Utilizing multi-stage behavior change theory to model the process of bike share adoption

Transport Policy 77 (2019) 30–45

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Utilizing multi-stage behavior change theory to model the process of bike share adoption

T

Alec Biehla, Alireza Ermagunb, Amanda Stathopoulosa,∗ a b

Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208-3109, USA Department of Civil and Environmental Engineering, Mississippi State University, 501 Hardy Road, 235 Walker Hall, MS 39762, USA

ARTICLE INFO

ABSTRACT

Keywords: Bike share Stages of change Factor analysis Discrete choice model Segmentation

This paper studies bike share adoption decisions as a dynamic change process from early contemplation to consolidated user status. This runs counter to the typical representation of mode adoption decisions as an instantaneous shift from pre to post usage. A two-level nested logit model that draws from the stage-of-change framework posited by the Transtheoretical Model is developed to study the adoption process. Using survey data collected from an online U.S. sample (n = 910), the model illustrates how personal, psychosocial, and community-oriented factors influence the probability of transitioning between different levels of readiness to participate in a bike share scheme. The findings suggest that encouraging forward movement in the contemplationuse ladder requires tailored, stage-specific interventions that are likely be overlooked if instead a one-size-fits-all psychological theory is applied to investigate travel behavior. In particular, the intermediate stages encapsulate more flexible (i.e. less habitual) orientation among respondents. Among the explanatory variables, the pronounced elasticities for active travel identity formation and norm integration are especially significant for crafting policies that influence bike share membership decisions. This paper adds to the nascent literature on the behavioral foundations of shared mobility adoption. The findings are translated to practical interventions, from operations to design and community-initiatives to guide practitioners seeking to promote bike share. The stagebased adoption representation helps to align interventions across the spectrum of user readiness to translate intention into behavior.

1. Introduction Bike share system development has exploded across Western countries (Parkes et al., 2013), as well as China (Zhang et al., 2014, 2015), during the past decade. In the United States, 35 million bike share trips were taken in 2017, a 25% increase over 2016 (NACTO, 2018). This is, however, only a marginal share of overall short-distance travel: the 2017 US National Household Travel Survey estimates that approximately 106.7 million walking and 9.8 million cycling trips occur on a daily basis (NHTS, 2017). Moreover, aside from revenue generation, bike share operators vary in their missions: furthering public health goals through the encouragement of physical activity, reducing traffic congestion and carbon emissions, improving access to transit services, and/or increasing the urban travel experience to residents and tourists alike. Recent research findings support the notion that these shared mobility systems, if well-designed, could offer numerous advantages to the active traveler, such as enhanced first- and last-mile connections with public (rail) transportation, the promotion of

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healthier lifestyles, and even acting as a conduit for bicycle ownership in some instances (Ahillen et al., 2016; Murphy and Usher, 2015). There are, however, important challenges associated with promoting the uptake of bike share, namely (a) uncertainty in understanding the transition from non-user to the occasional or active user (Shaheen et al., 2015), and (b) overcoming the socioeconomic disparity in its adoption related to gender, income, and ethnicity (Goodman and Cheshire, 2014)—which is also endemic in the general domain of cycling (Steinbach et al., 2011). A central question facing all innovative (shared) mobility services is how to promote their widespread adoption. Recent literature suggests the importance of behavior-focused soft policy initiatives for encouraging sustainable behaviors (Hiselius and Rosqvist, 2016; Richter et al., 2011). Accordingly, there is a crucial need to understand processes of behavioral change for creating efficacious policies that account for heterogeneity in motivation across individuals (Stathopoulos et al., 2017). In line with the focus on adoption of active mobility as a process rather than a static representation, this research draws on the

Corresponding author. E-mail addresses: [email protected] (A. Biehl), [email protected] (A. Ermagun), [email protected] (A. Stathopoulos).

https://doi.org/10.1016/j.tranpol.2019.02.001 Received 31 January 2018; Received in revised form 2 December 2018; Accepted 4 February 2019 Available online 11 February 2019 0967-070X/ © 2019 Elsevier Ltd. All rights reserved.

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Transtheoretical Model (TTM) from health psychology, a multi-stage theory that encapsulates varying levels of a person's readiness for change. This model framework is still in its infancy in the transportation setting, with limited applications in the area of sustainable transport mode adoption (Horiuchi et al., 2017; Redding et al., 2015), and as articulated by Armitage (2009), there is emphatic debate surrounding the utility of this model amongst psychologists. While some argue that the conceptual discrepancies and ambiguous evidence support dispensing with the framework, others espouse a more sanguine mindset in (re)discovering its prospective intellectual contributions. This research study, in the spirit of the latter perspective, investigates an adaptation of the TTM to study bike share stage memberships. The overarching goal is to investigate the contribution that stage-based frameworks can make to understand adoption processes and to develop interventions to promote active mobility programs, which are becoming more pivotal in the agendas of metropolitan planning organizations across the United States as interest in individual- and community-level well-being spreads (Lee and Sener, 2016; Roberts et al., 2017). Concretely, the research objectives are to (1) examine the role of stage-based theory in bike share adoption, (2) develop a model that allows estimation of stage-specific determinants of adoption, (3) assess the connection to transport policy design development where policies are tailored on specific stages. To this end, a two-level nested logit (NL) discrete choice model is applied to data collected using an online survey distributed on Amazon MTurk (n = 910). Two salient points of the model are worth pointing out. Respondents are assigned to a stage based on their answers to a series of questions, as opposed to self-identification of stage membership, thereby mimicking the behavioral proposition of the TTM. The use of the NL structure reveals that end stages are habitual while the middle stages are more flexible states of adoption. To our knowledge, this is the first application of its kind to the analysis of bike share systems. The paper is organized as follows. Section 2 reviews the literature on mode choice in the context of cycling/bike-sharing, in addition to relevant applications of the TTM. Section 3 describes the data collection procedure and the preliminary data analysis for constructing the choice model introduced in Section 4. Section 5 displays the empirical model as well as the direct elasticities to further illuminate its implications. The final section of this paper offers a synthesis of findings and suggestions for future work in relation to the development of bike share policy.

However, findings related to age and income are pronouncedly ambiguous. Both review papers recognize the role of latent factors in encouraging cycling behavior and the overall dearth of research on this topic. Central theoretical constructs include attitudes and preferences, habits, community/cultural norms, the social environment, ecological beliefs, and awareness of health and environmental benefits. Finally, an important direction for future research is to better account for variable causality, by means of structural equation and cross-lagged panel models. The literature on bike-sharing has a somewhat different thematic consistency, in part due to its functionality as a publicly sponsored shared mobility system. Overall, there are fewer studies on the determinants of usage compared to general cycling. Table 1 shows a representative selection of cycling adoption studies, with selection criteria of studies that; (1) have cycling adoption as the primary emphasis and (2) rely on data collected through surveys or choice experiments, since our research methodology falls under this category. Of the thirteen studies, five examine bike share explicitly, and only two of these examine psychological factors in detail. Castillo-Manzano et al. (2015) utilize two binary choice models to understand bike share use and the transition to private bicycle ownership, something that also deserves more attention (Fishman et al., 2013). In a more rigorous application of psychological theory, Kim et al. (2017) implement an extension of the Norm-Activation Model to determine willingness-to-pay for enrollment in a bike-sharing program. Thus, an investigation of bike share adoption within the random-utility framework is certainly valuable, particularly with considerations for non-cyclists and non-users of bike share. Outside the domain of disaggregate bike choice analysis, many research studies examine bike-sharing through three primary perspectives. One is through data mining and other big data approaches, mainly to classify usage patterns (Bordagaray et al., 2016; Borgnat et al., 2011; Kumar et al., 2016). Another is geospatial analysis, for purposes such as identifying hot spots for system expansion (Wang et al., 2016) and the impacts of weather/climate variables, built environment variables, and sociodemographic factors on ridership levels (El-Assi et al., 2017; Noland et al., 2016). The third is station rebalancing, since bike share providers must ensure bicycle availability to meet demand. Some researchers have delivered new approaches for estimating system usage to pinpoint efficient operational strategies (Médard de Chardon and Caruso, 2015), which are critical for reducing vehicle kilometers for bicycle reallocation (Fishman et al., 2014). The bike share market, however, draws mostly from other sustainable modes of transportation, which suggests a dissonance between intended and perceived benefits of bike share promotion. Finally, while habits, traffic safety concerns, and vehicle/bicycle ownership are shared determinants with general cycling, there is more ambiguity in gender distribution and clearer indications of age and income effects in bike share research. Other notable studies, related to cycling adoption in Table 1, reveal important insights for research involving psychological theories. Chen (2016), for instance, combines the Theory of Planned Behavior with a modified Technology Acceptance Model to recommend that policymakers engender loyalty to bike share through the development of subjective norms, perceived pleasure, and environmental attitudes. In the broader cycling context, Fernández-Heredia et al. (2014) apply confirmatory factor analysis to confirm the existence of four influential latent variables in cycling use: convenience (efficiency and flexibility), exogenous restrictions (available facilities and safety perceptions), physical determinants (fitness and topography), and pro-bike attitudes (economical, ecological). A latent class choice model developed by Maness and Cirillo (2016), meanwhile, draws upon social learning theory to demonstrate how information and norms spread through a network to stimulate bicycle ownership.

