The “derived demand” perspective on daily travel, which has become axiomatic in thetransportation field, holds that travel is derived from the demand to participate in spatiallyseparatedactivities. The act of traveling itself is not considered to offer any positive utility, andminimizing travel time is a primary goal of all travelers in all situations. This dissertationcontinues a recent effort to challenge this paradigm by directly modeling the interrelationshipsamong travel amounts, perceptions, affections (or liking), and desires, and, in doing so, asking:why do some individuals want to travel more, and others less? By modeling quantities such astravel affection and desire, I am, importantly, first acknowledging the existence of these measuresand, second, formally quantifying their relative impact on daily travel amounts and each other.Five short-distance (one-way trips less than 100 miles) and five long-distance categories of travelare examined, specifically: short-distance overall, commute, work/school-related, entertainment/social/recreation, and personal vehicle; long-distance overall, work/school-related, entertainment/social/recreation, personal vehicle, and airplane. The models are estimated using data collected in1998 from more than 1,300 commuting workers in the San Francisco Bay Area. Cross-modelanalysis reveals three robust relationships, namely: (1) myriad measures of actual travel amountswork together to affect qualitative perceptions of those amounts (e.g. “a little” or “a lot”); (2)those perceptions are consistently important in shaping desires to reduce or increase one’s travel;and (3) affections for travel have a positive influence on those desires. The second findingsuggests that two individuals who travel the same objective amount may not have the same desireto reduce their travel: how much each individual perceives his or her travel to be is important.The third point argues that the degree to which travel is enjoyed is a key determinant in shapingdesires to reduce travel: the more travel is enjoyed, the less the desire to reduce it. Each of the tenmodels is estimated with the following four estimation techniques: maximum likelihood,asymptotic distribution free, bootstrapping, and the Mplus approach. A cross-model econometriccomparison by estimation technique and sample size is included.The implications of the work are largely theoretical, but the ideas presented can lead to verypractical suggestions. For instance, those promoting travel demand management strategies, suchas telecommuting, should pay attention to the travel perceptions of their target audience. Eventhough someone may be objectively traveling a lot, if the individual does not perceive thoseamounts to be high, he may not embrace a policy aimed at reducing his travel. And the same canbe said for those who enjoy travel: those who see value in travel, perhaps because it provides abuffer between the work and home realms of daily life, will logically be less motivated to reducetheir travel amounts. The survey respondents exhibit a considerable degree of liking for travel ofall kinds studied, and this work unequivocally demonstrates the importance of travel liking totravel behavior.

Applications of new information and communication technologies (ICTs) are changing how and where we work, shop, play, travel, and in other ways live our lives. Yet because ICT development and use is in such a volatile state, many of those changes and impacts are poorly understood. This report summarizes the development and deployment of a survey instrument intended to gather information to allow better understanding of the transportation impacts of business-to-consumer (B2C) e-commerce. Although the business-to-business (B2B) segment dominates e-commerce in terms of the dollar value of transactions made, B2C remains important for its potential impacts on urban travel and land use patterns, including potential redistributions of retail land uses, and substantial increases of package delivery trips into residential neighborhoods.

We see an analysis of the transportation impacts of B2C e-commerce as having two components: (1) assessing the transportation impacts of a given level or pattern of adoption of B2C e-commerce, and (2) investigating the adoption of B2C e-commerce (who, under what circumstances, in what form). While transportation planners ultimately need to forecast transportation impacts, to do so accurately they need to understand adoption processes and trends. Thus, the data collection described here is intended to lead to modeling the adoption of B2C e-commerce, among other shopping “modes” (specifically store and catalog shopping). Because we take the consumer perspective, we will refer to the use of the internet for B2C e-commerce as “e-shopping”. We consider e-shopping to be a subset of “teleshopping”, which also includes catalog shopping (whether placing the order by phone or mail) and shopping from a television channel (generally by phone). To frame a manageable project, and because most TV shopping appears to be impulsive (Handy and Yantis, 1997), we do not include TV shopping in the survey.

