Course Project: Conduct a study of personal information behavior using autoethnographic methods introduced in class. Throughout the semester, develop four deliverables that will constitute your term project.

1. Proposal (Assignment 1) Submit a short (~500 words) study proposal; provide a problem statement (what you are investigating and why), describe methods you will use to collect (e.g. online diary, photo diary, blog, fitbit app log, etc.) and analyze (e.g. statistics, content analysis) data.

2. Environmental Scan (Assignment 2) Review multi-disciplinary scholarly literature related to your study. Summarize three most important articles that would illustrate your understanding of the existing knowledge on the topic/concept you’re investigating, the problem you’re solving, and/or the methods you’re using.

3. Data Analysis (Assignment 3) Analyze your study data and report your findings in a ~ 800 words (1.5 p) paper. You can use numbers and visualizations, but you should also include a textual explanation for your numbers and figures.

4. Discussion of Findings and Conclusion (Assignment 4) Write a short paper discussing your study findings in light of existing knowledge (literature, similar projects, theories/models) that helps to explain patterns in your behavior. If you cannot find a theory/model that explains your findings, come up with your own model/theory; explain how your model/theory can be used in broader information interaction contexts; explain practical implications/significance of your findings. Outline your study limitations and directions for future work.

Arushi Jaiswal LIS 608-01, Summer 2018 Assignment 4 – Findings and Conclusion Introduction

Being a millennial, I like to have choices in today’s information saturated world. But off-late, I

have been feeling overloaded with it. With a numerous amount of options, from Google to

Facebook, Medium to Quora, whenever I am in front of a screen, I am constantly consuming

information. While I have ease of access to it, I don’t find myself productive and am often

distracted from low priority information on the internet.

This project is based on the common problem of Information Overload and multitasking in

today’s digital era. It occurs when we are trying to deal with more information than we are able

to process to make sensible decisions.

In order to understand my information consumption pattern, I conducted a study to map my

interactions and timings around the two primary screens I interact with on a daily basis -

desktop and mobile. I recorded this personal information behavior using autoethnographic

methods over a span of two weeks. The data collected was then analyzed and the findings were

linked to existing HII models.

Methodology

Over a span of two weeks (Tuesday, May 29th to Thursday June 14th), total screen time including

app (mobile) and website (desktop) usage was recorded using three plug-ins: Moment, Usage

and Webtime Tracker. Additionally, a daily unstructured dairy was recorded by the actor. This

diary included daily productivity satisfaction level, feelings regarding the screen time spent and

task completion.

Data Collected

Mobile - The study was conducted for a total of 17 days. Out of which, the only day this data

capturing attempt was unsuccessful was on Friday, June 8th due to an app glitch. That makes it a

total of 16 days. Summary of the data collected – The total number of minutes spent on mobile

during these sixteen days were 2251 minutes (13 hours 31 minutes) making it an average of 2

hours 20 minutes on mobile per day.

Figure 1 – Graph showing a daily record of minutes spent by user on mobile per day

I also quantified the number of times I “picked up” my phone (i.e. the action where I unlock my

phone to see its content). During the span of these 16 days, I picked up my phone a total of 833

times, averaging to 52 pickups a day. Illustrated below is a detailed chart of each day –

Figure 2 – Graph showing a daily record of minutes spent by user on mobile per day compared with number of Pick-ups per day

Another thing to note about my mobile data is that it was used for leisure browsing, social

media and communication. The data collected was based on the assumption the mobile device

was used for the three uses mentioned above, and not work. I also tended to use it while I was

on my desktop, during breaks. During the data collection period, I used a total of 36 apps (see

Figure 3). Out of which, the most used were –

• Instagram (17%), Facebook (16%) for Social;

• WhatsApp (14%), Messages (11%), FaceTime (7%), Phone (3%) for Communication;

• Podcast + Audible (7%), YouTube (7%), Bumble (7%), Others (5% - News, Camera,

Weather, Games, etc.) for Leisure.

Figure 3 – Pie chart showing percentage of Mobile Apps used in 16 days Desktop – The total number of minutes spent on desktop during the sixteen days were 3754

minutes (62 hours 34 minutes) making it an average of 3 hours 54 minutes on desktop per day.

Out of these 3754 minutes, I spent 2012 minutes (33 hours 32 minutes) on Google Chrome and

1742 minutes (29 hours 1 minute) on other desktop apps (like Principle, Sketch, Slack, Preview

etc.), the latter being completely work related.

Figure 4 – Pie chart showing percentage of different apps on Desktop

To obtain the data on Google Chrome (Web), I used another plugin to determine the time spent

on each website. A total of 327 websites visited. These websites were later categorized into 4

distinct categories (see Figure 5):

1. Work – Websites used for the purpose of research for tasks and assignments, Google

Drive for creating documents and slides, online prototyping like Invision.

2. Mail – Since I currently have 4 active email accounts through work, school and

fellowship, managing email is tedious and does not exactly categorize under work.

3. Growth – Websites used for external reading for knowledge in design, portfolio making,

skill building, reading, news.

4. Distraction/Leisure – Websites that don’t contribute to growth or work but are

primarily used for random browsing for breaks or distraction like YouTube, Quora, etc.

Figure 5 – Graph showing percentage of time spent on different categories (divided in 4 categories) Out of the 2012 minutes spent on Google Chrome, I spent 48% of the time working i.e. 958

minutes (15 hours 57 minutes), 15% of the time using mail i.e. 299 minutes (4 hours 58

minutes), 15% of the time on growth 299 minutes (4 hours 59 minutes), and 23% of the time

on leisure or being distracted i.e. 456 minutes (7 hours 36 minutes).

