innovate. Within the model it is also possible to see that Resources acts as a mediator between culture and innovation propensity. Implicit in this mediated relationship is the perception that an innovative culture, as defined in this study, would impact on the degree to which ideas are taken seriously and appropriate resources allocated. In fact,

Innovation culture plays a very large part with regard to resource allocation, with 59.3% of the

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variance in Resources explained by this construct. This result thus reinforces much of the literature

and our own experience in this area, where the interaction between culture (encompassing leadership,

knowledge transfer and nurturing and development of people’s skills and competencies), resource

allocation and a propensity to innovate is a critical but unsurprising outcome. The firm’s intuitive

understanding of this relationship is also evident in the organisation’s values presented in Figure 3.

Competitive Environment emerged with the second highest relative impact on Propensity to

Innovate, thus supporting H1. With a clear focus on the need for innovation and a rapid response to

market dynamics, this organisation values these attributes and this in turn impacts on innovation

propensity and by inference on innovation outcomes. Again congruent with the firm’s values, the

organisation sees itself as an organisation that recognizes the importance of and the need for creativity

in achieving excellence in the market place.

Innovation Process follows next in terms of its contribution to Propensity to Innovate. Well

supported in the literature, this relationship highlights the centrality of strong processes in encouraging

innovation and ultimately bringing new ideas to fruition. This finding supports the organisation’s

strong focus on people, culture, the provision of appropriate resources and on incrementally

developing appropriate processes to support innovation.

The organisation has a clear strategy emphasizing delivering the best for their clients. This is

evident by their core values (Figure 3). The structural model illustrated in this study reflects this

approach with Market Orientation having a significant and positive impact on both the Propensity to

Innovate and Innovation Strategy constructs.

Perhaps one of the more surprising results was that while the literature identifies the development

of an innovation strategy as a key driver of innovativeness (Kim W.C. and Mauborgne R., 1999), this

relationship was not supported here (non-rejection of the null hypothesis for H6). One way to

interpret this may be that Innovation Strategy as defined in this study, focused primarily on a strategy

being in place in the organisation. As no clear Innovation Strategy per se was in place at the time, it

may have been this which gave rise to the non-rejection of the null hypothesis. Another possible

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explanation is that while the strategy per se did not emerge as a statistically significant mediator, it is those things that a strategy enables (processes and allocation of resources) which truly make the difference. This is a possible area for further exploration, with more of an emphasis on strategic focus and intent than was the case in this study. These insights were used to inform the design and development of the Retreat.

A Focus on Measures of Perceived Importance

In order to ensure that the retreat focused on areas that mattered and to augment our understanding regarding what makes a difference in the organisation and to explore perceptions of the organisation’s achievement in critical areas, further analysis was conducted on the “Perceived Importance” measurement. The top 4 “Perceived Performance” constructs and the top 4 “Perceived Importance” identified in a separate structural analysis are presented in Table 8.

Table 8 illustrates participant’s views that the most important elements affecting the propensity to innovate are relevant organisational processes, the presence of a clear strategy for innovation, the existence of the type of culture conducive to developing new approaches, and a concomitant willingness to expend resources in pursuit of innovation.

However, when considering how the organisation is perceived to perform with regard to the various constructs, a clear difference can be seen. Participants felt that their organisation was performing best in terms of culture, their response to the competitive environment, progress with regard to processes and allocation of resources. Again we see here the relatively lower perceived performance with regard to Innovation Strategy, in that it does not appear among the top four.

Though this analysis enabled the CIDQ team to identify gaps that the Retreat could focus on, it is critical to note that overall the organisation performed particularly well along most of the dimensions identified and the gaps identified were quite small – cracks rather than chasms! The message here is one of fine tuning and amplification, rather than radical change.

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Limitations and Future Research

The research has a number of limitations and identifies a number of prospective research

directions. The first limitation lies in the sample size for this study. Though the number of responses

was sufficient for this research focus it precluded analysis for the different role categories used by the

organisation. Differences were hinted at between the various organisational role groupings in the raw

data which would perhaps have been useful to explore further in terms of practical implications.

A further possible limitation is that this research was designed as a case study and while the

organisation in this study is typical of a professional service firm the findings are limited in terms of

generalisability. It is suggested that this study be replicated to include a wider pool of professional

service .

Conclusion

This project began when the organisation sought to capitalise further on their innovative practices.

For the organisation’s Managing Partner, the key question was how to enhance and grow the pockets

of innovation within the organisation “so all of our people understand that innovation is about

changing the way we do things so as to gain a competitive advantage; it is not just about technological

solutions; it can be fun and create a sense of buzz and becomes a 'way of life' within the organisation.”

As part of the wider project designed to support this aspiration, the study presented here provides a

clear guide to action in terms of the central drivers impacting on the propensity for innovation. The

results of this phase of the research allowed the project development to focus on the areas that most

added value to the propensity to innovate and thus achieving this aspirational objective.

