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Drawing on the social identity theory, this paper explores the impact of rider–driver ethnicity match on the driver’s expected ride satisfaction and willingness to perform, and rider’s trust on the driver.

The study relies on scenario-based online experiments with 291 ride-hailing drivers and 282 riders in the USA.

The findings indicate that ethnicity match between ride-hailing drivers and riders positively impact driver’s ride satisfaction and willingness to perform, and rider’s trust in the driver. The study also revealed a significant positive moderation effect of ethnic identity on the relationship of ethnicity match and those constructs.

While it may be challenging to influence an individual’s level of ethnic identity, managers can take steps to educate and train their employees regarding the impact of ethnic identity and discrimination, with a particular focus on those individuals who possess a strong sense of ethnic identity.

The findings of this research provide theoretical contributions to the existing literature on ride-hailing services and adds to the limited stream of logistics research that examines the impact of ethnicity on ride-hailing operations.

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.

The pandemic has severely affected sharing economy businesses specially ride-hailing services due to high levels of human-touch points. This research investigates the impact of COVID-19 infodemic, identification, and emotional support on intention to use ride-hailing. The authors further examine the moderating effects of internalization, compliance, social currency and informational support in the proposed conceptual model.

The data were collected from 412 respondents from Delhi in India, and hypothesized relationships were tested using hierarchical regression.

The results indicate that COVID-19 infodemic has a negative association with intention to use ride-hailing. Identification and emotional support show a positive association with intention to use ride-hailing. The results also support internalization and compliance as moderators between infodemic, identification and intention to use ride-hailing. Further, social currency and information support moderate the relationship between emotional support and intention to use.

This is the first research, which adds to the expanding literature on ride-hailing, especially during the post-pandemic period of “new normal”.

Underpinned by the legitimacy perspective, this study explores how ride-hailing services are legitimized through resource exchange among the industry players. The authors explore the types of legitimacy involved in the legitimation process. The authors also examine the political games being played by the actors to attain legitimacy.

This qualitative study involves thirty-one stakeholders/interviewees from emerging Asia. The interview data are supported by online documents and observations.

Thematic analysis shows that the industry players collaborate to achieve political, market, alliance, social, and investment legitimacy. The collaborations also legitimize industry players' existence through an eclectic mix of the numerous stakeholders' actions. This study shows how Dacin's proposed four types of legitimacy are coexisting and interconnected. It also highlights the neglected political legitimacy.

The findings guide the policymakers and ride-hailing operators experiencing competing requests to legitimize sustainable ride-hailing service development in urban cities.

The proliferation of ride-hailing businesses brings significant considerations for improving the driver's operational performance. Informed by the literature on sharing economy, general deterrence theory and protection motivation theory this research investigates the behavioral factors impacting ride-hailing drivers' operational performance.

The authors empirically test the antecedents impacting a ride-hailing driver's operational performance using an online survey dataset comprising 513 ride-hailing drivers working for Uber and Lyft in the United States.

Ride-hailing drivers' intention to comply with the ride-hailing company guidelines results in better operational performance for the driver. Moreover, drivers believe that ride-hailing companies have effective penalties to deter drivers from violating company guidelines. However, drivers also believe that the chances of being caught while ignoring the company guidelines are low.

The results of this research support the decision-making processes of ride-hailing company managers and offer insights on how managers can enhance the operational performance of their drivers.

This study provides unique contributions to emerging research at the intersection of peer-to-peer asset sharing, behavioral studies and technology management. This research is one of the first to explore the role of behavioral factors such as coping mechanisms on the operational performance of sharing economy workers.

This paper aims to show the implementation in the terrestrial trunked radio (TETRA)-based sensor network. The publicly available data show that, in Serbia, the annual damage caused by hailstorms in the past seven years has been estimated almost at an average level of 40m of euros. As the amount of hail was not changed, the hail suppression system of the Republic of Serbia has to be improved, both technically and organizationally, to get better efficiency and protection and to reduce the damage.

In this paper, the authors show the implementation and improvements in the modern terrestrial trunked radio (TETRA)-based sensor network, and they propose the scientific use of sensors for remote control of automatic hail suppression rocket stations.

The authors’ idea is that TETRA should be used as an operational and official telecommunicating system for hail suppression activities units. A number of sensors, connected in a network, are used to maintain a high-quality functioning of this digital radio system, managed remotely and controlled either by operators or automatically.

The presented study with a real example attempts to explain as to how the system functions and how it can improve hail suppression activities.

