Search results

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.

Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.

VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.

The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.

User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

This study aims to examine why tax reforms fail and explores how tax collection can be improved within a developing country context.

Data comprise 28 semi-structured interviews with taxpayers, tax experts and tax authority personnel based in Pakistan. The results are analysed using a combined lens of taxpayer trust and tax agencies’ capabilities.

Tax reforms failed to build taxpayers’ trust and tax agencies’ capabilities. Building trust is challenging and demands extensive ongoing engagement with taxpayers while yielding gradual permanent results. This requires enhancing confidence in government; educating taxpayers; removing complexities; introducing transparency and accountability in tax agencies’ operations and the tax system; promoting procedural and distributive justice; and reversing perceptions of corruption through reconciliation and stakeholder inclusivity. Developing tax agencies’ capabilities requires upgrading outdated technologies, systems and processes; implementing governance and organisational reforms; introducing an oversight board; and recruiting and training skilled professionals.

The findings can assist policymakers and tax collection authorities in understanding why tax reforms fail and identifying potential solutions.

This study contributes to the emerging literature by exploring tax administration failures in developing countries. It contributes to the literature by engaging stakeholders to understand why reforms fail and potential solutions to stimulate tax revenues.

This study aims to understand the interaction between tax fairness perceptions, equitable tax burden distribution and tax compliance within Morocco’s unique socio-economic context, with the goal of uncovering strategies to enhance tax compliance.

Using the Delphi method, this study engaged tax experts in the Moroccan context to explore the impact of taxpayers’ perception of fairness, tax rates and tax burden on compliance. Their responses were gathered and analyzed with the aid of IRaMuTeQ software, which helped the authors identify themes relevant to the research question.

The preliminary results indicate a positive correlation between perceptions of tax fairness and compliance behavior, corroborating earlier studies conducted in different contexts. Notably, a substantial majority of Moroccan taxpayers perceive the current tax system as inequitable, deeming tax rates too high and the tax burden unfairly distributed among various taxpayer categories. This perception potentially influences their voluntary tax compliance behavior.

The findings have significant policy implications for the Moroccan Government and stakeholders. They suggest that by improving tax fairness, particularly by aligning tax assessment and payment modalities for employees, civil servants and small to medium enterprises, policymakers can encourage higher voluntary tax compliance, thereby potentially enhancing the efficiency of the Moroccan tax system.

This study adds to the existing body of knowledge by exploring the dynamics of tax fairness and compliance behavior in Morocco, a context which has been significantly understudied.

This study aims to explain how negative workplace interactions are formed by the application of a performance management system (PMS).

The study draws from unique in-depth interviews with service workers who resigned from an accounting shared service centre (SSC), discussing the reasons behind the resignations. Following an abductive approach, organisational justice theory is used to analyse the service workers' perceptions of negative interactions and to link the negative interactions to the use of the PMS.

The findings suggest that negative workplace interactions are characterised by cost consciousness, inequality and competitiveness. These interactions are attributed to the use of a PMS in the centre and are related to perceptions of distributive, procedural and interactional injustice.

Managers and leaders of shared service–type organisations should not rely on PMSs as an all-encompassing solution; instead, they should acknowledge the fairness of the use of PMSs. Moreover, HR professionals should choose and train employees to apply PMSs fairly. Fairness is important in work allocation, resourcing, monitoring, giving feedback, recognising good performance, promotion and interaction between peers.

This study contributes to the literature by taking an overall perspective on PMSs to analyse and explain the unintended negative consequences of a PMS in a highly scripted and monitored work environment that is usually considered appropriate for such a system's use. Through the analysis, the study highlights pitfalls in the use of a PMS and the importance of interactional injustice not only between but also within organisational levels.

Justice perceptions describe an individual's evaluation of whether decisions or actions are fair or unfair. These perceptions are important because they affect individual attitudes and behaviors in different situations. Family firms develop and implement governance policies and structures (i.e., governance systems) to diminish the problems that can arise from the overlap between the business, the family, and the ownership systems of a firm. Governance systems help family firms have a clear structure of accountability and a clear understanding of the rights and responsibilities that family and non-family members have toward the family enterprise. Research on governance to date has focused on the practices and policies that exist and their effects on the family firm. However, in the governance context, individual perceptions are important because they are likely to affect the attitudes that family and other members have toward the family enterprise and the likelihood that they will follow the different policies when they are implemented. This chapter takes a receiver perspective to explain how individuals create justice perceptions based on governance mechanisms and the effects of these perceptions. The goal is to understand how we can use this information when developing governance practices in family firms.

Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area.

The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data.

Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.

The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain.

La Inteligencia Artificial (IA) ofrece muchos beneficios para mejorar la práctica del marketing predictivo. Sin embargo, plantea preocupaciones éticas relacionadas con la priorización de clientes, la concentración de cuota de mercado y la manipulación del consumidor. Este artículo explora estas preocupaciones éticas desde una perspectiva contemporánea, basándose en las experiencias y perspectivas de profesionales en IA y marketing predictivo. El estudio tiene como objetivo contribuir a la literatura de este ámbito al proporcionar una perspectiva moderna sobre las preocupaciones éticas del uso de la IA en el marketing predictivo, basándose en las experiencias y perspectivas de profesionales en el área.

Para realizar el estudio se realizaron entrevistas semiestructuradas durante seis semanas con 14 participantes con experiencia en sistemas habilitados para IA en marketing, utilizando técnicas de muestreo intencional y de bola de nieve. Se utilizó un análisis temático para explorar los temas que surgieron de los datos.

Los resultados revelan que el uso de la IA en marketing podría tener consecuencias no deseadas, como perpetuar sesgos existentes, violar la privacidad del cliente, limitar la competencia y manipular el comportamiento del consumidor.

El estudio identifica siete temas y los comparan con el modelo de Ashok para proporcionar una perspectiva estructurada para interpretar los resultados. El marco presentado por esta investigación es único y puede utilizarse para respaldar investigaciones éticas que abarquen aspectos sociales, tecnológicos y económicos dentro del ámbito del marketing predictivo.

人工智能（AI）为改进预测营销实践带来了诸多益处。然而, 这也引发了与客户优先级、市场份额集中和消费者操纵等伦理问题相关的观点。本文从当代角度深入探讨了这些伦理观点, 充分借鉴了人工智能和预测营销领域专业人士的经验和观点。旨在通过现代视角提供关于在预测营销中应用人工智能时所涉及的伦理观点, 为该领域做出有益贡献。

本研究采用了目的性和雪球抽样技术, 与14位在人工智能营销系统领域具有丰富经验的参与者进行为期六周的半结构化访谈。研究采用主题分析方法, 旨在深入挖掘数据中显现的主要主题。

研究结果表明, 在营销领域使用人工智能可能引发一系列意外后果, 包括但不限于加强现有偏见、侵犯客户隐私、限制竞争以及操纵消费者行为。

本研究通过明确定义七个独特的主题, 并采用阿肖克模型进行基准比较, 为读者提供了一个结构化的视角, 以解释研究结果。所提出的框架具有独特之处, 可有效支持在跨足社会、技术和经济领域的预测营销中展开的伦理研究。

This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

This research aims to investigate the impact of the collaborative governance mechanism on the implementation of rural governance information systems in developing countries. By integrating institutional logic theory, affordance theory and social identity theory, the authors propose modeling grassroots officials' affordance perception process and exploring the importance of multi-identities’ information technology (IT) goals in affordance perception.

Through an exploratory case study, the authors identified three affordances of rural governance information systems and investigated the mechanisms influencing the perception of these affordances among grassroots officials. Next, the authors established a research model and collected 490 valid questionnaires from grassroots officials in China and analyzed the data using structural equation modeling (SEM).

The authors' study challenges previous assumptions by integrating institutional logic, affordance and social identity theories. The authors establish a perceived affordance path for rural governance information systems, recognize substitute effects among technological affordances and extend the theory to explain social factors influencing IT perception. The authors' findings suggest providing technical training for grassroots officials to enhance IT capabilities, and governments should prioritize essential functionalities in rural governance information systems to optimize resources. Training on collaborative governance mechanisms can improve IT affordance perception, enhancing digital platform utilization in governance processes.

The study was conducted mainly in China, and therefore, the findings may not be universal to other developing countries. Researchers are therefore encouraged to test the proposal in locations with different rural cultures.

The paper includes implications for the development of IT perception in rural governance, the development of affordance perception theory and studying the relationship between IT goals and affordance perception.

Overall, this paper addresses the need to understand how grassroots officials perceive IT affordances in rural governance and study the relationship between multi-identities’ goals and affordance perception.