Leisure time and labor productivity: a new economic view rooted from sociological perspective

Cui, Dan; Wei, Xiang; Dian-ting, WU; Cui, Nana; Nijkamp, Peter

doi:10.5018/economics-ejournal.ja.2019-36

Cited by 10 publications

(8 citation statements)

References 68 publications

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“…The last independent test from this study, we found that the working hour variable had a positive and significant relationship to the labor productivity variable as indicated by the Pearson Chi-Square p-value of 0.000. This finding is in line with and supports the results of a study conducted by Cui et al (2019); Man & Ling (2014) and Lubis (2014) which found that working hours had a significant effect on labor productivity, the more hours worked the higher the productivity of the workforce and vice versa.…”

Section: Simultaneous Test Of Model Estimation Resultssupporting

confidence: 91%

“…Finally, the working hour variable with the category of more than 7 hours per day has a significant effect and has a greater opportunity to increase labor productivity, which is 0.251 compared to less than 7 hours per day. These findings are in line with and support the findings of study conducted by Cui et al (2019); Man & Ling (2014) and Lubis (2014) which found that working hours had a significant effect on labor productivity.…”

Section: Independent Test Between Independent and Dependent Variablesupporting

confidence: 92%

See 1 more Smart Citation

Identifying Factors Influencing the Labor Productivity of SMEs in South Sumatra

Atiyatna

Bashir

Hamidi

2021

JEP

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This study is to investigate the effect of human capital, labor domicile, gender, and working hours on the productivity of MSME workers in South Sumatra. The data used are primary data, data collection with a purposive sample approach as many as 196 samples as workers in SMEs. This study applies a logistic regression approach. The findings of this study indicate that independent tests, human capital, labor domicile, gender, and working hours have a significant relationship to labor productivity. Likewise, jointly the predictor variables such as junior high school, Local workers, male, and working hours of more than seven hours have the opportunity to get higher productivity than other categories in SMEs.

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Section: Simultaneous Test Of Model Estimation Resultssupporting

confidence: 91%

Section: Independent Test Between Independent and Dependent Variablesupporting

confidence: 92%

Identifying Factors Influencing the Labor Productivity of SMEs in South Sumatra

Atiyatna

Bashir

Hamidi

2021

JEP

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“…Celbis and Turkeli had shown that at the country level, innovation exhibits diminishing returns on work hours [50]. A similar relationship between leisure time and productivity in general is observed by Cui et al [51]. We also assume that innovative performance does not increase linearly with respect to the number of work hours, but that there is some sort of inverted U-shape relationship.…”

Section: Theoretical Backgroundmentioning

confidence: 58%

Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

et al. 2021

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This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.

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“…The income domain is concerned with decision making power over the allocation of income whereas leadership concerns one's ability to lead e.g., in common interest groups and religious organizations. Lastly, the time domain is about allocation of one's time between farm activities and non-farm activities that may be high paying as well as leisure that has been found to have a positive and significant effect on labor productivity ( Cui et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%