2. Literature review The literature Subsection 2.1 outlines the main factors identified to drive the adoption of biking and bike sharing drawing on recent reviews and representative modeling papers. This subsection also highlights the main trends in model structures used in this research area. Subsection 2.2 overviews the application of TTM in mobility analysis. 2.1. Adoption of cycling vs. bike share research Several papers in Transport Reviews synthesize recent trends in cycling (Handy et al., 2014; Heinen et al., 2010) and bike share (Fishman, 2016; Fishman et al., 2013) research. We present a handful of key points between these two strands of literature to establish the basis of our research objectives and refer readers to the cited papers for a more exhaustive treatment. For cycling, trip distance, the type of bicycle infrastructure available, and car/bicycle ownership are major determinants of cycling usage. Another important factor, as with other modes, is the direct cost of mode use, but more research is needed in a comparative setting. In terms of demographic characteristics, research studies typically find a gender imbalance in favor of men—though closer scrutiny reveals that this is not the case in countries with a strong cycling presence (Heinen et al., 2010)—in addition to physical ability and exercise engagement.

2.2. Transtheoretical Model of behavior change Past mobility innovation adoption studies incorporating the TTM 31

32

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Spain

Spain

Fernández-Heredia et al. (2016) Castillo-Manzano et al. (2015) Fishman et al. (2015)

Bicycle use (university)

U.S.

U.S.

U.K.

Handy and Xing (2011)

Wardman et al. (2007)

Only variables that are significant in the models are listed.

Bicycle use

Bicycle use

Bicycle use

Bicycle use (utilitarian vs. recreational)

Bicycle use (university)

Bicycle use (school)

U.K.

Canada

Greece, Cyprus U.S.

Willingness-to-pay for bike share Bicycle and bike share use

Maldonado-Hinarejos et al. (2014) Akar et al. (2013)

Motoaki and Daziano (2015) Habib et al. (2014)

Kamargianni (2015)

Transition from bike share to bike ownership Bike share membership

Spain

Braun et al. (2016)

Australia

Bicycle use

S. Korea

Kim et al. (2017)

Bicycle and bike share use as feeder to rail transit

China

Ji et al. (2017)

Topic

Country

Study

Hierarchical MNL choice

Binary logistic regression

MNL choice

Hybrid discrete choice

Mixed logit choice with panel effects Hybrid discrete choice w/ latent class Hybrid discrete choice

Binary logistic regression

Binary probit/logit choice

Hybrid discrete choice

Conditional logit choice

Structural equation model

Multinomial logit (MNL), Nested logit choice

Statistical Model

Table 1 Summary of mode choice studies with bicycle or bike share as the primary focus.

Bicycle: gender, egress time, region Bike share: income, bicycle theft, age, commuting trip purpose Travel cost, gender, bicycle/vehicle ownership, congestion avoidance Cost (other), travel time (cycling and other), gender, age, education level, bike lanes, stations near home, network connectivity, transit stops, hilliness, parking at work Travel time/cost, experience, gender, HH size, commuting habit, leisure trip purpose Education, residence status, experience, type of pass, commuting habit, sport use, congestion avoidance Riding frequency, age, income, work-station proximity, mandatory helmet legislation Travel time, parking, bike lanes, weather, gender, age, safety education availability Temperature, precipitation, travel time, hilliness, bike lanes, traffic flow, age, gender, access, commuting habit HH size and income, education level, residence length, neighborhood population and bike lane density, gender, age, nearby parks Parking facilities, travel time (cycle and car), age, gender, race/ethnicity, neighborhood type Travel time/cost, nearby bike trail or bus stop, gender, employment status, gas prices Gender, HH ownership, physical/social environment of workplace Travel time/cost, hilliness, air/noise pollution, facilities, age, gender, income

Personal-Situational Influences∗

Enjoy bike/transit, comfort, car dependence, goal to limit driving, cyclist community Safety concerns, tiredness, cycling ability, financial incentive

Safety concerns, variety in travel mode use

Pro-bike, context, image, stress

Quality of cycling infrastructure, comfort, safety concerns

Cyclist identity, safety concerns, physical health

N/A

Convenience

Comfort, lifestyle, ease of use

Pro-bike, convenience, physical health, safety concerns

Environmental norm activation; cycling impact on environment and health N/A

N/A

Socio-Cognitive Influences∗

A. Biehl, et al.

Transport Policy 77 (2019) 30–45

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have typically focused specifically on one component, namely the ‘stages of change’ construct (e.g. Handy et al. (2014) in reference to strategy development for encouraging travel behavior change). However, the TTM consists of three additional central constructs with promise in informing active mode adoption policies. All four constructs, along with some main caveats in their utilization, are summarized in this subsection. For a deeper treatment we refer readers to Prochaska et al. (2008). For behaviors such as active transportation and exercise adoption, the TTM typically consists of five mutually exclusive stages of change describing qualitatively distinct segments of individuals based on their readiness for change. The original stage definitions are as follows:

To summarize, three main gaps are identified from the literature. First, there is a dearth of knowledge regarding individual-level bike share adoption, particularly with respect to latent constructs. Second, no research study to our knowledge has rigorously investigated bike share use through the lens of the Stages of Change (e.g. the Transtheoretical Model). Third, only one research study (Langbroek et al., 2016) uses the stages of change in combination with a discrete choice model; the authors show that people in later stages are more likely to adopt an electric vehicle and are less price-sensitive. Our research addresses these gaps through a more comprehensive treatment of the stages of change by using a two-level nested logit model to illuminate the factors that distinguish stage membership.

(1) Precontemplation: individuals who do not consider change possible within the next six months; (2) Contemplation: individuals who consider change possible within the next six months; (3) Preparation: individuals who consider change possible within the next month; (4) Action: individuals who have been engaging in the desired behavior for less than six months; and (5) Maintenance: individuals who have been engaging in the desired behavior for six or more months.

3. Data and psychological construct analysis 3.1. Survey design and distribution We implemented a survey through an online respondent panel during three weeks in February 2017. To maintain quasi-control over climate effects and group lifestyles/values, which impact cycling (Buehler and Pucher, 2012; Heinen et al., 2010; Vandenbulcke et al., 2011) and general travel (Krueger et al., 2018; Van Acker et al., 2010) behaviors, we restricted the survey call to six Midwestern states, which together boast 11 distinct bike share systems (NACTO, 2017): Ohio, Michigan, Indiana, Illinois, Wisconsin, and Minnesota. Given this choice in sampling method—which is also nonrandom—the results presented cannot be directly generalized. We believe, however, that the behavioral implications are valid and should be the topic of further inquiry, given their grounding in multi-stage behavior change theory that is of burgeoning interest within academia (Andersson et al., 2018; Scheiner, 2018) and industry (Roberts et al., 2017) in the context of transportation demand management programs. The survey is comprised of three categories of questions designed to target many of the adoption factors identified in the literature review. Before beginning the survey, participants were introduced to the concept of bike-sharing and informed of their rights to privacy and anonymity in accordance with the university's Institutional Review Board policies. The relevant variables are:

A remaining challenge is the inconsistency of stage definitions and measurement, which limits comparability of research findings. To demonstrate, Nkurunziza et al. (2012) assess stage membership in one step via selfidentification while others (Nehme et al., 2016; Biehl et al., 2018) use a multi-step algorithm to assign respondents to stages, thus representing an indirect identification process. The second construct consists of the ten processes of change which denote specific cognitive or behavioral phenomena that induce transition between stages. According to the original formulation of the TTM, specific processes should be employed in different stages of change to most effectively encourage progression (forward movement) while preventing regression (backward movement). An obvious yet critical challenge for travel behavior researchers is to adapt the processes of change to specific research contexts. The remaining two constructs are decisional balance, or evaluation of pros and cons of behavior change, and self-efficacy, which covers both the confidence to participate in a desirable behavior and the temptation to take part in an undesirable behavior. These constructs typically function as dependent variables: forward stage movement is associated with an increase in pros and confidence while backward stage movement is linked to an increase in cons and temptation. Since the TTM is relatively new to the field of transportation, there are only a few applications of the model, but most relate to active travel modes. Aligning with the more general literature on cycling, the most popular context is the work commute. Cross-sectional surveys are the most common data collection mechanisms in both transportation and health psychology. For instance, Gatersleben and Appleton (2007) show through a survey and interviews that more positive attitudes, as well as changes in personal (e.g. physical fitness level) and structural (e.g. existence of bike lanes) barriers, relate to forward stage movement. Shannon et al. (2006), meanwhile provide mean score ratings for motivators and barriers across the stages of change, as well as for 17 hypothetical interventions that would spur greater rates of active commuting to a university campus. Meanwhile, Thigpen et al. (2015) employ a multilevel ordinal logistic regression model to classify respondents into earlier or later stages of change using four categories of variables. Their findings suggest that travel-related attitudes are the most important for committing to behavioral change, with perceived barriers, travel attributes, and demographic characteristics completing the ranking. The stage-of-change concept, however, is similar to the cyclist typology constructed by Dill and McNeil (2016), which relies on attitudinal assessment to distinguish among individuals’ dispositions for increasing their cycling activity. Although this typology does not emphasize temporal sequencing, it reinforces the importance of tailored policy.