Hailstorms are a major risk in agriculture. In order to mitigate the negative consequences on farm revenues, in the present paper the authors analyse the choice between insurance contracts and anti-hail nets. Furthermore, the authors discuss the consequences of anti-hail nets adoption on the actuarial soundness of the insurance market.

In this paper the authors firstly develop a theoretical model based on expected utility theory to compare the profitability of no-hedging against insurance and anti-hail nets. Subsequently, they test their theoretical model predictions with data of South Tyrolean apple producers.

The authors find that the benefit of anti-hail nets compared to insurance is an increasing function of the overall risk of hail damages, of the farmers' level of risk aversion and of the worth of the agricultural output.

Given the authors’ findings that anti-hail nets are more profitable for riskier, risk-averse and high-profitable farmers, the diffusion of anti-hail nets could be beneficial for the actuarial soundness of insurance markets.

The model developed in the paper is specifically designed to compare the profitability of different agricultural hedging options and can be easily extended to cover other hazards.

Despite the perceived role of customer value in post-adoption behaviour in the context of ride-hailing apps such as Uber, there has been limited research on the subject. This paper seeks to enrich the understanding of the relationships between customer perceived value, particularly hedonic value and economic value, customer satisfaction and continued use intentions of ride-hailing apps.

This analysis is based on field data collected from 567 users of ride-hailing apps in Ghana. Data collected from the survey were analysed using the partial least square (PLS) approach to structural equation modelling (SEM).

The paper provides evidence that hedonic value, as well as economic value, positively predicts customer satisfaction and continued use intentions of ride-hailing apps. Further analysis reveals customer satisfaction directly predicts continued use intentions in addition to partially mediating the influence of customer perceived value on continued use intentions of ride-hailing apps. Finally, the findings suggest that hedonic value has a stronger impact on continued use intentions than economic value, while economic value has a greater impact on satisfaction than hedonic value.

The study contributes to post-adoption behaviour research by providing evidence on the relationships among the study constructs in a developing country context. Overall, the findings will stimulate future empirical debates on the subject and guide practitioners in decision-making concerning customers' usage of ride-hailing apps.

The risk of hail-impact occurrence that can decrease local strength property must be taken into account in the design of primary airframe structures in aviation, energy and space industries. Because of the high-speed of hail impact in operation, it can affect the load carrying capacity. Testing all impact scenarios onto real structure is expensive and impractical. The purpose of this paper is to present a cost-effective hybrid testing regime including experimental tests and FEM-based simulations for airframe parts that are locally exposed to the impacting hail in flight.

Tested samples (specimens) are flat panels of laminated and sandwich carbon/epoxy composites that are used in designing lightweight new airframes. The presented numerical simulations provide a cost effective and convenient tool for investigating the hail impact scenarios in the design process. The smoothed particle hydrodynamics (SPH) technique was selected for the simulation of projectiles. The most commonly used shape of projectiles in hail impact tests is the ice ball with a defined diameter. The proposed simulation technique was verified and validated in tests on flat composite panels (specimens).

Integration of the numerical analyses with high-speed impact tests of hail onto flat laminated and sandwich composite shells has been presented, and a developed simulation model for impact results assessment was obtained.

The tested coupons (specimens) are flat panels as representative of structural design deployed in real aircraft structures. These numerical simulations provide a cost effective and convenient tool for hail impact scenarios in the design process.

Hail risk management is essential for successful farm management in German fruit production, particularly because hail events and associated losses have increased in recent years. The purpose of this paper is to conduct a detailed risk analysis comparing different strategies to manage hail risk, taking into account farmers’ risk aversion and farm-specific conditions.

Within an expected utility framework, two different strategies for managing hail risk are compared: one belonging to the group of financial instruments (hail insurance) and the other to the group of technical instruments (anti-hail net). A unique data set comprising a ten-year time series of orchard-specific hail damage and hail insurance data is used.

For orchards with low local hail risk and low yield potential, not using hail risk mitigation is most efficient. For orchards with high local hail risk and high yield potential, anti-hail nets provide the highest certainty equivalents. For orchards with high local risk, but low yield potential, hail insurance is most efficient. For orchards, with low local risk, but high yield potential, the certainty equivalents are higher for anti-hail net, when the farmer is risk neutral or slightly risk-averse. With increasing risk aversion, hail insurance is most efficient, which can be explained by the greater degree of the instrument’s flexibility.

The novelty of the study lies in the direct comparison of the risk effects of anti-hail nets and hail insurance in fruit production.