Hybrid Choice Models: Choice of Internet Access and IP Telephony

1. Hybrid Choice Models:Choice of Internet Access and IP Telephony Denis Bolduc Moshe Ben-Akiva PROCESSUS Second International Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation Models: Frameworks, Models and Applications Toronto, June 15, 2005

2. Background 1/2 • Modeling Internet Access and IP telephony awareness and service choice • Complexity of Behavior (Focus of conference) • Simplicity of Models • Workhorses: logit, nested logit, mixed logit • Advances in • Discrete Choice Methods • Data Acquisition • Computational Power

3. Background 2/2 Internet Access and IP telephony • There are few empirical models of the demand for Internet • We employ discrete choice models and micro simulation to estimate the demand for Internet services for a wide range of potential future scenarios. • The models are estimated using an original market research survey conducted in Japan during 2004 with stated preferences choice experiments of both Internet Access and IP Telephony. • Preliminary results indicate that increasing awareness of IP Telephony has the potential to dramatically increase the penetration of IP telephony. Our results also indicate a great variability among income groups in price sensitivity.

4. Hybrid Choice Model • Core: standard discrete choice model • Relax simplifying assumptions + flexible covariance structures + random taste variation • Enrich with latent variables + attitudes and perceptions + latent market segments • Unified framework • Allows for efficient use of data

5. Standard Discrete Choice Model Model structural equations measurement equations Outputs Probability of n choosing alternative i. E.g., multinomial logit:

6. Hybrid Choice Model (HCM) • Standard: • HCM: • Latent variables • Latent classes • Flexible disturbances Also make use of additional Behavioral Indicators: • Stated Preferences • Psychometric Data

7. Choice and Latent Variable Models Hypothesis Preferences are a function of latent explanatory variables. Latent Variable Models Measurement and structural equations quantify latent variables (e.g., Factor Analysis, MIMC, LISREL).

8. Latent Variable Models Objective: To measure unobservable constructs. Structural equations Measurement equations e.g., e.g.,

9. Example of Psychometric Indicatorsin the case of Internet Access and IPT • IP Telephony Awareness • IP telephone is different than fixed phone in terms of operation. • A PC is needed to use IP phone. • Communication and computer affinity • I am very computer literate. • I am not proficient at inputting by keyboard. • I prefer emailing than talking on the phone. • Attitudes about decision making & information acquisition • I prefer to adopt new products ahead of other people. • I like to use products that nobody else is using.

10. Choice and Latent Variable Model Latent Variable Measurement Model Latent Variable Structural Model Choice Model

11. Logit Kernel / Mixed Logit Flexible disturbance and/or RTV: • Resolves limitations of logit: • Unrestricted substitution patterns • Random taste variation (RTV) • Correlations over space and time • Computationally Feasible FlexibleDisturbance i.i.d. Extreme Value Disturbance

12. HCM Choice Probability Flexible Disturbances Choice Model Kernel Latent Variable Structural Model

14. The Internet Access sub-Model

15. Today’s focus:The IP Telephony sub-Model

16. Model Formulation Choice Model Latent Variable Model (1 latent variable) Measurement Model ( R indicators)

19. Conclusion • Hybrid Choice Model provides powerful, practical, & statistically grounded method for enriching demand models. • Issues • More experience in applications • Forms of the kernel • Better understanding of identification • Data Requirements • Large Scale Models