FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893124375> ?p ?o ?g. }

• W2893124375 abstract "In the long-haul trucking industry, the turnover rate for drivers has been consistently near or higher than 100% for many years (Fournier, Lamontagne, & Gagnon, 2012; LeMay, Williams, & Garver, 2009). There are many complexly-interacting contributors to this high rate, including competition among industry members for the short supply of qualified drivers, characteristics of driving assignments, family needs, and interpersonal relationships among coworkers. Several costs to trucking companies are associated with a driver quitting their current position, including lost profit, underutilization of equipment, training costs, insurance investments, and administration costs. The overall turnover cost to the industry is estimated to be around $2.8 billion per year (Morrow et al., 2005), which is eventually passed on to consumers. The current and expected increase in the shortage of drivers provides additional motivation to investigate the high turnover problem. Several past studies have looked into the possible causes of high driver turnover, although only within consideration of current trucking operations. In addition to only considering the problem within current operations, most of these studies focused only on a particular subset of the possible issues. A driver survey was created as part of this research effort to take into account a broader range of issues and with a more generalizable approach to allow for consideration of new operational paradigms. In addition to gathering survey results, a key activity in this research effort was to develop a model that can accurately predict the likelihood that a truck driver will quit or stay at their job. When we reach a point when a driver’s decision to stay or quit can be accurately predicted based on the explored issues, it will reveal which combination of issues most strongly influences this decision. Once these issues are discovered, they can inform the development of new operational paradigms that may lead to a decrease in the high turnover problem. A thorough literature review informed the creation of the content of the survey. This was followed by multiple revisions based on feedback from an in-person focus group with truckers, a test launch with a sample of students (non-drivers), and a second test launch with a sample of local drivers. The survey was then administered and 308 valid responses were obtained. The final survey consisted of 84 questions covering demographic information, training and education, job preferences, job nature, management practices, relationship with supervisor, job risks and benefits, work history, job seeking experience, and alternate job opportunities. One survey question which asked: “How do you feel today about the likelihood you'll stay with your current company for the foreseeable future?” resulted in the dependent variable (range of 0.00 to 1.00) used in the analysis. The data was analyzed using the free statistical software environment, R. The first attempt to create a prediction model involved performing multiple linear regression with most of the independent variables and the stated dependent variable. From this analysis, the variables deemed significant included the following: how routes are communicated to the driver (in-person, by phone/radio, by text/email, or other), how often pre-planned routes are given to the driver, what the driver thinks about their work amount, how the driver feels about being affiliated with their company, how well the driver feels their supervisor represents them, how satisfied the driver is with their pay, the driver’s flexibility in being able to take time off work, how interesting the driver considers their job to be, and how frequently the driver interacts with other drivers by radio. This resulting linear model had an adjusted R-squared value of 0.4658. Further data analysis involving other variable selection methods and the creation of complex models will be performed in hopes of obtaining a more accurate model. Future analysis may also include clustering the drivers into groups based on their characteristics and preferences and then seeing if the variables that predict the job plans of the drivers in these groups are different." @default.
• W2893124375 created "2018-10-05" @default.
• W2893124375 creator A5002000186 @default.
• W2893124375 creator A5004934747 @default.
• W2893124375 creator A5010343003 @default.
• W2893124375 creator A5017919558 @default.
• W2893124375 creator A5071205368 @default.
• W2893124375 date "2018-09-01" @default.
• W2893124375 modified "2023-09-27" @default.
• W2893124375 title "Survey and Modeling Approach to Predicting Driver Turnover in Long-Haul Trucking" @default.
• W2893124375 cites W1489942254 @default.
• W2893124375 cites W2011709394 @default.
• W2893124375 cites W29959721 @default.
• W2893124375 doi "https://doi.org/10.1177/1541931218621315" @default.
• W2893124375 hasPublicationYear "2018" @default.
• W2893124375 type Work @default.
• W2893124375 sameAs 2893124375 @default.
• W2893124375 citedByCount "1" @default.
• W2893124375 countsByYear W28931243752020 @default.
• W2893124375 crossrefType "journal-article" @default.
• W2893124375 hasAuthorship W2893124375A5002000186 @default.
• W2893124375 hasAuthorship W2893124375A5004934747 @default.
• W2893124375 hasAuthorship W2893124375A5010343003 @default.
• W2893124375 hasAuthorship W2893124375A5017919558 @default.
• W2893124375 hasAuthorship W2893124375A5071205368 @default.
• W2893124375 hasConcept C127413603 @default.
• W2893124375 hasConcept C138885662 @default.
• W2893124375 hasConcept C144133560 @default.
• W2893124375 hasConcept C146978453 @default.
• W2893124375 hasConcept C162324750 @default.
• W2893124375 hasConcept C162853370 @default.
• W2893124375 hasConcept C175444787 @default.
• W2893124375 hasConcept C181622380 @default.
• W2893124375 hasConcept C187736073 @default.
• W2893124375 hasConcept C194051981 @default.
• W2893124375 hasConcept C21547014 @default.
• W2893124375 hasConcept C22212356 @default.
• W2893124375 hasConcept C26077564 @default.
• W2893124375 hasConcept C2778137410 @default.
• W2893124375 hasConcept C2910182162 @default.
• W2893124375 hasConcept C41895202 @default.
• W2893124375 hasConcept C42475967 @default.
• W2893124375 hasConcept C52121051 @default.
• W2893124375 hasConceptScore W2893124375C127413603 @default.
• W2893124375 hasConceptScore W2893124375C138885662 @default.
• W2893124375 hasConceptScore W2893124375C144133560 @default.
• W2893124375 hasConceptScore W2893124375C146978453 @default.
• W2893124375 hasConceptScore W2893124375C162324750 @default.
• W2893124375 hasConceptScore W2893124375C162853370 @default.
• W2893124375 hasConceptScore W2893124375C175444787 @default.
• W2893124375 hasConceptScore W2893124375C181622380 @default.
• W2893124375 hasConceptScore W2893124375C187736073 @default.
• W2893124375 hasConceptScore W2893124375C194051981 @default.
• W2893124375 hasConceptScore W2893124375C21547014 @default.
• W2893124375 hasConceptScore W2893124375C22212356 @default.
• W2893124375 hasConceptScore W2893124375C26077564 @default.
• W2893124375 hasConceptScore W2893124375C2778137410 @default.
• W2893124375 hasConceptScore W2893124375C2910182162 @default.
• W2893124375 hasConceptScore W2893124375C41895202 @default.
• W2893124375 hasConceptScore W2893124375C42475967 @default.
• W2893124375 hasConceptScore W2893124375C52121051 @default.
• W2893124375 hasLocation W28931243751 @default.
• W2893124375 hasOpenAccess W2893124375 @default.
• W2893124375 hasPrimaryLocation W28931243751 @default.
• W2893124375 hasRelatedWork W1516455402 @default.
• W2893124375 hasRelatedWork W1528638232 @default.
• W2893124375 hasRelatedWork W1541087157 @default.
• W2893124375 hasRelatedWork W2046386119 @default.
• W2893124375 hasRelatedWork W2062711313 @default.
• W2893124375 hasRelatedWork W2140805093 @default.
• W2893124375 hasRelatedWork W21469179 @default.
• W2893124375 hasRelatedWork W605816536 @default.
• W2893124375 hasRelatedWork W617987602 @default.
• W2893124375 hasRelatedWork W773500611 @default.
• W2893124375 isParatext "false" @default.
• W2893124375 isRetracted "false" @default.
• W2893124375 magId "2893124375" @default.
• W2893124375 workType "article" @default.