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1 – 3 of 3Kiyavash Irankhah, Soheil Asadimehr, Golnaz Ranjbar, Behzad Kiani and Seyyed Reza Sobhani
To effectively combat the increasing rates of obesity, it is crucial to explore how environmental factors like sidewalk access impact weight-related outcomes. This study aimed to…
Abstract
Purpose
To effectively combat the increasing rates of obesity, it is crucial to explore how environmental factors like sidewalk access impact weight-related outcomes. This study aimed to systematically examine the association between sidewalk accessibility and weight-related outcomes.
Design/methodology/approach
Databases were searched by keywords for relevant articles, which were published before March 3, 2024, to report the role of neighborhood sidewalk access on weight-related outcomes. The main findings of the selected articles were extracted from eligible studies by two independent reviewers.
Findings
A total of 20 out of 33 studies indicated a significant negative relationship between access to sidewalks and weight-related outcomes. Three studies demonstrated an indirect relationship between access to sidewalks and weight-related outcomes by greater access to physical environments. In addition, five studies reported no clear relationship, and three studies reported a significantly positive relationship between access to sidewalks and weight-related outcomes.
Practical implications
In general, people who live in urban areas with better sidewalk access benefit from better weight-related outcomes. Adults showed this correlation more prominently than adolescents and children. Therefore, sidewalks that have a positive effect on physical activity levels could be considered as a preventive measure against obesity.
Originality/value
One of the weight-related outcomes is obesity. Every community faces numerous challenges due to obesity, which adversely affects the quality of life and health. Environmental factors such as access to sidewalks could be associated with body weight due to lifestyle influences.
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A large number of studies indicate that coercive forms of organizational control and performance management in health care services often backfire and initiate dysfunctional…
Abstract
Purpose
A large number of studies indicate that coercive forms of organizational control and performance management in health care services often backfire and initiate dysfunctional consequences. The purpose of this article is to discuss new approaches to performance management in health care services when the purpose is to support innovative changes in the delivery of services.
Design/methodology/approach
The article represents cross-boundary work as the theoretical and empirical material used to discuss and reconsider performance management comes from several relevant research disciplines, including systematic reviews of audit and feedback interventions in health care and extant theories of human motivation and organizational control.
Findings
An enabling approach to performance management in health care services can potentially contribute to innovative changes. Key design elements to operationalize such an approach are a formative and learning-oriented use of performance measures, an appeal to self- and social-approval mechanisms when providing feedback and support for local goals and action plans that fit specific conditions and challenges.
Originality/value
The article suggests how to operationalize an enabling approach to performance management in health care services. The framework is consistent with new governance and managerial approaches emerging in public sector organizations more generally, supporting a higher degree of professional autonomy and the use of nonfinancial incentives.
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Weimo Li, Yaobin Lu, Peng Hu and Sumeet Gupta
Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic…
Abstract
Purpose
Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.
Design/methodology/approach
Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.
Findings
This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.
Originality/value
This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.
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