1. Travel Attributes. Participants are screened on their neighborhood type: urban, suburban, urban-suburban mix, or rural (the latter response terminated the survey). Other variables include: possession of a Driver's license, number of household vehicles and bicycles, assessment of general travel experiences, walking and biking habits, questions to identify the bike share stages of change, and perceptions of local built environment. 2. Psychosocial Attributes. Participants responded to four sets of Likert scale statements relating to subjective factors identified to be relevant: active transportation identity and norms, environmental spatial ability, psychological well-being, and sense of community. We provide more details on the exploratory factor analyses conducted on these factors in the next subsection. 3. Personal Attributes. These variables include employment status and schedule flexibility, year of birth, gender, race/ethnicity, annual household income (before taxes), education level, and household composition. A total of 1,253 individuals responded to the survey. However, following careful screening— excluding rural dwellers, failure at one of two attention checks,1 incompleteness or untrustworthily short completion times (less than 5 min), hostile commentary, or missing crucial information—the final sample size retained is 910. This corresponds to 1 The two attention checks were placed in the Travel Well-Being and Psychological Well-Being survey scales to catch negligent respondents, with instructions: “Please indicate somewhat disagree for this statement.”

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study are not asked to explicitly self-identify their stage of use; rather, they are assigned to a stage based on their responses. The goal was to reduce the temptation to give idealized responses intended to satiate what they conjectured to be ‘desired’ outcomes by the researchers. Table 3 outlines this identification process, starting with the extremes, then identifying the middle stages. An important observation is that respondents are asked to consider ‘good weather’ and weekly travel patterns. By prompting individuals in this manner, the fact that the survey was distributed in February should not impact response patterns related to stage-of-change placement. However, we cannot truly verify if there would be a seasonal effect on our staging algorithm, while this should be mitigated by asking people to consider adoption in the next six months (thus covering summer months).

Table 2 Summary statistics for important objective respondent variables (n = 910). Variable a Dummy category tested in model

Categories

Breakdown (Percent)

Neighborhood Type a Suburban:= 1 if respondent lives in suburban area, 0 otherwise

Urban Urban-Suburban “hybrid” Suburban Yes No

26.7 34.3 39.0

0 1 2 3+ 0 1 2 3+ Yes No

8.8 36.2 43.5 11.5 22.4 30.4 25.7 21.3 63.0 36.6

Yes No

7.6 92.0

Very flexible Somewhat flexible Not at all flexible 18–29 30–39 40–54 55+ Male Female Other White only Other

22.0 57.7 19.7 33.7 33.0 20.4 12.9 40.7 59.0 0.3 82.9 17.1

Less than $30K [$30K, $50K) [$50K, $70K) [$70K, $90K) $90K or more No college degree Associate's or Bachelor's degree Advanced degree 1 2 3–4 5 or more

21.6 23.2 17.9 14.1 20.8 31.2 49.1 19.5

Driver's License? a No_License:= 1 if respondent does not possess a license, 0 otherwise # Household Vehicles a No_Vehicle:= 1 if respondent does not own a vehicle, 0 otherwise # Household Bicycles (1 NA) a Own_Bicycle:= 1 if respondent owns bicycle, 0 otherwise Full-time Worker? (4 NA) a Full_Time_Worker:= 1 if respondent works full time, 0 otherwise Full-time Student? (4 NA) a Full_Time_Student:= 1 if respondent goes to school full time, 0 otherwise Schedule Flexibility (6 NA) a No_Flex:= 1 if respondent has no flexibility, 0 otherwise Age (4 NA) a Treated as continuous variable in NL model Gender a Female:= 1 if respondent is female, 0 otherwise Race/Ethnicity a Nonwhite:= 1 if respondent is not white, 0 otherwise Annual Household Income Level (23 NA) a Low_Income:= 1 if respondent makes less than $30K, 0 otherwise Education Level (2 NA) a No_College:= 1 if respondent does not hold a college degree, 0 otherwise # Household Members (3 NA) a Single_HH:= 1 if respondent lives alone, 0 otherwise

92.7 7.3

3.3. Single-item scores We rely on both latent (factor analytic) and observed (single-item) modeling treatments of the subjective variables used to explain stages of bike share adoption, the latter arising for scales that exhibited low internal consistency based on Cronbach's alpha (Tavakol and Dennick, 2011) or produced an unclear factor structure. Thus, respectively, the statements comprising the Travel Well-Being (TWB) and Perceptions of the Local Built Environment (PLBE) scales are treated as single-item scores. The TWB scale contains five statements rated on a 5-point agreement scale that reflect elements of the Satisfaction with Travel Scale (Friman et al., 2013) as well as other probable determinants of stage membership, such as variety-seeking tendencies (Schüssler and Axhausen, 2011). The PLBE scale contains eleven neighborhood features rated on a 5-point rating scale that represent external factors that could influence bike share use. Table 4 displays the two scales with mean scores for each stage. We note that results using single-item scores should be interpreted with cautionsince they omit variable interactions that multivariate methods encapsulate. 3.4. Factor analysis scores Meanwhile, we use the psych package in the R programming environment to conduct exploratory factor analysis on the remaining four sets of statements, measured with 7-point agreement scales. We then calculate factor scores based on the selected solutions. The Thurstone method, which calculates regression-based weights from the product of the inverse correlation matrix and factor structure (or pattern, with an oblique rotation) matrix is applied. The factor scores are then calculated from the product of the raw score matrix and this weight matrix (Revelle, 2016). Although scores are typically calculated across all scale items, in this work we consider only items whose loadings are at least 0.35 to obtain our scores; this essentially mimics a confirmatory factor analytic approach, so the extracted factor correlations do not carry over to the variable extraction procedure implemented and thus warrant caution regarding multicollinearity issues. In general, the processed scores are better than using the direct item responses for each respondent because they are more reliable and offer a deeper representation of the measured latent factor (Everitt and Hothorn, 2011). Tables 9–12, located in the Appendix, present the final factor solutions and report indicator statements in full. The Active Travel Identity and Norms (ATIN) scale produces two factors. Factor 1 comes from the Identity Process Theory (IPT) in social psychology (Breakwell, 2015). In IPT the “self-concept” consists of four constructs: self-esteem (emotional appraisal), self-efficacy (confidence level), continuity (temporal consistency of the self), and distinctiveness (sense of individualism). Murtagh et al. (2012) apply this theory to demonstrate that its constructs represent potential sources of resistance to travel behavior change if it does not comply with an individual's selfidentity. The recognition of identity as an important construct is a recent phenomenon in the field of transportation (Heinen, 2016). Factor 2 is based on the norm taxonomy presented by Thøgersen (2006). That is,

21.2 33.8 36.6 8.2

Note: (# NA) denotes the number of missing survey responses for certain objective variables. a Demarcates dummy variable tested in model development.

73% retention and these respondents were rewarded 1 USD for their participation. Table 2 provides descriptive statistics of the relevant objective variables for the sample. 3.2. Stages of change identification One of the most contentious aspects surrounding the TTM is the validity of the stage-of-change construct. More specifically, the apparent arbitrariness of stage boundaries leads to questions regarding its accuracy in representing a progressive sequence of readiness for change (Brug et al., 2005). Although any measurement scheme is susceptible to criticism, it is important for researchers to employ sound reasoning in the construction of stage definitions. Therefore, we propose a stageidentification procedure for bike share adoption that attempts to distinguish membership based on mindsets and actions appropriate given the structure of the public bicycle system. That is, respondents in our 34

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Table 3 Delineation of the stages of change (n = 910). Identification Steps

Response

Result

Stage

# of Respondents

% of Sample

Assuming “good weather,” would you expect to use bike share at least once per week? Would you ever contemplate using this mode?

