Search results

Most process FMEA systems in the market simply fill out the worksheet/form, and can not effectively express, organize, and utilize process failure knowledge of production process during process planning. This paper proposes a knowledge‐enriched representation model for enhancing process FMEA in process planning. This model can effectively represent the process FMEA knowledge other than specific process failure instances or data. Furthermore, in order to effectively utilize the previous process FMEA knowledge, an algorithm is proposed to calculate the similarity among process failure problems based on “concept distance”. A knowledge‐enriched process FMEA tool has been developed by adopting Web technique so as to provide cooperative process FMEA services for process planner on the Internet or Intranet.

This paper gathers tourism digital footprint from online travel platforms, choosing social network analysis method to learn the structure of destination networks and to probe into the features of tourist flow network structure and flow characteristics in Guilin of China.

The digital footprint of tourists can be applied to study the behaviors and laws of digital footprint. This research contributes to improving the understanding of demand-driven network relationships among tourist attractions in a destination.

(1) Yulong River, Yangshuo West Street, Longji Terraced Fields, Silver Rock and Four Lakes are the divergent and agglomerative centers of tourist flow, which are the top tourist attractions for transiting tourists. (2) The core-periphery structure of the network is clearly stratified. More specifically, the core nodes in the network are prominent and the core area of the network has weak interaction with the peripheral area. (3) There are eight cohesive subgroups in the network structure, which contains certain differences in the radiation effects.

This research aims at exploring the spatial network structure characteristics of tourism flows in Guilin by analyzing the online footprints of tourists. It takes a good try to analyze the application of network footprint with the research of tourism flow characteristics, and also provides a theoretical reference for the design of tourist routes and the cooperative marketing among various attractions.

Although the prior literature has identified the relevance of dealer participation for multinational enterprises (MNEs), it is unclear whether such participation could also be an important means for local dealers to learn from MNEs. By adopting local firms’ viewpoint, our study draws on organizational learning theory to examine how local dealers benefit from their participation with foreign suppliers in Africa.

The empirical setting is a combinative dataset of secondary data and primary survey of 164 small- and medium-sized local dealers with nine subsidiaries of a Chinese motorcycle company in six countries of Sub-Saharan Africa.

This research shows that dealer participation is positively associated with dealer performance, and this positive effect is stronger when local dealers operate in regions with low government corruption and high government support. However, the positive relationship is weaker when local dealers use the local tongue extensively but becomes stronger when their foreign suppliers have a high dealer coverage.

By taking a local-participant perspective, our study extends the participation literature to show how firms from a resource-constrained region may benefit from their proactive participation with foreign counterparts. Additionally, we identify the boundary conditions of institutional factors and strategic choices of local dealers and foreign suppliers, providing a nuanced understanding of firm behaviors in complex and uncertain markets.

In this paper, an attempt is made to obtain buckling loads, ultimate bearing capacity and other required structural characteristics of grid structure panels. The numerical method for post-buckling behavior analysis of panels involving multiple invisible damages is also presented.

In this paper, two bidirectional stiffened composite panels are manufactured and tested. Multiple discrete invisible damages are introduced in different positions of the stringers, and the experimental and simulation investigation of buckling and post-buckling were carried out on the damaged stiffened panels.

The simulation load–displacement curves are compared with the experimental results, and it is found that the simulation model can well predict the occurrence of buckling and failure loads. The strain curve shows that the rate of strain change at the damaged site is greater than that at the undamaged site, which reflects that the debond is more likely occurred at the damaged site. The simulation verifies that the panel is usually crushed due to matrix compression and fiber–matrix shear.

In this paper, post-buckling tests and numerical simulations of bidirectional stiffened composite panels with impact damage were carried out. Two panels with four longitudinal stringers and two transverse stringers were manufactured and tested. The buckling and post-buckling characteristics of the grid structure are obtained, and the failure mechanism of the structure is explained. This is helpful for the design of wall panel structure.

This study aims to contribute novel insights into understanding and mitigating the harmful consequences of abusive supervision (AS) by examining the association between AS experiences, revenge, forgiveness, and the moderating role of emotional intelligence (EI). The key argument is that employees' EI can influence the AS experience through affective processes, countering supervisors' abusive behaviors.

A between-person scenario-based experiment was conducted with 366 participants divided into AS and control groups. The study explored the association between AS experience and revenge/forgiveness, mediated by core affect (valence and activation). EI abilities were measured as a moderator. Data analysis examined the relationships and interactions among AS, revenge/forgiveness, EI, and affective experiences.

The study reveals significant findings indicating that AS experiences were positively associated with revenge and negatively associated with forgiveness. The mediation analysis confirmed the role of core affect in these relationships. EI emerged as a moderator, shaping the association between AS experiences and revenge/forgiveness. Importantly, participants with higher EI exhibited lower revenge intentions, demonstrating the potential of EI to mitigate the adverse effects of AS. Unexpectedly, individuals with high EI also expressed fewer forgiveness intentions.

