Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A holistic methodology for modeling consumer response to innovation.

R P Bagozzi

    Operations Research
    |December 12, 1982
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Racial Disparities in Type 2 Diabetes Health Care Utilization in Medicaid Adults With Developmental Disabilities.

    Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2016
    Same author

    An Examination Of The Validity Of Two Models Of Attitude.

    Multivariate behavioral research·2016
    Same author

    An Examination of the Etiology of the Attitude-Behavior Relation for Goal-Directed Behaviors.

    Multivariate behavioral research·2016
    Same author

    Canonical Correlation Analysis As A Special Case Of A Structural Relations Model.

    Multivariate behavioral research·2016
    Same author

    The Construct Validity Of The Affective, Behavioral, And Cognitive Components Of Attitude By Analysis Of Covariance Structures.

    Multivariate behavioral research·2016
    Same author

    Gender differences in the self-regulation of hypertension.

    Journal of behavioral medicine·2001
    Same journal

    Quantile Markov Decision Processes.

    Operations research·2022
    Same journal

    Dynamics of Drug Resistance: Optimal Control of an Infectious Disease.

    Operations research·2021
    Same journal

    Inverse Optimization: A New Perspective on the Black-Litterman Model.

    Operations research·2014
    Same journal

    Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors.

    Operations research·2011
    Same journal

    Controlling Co-Epidemics: Analysis of HIV and Tuberculosis Infection Dynamics.

    Operations research·2009
    Same journal

    A model for making project funding decisions at the National Cancer Institute.

    Operations research·1992
    See all related articles

    This study presents a new structural equation model to understand consumer responses to innovation. The model improves upon existing frameworks by accounting for measurement error and complex relationships, aiding product design and marketing strategies.

    Area of Science:

    • Marketing Science
    • Consumer Behavior Research
    • Quantitative Marketing

    Background:

    • Existing models for consumer response to innovation have limitations.
    • Previous frameworks, such as Hauser and Urban's, do not fully account for measurement error or complex interrelationships.
    • There is a need for a more comprehensive model to understand consumer choice behavior in response to new products and marketing efforts.

    Purpose of the Study:

    • To derive and illustrate a general structural equation model for consumer response to innovation.
    • To extend and complement existing models by incorporating measurement error and complex hypothesis testing.
    • To provide a framework for modeling the influence of controllable marketing stimuli on consumer choice.

    Main Methods:

    • Development of a general structural equation model.

    Related Experiment Videos

  • Explicitly accounting for measurement error in consumer response variables.
  • Estimation of intercorrelations among exogenous factors.
  • Modeling of environmental and managerially controllable stimuli.
  • Development of four generic response models.

    • Main Results:

    • The proposed model offers a unique statistical solution and can test complex hypotheses, including simultaneity and feedback loops.
    • It explicitly incorporates measurement error, improving the accuracy of consumer response estimation.
    • The model allows for the integration of controllable marketing factors (e.g., product design, persuasive appeals) into the analysis of consumer choice.
    • Four generic response models are developed to guide the application of the structural equation framework.

    Conclusions:

    • The derived structural equation model provides a robust and flexible framework for analyzing consumer response to innovation.
    • This approach enhances the ability to understand and predict consumer choice behavior by accounting for complex relationships and marketing influences.
    • The model and associated response frameworks offer valuable insights for managers aiming to optimize product design and marketing strategies.