Search results

The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.

The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.

The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33–0.41 mm, and the average seam tracking error is 0.11–0.22 mm.

The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.

This study aims to explore how the concept of extended self-influences luxury shopping tourism consumption among Chinese tourists. It explores why luxury shopping is important for Chinese tourists. Specifically, this study focuses on a strategically important emerging market segment: post-1990s female Chinese tourists.

To explore the link between the extended self and luxury shopping tourism consumption among post-1990s Chinese female tourists, this study adopted a qualitative and interpretive approach. A total of 22 semi-structured in-depth interviews were carried out to collect the data.

This qualitative inquiry found that luxury shopping during overseas holidays has some subtle differences from luxury shopping in China, as the conceptualisation of luxury is highly context-based. Through a focus on gender and generational differences, the current study reveals that the idea of individuality has started to influence their luxury purchases.

The study shows how different selves are associated with luxury shopping. It thus provides empirical evidence regarding the reasons behind their motivation, especially for shopping overseas to get a good price and an exclusive and enjoyable luxury shopping experience abroad. Also, it was found that curiosity about buying luxury products is viewed as less favoured and logical shopping will be a future trend. Individuality is becoming a trend for younger consumers.

Theoretically, by linking the “extended self” with luxury shopping tourism, this study provides the social-psychological aspects of luxury shopping tourism. Instead of focusing on particular destinations, this study provides compressed but also focused inquiries to explore how the concept of the self-influences post-1990s female Chinese tourists’ shopping consumption while on holiday, and how this luxury shopping experience influences their concept of the self.

本文运用“延伸自我”概念对中国泳客在境外购买奢侈品的行为和意义进行探讨, 从而进一步了解中国游客对奢侈品的消费形态。 研究主要专注在中国90后女性市场, 女性市场已被许多学者以即市场营销人员认定为新兴市场领域。

通过22个深度访谈的所得数据表明境外奢侈品高买行为与自我概念有着紧密的联系, 并为消费者购买动机背后的原因提供了实证

在境外旅游过程中购买奢侈品有非常大的价格优势, 同时又能为消费者提供独特的购物体验, 中国90后女性的奢侈品购买行为日趋理性并在出行之前做好详细的购物计划, 个性的展示将成为中国年轻女性奢侈品消费的主导因素。

该研究的理论贡献是探索自我概念如何影响90年代后中国女性游客在境外旅行度假时的奢侈品购物行为, 以及这种奢侈品购物体验如何影响其自我概念。

关键词 购物，中国游客，女性， 延伸自我，自我概念

文章类型 研究论文

El estudio se dedica a explorar cómo influir el conceptöauto-extensión¨ al comportamiento de comprar los artículos de lujo en el extranjero entre los chinos turísticos, también a explorar la importancia de tal caso para ellos.

Este estudio ha adaptado un enfoque cualitativo entre 22 entrevistas en profundidad semiestructuradas para la recopilación de datos y muestra cómo se asocian diferentes seres con las compras de lujo.

Por lo tanto, se proporciona las pruebas evidentemente sobre las razones detrás de su motivación. Comprar artículos de lujo durante los viajes al extranjero tiene una gran ventaja de precio, al tiempo que las ofrece una experiencia especial. Estos actos se están volviendo más racionales y tienen planes de compras detallados antes de viajar. La exhibición de la personalidad se convertirá en el factor dominante en cuanto al consumo de artículos de lujo por parte de tales chinas.

Principalmente se enfoca a un nuevo campo del mercado en lo cual los consumidores son las chicas quienes nacieron después del año 1990.

Based on the extended resource-based view (ERBV), this paper aims to investigate the relationship between sustainable supply chain management (SSCM), dynamic capabilities (DCs) and enterprise economic performance (EEP). Both the direct effects of SSCM on economic performance and the mediation effect of DCs are investigated. This empirical study also examines the moderating role of firm size.

This study applies hierarchical regression analyses to test our hypotheses, and then the mediation test was performed using the macro PROCESS. Data were collected from 178 Chinese manufacturing firms.

The findings show that SSCM practices significantly and positively influence both economic performance and DCs. The results indicate that DCs partially mediate the relationship between SSCM practices and EEP. Moreover, firm size has a moderating effect on external SSCM practices that influence EEP, but the moderating effect was not found to be significant for the effects of internal SSCM practices on economic performance and SSCM practices on DCs.

This study reveals insights into the potential benefits for large enterprises and SMEs related to the utilization of SSCM practices in China and puts forward differentiated suggestions for SSCM practices in large enterprises and SMEs.

Drawing on the ERBV, this study provides a deeper perspective on the relationship between SSCM and EEP by regarding DCs as a mediating variable and firm size as a moderating variable.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Abnormal behaviors such as rework, backlog, changes and claims generated by project organizations are unavoidable in complex projects. When abnormal behaviors emerge, the previously normal state of interactions between organizations will be altered to some extent. However, previous studies have ignored the associations and interactions between organizations in the context of abnormal organizational behaviors (AOBs), making this challenging to cope with AOBs. As a result, the objective of this paper is to explore how to reduce AOBs in complex projects at the organizational level from a network perspective.

To overcome the inherent limitations of a single case study, this research integrated two data collection methods: questionnaire survey and expert scoring method. The questionnaire survey captured the universal data on the influence possibility of AOBs between complex project organizations and the expert scoring method got the influence probability scores of AOBs between organizations in the case. Using these data, four organizational influence network models of AOBs based on a case were developed to demonstrate how to destroy AOBs networks in complex projects using network attack theory (NAT).