This study provides a comprehensive understanding of how employees can effectively counterbalance the impact of AS through higher levels of strategic EI. Examining core affect as a mediator offers novel insights into coping mechanisms in response to AS experiences and their consequences.

The study acknowledges several limitations, as the scenarios may only partially capture the complexities of real-life AS situations. The focus on a specific context and the sample characteristics limit the generalizability of the findings. Future research should explore diverse organizational contexts and employ longitudinal designs.

The findings have practical implications for organizations as enhancing employees' EI skills through training programs interventions and integrating EI into organizational culture and leadership conduct.

Line–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system.

First, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms.

Computations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown.

In this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.

This study investigates the impact of customer stability on the cost of debt and the moderating effect of environmental uncertainty on the above relationship.

An empirical analysis based on the ordinary least squares (OLS) regression model is conducted using China’s A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2007 to 2021.

The results indicate that customer stability significantly inhibits the cost of debt, and the higher the environmental uncertainty, the more significant the inhibitory effect of customer stability on the cost of debt. The results of heterogeneity analyses indicate that the more intense the industry competition, the higher the customer concentration or the older the average customer age, the more significant the inhibiting effect of customer stability on the cost of debt.

This study highlights the importance of customer relationship management and supply chain risk management, which have both theoretical and managerial implications. Despite its contributions, this study has limitations, such as China’s institutional context limits, which the generalisability of our results, and the sample size for this study is small because of limitations in measuring customer stability.

Existing literature has not yet reached a consistent conclusion on how customer relationships affect the cost of debt, and such studies are mainly centered around perspectives such as customer concentration and the contagion effect of supply chains. This study constructs an indicator of customer stability using detailed information on the top five customers of China’s A-share listed companies and dynamically examines the impact of customer stability on the cost of debt, which expands the research on the influencing factors of the cost of debt, the economic consequences of customer stability and the theory of customer relationship management.

The purpose of this paper is to explore suzhi requirements and expectations to double-shouldered academics as middle-level cadres (双肩挑处级干部) in a Chinese higher education institute (HEI) as an initial step to examine the interplays between suzhi requirement and expectations and organizational operational mechanism in the Chinese context.

The study adopted an exploratory single-case approach for the study. In this study, 22 participants composed of middle-level cadres, other stakeholders at the university, college/department and business unit levels were interviewed.

Suzhi requirements for the cadres followed the Communist Party of China’s (CPC) guideline, the required de, neng, qin, ji and lian (德, 能, 勤, 绩, 廉) was abstract and vague. With the parallel systems, the CPC and administrative lines, there were discrepancies between the CPC required suzhi and those expected by the stakeholders. A social phenomenon, “official rank-oriented standard” (ORS, guan ben wei, 官本位), was found significantly intertwining with the self suzhi expectation of the cadres, unveiling a more complex dynamics than most research reported for the Chinese public sector organizations (PSOs).

With the initial qualitative findings unveiling suzhi as an organizational construct, this study informs future empirical research in the indigenous suzhi phenomenon in organizational setting. The conceptualized results of our study offer new insight for future indigenous Chinese management research in all PSOs including state-owned enterprises (SOEs).

As an initial step, this study endeavored to explore suzhi as an organizational construct in a Chinese HEI. The paper contributes to the literature by unveiling the complexity of PSOs in the interplays of dual management systems and ORS coupled with dual-role suzhi requirements for the cadres.

Due to the worldwide internet usage and online presence of businesses, digital marketing is an integrated part of the overall marketing strategy. One of the main investments in this field is the investment on more creative content that engages more potential customers. Online brand communities (OBCs) are used by businesses as a tool to have higher rates of engagement. Many researchers have developed customer engagement (CE) models to better understand it, but the transition economies like the countries of the Balkan are somehow left aside. Due to this identified gap in the literature, this paper aims to provide a model explaining the drivers and outcomes of CE, specifically on OBCs.

The research is based on primary data collected with an online questionnaire. Sample consisted of 764 respondents from four Balkan countries (Kosovo, North Macedonia, Albania and Montenegro). Data were analyzed using SPSS Statistics and IBM AMOS 26. To test the research model and hypotheses, confirmatory factor analysis and structural equation modeling were used.

First, the study found a positive impact of entertainment and customer satisfaction, and celebrities’ endorsement in CE. Furthermore, the results revealed a positive impact of CE on customer purchase intention and customer loyalty.

The research findings aim to help businesses of Balkan countries to better understand the importance and operation of CE in OBC.

This paper enriches the existing digital marketing literature of transition economies with a model of online CE.