Though the survey instrument collects data on the pre-purchase browsing mode(s) as well as the transaction mode choice, our definition of shopping requires a purchase to occur, not just informationgathering or “window shopping”. Because (1) the nature of the shopping process is presumed to be quite heterogeneous depending on the type of good being considered; (2) due to resource constraints our sample will number in the (high) hundreds rather than many thousands; and (3) attempting to survey respondents in-depth with respect to many types of goods would pose too great a burden; we focus on two specific product types. Product classes can be characterized along a number of dimensions, including frequency of purchase, cost, search area (local, regional, broader), tangibility (whether a service, a digital good, or a material good), perishability, “differentiability” (the extent to which retailers can distinguish their offering of the product from others’), product information complexity, and as a “search good” (having features “that can be evaluated from externally provided information”) versus an “experience good” (needing “to be personally inspected or tried”; Peterson, et al., 1997).

To yield a sufficient number of purchase occasions in our sample, we focus on low-cost, medium-/highfrequency- of-purchase goods. Specifically, we take the tangible product classes of books/physical CDs/DVDs/videotapes and clothing/shoes to be analyzed in depth. Books/CDs/DVDs/videotapes per se are basically search goods with low intrinsic differentiability, though the bookstore experience (browsing contents, having coffee) may offer some advantages to some customers. Clothing and shoes are basically experience goods, although decades of catalog shopping history show that some customers are willing to buy without trying. The survey also asks a few questions about internet shopping activity of all kinds, especially internet-based purchases.

The main research questions to be addressed by this exploratory project build on a number of prior studies: (1) For the product class in question, what are the advantages (motivators and facilitators for choosing) and disadvantages of (constraints on choosing) each shopping mode? A number of authors have identified the potential advantages of e-shopping and/or store shopping (e.g. Brynjolfsson and Smith, 2001; Salomon and Koppelman, 1988; Tauber, 1972). Store shopping is so far still very different from e2 shopping in terms of such attributes as the information provided, the sensual stimulation, the ability to compare prices and to attain immediate ownership. Beyond the functional attributes related to shopping and purchasing, store shopping also offers numerous other experiences, which to varying degrees are less amenable to electronic platforms. These include, for example, the ability to interact with real salespeople and to bargain, the opportunity to be outside the home or work environment, some physical exercise, and so on. Shopping, under many circumstances, is a combined maintenance - leisure activity. Thus, the choice between store shopping and e-shopping is not unambiguous (Handy and Yantis, 1997).

(2) Can market segments with different propensities to use alternative modes be identified? A composite of several studies (Cairns, 1996; Koppelman, et al., 1991; Tacken, 1990; Gould and Golob, 1997; Gould et al., 1998; Burke, 1998; Farag et al., 2006; Ferrell, 2005; Ren and Kwan, 2005) identifies four segments of the population that are likely to be early adopters of e-shopping: the mobility-limited, time-starved, technophilic, and shopping-phobic. These four segments may well be generalizable to many e-shopping contexts. In reality, however, they probably represent (and in general we will treat them as) four continuous dimensions, with individuals separately falling somewhere along each of them, rather than four mutually-exclusive and collectively-exhaustive group membership indicators.

(3) To what extent do customers perceive there to be viable alternative modes for a given shopping occasion? Previous studies have largely neglected this question, implicitly or explicitly assuming that each shopping activity involves a true choice among competing alternatives. It is obviously important to test that assumption, and identify variables associated with perceived mode captivity versus choice.

(4) Are the various shopping modes substitutes, or complements? For example, do people who do a lot of e-shopping tend to do less store shopping, or do high amounts of one tend to be associated with high amounts of the other? Do former catalog shoppers replace the catalog with the internet, or supplement it; conversely, do internet shoppers become new catalog shoppers as retailers engage in cross-channel marketing? Are there different relationships for different segments of the population? For example, shopping modes may be complementary for innate “shopaholics”, but substitutionary for the timepressured. Although to keep the survey at a reasonable length it is not possible to obtain the data for a rigorous analysis of transportation impacts, the answers to these questions will have direct implications for the likely transportation impacts of e-shopping.

The remainder of this report focuses on the administration of a survey instrument that will facilitate the research directions discussed above. The organization of the document is as follows. Section 2 discusses the design of the sampling plan for the data collection. The following section then describes the actual sampling process. The fourth section briefly presents the survey design. Section 5 discusses the development of the survey instruments and Section 6 outlines the deployment of the survey. Next, a section is devoted to the steps taken to build the final sample through data cleaning. A concluding section ends the report.