This research adopted a SEM modelling approach to gain necessary insights and identify gaps in

areas that the organisation needed to focus on to improve the organisation’s propensity to innovate.

While the research focuses on a particular professional services firm, the approach to designing of a

developmental program is general enough to have wider application and relevance for both researchers

and practitioners.

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References

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Hurley, R. F. & Hult, G. T. M. (1998) Innovation, Market Orientation, and Organizational Learning: An Integration and Empirical Examination. Journal of Marketing, 62 , 42- 54. Hurley, R. F., Hult, G. T. M. & Knight, G. A. (2005) Innovativeness and capacity to innovate in a complexity of firm-level relationships: A response to Woodside (2004). Industrial Marketing Management, 34 , 281-283. Igbaria, M., Zinatelli, N., Cragg, P. & Cavaye, A. L. M. (1997) Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model. MIS Quarterly , 279-302. Jaworski, B. & Kohli, A. (1993) Market orientation: antecedents and consequences. Journal of Marketing, 52 53-70. Kim W.C. & Mauborgne R. (1999) Strategy Value Innovation and the Knowledge Economy. Sloan Management Review . Lukas, B. & Ferrell, O. (2000) The effect of market orientation on product innovation. Journal of the Academy of Marketing Science, 28 , 239-247. Maister, D. (2006) Are Law Firms Manageable? . The American . Mankin, E. (2006) Looking for Law Firm Innovation ICE Update. Martensen, A., Dahlgaard, J. J., Park-Dahlgaard, S. M. & Grønholdt, L. (2007) Measuring and diagnosing innovation excellence - simple contra advanced approaches: a Danish study. Measuring Business Excellence., 11 , 51-65. Mckinsey (2007) How companies approach innovation: A McKinsey Global Survey McKinsey Quarterly. Morris, W. (2005) Morris W. A Survey of Organisational Creativity jpb.com Narver, J. C. & Slater, S. F. (1990) The effect of a market orientation on business profitability. Journal of Marketing, 54 , 20-35. Rao, H. & Sutton, R. (2008) The ergonomics of innovation. McKinsey Quarterly , 130-141. Robert, G. C. (2008) Perspective: The Stage-Gate Idea-to-Launch Process;Update, What's New, and NexGen Systems. Journal of Product Innovation Management, 25 , 213-232. Slater, S. F. & Narver, J. C. (1998) Customer-led and market oriented: let’s not confuse the two. Strategic Management Journal, 19 , 1001-1006. Slater, S. F. & Narver, J. C. (1999) Market-oriented is more than being customer-led. Strategic Management Journal, 20 , 1165-1168. Sloan, P. (2008) Innovation Questionnaire. Destination Innovation. Smith, M., Busi, M., Ball, P. & Van Der Meer, R. (2008) Factor's influencing an organisation's ability to manage innovation: a structured literature review and conceptual model". International journal of innovation management, 12 , 655-676. Verhees, F. J. H. M. & Meulenberg, M. T. G. (2004) Market Orientation, Innovativeness, Product Innovation, and Performance in Small Firms. Journal of Small Business Management, 42 , 134-154. Vitasovic, D. & Wood, M. (2009) ‘Thinking Differently’ in a Professional Services Firm. IPSIM 2009: The Future of Innovation. Vienna, Austria. Woodside, A. G. (2004) Firm orientations, innovativeness and business performance: Advancing a systems dynamics view following a comment on Hult, Hurley, and Knight's 2004 study. Industrial Marketing Management 34 , 275-279.

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FIGURES

Figure 1: Propensity to Innovate Model

Competitive Environment Innovation Culture

Propensity to Innovate

Resources

Market Orientation

Innovation Innovation Process Strategy

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Figure 2: Final Propensity to Innovate Model

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Figure 3: The Organisation’s Values

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TABLES

Table 1. Supporting Studies for the Constructs developed for the model

Factors included in the Propensity to Supporting Studies (References) Innovate model Competitive Environment/Advantage (AusInnovation, 2008, Barsh et al., 2008, Hult et al., 2004) Market Orientation (AusInnovation, 2008, Hult and Ketchen, 2001, Jaworski and Kohli, 1993, Lukas and Ferrell, 2000, Narver and Slater, 1990, Slater and Narver, 1998, Slater and Narver, 1999, Verhees and Meulenberg, 2004) Baker and Sinkula, 1999) Innovation Process (de Berntani, 2001, Dobni, 2006, Hansen and Birkinshaw, 2007, Martensen et al., 2007, Robert, 2008, Smith et al., 2008) Role of Culture (Dobni, 2006, Dombrowski et al., 2007, Martensen et al., 2007, McKinsey, 2007, Rao and Sutton, 2008, Smith et al., 2008) Innovation Strategy (Dobni, 2006, Kim W.C. and Mauborgne R., 1999, Martensen et al., 2007, Smith et al., 2008) Innovation and Resources (Christensen and Bower, 1996, Christensen and Raynor, 2003), (Martensen et al., 2007, Smith et al., 2008) Propensity to Innovate (Cannon, 1985, Dobni, 2006, Hansen and Birkinshaw, 2007, Hurley R. F. and G.T.M. Hult G. T. M., 1998, Hurley et al., 2005, Morris, 2005, Sloan, 2008, Woodside, 2004)

