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210604 hci seminar long term use
1. “long term use”
of digital products
June 4th, 2021.
Byounghern Kim
CDE 20208002 1
2. Longitudinal method on UX Assessment
• Qualitative approach
“User experience overtime: an initial
framework”
• Quantitative approach
“Understanding usage style transformation
during long-term smartwatch use”
The Datacatcher
Smart speakers
2
3. • Diary methods for self-reporting experiences during field studies
• Experience sampling method (ESM) for self-reporting during field studies
• Day reconstruction method (DRM) – story-telling to reveal the
meaningful experiences during field studies
• AttrakDiff questionnaire for overall UX evaluation
• User experience questionnaire (UEQ) (available in several language
versions)
• Ladder interviews – e.g. to find out attitudes or values behind behaviour or
experience
• Holistic user experience (HUX) identifying the relevant product factors for
holistic user experience
Longitudinal method on UX Assessment
3
4. User experience over time:
an initial framework
4
“Can users’ experiencesbe articulatedin distinctphasesin the adoption
of the product?”
6 participants
iPhone
1 + 4 Weeks
Axial coding
Open coding
Quantitative analysis
Day Reconstruction
Method
Good afternoon everyone, This is the presentation about “Long tern use or digital products”,
Given three papers are quite interesting. I organized this presentation as shown, the “Qualitative approach” paper , “Quantitative approach” paper, and the digital products.
Although The datacatcher (batch production; batch deployment) approach, I tried matching the factors of each product on framework because I want to focus the framework For the data catcher and famous digital products which is smart speaker,
Before starts, There are many methods utilized to uncover how a person perceives a system before, during and after interacting with it.
In this presentation, both DRM and AttrakDiff methods are applied. If you get any interest about it, try searching keywords of UX Assessment.
The author, Evangelos Karapanos, address three questions and one of them is “Can users’ experiences be articulated in distinct phases in the adoption of the product”
This paper presents an in-depth, fiveweek ethnographic study that followed 6 individuals during an actual purchase of the Apple iPhone. During 1+4 Weeks participants experiences are collected thru DRM, any they analyze with several steps to distinct phases.
As a result, they figured out “~~~~” “~~~~” “~~~~” “~~~~” phases based on the charts below.
the forces “familiarity, functional dependency, and emotional attachment” are found as well. These forces motivate the transition of users’ experience across three phases in the adoption of the product: orientation, incorporation, and identification
They defined major factors of each phase,
~~ ~~ for orientation,
~~ ~~ for incorporation,
~~ ~~identification.
Anticipation
the act of anticipating an experience resulting in the formation of expectations, happens prior to any actual experience of use
Orientation refers to users’ initial experiences that are pervaded by a feeling of excitement as well as frustration as we experience novel features and encounter learnability flaws.
Incorporation
we reflect on how the product becomes meaningful in our daily lives.
Identification
as we accept the product in our lives, it participates in our social interactions, communicating parts of our self-identity that serve to either differentiate us from others or connect us to others by creating a sense of community
On the other hands, there’s quantitative approach such as “~~~”.
In their paper, data-driven approach, they have aggregated data points collected from 81 individual Android smartwatches’ notification, screen and battery sensors, to 468 unique 30-day usage characteristics that highlight the differences of individuals smartwatch usage behaviour.
They have created 33 unique usage behaviour types using a k-means clustering approach, each average from samples over a 30-day time period. The daily behaviour variance within the 30-day segments is low enough to warrant generalising the behaviour within the specified time period
Exploratory behaviours: 10 behaviours decreased in popularity over time.
Accepted behaviours: 16 behaviours became increasingly popular over time
By the quantitative analysis, they validate previous assumptions.
They had identified two distinct types of usage behaviours, exploratory and accepted behaviours, and initially analysed how users transition between these behaviours. Users tend to explore new usage behaviours (new ways to use their technology), and if they fall back to previously used behaviours, they tend to favour the most recent behaviour (one prior to change).
After separating elicited usage behaviours to accepted and exploratory, we reveal 24 key usage differences between the behaviours. The accepted behaviours tend to be affiliated with less active use, showcasing fewer daily usage sessions, less notifications and shorter longitudinal usage streaks and phases. In general, the accepted smartwatch usage behaviour tends to be more passive.
The way different users enable themselves to use more complex features on their smartwatches is highly individual, thus from our findings we cannot offer comprehensive design guidelines for increasing user engagement and remains an interesting challenge for future work.
Datacatchers are custom-built, location-aware devices that stream messages about the area they are in.
The messages simultaneously draw attention to the sociopolitical topology of the lived environment and to the nature of big data itself.
