Examining the influence of social norms on orderly parking behavior of dockless bike-sharing users
TL;DR: Zhang et al. as discussed by the authors proposed an integrated model and recruited 1722 participants from diverse professions and geographic locations in China, examining the influence of individual and social environmental factors on orderly parking behavior.
Abstract: With the rapid global expansion of dockless shared bicycles, disorderly parking has not only generated convenience to users but also negative consequences to enterprises and urban management. We proposed an integrated model and recruited 1722 participants from diverse professions and geographic locations in China, examining the influence of individual and social environmental factors on orderly parking behavior. It was shown that descriptive social norms played an important role in shaping user’s attitudes toward orderly parking directly and indirectly via personal norms, and thus influence the behavioral intention of orderly parking. Cultural tightness-looseness further moderated the effect of descriptive social norms. At the individual level, antecedents of personal norms (moral awareness, awareness of consequences, and ascription of responsibility) were investigated based on Norm Activation Model. This study indicates that descriptive social norms can act as a complimentary policy and regulations of dockless bike-sharing, which provides valuable insights on urban operation and policy making concerning shared bicycles.
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TL;DR: The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
Abstract: Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.
20 citations
Cites background from "Examining the influence of social n..."
...The bike-sharing system, as a relatively new alternative and environmentally friendly travel choice, has been increasingly prevalent in major metropolises around the world (Chen et al., 2020; Gao et al., 2021; Jin et al., 2015; Lazarus et al., 2020; Wang et al., 2021)....
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TL;DR: Wang et al. as mentioned in this paper investigated the riding behavior in the time and space dimensions based on multisource datasets, and compared the DBS usage based on the traffic grid between the two study areas.
Abstract: To better understand dockless bike-sharing (DBS) usage and advance the knowledge on shared bicycle service, this study empirically investigated the riding behavior in the time and space dimensions based on multisource datasets. Taking Central Business District (CBD) and Beijing West Railway Station (BWRS) as study areas, this study analyzed and compared the DBS usage based on the traffic grid between the two study areas. Furthermore, the random forest (RF) model was applied to investigate the contribution of influencing factors on origin/ destination and origin–destination pair trip volume. Partial Dependence Plots (PDP) analysis was conducted to explore the nonlinear effects of influencing factors. Results show considerable variation across different scenarios. Variables such as government agencies, restaurants, bus stop distance, and metro distance show nonlinear and threshold effects on DBS usage. The findings offer valuable insights for urban infrastructure development and bike rebalancing strategies, and the formulation of green and sustainable transportation policies.
17 citations
TL;DR: Wang et al. as discussed by the authors investigated the influencing factors accounting for the intention to sort household waste using Jiangsu province of China as a case and found that attitude and subjective norm cardinally influenced intention towards waste sorting.
14 citations
TL;DR: Wang et al. as mentioned in this paper investigated the characteristics of drivers' scrambling behavior and its influencing factors based on the theory of planned behavior and found that positive attitudes towards scrambling behavior, subjective norms and perceived behavior control increased the intention of scrambling behavior.
Abstract: Scrambling behavior is one of the main causes of road traffic accidents in China. This study aimed to investigate the characteristics of drivers’ scrambling behavior and its influencing factors based on the theory of planned behavior. A total of 388 drivers answered the questionnaire and 359 provided valid data. The structure equation model of scrambling behavior showed that positive attitudes towards scrambling behavior, subjective norms and perceived behavior control increased the intention of scrambling behavior. Furthermore, the path coefficient of the structural equation model for the scrambling behavior revealed that attitude was the most important factor influencing scrambling behavior. Thus, to prevent drivers from scrambling, traffic regulators should focus on improving drivers’ attitude towards this behavior, while auxiliary measures should be enacted to regulate drivers' subjective norms and perceptual behavior control. Implications on intervention strategy and policy to reduce scrambling behavior are discussed.
12 citations
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TL;DR: Ajzen, 1985, 1987, this article reviewed the theory of planned behavior and some unresolved issues and concluded that the theory is well supported by empirical evidence and that intention to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior.