Yes No Yes No No & Likert scale response: 1-2 Yes & Likert scale response: 1-2 No & Likert scale response: 3-5 Yes & Likert scale response: 3-5

Assignment Next Step Next Step Assignment Assignment

Action-Maintenance – – Precontemplation Contemplation 1

166 – – 424 146

18.2% – – 46.6% 16.0%

Assignment

Contemplation 2

80

8.8%

Assignment

Preparation 1

55

6.0%

Assignment

Preparation 2

39

4.3%

Is bike share currently accessible to you? ∼&∼ What is the likelihood of using bike share in the next six months? (5point scale)

Note that four respondents are removed due to missing information pertinent to the final choice model presented in Section 5. Furthermore, since self-reported accessibility is not addressable through behavior-based tailored interventions, in addition to low membership totals, the two Contemplation stages (n = 226, 24.8%) and the two Preparation stages (n = 94, 10.3%) were combined to represent a single stage each. In total, 166 of the survey respondents habitually use bike share on a weekly basis.

our scale has one item for each of the following types of personal norms: introjected, integrated, moral, and felt obligation. The factor solution employing an oblique promax rotation reveals a factor correlation of 0.75. The Environmental Spatial Ability (ESA) scale also produces two factors; while the former is a direct modification of the Santa Barbara Sense of Direction scale (Hegarty et al., 2002, 2006), the latter touches on how having access to mobile devices impacts daily mobility (Dal Fiore et al., 2014). Thus, Factor 3 represents sense of direction while Factor 4 represents affinity to (GPS) technology. These factors target an individual's wayfinding competency: while the former relates to internal capability through the formation of a complete and accurate cognitive map, the latter relates to external aids through navigation assistance as well as general amenities associated with technology access. The factor solution employs an orthogonal quartimax rotation. The Sense of Community (SC) scale produces four factors. This term emphasizes affiliation, collaboration, and a feeling of belonging among community members. It has been shown that a greater sense of community is associated with greater levels of civic participation as well as physical, mental, and subjective well-being (Francis et al., 2012; Jorgensen et al., 2010). Our statements draw upon these references as well as the Sense of Community scale by Chavis et al. (2008) while restricting the scope to the individual's neighborhood of residence. Based on survey pilot inputs, we allowed for a ‘not sure’ option on this scale since some individuals may not be well connected to their

neighborhoods. In this analysis, we treated such responses as equivalent to indicating a middle response (i.e. ‘4’); in other words, we assume that uncertainty is the same as neither agreeing nor disagreeing with a statement. We interpret Factor 5 as community social cohesion, Factor 6 as the interplay of place identity and active travel, Factor 7 as confidence about the future, and Factor 8 as symbiotic identity between individual and neighborhood. The factor solution employs an oblique promax rotation with factor correlations ranging from 0.44 to 0.73. The Psychological Well-Being (PWB) scale also produces four factors, which are rooted in Diener's Satisfaction with Life scale (Diener et al., 1985) and Ryff's scale measuring six dimensions of psychological well-being (Ryff and Keyes, 1995). We chose to include well-being metrics in our study as interest in this topic has burgeoned in the field of transportation due to its emerging overlap with public health interests. For example, Vella-Brodrick and Stanley (2013) illuminate the relationship between these two incarnations of well-being and one's perceived/realized level of mobility. We interpret Factor 9 as general life happiness, Factor 10 as openness to learning and novelty, Factor 11 as perseverance, and Factor 12 as sense of autonomy. The factor solution employs an orthogonal quartimax rotation. 4. Nested logit model Discrete choice modeling is one of the fundamental pillars of travel behavior research. Relying on random utility theory from microeconomics,

Table 4 Distribution of single-item scores across the stages of change. Variable

Travel Well-Being (TWB) 1 = Strongly agree to 5 = Strongly disagree

Stage 1 Precont.

Stage 2 Cont.

Stage 3 Prep.

Stage 4 Action-Main.

Satisfaction Boredom Variety_Seeking Relaxed Enjoyment

I am satisfied with the choices I make regarding my travel. I tend to feel bored while traveling. The idea of adding variety to my travel habits is appealing to me. I usually feel relaxed during travel. My travel environment is enjoyable.

1.710 3.349 2.620 2.139 2.231

1.867 3.257 2.217 2.301 2.305

2.032 2.957 2.085 2.266 2.521

1.934 3.078 2.084 2.102 2.241

3.243 2.854 3.108 3.387 3.557 3.767 3.585 3.493 3.172 3.198 3.403

3.274 2.814 3.243 3.345 3.571 3.748 3.633 3.491 3.376 3.319 3.566

3.096 2.702 3.170 3.191 3.351 3.691 3.670 3.500 3.340 3.213 3.543

3.307 3.139 3.265 3.319 3.434 3.717 3.602 3.675 3.476 3.283 3.452

Perceptions of the Local Built Environment (PLBE)1 = Terrible to 5 = Excellent Street_Infrast Biking_Infrast Walking_Infrast Safety_Traffic Safety_Crime Access_Grocery Access_Health Shopping_Opp Social_Venue Arch_Land Public_Space

Street infrastructure Biking infrastructure Walking infrastructure Safety from traffic Safety from crime Access to grocery stores Access to health services Shopping opportunities Social venues Attractiveness of architecture and landscaping Public (green) space

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this collection of statistical methods is used to model past and forecast future choice-making behavior to create effective policy solutions (Ben-Akiva and Lerman, 1985). The studies presented in Table 1 employ some form of choice model to understand cycling behavior, ranging from the simple Multinomial logit (MNL) model (Akar et al., 2013) to the complex hybrid formulation that integrates a latent variable structure in the analytical framework (Motoaki and Daziano, 2015). Other noteworthy applications from papers discussing the TTM include intention-adoption frameworks for electric vehicles (Langbroek et al., 2016), solar vehicle technologies (de Luca et al., 2015), voluntarily travel behavior change (Meloni et al., 2013), and the impact of cognitive mismatch on rail transit use (Creemers et al., 2015). None of these studies, however, holistically consider a multi-stage process of behavior change as put forth by the TTM, which we examine in this paper in the context of bike-sharing adoption. More specifically, we employ a two-level nested logit model that reflects a “choice” to be in one of the four stages of change, determined by their responses to stage-assignment questions (see Section 3.2). Fig. 1 illustrates the tree structure that performed the best following extensive nesting specification testing. The three boxes in the middle layer, moving from left to right, represent the conceptual distinctions comprising the upper-level choice of (1) never contemplating bike share usage, (2) contemplating use of bike share, and (3) currently using bike share during the average week. The lower-level choice, demarcated by the four boxes comprising the bottom layer, distinguishes Contemplation from Preparation within the stage-of-change framework. The value of using the nesting structure for analyzing the stages lies in the ability to allow the level of substitution to vary as a function of the maturity of adoption. The degenerate nests, representing Precontemplation and Action-Maintenance, represent diametrically opposed habitual behaviors. The two middle stages have a degree of correlation between the errors in the utility functions, as the independence of irrelevant alternatives assumption is relaxed in the second nest. This higher substitutability is consistent with the intuition that movement between these stages is more easily affected by the subjective factors accounted for in this research. With this two-level structure, the probability of choosing alternative k in nest n (k ∈ n) is

Pk = P(k|n) P(n)

Pk|n =

exp (Vk/ n ) exp (V k'/ n )

k' n

exp (Vk / n ) exp (IVn)

(2)

and the marginal probability of a nest n being chosen is

P(n) =

exp( n IVn) exp( n' /IVn') n'

IVn = ln

exp (Vk'/ n ) k' n

(3) (4)

Here, Vk is the systematic utility of alternative k in nest n, k ' n is the set of all alternatives included in nest n out of the alternative set k '= 1, …, 4 . IVn is the logsum parameter of nest n, whereas n is an index of similarity among alternatives in the nest. To be consistent with utility maximization, 0 < n 1, with lower values implying higher correlation (Koppelman and Wen, 1998; McFadden, 1978). It follows that Vn , the utility of nest n, is equal to n IVn . 5. Model results and discussion 5.1. Drivers of stage membership Table 5 displays the results of our NL model as well as the reference MNL model, estimated using NLOGIT 5 software. We employed a stepwise approach to adding variables to the systematic utility functions, using a significance level of alpha = 0.1 and theoretical relevance based on our literature analysis as criteria for inclusion. The n parameter for the ‘consider adoption’ middle-stage nest is associated with a 95% confidence interval of (0.441, 0.999), which supports the appropriateness of the nested structure against the base model in addition to model stability. Other nesting structures were tested whereas the one presented in Fig. 1 yields the best solution. To establish a comparative performance with the MNL structure an equivalent base model is estimated. Comparatively, while the improvement in fit is limited, the significant nesting coefficient along with important differences in several parameter and elasticity values – with a corresponding behavioral interpretation - justifies the nested structure. The most notable differences are observable in the coefficients for the Contemplation and Preparation stages. This finding aligns with behavioral readiness for change being more pronounced in the intermediate stages of change and will be re-examined in the discussion of elasticity measures. We use coded variable names to simplify notation, preserve space and avoid cluttering the

(1)

where the conditional probability of k being chosen in nest n is

Fig. 1. Nested logit structure for modeling bike share adoption. Level 1 represents the four choice alternatives, i.e. the stages of change. Level 2 demarcates the broader behavioral dispositions towards bike share usage. 36

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Table 5 Results of the estimated stages-of-change models (n = 910). Bolded variables indicate non-specific utility specification. Stages

Variables

Polarity

MNL Coeff.

t-stat

NL Coeff.

t-stat

Precontemplation (Stage 1)

Constant Age Own_Bicycle Variety_Seeking

– – – (−)

2.174** 0.019** −1.282*** 0.239***

2.57 2.50 −4.11 2.71

2.354*** 0.018** −1.270*** 0.266***

2.73 2.46 −4.06 3.02

Contemplation (Stage 2)

Constant Age Own_Bicycle Variety_Seeking

– – – (−)

−0.681 0.021*** −1.267*** 0.239***

−0.75 2.63 −3.81 2.71

−0.224 0.025** −1.709*** 0.266***

−0.18 2.53 −3.14 3.02

Nonwhite Identity No_Vehicle Public_Space

– (+) – (+)

