Individual differences limit predicting well-being and productivity using software repositories: a longitudinal industrial study

Kuutila, Miikka; Mäntylä, Martti; Claes, Maelick; Elovainio, Marko; Adams, Bram

doi:10.1007/s10664-021-09977-1

Cited by 10 publications

(5 citation statements)

References 93 publications

(94 reference statements)

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“…The well-being, happiness, and work settings of software professionals have a remarkable effect on the quality of the output, their motivation, and hence on the success of the whole software development process ( Nakata, 2017 , Nieminen, 2019 , Sharp et al, 2009 ). The prediction of the software developers’ well-being could be possible through individual prediction models ( Kuutila et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Software professionals during the COVID-19 pandemic in Turkey: Factors affecting their mental well-being and work engagement in the home-based work setting

Tokdemir

2022

Journal of Systems and Software

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Section: Introductionmentioning

confidence: 99%

Software professionals during the COVID-19 pandemic in Turkey: Factors affecting their mental well-being and work engagement in the home-based work setting

Tokdemir

2022

Journal of Systems and Software

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“…Perhaps even more important, the lack of association can be due to high variability within subjects for these particular data that is, individual differences. The importance of individualised studies has been recently highlighted in a study to predict developers' wellbeing and productivity [53].…”

Section: Results From Stagementioning

confidence: 99%

Profession‐centric measures and indicators for occupational stress: An empirical study with novice software developers in Mexico

et al. 2022

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Occupational stress refers to job-related uneasiness and anxiety, which affect people's emotional or physical health. Although occupational stress has been studied in several industries, it has remained largely unexplored in software developers, particularly in emerging economies such as Mexico. In this work, we propose a set of measures for supporting the assessment of occupational stress in software developers, which concurs with the types of tasks performed by software developers daily. For this, we first identified several stressors found in the literature. Then, we carried out 10 semi-structured interviews with novice software developers to further understand stressors at the workplace. Afterwards, we conducted a study with 30 novice software developers for associating stress with workplace measures. From our work, we identified the workload, mental work fatigue, and work distraction as relevant measures associated with a certain level of stress in software developers. Our results suggest that some indicators can be used to monitor measures that are associated with occupational stress in software developers. | INTRODUCTIONThe software industry in Mexico has been growing steadily for the last two decades. It has several strengths, which make it attractive for investment in the global market. According to A. T. Kearney, a global consulting firm, Mexico ranks 8th for providing information technology services worldwide [1]. In the U.S. from 1997 to 2012, software industry production grew from $149 billion to $425 billion USD. This growth outpaced the rest of the economy, the software industry's direct share of U.S. Gross Domestic Product increased from 1.7% to 2.6% [2]. This growth was also reflected in Mexico where software companies hired thousands of new professionals until 2017, as reported in [3]. Several news reports have dubbed the city of Guadalajara as the Mexican Silicon Valley [4][5][6][7]. However, Mexico tops the world ranking in occupational stress, above China and the U.S. [8]. Symptoms of occupational stress are physical and psychological for the worker and are present at the organizational level. Workers can present cardiovascular and muscular disorders, increased blood pressure, insomnia, lack of appetite, flu, chronic pain, rheumatoid arthritis, etc., among others. They can also present psychological symptoms such as excessive worrying, anxiety, anger, frustration, inability to make decisions, mental blockage, bad mood, frequent forgetfulness, drug and alcohol addictions, depression, low self-esteem, and deterioration of interpersonal relationships. Regarding the organization level, the symptoms are decreased performance and productivity, absenteeism, increased costs due to illness, high job turnover, lack of cooperation between colleagues, higher rate of errors, accidents, and incidents [9].Software developers, in particular, show at least one symptom of occupational stress, and at least 60% of them have high levels of stress [10]. Thus, there are psycho-social risks associated with software ...

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“…Huang et al [46], Kim et al [47] focused specifically on speech features. Regarding work-related environments, Kuutila et al [48] used software repositories to predict well-being without collecting audio data. Izumi et al [49] took a multi-modal approach including audio and speech data.…”

Section: Individual Well-being Data Analysismentioning

confidence: 99%

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Zeulner,

Hagerer,

Müller

et al. 2024

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Current methods for assessing individual well-being in team collaboration at the workplace often rely on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal was to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We focus on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to explore which features are predictors of self-reported satisfaction scores. We applied the toolchain to a case study, where we collected videos of 20 teams with 56 participants collaborating over a four-day period in a team project in an educational environment. Our audiovisual speaker diarization extracted individual speech features from a noisy environment. As the dependent variable, team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores were predicted using speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain was able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

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Individual differences limit predicting well-being and productivity using software repositories: a longitudinal industrial study

Cited by 10 publications

References 93 publications

Software professionals during the COVID-19 pandemic in Turkey: Factors affecting their mental well-being and work engagement in the home-based work setting

Software professionals during the COVID-19 pandemic in Turkey: Factors affecting their mental well-being and work engagement in the home-based work setting

Profession‐centric measures and indicators for occupational stress: An empirical study with novice software developers in Mexico

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

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