This paper studies the design and pricing of a novel two-stage extended warranty (EW) menu considering customer heterogeneity. We consider the scenario in which preventive maintenance and predictive maintenance mechanisms (PM/PPM) already exist, and mainly focus on how manufacturers can utilize the degradation information collected during the base warranty (BW) period to design differentiated extended warranty menus. At the end of the BW period, customers are classified into different risk categories based on their usage and product condition information (including failure history and degradation level). Based on profit-driven posterior risk assessment, the manufacturer allocates different extended warranty menus for different customer groups, and the customer’s purchasing behavior is described by a random utility model. In the proposed two-stage EW policy, the first stage offers a limited number of free imperfect repairs, while the second stage adopts a cost-sharing scheme between the manufacturer and the customer. From the manufacturer’s perspective, the goal of the EW menu design is to maximize the expected profit by determining the menu prices, the number of free repairs in the first stage, and the proportion of customer cost-sharing in the second stage. The numerical results show that customer classification helps manufacturers provide menus that better match the risk profiles of different customer segments. Moreover, the research results also indicate that the proposed segmented two-stage EW strategy outperforms the unified warranty menu in terms of expected profit. Sensitivity analysis further examines the impact of different maintenance costs and warranty periods on the optimal menu decision.

With rapid advances in digital technologies and artificial intelligence, autonomous vehicles (AVs) have developed substantially, yet consumer adoption remains constrained by psychological barriers. Against this backdrop, this study examines the factors shaping consumers' willingness to adopt AVs, with particular emphasis on trust. Drawing on a UTAUT2-informed perspective on technology adoption and extending it with trust, we develop and test a framework for explaining consumers' willingness to adopt AVs in the context of Level 3 AVs. Evidence from a field experiment conducted in China (N = 400) shows that trust serves as a critical mechanism through which consumers' perceptions translate into willingness to adopt AVs. Specifically, performance expectancy, effort expectancy, perceived enjoyment, and anthropomorphism are all positively associated with trust. The findings further show that perceived privacy risk weakens all of these positive effects on trust except the effect of anthropomorphism. By clarifying the mediating role of trust and identifying perceived privacy risk as an important boundary condition, this study advances research on AV technology acceptance. It also offers practical insights for firms and designers seeking to strengthen consumer trust through better system performance, more intuitive user experiences, and clearer privacy protections.

Online reviews provide valuable insights into customer preference and product attributes (PAs), enabling companies to formulate effective product improvement strategies. In addition to PAs, however, reviews often contain noise, such as information about logistics and marketing strategies. While managing this noise is crucial to improving PA extraction and categorization accuracy and efficiency, existing studies have largely overlooked or handled noise inadequately. To address this gap, this study proposes a hybrid AI-driven framework for identifying and categorizing PAs while filtering out noise from online reviews. First, we use bidirectional encoder representations from transformers (BERT) to identify informative reviews (i.e., those containing at least one PA). Then, we integrate latent Dirichlet allocation and Word2Vec to extract irrelevant information, aiming to isolate noise and extract PAs from the reviews. Furthermore, we categorize PAs using importance-performance analysis (IPA) and IPA-GAP1. In this process, the importance of attributes is calculated by fitting the relationship between customer sentiment toward attributes and customer satisfaction using random forest, and customer sentiments are determined using BERT-based sentiment analysis. Additionally, we use importance-performance competitor analysis to assess attribute performance and importance across different products. Finally, we propose an Improvement Priority Score (IPS), which integrates attribute importance, performance, and competitive performance gap to provide companies with actionable insights for product optimization under limited resources. The proposed framework is validated through a case study using phone reviews from JD.com.

The proliferation of online consumer reviews offers new opportunities for data-driven market segmentation. However, traditional approaches that rely on surveys or demographic profiling often fail to capture the evolving, heterogeneous nature of customer preferences. Addressing this gap, we propose a novel hybrid framework to identify distinct customer segments and infer their preferences from unstructured text. First, we integrate BERTopic and Word2Vec for robust product feature extraction, followed by a Teacher-Student distillation strategy (distilling knowledge from Qwen-Max to Qwen 2.5-7B) to extract granular feature-level sentiment. We then identify latent customer segments by applying multi-dimensional K-means clustering to a composite of Feature Mention Vectors (representing customer attention) and product metadata (reflecting choice behaviors). Finally, we model segment-specific preferences using Ordinal Logistic Regression (OLR) to identify asymmetric satisfaction drivers and classify features according to the Three-Factor Theory. Empirical validation on a large-scale dataset of iPhone and AirPods reviews from Jingdong.com proves that feature classification is neither universal nor static, as it varies with time and across customer segments. The results validate our framework’s ability to uncover preference heterogeneity and track temporal shifts across product generations, providing firms with a scalable tool for targeted product iteration and personalized marketing.

