KingVote
KingVote is designed to make voting more secure, transparent, and efficient, with a focus on increasing voter trust and engagement in student elections.
The goal was to design a user-friendly and intuitive interface that corresponds with Kingston University students' needs and expectations, while also maintaining the integrity and security of the voting process through the use of Blockchain technology
Role :
UI/UX Designer
Duration :
3 Months
Industry :
Current voting system at Kingston :
Through Emails!
Candidates fill their registration forms and get themselves registered and it has to be done, a week before the commencement of the elections, and they run campaigns for two weeks, then email links are sent to the students and they can place their votes accordingly.
Majority of these students don't really open these links or even open these emails sent to them.
Problem :
The traditional method of conducting elections for university students through email links for voting is not secure, transparent, and easily manipulated, leading to mistrust and dissatisfaction among the student body.
Therefore, there is a need for a more secure and transparent digital voting system that can ensure the integrity and privacy of each vote, prevent voter fraud and manipulation, and increase the trust and participation of the student community in the election process
Objectives :
Primary Research :
Once all the information was gathered, the researcher started framing the set of carefully prepared interview questions for the target users (Kingston University Students) based on all the collected data regarding the election process.
User Survey :
User Interviews and surveys were done in order to gather quantitative as well as qualitative data directly from the users to inform the design process. The researched maintained to get a total of 7 responses from the survey conducted and some key insights were extracted from it :
Once all the information was gathered, the researcher started framing the set of carefully prepared interview questions for the target users (Kingston University Students) based on all the collected data regarding the election process.
User Interviews
Three face-to-face interviews were carried out with Kingston university students. Each Participant was provided with an information sheet and a consent form prior to their interviews. The Interviews that were conducted were semi-structured wherein some questions were predefined by the researcher and the other questions were open ended follow up questions to receive detailed feedback from the participants. Some of the key Highlights from the interviews are stated below :
Affinity Diagramming
In order to visualise the data that was received from the previously conducted interviews and surveys and in order to organise it affinity diagrams were created. At this stage data cleaning and analysis of the data was conducted from the transcripts of the user interviews and the data achieved from the survey. This analysed data was then grouped in the form of separated colored themes as given below:
Assumptions
On the basis of the existing data, assumptions were formed. They were categorized into business assumptions and user assumptions
These Assumptions served as preliminary hypotheses or educated estimates about corporate objectives, user demands, and solution concepts
Assumption prioritization
The assumptions were then prioritized on the basis of the risk/validation chart as it helps to focus on the most crucial and significant assumptions, ensuring efficient and effective product development. Prioritisation was based on findings from secondary and primary research. The risk/validation chart assisted the researcher in better understanding and visualising which assumptions were risky and which were easy to validate.
Competitor's Analysis
Competetive analysis was carried out by the researcher in order to understand what other similar platforms are doing and to better understand where things could be improved and in order to learn get more data. The painpoints and highlights were taken from various app reviews and feedbacks by users on google playstore and appstore.
Proto Personas
Two Proto-personas were created on the basis of the qualitative and quantitative data received from the interviews and surveys and some assumptions in order to give a shared understanding of the target users and their demands, and to assist decision-making during the design process
Customer Journey Maps
Emphasizing with the users helped the researcher to visualise the experience from their perspective. It assisted the researcher in understanding the customer's point of view and identifying possibilities to improve the customer experience and eventually to gather insights about a customer's ideas, feelings, actions, and touch-points at each stage of their journey, from initial awareness to post-service involvement.
How Might We
How might we statements provided a structured approach to refine and expand upon the user stories. Four how might we statements were generated, each of which provided a usercentred perspective on a particular opportunity or problem. As a part of progressive idea generation and generating ideas impromptu for the same, particular ideas were also developed for each How might we assertion.
Rapid Idea Generation
After creating the How Might We assertions, rapid idea production was facilitated to develop a huge number of ideas in a short amount of time, free of limits imposed by opinions or perceived limitations. To accomplish so, eight distinct corporations (such as Facebook, Samsung, CapGemini, Elon Musk, Crocs, Forbes, Narendra Modi, and Don Norman) were regarded as the source or point of view from which the ideas will be developed. The researcher chose these entities at random to generate a wide range of ideas because they were all completely different from one another, and this activity served as a remarkably effective technique in producing fresh and innovative ideas that could solve our How Might We statements and ultimately solve our end user's problems.
Hypothesis Statements
Hypothesis Statements were created in order to express the researcher’s ideas, describe the intended outcomes, and direct the trial and validation efforts
Hypothesis Prioritization
These hypothesis statements were then prioritised in order to identify which assumptions should be tested first and which assumptions should be given more priority. This was useful in determining which hypotheses to ship and measure, test, not to test or build, or trash for future project plans
Feature Prioritization
The Moscow feature prioritizing technique was used to prioritize features according to Must Have, Should Have, Could Have, and Won't Have. Where Must Have specifies the features required for the application. Should Have's were features that could be useful if they were merged, could have's were features that could be introduced, and won't have's were features that were not to be included further.
Mental Model
A mental model was created by the researcher to understand how the user approaches to use the product and the mental model was divided in two phases for before voting as well as after voting.
Information Architecture
For KingVote, a complex system integrating blockchain technology, clear and logical information architecture ensured that users could easily understand the voting process, verify their actions, and trust the security of the platform. A well-defined IA not only streamlined the user journey but also contributed to the overall transparency and legitimacy of the voting system.
Sitemap
User Flow Diagram
Low-Fidelity Prototypes
After the feature sets were established, the low-fidelity paper prototypes were built. The visual aspect of the application is not important at this stage, but it was primarily used to check and test the app's functionality because it allows for early user validation of the design concept and feature sets.
Mid-fidelity Prototypes
Moodboard
Styleguide
Hi-Fidelity Wireframes
The Midfidelity Prototypes perepared in the previous stage were then used as a skeleton to create the Hi-Fidelity prototypes followed by the styleguide to design the prototypes. These prototypes served as a bridge between design concepts and the actual implementation, facilitating informed decision-making and fostering a shared understanding of the final user interface.
Feedback testing
By obtaining real-world feedback on prototypes and design iterations, designers could identify usability issues, validate design decisions, and address concerns specific to the integration of blockchain technology. Feedback testing played a pivotal role in refining the user experience, ensuring that the app was not only aligned with user expectations but also compliant with legal and security standards. A summary of the feedback testing is given which demonstrates the views of 3 different particiapants with their highlights as well as the painpoints.
SUS Test (System Usability Scale)
By administering the SUS questionnaire after users interacted with prototypes or the application itself, designers gained valuable insights into the overall usability and user experience. SUS scores helped quantify user satisfaction, identify areas for improvement, and prioritize design enhancements. An average score of 87.5 resulted after calculating the SUS score for each participant.
An average score of 87.5 resulted after calculating the SUS score for each participant.
Hypothesis Evaluation and Validation
This iterative process of hypothesis evaluation ensured that design decisions were grounded in user needs and actual user experiences, leading to a more user-centric and effective blockchain-based voting application.
