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

Wood Science & Engineering - Oregon State University

Developing a Measure of Innovativeness in the North

American Forest Products Industry

Chris KnowlesResearch Assistant, Forest Products Marketing

Oregon Wood Innovation CenterWood Science and Engineering

Oregon State University

Eric HansenProfessor, Forest Products Marketing

Wood Science and EngineeringOregon State University

IUFRO All-Division 5 Conference Thursday, November 1, 2007


Outline

• Study objectives

• Principles of scale development

• Scale development procedure

• Future work


Objective

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


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)


Stage I

Literature review

Identify critical factors of innovativeness

Generation of items

Scale refinement with expert opinions

Questionnairedevelopment

Data collection

Scale refinement

Pretest

Refine questionnaire

Data collection

Scale refinement

Innovativeness instrument

Stage IIA Two-Stage

Approach

Based on: Churchill (1979), DeVellis (2003) and Netermeyer et al. (2003)


In this study, innovativeness is defined as:

The propensity of firms to create and/or adopt new products, processes, and

business systems.

Literature Review


Previous Measures of Innovativeness


Previous Innovativeness Measures

• Current processing technology

• Self-rating

• Intellectual property

• R&D spending


Critical factors of innovativeness

• Wang and Ahmed (2004) identified five aspects of innovativeness– Product– Market– Market– Behavior– Strategic


Critical factors cont.

• Hovgaard and Hansen (2004) identified three aspects– Product– Process– Business systems

• Hansen et al. 2007 confirmed this view


Theoretical Frame of ReferencePropensity

to createnew products

Innovativeness

Propensity to create

new mfg. processes

Propensityto create

new bus systems

Propensityto adopt

new bus systems

Propensityto adopt

new mfg. processes

FinancialPerformance


Generation of items

• 25 items generated

• 5 for each aspect of innovativeness

• Adapted previously developed items when possible


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.


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


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)


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%)


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%)


Stage I


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)


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


Results of CFAModel Chi-square Degrees of Freedom Change in chi-square p-value

One-factor model 1501.5 301

Propensity to create and adopt model constrained 1399.3 299 102.2 <0.001

Propensity to create and adopt model unconstrained1 870.6 284 528.7 <0.001

Product, process, and business systems model constrained

605.5 209 265.1 <0.001

Product, process, and business systems model unconstrained2

526.0 203 79.5 <0.001

1Chi sq = 870.6, df = 284, CFI = 0.91, Delta2 = 0.91, RNI = 0.89, RMSEA = 0.160, NNFI = 0.89








605.5 209 265.1 <0.001


526.0 203 79.5 <0.001









605.5 209 265.1 <0.001


526.0 203 79.5 <0.001




Refined Theoretical Frame of Reference

Propensity to create/adopt new products

InnovativenessPropensity

to create/adoptnew mfg. processes

Propensityto create/adopt

new bus systems

FinancialPerformance


Stage II


Scale Refinement

• Followed procedure used in Stage 1


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)


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


Model Chi-square D.F. Change in chi-square

p-value



1052.1 209 215.6 <0.001


810.2 203 241.9 <0.001

Refined product, process, and business systems model constrained

601.5 151 208.7 <0.001

Refined product, process, and business systems model unconstrained2

494.1 142 107.4 <0.001





p-value



1052.1 209 215.6 <0.001


810.2 203 241.9 <0.001


601.5 151 208.7 <0.001


494.1 142 107.4 <0.001





p-value



1052.1 209 215.6 <0.001


810.2 203 241.9 <0.001


601.5 151 208.7 <0.001


494.1 142 107.4 <0.001




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


So, do more innovative firms perform better?


The proposed model

Innovativeness Performance

Return on Sales

SalesGrowth

Return onAssets

Competitiveness

Product

Process

Business Systems


Relationship between innovativeness and performance

Chi-Square = 18.13, df = 11 p-value = 0.08, RMSEA = 0.077

All relationships significant

Innovativeness Performance

Return on Sales

SalesGrowth

Return onAssets

Competitiveness

Product

Process

Business Systems


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


Questions?

Documents

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