Search results

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

This study aims to investigate whether the contract manufacturer (CM) should take the first-mover advantage in the end-product without supplying core components to the original equipment manufacturer (OEM) immediately, or should fully squeeze the benefit of the learning effect through an amplified production quantity by letting the OEM enter the end-product market early.

The authors propose a two-period model for a supply chain consisting of a CM and an OEM where the CM has four alternative entry strategies concerning it competition to the OEM in the end-product market. For each strategy, the authors derive the equilibrium solutions of the two firms using a backward approach. Comparison leads to the CM’s final choices among the four strategies.

For both CM and OEM, the monopoly and the first-entry strategies will be dominated by either the post-entry or the simultaneous-entry strategy, and thus, their preferred strategy is chosen from the latter two. Regarding the two firms choices between the post- and simultaneous-entry strategy, the CM prefers the post-entry strategy when the OEMs brand premium is at a moderate level, whereas the OEM prefers the post-entry strategy when its brand premium is low, and the learning effect can amplify the interval for the CMs adopting the post-entry strategy as well as changes the interval for the OEMs preference related to the two strategies.

This paper is the first one to explore the optimal strategy for a CM to maximize its profit in a co-opetitive supply chain situation with a CM and an OEM. The authors believe that our paper contributes to both literature and the market.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This paper enhances our understanding of how national culture impacts manufacturing performance (assembly speed, consistency between teams, etc.) during a production process move. The authors also investigate the efficacy of co-location as a strategy to enhance knowledge transfer from one organization to another.

To study the impact of national culture on production process moves, the authors develop and employ a team-based behavioral experiment within and between an individualist society (the United States) and a collectivist one (China). The authors also examine the impact of co-location on knowledge transfer effectiveness within and between these two unique cultures.

Interestingly, co-location has little impact on the performance of US recipient teams. Without co-location, Chinese recipient team performance lags significantly behind the US teams. However, firms can overcome these knowledge transfer challenges by co-locating source and recipient team members. These results suggest that firms should assess the national cultural context when considering co-location to manage their production move. There are contexts where co-location may be incredibly useful to facilitate an effective knowledge transfer (e.g. collectivist cultures like China) and contexts where this approach may not be as valuable (e.g. individualistic cultures such as the United States).

This research contributes to the academic literature in several ways. First, while past research demonstrates that national culture can be an essential barrier to information and knowledge sharing, this paper extends these findings showing that co-location may effectively overcome this barrier. After the authors offer and test the merits of co-location, they also establish the boundary conditions of this approach by showing that the effect of co-location on knowledge transfer is contingent on the cultural context. This contribution enhances our understanding of the relationship between national culture and knowledge sharing and has implications for managers developing approaches to transfer knowledge between cultures. Second, the authors develop and execute a novel cross-country experimental design. While cross-country experiments have been done before (e.g. Ozer et al. 2014, Kuwabara et al. 2007, etc.), it is still rare to see such experiments due to them being “technically difficult and costly” (Ozer et al. 2014, p. 2437). This research not only offer insights into how teams of people from individualist and collectivist societies send, receive and comprehend production knowledge. It also documents how these teams convert this knowledge into production results.

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.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

This study aims to investigate the factors affecting (i.e. determinants) the continuance of mobile learning adoption in an informal setting among higher education learners from a rural region in the Philippines. It assesses the extent of the determinants of mobile learning adoption continuance and their interrelationships and the role of a personality trait (e.g. locus of control) on its determinants.

This study used a rigorous literature review method that led to a mobile learning adoption continuance model. This proposed model analyzed the perceptions of higher education learners’ experiences on mobile learning adoption during the COVID-19 pandemic (i.e. informal setting). The data collection was self-administered using an online survey from a convenience sample size of 434 using adapted questionnaire instruments. The study used factor analysis by using a structural package for social sciences (SPSS) and analysis of the moment of the structure. The effect sizes of the direct effect, simple and serial mediation and interaction effects in a path model were analyzed by using user-defined estimand and orthogonalized approaches.

