Understanding Personal Productivity

Abstract

Productivity tracking tools often determine productivity based on the time interacting with work-related applications. To deconstruct productivity's diverse and nebulous nature, we investigate how knowledge workers conceptualize personal productivity and delimit productive tasks in both work and non-work contexts. We report a 2-week diary study followed by a semi-structured interview with 24 knowledge workers. Participants captured productive activities and provided the rationale for why the activities were assessed to be productive. They reported a wide range of productive activities beyond typical desk-bound work-ranging from having a personal conversation with dad to getting a haircut. We found six themes that characterize the productivity assessment-work product, time management, worker's state, attitude toward work, impact & benefit, and compound task-and identified how participants interleaved multiple facets when assessing their productivity. We discuss how these findings could inform the design of a comprehensive productivity tracking system that covers a wide range of productive activities. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI.

Today Was a Good Day: The Daily Life of Software Developers

“…Positive affective states are proxies of happiness and were previously shown to have a positive effect on developers' problem solving skills and productivity [1], [2], [4], [27]. Similarly, aspects of the job that motivate developers or tasks that bring them enjoyment were also shown to lead to higher job satisfaction and productivity [28], [29]. Self-reported satisfaction levels of knowledge workers [30], and more specifically, self-reported affective states of software developers [3], have further been shown to be strongly correlated with productivity and efficiency.…”

TimeToFocus

“…Participants in the notification condition made significantly shorter switches (M=4.76s, SD=1.65s) than those in the control condition (M=7.13s, SD=3.05), U(24, 23) = 406, p < .01, r =.44. The number of switches per trial was on average 10.6 in both conditions, and there was no significant difference in number of switches between conditions, U (24,23)=243, p = .60. As there were ten codes to be entered per trial, this suggests participants switched once for every piece of data entered.…”

“…Participants could complete the study at any time. Though people may receive more email during working hours, the blurring boundaries of people's working hours [23] and when people receive and manage email [9] make it difficult to control for the frequency of email across participants. Future work could examine whether time of day and frequency of incoming email affect people's self-interruption behaviour.…”

Productivity metrics in the context of knowledge work: literature vs practice

“…Finally, regarding platforms and applications for automatic monitoring of labor productivity, some interviewees report the use of a variety of them, such as Jira, Trello, Cronowork, Monday and Web of Project, as well as techniques and methodologies such as Function Points, Balance Scorecard, Expert Judgment and Agile Methodologies, while others report using self-developed tools. In this regard, Kim et al (2019) emphasize several restrictions that automatic productivity tracking platforms present, such as that these do not allow capturing a diversity of productive activities that people perform without devices, and consequently, the activities recorded in such applications do not reflect the real productivity of the person. Therefore, their use would be more oriented as a support tool that provides information on certain relevant aspects that can be incorporated into different productivity monitoring strategies.…”