Wood Science & Engineering - Oregon State University 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

Wood Science & Engineering - Oregon State University Outline Study objectives Principles of scale development Scale development procedure Future work

Wood Science & Engineering - Oregon State University Objective To develop a valid and reliable measure of firm innovativeness for firms in industrial manufacturing industries

Wood Science & Engineering - Oregon State University 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)

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

Wood Science & Engineering - Oregon State University In this study, innovativeness is defined as: The propensity of firms to create and/or adopt new products, processes, and business systems. Literature Review

Wood Science & Engineering - Oregon State University Previous Measures of Innovativeness

Wood Science & Engineering - Oregon State University Previous Innovativeness Measures Current processing technology Self-rating Intellectual property R&D spending

Wood Science & Engineering - Oregon State University Critical factors of innovativeness Wang and Ahmed (2004) identified five aspects of innovativeness –Product –Market –Behavior –Strategic

Wood Science & Engineering - Oregon State University Critical factors cont. Hovgaard and Hansen (2004) identified three aspects –Product –Process –Business systems Hansen et al confirmed this view

Wood Science & Engineering - Oregon State University Theoretical Frame of Reference Propensity to create new products Innovativeness Propensity to create new mfg. processes Propensity to create new bus systems Propensity to adopt new bus systems Propensity to adopt new mfg. processes Financial Performance

Wood Science & Engineering - Oregon State University Generation of items 25 items generated 5 for each aspect of innovativeness Adapted previously developed items when possible

Wood Science & Engineering - Oregon State University 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.

Wood Science & Engineering - Oregon State University 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

Wood Science & Engineering - Oregon State University 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)

Wood Science & Engineering - Oregon State University 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 mills (18.6%)

Wood Science & Engineering - Oregon State University 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 mills (25.1%)

Wood Science & Engineering - Oregon State University Stage I

Wood Science & Engineering - Oregon State University Exploratory factor analysis 5 factor solution 3 items not loading as predicted – deleted because of wording Kaiser–Meyer–Olkin coefficient = Bartlett test of Sphericity statistically significant (chi-sq = , d.f. 406, P < 0.001)

Wood Science & Engineering - Oregon State University 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

Wood Science & Engineering - Oregon State University Results of CFA ModelChi-squareDegrees of FreedomChange in chi-squarep-value One-factor model Propensity to create and adopt model constrained <0.001 Propensity to create and adopt model unconstrained <0.001 Product, process, and business systems model constrained <0.001 Product, process, and business systems model unconstrained < Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = Chi sq = 526.0, df = 203, CFI = 0.93, Delta2 = 0.93, RNI = 0.88, RMSEA = 0.137, NNFI = 0.92

Wood Science & Engineering - Oregon State University Results of CFA ModelChi-squareDegrees of FreedomChange in chi-squarep-value One-factor model Propensity to create and adopt model constrained <0.001 Propensity to create and adopt model unconstrained <0.001 Product, process, and business systems model constrained <0.001 Product, process, and business systems model unconstrained < Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = Chi sq = 526.0, df = 203, CFI = 0.93, Delta2 = 0.93, RNI = 0.88, RMSEA = 0.137, NNFI = 0.92

Wood Science & Engineering - Oregon State University Results of CFA ModelChi-squareDegrees of FreedomChange in chi-squarep-value One-factor model Propensity to create and adopt model constrained <0.001 Propensity to create and adopt model unconstrained <0.001 Product, process, and business systems model constrained <0.001 Product, process, and business systems model unconstrained < Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = Chi sq = 526.0, df = 203, CFI = 0.93, Delta2 = 0.93, RNI = 0.88, RMSEA = 0.137, NNFI = 0.92

Wood Science & Engineering - Oregon State University Refined Theoretical Frame of Reference Propensity to create/adopt new products Innovativeness Propensity to create/adopt new mfg. processes Propensity to create/adopt new bus systems Financial Performance

Wood Science & Engineering - Oregon State University Stage II

Wood Science & Engineering - Oregon State University Scale Refinement Followed procedure used in Stage 1

Wood Science & Engineering - Oregon State University Exploratory Factor Analysis 4 factor solution Items generally loaded as expected Kaiser–Meyer–Olkin coefficient = Bartlett test of Sphericity statistically significant (chi-sq = , d.f. 153, P < 0.001)

Wood Science & Engineering - Oregon State University 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

Wood Science & Engineering - Oregon State University ModelChi-squareD.F.Change in chi-square p-value One-factor model Product, process, and business systems model constrained <0.001 Product, process, and business systems model unconstrained <0.001 Refined product, process, and business systems model constrained <0.001 Refined product, process, and business systems model unconstrained < Chi sq = 810.2, df = 203, CFI = 0.89, Delta2 = 0.89, RNI = 0.85, RMSEA = 0.166, NNFI = Chi sq = 494.1, df = 142, CFI = 0.91, Delta2 = 0.91, RNI = 0.86, RMSEA = 0.152, NNFI = 0.89

Wood Science & Engineering - Oregon State University ModelChi-squareD.F.Change in chi-square p-value One-factor model Product, process, and business systems model constrained <0.001 Product, process, and business systems model unconstrained <0.001 Refined product, process, and business systems model constrained <0.001 Refined product, process, and business systems model unconstrained < Chi sq = 810.2, df = 203, CFI = 0.89, Delta2 = 0.89, RNI = 0.85, RMSEA = 0.166, NNFI = Chi sq = 494.1, df = 142, CFI = 0.91, Delta2 = 0.91, RNI = 0.86, RMSEA = 0.152, NNFI = 0.89

Wood Science & Engineering - Oregon State University ModelChi-squareD.F.Change in chi-square p-value One-factor model Product, process, and business systems model constrained <0.001 Product, process, and business systems model unconstrained <0.001 Refined product, process, and business systems model constrained <0.001 Refined product, process, and business systems model unconstrained < Chi sq = 810.2, df = 203, CFI = 0.89, Delta2 = 0.89, RNI = 0.85, RMSEA = 0.166, NNFI = Chi sq = 494.1, df = 142, CFI = 0.91, Delta2 = 0.91, RNI = 0.86, RMSEA = 0.152, NNFI = 0.89

Wood Science & Engineering - Oregon State University Innovativeness Instrument Composed of 15 items –6 product, 4 process, 5 business systems Reliability – Cronbach’s alpha –Full 15-item scale – –Component items Product – Process – Business systems – 0.883

Wood Science & Engineering - Oregon State University So, do more innovative firms perform better?

Wood Science & Engineering - Oregon State University The proposed model InnovativenessPerformance Return on Sales Growth Return on Assets Competitiveness Product Process Business Systems

Wood Science & Engineering - Oregon State University Relationship between innovativeness and performance Chi-Square = 18.13, df = 11 p-value = 0.08, RMSEA = All relationships significant InnovativenessPerformance Return on Sales Growth Return on Assets Competitiveness Product Process Business Systems

Wood Science & Engineering - Oregon State University 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

Wood Science & Engineering - Oregon State University Questions?