Road crossing behavior and preferences among pedestrians: From the lens of the theory of interpersonal behavior

References

• Adjei E, Behrens R. 2012. Travel behavior change theories and experiments: a review and synthesis. SATC. 2012:55–69.
   Google Scholar
• Afukaar FK, Agyemang W, Most I. 2008. Accident statistics 2007. Kumasi: Building and Road Research Institute, Council for Scientific and Industrial Research.
   Google Scholar
• Amin H, Abdul-Rahman AR, Abdul-Razak D. 2016. Malaysian consumers’ willingness to choose Islamic mortgage products: an extension of the theory of interpersonal behavior. Int J Bank Mark. 34(6):868–884.
   Web of Science ®Google Scholar
• Amoako C, Cobbinah PB, Niminga-Beka R. 2014. Urban infrastructure design and pedestrian safety in the Kumasi central business district, Ghana. J Transport Safety Security. 6(3):235–256. doi:10.1080/19439962.2013.861887.
   Web of Science ®Google Scholar
• Anand IM, Gaur SS. 2019. Consequences of consumers’ emotional responses to government’s green initiatives: insights from a scenario-based experimental study. MEQ. 30(1):243–259. doi:10.1108/MEQ-02-2018-0045.
   Google Scholar
• Anciaes PR, Jones P. 2016. Estimating preferences for pedestrian crossing facilities. In Universities’ Transport Study Group 48th Annual Conference Proceedings (Vol. 2016). Universities’ Transport Study Group.
   Google Scholar
• Anderson JC, Gerbing DW. 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull. 103(3):411–423. doi:10.1037/0033-2909.103.3.411.
   Web of Science ®Google Scholar
• Chin WY, Chua HN. 2021. Using the theory of interpersonal behavior to predict information security policy compliance. In 2021 Eighth International Conference on eDemocracy & eGovernment (ICEDEG) (pp. 80-87). IEEE. doi:10.1109/ICEDEG52154.2021.9530849.
   Google Scholar
• Dada M, Zuidgeest M, Hess S. 2019. Modelling pedestrian crossing choice on Cape Town’s freeways: caught between a rock and a hard place? Transp Res Part F Traffic Psychol Behav. 60:245–261. doi:10.1016/j.trf.2018.10.005.
   Web of Science ®Google Scholar
• Damsere-Derry J, Bofah NA. 2023. Road safety benefits and challenges associated with pedestrian footbridge patronage along the Madina-Adenta highway. Urban Plan Transp Res. 11(1):2193240.
   Google Scholar
• De Vos J, Singleton PA, Gärling T. 2022. From attitude to satisfaction: introducing the travel mode choice cycle. Transp. Rev. 42(2):204–221. doi:10.1080/01441647.2021.1958952.
   Web of Science ®Google Scholar
• Evans D, Norman P. 2003. Predicting adolescent pedestrians’ road-crossing intentions: an application and extension of the Theory of Planned Behavior. Health Educ Res. 18(3):267–277. doi:10.1093/her/cyf023.
   PubMed Web of Science ®Google Scholar
• Gou JQ, Zou QG, Wang L. 2012. An analysis of teenagers’ traffic violation behavior based on the improved theory of planned behavior. J Beijing Jiaotong Univ. 11(3):85–90.
   Google Scholar
• Guo M, Yuan Z, Janson B, Peng Y, Yue R, Zhang G. 2023. Do factors associated with older pedestrian crash severity differ? A causal factor analysis based on exposure level of pedestrians. Traffic Inj Prev. 24(4):321–330. doi:10.1080/15389588.2023.2183080.
   PubMedGoogle Scholar
• Hair JF, Black WC, Babin BJ, Anderson RE. 2013. Multivariate data analysis: Pearson new international edition. 7th ed. Harlow: Pearson Education Limited. doi:10.1007/978-3-319-01517-0_3.
   Google Scholar
• Hasan R, Napiah M. 2018. The perception of Malaysian pedestrians toward the use of footbridges. Traffic Inj Prev. 19(3):292–297. doi:10.1080/15389588.2017.1373768.
   PubMed Web of Science ®Google Scholar
• Hussein M, Sayed T, Reyad P, Kim L. 2015. Automated pedestrian safety analysis at a signalized intersection in New York City: automated data extraction for safety diagnosis and behavioral study. Transport Res Record. 2519(1):17–27. doi:10.3141/2519-03.
   Google Scholar
• Ibrahim A, Knox K, Rundle-Thiele S, Arli D. 2018. Segmenting a water use market: theory of interpersonal behavior insights. Soc Mark Q. 24(1):3–17. doi:10.1177/1524500417741277.
   Web of Science ®Google Scholar
• Jenatabadi HS, Ismail NA. 2014. Application of structural equation modelling for estimating airline performance. J Air Transp Manag. 40:25–33. doi:10.1016/j.jairtraman.2014.05.005.
   Web of Science ®Google Scholar
• Macorr. 2019. Sample size methodology. [Online]. [accessed 2022 May 12]. https://www.macorr.com/sample-size-methodology.htm.
   Google Scholar
• Mallinckrodt B, Abraham W, Wei M, Russell D. 2006. Advances in testing the statistical significance of mediation effects. J Couns Psychol. 53(3):372–378. doi:10.1037/0022-0167.53.3.372.
   Web of Science ®Google Scholar
• Moody GD, Siponen M. 2013. Using the theory of interpersonal behavior to explain non-work-related personal use of the Internet at work. Inf Manag. 50(6):322–335. doi:10.1016/j.im.2013.04.005.
