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In this paper, we explore the role of education in household financial technology (FinTech) adoption.

Using representative nationwide household data from the 2017 China Household Finance Survey, we employ the change in China’s compulsory schooling law in the 1980s as an instrumental variable for educational attainment.

We find that among Chinese households, education has statistically significant and economically important effects on the use of various FinTech services, including digital banking, mobile payment, digital wealth management and digital consumer credit. Further analysis indicates that exogeneous increases in education lead to higher levels of financial literacy and social trust, both of which are potential drivers of FinTech adoption. Our findings provide new insights into the importance of education for household financial decision-making and technology adoption.

The contribution of our study is mainly twofold. First, we provide evidence on the role of education in household financial decision making. Second, this study adds to the literature on household adoption of technological innovation in finance. Our findings are also policy-relevant.

The paper aims to explore and examine the factors that influence the post-consumption behavioral intentions of education consumers with the help of online reviews from a Massive Open Online Course (MOOC) platform in the knowledge payment context.

The paper adopted a novel mixed-method approach based on natural language processing (NLP) techniques. Variables were identified using topic modeling drawing upon 14,585 online reviews from a global commercial MOOC platform (Udemy.com). The relationships among identified factors, such as perceived quality dimensions, consumption emotions, and intention to recommend, were then tested from a cognition-affect-behavior (CAB) perspective using partial least squares structural equation modeling (PLS-SEM).

Results indicate that course content quality, instructor quality, and platform quality are strong predictors of consumers' emotions and intention to recommend. Interestingly, course content quality displays a positive effect on invoking negative emotions in the MOOC context. Additionally, positive emotions mediate the relationships between three perceived qualities and the intention to recommend.

Limited research has been conducted regarding MOOC consumers' post-consumption intentions in the knowledge payment context. Findings of this study address the limited literature on MOOC qualities and consumer post-consumption behaviors, which contribute to a comprehensive understanding of MOOC learners' experiences at a meso-level for future paid-MOOC creators.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2021-0482/

This chapter explores the ethical challenges arising from the integration of advanced artificial intelligence (AI) technologies into intelligent manufacturing systems. Machine learning (ML), augmented reality/virtual reality (AR/VR), digital twins, and human–robot collaboration (HRC) redefine industrial production, they bring forth unprecedented efficiencies and capabilities but also introduce complex ethical considerations. The text delves into issues such as data privacy, job displacement, the impact of automation on workforce dynamics, and the psychological effects of working alongside AI-powered systems. Through a detailed examination of these technologies and their implications, the chapter advocates for a dynamic ethical framework that evolves alongside technological advancements. This framework should prioritize human dignity, safety, and rights, involving all stakeholders in its development and implementation. By addressing the ethical implications of AI, AR/VR, digital twins, and HRC, the chapter underscores the necessity of balancing technological innovation with ethical responsibility. It calls for collaborative efforts involving policymakers, industry leaders, workers, and consumers to navigate the ethical landscape of intelligent manufacturing, aiming to harness the potential of these technologies responsibly for the betterment of society and the workforce.

The development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them.

A three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods.

Although the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively.

The model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs.

The Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

This paper examines the impact of corporate digital transformation on employee satisfaction. Therefore, this study extends our understanding of the economic consequences of corporate digital transformation from employees’ perspectives.

The data used to construct our main proxy of employee satisfaction are collected from Kanzhun.com, which provides reviews by rank-and-file employees on their employers. This study uses a large sample of Chinese firms and adopts various empirical methods to examine the impact of digital transformation on employee satisfaction.

We find a significant positive relationship between corporate digital transformation and employee satisfaction. Moreover, we document that the relationship between corporate digital transformation and employee satisfaction is more pronounced in firms with higher labor intensity and in state-owned enterprises (SOE).

One significant limitation is that corporate digital transformation is constructed based on word frequency analysis. This approach may be influenced by variations in corporate disclosure practices and might not accurately capture the true extent of corporate digital transformation. This limitation is not only present in our research but is also pervasive in many other studies that utilize similar methodologies. Therefore, our results should be interpreted with this caveat in mind.

Our study suggests that corporate digital transformation enhances employee satisfaction, providing direct evidence for managers and regulators to promote corporate digital transformation. Through digital transformation, companies can not only improve operational efficiency but also foster employee satisfaction. This dual benefit underscores the importance of investing in corporate digital transformation for long-term success.