The findings indicate that the effect of perceived security risks along with perceived functional benefit and learner value affect the mobile learning adoption continuance. The perceived learner value mediates the perceived functional benefit relationship on mobile learning adoption continuance. Perceived security risk indirectly affects mobile learning adoption continuance through perceived functional benefit and learner value. In addition to this, the internal locus of control strengthens the positive relationship between perceived functional benefit and mobile learning adoption continuance. However, it dampens the positive relationship of perceived learner value.

The study provides an essential foundation on the mobile learning adoption model that focuses on its continuance. This model integrated perceived security risks, functional benefits and learner value aspects of continuance intention that higher education institutions may consider in their mobile learning initiative. It further provides evidence to intensify the important moderating role of locus of control that intervenes on the determinants of mobile learning adoption continuance.

Drawing on the knowledge-based view, this study investigates how IT–business alignment influences business model design via organizational learning and examines the moderating role of data-driven culture in the relationship between IT–business alignment and business model design via organizational learning.

Using multi-respondent survey data collected from 597 Chinese firms, mediation and moderated mediation analyses were used to examine this study's hypotheses.

The mediation test results revealed organizational learning served as a mediator between IT–business alignment and two types of business model design (i.e. novelty- and efficiency-centered). In addition, data-driven culture strengthened the indirect effects of IT–business alignment on these two types of business model design via organizational learning.

This study extends current understandings of the relationship between IT–business alignment and business model design by revealing the mediating role of organizational learning and investigating its indirect effects under various degrees of data-driven culture. As such, it contributes to the literature on the business model and IT–business alignment and provides insights for managers seeking to achieve the expected business model design.

Researchers have previously utilized the project-based 6E learning model and the problem-based quantum learning model in various courses, such as the instructional principles and methods course and the character and values education course. These models were evaluated for their impact on students in different subjects, including developing skills, values, democracy perceptions, attitudes towards cooperative learning, metacognitive thinking skills and teacher self-efficacy perceptions. In 2023, Ökmen, Sahin and Kiliç reported positive outcomes, while Sahin and Kiliç reported similar findings in 2023a, 2023b and 2023c. There has been no investigation into how the models affect students' critical thinking and academic literacy. This study seeks to determine the impact of both models on these skills, gain more insight into their effectiveness and determine which is more beneficial. The results will guide the decision-making process for the character and values education course and other courses in the future. Specifically, this research aims to compare the effects of the project-based 6E learning model and problem-based quantum learning model on critical thinking and academic literacy.

This research employed the Solomon four-group experimental design to assess the efficacy of the applications. Prior knowledge and experience of the participants were evaluated through pretests. However, it should be noted that pretests may impact posttest scores either positively or negatively. For instance, participants taking the test multiple times may become more interested or attentive to the subject matter. The Solomon four-group design was deemed appropriate to analyze the influence of pretesting. This design enables the investigation of the application effect, pretest effect and interactive effect of pretest and application (van Engelenburg, 1999).

It was concluded that the project-based 6E learning model was effective in developing critical thinking in students, but not significantly. It was concluded that the problem-based quantum learning model significantly improved students' critical thinking skills. It was concluded at the end of the study that the project-based 6E learning model notably enhanced students' academic literacy. It was concluded that the problem-based quantum learning model had a significant positive impact on students' academic literacy. According to research, it has been determined that the problem-based quantum learning model is superior in enhancing critical thinking abilities compared to the project-based 6E learning model. Nevertheless, there seems to be no detectable disparity in the academic literacy advancement of pupils between the problem-based quantum learning model and the project-based 6E learning model.

There has been no investigation into how the models affect students' critical thinking and academic literacy. This study seeks to determine the impact of both models on these skills, gain more insight into their effectiveness and determine which is more beneficial. The results will guide the decision-making process for the character and values education course and other courses in the future.