role of perceived satisfaction and the built environment on the frequency of cycle-commuting

Understanding how several street attributes influence the frequency of cycle commuting is relevant for policymaking in urban planning. However, to better understand the impact of the built environment on people's choices, we must understand the subjective experience of individuals while cycling. This study examines the relationship between perceived satisfaction and the attributes of the built environment along the route. Data was collected from a survey carried out within one district of Santiago’s central business district (N=2,545). It included socio-demographic information, origin-destination and route, travel behavior habits, and psychometric indicators. Two models were estimated. The first, a satisfaction latent variable model by mode, confirms previous findings in the literature, such as the correlation between cycling and a more enjoyable experience, while adding some new findings. For instance, satisfaction increases with distance and the number of trips per week. The second is a hybrid ordered logit model for cycle commuting frequency that includes satisfaction, through a structural equation, that shows this latent variable plays a significant role in travel behavior. The presence of buses along the route decreases cycling satisfaction and frequency, while the trip length and the availability of cycle paths has the opposite effect for male and female cyclists. These results allow us to understand the main factors that deliver satisfaction to cyclists and therefore induce frequent cycle commuting. Overall, our study provides evidence of the need for policymakers to focus their strategies so as to effectively promote cycling among different types of commuters.

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Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

10.31234/osf.io/68s4f ◽

2020 ◽

Author(s):

Jonathan Rush ◽

Philippe Rast ◽

Scott Michael Hofer

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Structural Equation ◽

Structural Equation Models ◽

Simulated Data ◽

Variable Model ◽

Alternative Approach ◽

Scale Scores ◽

Weighted Model ◽

The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

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Examining the interrelationships among reward systems, organisational climate and cultural changes in the hospitality industry

Tourism Review International ◽

10.3727/154427220x16064144339174 ◽

2020 ◽

Author(s):

Christos Kakarougkas ◽

Theodoros Stavrinoudis

Keyword(s):

Cultural Change ◽

Latent Variable ◽

Structural Equation ◽

Reward System ◽

Senior Executives ◽

Cultural Changes ◽

Organisational Climate ◽

Individual Variables ◽

The Impact ◽

Method Of Extraction

This paper aims to explore the impact of a hotel’s reward system on strengthening: positiverelationships and communication among employees; the creation of a change-friendlyorganisational climate and cultural change barriers, within the context of a cultural changeprocess in a hotel. Quantitative data were collected from a proportionally stratified,representative sample of 207 Greek five-star hotels’ senior executives and analysed with theprincipal component method of extraction and Structural Equation Modelling. This led to thecreation and validation of three prototype second-order latent variable models, whichhighlight and depict the impact of individual variables and their importance for a rewardsystem creating an organisational climate for or against cultural change in hotels. Theoriginality of the paper lays on both theoretical and practical levels. On a theoretical level, thepaper’s findings manage to fill a knowledge gap through a novel modelling of a rewardsystem on a hotel’s organisational climate in times of cultural change. On a practical level, thepaper findings enable hotels’ executives to focus on specific variables of a reward system thatcan enhance and/or prevent a cultural change initiative.

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Using an Integrated Choice and Latent Variable Model to Understand the Impact of 'Professional' Respondents in a Stated Preference Survey

SSRN Electronic Journal ◽

10.2139/ssrn.3368948 ◽

2019 ◽

Author(s):

Erlend Dancke Sandorf ◽

Lars Persson ◽

Thomas Broberg

Keyword(s):

Latent Variable ◽

Stated Preference ◽

Latent Variable Model ◽

Variable Model ◽

Stated Preference Survey ◽

The Impact

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The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

Journal of Sustainable Real Estate ◽

10.22300/1949-8276.11.1.108 ◽

2019 ◽

Vol 11 (1) ◽

pp. 108-129

Author(s):

Andrew G. Mueller ◽

Daniel J. Trujillo

Keyword(s):

Land Use ◽

Built Environment ◽

Discrete Choice ◽

Travel Behavior ◽

Choice Model ◽

Mode Choice ◽

Land Use Regulation ◽

Policy Action ◽

Development Goals ◽

The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

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Using Deep Learning to Understand Travel Demands in Different Urban Districts

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994582 ◽

2021 ◽

pp. 036119812199458

Author(s):