Applications of new information and communication technologies (ICTs) are changing how and where we work, shop, play, travel, and in other ways live our lives. Yet because ICT development and use is in such a volatile state, many of those changes and impacts are poorly understood. This report summarizes the development and deployment of a survey instrument intended to gather information to allow better understanding of the transportation impacts of business-to-consumer (B2C) e-commerce. Although the business-to-business (B2B) segment dominates e-commerce in terms of the dollar value of transactions made, B2C remains important for its potential impacts on urban travel and land use patterns, including potential redistributions of retail land uses, and substantial increases of package delivery trips into residential neighborhoods.

We see an analysis of the transportation impacts of B2C e-commerce as having two components: (1) assessing the transportation impacts of a given level or pattern of adoption of B2C e-commerce, and (2) investigating the adoption of B2C e-commerce (who, under what circumstances, in what form). While transportation planners ultimately need to forecast transportation impacts, to do so accurately they need to understand adoption processes and trends. Thus, the data collection described here is intended to lead to modeling the adoption of B2C e-commerce, among other shopping “modes” (specifically store and catalog shopping). Because we take the consumer perspective, we will refer to the use of the internet for B2C e-commerce as “e-shopping”. We consider e-shopping to be a subset of “teleshopping”, which also includes catalog shopping (whether placing the order by phone or mail) and shopping from a television channel (generally by phone). To frame a manageable project, and because most TV shopping appears to be impulsive (Handy and Yantis, 1997), we do not include TV shopping in the survey.

Though the survey instrument collects data on the pre-purchase browsing mode(s) as well as the transaction mode choice, our definition of shopping requires a purchase to occur, not just informationgathering or “window shopping”. Because (1) the nature of the shopping process is presumed to be quite heterogeneous depending on the type of good being considered; (2) due to resource constraints our sample will number in the (high) hundreds rather than many thousands; and (3) attempting to survey respondents in-depth with respect to many types of goods would pose too great a burden; we focus on two specific product types. Product classes can be characterized along a number of dimensions, including frequency of purchase, cost, search area (local, regional, broader), tangibility (whether a service, a digital good, or a material good), perishability, “differentiability” (the extent to which retailers can distinguish their offering of the product from others’), product information complexity, and as a “search good” (having features “that can be evaluated from externally provided information”) versus an “experience good” (needing “to be personally inspected or tried”; Peterson, et al., 1997).

To yield a sufficient number of purchase occasions in our sample, we focus on low-cost, medium-/highfrequency- of-purchase goods. Specifically, we take the tangible product classes of books/physical CDs/DVDs/videotapes and clothing/shoes to be analyzed in depth. Books/CDs/DVDs/videotapes per se are basically search goods with low intrinsic differentiability, though the bookstore experience (browsing contents, having coffee) may offer some advantages to some customers. Clothing and shoes are basically experience goods, although decades of catalog shopping history show that some customers are willing to buy without trying. The survey also asks a few questions about internet shopping activity of all kinds, especially internet-based purchases.

The main research questions to be addressed by this exploratory project build on a number of prior studies: (1) For the product class in question, what are the advantages (motivators and facilitators for choosing) and disadvantages of (constraints on choosing) each shopping mode? A number of authors have identified the potential advantages of e-shopping and/or store shopping (e.g. Brynjolfsson and Smith, 2001; Salomon and Koppelman, 1988; Tauber, 1972). Store shopping is so far still very different from e2 shopping in terms of such attributes as the information provided, the sensual stimulation, the ability to compare prices and to attain immediate ownership. Beyond the functional attributes related to shopping and purchasing, store shopping also offers numerous other experiences, which to varying degrees are less amenable to electronic platforms. These include, for example, the ability to interact with real salespeople and to bargain, the opportunity to be outside the home or work environment, some physical exercise, and so on. Shopping, under many circumstances, is a combined maintenance - leisure activity. Thus, the choice between store shopping and e-shopping is not unambiguous (Handy and Yantis, 1997).