Our study suggests that corporate digital transformation enhances employee satisfaction, providing direct evidence for managers and regulators to promote corporate digital transformation. Through digital transformation, companies can not only improve operational efficiency but also foster employee satisfaction. This dual benefit underscores the importance of investing in corporate digital transformation for long-term success.

Our study contributes to the literature on the economic consequences of corporate digital transformation and extends existing research on the determinants of employee satisfaction. Additionally, it provides a novel measurement of employee satisfaction for a large sample of Chinese firms.

Course recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.

The study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.

The main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.

The findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.

This study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

Although digitalization in the workplace is burgeoning, tools are needed to facilitate personalized learning in informal learning settings. Existing knowledge recommendation techniques do not account for dynamic and task-oriented user preferences. The purpose of this paper is to propose a new design of a knowledge recommender system (RS) to fill this research gap and provide guidance for practitioners on how to enhance the effectiveness of workplace learning.

This study employs the design science research approach. A novel hybrid knowledge recommendation technique is proposed. An experiment was carried out in a case company to demonstrate the effectiveness of the proposed system design. Quantitative data were collected to investigate the influence of personalized knowledge service on users’ learning attitude.

The proposed personalized knowledge RS obtained satisfactory user feedback. The results also show that providing personalized knowledge service can positively influence users’ perceived usefulness of learning.

This research highlights the importance of providing digital support for workplace learners. The proposed new knowledge recommendation technique would be useful for practitioners and developers to harness information technology to facilitate workplace learning and effect organization learning strategies.

This study expands the scope of research on RS and workplace learning. This research also draws scholarly attention to the effective utilization of digital techniques, such as a RS, to support user decision making in the workplace.

This era is an era of social media (SM); thus, it is an essential tool for communication among individuals and organizations. The excessive use of SM by employees has raised many questions about their job performance. Therefore, there is a dire need to investigate the effects of SM use on an employee’s job performance mediated by knowledge exchange. Furthermore, the purpose of this paper is to examine how the organization’s SM rules can moderate the relationship between personal and work-related use of SM with information sharing and obtaining information.

Quantitative methodology was used and randomly 1,200 questionnaires data were collected physically from the employees of the public and private sectors in Pakistan. To examine the hypothesized relationships, partial least squares (PLS), rather than covariance-based structural equation modeling, was used to analyze the data. For this reason, multivariate technique, Smart PLS-3.2.1, was used for data analysis.

The findings of this study demonstrated that personal and work-related use of SM could enhance employees’ job performance through knowledge exchange, and SM rules have adverse impacts on the relationships between SM use and knowledge exchange.

This study provides a novel model for the investigation of whether SM use affects employees’ job performance. Furthermore, it will help the policy makers and researchers regarding the management of SM use at work.

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

This research conceptualizes service recovery process (SRPs) within pre-recovery, recovery and post-recovery. This study aims to provide a summary of factors and strategies with respect to SRPs. Also, this research highlights different responses by organizations to SRPs. These responses are synthesized in this research in the context of SRPs.

This study provides a systemic literature review that considers only studies that have been published within the past 11 years to highlight the different response options used today. This study only selected papers that are included in a rigorous review process such that they explicitly contribute towards practice, theory and policy.

The pre-recovery is about the awareness of the problem whereby communication between the customer and organization is initiated to resolve the issue, and it provides a critical foundation for the recovery expectations. The recovery phase concluded with either a satisfactory resolution of the problem or when the customer gives up on his/her query due to another failure of the organization. Post-recovery encompasses the period in which the recovery efforts have concluded, and the customers have now started to evaluate their experience of preceding phases. A major contribution of this study is that it provides a summary of factors and strategies with respect to SRPs.

The managers of service-providing organization can use this synthesis to evaluate the response of their organization to different instances of service failures along SRPs. They can then modify their responses. Managers can also use this synthesis as part of an employee training programme to ensure wide coverage of potential responses of the organization following a failure of service.

This research then highlights different questions that can be explored in future studies regarding the various phases involved in SRPs. Finally, this research outlines the recommendations for businesses looking to benefit from adopting SRPs by also considering the related managerial implications. This study will provide a conceptual framework as to the future direction of the overall study through highlighting gaps of understanding related to SRPs.