Understanding Perceptions: User Responses to Browser Warning Messages

2019 ◽  
pp. 164-175 ◽  
Author(s):  
Heather Molyneaux ◽  
Irina Kondratova ◽  
Elizabeth Stobert
Keyword(s):  
Warning Messages

Consumer-citizen willingness to pay for healthy eating messages

2019 ◽  
Vol 31 (2) ◽  
pp. 890-909 ◽  
Author(s):  
Yuxia Ouyang ◽  
Amit Sharma
Keyword(s):  
Willingness To Pay ◽  
Descriptive Analysis ◽  
Content Type ◽  
Health Warning ◽  
Warning Messages ◽  
Related Information ◽  
Warning Message ◽  
Home Context ◽  
Health Related ◽  
Amazon's Mechanical Turk

PurposeThe purpose of this study was to investigate the preference of health-warning message labeling in an eating-away-from-home context. The authors assessed individuals’ preference valuation of such messaging from a dual – consumer and citizen – perspective and with associated expected risk reduction (RR) level.Design/methodology/approachIn an online stated choice experiment on Amazon’s Mechanical Turk (N = 658), participants were asked to provide willingness to pay (WTP) preferences for health-warning messages and based on the expected RR from health-warning messages. Two types of multiple price list questions were used for consumer and citizen contexts. Interval regression and descriptive analysis methods were applied to analyze the data.FindingsThe study found that individuals placed a higher value (higher WTP) on health-warning message labeling when acting as citizens rather than as consumers. An RR expectation of 50 per cent was most effective in increasing participants’ WTP. Individuals who ate out frequently were more concerned about healthier food messages, and the influence of gender and age on WTP was conditional on individuals’ roles as consumers versus citizens.Originality/valueThis study extends the theory of consumer-citizen duality to the context of health-related information labeling, thus opening the discussion to extending such labeling from traditionally risky behavior such as alcohol and tobacco to also including food choice behavior. The authors also highlight implications on policy and industry practices to promote healthy food choices through such messages.

Cognitive and affective responses of Florida tourists after exposure to hurricane warning messages

2012 ◽  
Vol 66 (1) ◽  
pp. 97-116 ◽  
Author(s):  
Jorge Villegas ◽  
Corene Matyas ◽  
Sivaramakrishnan Srinivasan ◽  
Ignatius Cahyanto ◽  
Brijesh Thapa ◽  
...  
Keyword(s):  
Affective Responses ◽  
Warning Messages

Effectiveness of Truck Rollover Warning Systems

2001 ◽  
Vol 1779 (1) ◽  
pp. 134-140 ◽  
Author(s):  
Derek Baker ◽  
Rob Bushman ◽  
Curtis Berthelot
Keyword(s):  
North America ◽  
Warning System ◽  
Warning Systems ◽  
Commercial Vehicles ◽  
Decision Criteria ◽  
Warning Messages ◽  
System A ◽  
Different Types ◽  
Rollover Warning

Different types of intelligent rollover system deployed by road agencies across North America are investigated. The importance of weight is addressed for maximum effectiveness of rollover warning messages for commercial vehicles in a potential rollover situation on sharp curves or exit ramps. The type of information that may be used to activate a rollover is discussed to analyze the number of correctly warned vehicles compared with the number of false warnings generated by the rollover warning system. A case study of the effectiveness of an intelligent rollover system is presented. On the basis of this case study, it was found that speed-based rollover warning systems generated anywhere from 44 percent to 49 percent more false rollover warnings for commercial vehicles than did rollover warning systems that employed weight information in the rollover decision criteria.

An improved car-following model considering the impact of safety messages

2018 ◽  
Vol 32 (32) ◽  
pp. 1850398 ◽  
Author(s):  
Tenglong Li ◽  
Fei Hui ◽  
Xiangmo Zhao
Keyword(s):  
Traffic Congestion ◽  
Dominant Factor ◽  
Velocity Difference ◽  
Car Following ◽  
Safety Message ◽  
Warning Messages ◽  
Gray Correlation ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

scholarly journals Air quality sensors in public transport stops and cross-system communication

2021 ◽  
Vol 3 (1) ◽  
pp. 5-10
Author(s):  
Dan-Marius Mustață
Keyword(s):  
Air Quality ◽  
Public Transport ◽  
Traffic Management ◽  
Transport Systems ◽  
View Point ◽  
System Communication ◽  
Warning Messages ◽  
Different Types ◽  
Cross System ◽  
State Of Art

The purpose of this article is to present a state of art implementation of air quality sensors in public transport stops. Effects on health due to different types of pollutants are summarized as well. Functional scope of the solutions, via warning messages displayed for passengers waiting at these stops, including a cross system communication between traffic management and public transport systems, are also focused. Analysis of existing sensor types from multiple view point including functions, types of measured pollutants, price ranges and comparisons are outlined.

Sign in / Sign up

Export Citation Format

Share Document