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Developing a Measure of Innovativeness in the North American Forest Products Industry

Developing a Measure of Innovativeness in the North American Forest Products Industry. Chris Knowles Research Assistant, Forest Products Marketing Oregon Wood Innovation Center Wood Science and Engineering Oregon State University Eric Hansen Professor, Forest Products Marketing

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Developing a Measure of Innovativeness in the North American Forest Products Industry

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  1. Developing a Measure of Innovativeness in the North American Forest Products Industry Chris Knowles Research Assistant, Forest Products Marketing Oregon Wood Innovation Center Wood Science and Engineering Oregon State University Eric Hansen Professor, Forest Products Marketing Wood Science and Engineering Oregon State University IUFRO All-Division 5 Conference Thursday, November 1, 2007

  2. Outline • Study objectives • Principles of scale development • Scale development procedure • Future work

  3. Objective • To develop a valid and reliable measure of firm innovativeness for firms in industrial manufacturing industries

  4. Why develop a new measure? • Inconsistent results from previous research • Largely due to inconsistent measures and/or conceptualizations • Call for development in previous literature • Deshpande and Farley (2004) • Crespell et al. (2006)

  5. Stage I Literature review Identify critical factors of innovativeness Generation of items Scale refinement with expert opinions Stage II Questionnaire development Refine questionnaire Pretest Data collection Data collection Scale refinement Scale refinement Innovativeness instrument A Two-Stage Approach Based on: Churchill (1979), DeVellis (2003) and Netermeyer et al. (2003)

  6. Literature Review In this study, innovativeness is defined as: The propensity of firms to create and/or adopt new products, processes, and business systems.

  7. Previous Measures of Innovativeness

  8. Previous Innovativeness Measures • Current processing technology • Self-rating • Intellectual property • R&D spending

  9. Critical factors of innovativeness • Wang and Ahmed (2004) identified five aspects of innovativeness • Product • Market • Market • Behavior • Strategic

  10. Critical factors cont. • Hovgaard and Hansen (2004) identified three aspects • Product • Process • Business systems • Hansen et al. 2007 confirmed this view

  11. Theoretical Frame of Reference Propensity to create new products Propensity to create new mfg. processes Propensity to create new bus systems Innovativeness Financial Performance Propensity to adopt new mfg. processes Propensity to adopt new bus systems

  12. Generation of items • 25 items generated • 5 for each aspect of innovativeness • Adapted previously developed items when possible

  13. Generation of items • 25 items generated • 5 for each aspect of innovativeness • Adapted previously developed items when possible Example item for propensity to adopt new processes Our company tends to be an early adopter of new manufacturing processes.

  14. Scale refinement – expert opinions • Two stages • Stage 1 – review by Forest Business Solutions Team • Stage 2 – review by outside experts • 3 from academia • 3 industry managers • 2 industry consultants

  15. Scale refinement • Exploratory factor analysis • Allows exploration of data • Don’t specify number of factors • Deletion of items with cross-loading • SPSS • Confirmatory factor analysis • Used to confirm proposed factor structure • Specify number of factors • LISREL Netermeyer et al. (2003)

  16. Data collection – Stage I • 500 sawmills in North America randomly selected from The Big Book • Target respondent was mill manager • 53 undeliverables / closed mills • Adjusted sample size of 447 • 83 mills (18.6%)

  17. Data Collection – Stage II • 463 sawmills in North America randomly selected from The Big Book • Sawmills not used in Stage 1 • Target respondent was mill manager • 29 undeliverables / closed mills Adjusted sample size of 434 • 109 mills (25.1%)

  18. Stage I

  19. Exploratory factor analysis • 5 factor solution • 3 items not loading as predicted – deleted because of wording Kaiser–Meyer–Olkin coefficient = 0.899 Bartlett test of Sphericity statistically significant (chi-sq = 2512.1, d.f. 406, P < 0.001)