This paper investigates the joint optimization problem for imperfect manufacturing systems with two production states (controlled and uncontrolled) and one failure state. To simultaneously optimize the quality, maintenance, and production strategies, researchers typically employ a fixed buffer stocking time. However, this approach can lead to excessive holding costs or stockouts. Therefore, we formulate a dynamic buffer replenishment problem in which the buffer replenishment time is treated as a variable that adjusts according to the actual production conditions. Additionally, we consider the effect of the defect rate in the uncontrolled state, where there is a probability of producing defective products; this allows for the real-time detection of uncontrolled systems. Then, we present a new joint control model constructed to derive optimal production, maintenance, and quality strategies by minimizing the expected average cost per unit of time. We use a numerical example to evaluate the proposed model, and the numerical results show that our strategy is more cost-effective than the model with a fixed buffer time that does not consider the quality strategy. The results indicate that the proposed model is more realistic than existing models and can help companies achieve higher profits.

Optimizing productivity in manufacturing is crucial for increasing output and reducing costs; however, it can also negatively impact product quality and accelerate system degradation. This study is the first to propose a method for dynamically adjusting productivity while considering both system degradation and product quality. We construct a dynamic programming model using optimal control theory to address both fixed maintenance cycles and the joint optimization of production and maintenance strategies. Our approach identifies optimal production strategies for various scenarios, showing that integrating product quality considerations with productivity and degradation significantly enhances overall outcomes. Extensive numerical studies validate our results, demonstrating that this comprehensive optimization scheme not only increases production system revenue but also reduces maintenance costs as well as product defects. By accounting for the dual impact of productivity on system degradation and product quality, this research provides a more holistic and practical strategy for maximizing manufacturing revenue and product reliability. The findings offer significant theoretical and practical value, guiding enterprises toward achieving a balance between high productivity, system longevity, and product quality.

Given the flexible and configurable characteristics of smart manufacturing systems with a limited time per manufacturing task, the assumption of infinite time for prostems is no longer applicable to the joint-control strategy. Consequently, a joint-control model that considers production, inspection, and maintenance within a finite-time scenario for smart manufacturing systems is proposed in this paper. The objective is to optimize overall production and maintenance functions to minimize the total system cost. Comparing the joint strategy under infinite time with the proposed finite-time approach reveals significant differences in unit costs between the two scenarios. To enhance the effectiveness of the model, a discrete iterative algorithm with multiple loops was developed. Through a case study, it was observed that 1) joint strategies implemented within a finite time horizon were more cost-effective than those under infinite time, thus emphasizing the need for business managers to develop strategies within a finite time frame; 2) different production planning and efficiency levels had varying effects on the final joint strategy, necessitating customized strategies based on different production durations. Overall, the research gap regarding joint strategies within a finite-time context was addressed in this research, serving as a methodological foundation for practitioners to develop various strategies that minimize total costs across diverse real-world scenarios.

The traditional 76 standard solutions have several deficiencies making it difficult to use: 1) The 76 standards and the classification is not well organized for ease of usage; 2) The existing problem-solving processes are not well organized and does not cover all standards; (3) The 76 standard solutions only have textual descriptions without graphical structures making them difficult to organize. The research developed a novel operation-operand:location, 3-element mapping approach, to map from problem su-field model to solution model for problem-solving. By using the 3-element mapping from the problem su-field structure, more than twice the solution structures can be obtained than those obtained from Altshuller’s standards. With the addition of solution characteristic attributes, characteristics of Altshuller’s standards can be easily used to stimulate ideas for specific solutions for a given problem. A problem identification process to identify the su-field model of the core problem before applying the 3-element mapping approach is also proposed to complete a problem-solving process.Contributions of this research include: 1) Establishing su-field analysis and problem-solving approach using the novel 3-element mapping approach enabling comprehensive identification of all possible su-field structures with additional 46 new su-field structures over conventional approaches allowing generation of many more solution ideas. 2) The 3-element structure mapping along with solution characteristic attributes greatly simplified the understanding and usage of solution standards while covering various solution ideas of the 76 standards and beyond to aid idea triggering for solution development. 3) The 3-element structure mapping of the problem array to solution array allows for future conversion of TRIZ logical reasoning of problem-solving to mathematically computable approach for solution identifications.