   Web of Science ®Google Scholar
• Nitzl C, Roldan JL, Cepeda G. 2016. Mediation analysis in partial least squares path modeling. IMDS. 116(9):1849–1864. doi:10.1108/IMDS-07-2015-0302.
   Google Scholar
• Ojo TK, Appiah AB, Obiri-Yeboah A, Adebanji AO, Donkor P, Mock C. 2022. An intercept survey of the use and non-use of footbridges in Ghana. Case Stud Transp Policy. 10(3):1581–1590. doi:10.1016/j.cstp.2022.05.016.Published by Elsevier Ltd.
   Web of Science ®Google Scholar
• Ourania Skandami M, Anapali IS, Basbas S. 2020. Choosing footbridge or signalized crossing in an urban area: what triggers pedestrians? ToTS. 11(3):37–51. doi:10.5507/tots.2020.013.
   Google Scholar
• Oviedo-Trespalacios O, Scott-Parker B. 2017. Footbridge usage in high-traffic flow highways: the intersection of safety and security in pedestrian decision-making. Transp Res Part F Traffic Psychol Behav. 49:177–187. doi:10.1016/j.trf.2017.06.010.
   Web of Science ®Google Scholar
• Pfeffer K, Hunter E. 2013. The effects of peer influence on adolescent pedestrian road-crossing decisions. Traffic Inj Prev. 14(4):434–440. doi:10.1080/15389588.2012.725259.
   PubMed Web of Science ®Google Scholar
• Poó FM, Ledesma RD, Trujillo R. 2018. Pedestrian crossing ­behavior, an observational study in the city of Ushuaia, Argentina. Traffic Inj Prev. 19(3):305–310. doi:10.1080/15389588.2017.1391380.
   PubMed Web of Science ®Google Scholar
• Rasanen M, Lajunen T, Alticafarbay F, Aydin C. 2007. Pedestrian self-reports of factors influencing the use of pedestrian bridges. Accid Anal Prev. 39(5):969–973. doi:10.1016/j.aap.2007.01.004.
   PubMed Web of Science ®Google Scholar
• Rhemtulla M, Brosseau-Liard PÉ, Savalei V. 2012. When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychol Methods. 17(3):354–373. doi:10.1037/a0029315.
   PubMed Web of Science ®Google Scholar
• Robinson J. 2010. Triandis’ theory of interpersonal behavior in understanding software piracy behavior in the South African context [Doctoral dissertation], University of the Witwatersrand.
   Google Scholar
• Russell SV, Young CW, Unsworth KL, Robinson C. 2017. Bringing habits and emotions into food waste behavior. Resour Conserv Recycl. 125:107–114. doi:10.1016/j.resconrec.2017.06.007.
   Web of Science ®Google Scholar
• Sai Leung N. 2020. Knowledge–intention–behavior associations and spillovers of domestic and workplace recycling. Soc Sci J. 60(2):254–273. doi:10.1080/03623319.2020.1735857.
   Google Scholar
• Shoabjareh AH, Mamdoohi AR, Nordfjærn T. 2021. Analysis of pedestrians’ behavior: a segmentation approach based on latent variables. Accid Anal Prev. 157:106160. doi:10.1016/j.aap.2021.106160.
   PubMed Web of Science ®Google Scholar
• Sullman MJ, Gras ME, Font-Mayolas S, Masferrer L, Cunill M, Planes M. 2011. The pedestrian behavior of Spanish adolescents. J Adolesc. 34(3):531–539. doi:10.1016/j.adolescence.2010.05.011.
   PubMed Web of Science ®Google Scholar
• Sung K, Cooper T, Kettley S. 2019. Factors influencing upcycling for UK makers. Sustainability. 11(3):870. doi:10.3390/su11030870.
   Web of Science ®Google Scholar
• Triandis H. 1977. Interpersonal behavior. Monterey (CA): Brooks/Cole Pub. Co.
   Google Scholar
• Triandis HC. 1980. Values, attitudes, and interpersonal behavior. In: H. Howe & M. Page, editors. Nebraska symposium on motivation 1979. Lincoln (NE): University of Nebraska Press. p. 195–295.
   Google Scholar
• Turaga RMR, Howarth RB, Borsuk ME. 2010. Pro‐environmental behavior: rational choice meets moral motivation. Ann N Y Acad Sci. 1185(1):211–224. doi:10.1111/j.1749-6632.2009.05163.x.
   PubMed Web of Science ®Google Scholar
• Van der Linden S. 2014. On the relationship between personal experience, affect and risk perception: the case of climate change. Eur J Soc Psychol. 44(5):430–440. doi:10.1002/ejsp.2008.
   PubMed Web of Science ®Google Scholar
• Wang X, McClung SR. 2012. The immorality of illegal downloading: the role of anticipated guilt and general emotions. Comput Hum Behav. 28(1):153–159. doi:10.1016/j.chb.2011.08.021.
   Web of Science ®Google Scholar
• Xiao Y, Liu Y, Liang Z. 2021. Study on road-crossing violations among young pedestrians based on the theory of planned behavior. J Adv Transp. 2021:1–11.
   Web of Science ®Google Scholar
• Xu L, Ling M, Lu Y, Shen M. 2017. Understanding household waste separation behavior: testing the roles of moral, past experience, and perceived policy effectiveness within the theory of planned behavior. Sustainability. 9(4):625. doi:10.3390/su9040625.
   Web of Science ®Google Scholar