This chapter elaborates on the role of clusters and innovational networks in Russia and interprets that the future growth of Russia’s economy depends on the development of clusters and innovational networks. We substantiated that clusters and innovational networks possess considerable potential in the sphere of provision of sustainable growth of Russia’s economy. Today, this potential is not used due to unequal distribution of clusters and innovational networks on Russia’s vast territory and due to their being at the initial stage of development – despite their prolonged period of existence. However, even in the current conditions, clusters and innovational networks make a significant contribution to Russia’s GDP (15.58% in 2018). By 2024, by implementing our described optimal scenario of clusters and innovational networks development, it would be possible to expand Russia’s GDP by 15 times, which will allow increasing the country’s GDP by 2.5 times. This positive effect will be achieved due to the provision of balance and stability of economic growth, which will make it more sustainable. Clusters and innovational networks should become a basis for sustainable growth of Russia’s economy. We recommend a scenario of sustainable growth based on a more equal distribution of clusters and innovational networks across Russia and a quicker development of clusters and innovational networks via more extensive involvement of private businesses.

This study explores how controlling leadership behaviors, such as authoritarian leadership and abusive supervision, affect police officers’ proactive behavior. Specifically, it examines how perceived powerlessness mediates the relationship between these leadership styles and officers’ proactive behavior.

We employed structural equation modeling with partial least squares analysis to test our hypotheses, utilizing a sample of police officers employed within a regional police institution in Indonesia.

The findings of this study indicate that both authoritarian leadership and abusive supervision have the potential to diminish or impede the proactive behavior of police officers by shaping their perception of powerlessness.

This study contributes to the broader understanding of how leader-controlling behaviors, such as authoritarian leadership and abusive supervision, impact the proactive behavior of police officers within the specific context of Indonesia.

In 2001, the US moved to regulate internal control reporting by management and auditors. While some jurisdictions have followed the lead of the US, many others have not. An important question, therefore, is the relevance of internal control to stakeholders. The more specific issue of the benefits of US-style regulation of internal control reporting is also topical. We review studies on the determinants of internal control quality and its economic consequences for stakeholders including investors, creditors, managers, auditors and financial analysts. We extend previous reviews by focusing on US studies published since 2013 as well as all non-US studies investigating IC quality including countries regulating IC disclosure as well as unregulated settings and both developed and developing economies. In doing so, we identify research questions where evidence remains mixed and new directions in which there are research opportunities.

Three main insights arise from our analysis. First, evidence on the economic consequences of internal control quality suggests that the quality of internal control can have a significant effect on decision making by users of financial information. Second, the results of research on the empirical association between ownership structure, certain board characteristics and internal control quality is generally mixed. Empirical evidence concerning the association between audit committee characteristics and internal control quality generally supports a positive and significant association. Finally, while studies in non-US jurisdictions are increasing, opportunities remain to explore the determinants and consequences of internal control in other jurisdictions. Our review provides evidence for policy makers of whether there are benefits from requiring management and auditors to report on internal control over financial reporting.

This study aims to investigate technology-based health and safety (H&S) management to control the spread of disease on construction sites using a partial least squares structural equation modelling (PLS-SEM) approach.

An extensive literature review is conducted to develop a conceptual framework. The variables identified from the literature review are included in a cross-sectional survey which gathered a total of 203 valid feedback. The variables for challenges are grouped under their relevant construct using exploratory factor analysis. Then, a hypothesized model is developed for PLS-SEM analysis using Smart PLS software. Later, the outcome of the model is further validated by nine construction experts using a semi-structured questionnaire survey.

The results rationalized the relationships between the COVID-19 H&S measures, challenges in implementing COVID-19 H&S measures on construction sites and the innovative technologies in transforming construction H&S management during the COVID-19 pandemic. The possible challenges that obstruct the implementation of H&S measures are highlighted. The potential technologies which can significantly transform H&S management by reducing the impact of challenges are presented.

The findings benefited the industry practitioners who are suffering disruption in construction operations due to the pneumonic plague.

By developing a conceptual model, this study reveals the contribution of technology-based H&S management for construction projects during the COVID-19 pandemic, which remains under-studied, especially in the context of the developing world.

Research into older adults' use of social media remains limited. Driven by increasing digitalisation in China, the authors focus on Chinese older adults (aged 60–75)’ use of WeChat.

This study used a qualitative interpretive approach and interviewed Chinese older adults to uncover their social practices of WeChat use in everyday life.

By using social practice theory (SPT), the paper unfolds Chinese older adults' social practices of WeChat use in everyday life and reveals how they adopt and resist the drastic changes in Chinese society.

The study contributes to new understandings of SPT from technology use by emphasising the dynamic characteristics of its three elements. The authors synthesise both adoptions and resistance in SPT and highlight the importance of understanding three elements interdependently within specific contexts, which are conditioned by structure and agency.

The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems.

On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects.

The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model.

