484 research outputs found

    Modeling the Choice of Telecommuting 3: Identifying the Choice Set and Estimating Binary Choice Models for Technology-Based Alternatives

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    Previous papers in this series have presented a conceptual model of the individual decision to telecommute and explored relationships among constraints, preference, and choice. A related paper has developed a binary model of the preference for home-based telecommuting. Noting that there is a wide gap between preferring to telecommute (88% of the sample) and actually telecommuting (13%), this paper develops binary logit models of telecommuting adoption. Two approaches to dealing with constraints are compared: incorporating them directly into the utility function, and using them to define the choice set. Models using the first approach appear to be statistically superior in this analysis, explaining 63-64% of the information in the data. Variables significant to choice include those relating to work and travel drives, and awareness, manager support, job suitability, technology, and discipline constraints. The best model was used to analyze the impact of relaxing three key constraints on the 355 people in the sample for whom telecommuting was previously identified to be a Preferred Impossible Alternative. When unawareness, lack of manager support, and job unsuitability constraints are relaxed, 28% of the people in the PIA category would be expected to adopt telecommuting. The importance of behavioral models to accurately forecasting telecommuting adoption is emphasized and is suggested to have wider implications for predicting technology-based activity changes.telecommuting, teleworking, discrete choice, choice set

    Modeling the Choice of Telecommuting 2: A Case of the Preferred Impossible Alternative

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    A conceptual model of the choice to telecommute was advanced in an earlier paper (Mokhtarian and Salomon, 1994). In this paper, we present empirical data from a non-representative sample of 628 City of San Diego employees on key variables and relationships in that model. The relationships among possibility, preference, and choice are examined. A key finding is the existence of a large group of people (57% of the sample) for whom telecommuting is a Preferred Impossible Alternative. Dichotomous and continuous constraints are distinguished, and three dichotomous constraints are defined. Lack of awareness is active for 4%, job unsuitability for 44%, and manager disapproval for 51% of the sample. For 68% of the sample, at least one of these constraints is active. Even among those for whom none of the dichotomous constraints is in force, most people do not choose telecommuting due to the presence of active continuous constraints. For only 11% of the entire sample, telecommuting is possible, preferred, and chosen. The potential impacts of self-selection bias are estimated, and sampling bias is qualitatively assessed. This analysis provides a crude but useful estimate of the potential of telecommuting in the population, and more specifically, the relative share of potential telecommuters who are prevented by key dichotomous constraints from choosing that option.telecommuting, teleworking

    What about people in behavioral modeling? Ryuichi Kitamura (1949 - 2009)

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    Editorial

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    Trivariate Probit Models of Pre-purchase/ Purchase Shopping Channel Choice: Clothing Purchases in Northern California

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    This study analyzes the joint choice of pre-purchase and purchase shopping channels for clothing purchases, using data collected from an internet-based survey of two university towns in Northern California (final Ns=390 and 452). Descriptive analysis clearly shows dependence across these three choices: in particular, the “sticky” combinations of {only-store pre-purchase + store purchase} and {only internet pre-purchase + internet purchase} occur substantially more often than independent choices would predict. We develop two trivariate probit (TVP) models, consisting of two binary choice equations for the pre-purchase channel (respectively measuring the use of store or not, and the use of internet or not) and one binary choice equation for the purchase channel (store or internet). One model allows prior channel purchase experience variables to enter while the other model excludes them. The results further confirm the dependency among pre-purchase and purchase channel choices, with all three error term correlations strongly significant. In addition to breadth and depth of experience variables and channel-specific perceptions (post-purchase satisfaction, cost savings, enjoyment, and convenience), significant explanatory variables include general shopping-related attitudes (pro-exercise, shop enjoyment, and store enjoyment), context variables, and sociodemographic traits (age and income). Prediction of joint choice probabilities was considerably better for the TVP model than for independent binary choice models, confirming the value of simultaneously modeling pre-purchase and purchase channel choice bundle
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