(2) Can market segments with different propensities to use alternative modes be identified? A composite of several studies (Cairns, 1996; Koppelman, et al., 1991; Tacken, 1990; Gould and Golob, 1997; Gould et al., 1998; Burke, 1998; Farag et al., 2006; Ferrell, 2005; Ren and Kwan, 2005) identifies four segments of the population that are likely to be early adopters of e-shopping: the mobility-limited, time-starved, technophilic, and shopping-phobic. These four segments may well be generalizable to many e-shopping contexts. In reality, however, they probably represent (and in general we will treat them as) four continuous dimensions, with individuals separately falling somewhere along each of them, rather than four mutually-exclusive and collectively-exhaustive group membership indicators.

(3) To what extent do customers perceive there to be viable alternative modes for a given shopping occasion? Previous studies have largely neglected this question, implicitly or explicitly assuming that each shopping activity involves a true choice among competing alternatives. It is obviously important to test that assumption, and identify variables associated with perceived mode captivity versus choice.

(4) Are the various shopping modes substitutes, or complements? For example, do people who do a lot of e-shopping tend to do less store shopping, or do high amounts of one tend to be associated with high amounts of the other? Do former catalog shoppers replace the catalog with the internet, or supplement it; conversely, do internet shoppers become new catalog shoppers as retailers engage in cross-channel marketing? Are there different relationships for different segments of the population? For example, shopping modes may be complementary for innate “shopaholics”, but substitutionary for the timepressured. Although to keep the survey at a reasonable length it is not possible to obtain the data for a rigorous analysis of transportation impacts, the answers to these questions will have direct implications for the likely transportation impacts of e-shopping.

The remainder of this report focuses on the administration of a survey instrument that will facilitate the research directions discussed above. The organization of the document is as follows. Section 2 discusses the design of the sampling plan for the data collection. The following section then describes the actual sampling process. The fourth section briefly presents the survey design. Section 5 discusses the development of the survey instruments and Section 6 outlines the deployment of the survey. Next, a section is devoted to the steps taken to build the final sample through data cleaning. A concluding section ends the report.

Using socio-demographic, personality, and attitudinal data from 1,680 residents of the San Francisco Bay Area, we develop and estimate binary, multinomial, and nested logit models of the choice to work or not, whether or not to work at home, and whether to commute all of the time or some of the time (either by only working part time, or by working a compressed work week, or by telecommuting some of the time). To our knowledge, these are the first models of all these choices simultaneously. This work is relevant both to travel demand modeling, which usually bases trip or activity generation models on a given set of employment status inputs, and to labor force engagement modeling, which typically ignores the impact of travel-related variables. The model results indicate that the typical predictors of labor force engagement (gender, household income, and education) play an important role here, with family variables having an especially complex effect. Other interesting findings are that telecommuters tend to be adventure-seekers and home-based workers tend to be workaholics; those who like travel tend to commute five or more times per week; and mobility constraints are significant in the decisions to work part-time and to commute full-time.

Using retrospective data collected from a survey of more than 200 State of California workers, including current, former, and non-telecommuters, this study analyzes the relationship between telecommuting and commute time, distance, and speed over the ten-year period from 1988 to 1998. We find that telecommuters consistently live farther from work (in terms of time and distance) compared to former and future telecommuters. These longer one-way distances are ameliorated, however, by telecommuters traveling at higher speeds and commuting less frequently than their counterparts, with an end result of telecommuters commuting fewer person-miles and person-minutes, on average. About 70% of the commute time savings can be attributed to the faster speeds, with the remainder due to the lower distances traveled. Interestingly, in the aggregate, those who telecommute at some point in the ten-year period average slightly (2.6%) more commute person-miles traveled (although 4.4% less time) over the decade (even with the reductions offered by telecommuting) than those who do not telecommute at all. This finding suggests either that telecommuting is at best an ineffective travel reduction policy (i.e. those who engage in it do not travel any less) or that telecommuting disproportionately attracts those who would otherwise commute even more (making it an effective tool). A companion study addresses this question and finds that the latter explanation is the stronger one.

Using socio-demographic, personality, and attitudinal data from 1,680 residents of the San Francisco Bay Area, we develop and estimate binary, multinomial, and nested logit models of the choice to work or not, whether or not to work at home, and whether to commute all of the time or some of the time (either by only working part time, or by working a compressed work week, or by telecommuting some of the time). To our knowledge, these are the first models of all these choices simultaneously. This work is relevant both to travel demand modeling, which usually bases trip or activity generation models on a given set of employment status inputs, and to labor force engagement modeling, which typically ignores the impact of travelrelated variables. The model results indicate that the typical predictors of labor force engagement (gender, household income, and education) play an important role here, with family variables having an especially complex effect. Other interesting findings are that telecommuters tend to be adventure-seekers and home-based workers tend to be workaholics; those who like travel tend to commute five or more times per week; and mobility constraints are significant in the decisions to work part-time and to commute full-time.