Shunhua Bai ◽

Junfeng Jiao

Keyword(s):

Built Environment ◽

Travel Behavior ◽

Prediction Accuracy ◽

Travel Demand ◽

Short Term Memory ◽

Future Research ◽

Demand Forecast ◽

Time And Space ◽

The University ◽

The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

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Assessing the Impact of Metropolitan-Level, County-Level, and Local-Level Built Environment on Travel Behavior: Evidence from 19 U.S. Urban Areas

Journal of Urban Planning and Development ◽

10.1061/(asce)up.1943-5444.0000226 ◽

2015 ◽

Vol 141 (3) ◽

pp. 04014031 ◽

Cited By ~ 14

Author(s):

Arefeh Nasri ◽

Lei Zhang

Keyword(s):

Built Environment ◽

Travel Behavior ◽

Urban Areas ◽

Local Level ◽

County Level ◽

The Impact

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Linking employee perceptions and employee attitudes through their levels of organizational identification: a field investigation in India

International Journal of Organization Theory and Behavior ◽

10.1108/ijotb-09-2020-0164 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Soumendu Biswas

Keyword(s):

Social Exchange ◽

Organizational Support ◽

Organizational Identification ◽

Latent Variable ◽

Structural Equation ◽

Employee Attitudes ◽

Equation Modeling ◽

Full Time ◽

Variable Model ◽

Content Type

PurposeDespite organizational socialization and support, contemporary managers often perceive employees to be less engaged and attached to their workplace, multiplying their workload with unsolicited vexations and worries. In this connection, the purpose of this paper is to explore and possibly confirm the ameliorative role of organizational identification as a mediator between employees' perceptions of organizational support and justice and their favorable association to their levels of engagement and attenuation of their intentions to quit.Design/methodology/approachSuitable theories such as the social exchange and fairness heuristics theories were examined to select and support the study constructs. Accordingly, the literature was reviewed to formulate the study hypotheses and connect them through a conceptual latent variable model (LVM). Data were collected from 402 full-time managerial executives all over India. The data thus collected were subjected to structural equation modeling (SEM) procedures.FindingsAll the measures used in this study had acceptable reliabilities as indicated by their Cronbach's Alpha values. Based on the SEM procedures all the study hypotheses and one of the competing LVMs labeled as LVM5 was finally accepted.Originality/valueThe distinctive feature of this study is the theoretical compilation of all the study constructs in one LVM and subsequent empirical verification of the same. This study is, perhaps, the first of its kind to examine the implications of such justice-based perceptions of social exchange relations between employees and their organizations in India more so, since it considers support and justice to complement each other as an interactive whole.

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A Hierarchical Latent Variable Model of Leadership Styles using PLS-SEM

Jurnal Teknologi ◽

10.11113/jt.v69.3245 ◽

2014 ◽

Vol 69 (6) ◽

Cited By ~ 1

Author(s):

Azlin Shafinaz Arshad ◽

Chin Fei Goh ◽

Amran Rasli

Keyword(s):

Transformational Leadership ◽

Leadership Styles ◽

Transactional Leadership ◽

Latent Variable ◽

Structural Equation ◽

Second Order ◽

Partial Least Square ◽

Least Square ◽

Variable Model ◽

Component Models

The aim of this article is to propose second order hierarchical component models to analyze the two leadership styles (transformational leadership and transactional leadership) for technology-based SMEs. We adopted the two-stage approaches in partial least square-structural equation modelling to examine the appropriateness of hierarchical modelling for both leadership styles. The findings indicate that the conceptual properties of transformational leadership and transactional leadership are matched with reflective-formative type of second order hierarchical component models. In addition, the study offers an alternative avenue to those researchers who are intending to introduce hierarchical component models in modelling leadership styles.

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Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

Journal of Advanced Transportation ◽

10.1155/2017/8749040 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Chuan Ding ◽

Yu Chen ◽

Jinxiao Duan ◽

Yingrong Lu ◽

Jianxun Cui

Keyword(s):

Travel Behavior ◽

Traffic Congestion ◽

Structural Equation ◽

Choice Model ◽

Mode Choice ◽

Influential Factors ◽

Travel Mode ◽

Travel Mode Choice ◽

The Impact ◽

Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

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