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Table 2: Measurement Items included in the final model

Competitive Environment Q39P This organisation needs innovative and creative ideas to excel in the market place Q38P We rapidly respond to competitive threats and opportunities in the market

Propensity to Innovate Q53P. I feel motivated to "thinking differently" in my work situation Q54P People in this organisation demonstrate a willingness to explore, accept and adopt "external" ideas

Q55P People are ready to take risks without fear of failure Q56P We deliberately copy and adapt good ideas from outside our field Q57P This organisation is willing to take the risk in supporting and investing in radical ideas

Market Orientation Q47P This organisation understands the needs of its clients Q48P We formally measure client’s satisfaction Q49P This organisation systematically acquires information on existing and potential competitors Q50P This organisation works in ways to create superior value for our clients

Innovation Strategy Q40P This organisation has developed a strategy for innovation Q41P Strategies for innovation are communicated clearly to everybody Q42P Processes are in place to accommodate and evaluate suggestions for improvements to the innovation process

Innovation Culture Q2P This organisation develops its people with the necessary skills and competencies to be creative and innovative Q27P This organisation is characterised by an innovation culture e.g. think, explore ideas, dream, connect with others and reflect Q28P Creating, acquiring and transferring of knowledge and skills are a part of our culture Q29P Our culture makes it easy for people to put forward novel ideas Q31P Leadership in this organisation actively support and encourage creativity and innovation Q32P Support for implementing approved projects is more than lip service

Resources Q14P Ideas are taken seriously in this organisation Q16P Ideas which are recognised and approved receive appropriate resources Q17P This organisation budgets sufficient time and money to support and encourage innovation among all its members Q22P It is often very easy to get ideas funded

Innovation Process Q5P A process exists to objectively consider new ideas Q11P There is an effective process in place to encourage and support the development of new ideas Q12P We roll out new services and processes within appropriate timeframes Q13P Fresh ideas are encouraged and tried out

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Table 3: Item Loadings

Construct Name Item & Loading Q35P 0.5771 Competitive Environnent Q36P 0.6358 Q37P 0.7048 Q38P 0.7736

Innovation Strategy Q39P 0.6481 Q40P 0.8516 Q41P 0.8990 Q42P 0.7946

Propensity to Innovate Q52P 0.6935 Q53P 0.8201 Q54P 0.7962 Q55P 0.7740 Q56P 0.7876 Q57P 0.7706

Market Orientation Q45P 0.6022 Q46P 0.6349 Q47P 0.7632 Q48P 0.7442 Q49P 0.7123 Q50P 0.8310 Q51P 0.6501

Innovation culture Q1P 0.6069 Q2P 0.7072 Q3P 0.5683 Q4P 0.2629 Q25P 0.6347 Q26P 0.6832 Q27P 0.7495 Q28P 0.7638 Q29P 0.8047 Q30P 0.6817 Q31P 0.7595 Q32P 0.7694 Q33P 0.5465 Q34P 0.6058 Q43P 0.4755 Q44P 0.4273

Resources Q10P 0.6380 Q14P 0.7250 Q15P 0.6888 Q16P 0.7233 Q17P 0.7266 Q18P 0.6846 Q19P 0.2529 Q20P 0.6313 Q21P 0.6819 Q22P 0.7079 Q23P 0.6776 Q24P 0.6935

Innovation Process Q5P 0.7101 Q6P 0.6971 Q7P 0.4825 Q8P 0.6538 Q9P 0.4869 Q11P 0.7353 Q12P 0.7544 Q13P 0.8455

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Table 4: Internal Consistency of Latent Variables

Latent Variable Internal Consistency Competitive Environment 0.77 Propensity to Innovate 0.90 Market Orientation 0.875 Innovation Strategy 0.878 Innovation Culture 0.922 Resource 0.909 Innovation Process 0.880

Table 5: Discriminant Validity - Square Root of AVE and Latent Variable Correlations

Competitive Propensity Market Innovation Innovation Resources Innovation Environment to Orientation Strategy Culture Process Innovate Competitive 0.803 Environment Propensity to 0.661 0.800 Innovate Market 0.476 0.649 0.819 Orientation Innovation 0.582 0.601 0.559 0.880 Strategy Innovation 0.657 0.800 0.660 0.647 0.795 Culture Resources 0.496 0.705 0.541 0.583 0.770 0.794 Innovation 0.438 0.694 0.641 0.656 0.731 0.680 0.772 Process

