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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.

The study examines the instigating factors behind the development of the local content (LC) policy in Ghana and it further investigates the accountability mechanisms that drive the LC policy implementation to promote sustainable development.

The study reports on a series of interviews with key actors using Institutional Theory and the application of Bovens’ (2010) Global Accountability Framework as a lens for discussion and interpretation of results.

The results reveal that two forces instigated LC policy enactment. One is external funding pressure from the Norwegian government and the World Bank. The other is the government’s engagement of Civil Society Organisations and other internal stakeholders to justify its activities and missions to signal adherence to impartiality, neutrality, and, to a lesser extent, solidarity. The analysis also reveals tensions in how accountability legitimacy relates to implementation of the LC policy. The study further discovers that while participation, transparency, monitoring, and evaluation are frequently invoked as de jure institutional legitimacy in oil and gas contracts, actual practices follow normative (de facto) institutionalism rather than what the LC policy law provides.

The interview had a relatively small number of participants, which can be argued to affect the study’s validity. Nevertheless, given the data saturation effect and the breadth of the data obtained from the respondents, this study represents a significant advancement in LC policy enactment knowledge, implementation mechanisms and enforcement in an emerging O&G industry.

The findings of this study suggest that future policy development in emerging economies should involve detailed consultations to increase decision-maker knowledge, process transparency and expectations. This will improve implementation and reduce stakeholder tension, conflict and mistrust.

The findings of this study build on earlier investigations into legitimacy, accountability and impression management in and outside the O&G sector. Also, the findings reveal the legitimising tactics used by O&G actors to promote local content sustainable development targets.

It is well known that culture is a catalyst for change, helping economies respond to societal problems and demands and that culture is where people turn to in moments of crisis. In this case study around designing and implementing evaluation methodologies/frameworks for Coventry UK City of Culture 2021, it is suggested that in English public policy and within publicly invested arts there is a maturation of thinking around recognising/measuring the public value of culture including its social value. The purpose of this paper is to chart the recent policy of justifying cultural expenditure with social value claims and highlight challenges for evaluating activity within Coventry UK CoC 2021 as a change in wider policy is taking place.

This paper provides creative insights into the design and implementation of the evaluation methodologies/frameworks for Coventry UK City of Culture 2021. The authors of this paper as the collective team undertaking the evaluation of Coventry's year as UK City of Culture 2021 bring first-hand experiences of challenges faced and the need for a cultural mega-event to evidence its value.

The case study aims to address the concepts of measuring value within cultural events and argues that a paradigm shift is occurring in methods and concepts for evidencing the aforementioned value.

The case study within this paper focuses on the build-up period to the UK City of Culture 2021 year and the thinking and logic behind the creation of the evaluation/measurement framework and therefore does not include findings from the actual cultural year.

It is acknowledged that there are papers examining measuring and evidencing the “value” of cultural mega-events, the authors bring real-life first-hand experience of the concepts being utilised by them on the ground in the delivery and evaluation design of Coventry, UK City of Culture 2021.

Breast cancer has become the largest cancer in the world today. Health problems for women with breast cancer need to be addressed urgently. This study aims to select the best method for preparing temperature-sensitive sports underwear, and to verify the feasibility of using K-type thermocouple threads in underwear fabrics.

In the experiments, two samples were designed for temperature-sensitive performance tests and the effects produced by different outer layer structures were investigated. In the second step, K-type thermocouple wires were integrated into sports underwear. The comfort and feasibility of the temperature-sensitive underwear were investigated.

It was finally verified to obtain the best comfort and temperature-sensing performance of K-type thermocouple filaments integrated into sports underwear with plain stitching.

The underwear has a certain prospect for the application of smart apparel based on breast cancer health monitoring, which is of some significance for monitoring smart apparel.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

Socioeconomic aims and impacts are an explicit part of the UK City of Culture (UKCoC) application, bidding, delivery and evaluation stages. This article engages with existing debates on evaluating cities of culture and introduces perspectives from critical data studies to examine the collection and analysis of different data for the purposes of the CoC application and evaluation processes.

The meta-methodological concept of accompanying researcher is used to analyse the experiences of researchers based within a city bidding for UKCoC 2025 in dialogue with the evaluation reports from past UKCoC host cities.

Findings are analysed under three themes: defining data morsels; local histories and infrastructures of data generation and sharing; and resources, capacities and expertise for data generation and evaluation. The discussion examines data still to be generated and/or brought into relation; tensions around data and measurement; and how constructing an evaluation baseline is generative—creating new organisations, relationships and practices.

The conceptual and methodological approach and empirical findings will be relevant for academic, policymakers and practitioners engaging with cultural evaluation.

In focussing on the bidding stage in real time through the accompanying researcher position, this article presents original empirical insights into the process of creating a baseline for cities of culture evaluation. The conceptual originality of this article is in using critical data studies to explain strategies of data generation and analyse data relations and frictions.

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.