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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.

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.

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.

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.

President Trump's tenure was accompanied by a series of protectionist measures that intended to reinvigorate US-based production and make manufacturing supply chains more “local”. Amidst these increasing institutional pressures to localise, and the business uncertainty that ensued, this study investigates the extent to which manufacturers reconfigured their supply bases.

Bloomberg's Supply Chain Function (SPLC) is used to manually extract data about the direct suppliers of 30 of the largest American manufacturers in terms of market capitalisation. Overall, the raw data comprise 20,100 quantified buyer–supplier relationships that span seven years (2014–2020). The supply base dimensions of spatial complexity, spend concentration and buyer dependence are operationalised by applying appropriate aggregation functions on the raw data. The final dataset is a firm-year panel that is analysed using a random effect (RE) modelling approach and the conditional means of the three dimensions are plotted over time.

Over the studied timeframe, American manufacturers progressively reduced the spatial complexity of their supply bases and concentrated their purchase spend to fewer suppliers. Contrary to the aims of governmental policies, American manufacturers increased their dependence on foreign suppliers and reduced their dependence on local ones.

The research provides insights into the dynamics of manufacturing supply chains as they adapt to shifting institutional demands.

This study aims to explore how CEO narcissism drives investment in corporate social responsibility (CSR) and its mediating mechanism.

This study includes panel regression based on archival data.

CEO narcissism leads to signaling of organizational virtuous orientation that results in increase in CSR investment.

Relevance of CEO traits on CSR remains unexplored in emerging markets context, especially the underlying mechanism. This study uncovers these mechanisms.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

The purpose of this article is to discuss how the mastery of resilience in operations and supply chains plays a significant role in the transition to a more sustainable future. Furthermore, it is supposed to propose avenues for future research on operational and supply chain resilience, interacting with the sustainability literature in our field.

A conceptual review of resilience and sustainability themes within operations and supply chain management research is conducted. Reflections on the topic are informed by relevant literature published over the last decade.

The major conceptual contributions are threefold: (1) This article elaborates on the understanding of operational resilience and supply chain resilience concepts and reviews their respective primary research streams. (2) It proposes resilience as the missing element in the pursuit of excellence in organizations that want to contribute to a more sustainable future. (3) The article offers a research framework that provides a future research agenda at the intersection of resilience and sustainability in operations and supply chain management research.

The article highlights gaps in current research and illustrates further areas of research that need to be addressed to maximize the contribution of operations and supply chain management research in supporting practitioners to achieve a more sustainable future.

Research highlights that residential care experienced children and young people in Scotland have poorer educational outcomes than their peers within the wider population. Despite this, poor educational attainment is not inevitable, and further research is needed to increase the understanding of long-term trajectories. This paper aims to address a gap in contemporary literature that is of benefit to practitioners, academics and policymakers. Despite experiencing adversity, attachment, separation and loss, school attainment data on leaving care only reflects part of the educational journey.

Using a mixed methodology and social constructionist theoretical framework, a practitioner-led PhD study gathered data from questionnaires and qualitative information from 13 semi-structured interviews with young people who had experienced residential care in Scotland. Recruitment was through a gatekeeper within a national third-sector organisation. The educational trajectories for young people with experience of residential care in Scotland are complex. A lived experience perspective from a PhD study illustrates that statistical data only captures part of the journey and the author needs to reconsider how success is measured.

Of the 13 participants in the study, 12 achieved success educationally, although for the majority of those interviewed, attainment continued after leaving compulsory education. Barriers to greater success included placement uncertainty and movement, stigma, low expectations, pressure to not become a statistic, procedural obstacles and inconsistency or poor relationships.

Supportive relationships and stable placements can create circumstances conducive to effective learning, but evidence reflects that support is necessary throughout the life course if children, young people and adults with care experience are to reach their full academic potential.

Research into the educational outcomes for those with experience of residential care in Scotland is limited. This paper, from a PhD, provides lived experience accounts from a practitioner-led study.

This study aims to assess the knowledge-sharing risks and controls in the government sector from the knowledge workers’ perspective.

This qualitative study combines two techniques. First, the study uses the Delphi technique to identify the risks and rank them. Second, the study used a follow-up interview approach to identify the needed controls to mitigate these identified risks.

The Delphi study revealed the top knowledge-sharing risks are related to organizational and individual risks. Furthermore, the study identified the top controls that needed to mitigate these identified risks from technology, process and people dimensions. The study findings suggested that implementing controls on people and processes is the most important, and the focus must be on them, especially in the government sector.

The study offers several practical implications for the government sector to establish a knowledge-sharing risks management strategy. Such study has been given little attention in previous research, especially in developing countries.