ENVIRONMENTAL COMMUNICATIONS WITH LCA INFORMATION: AN EXPLORATORY - - PowerPoint PPT Presentation

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ENVIRONMENTAL COMMUNICATIONS WITH LCA INFORMATION: AN EXPLORATORY STUDY WITHIN THE BUILDING INDUSTRY

Sergio A. Molina –Murillo Post‐Doctoral Associate/Instructor Strategic Environmental Management & Policy University of Minnesota‐Twin Cities 3rd International Conference on Life Cycle Management Zurich, Switzerland August 27-29, 2007 Timothy M. Smith Associate Professor Environmental Science, Policy & Management University of Minnesota‐Twin Cities

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Environmental Messages are difficult to communicate

• Environmental issues are themselves complex and require

significant disclosures in light of many information asymmetries.

• The legitimacy of multiple stakeholders, each one providing
• ften conflicting expert information to an already crowed

media marketplace.

• Consumers and buyers function within a skeptical‐mode

when exposed to environmental messages.

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Our Footprint Label

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British supermarket chain Tesco, would spend almost $1 billion over the next five years to lead "a revolution in green consumption”

(New York Times, March 6th, 2007).

“To create a mass movement in green consumption we must provide better information”

(Sir Terry Leahy, Tesco CEO 01/18/2007)

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Environmentally Preferable Products

• The U.S. Federal Government procures $200 billion

annually and several executives’ orders encourage to give preference to “green” products.

• Wal‐Mart’s recent announcement to aggressively

encourage its 60,000 suppliers to create products that don't harm the environment (Hudson 2007).

• The end-consumer market size of sustainable products is over $200

billion in the U.S. (LOHAS 2005)

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Product: bundle of benefits

Environmental communications are often considered in isolation and not in conjunction with functional product attributes/benefits.

Functional benefits: “ingredients necessary for performing the product function as viewed by consumers” (Keller 1993)

Environmental communications are often considered in isolation and not in conjunction with functional product attributes/benefits.

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Does environmental information (non-functional) complement or impair functional product performance information?

Research Question

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Information processing & persuasion

• Dual‐Process Models of Persuasion:

(Petty and Cacciopo 1981,1984; Petty, Cacciopo and Schumman 1983; Chaiken 1980, 1994).

2 Routes of Persuasion

• Central route: people examine most of the information

presented to uncover the reasons in support of the proposal.

• Peripheral route provides a quick accept or reject decision

without deep consideration of the information content.

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Conceptual Model

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Methods

Sample and product: Architects Insulation Web‐based survey, architects randomly assigned to a single ad.

• Dependent Variables:

AAd, ABrand, ACompany, Purchase Intention, Credibility, Complexity

• Independent Variables:

Type of message (Functional/ Non‐functional) Explicit claim (Yes/No); Private disclosure (FUN, FIN, HEA)

• Control Variables:

Gender, Familiarity with product category, Environmental Concern, Ad message Involvement, Attitude towards Advertising, Familiarity with Energy Star program, Years of professional experience.

• 8 Different Ads
• Response rate= 21.5% (1,346); median = 10 minutes.
• Included in analysis responses between 5‐20 minutes only respondents with English as

the first language = 1,062).

• No response bias.

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Measurement Scales

• Reliability (Cronbach α): .852 (CRE) ‐ .945 (ABR).

Construct

• No. of

Items References Complexity (COM)

3 Keller and Block (1997).

Credibility (CRE)

3 Cottle et al. (2005); Newell and Goldsmith (2001), Golberg and Hartwick (1990).

Attitude toward the ad (AAD)

3 MacKenzie and Lutz (1989); Muehling (1987a).

Attitude toward the brand (ABR)

3 MacKenzie and Lutz (1989); Muehling (1987a)

Attitudes toward the company (ACO)

8 Newell and Goldsmith (2001).

Purchase intention (PI)

3 Yi (1990); Putrevu et al. (2004); MacKenzie, Lutz and Belch (1986).

Product knowledge (KNO)

3 Kent and Allen (1994).

Energy Star’ familiarity (STA)

3 Oliver and DeSarbo (1985)

Environmental concern (EC)

5 Cordano et al. (2003); Cordano et al. (2004)

Attention devoted to the message (ATT)

4 Ha (1996); Laczniak and Muehling (1993).

Attitudes toward advertising (ADV)

9 Obermiller and Spangenberg (1998); Muehling (1987b)

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Functional No Explicit Non-functional (Environmental) Explicit

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Regression results of all dependent variables.