This paper analyzes empirically measured values of Travel Liking-how much individuals like to travel, in various overall, mode-, and purpose-based categories. The study addresses two questions: what types of people enjoy travel, and under what circumstances is travel enjoyed? We first review and augment some previously hypothesized reasons why individuals may enjoy travel. Then, using data from 1358 commuting residents of three San Francisco Bay Area neighborhoods, a total of 13 ordinary least-squares linear regression models are presented: eight models of short-distance Travel Liking and five models of long-distance Travel Liking. The results indicate that travelers' attitudes and personality (representing motivations) are more important determinants of Travel Liking than objective travel amounts. For example, while those who commute long distances do tend to dislike commute travel (as expected), the variables entering the models that hold the most importance relate to the personality and attitudes of the traveler. Most of the hypothesized reasons for liking travel are empirically supported here. (C) 2004 Elsevier Ltd. All rights reserved.

Researchers have questioned whether the ability to telecommute is encouraging workers to relocate to more desirable residences farther from work, and in doing so, exacerbate sprawl and increase their net vehicle-miles traveled. The research presented here directly asks, is telecommuting a “friend or foe” of travel-reducing policies? Given that telecommuters tend to have longer commutes than non-telecommuters, is the ability to telecommute prompting workers to move farther away from work? Or rather, does the ability to telecommute allow those who for other reasons have already chosen, or would in any case choose, to live in more distant locations to commute less frequently? These questions are addressed using data collected from more than 200 State of California workers, including current, former, and non-telecommuters. The survey inquired retrospectively about their residential relocations, as well as their telecommuting engagements, over a 10-year period. The results indicate that, as expected, residential moves that are temporally associated with telecommuting episodes tend to increase commute time and length compared to other moves—although the differences are not statistically significant. Analyzing the temporal order of telecommuting engagement and residential relocation, the data show that those who are telecommuting and then move actually tend to relocate closer to their workplace, whereas those who begin telecommuting following a residential relocation tended to have moved much farther from their workplace. Analysis of the stated importance of telecommuting to specific residential relocations did not show a convincing effect toward more distant moves. Thus, the evidence more strongly supports the positive view of telecommuting, that it is ameliorating the negative transportation impacts of moves that occur for other reasons.

Using socio-demographic, personality, and attitudinal data from 1,680 residents of the San Francisco Bay Area, we develop and estimate binary, multinomial, and nested logit models of the choice to work or not, whether or not to work at home, and whether to commute all of the time or some of the time (either by only working part time, or by working a compressed work week, or by telecommuting some of the time). To our knowledge, these are the first models of all these choices simultaneously. This work is relevant both to travel demand modeling, which usually bases trip or activity generation models on a given set of employment status inputs, and to labor force engagement modeling, which typically ignores the impact of travel-related variables. The model results indicate that the typical predictors of labor force engagement (gender, household income, and education) play an important role here, with family variables having an especially complex effect. Other interesting findings are that telecommuters tend to be adventure-seekers and home-based workers tend to be workaholics; those who like travel tend to commute five or more times per week; and mobility constraints are significant in the decisions to work part-time and to commute full-time.

Travel demand models focus on explaining how much individuals actually travel but offer no insight into how much individuals think they travel. The authors propose that the latter is an important determinant of traveler behavior, and that actual mobility is refracted through a variety of filters that magnify or diminish those subjective evaluations of travel amounts. Linear regression models of subjective mobility measures provided by 1,358 San Francisco Bay Area commuters were estimated earlier; the focus of this article is on identifying the potential cognitive and affective mechanisms that influence subjective mobility upward or downward, after controlling for objective mobility. The authors find three major types of mechanisms: awareness-heightening, affective, and comparison-inducing. Recurring patterns of effects in these three categories are analyzed in the light of psychological and marketing research concepts including the availability heuristic, social comparison, relative deprivation, autobiographical memory, and motivation theory.