Positive Psychological Well-Being at Work: The Role of Eudaimonia

Kinderen, Susan der; Khapova, Svetlana N.

doi:10.1007/978-3-030-30025-8_79

Cited by 5 publications

(8 citation statements)

References 116 publications

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“…The two conceptualizations of hedonic and eudaimonic wellbeing can also be found in studies of employee PWB, where the concept of job satisfaction has been termed a hedonic measure since it captures employees' pleasure or enjoyment derived from having or doing a job, i.e. subjective reports of positive affect (related to hedonia, i.e., feeling good, happy, and satisfied) (Rothausen et al, 2012;der Kinderen and Khapova, 2021). The eudaimonic perspective, on the other hand, examines the presence of factors that support outcomes related to one's skill development, growth, sense of contribution, personal relationships and the construction of life in a general sense (Rothausen et al, 2012;Budd and Spencer, 2015).…”

Section: Role Of Workplace Psychological Well-beingmentioning

confidence: 99%

Beyond productivity and efficiency: design tools, methods and frameworks for psychological well-being, and (un)exploited potentials to assist employees at work

Dhiman,

Nemoto,

Fellmann

et al. 2023

Front. Comput. Sci.

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Research has repeatedly demonstrated that fostering employees' psychological well-being is linked to several positive outcomes, both for the employee and the organization, and yet, it has rarely been considered as a design goal, especially when it comes to developing digital solutions to assist employees in their everyday tasks. In this paper, we take the first steps toward using well-being concepts to (re)design assistants. We motivate the problem by providing an overview of assistance technologies in the industrial context and their current state of development. Next, we elucidate and map the facets of psychological well-being at work, and highlight the importance of workplace eudaimonic well-being. To help designers and researchers adopt it as a design objective, we carried out a literature review to synthesize the state-of-the-art frameworks and methods that have been proposed to incorporate psychological well-being into design. Our investigation reveals that no one framework targets eudaimonic well-being, and more work may be necessary to develop a comprehensive approach that targets the various facets of workplace eudaimonic well-being. Consequently, we discuss challenges and opportunities for developing digital assistance that could foster employees' psychological well-being.

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Section: Role Of Workplace Psychological Well-beingmentioning

confidence: 99%

Beyond productivity and efficiency: design tools, methods and frameworks for psychological well-being, and (un)exploited potentials to assist employees at work

Dhiman,

Nemoto,

Fellmann

et al. 2023

Front. Comput. Sci.

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“…A recurrent neural network was initially used to classify six different emotions. Their performances were then compared to multivariate linear regression (MLR) and support vector machine techniques [12] . Machine learning algorithms have been proven to be effective and reliable for opinion mining as well as sentimental classification [13].…”

Section: Previous Researchmentioning

confidence: 99%

“…Machine learning algorithms have been proven to be effective and reliable for opinion mining as well as sentimental classification [13]. The use of emotion recognition has been so far used in different applications [10] [9] [12]. Unfortunately, emotional states of employees and it's impacts have not significantly been highlighted in most researches.…”

Section: Previous Researchmentioning

confidence: 99%

“…This is called additive strategy. The entire process can be broken as follows [12]. In every step, the residual gets calculated: hm(x) = yFm(x) here hm(x) can be any model.…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

“…If is small and M is large enough then sm will be the minimum value of L 's position. XGBoost also has incredible features [12] making it one of a kind that includes Handling sparse data: Missing values or data processing steps like one-hot encoding can render data sparse. XGBoost implements a sparsity-aware algorithm for split finding that can take care of various sparsity patterns in the data.…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

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Multi-modal Emotion Recognition for Determining Employee Satisfaction

Hussain¹,

Zaman²,

Zaman³

et al. 2021

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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Emotion Detection has been very popular in the field of research for a couple of years. In the past, emotion recognition has been studied and applied in order to detect the overall emotional state of a person using individual modalities such as facial recognition from images. However, in order to ensure the authenticity of the real time emotional state detected from the data that is received, it is required to use multiple modes. In our research, we have classified emotional states into six specific entities which are: Happiness, Sadness, Neutral, Disgust, Anger and Surprise. The real time emotional state of the candidate is classified into one of these entities according to the candidate's response. We have used two important modes to detect the real time emotion of the candidate, Emotion recognition from facial expression as well as emotion recognition from sentimental analysis. For our research, we have mainly used the Convolutional Neural Network(CNN). We have trained and tested both the facial recognition and sentimental analysis datasets with all the six entities. Therefore, results can be obtained from both the modes in order to justify the candidate's emotional state in real time. The two separate results from the individual independent artificial neural networks are then fed into a machine learning algorithm called Support Vector Machine (SVM) so that the final emotional state can be achieved. Our goal is to apply this multimodal emotion detection technique on employees in various offices and workplaces by asking them questions regarding their work so that their genuine emotions can be obtained from their answers. This is important because in this way, employee satisfaction in workplaces can be recognized which is vital for mental health as well as productivity. In fact, the mental health of the employees not only affects their individual well-being but it affects the overall productivity and environment of the workplace. In order to improve certain aspects of a workplace for better performance along with employee satisfaction and productivity, determining the emotional condition of each employee is vital.

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Intelligent Assistants

et al. 2022

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Intelligent assistants are an increasingly commonplace class of information systems spanning a broad range of form and complexity. But what characterizes an intelligent assistant, and how do we design better assistants? In the paper, the authors contribute to scientific research in the domain of intelligent assistants in three steps, each building on the previous. First, they investigate the historical context of assistance as human work. By examining qualitative studies regarding the work of human assistants, the authors inductively derive concepts crucial to modeling the context of assistance. This analysis informs the second step, in which they develop a conceptual typology of intelligent assistants using 111 published articles. This typology explicates the characteristics (what or how) of intelligent assistants and their use context (who or which). In the third and final step, the authors utilize this typology to shed light on historical trends and patterns in design and evaluation of intelligent assistants, reflect on missed opportunities, and discuss avenues for further exploration.

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Positive Psychological Well-Being at Work: The Role of Eudaimonia

Cited by 5 publications

References 116 publications

Beyond productivity and efficiency: design tools, methods and frameworks for psychological well-being, and (un)exploited potentials to assist employees at work

Beyond productivity and efficiency: design tools, methods and frameworks for psychological well-being, and (un)exploited potentials to assist employees at work

Multi-modal Emotion Recognition for Determining Employee Satisfaction

Intelligent Assistants

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