COM CRE AAD ABR ACO PI

INDEPENDENT VARIABLES Intercept term (TDF) a 4.082* 3.869* 3.358* 2.091* 2.077*

• 0.471*

Functional Disaggregated (TDFD) 0.214

• 0.022

0.018

• 0.084

0.057 0.125 Environmental Non-Disaggregated (TDE) 0.201

• 0.206*
• 0.024
• 0.035
• 0.079

0.172* Environmental Disaggregated (TDED) 0.078 0.244* 0.239*

• 0.039

0.046 0.115* FIN 0.171

• 0.034

0.068

• 0.028
• 0.014

0.010 HEALTH 0.20** 0.026

• 0.035

0.033 0.096*

• 0.112**

Complexity (COM)

• 0.321*
• 0.035
• 0.004
• 0.078*

Credibility (CRE) 0.349* 0.149* 0.320* 0.256* Attitude toward the ad (AAD 0.356* 0.121* 0.168* Attitude toward the brand (ABR) 0.186* Attitude toward company (ACO) 0.348* CONTROL VARIABLES Gender (GEN) 0.099

• 0.004
• 0.080
• 0.054

0.019

• 0.072

Product Knowledge (KNO) 0.002

• 0.077*

0.029

• 0.049
• 0.037*

0.051** Energy Star Familiarity (STA) 0.060 0.037

• 0.017

0.065 0.039** 0.024 Environmental Concern (EC) 0.071** 0.019 0.001

• 0.054

0.013 0.062* Processing effort (ATT)

• 0.320*
• 0.004

0.113* 0.005

• 0.048*

0.014 Professional Experience (EXP) 0.007

• 0.014*
• 0.009*
• 0.004
• 0.005*
• 0.008*

Attitude toward Advertising (ADV)

• 0.100*

0.319* 0.099* 0.078* 0.073* 0.061*

• Adj. R2

0.095 0.154 0.362 0.282 0.450 0.546

a The base line advertisement includes a Functional Non-Explicit theme disclosure (TDF) with a Functional Private Disclosure (FUN).

* Significant (p≤0.05) ** Significant (p ≤ 0.10)

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Structural Equations Model

TDF

ξ8

TDE

ξ10

Gender

ξ1

EC

ξ4

Knowledge

ξ2

TDFD

ξ9

TDED

ξ11

FIN

ξ13

HEA

ξ14

• A. Advertising

ξ7

FUN

ξ12

PI

η6

• A. Company

η5

• A. Brand

η4

Credibility

η2

• A. Ad

η3

Complexity

η1

GEN1 EC1 EC2 EC3 EC4 EC5 KNO1 KNO2 AAD1 AAD2 AAD3 TDF1 TDED1 TDE1 FUN1 HEA1 FIN1 COM1 COM2 COM3 CRE1 CRE2 ABR1 ABR2 ABR3 CO1 CO2 PI1 PI2 PI3 AAD1 AAD2 AAD3 TDFD1

Β32= 0.30∗ Β42= 0.18∗ Β62= 0.18∗ Β43= 0.42∗ Β63= 0.28∗ Β52 = 0.72∗ Β31= −0.47∗ Β61= −0.08∗ Β51=0.03 Β65=0.33∗ Β41= −0.02 Β64=0.15∗ Β53= 0.17∗ ε1 ε2 ε5 ε4 ζ21 ζ54

CRE3

Energy Star

ξ3

STA1 STA2 STA3 KNO3 KNO4

ATT

ξ5

ATT1 ATT2 ATT3 ATT4

EXP

ξ6

EXP1 CRE4

γ1 1−14 γ2 1−14 γ3 1−14

TDF

ξ8

TDE

ξ10

Gender

ξ1

EC

ξ4

Knowledge

ξ2

TDFD

ξ9

TDED

ξ11

FIN

ξ13

HEA

ξ14

• A. Advertising

ξ7

FUN

ξ12

PI

η6

• A. Company

η5

• A. Brand

η4

Credibility

η2

• A. Ad

η3

Complexity

η1

GEN1 EC1 EC2 EC3 EC4 EC5 KNO1 KNO2 AAD1 AAD2 AAD3 TDF1 TDED1 TDE1 FUN1 HEA1 FIN1 COM1 COM2 COM3 CRE1 CRE2 ABR1 ABR2 ABR3 CO1 CO2 PI1 PI2 PI3 AAD1 AAD2 AAD3 TDFD1