65,095 citations
TL;DR: The extent to which method biases influence behavioral research results is examined, potential sources of method biases are identified, the cognitive processes through which method bias influence responses to measures are discussed, the many different procedural and statistical techniques that can be used to control method biases is evaluated, and recommendations for how to select appropriate procedural and Statistical remedies are provided.
Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
52,531 citations
Book•
06 May 2013
TL;DR: In this paper, the authors present a discussion of whether, if, how, and when a moderate mediator can be used to moderate another variable's effect in a conditional process analysis.
Abstract: I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1. A Scientist in Training 1.2. Questions of Whether, If, How, and When 1.3. Conditional Process Analysis 1.4. Correlation, Causality, and Statistical Modeling 1.5. Statistical Software 1.6. Overview of this Book 1.7. Chapter Summary 2. Simple Linear Regression 2.1. Correlation and Prediction 2.2. The Simple Linear Regression Equation 2.3. Statistical Inference 2.4. Assumptions for Interpretation and Statistical Inference 2.5. Chapter Summary 3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation 3.2. Partial Association and Statistical Control 3.3. Statistical Inference in Multiple Regression 3.4. Statistical and Conceptual Diagrams 3.5. Chapter Summary II. MEDIATION ANALYSIS 4. The Simple Mediation Model 4.1. The Simple Mediation Model 4.2. Estimation of the Direct, Indirect, and Total Effects of X 4.3. Example with Dichotomous X: The Influence of Presumed Media Influence 4.4. Statistical Inference 4.5. An Example with Continuous X: Economic Stress among Small Business Owners 4.6. Chapter Summary 5. Multiple Mediator Models 5.1. The Parallel Multiple Mediator Model 5.2. Example Using the Presumed Media Influence Study 5.3. Statistical Inference 5.4. The Serial Multiple Mediator Model 5.5. Complementarity and Competition among Mediators 5.6. OLS Regression versus Structural Equation Modeling 5.7. Chapter Summary III. MODERATION ANALYSIS 6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny? 6.2. Confounding and Causal Order 6.3. Effect Size 6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously? 6.5. Reporting a Mediation Analysis 6.6. Chapter Summary 7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects 7.2. An Example: Sex Discrimination in the Workplace 7.3. Visualizing Moderation 7.4. Probing an Interaction 7.5. Chapter Summary 8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator 8.2. Interaction between Two Quantitative Variables 8.3. Hierarchical versus Simultaneous Variable Entry 8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance 8.5. Chapter Summary 9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering 9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis 9.3. Artificial Categorization and Subgroups Analysis 9.4. More Than One Moderator 9.5. Reporting a Moderation Analysis 9.6. Chapter Summary IV. CONDITIONAL PROCESS ANALYSIS 10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature 10.2. Conditional Direct and Indirect Effects 10.3. Example: Hiding Your Feelings from Your Work Team 10.4. Statistical Inference 10.5. Conditional Process Analysis in PROCESS 10.6. Chapter Summary 11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study 11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model 11.3. Visualizing the Direct and Indirect Effects 11.4. Mediated Moderation 11.5. Chapter Summary 12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis 12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect? 12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation 12.4. The Pitfalls of Subgroups Analysis 12.5. Writing about Conditional Process Modeling 12.6. Chapter Summary Appendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS
26,144 citations
Book•
11 Oct 1985TL;DR: In this paper, models of Human Nature and Casualty are used to model human nature and human health, and a set of self-regulatory mechanisms are proposed. But they do not consider the role of cognitive regulators.
Abstract: 1. Models of Human Nature and Casualty. 2. Observational Learning. 3. Enactive Learning. 4. Social Diffusion and Innovation. 5. Predictive Knowledge and Forethought. 6. Incentive Motivators. 7. Vicarious Motivators. 8. Self-Regulatory Mechanisms. 9. Self-Efficacy. 10. Cognitive Regulators. References. Index.
11,264 citations
TL;DR: The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or differentiation criteria.
Abstract: Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or...
8,248 citations