First, the findings show that controlling AOBs generated by key organizations preferentially and improving the ability of key organizations can weaken AOBs network, enabling more effective coping strategies. Second, the owners, government, material suppliers and designers are identified as key organizations across all four influence networks of AOBs. Third, change and claim behaviors are more manageable from the organizational level.

Project managers can target specific organizations for intervention, weaken the AOBs network by applying NAT and achieve better project outcomes through coping strategies. Additionally, by taking a network perspective, this research provides a novel approach to comprehending the associations and interactions between organizations in the context of complex projects.

This paper proposes a new approach to investigating AOBs in complex projects by simultaneously examining rework, backlog, change and claim. Leveraging NAT as a novel tool for managing the harmful effects of influence networks, this study extends the knowledge body in the field of organizational behavior (OB) management and complex project management.

Most of the existing studies on the evolution of emergency topics in social media focused on the emergency information demand of fixed user type in emergency while ignoring the changing roles of stakeholders during the emergency. Thus in this study, a three-dimensional dynamic topic evolution model is proposed, in which fine grained division of time, dynamic identification of stakeholders in the emergency, and emergency topic evolution based on both timeline and stakeholder's type are all considered.

Particularly the relevance between the tweets posted and the topic of emergency, the influence on the social network, and the attention of emergency topic are as well taken into account to quantitatively calculate the weight and ranking of stakeholders at different stages of the emergency. To verify the proposed model, an experimental demonstration was carried out under an emergency event posted on social media.

The results show that (1) based on the three-dimensional dynamic topic evolution model, the composition and ranking of stakeholders have obvious differences at different stages; (2) the emergency information needs and the sharing behavior of stakeholders on emergency information also indicate different preferences where the topic concerns of stakeholders at different stages have a strong relationship with their weight ranking; (3) the emergency topic evolution considering both the dynamics of emergency stakeholders and emergency information demand could more accurately reflect the changing regularity of social media users' attention to information in emergency events.

This study is one of first to investigate the emergency topic evaluation on social media by considering the dynamic changes of various stakeholders in emergency. It could not only theoretically provide more accurate method to understand how users share and search emergency information in social media, but also practically signify an information recommendation way in social media for emergency tracking.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2021-0098.

Browsing knowledge documents in a peer-to-peer (P2P) environment is difficult because knowledge documents in such an environment are large in quantity and distributed over different peers who organize the documents according to their own views. This paper aims to propose a method for constructing a personal knowledge map for a peer to facilitate knowledge browsing and alleviate information overload in P2P environments.

The research presents a method for constructing a personal knowledge map. The method adopts an ontology-concept-tree-based classification algorithm to recognize a peer’s personal knowledge structure and construct a personal knowledge map, and uses a self-organizing map algorithm to cluster and visualize the knowledge documents. The correctness of the created knowledge map is evaluated with a collection of abstracts of academic papers.

The method for constructing a personal knowledge map is the main finding of this research. The evaluation shows that the created knowledge map is good in quality.

The proposed method provides a way for P2P platforms to understand their users’ knowledge background, as well as to improve the P2P platform environment. However, the proposed method will not help a peer when he has nothing in his individual knowledge document repository (i.e. the “cold start” problem). The method also requires a relatively good ontology base for a P2P document sharing system to use the method effectively.

It is novel that the proposed method organizes the knowledge documents related to a peer’s knowledge background into a personal knowledge map. Moreover, the created knowledge map combines the advantages of a hierarchical display and a map display. It has values for a distributed P2P environment to facilitate users’ knowledge browsing and to alleviate information overload.

The classification of aircraft failures has been a significant part of functional hazard analysis (FHA). Aiming at the shortcomings of the traditional FHA method in the evaluation of aircraft risk, the purpose of this paper is to put forward a new approach by combining the gray comprehensive relation calculation method in the gray system theory with the traditional FHA in order to deal with the problem of “little data, poor information.”

This paper combines FHA, 1–9-scale method and gray relation analysis. At first, aircraft failure scenarios are chosen and data from experts are collected; then gray system theory is applied to find the relevance of such scenarios. Finally, the classification according to relevance is determined.

In the past, “little data, poor information” made it difficult for researchers to implement FHA. In this paper, the authors manage to deal with the problem of “poor information” and provide an approach to find the seriousness of aircraft failure.

Due to the use of expert-evaluating methods, the classification of failures is still a little subjective and can be improved in this area. In the future, the method can be improved from the perspective of combining FMEA to analyze more complex indicators or using multisource heterogeneous solutions to solve fuzzy numbers, probabilities, gray numbers and indicators that cannot be assigned.

The paper uses FHA to divide the failure state and establishes a gray evaluation model of the aircraft failure state classification to verify the relevant method. Some aircraft safety design requirements are used to check the safety hazards of the aircraft during the design process, and to provide rational recommendations for the functional design of the aircraft.

Improving the safety of aircraft is undoubtedly of great practical significance and has become a top priority in the development of the civil aviation industry. In this paper, the FHA method and the failure state of the aircraft are studied. The original FHA method is innovated by using the gray system theory applicable to the poor information state. Therefore, to some extent, this study has significance for improving the safety of civil aircraft flight, ensuring people’s travel safety and enhancing the society’s trust in civil aviation.

The main innovation of this paper is integrating the FHA method and the gray system theory. This study calculates the comprehensive relation degree of each failure under different flight stages, and uses FHA to divide the failure state, and finally establishes a gray evaluation model of the aircraft failure state classification to analyze the different conditions of the landing gear brake system, so that it improves the present situation, and the problem with the character of “little data, poor information” can be addressed better.

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM).

Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution.

It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration.

The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation.

The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.