−0.777*** 0.184*** 0.668** 0.134*

−3.17 6.59 2.47 1.84

−0.901*** 0.252*** 0.838** 0.189*

−3.10 4.48 2.41 1.74

Preparation (Stage 3)

Constant Suburban Own_Bicycle Satisfaction Identity Norms GPS_Tech_Affinity Full_Time_Student Public_Space_

– – – (−) (+) (+) (−) – (+)

−1.143 −0.462*** −0.952** 0.209** 0.179*** 0.100** −0.192** 0.566* 0.134*

−1.57 −2.63 −2.40 1.76 5.64 2.25 −2.19 1.65 1.84

−0.705 −0.462*** −1.350** 0.224* 0.193*** 0.142** −0.188** 0.624ˆ 0.189*

−0.53 −2.61 −2.33 1.72 5.68 2.53 −2.00 1.63 1.74

Action-Maintenance (Stage 4)

Low_Income Suburban Full_Time_Worker Biking_Infrast Identity Perseverance Nbhd_Travel

– – – (+) (+) (+) (−)

0.829*** −0.462*** 0.640*** 0.213** 0.179*** −0.168** −0.095**

3.74 −2.63 3.01 2.13 5.64 −2.32 −2.11

0.826*** −0.462*** 0.657*** 0.218** 0.193*** −0.169** −0.088*

3.72 −2.61 3.08 2.18 5.68 −2.34 −1.96

–

0.720***

5.05

−1134.4 −1010.9 0.1007

−1134.4 −1009.6 0.1019

n

Log Likelihood: constants-only Log Likelihood: convergence Adjusted Pseudo-R2

*Significant at alpha = 0.1, **Significant at alpha = 0.05, ***Significant at alpha = 0.01, ˆBorderline significance.

tables; we are careful to designate codes that convey the variable meaning as much as possible with the interpretation of results that comprise the text clarifying what the tables represent. Examining the objective variables first, we observe that older respondents are more likely to be in the first two stages of change, which is unsurprising given that shared mobility systems typically cater to younger individuals and their interest in collaborative consumption. Bicycle ownership is positively associated with bike share usage. This finding reflects the relationship between public and private forms of cycling illuminated in several studies (Castillo-Manzano et al., 2015; Kim et al., 2017; Murphy and Usher, 2015). Bike owners are by far most likely to belong to the stable Action-Maintenance stage. On the other hand, not owning a vehicle is associated most with Contemplation, meaning that car-free households would be receptive to campaigns designed to promote the early uptake of bike share. Being a student is associated with Preparation while being a (full-time) worker and in the lowest income groups are associated with Action-Maintenance. A possible interpretation is either that commuting is a primary trip purpose for bike share, or that employees use bike-sharing to run errands during the working day (Ahillen et al., 2016; Ji et al., 2017; Karki and Tao, 2016). The positive impact of low-income status on the probability of being in Action-Maintenance appears to contradict the notion that this travel mode is generally exclusive to the economically privileged (Murphy and Usher, 2015). On the other hand, the findings may in part echo the message Goodman and Cheshire (2014) provide for London, which describes the successful uptake of bike-sharing by residents of traditionally deprived neighborhoods following a major system expansion. It is plausible that individuals of lower socioeconomic status

could ‘be ready’ for bike share if equipped with the necessary mobility capital (Schwanen et al., 2015). Finally, suburban residents are more likely to be infrequent users, belonging to Precontemplation or Contemplation, which makes sense given that these neighborhoods are often auto-oriented. Turning to analyze the subjective indicator measurements, two items from the TWB scale, rated from strongly agree to strongly disagree, are included in the model. Those who find adding variety to their travel habits unappealing are more likely to be in Precontemplation and Contemplation while those who are less satisfied with their current travel choices are more likely to be in Preparation. This suggests that policymakers and bike share operators should target ‘dissatisfied travelers’ who might view different forms of mobility as opportunities to diversify their repertoire of travel modes. Two variables in the model come from the PLBE scale, which are rated from terrible to excellent. A more positive rating of local public green space is associated with membership in Contemplation and Preparation, which is suggestive of the importance of community-oriented spaces in encouraging (early stage) bike share adoption (Francis et al., 2012). Moreover, adequate biking infrastructure is a must for a bike-sharing system—and cycling in general—to survive within the urban landscape (Braun et al., 2016; Gatersleben and Appleton, 2007; Habib et al., 2014). The current findings suggest that features such as expansive bicycle networks, a diverse set of available bicycle facilities (e.g. cycle tracks vs. bike lanes) as well as ample parking/docking locations (Handy et al., 2014) are imperative for fostering ‘habitual’ travel by bike share. Although the current research study did not assess specific infrastructure components via the survey, the cited studies offer relevant insights for policymakers to utilize. Five of the latent factors are also significant indicators of stage 37

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membership. First, identity formation as it relates to active transportation appears to be important for most of the bike share adoption process. Curiously, this variable has a stronger effect in Contemplation compared to Preparation and Action-Maintenance. One possible interpretation is that decreased resistance to travel behavior change, beginning in Contemplation, gives rise to conscious identity formation connected to active travel engagement, which is then a driving force for progression through the later stages of change. Norms, meanwhile, appears to be most important during Preparation, therefore indicating the importance of active travel norms for the critical decision-point to initially adopt bike share. Taken together, the effects of the ATIN scale factors seem to suggest the vital intercorrelation between self-identity and personal norm formation within the framework. Additionally, numerous studies have validated the importance of these two constructs in relation to pro-environmental behaviors (Bamberg et al., 2007; Carfora et al., 2017; Donald et al., 2014; Thøgersen, 2014; Walton and Jones, 2018; Whitmarsh and O'Neill, 2010), of which bike share adoption is an emerging phenomenon; hence more research is warranted in this context. Switching gears, individuals in Preparation also appear to have the highest affinity toward (GPS) technology. This might suggest that individuals attracted to (technological) innovation, or those possessing adequate skills to effectively utilize navigational devices, find bike share appealing—though still face adoption barriers. This has important ramifications for third- and fourth-generation systems (Parkes et al., 2013) in terms of equity, as individuals and communities with low access to technological resources would indubitably struggle to participate—or even enroll—in these more sophisticated bike share schemes. Finally, members of Action-Maintenance, compared to earlier stages, possess greater perseverance when it comes to goal-setting and learning, in addition to a stronger sense of place-based community in relation to (active) mobility innovation.

experiences such as bicycle theft (Ji et al., 2017). We also see that a low-income status generates a stronger gap between the Action-Maintenance membership compared to earlier stages of bike-sharing than status as a full-time worker. Making less than $30,000 annually increases the likelihood of habitual bike share use by about 81% compared to individuals with higher incomes, which supports the notion that removing enrollment and access barriers for low-income households would be a fruitful endeavor—though this remains a major roadblock for system operators and policymakers to overcome. The interpretation of the continuous variables is different. Consider the ATIN factors, for example, the only two variables that are found to be demand elastic in our model. We concentrate our interpretation on the Preparation stage since both constructs appear in this utility function. For a 1% increase in the Identity and Norms variables, we expect the likelihood of membership in Preparation to increase by 1.52% and 1.00%, respectively. This points to the crucial role that identity formation and norm integration play at the decision-point to adopt bike share (or not), with the former construct appearing to be critical throughout the behavior change process. Furthermore, both variables exhibit stronger elastic effects in the NL model compared to the MNL model. Meanwhile, a 1% increase in the Perseverance and Nbhd_Travel variables, reflecting less determination in adopting new challenges and support from the local community for investment in active travel modes, correspond to decreases in likelihood of Action-Maintenance membership by 0.64% and 0.52%, respectively. These findings highlight the importance of both cultivating determination and encouragement to make active mobility an abiding habit. However, comparing the two models, the effect of Perseverance is roughly the same while Nbhd_Travel exhibits a weaker inelastic effect in the NL model. 6. Policy recommendations

5.2. Elasticity calculations

6.1. Stage-tailored policies for bike share adoption

To clarify the policy implications of our model results, we calculate the elasticities for the variables in both models. We do not, however, standardize the continuous variables along Likert scales, normally done to account for differences in scale length; this is because the elasticity calculations would all be close to zero, disallowing meaningful interpretation. For continuous variables both direct and indirect effects are computed. Direct elasticities denote the percent change in the probability of choosing an alternative (i.e. being a member of a stage) associated with a one percent change in a specified attribute in the alternative's utility function. Indirect (cross) elasticities denote the percent change in the probability of being in the three remaining stages, associated with a one percent change in this same attribute. These percentage change definitions, however, are not meaningful for dummy variables. Accordingly, we calculate pseudo-elasticities, which represent the percent change in the probability of choosing an alternative resulting from a change in the baseline attribute level (coded 0) to the model attribute (coded 1). Table 6 displays the weighted average of pseudo-elasticities across the stages of change for the dummy variables, while Table 7 displays the direct and cross elasticities for the continuous variables. The largest differences between model coefficients are found in the variables representing nonwhite and full-time student status for pseudo-elasticities and in the variables representing public space perceptions and norm integration for direct elasticities. This finding is intuitive given that these variables are restricted to the nested middle stages and thus are impacted by the relaxed IIA assumption in the final model. Delving into the elasticity results, there are strong stage effects for bike ownership. Changing status from no-bike to yes-bike increases the probability of being in the mature bike share stage by nearly a factor of two. This result reflects bike-sharing as a potential conduit towards purchasing a bicycle, as well as the interest of current cyclists in bike share participation, potentially triggered by negative ownership

