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Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

This study aims to examine the association between apprenticeship training and self-employment and how gender moderates the association.

Secondary data from the World Bank’s Skills Towards Employment and Productivity (STEP) survey on Ghana were analysed using a binary choice (logit regression) model. The STEP survey drew its nationally representative sample from the working-age population (15–64 years) in urban areas.

After controlling for several factors identified in the literature as determinants of self-employment, the results indicate that completing apprenticeship training increases the likelihood of being self-employed. However, women who have completed apprenticeship training are more likely to be self-employed than men.

By examining the moderating effect of gender on the association between apprenticeship training and self-employment, this study has offered new evidence that policymakers can use to promote self-employment, especially among women, to reduce the entrepreneurial gap between men and women.

Electromagnetic field problems arising in electrical machinery and devices are in general truly three dimensional in nature. In many devices natural excitation is the voltage excitation, for example: relays and electrical motor. Several research papers have been published on the subject of three dimensional field solutions. Many different formulations of the 3D electromagnetic problems are possible. They involve differential or integral formulations in terms of scalar or vector unknowns. The aim of this paper is to present a method capable of solving general 3D eddy currents with voltage excitation in nonlinear magnetic field using the A, A‐V formulation. The method of the finite difference equations formulation based on minimising of the energy functional has been given.

Knowledge management (KM) is widely adopted by organisations to improve their performance and make informed decisions. Prior research has confirmed that Information Systems (IS) play a critical role in effective KM. The purpose of this study is to examine the existing literature on the role of cloud-based KM systems (C-KMS) in small- and medium-sized enterprise (SMEs) by understanding its impact on the five KM processes: knowledge acquisition, creation, storage, sharing and usage.

This study conducted a systematic literature review by examining 133 journal articles and 24 conference papers from 2010 to 2021 on the role of cloud computing in KM for SMEs.

This study revealed that there are numerous empirical analyses on KM processes and tools in SMEs; however, only few studies demonstrate how the whole gamut of KM processes can adopt cloud computing in SMEs. Therefore, SMEs are ineffective at KM with limited IS intervention. This paper offers a proposition on how C-KMS can impact all five KM process, thereby increasing its effectiveness of KM in SMEs. This study analysed the benefits of C-KMS that brings to SMEs in terms of availability, scalability, reliability, security and cost.

This systematic review is restricted to certain databases (ScienceDirect, Sage journals, Scopus and Emerald Insight) and specific IS conference proceedings to source articles. The selection of search criteria and time frame is based on this study’s assessment and choice. This study adds value to our understanding of the role of KM in SMEs, and it reinforces the role of cloud computing in effectively managing knowledge in SMEs. The proposal of C-KMS for the enhancement of KM has significant implications for SMEs to effectively use knowledge for their survival and superior performance.

This study suggests three practical implications. First, adopting and using C-KMS provide a strong foundation to manage knowledge for SMEs in a cost-effective way. Second, C-KMS improves the effectiveness of KM by increasing availability of knowledge artifacts, which in turn aids SMEs’ growth. Third, C-KMS is useful to codify SME’s knowledge, and accordingly supports employees to acquire and use knowledge based on their requirements.

This study discussed C-KMS with contemporary social issues, such as the COVID-19 pandemic challenges for SMEs and demonstrated how C-KMS can support SMEs to handle such crises by managing knowledge effectively.

This research highlights the importance of the implementation of a C-KMS for the enhancement of KM in SMEs. The review provides empirical evidence on the challenges faced by SMEs regarding KM, as they often only have enough resources to focus on a single KM process, predominantly knowledge sharing. Consequently, a holistic approach to KM cannot be realised by SMEs. In this context, the findings of this study offer theoretical and practical insights into the role of cloud computing by addressing the challenges of KM in SMEs.

The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.

We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.

The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.

This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.