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This case explores how driver training school create experience value for their trainees. It describes the development of driver training industry, the foundation and new training mode of Rongan Driving School, changes and challenges of environment for Rongan facing and so on, which will guide readers to discuss six influence factors of customer experience, six dimensions of customer-experience value, the relationship between them, and the influence of social environment. Rongan's innovative training mode of “pay after learning, time-based billing, one car for one person”, provides a good training experience for driving trainees. It has become the benchmark of the national driving training industry within six years.

This research aimed to examine the current status of artificial intelligence's (AI's) integration into Chinese adult education, by analyzing the influences that AI has had on current adult education practices in China and by discussing the opportunities and challenges that adult education in China is faced with under the rapid AI development in the past 12 years.

This research employed systematic literature analysis. CNKI (China National Knowledge Infrastructure) Chinese Journals Full-text Database was used to collect scholarly publications on the use of AI in adult education in China that was published in the past decade. Data analysis included the following steps: identifying key words and phrases, detecting underlying meanings, searching for logical connections and relationships, collecting and connecting evidence to the research questions, and drawing logical and credible conclusions.

The findings indicated that AI has been gradually integrated into Chinese adult education through innovations and explorations and AI's influence is broad and profound. More specifically, the following five main themes were identified. The field's understanding of AI technology and AI's influence on adult education has evolved and become more comprehensive; AI challenges traditional Chinese adult education practices by helping to actualize personalized learning and precision education; AI transforms adult learning resource development; AI helps to turn learning environment into an open intelligent learning system; and lastly, AI urges the shift of adult educator's role in adult learning.

This study is not without limitations. Contextualized in China, this study shares the limitations with other single country studies. One such limitation is “cumulation” issue. This study should be replicated in other country contexts to further validate the generalizability of the five main themes identified in this research.

The five themes identified in this study can help understand the promises and challenges that AI brings to the field of adult education in China. These five themes can also serve as an integrated lens through which one can make sense of AI's integration into other countries' adult education practices.

This paper fulfills an identified need of understanding the current status of AI's integration into and influence on the field of adult education in China.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

This paper aims to shed light on how the composition of future blended learning (BL) courses can be changed to provide students with quality academic learning experiences. The model suggested in this study will guide instructors on how to design their course learning outcomes to ensure effective delivery.

The new model has been developed by combining Bloom's taxonomy and Carman's model. Later, a new framework entitled “PATHCO” based on an extensive literature review is applied to enhance the quality of all five components of Carman's model.

The PATHCO conceptual framework has been developed to ensure quality in the five main teaching and learning factors. This framework covers criteria like pedagogical, assessments, technical, health care and organizational. Further research is required to broaden the main elements of the suggested framework and to validate this research through a case study.

The COVID-19 pandemic has transformed the landscape of the education sector by encouraging an extensive acceptance of technology-enhanced learning and teaching. Blended learning (BL) has become the most appropriate medium to deliver online learning (OL). However, educators and students have reported dissatisfaction with the BL mode of delivery. To address this dissatisfaction, this study outlines, using the PATHCO model, all the essential building blocks which are required to find the right blend of both face-to-face and online components.

During the COVID-19 pandemic, many working mothers in India adopted involuntary telecommuting work option for the first time. However, no research explored their adjustments and experiences in the new work setting. This paper aims to gain an in-depth understanding of Indian working mothers' lived experience of involuntary telecommuting.

A phenomenological research design was adopted. The authors conducted 14 in-depth, semi-structured telephonic and online interviews. Data were analyzed using Interpretative Phenomenological Analysis framework.

The data analysis yielded two interconnected superordinate themes in this research: (1) characteristics of involuntary telecommuting and (2) the impact of involuntary telecommuting. Under the first theme, four sub-themes emerged: long working hours, increased family demands, reduced interaction with coworkers and technology-enabled communication with supervisors. The second theme comprised five sub-themes: time-based work interference with family, time-based family interference with work, strain-based family interference with work, absence of emotional and professional support and performance management concerns. Involuntary telecommuting mothers faced challenges due to lack of control over their daily work schedule and demands, along with an increased burden of unpaid household work, leading to difficulties in managing their work schedule and negotiating their professional role identity within the family. These findings emphasize that working mothers who participated in involuntary telecommuting encountered bidirectional time-based conflicts and unidirectional strain-based conflict.

The study examines a particular subset of women telecommuters who were working mothers with young children. These potential limitations are to be addressed in future research.

The findings suggest that managers should develop HR policies and telecommuting ecosystems in order to enhance effectiveness of telecommuting. Specifically, organizations offering telecommuting work options should create opportunities for informal interaction among peers and formal one-to-one interaction with managers. Moreover, HR managers should develop and implement employee-friendly telecommuting policies.