This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

Consumer adoption of online shopping continues to increase each year. At the same time, online retailers face intense competition and few are profitable. This suggests that businesses and researchers still have much to learn regarding key antecedents of online shopping adoption and success. Based on extensive past research that has focused on the importance of various online shopping antecedents, this work seeks to provide an integrative, comprehensive nomological network.

The authors employ a mixed-methods approach to develop a comprehensive model of consumers online shopping behavior. To that end, in addition to a literature review, qualitative data are collected to identify a broad array of possible antecedents. Then, using a longitudinal survey, the model of consumer shopping intentions and behaviors is validated among 9,992 consumers.

The authors identified antecedents to online shopping related to culture, demographics, economics, technology and personal psychology. Our quantitative analysis showed that the main drivers of online shopping were congruence, impulse buying behavior, value consciousness, risk, local shopping, shopping enjoyment, and browsing enjoyment.

The validated model provides a rich explanation of the phenomenon of online shopping that integrates and extends prior work by incorporating new antecedents.

The assessment of risk to existing tunnels within the context of pit construction is influenced by a multitude of factors. The conventional fuzzy analytic hierarchy process (FAHP) method may lack precision due to its inability to incorporate the inherent randomness associated with numerous risk factors. To enhance the precision of risk evaluation for existing tunnels, this research introduces an improved FAHP approach grounded in cloud modeling theory.

We developed a risk assessment index system for existing tunnels, categorizing risk sources into three areas: hydrogeological conditions, foundation pit construction and tunnel structural bearing capacity. The system includes 11 evaluation indicators linked to these sources, with defined risk level thresholds for each. Using the cloud model, we calculated the membership degree of these indicators to risk levels, replacing traditional membership function formulas. The cloud model’s three digital characteristics (Ex, En and He) account for the randomness and ambiguity between qualitative descriptions and quantitative values, enhancing assessment accuracy. We applied hierarchical analysis to determine the weights of each risk factor and combined these with the membership degrees to evaluate overall risk levels. Engineering applications and model comparisons confirmed the method’s reliability, while sensitivity analysis identified key risk indicators affecting evaluation outcomes, allowing for targeted risk control measures to safeguard existing tunnels during foundation pit construction.

The evaluation results of engineering applications show the same results with the traditional FAHP method, which proves the reliability of the improved method. Furthermore, when comparing the evaluation result vectors between the two methods, it is observed that the outcomes of the improved method are more concentrated on a specific risk level compared to the traditional FAHP. This concentration mitigates the potential for bias in the evaluation results, thereby enhancing their accuracy. Through sensitivity analysis, four indicators were identified to have a significant influence on the evaluation result. After implementing targeted risk control measures, a downgrade in risk level to III was revealed. This aligns with the actual construction circumstances, as no safety incidents occurred in the Line 1 metro tunnel throughout the duration of the pit construction. This confirms the efficacy of the measures taken based on the evaluation results.

The novelty of this study is demonstrated through two key advancements. First, in response to the lack of a mature evaluation index system for risk assessment of existing tunnels during pit construction, the authors have meticulously curated a comprehensive risk evaluation index system. This system provides a valuable reference for the selection of appropriate risk evaluation indices in similar projects. Second, building upon the established index system, the study introduces a cloud model FAHP risk evaluation method. This method automates the generation of the membership degree between indicators and risk levels. The improved method has good reliability for the risk evaluation of existing tunnels, and it can provide decision-making reference for related studies when they carry out risk evaluations of similar projects.

In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

This study explores the deep integration of digital technology and cultural heritage to promote the preservation and inheritance of cultural heritage. Focusing on Digital Cultural Heritage (DCH), this research investigates its key role in activating theoretical research and practical applications in cultural heritage.

This study conducted an extensive bibliometric analysis utilizing VOSviewer and Bibliometrix visualization software to meticulously examine DCH research. Insights were gleaned from a dataset comprising 2,997 DCH-related publications harvested from the Web of Science database.

The bibliometric analysis reveals several notable findings: driven by active contributions from Italy, China, Spain, and the USA, the number of DCH publications shows a linear upward trend. Consiglio Nazionale delle Ricerche in Italy emerges as a prominent institution, while the Journal of Cultural Heritage stands out as the most influential journal in the DCH field. Scholars such as Remondino, Guidi, Barazzetti, and Carrozzino have significantly impacted DCH research. Furthermore, an in-depth analysis of keyword co-occurrence networks elucidates six major research trajectories in the DCH field, covering various aspects from cultural heritage digitization to digital humanities.

The study emphasizes the value of global knowledge exchange, interdisciplinary collaboration, innovative technology applications, and digital content provision practices in advancing DCH research.

By delving into the multifaceted landscape of DCH research, this study brings forth original insights into the escalating trends, pivotal contributors, and burgeoning research directions.