  20. Confirmatory factor analysis The following measurement models were compared: • One-factor model – all items load onto one latent variable* • Propensity to create and adopt model – Items load on latent variables according to the proposed model • Model 1 with covariances of latent variables constrained at 1 • Model 2 with covariances of latent variables unconstrained • Product, Process, Business Systems model – items load on latent variables Latent variable* – variable not directly observed

  21. Results of CFA 1Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = 0.89 2Chi sq = 526.0, df = 203, CFI = 0.93, Delta2 = 0.93, RNI = 0.88, RMSEA = 0.137, NNFI = 0.92

  22. Results of CFA 1Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = 0.89 2Chi sq = 526.0, df = 203, CFI = 0.93, Delta2 = 0.93, RNI = 0.88, RMSEA = 0.137, NNFI = 0.92

  23. Results of CFA 1Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = 0.89 2Chi sq = 526.0, df = 203, CFI = 0.93, Delta2 = 0.93, RNI = 0.88, RMSEA = 0.137, NNFI = 0.92

  24. Refined Theoretical Frame of Reference Propensity to create/adopt new products Propensity to create/adopt new mfg. processes Innovativeness Financial Performance Propensity to create/adopt new bus systems

  25. Stage II

  26. Scale Refinement • Followed procedure used in Stage 1

  27. Exploratory Factor Analysis • 4 factor solution • Items generally loaded as expected Kaiser–Meyer–Olkin coefficient = 0.921 Bartlett test of Sphericity statistically significant (chi-sq = 1551.2, d.f. 153, P < 0.001)

  28. Confirmatory factor analysis • One-factor model – all 18 items from the product, process and business systems model load onto one latent variable • Propensity to create and adopt model – Items load on latent variables according to the proposed model • Model 1 with covariances of latent variables constrained at 1 • Model 2 with covariances of latent variables unconstrained • Refined product, process, business systems model • Model 1 with covariances of latent variables constrained at 1 • Model 2 with covariances of latent variables unconstrained

  29. 1Chi sq = 810.2, df = 203, CFI = 0.89, Delta2 = 0.89, RNI = 0.85, RMSEA = 0.166, NNFI = 0.88 2Chi sq = 494.1, df = 142, CFI = 0.91, Delta2 = 0.91, RNI = 0.86, RMSEA = 0.152, NNFI = 0.89

  30. 1Chi sq = 810.2, df = 203, CFI = 0.89, Delta2 = 0.89, RNI = 0.85, RMSEA = 0.166, NNFI = 0.88 2Chi sq = 494.1, df = 142, CFI = 0.91, Delta2 = 0.91, RNI = 0.86, RMSEA = 0.152, NNFI = 0.89

  31. 1Chi sq = 810.2, df = 203, CFI = 0.89, Delta2 = 0.89, RNI = 0.85, RMSEA = 0.166, NNFI = 0.88 2Chi sq = 494.1, df = 142, CFI = 0.91, Delta2 = 0.91, RNI = 0.86, RMSEA = 0.152, NNFI = 0.89

  32. Innovativeness Instrument • Composed of 15 items • 6 product, 4 process, 5 business systems • Reliability – Cronbach’s alpha • Full 15-item scale – 0.946 • Component items • Product – 0.903 • Process – 0.808 • Business systems – 0.883

  33. So, do more innovative firms perform better?

  34. The proposed model Return on Sales Product Sales Growth Innovativeness Performance Process Return on Assets Business Systems Competitiveness

  35. Relationship between innovativeness and performance Return on Sales Product All relationships significant Sales Growth Innovativeness Performance Process Return on Assets Business Systems Competitiveness Chi-Square = 18.13, df = 11 p-value = 0.08, RMSEA = 0.077

  36. Conclusions • Scale refinement • Stage 1 – went from 25 to 18 items • Stage 2 – went from 18 to 15 items • Strong fit for proposed model • Significant, positive relationship between innovativeness and performance

  37. Questions?

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