Over the past several years, companies and organizations have published reports that outline strategies for urban planners and service providers to successfully implement and manage bike share systems (ITDP, 2014; Roland Berger, 2016; Toole Design Group, 2012). These reports, while highlighting the potential to attract new cyclists by refurbishing “the image of the city” through the lenses of mobility culture and sustainable lifestyle, provide minimal detail regarding the behavioral processes underpinning the choice to use bike-sharing, though “changing values, perceptions, and behaviors” has been identified as critical to the proliferation of active transportation.2a Therefore, to further develop policy expertise in this domain, the aim of Section 6 is to give concrete suggestions to practitioners about possible strategies from the perspective of behavior change through lens of the stage-of-change framework. Fig. 2 shows the stages as a set of steps on a ladder, highlighting the profile of users in each stage (labeled Who?) along with suggestions for drivers of adoption, represented by elongated boxes. The drivers correspond to specific transitions across the stages of change: (1) Contemplation to Preparation, (2) Preparation to Action, and (3) Maintenance of adopted behavior. The following subsections 6.2-6.5 discuss the most salient determinants along with specific operational strategies to attract new customers, encourage increased use or retaining existing users. 6.2. Understanding lack of bike share adoption: removing barriers Notice that the policy box corresponding to the transition between Precontemplation and Contemplation indicates further research required. 2 http://www.pedbikeinfo.org/programs/promote_changing.cfm 25 October 2018).

38

(accessed

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Table 6 Pseudo-elasticities for dummy variables in the choice models. Variable

Suburban Own_Bicycle Nonwhite No_Vehicle Full_Time_Student Low_Income Full_Time_Worker

MNL Utility Functions

NL Utility Functions (% change from MNL)

Stage 1

Stage 2

Stage 3

Stage 4

Stage 1

Stage 2

Stage 3

Stage 4

11.15 −19.37 17.68 −20.61 −6.10 −21.26 −9.45

11.15 −13.14 −46.04 53.68 −6.10 −21.26 −9.45

−26.83 12.11 17.68 −20.61 64.36 −21.26 −9.45

−26.83 189.88 17.68 −20.61 −6.10 81.41 66.56

9.89 (−11.3%) −19.67 (−1.5%) 14.81 (−16.2%) −18.86 (8.5%) −4.61 (24.4%) −21.23 (0.1%) −9.68 (−2.4%)

12.57 (12.7%) −13.32 (−1.4%) −44.66 (3.0%) 52.57 (2.1%) −9.47 (−55.2%) −21.23 (0.1%) −9.68 (−2.4%)

−25.23 (6.0%) 17.42 (43.8%) 37.93 (114.5%) −34.08 (−65.4%) 65.82 (2.3%) −21.23 (0.1%) −9.68 (−2.4%)

−27.01 (−0.7%) 185.43 (−2.3%) 14.81 (−16.2%) −18.86 (8.5%) −4.61 (24.4%) 81.01 (−0.5%) 69.19 (4.0%)

Table 7 Direct (bolded) and cross elasticities for continuous variables in the choice models. Variable

Age Variety_Seeking Satisfaction Public_Space Biking_Infrast Identity Norms GPS_Tech_Affinity Perseverance Nbhd_Travel

Stage

Precontemplation Contemplation Precontemplation Contemplation Preparation Contemplation Preparation Action-Maintenance Contemplation Preparation Action-Maintenance Preparation Preparation Action-Maintenance Action-Maintenance

MNL Utility Functions

NL Utility Functions

% Change

1

2

3

4

1

2

3

4

(direct elast.)

0.367 −0.206 0.281 −0.139 −0.044 −0.119 −0.049 −0.122 −0.468 −0.207 −0.350 −0.106 0.032 0.136 0.110

−0.336 0.583 −0.283 0.426 −0.044 0.347 −0.049 −0.122 1.282 −0.207 −0.350 −0.106 0.032 0.136 0.110

−0.336 −0.206 −0.283 −0.139 0.338 −0.119 0.417 −0.122 −0.468 1.500 −0.350 0.756 −0.361 0.136 0.110

−0.336 −0.206 −0.283 −0.139 −0.044 −0.119 −0.049 0.492 −0.468 −0.207 1.357 −0.106 0.032 −0.630 −0.563

0.342 −0.173 0.311 −0.111 −0.034 −0.121 −0.050 −0.125 −0.463 −0.157 −0.380 −0.107 0.023 0.137 0.103

−0.313 0.552 −0.319 0.386 −0.068 0.402 −0.101 −0.125 1.467 −0.312 −0.380 −0.213 0.048 0.137 0.103

−0.313 −0.365 −0.319 −0.243 0.340 −0.252 0.553 −0.125 −0.931 1.523 −0.380 1.003 −0.337 0.137 0.103

−0.313 −0.173 −0.319 −0.111 −0.034 −0.121 −0.050 0.503 −0.463 −0.157 1.455 −0.107 0.023 −0.635 −0.524

−6.8% −5.3% 10.7% −9.4% 0.6% 15.9% 32.6% 2.2% 14.4% 1.5% 7.2% 32.7% 6.6% −0.8% 6.9%

This is because the model is not conclusive on what actions might be taken to spur this forward transition. When comparing ‘who’ falls within each stage, however, what appears to distinguish membership are race/ethnicity and vehicle ownership. This suggests that there is a significant challenge in convincing ‘precontemplaters’ with either characteristic to consider bike share usage, therefore warranting a reexamination of this travel mode in terms of equity—how should this mode be promoted to traditionally disadvantaged populations3—and sustainability—is this mode incentivizing a decrease in private vehicle use or drawing from other ‘green’ modes (Roland Berger (2016), p.12). Rather than disregarding this stage-based segment for containing individuals that are the ‘least ready’ to adopt bike-sharing (Savan et al., 2017), system operators and planners should consider the contextual roadblocks individuals and communities must overcome to view travel by this mode as feasible. We recommend further work to uncover the early usage potential among non-users. In accordance with this line of thinking, Table 8 summarizes information regarding why survey respondents in Precontemplation would never consider using bike share. Of the 424 individuals asked this question, 53.5% credited this decision to the fact that the option does not exist where they live, while 33.5% feel that they do not have enough information about how bike-sharing operates. Regarding the former statistic, it is important to note that, as shown in Table 3, 146 ‘contemplators’ and 55 ‘preparers’ do not have access to bike share—or perceive this to be the case—yet evidently would consider using it (with varying degrees of readiness). Thus, the local inexistence of bike-sharing does not deter people from considering adopting it, which bodes well for local governments seeking to identify potential early adopters. Additionally, 37 respondents would not use

this mode due to already owning their own bicycles. This finding needs to be compared against the nested logit model results, where bicycle ownership is positively associated with later stages of change. That is, bike ownership might be substitutional for some early adopter groups while it is complementary in the context of mature use. Given this, cities that have deployed or are considering implementing a bike share scheme would benefit from collecting data on different stages of consideration/adoption to study barriers and facilitators for multiple stage transitions. Barriers to bike share adoption, particularly from the lens of equity, are thoroughly investigated by McNeil et al. (2017) in a report that covers survey responses from operators and residents in Brooklyn (New York City), Chicago, and Philadelphia. In addition to barriers listed in Table 8, it would be beneficial for bike share operators to work with community organizations on matters related to credit/debit card and smartphone/Internet access, as well as increasing awareness (i.e. TTM process of change called consciousness raising) of local cycling laws and the enrollment process—particularly for residents of underserved neighborhoods. The Divvy for Everyone scheme in Chicago (and Evanston) is one model that cities might adopt and adapt to diversify their bike share user base. To expound, Divvy offers $5 annual membership without the use of a credit card to residents whose annual income is less than three times the federal poverty line; sign-up takes place at 7-Eleven and Family Dollar stores.4 Thus, local businesses and community organizations could function as trusted bike share information dissemination centers. 6.3. Encouraging movement beyond contemplation: changing habits Based on our model results, policymakers should consider two sets

3 https://www.usnews.com/news/national-news/articles/2018-06-14/bikeshare-still-has-a-race-problem (accessed 25 October 2018).

4

39

https://www.divvybikes.com/pricing/d4e (accessed 25 November 2018).