The research contributes to HRM and gender literature.

The paper contributes to the discourses of work-life balance, workplace relationships and work policies within telecommuting literature.

This article aims to link discussions of the role of earnings-related pension measures with time in Hong Kong (HK) and the United Kingdom (UK). It presents a new conceptual “time-based framework” to explore two related types of government response to the way people accumulate pension incomes through participation in paid work. The first is to consider governments' perceptions of appropriate time in work and retirement. The second is to consider how governments use pension measures to influence the connection between the amount of time people spend in paid work and retirement.

This is a conceptual paper. The time-based framework is developed using literature concerning discretionary time and the social construction of time. To explore the empirical significance of this framework, the authors discuss how it can be applied to the analysis of earnings-related pension measures in HK and the UK.

The evidence generated from the discussion of the earnings-related pension measures in HK and the UK shows that pension policies can serve both as a financial and time instrument. At the same time as influencing the connection between the amount of time people spend in paid work and the pensions they can accumulate, pension policies can be used to convey the government's views on important time issues, namely the appropriate length of time in work and retirement, and the relative value of the time spent in paid work and providing informal care.

A new framework is developed to explore the connection between the studies of earnings-related pension measures and time, which is an understudied area.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

The evaluation of time-based performance is a valued approach in the lean management thinking which is based on delivering value from customer's perspective. This approach contributes for long-term competitiveness and success in today's business environment. The focus of this study is to analyse the cycle time and manufacturing lead time with value stream mapping (VSM) in the preparatory stage of the textile fabric manufacturing process and to identify and improve the non-value adding activities in the value chain (VC). The study presents an insight on the translation of performance improvement across functions and how upstream supply chain (SC) segments can be linked in the performance improvement program. It also covers how the application of VSM improves visibility and planning flexibility in textile fabric manufacturing process.

The time-based performance was evaluated using VSM and recording of the activity times in the existing process. The impact of the quality of supplier's raw material was also measured contributing to identify the strategy for procurement and the means to establish a feedback system to the upstream segments of the SC. The methodology of VSM, observation of the practice and the expertise of the individuals involved with the process were utilised to develop the value stream maps and to identify value adding activities, non-value adding activities, existing gaps and plans for improvement.

The means for improving the time-based performance were identified and their impact was measured. The factors responsible for improvement are related to the production system and with the procurement strategy. The improvement was achieved in terms of available capacity utilisation, balancing the work flow in the preparatory stages, visibility of the process by measuring its capability and flexibility for the planning function. The study revealed that the effectiveness and enhancement of VSM and related tools should be adopted to address the issue of limiting success rates of long term and repeating application of such tools. Continuous improvement, innovations and the systematic embedding of VSM in the process life cycle provide the ways for achieving long-term success.

The paper presents a real and in-depth study on the application of VSM in the textile manufacturing process. The scope of the study is broad; it covers activities across functions with actual estimates of activity times in the manufacturing process for the focused value streams. It offers researchers the opportunity to analyse the translation of productivity improvement across functions and how upstream SC segments can be linked in a performance improvement program.

The study offers useful insight for the managers in textile manufacturing and other sectors for improving the time-based performance and achieving higher utilisation of capacity. It identified the production factors and their impact on warping and sizing cycle time in selected value streams and those which share common activities. It also identified the directions for future research when repeating the application of VSM in the continuous improvement cycle. Furthermore, since the industries need to progress towards advanced systems including Industry 4.0 standards, adoption of advanced VSM tool with relevant technology can align their production systems to develop the required capability. This will also bring a sustainable competitive advantage in the system.

The focused sector is stagnant in terms of productivity and innovation. The adoption of the advanced tools can facilitate the implementation of continuous improvement and innovation strategies.

The main focus of this study is to analyse and improve the cycle time in the preparatory stage of the fabric manufacturing process. This has impact on other important and tangible measures including capacity utilisation and work flow and intangible measures including production planning flexibility and process visibility. The improvement impact is across departments.

This chapter is written from the perspective of an experienced middle leader reflecting on what would have been a powerful approach to solving curriculum challenges at the launch of their middle leadership journey. The approach is learning and leading through collaborative inquiry; a process that provides the pathway and direction for a curriculum middle leader (CML) to engage in inquiry to solve a challenge of practice. A CML invests a great deal of time and effort in unpacking and understanding the ‘why’ of a curriculum challenge. This is achieved through unpacking and understanding the ‘how’ which led to the challenge, and thus, strong strategic skills are required. Additionally, effective CMLs do not undertake this work in isolation, rather, they build and facilitate a professional learning community (PLC) (Stoll et al., 2006) of curriculum enthusiasts to undertake this journey with them, which requires substantial relational trust.