Understanding Personal Productivity: How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity

Young-Ho Kim
Published in
6 min readApr 10, 2019

This article summarizes a paper authored by Young-Ho Kim, Eun Kyoung Choe, Bongshin Lee, and Jinwook Seo, reporting a collaborative research by Seoul National University, University of Maryland, and Microsoft Research. This paper will be presented at CHI 2019, a conference of Human-Computer Interaction, on Tuesday 7th May 2019 at 09:00 in the session Knowledge Work held in room Boisdale 1.

A 30-sec video preview for this study

“What’s your working definition of productivity and what makes you feel more productive?”

To explore these questions, we observed 24 knowledge workers’ everyday activities to understand how they conceptualize personal productivity and the rationale behind assessing the productivity. In this article, we invite you to learn more about the multi-faceted nature of productivity assessment consisting of six themes–work product, time management, worker’s state, attitude toward work, impact & benefit, and compound task–in addition to a wide range of reported productive activities beyond typical desk-bound work. We also provide implications for designing better self-tracking technologies in terms of customizations and personalization.


Boosting productivity is important for knowledge workers including software developers, writers, researchers, and designers, and self-tracking personal productivity is a common technique that people adopt to improve their productivity. In fact, we can find many productivity tracking apps and services, such as Moment and RescueTime, on the market.

However, existing tools are usually not sufficiently designed to capture the diverse and nebulous nature of individuals’ activities: For example, screen time trackers such as RescueTime does not support capturing work activities that do not involve digital devices. Although the specifics of individuals’ day-to-day activities vary, existing technologies track activities that are easy to capture; for example, the usage duration of each application or the device. As the distinction between work and life has become fuzzy, we need a more holistic understanding of how knowledge workers conceptualize their productivity in both work and non-work contexts. Such knowledge would inform the design of productivity tracking technologies.

In this article, we present twofold insights learned from our study:

  1. The concept of how knowledge workers delimit productivity-related activities and evaluate their productivity.
  2. Implications for designing comprehensive productivity tracking tools that cover a wide range of activities.

The Research

In this study, we conducted a diary study, a data collection method for capturing in-situ data on events and experiences in an ecologically valid way.

Screenshots of the diary app used in this study, built with OmniTrack.

Goal: To understand how people perceive and evaluate the productivity of their activities.

Participants: 24 knowledge workers with 11 distinct occupations.

Method: We deployed a mobile diary to be used for two weeks (10 working days + 4 voluntary weekends). After each productive activity, participants recorded the following information:

  • The timestamp and duration of the activity
  • Task types (e.g., paperwork, email, meeting)
  • Perceived productivity level
  • A rationale for the productivity assessment (why a certain activity was rated “very productive”)

We encouraged participants to freely define their own meaning of productivity along with its details and criteria.

Analysis: The journal entries were analyzed qualitatively using Thematic Analysis and iterative categorization.

How Should We Design Productivity Monitoring Tools?

1. Productivity is a multifaceted concept, rather than a homogeneous one.

We identified six themes of productivity that participants consider when evaluating their productivity. The way people assess productivity was more diverse and complex than we thought.

Sometimes, like the traditional concept of productivity, work product and time spent was analogous to the level of productivity.

“Answered all the emails in the inbox before the time to go home!”
— very productive [P7, project manager]

Interestingly though, participants also reflected on conceptual achievements.

“Although it [seminar] was outside of my research area, it inspired me to reflect on my research directions.” — productive [P11, graduate student].

Other times, a predisposition to a task — whether the task is significant or trivial — had a strong impact on the productivity perception.

“It was just a Monday weekly meeting, rarely meaningful to me.” — neutral [P21, UI designer]

Many of the attributes of productivity we identified are not well reflected in current productivity tool designs. For example, existing productivity tools rarely capture work product, even though this is one of the two defining factors of productivity (i.e., “output”) according to the traditional concept. We suggest that productivity should be treated as a multifaceted concept, rather than a homogeneous concept that can be represented with a single variable.

2. Consider a wide range of tasks and contexts

Participants produced 183 unique task names, which were categorized into 13 task types. Focusing on the fact that a large portion of knowledge work involves digital devices, existing productivity tracking tools have focused primarily on the device usage time. However, our result shows that many work activities do not involve digital devices (e.g., business meetings, reading papers), and even non-work activities can also be considered productive. For example, some participants considered meaningful conversations to be productive.

“Talked with my colleague about why I wanted to resign from the company, what was tough for me and what I couldn’t resolve here … I thought I should consider more alternatives including different types of startup companies” — very productive [P22, UI designer]

In addition, personal matters can make non-work activities to be as important as work activities. P22 from the above quote was thinking about switching her job. Consequently, she valued activities such as updating resume and portfolio much more than the tasks for her primary work.

Therefore, to design comprehensive productivity tracking tools, we should recognize that productivity-related activities may occur in both work and nonwork contexts, at any time, outside the typical work hours and traditional work settings.

3. Tracker Customization and Personalization

There were large individual differences in task names, criteria of productivity levels, and prioritization of the attributes of productivity. This indicates the need and opportunity for customizing productivity trackers to fit an individual’s context and preference. Because our diary study had a format of manual tracking, participants were able to capture diverse “analog” activities. However, participants also reported that automated methods would be more accurate for capturing some attributes: e.g., how much time they were distracted on Facebook. Therefore, properly balancing manual and automated capture methods would be important for designing productivity monitoring tools that can depict accurate snapshots of a user’s productive activities. More practically, we recommend Semi-automated tracking as a promising approach for this. People can leverage flexible tracker creation platforms like OmniTrack to build semi-automated productivity trackers.


In this article, we introduced a diary study conducted to understand how knowledge workers conceptualize their personal productivity. From the data, we distilled rich contexts and various aspects that affect the perception of productivity, which are broad and highly personalized. We hope this study can help others working in the field gain insight regarding the ways to better support comprehensive tracking of personal productivity.

Full Citation:
Young-Ho Kim, Eun Kyoung Choe, Bongshin Lee, and Jinwook Seo. 2019. Understanding Personal Productivity: How Knowledge Workers Define, Evaluate, and Reflect on Their Productivity. In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3290605.3300845

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Young-Ho Kim
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Human Computer-Interaction Postdoc Researcher @ Seoul National University