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Fig. 2. Ladder of bike-share adoption: Policy guidance for operators and planning organizations PC = Precontemplation, C = Contemplation, P = Preparation, AM = Action-Maintenance.

the new behavior should be mobilized. Policy campaigns should thus be designed to demonstrate (a) how bike-sharing aligns with self- and place identities, and (b) the achievability of healthier lifestyles by breaking mobility habits. Practical examples of policies to move beyond contemplation should focus on changing relevant perceptions towards bike share and more general attitudes regarding active travel, which create the foundation for developing habitual mobility patterns. Importantly, designing outreach material for underserved communities that highlight the arrival/ expansion of stations is valuable. For instance, in the Brooklyn neighborhood Bedford-Stuyvesant, which is majority Black American, the local bike share operator designed promotional material aligned with this development. It informed potential users about practical enrollment but also showed imagery of people of color and members of the local community participating to highlight that biking is for everyone (McNeil et al., 2017). In addition, Hannig (2015) explores innovative strategies for promoting bike share usage in Minneapolis/St. Paul, Minnesota and Milwaukee, Wisconsin. The author gives four major recommendations for bolstering equitable outcomes: (a) relationship building among bike share operations, transportation planners, and community partners; (b) station placement near parks and transit stations to market bike share as a form of recreational activity; (c) longterm bicycle rental programs and skill-building workshops to change perceptions of active travel through education; and (d) empowering families and other social units by community-biking initiatives. These ideas can be adapted to accommodate the psychosocial factors detailed in the previous paragraph, tailoring to the needs of both individuals and communities to ‘prepare’ potential riders for action.

Table 8 List of reasons why individuals would not consider adopting bike-sharing as a form of mobility (n = 424). Reasons

# of Respondents

It does not exist near me. I do not know enough about how the program works. It is too expensive for me. Bicycle station locations are inconvenient. Uncertainty regarding bicycle availability at the trip origin or parking availability at the trip destination. I already own a bicycle. (This reason appears as a comment by respondents selecting the ‘other’ category.)

227 142 31 53 92 37

of actions for urging ‘contemplators’ forward through the stages of change. First, individuals in Contemplation should be encouraged to explore ways to change their daily routines, whether it be related to mobility (e.g. try out a new route to a familiar destination) or activity (e.g. travel to a new destination to meet needs). This movement away from current travel habits, which could be facilitated by a personalized travel planning app, would function to mold a mindset that is open to change. Second, to begin fostering an active travel identity, travel behavior change campaigns should target the continuity (items 8 & 9) and self-efficacy (items 7 & 12) constructs from the Identity Process Theory (see subsection 3.4), since these item responses exhibit the largest positive changes comparing Contemplation to Preparation stage members. In other words, to convince hesitant users to try bike-sharing, their identity aspiration and perceived competence to successfully engage in 40

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6.4. Progressing from preparation to action: skill-building

nurturing bike share usage once it is adopted. Logically, the regular upkeep and expansion of cycling networks is key to attracting users to bike-sharing systems and offering more feasible trip-making opportunities as well as mode substitutions. Furthermore, this final stage of active travel identity maturation should emphasize the self-esteem (items 5 & 11) construct through personalized travel planning programs. These could utilize feedback techniques (Savan et al., 2017) for disseminating information on quality-of-life benefits, which should reinforce values learned during membership in the previous stage. Regarding the significant factors from the PWB and SC survey scales, these could respectively translate into helping relationships and social liberation processes of change. While the former process denotes the creation and utilization of a social support system, the latter processes refers to the realization that subjective social norms endorse the new behavior (Prochaska et al., 2008). To contextualize in terms of practical guidance for bike share adoption, social support through community social networks and cycling clubs (Savan et al., 2017) would boost individuals' capabilities to persevere through the challenges of being a member of a bike-sharing scheme and while motivating continued participation through engaging group dynamics. In addition, bike share operators and policymakers could leverage civic engagement opportunities to bolster a collective consciousness surrounding contributions to ‘societal good’ through usage of this mode. Social media is one conduit to market bike share programs in this manner and has been a prevalent driver of equitable growth for the Indego Bike Share in Philadelphia.10

For individuals who are preparing to be bike share users, but not quite ready to adopt this mode, several policy strategies are recommended. First, the promotion of public space accessible by bicycle is key to encouraging usage. This could be accomplished through neighborhood walking or biking tours, during which participants are able to familiarize themselves with leisure activity areas and enhance their ‘place attachment’ to local spaces. Minnesota's Nice Ride bike share program launched a Neighborhood pilot program in 2014 to promote use in underserved and low-income neighborhoods. By teaming with local community organizations and bike-shops, the pilot promoted group-rides where participants could build skills and gain confidence as bikers.5 In Chicago, the Go Bronzeville initiative offers guidance to locals in using active and shared mobility for navigating the surrounding areas, in addition to encouraging visitors to explore socio-historic features of the neighborhood. 6 Second, continuing the process of active travel identity formation, travel behavior change campaigns should aim to foster a sense of distinctiveness (items 6 & 10) at this stage, since the items on the ATIN scale corresponding to this IPT construct are the only to exhibit an increase between Preparation and Action stage members. A possible policy could be to develop local programming to foster pride within the local community through the opportunity to act as a bike-sharing exemplar. As an example, Detroits MoGo system promotes ‘neighborhood ambassadors’ to spread the word about bike share via group rides and community meetings.7 On the side of operator strategies, Chicago's Divvy has used gamified limited edition decorated ‘unicorn’ bikes, as a distinctive promotional strategy.8 Third, personal norm development appears to be critical for spurring the adoption of bike share, which aligns with established behavior change techniques that rely on normative information as social influence (Savan et al., 2017). To expound, interventions should emphasize the integration of active (and shared) travel norms into individuals’ belief systems, which might be accomplished through the promotion of values aligned with environmentalism or collectivist thinking. An early grassroots initiative to builds skills and fosters place attachment is Slow Roll, a movement founded in Detroit in 2010, that hosts neighborhood-based bicycle rides in partnership with local community organizations.9 Finally, it is imperative to improve access to and education in the technological resources required to navigate the physical and financial infrastructure of bike-sharing systems. This is important in the case of dock-less bike-sharing, which relies on GPSbased apps to disseminate information to users. On a final note, issues regarding crime- and traffic-related safety are predominant cycling barriers across socio-economic strata (McNeil et al., 2017). Though the single-item indicators for these variables were not included in our final model, Table 4 shows that individuals in Preparation scored lowest on these items. This suggests that safety perceptions could be construed as one of the ‘final barriers’ to overcome. From the operator side, cooperation with local governments to spur investment in neighborhood watch programs and traffic calming measures—especially in and around public spaces—would ideally assuage fears from an objective perspective; from the rider side, educational workshops on ‘defensive cycling’ and ‘safety in numbers’ could reduce these barriers sufficiently as to encourage bike share usage.

7.0. Conclusions and future research This research examines bike share adoption by combining stagebased behavioral theory with to discrete choice modeling. More specifically, we construct a two-level nested logit model drawing on the Transtheoretical Model framework to represent bike-sharing as a stagebased adoption process. To study the progression up the ladder of adoption from Pre-Contemplation to Maintenance, a number of behavioral theories and factors are included in the model. The motivation for this research is to investigate whether a stage-based model can lead to better understanding of the adoption process than traditional discrete choice frameworks. Specifically, the identification of adoption stages opens the door to defining stage-tailored guidance for policy design to promote forward movement of users in active mobility initiatives. This research shows that stage-based theory can be used to study bike share adoption via careful definition of stage membership levels. The NL model structure employed in this paper to represent the adoption stages reveals the middle two stages as distinct from the endpoint stages, in that Contemplation and Preparation both represent more flexible (i.e. less habitual) mindsets regarding openness to change. The combination of objective variables, reflecting traditional market segmentation practices, and psychological and community-based subjective variables, offers a toolbox for policymakers to draw upon amongst efforts to expand bike share usage. The model also allows for the examination of both users and nonusers that is lacking in the literature (Fishman, 2016). In terms of policy design, this framework can be used for devising bottom-up, neighborhood-oriented campaigns to establish a nurturing environment that promotes the diffusion of bike-sharing. Importantly, the results indicate that identity and norm constructs are fundamental to theories attempting to explain the adoption of bike-sharing, which stands at the nexus of shared and active mobilities. To expound, Identity Process Theory has been identified as a key perspective on coping with social change (de la Sablonnière, 2017), whereas accumulating evidence is in support of both moral and social norms as

6.5. Maintaining bike share membership The model presented in this paper offers several insights for 5 http://betterbikeshare.org/wp-content/uploads/2016/06/NRN-trifold.pdf (accessed 30 Nov 2018). 6 http://gobronzeville.org/(accessed 1 July 2018). 7 https://mogodetroit.org/mogo-neighborhood-ambassadors/(accessed 30 Nov 2018). 8 https://www.divvybikes.com/red (accessed 30 Nov 2018). 9 http://slowroll.bike/index.php (accessed 1 July 2018).

10 http://betterbikeshare.org/2017/03/28/qa-part-coordinate-bike-sharemarketing-goals-across-organizations/(accessed 25 Nov. 2018).