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Table 6: Discriminant Validity - Cross Loading Values

Competitive Propensity Market Innovation Innovation Resources Innovation Environment to Orientation Strategy Culture Process Innovate Q37P 0.783489 0.506878 0.295483 0.399581 0.474728 0.389403 0.313454 Q38P 0.82241 0.553675 0.461725 0.52967 0.576325 0.407762 0.387065 Q53P 0.638996 0.840555 0.602381 0.538762 0.719061 0.593359 0.654298 Q54P 0.423132 0.815537 0.576782 0.429571 0.649442 0.570256 0.580526 Q57P 0.58958 0.781939 0.45142 0.55783 0.617061 0.67351 0.565307 Q55P 0.511597 0.780087 0.42588 0.435897 0.621118 0.485832 0.461888 Q56P 0.456953 0.779604 0.527166 0.42443 0.579143 0.478926 0.48791 Q50P 0.46575 0.619454 0.872927 0.554134 0.632414 0.512347 0.58173 Q47P 0.356944 0.525804 0.821324 0.361619 0.559788 0.395235 0.490276 Q48P 0.332138 0.482561 0.826163 0.389847 0.494125 0.382686 0.51103 Q49P 0.381006 0.47737 0.749197 0.492853 0.456472 0.461894 0.502478 Q40P 0.630007 0.527124 0.434678 0.859022 0.589791 0.467666 0.585751 Q41P 0.504004 0.577367 0.525386 0.91139 0.5607 0.539381 0.52833 Q42P 0.40677 0.478059 0.511084 0.868523 0.561591 0.529745 0.626158 Q27P 0.523926 0.597811 0.461782 0.558529 0.8014 0.59402 0.662778 Q28P 0.562486 0.627026 0.597599 0.499006 0.813885 0.632255 0.494885 Q29P 0.562118 0.722392 0.524692 0.532996 0.823295 0.684836 0.63969 Q2P 0.470575 0.574704 0.508403 0.487848 0.752805 0.577614 0.483872 Q31P 0.460061 0.609272 0.610573 0.486232 0.780773 0.591652 0.616269 Q32P 0.543037 0.667442 0.446526 0.521101 0.793126 0.580664 0.580132 Q17P 0.322377 0.549871 0.410493 0.494707 0.631873 0.812075 0.542304 Q16P 0.442048 0.524267 0.463339 0.493572 0.583131 0.761294 0.554859 Q14P 0.403474 0.59172 0.433768 0.394228 0.618978 0.809859 0.534718 Q22P 0.41204 0.571229 0.414646 0.47329 0.610291 0.791497 0.529993 Q5P 0.339006 0.490887 0.473865 0.638452 0.609293 0.493474 0.707647 Q11P 0.319864 0.506583 0.522523 0.67292 0.571767 0.60177 0.730269 Q12P 0.386658 0.535488 0.45246 0.439932 0.56833 0.496608 0.771955 Q13P 0.348098 0.602626 0.537999 0.434108 0.581711 0.538896 0.868692

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Table 7: Statistical Analysis for the standardised path values of the Structural Model.

Hypothesis Description Standardised Path Significance** Value

There is a positive and significant relationship between H1 0.255 Significant Competitive Environment and Propensity to Innovate There is a positive and significant relationship between H2 0.114 Significant Market Orientation and Propensity to Innovate There is a positive and significant relationship between H3 0.182 Significant Innovation Process and Propensity to Innovate There is a positive and significant relationship between H4 0.3 Significant Innovation Culture and Propensity to Innovate There is a positive and significant relationship between H5 0.169 Significant Resources and Propensity to Innovate There is a positive and significant relationship between H6 -0.04 Not Significant Innovation Strategy and Propensity to Innovate There is a positive and significant relationship between H7 0.77 Significant Innovation Culture and Resources There is a positive and significant relationship between H8 0.559 Significant Market Orientation and Innovation Strategy Resources positively and significantly mediates the H9 Used the three stage Partial Mediation relationship between Innovation Culture and Propensity to process suggested by Innovate. Baron and Kenny (1986) Innovation Strategy positively and significantly mediates H10 Used the three stage No Mediation the relationship between Market Orientation and process suggested by Propensity to Innovate Baron and Kenny (1986)

Table 8: Structural Model Standardised Score for “Perceived Importance” and “Perceived

Performance”

Perceived Importance Perceived Performance

Construct Standard Score Construct Standard Score

Innovation Process 0.252 Innovation Culture 0.300 Innovation Strategy 0.251 Competitive Environment 0.255 Innovation Culture 0.208 Innovation Process 0.182 Resources 0.180 Resources 0.169

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