Β32= 0.30∗ Β42= 0.18∗ Β62= 0.18∗ Β43= 0.42∗ Β63= 0.28∗ Β52 = 0.72∗ Β31= −0.47∗ Β61= −0.08∗ Β51=0.03 Β65=0.33∗ Β41= −0.02 Β64=0.15∗ Β53= 0.17∗ ε1 ε2 ε5 ε4 ζ21 ζ54

CRE3

Energy Star

ξ3

STA1 STA2 STA3 KNO3 KNO4

ATT

ξ5

ATT1 ATT2 ATT3 ATT4

EXP

ξ6

EXP1 CRE4

γ1 1−14 γ2 1−14 γ3 1−14

• 915 observations with a total of 45 observed variables specified for 20 latent variables

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LVSEM standardized coefficients of exogenous variables.

Specified relationship Parameter Estimate t Value TDF Complexity

γ18

• 0.02
• 0.03

TDF Credibility

γ28

0.05 0.07 TDF AAD

γ38

• 0.01
• 0.01

TDFD Complexity

γ19

0.03 0.86 TDFD Credibility

γ29

0.01 0.07 TDFD AAD

γ39

• 0.01
• 0.05

TDE Complexity

γ110

0.02 0.03 TDE Credibility

γ210

• 0.05
• 0.07

TDE AAD

γ310

0.01 0.01 TDED Complexity

γ111

• 0.02
• 0.50

TDED Credibility

γ211

0.14 4.11 TDED AAD

γ311

0.08 2.74

χ2

(806) = 1,564.61 (p=0.001)

Root Mean Square Error of Approximation (RMSEA) = 0.031 Normed Fit Index (NFI) = 0.97 Incremental Fit Index (IFI) = 0.99

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Estimated Structural Model standardized coefficients (cont.)

Specified relationship Parameter Estimate t Value Complexity AAD Β31

• 0.47
• 14.48

Complexity ABR Β41

• 0.02
• 0.63

Complexity ACO Β51 0.03 0.87 Complexity PI Β61

• 0.08
• 2.54

Credibility AAD β32 0.30 8.88 Credibility ABR β42 0.18 5.01 Credibility ACO β52 0.72 15.16 Credibility PI β62 0.18 3.47 AAD ABR β43 0.42 9.25 AAD ACO β53 0.17 4.18 AAD PI β63 0.28 7.09 ABR PI β64 0.15 5.08 ACO PI β65 0.33 5.59

χ2

(806) = 1,564.61 (p=0.001)

Root Mean Square Error of Approximation (RMSEA) = 0.031 Normed Fit Index (NFI) = 0.97 Incremental Fit Index (IFI) = 0.99

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Results

– Non‐functional (environmental) information increases credibility and AAD , but

• nly when they are explicit. Just the mention of LCA doesn’t help.

– Functional‐only messages did not improve communications effectiveness when they were elaborated (explicit). – The two mediating constructs (complexity and credibility) had significant effects

• n communication effectiveness measures.

– Complexity (COM) is negatively related to AAD and influences purchase intent. – Credibility, to a high extent, compensates the effect of complexity on the attitude toward the ad, and in fact, it strongly influences in a positive manner all attitudinal variables and the intention to purchase the product.

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Concluding Thought

• Very strong likelihood that LCA‐based data communications in an

advertising context makes for bad advertising appeal … …BUT, may help build credibility that reflects favorably on

• ther variables (AAD, ABR, ACO, PI).
• Future ideas to explore:

– When should firms communicate environmental performance information (early in the dialogue, late in the dialogue, only after they have seen it in print…)? – Test on companies with different corporate reputation

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Sponsors

– North American Insulation Manufacturers Association (NAIMA) – Forest Products Management Development Institute (FPMDI) – U.S. Green Building Council (USGBC), Tom Dietsche

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THANK YOU!

sergiomolina@umn.edu +1 (612) 624 3223