Dairy – I maintained an informal journal, where I wrote a paragraph about my day and marked

my days as satisfactory or unsatisfactory. In order to collect data, I picked the top 4 phrases

from each day and kept the satisfaction marking.

Figure 6 – Table showing Top 4 phrases used per day

To make sense of the dairy data and see patterns from activity, I combined all of them and

created a chart to observe what activities make a day productive or not (see Figure 7).

Figure 7 – Detailed graph chart comparing all the activities (time) on every screen and marked a Satisfactory/Unsatisfactory

Findings Higher mobile and desktop device usage leads to stress despite the productivity level and

vice-versa

I observed that the days I browsed erratically on desktop (indicated by high web usage minutes

in distraction and/or growth category) and mobile (indicated by high usage in mobile minutes),

were also the days I felt “overwhelmed” by information, “stressed”, “anxious” and “aimless”.

Whereas the days I focused more on work and didn’t browse erratically, I felt that I had a

“sense of direction”. I felt “inspired”, “excited” and “curious”.

For example on Tuesday, 5/29/18 the top 4 phrases described the day as “anxious,

overwhelmed, unproductive and aimless” and it was ultimately marked as unsatisfactory. The

usage on each screen is above the average of the days in total. On an average I spend 19

minutes on growth as opposed to 44 minutes in which I went through 25 websites giving myself

about 0.6 minutes for each. Similarly, on an average under the leisure/distraction category I

spend 28 minutes but on 5/29/18, I spent 50 minutes during which I went through 23 websites

giving myself about 0.56 minutes per page.

Figure 8 – Table showing total number of activities quantifiable by minutes for 5/29/18

A good day’s example would be Wednesday 6/6/18. The top 4 phrases described the day as

“inspired, productive, focused and fulfilling deadlines”.

Figure 9 – Table showing total number of activities quantifiable by minutes for 6/6/18

Levitin (2015) states that even though we think we’re doing several things at once, multitasking

is an illusion. He further quotes Earl Miller, a neuroscientist at MIT and one of the world experts

on divided attention, stating that our brains are “not wired to multitask well… When people

think they’re multitasking, they’re actually just switching from one task to another very rapidly.

And every time they do, there’s a cognitive cost in doing so.”

He states that even though we think we’re getting a lot done by multitasking, we’re actually

being less efficient.

In fact, he further explains how multitasking has been found to increase the production of the

stress hormone cortisol and adrenaline, which can overstimulate your brain and cause “mental

fog or scrambled thinking”. He states that multitasking creates a “dopamine-addiction

feedback loop”. In this loop dopamine addiction rewards the brain for losing focus and for

constantly searching for external stimulation.

He calls multitasking “the ultimate empty-caloried brain candy”, because instead of making an

effort for bigger tasks which can be achieved from sustained, focused effort, we tend to work

on completing more and more smaller, low effort ones.

This brings me to my second finding – Rapid new information browsing limits productivity. I

felt more overwhelmed and stressed the days I had higher “growth” category on the web. The

reason for that being, I was jumping from topic to topic, tab to tab with new information on

how to grow as a designer. This knowledge revolved around coding, design tools etc.

Consuming more information like this made me more aware of what I can do, but stressed me

out because I couldn’t do it instantly. I was further stressed because I couldn’t focus on the high

priority tasks of the day which were just a tab away.

I also tended to have a higher number of mobile pickups on the days I’m stressed, anxious,

overwhelmed etc. This has got more to do with instant gratification through social media.

Sapolsky (2017) talks about the idea of the “Magic maybe” – i.e. the act of looking into our

phone and maybe there’s a text there or maybe there’s not. When it does show up we get a

great spike in dopamine. But the feeling of that pleasure disappears quickly after it comes.

Therefore, it’s easy to get in as Levatin (2015) stated a dopamine induced loop. Dopamine starts

us seeking, then we get rewarded for the seeking which makes us seek more. It becomes harder

and harder to stop looking at email, stop texting, or stop checking our cell phone to see if we

have a new notification.

User Multi-tasking Model

Based on my research and findings, I put together a model to better communicate the research.

In this model the “User” has two networks of attention system where only one state is active at

a time – Central Executive System (task positive) and daydreaming mode (task negative) which

is the default state of the brain. Switching between these states is possible and can be induced

through various activities which Levitan talked about during his interview with O’Callaghan

(2014). Using any one of these systems, the user consumes information through an

overwhelming amount of media which leads to multi-tasking and ultimately instant

gratification causing Information Overload. Once the user has the information from all the

different channels after multitasking, it tends to get mix-matched causing stress leading to

confusion and overload.

Figure 10 – HII Model on User Multi-tasking

References

Crawford, Walt. (2004). May I have your Attention, Please? EContent, 27(7/8), 42-43.

Koltay, T. (2017). The bright side of information: Ways of mitigating information

overload. Journal of Documentation,73(4), 767-775. doi:10.1108/jd-09-2016-0107

Levitin, D. J. (2015, January 18). Why the modern world is bad for your brain. Retrieved from

https://www.theguardian.com/science/2015/jan/18/modern-world-bad-for-brain-daniel-j-

levitin-organized-mind-information-overload

Microsoft Attention Spans Research Report. (2015). Retrieved June, 2018, from

https://www.scribd.com/document/265348695/Microsoft-Attention-Spans-Research-

Report#download

O'Callaghan, T. (2014). It's all too much!. New Scientist, 223(2982), 26-27.

Sapolsky, R. (2017, February 21). Chasing Dopamine: Porn, Social Media, and Alcohol. Retrieved

from http://bigthink.com/videos/steven-kotler-on-addictions-and-dopamine