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effective behavioral ‘nudges’ towards a desired outcome (Anderson and Dunning, 2014). It is therefore imperative for policymakers to give serious consideration to social context given the growing sharing economy and push for healthier lifestyles. This supplements the research agenda suggested by Handy et al. (2014), which emphasizes the need to investigate local cultural and community influences impacting cycling-oriented perceptions and behaviors. Policymakers can hence tailor information campaigns and interventions to promote bike-sharing using the stage-specific insights. In particular, the user profiles, internal, and socio-spatial drivers differ between earlier adoption stages and more mature usage. Depending on whether practitioners are seeking to increase propensity to ‘try out’ bike-sharing, to increase first time usage for groups traditionally associated with low adoption rates, or to encourage aspiring users to overcome remaining barriers, these results show the way for customized interventions. Along the same lines, to the extent that the fundamental traits and believes can be acquired, the insights can also guide the design of training and educational activities. For example, we can consider the finding that respondents who are dependent on technology to navigate the urban environment are less likely to be ‘preparing’ to use bike-sharing. Local agencies can plan activities to remove the navigation skill barrier such as organizing neighborhood bike share tours or supporting local initiatives of collective cycling excursions that are present in many neighborhoods. Several avenues of future research are worth noting. First and foremost, the stages of change framework we present is one of several variants, whereas further work is needed to test the implications of different change-process designs on modeling outcomes. Second, employing other discrete choice or latent variable models could further illuminate complexities in the process of behavior change, especially if multiple behaviors are simultaneously studied (e.g. bike share and transit use). More research is also needed to understand adoption in continuously evolving systems. Researchers are only beginning to study new business models, such as dock-less bike share. Many traditional determinants appear to be relevant for users in these systems, such as

weather, cycling infrastructure, and transit proximity (ITDP, 2014; Shen et al., 2018). However, traditional barriers and facilitators are likely to change in the new services, opening the door for more research on adoption dynamics. For example, it is possible that lessons from user behavior in dock-less systems can benefit station placement in traditional fixed systems. Tentatively, free-flowing systems have the potential to overcome some of the equity challenges by facilitating access in low-income communities. An example of a bike-sharing initiative in this vein is the launch of dock-less bicycle-libraries planned by various operators in the southside of Chicago with support and regulation by the Chicago Department of Transportation. The extension will complement traditional bike-sharing and is aimed specifically at neighborhoods with primarily low-income Black populations, fueled by community-based initiatives and discounted passes (Wisniewski, 2018). An important caveat to note is that findings from our analysis are limited in scope due to the cross-sectional nature of the survey—meaning that the models do not embody the true stage transitions that would be revealed in panel studies—and the nonrandom sampling procedure. However, the geographically-targeted sampling frame, compared to many previous studies, partially circumvents the issue of application context. Additionally, we did not collect further information on bike share system features and perceptual barriers (e.g. ease of access, affordability, credit card access) that may encourage or discourage adoption. These variables can be included as supplemental constructs to those explored in the current analysis, defining progression among stages or even respondent stage membership. Despite these limitations, the current research illustrates the viability and value of using a stage-based analysis of active mobility adoption, along with the potential to derive tailored policy guidance from this innovative a priori market segmentation approach. Acknowledgements The study was approved by Northwestern University Institutional Review Board with study number STU00204357.

APPENDIX Table 9

2-factor solution for Active Travel Identity and Norms (ATIN) scale. Statement: Strongly disagree to Strongly agree

F1 Identity

F2 Norms

1. It weighs on my conscience if I do not use active transportation for a trip when it is a reasonable alternative. 2. Finding more opportunities to travel using active transportation is meaningful to me. 3. I think it is right to take advantage of opportunities for me to travel using active transportation. 4. I feel I should attempt to integrate more trips by active transportation into my weekly travel patterns. 5. Investment in active transportation infrastructure in my neighborhood would make me feel valued in society. 6. Investment in active transportation infrastructure would distinguish my neighborhood from others nearby. 7. Increased availability of active transportation would make me more capable of traveling where I need. 8. Increased availability of active transportation would create a neighborhood that aligns more with how I view myself. 9. Greater popularity of active transportation could make me feel pressured to change how I travel. 10. Greater popularity of active transportation would give me a greater sense of pride in my neighborhood. 11. Participating in more active transportation would allow me to adhere more strongly to personal values. 12. Participating in more active transportation would increase my confidence in being able to enjoy my ideal lifestyle.

– – – – 0.723 0.761 0.609 0.813 0.602 0.918 0.778 0.764

0.728 0.913 0.642 0.676 – – – – – – – –

SS Loadings Proportion of variance explained Cumulative variance explained Cronbach's alpha = 0.94 Tucker Lewis index = 0.952 RMSEA index = 0.081

4.584 0.382 0.382

2.320 0.193 0.575

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Table 10

2-factor solution for Environmental Spatial Ability (ESA) scale.

a

Statement: Strongly agree to Strongly disagree

F3 Sense_Direction

F4 GPS_Tech_Affinity

1. I am good at giving directions.a 2. I easily get lost when traveling in an unfamiliar area. 3. I have trouble understanding directions. 4. I am good at reading maps.a 5. I prefer someone else to do the travel planning for trips in unfamiliar areas. 6. I do not have a good mental map of my local environment. 7. I could easily travel to a new location without on-the-go access to GPS technology.a 8. I am confident in my abilities to use GPS technology.a 9. It is important to be able to access information on the Internet while traveling. 10. I enjoy trying out new routes to familiar destinations.a 11. I am easily stressed when I feel lost during travel. 12. More often than not, I depend on GPS technology to help me travel. 13. GPS technology has allowed for more variety in my everyday travel. 14. I feel that I get more accomplished because of technology.

0.818 0.827 0.836 0.769 0.675 0.578 0.744 – – 0.460 0.597 0.414 – –

– – – – – – – −0.562 0.600 – – 0.675 0.764 0.748

SS Loadings Proportion of variance explained Cumulative variance explained Cronbach's alpha = 0.86 Tucker Lewis index = 0.947 RMSEA index = 0.063

4.867 0.348 0.348

2.394 0.171 0.519

Indicates reverse scoring.

Table 11

4-factor solution for Sense of Community (SC) scale. Statement: Strongly agree to Strongly disagree

F5 Cohesion

F6 Nbhd_Travel

F7 Confidence

F8 Symbiotic_ID

1. Neighborhood members and I value the same things. 2. Being a member of my neighborhood makes me feel good. 3. I put a lot of time and effort in being a part of my neighborhood. 4. Being a member of my neighborhood is an important part of my identity. 5. I fit in very well with the people in my neighborhood. 6. I enjoy interacting with other neighborhood residents. 7. Local development trends make me feel more confident about the future of my neighborhood. 8. Local development trends make me feel more confident about my own future. 9. I am confident in my knowledge of where places are in my neighborhood and how to get to them. 10. My neighborhood is a role model for other neighborhoods. 11. My neighborhood has symbols of membership such as signs, art, architecture, logos, and landmarks that people recognize. 12. People in my neighborhood have similar needs, priorities, and goals. 13. People in my neighborhood embrace innovation in transportation services. 14. The local government successfully meets the needs of my neighborhood. 15. Growth in active transportation use is a priority in my neighborhood.

0.707 0.883 – – 0.829 0.678 – – 0.521 0.386 –

– – – – – – – – – – 0.487

– – – – – – 0.878 0.860 – – –

– – 0.718 0.756 – – – – – – –

0.630 – – –

– 0.880 0.403 0.898

– – – –

– – – –

SS Loadings Proportion of variance explained Cumulative variance explained Cronbach's alpha = 0.92 Tucker Lewis index = 0.961 RMSEA index = 0.057

3.500 0.233 0.233

2.091 0.139 0.373

1.590 0.106 0.479

1.256 0.084 0.563

Table 12

4-factor solution for Psychological Well-Being (PWB) scale. Statement: Strongly disagree to Strongly agree

F9 Happiness

F10 Openness

F11 Perseverance

F12 Autonomy

1. 2. 3. 4. 5. 6. 7. 8. 9.

0.908 0.933 0.873 0.660 – – – 0.620 0.669

– – – – – – – – –

– – – – – – – – –

– – – – 0.832 0.395 0.422 – –

In most ways my life is close to ideal. I am satisfied with my life. So far I have been able to obtain the things I want in life. If I could live my life over, I would change almost nothing. My decisions are usually not influenced by what everyone else is doing. I tend to worry about what other people think of me.a I judge myself by what I think is important, not by the values of what others think is important. In general, I feel I am in charge of what is happening in my life. I have been able to build a healthy lifestyle that is much to my liking.

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Table 12 (continued) Statement: Strongly disagree to Strongly agree

F9 Happiness

F10 Openness

F11 Perseverance

F12 Autonomy

10. 11. 12. 13. 14. 15.

– – – – – –

0.786 0.713 0.435 – – 0.447

– – – 0.660 0.747 –

– – – – – –

4.134 0.276 0.276

1.660 0.111 0.386

1.379 0.092 0.478

1.190 0.079 0.557

I am interested in activities that could give me a new perspective in life. I enjoy being in new situations that require me to rethink my habits. My life has been a continuous process of learning, changing, and growth. I do not set ambitious goals for myself because I am afraid of failure.a When trying to learn something new, I tend to give up if I am not initially successful.a I try to learn new things, even when they look too difficult for me.

SS Loadings Proportion of variance explained Cumulative variance explained Cronbach's alpha = 0.86 Tucker Lewis index = 0.977 RMSEA index = 0.039 a

Indicates reverse scoring.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.tranpol.2019.02.001. Conflicts of interest None.

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