Effective key quality characteristic (KQC) selection is essential for follow-up quality improvement. Customers' demands for different product QCs affect product popularity. However, little research has integrated customer attention into KQC selection under imbalanced data in e-commerce, which can lead to a follow-up product with good quality but no popularity. This study, therefore, investigated KQC selection incorporating customer attention in the scenario of imbalanced data for popular products. First, KQC selection incorporating customer attention was defined as a multi-objective problem, aiming to minimise the percentage of selected QCs and maximise the importance of QCs, as well as cumulative attention to selected QCs. A collaborative filtering algorithm-based method was applied to extract customer attention from historical data when filtering key QCs. Second, an adaptive hybrid whale optimisation algorithm (AHWOA) was proposed to solve KQC selection. Here, simulated annealing was incorporated into the WOA agent search, and an adaptive convergence-acceleration mechanism and a fast non-dominated sorting algorithm with an improved crowding-distance measure were integrated into WOA. Third, the proposed AHWOA was evaluated on five datasets from the UCI repository, and the results show AHWOA's advantages over five existing benchmark algorithms.

E-commerce provides a vast space for user-generated content, including user reviews starting at the initial use of products or services to subsequent usage. Existing methods mainly focus on unmodified reviews and ignore customer perception after more experience with products. To address this, we propose a Markov chain-based bi-channel dynamic topic model (BDTM), which extends the sequential structure of the dynamic topic model and incorporates initial customer reviews and additional reviews to reflect topic shifts caused by customer experience perception. Then, a control chart based on word mover's distance (WMD), called a BDTM-WMD (B-W) chart, is proposed to monitor topic shifts under BDTM. An alternative multiscale dynamic topic model (M-K) chart is constructed for comparison. Using a simulation approach, we find that compared with the existing sequential reverse joint sentiment-topic (SRJST) chart and joint sentiment topic-rating meets review (JSTRMR) chart, the proposed B-W chart is more sensitive to small shifts. Case study 1, using real data, shows the advantages of the proposed BDTM, B-W chart and M-K chart under both initial and additional reviews. Case study 2 shows that with additional reviews, the proposed B-W chart triggers out-of-control signals earlier than those by the existing JSTRMR chart.

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy.

The joint optimization of production, maintenance, and quality control has shown effectiveness in reducing long-term operational costs in production systems. However, existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant. To address this limitation, we propose an innovative method based on the continuous ranking probability score (CRPS). This method enables the simultaneous detection of changes in mean and variance in nonconformities, thus removing the assumption of a specific distribution for quality characteristics. Our approach focuses on developing optimal strategies for production, maintenance, and quality control to minimize cost per unit of time. Additionally, we employ a stochastic model to optimize the production time allocated to the inventory buffer, resulting in significant cost reductions. The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments, sensitivity analysis, and a comparative study. The results show that our method can achieve cost reductions compared to several other related methods, highlighting its practical applicability for manufacturing companies aiming to reduce costs.

How does intellectual property rights (IPR) enforcement influence innovation and performance of international new ventures (INVs)? According to the literature on IPR enforcement, innovation, and internationalization, we predict that IPR enforcement directly and indirectly (via innovation speed and innovation quality) impacts firm performance of INVs, and international intensity (INT) moderates the indirect effects. Based on both primary data and secondary data, the empirical results indicate that IPR enforcement promotes firm performance. In addition, innovation speed and innovation quality partially mediate the relationship between IPR enforcement and firm performance. Moreover, the relationship between IPR enforcement and innovation quality is weakened with high levels of INT. Theoretical contributions, practical implications, limitations, and future research avenues are discussed.

"加快一流大学和一流学科建设,实现高等教育内涵式发展"是党中央对高校提出的明确要求.制定和落实高校"十四五"规划,是学校实现长远发展的抓手和实行日常管理的重要手段.文章以天津大学为例,研究天津大学国际合作改革现状与策略.首先,介绍了天津大学国际合作改革的现状;其次,简析了国内外主要高校国际合作改革方案;进而,提出天津大学国际合作改革的目标与原则;最后,提出国际合作改革的策略,主要包括领导基层协同化、扬长补短全面化、人才培养国际化、科学研究国际化、服务功能国际化.

