Open AccessCorrelation of driving behavior with the need for self-recovery, work motivation, and emotional intelligence of an app-based motorbike taxi drivers
Author(s) -
Annisa Azzahrah Subroto,
Rida Zuraida
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/794/1/012071
Subject(s) - emotional intelligence , correlation , psychology , affect (linguistics) , logistic regression , applied psychology , work (physics) , social psychology , statistics , mathematics , engineering , mechanical engineering , geometry , communication
The increasing mobility drives demand for fast, reliable, and safe public transportation. Nowadays, the citizen of the main city in Indonesia relies on app-based transportation to fulfill these needs, especially the motorbike taxi services. Many factors affect the convenience and safety of the passenger, especially a driver’s driving behavior. This research aims to measure a motorbike taxi driver driving behavior level using a Manchester Driving Behavior Questionnaire (MDBQ). Furthermore, the correlation between emotional intelligence (X1), the need for self-recovery (X2), and work motivation (X3) variables with driving behavior (Y) was measured. Using a set of questionnaires, the 200 app-based motorbike taxi drivers’ opinions on those variables were collected. The participants were drivers who operate in the Indonesian capital city and its four adjacent cities. To see the correlation of the variables, a Binary logistic regression method was applied. One way ANOVA was also utilized to find out whether the age, driver’s income, and driving duration differentiate the level of Y, X1, X2, and X3. The result showed that X1 had a negative influence on the Y, while X2 and X3 had a positive influence. The correlation can be explained by the equation Y=- 7,491 + 0,067X 1 + 1,388X 2 + 0,577X 3 . One way ANOVA results showed that the emotional intelligence level statistically different among the age class ( p = 0.095), the work motivation level statistically different among drivers with different driving duration per day ( p = 0.035). This research provides a new perspective that driving behavior is influenced by those variables. The implication, to manage or to correct the driving behavior of drivers, the app-based transportation company service could set some regulations related to this matter. The company may consider limiting the driving duration and give rewards to the drivers who able to manage their driving duration and their driving behavior.