Augmenting the Technology Acceptance Model with Trust: Commercial Drivers’ Attitudes towards Monitoring and Feedback

This study evaluates truck drivers’ attitudes toward an on-board monitoring system (OBMS), using an extended version of the Technology Acceptance Model (TAM) that accounts for drivers’ trust in OBMS. Crashes that involve trucks incur a high cost to society and driver-related factors contribute to about one third of all large truck fatal crashes in the US. Therefore, safety initiatives that can increase drivers’ awareness of their risky behaviors are highly desirable. In-vehicle feedback systems are designed to serve this purpose; however, their benefits will not be realized unless their information can positively influence safe driving. Acceptance constructs for the proposed model were measured using a survey administered after the monitoring system was introduced to the drivers but before the system was actually installed in their trucks. In line with the TAM, the results demonstrated that perceived usefulness is the most important determinant of intention to use the OBMS. Trust was also a major determinant of intention to use, suggesting that the acceptance model can be usefully augmented by this construct.

[1]  Linda Ng Boyle,et al.  Extending the Technology Acceptance Model to assess automation , 2011, Cognition, Technology & Work.

[2]  Jean-Michel Hoc,et al.  Objective and subjective evaluation of motor priming and warning systems applied to lateral control assistance. , 2010, Accident; analysis and prevention.

[3]  A. PavlouPaul Consumer Acceptance of Electronic Commerce , 2003 .

[4]  Wei Wang,et al.  Analyzing Travelers’ Intention to Accept Travel Information , 2010 .

[5]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[6]  John D. Lee,et al.  Trust, self-confidence, and operators' adaptation to automation , 1994, Int. J. Hum. Comput. Stud..

[7]  John D. Lee,et al.  Designing Feedback to Mitigate Distraction , 2009 .

[8]  George Mason Situation Awareness, Mental Workload, and Trust in Automation:Viable, Empirically Supported Cognitive Engineering Constructs , 2011 .

[9]  Manfred Tscheligi,et al.  Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour , 2009, AutomotiveUI.

[10]  Makoto Itoh,et al.  Support by Warning or by Action: Which is Appropriate under Mismatches between Driver Intent and Traffic Conditions? , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[11]  Fred D. Davis,et al.  A critical assessment of potential measurement biases in the technology acceptance model: three experiments , 1996, Int. J. Hum. Comput. Stud..

[12]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[13]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[14]  Greg Stark,et al.  Evaluation of the Mack Intelligent Vehicle Initiative Field Operational Test , 2006 .

[15]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[16]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[17]  Emeli Adell,et al.  ACCEPTANCE OF DRIVER SUPPORT SYSTEMS , 2010 .

[18]  Akhilesh Bajaj,et al.  A feedback model to understand information system usage , 1998, Inf. Manag..

[19]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[20]  AgarwalRitu,et al.  Reconceptualizing compatability beliefs in technology acceptance research , 2006 .

[21]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[22]  Naresh K. Malhotra,et al.  A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena , 2005, Manag. Sci..

[23]  Mark R. Lehto,et al.  Foundations for an Empirically Determined Scale of Trust in Automated Systems , 2000 .

[24]  Nancy McMillan,et al.  Volvo Trucks Field Operational Test: Evaluation of Advanced Safety Systems for Heavy Trucks , 2007 .

[25]  France Bélanger,et al.  The utilization of e‐government services: citizen trust, innovation and acceptance factors * , 2005, Inf. Syst. J..

[26]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[27]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[28]  Pei-Chun Chen,et al.  Applying the TAM to travelers' usage intentions of GPS devices , 2011, Expert Syst. Appl..

[29]  Daniel V. McGehee,et al.  Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator , 2002, Hum. Factors.

[30]  Diane M. Strong,et al.  Extending the technology acceptance model with task-technology fit constructs , 1999, Inf. Manag..

[31]  Paul A. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..

[32]  Reinhard Pfliegl,et al.  Driver Behavior and User Acceptance of Cooperative Systems Based on Infrastructure-to-Vehicle Communication , 2009 .