2025
Yeaminur Rahman
Adapting Eco-Driving Feedback and Historical Visualization for Vessel Dashboards Masters Thesis
2025.
Abstract | Links | BibTeX | Tags: behaviour change, climate, dashboard, geospatial analytics, mobile, navigation, peripheral vision, simulation, training, virtual environment, visualization, wayfinding
@mastersthesis{nokey,
title = {Adapting Eco-Driving Feedback and Historical Visualization for Vessel Dashboards},
author = {Yeaminur Rahman},
url = {https://hdl.handle.net/10222/85531},
year = {2025},
date = {2025-11-25},
urldate = {2025-11-25},
abstract = {Maritime navigation is a significant source of greenhouse gas emissions. While large-scale cargo shipping is the major contributor, smaller maritime operations, including patrolling, fishing, public transit, and recreation, present unique challenges and opportunities for power management. Fuel consumption, power conversion, and environmental data can permit environmentally conscious and cost-effective decision-making when driving a boat. To achieve this, we need to understand how best to integrate such data into boat dashboard interfaces. In this work, we design an Eco Dashboard inspired by eco-driving feedback dashboards in the automotive industry, as well as a variant of the Eco Dashboard that additionally visualizes historical route and fuel consumption data (Eco + Historical Dashboard). In an experimental simulation (N = 30) involving 12 experienced mariners and 18 novices, we compared both interfaces with a typical boat dashboard that presented fuel and speed. Our findings suggest that dashboards incorporating historical data, alongside eco-driving features, improve fuel efficiency and decision-making, particularly for non-experienced users. The Eco Dashboard supported real-time adjustments during complex navigation, whereas the Eco + Historical Dashboard enhanced route planning and confidence in longer-term decisions. Participants also reported greater confidence and reduced cognitive load when using these systems. These results provide valuable insights for the future design of maritime dashboard systems, offering a pathway to more effective and environmentally conscious navigation tools.},
keywords = {behaviour change, climate, dashboard, geospatial analytics, mobile, navigation, peripheral vision, simulation, training, virtual environment, visualization, wayfinding},
pubstate = {published},
tppubtype = {mastersthesis}
}
Maritime navigation is a significant source of greenhouse gas emissions. While large-scale cargo shipping is the major contributor, smaller maritime operations, including patrolling, fishing, public transit, and recreation, present unique challenges and opportunities for power management. Fuel consumption, power conversion, and environmental data can permit environmentally conscious and cost-effective decision-making when driving a boat. To achieve this, we need to understand how best to integrate such data into boat dashboard interfaces. In this work, we design an Eco Dashboard inspired by eco-driving feedback dashboards in the automotive industry, as well as a variant of the Eco Dashboard that additionally visualizes historical route and fuel consumption data (Eco + Historical Dashboard). In an experimental simulation (N = 30) involving 12 experienced mariners and 18 novices, we compared both interfaces with a typical boat dashboard that presented fuel and speed. Our findings suggest that dashboards incorporating historical data, alongside eco-driving features, improve fuel efficiency and decision-making, particularly for non-experienced users. The Eco Dashboard supported real-time adjustments during complex navigation, whereas the Eco + Historical Dashboard enhanced route planning and confidence in longer-term decisions. Participants also reported greater confidence and reduced cognitive load when using these systems. These results provide valuable insights for the future design of maritime dashboard systems, offering a pathway to more effective and environmentally conscious navigation tools.
2018
Rita Orji; Derek Reilly; Kiemute Oyibo; Fidelia A. Orji
Deconstructing persuasiveness of strategies in behaviour change systems using the ARCS model of motivation Journal Article
In: Behaviour and Information Technology, 2018.
Abstract | Links | BibTeX | Tags: ARCS model, behaviour change, personalisation, Persuasive strategies, persuasive technology
@article{orji2018BIT,
title = {Deconstructing persuasiveness of strategies in behaviour change systems using the ARCS model of motivation},
author = {Rita Orji and Derek Reilly and Kiemute Oyibo and Fidelia A. Orji},
doi = {10.1080/0144929X.2018.1520302},
year = {2018},
date = {2018-09-17},
journal = {Behaviour and Information Technology},
abstract = {Persuasive technologies (PTs) motivate behaviour change using various persuasive strategies. However, there is still a dearth of knowledge on how PTs motivate behaviour change and how to design systems to increase their persuasiveness. To provide empirical insight into the mechanism through which PTs persuade, we conducted a large-scale study with 543 participants to investigate the relation between Attention, Relevance, Confidence, and Satisfaction constructs from the ARCS model of motivation and 10 strategies that are commonly used in persuasive systems design. Our results show that the ARCS constructs collectively explain between 82% and 91% of the variance in persuasiveness across the ten strategies. Relevance, followed by Attention, has the strongest association with persuasiveness. The result generalises across gender groups. Therefore, to increase a system’s persuasiveness, designers should focus on designing to increase relevance and to capture user’s attention, while also promoting confidence and a feeling of satisfaction. We contribute to Human–Computer Interaction (HCI) and Persuasive Technology by offering design guidelines for PTs to increase their persuasiveness and hence efficacy.},
keywords = {ARCS model, behaviour change, personalisation, Persuasive strategies, persuasive technology},
pubstate = {published},
tppubtype = {article}
}
Persuasive technologies (PTs) motivate behaviour change using various persuasive strategies. However, there is still a dearth of knowledge on how PTs motivate behaviour change and how to design systems to increase their persuasiveness. To provide empirical insight into the mechanism through which PTs persuade, we conducted a large-scale study with 543 participants to investigate the relation between Attention, Relevance, Confidence, and Satisfaction constructs from the ARCS model of motivation and 10 strategies that are commonly used in persuasive systems design. Our results show that the ARCS constructs collectively explain between 82% and 91% of the variance in persuasiveness across the ten strategies. Relevance, followed by Attention, has the strongest association with persuasiveness. The result generalises across gender groups. Therefore, to increase a system’s persuasiveness, designers should focus on designing to increase relevance and to capture user’s attention, while also promoting confidence and a feeling of satisfaction. We contribute to Human–Computer Interaction (HCI) and Persuasive Technology by offering design guidelines for PTs to increase their persuasiveness and hence efficacy.