Since the coronavirus disease outbreak in 2019, the development of automated driving vehicles (ADVs) has attracted increasing interest worldwide, affecting several areas, such as transportation, energy, government, and the environment. However, the adoption of ADVs lags far behind predictions. Therefore, it is necessary to explore the psychological mechanism behind customers’ willingness to adopt ADVs. Drawing on the cognitive appraisal theory of emotions, this study explores the antecedents and consequences of customers’ trust in ADVs. Using data from a sample of 349 survey participants in China, this empirical study finds that customers’ willingness to travel in ADVs is determined by their trust arising from their appraisal of ADVs. Specifically, cognitions (performance expectancy, effort expectancy, perceived enjoyment, and anthropomorphism) are the antecedents of customers’ trust in ADVs. Further, trust is positively related to customers’ willingness to travel in ADVs; therefore, trust plays a mediating role between cognitions and willingness. In addition, cost weakens the relationship between trust and willingness to adopt. Thus, this study advances the literature on technology acceptance studies and provides practical implications for customers and businesses.

Monitoring surgical outcome quality by risk‐adjusted control charts has attracted wide attention. The hidden medical errors may cause increasing of adverse events such as infection, rehospitalization, and even death. Quickly and timely detecting abnormal changes of surgical performance helps reduce the probability of adverse events and improve health care quality. Most existing monitoring schemes focus on the binary surgical outcomes. However, continuous survival times of patients should be considered for more accurate monitoring. In this paper, a new exponentially weighted moving average (EWMA) control chart is proposed for monitoring continuous surgical outcomes. To describe surgical performance, a patient's actual survival time and predicted mortality are combined in an illustrative and interpretable way. Performance of the proposed chart is evaluated with different chart parameters under different shifts by a simulation study. We compare our chart with the risk‐adjusted survival time cumulative sum chart, and the simulation results demonstrate that the proposed monitoring scheme has better efficiency. The implementation of the proposed chart is illustrated by a real example. Besides an analysis of the entire dataset, the surgical performance of each surgeon is monitored, because each of them has patients with different risk levels.

MIT新工程教育改革引发了全球关注,但已有研究对其课程改革缺乏有效分析.以项目为中心、采用串编方法、面向新机器和新系统人才需求开展课程组织,是NEET改革的精髓.本文采用文本材料与实地调研交互推进、交叉验证的形式,围绕NEET课程改革"面向未来与重大挑战的课程理念变革""以项目为中心的课程内容变革"及"以串编为模式的课程组织变革"三个层次展开讨论,对其课程改革的重要意义与核心价值进行深层次探究,为我国工程教育改革理念变革、体系设计与具体实践提供启示.

Universities and institutes are increasingly recognized as important sources in national innovation systems. As such, an increasing number of academics are participating in entrepreneurial and other commercial activities, and the topic of academic entrepreneurship has attracted wide attention. This paper aims to explore the effect of entrepreneurial identification on academic entrepreneurship from the social identity theory viewpoint and consider the effects of context (social capital inertia, entrepreneurial narrative). On the basis of 248 academic entrepreneur samples, empirical results indicate that the relationship between entrepreneurial identification and academic entrepreneurship performance is positive. In addition, the abovementioned relationship is negatively moderated by social capital inertia while positively moderated by entrepreneurial narrative. Moreover, entrepreneurial identification is best for academic entrepreneurship performance in the context of low levels of social capital inertia and high levels of entrepreneurial narrative. Theoretical contributions, practical implications, limitation and future research are discussed.

Purpose The study clarifies the relationship between students’ perceptions of university support and heterogeneous entrepreneurial intentions in the Chinese context. It proposes a new construct with the classification of growth- and independence-oriented intentions and examines the moderating role of the Chinese sense of face. This study aims to enrich entrepreneurship education research by incorporating cultural factors. Design/methodology/approach The study uses a questionnaire survey to examine the research hypotheses. Further, the authors collected data from 374 students from Mainland China and applied a regression analysis. Findings The study clarifies the positive relationship between perceived university support and growth-oriented/independence-oriented entrepreneurial intentions. Further, it proposes the differences in the moderating role of the Chinese sense of face in the relationships between entrepreneurial self-efficacy and growth- and independence-oriented intentions. Research limitations/implications Because of the chosen method, the study results may lack generalizability. Hence, future studies are encouraged to test the proposed hypotheses. Practical implications The study results have important implications for entrepreneurship education development. Social implications The study is conducted against the background of the “mass entrepreneurship and innovation” policy in China and combines country-specific characteristics to enrich entrepreneurial education and social entrepreneurship. Originality/value This study fulfills the intention to examine the influence of cultural factors on entrepreneurship education and identify the heterogeneous entrepreneurial intentions in a single construct.