Search results

This study aims to examine how three types of information technology (IT) capability (supplier technological capability, customer technology-sensing capability and relatedness of IT infrastructure) facilitate knowledge acquisition by the customer when the supplier is dominant in the supplier-customer relationship.

The unit of analysis was project. The authors designed two different questionnaires that were responded by the project manager of an enterprise resource planning (ERP) software supplier and the contact person of the customer organization in the same project, respectively. The two questionnaires were matched by means of project name. The final sample included a total of 136 projects. The authors used ordinary least squares to test the research hypotheses.

The authors found that supplier power advantage negatively influenced knowledge acquisition by the customer. The three types of IT capability did not have direct impacts on knowledge acquisition. The moderating effect of customer technology-sensing capability was not significant either. However, supplier technological capability and relatedness of IT infrastructure attenuated the negative effect of supplier power advantage on knowledge acquisition, indicating that both factors promoted knowledge acquisition.

Knowledge acquisition is important for the success of software implementation in the supplier-customer relationship. There is limited evidence in the literature on how to apply externally oriented IT capability to enhance knowledge management, improve knowledge acquisition and manage the business relationship that is typically dominated by the software supplier. The authors provide evidence to examine related issues.

This study aims to fill several gaps in the literature. First, it examines the Umrah industry from the supply side, investigating the pivotal factors for travel agencies’ performance. Second, it empirically investigates service leaders’ competencies specific to the hospital and tourism industry. Third, it clarifies whether there is a direct impact of organisational service orientation on business performance. Fourth, it explores the influence of network capabilities in a service context, specifically in travel agencies, which has rarely been discussed.

This is a mixed-method study with sequential explanatory research design. First, a quantitative approach was conducted with 150 authorised travel agencies in Indonesia, with two manager-level employees representing each agency. The data were analysed using descriptive statistics and structural equation modelling. A qualitative study was conducted to enrich the findings by interviewing the Director of Umrah and Hajj Development of the Ministry of Religious Affairs of the Republic of Indonesia and three other respondents.

Service leaders’ competencies and resource capacity significantly influence organisational service orientation, leading to enhanced perceived service quality and performance. In addition, resource capacity influences network capabilities, improving performance.

This study identifies factors affecting the performance of Umrah travel agencies in an intensely competitive environment, which has rarely been discussed. This sheds light on how travel agencies can survive and succeed in this competitive industry. Moreover, this study provides evidence regarding the role of network capabilities in the tourism industry and the impact of organisational service orientation, both directly and indirectly, on performance.

Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.

The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.

The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.

In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.

The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

Export manufacturing firms from emerging markets can better meet customer needs by providing customization, which leads to competitive advantages. Although both practice and academic research have deeply discussed customization, the question of whether customization promotes export manufacturing firms' product innovation in the global B2B market is largely unexplored. The purpose of this paper is to address this issue.

This paper collects survey data from 2,248 export manufacturing firms in China and uses hierarchical moderated regression to explore the relationship between customization and product innovation in the global B2B market and their boundary conditions.

This research shows that customization positively affects export manufacturing firms' product innovation in the context of the global B2B market, and it shows that internal governance structure (contract governance and relationship governance) and external governance structure (legal enforceability) can be used as boundary conditions that affect the relationship. Specifically, contract governance has an inverted U-shaped moderating effect on the relationship between customization and product innovation; moreover, relationship governance and legal enforceability can strengthen the positive relationship between customization and product innovation.

The study explores the relationship between customization and product innovation in the global B2B market and examines the moderating effect of internal and external governance structures. In addition, the study enriches the research related to customization and product innovation in the context of the global B2B market and provides essential practical insight into the survival of export manufacturing firms from emerging markets.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

This paper aims to clarify the spatial connection characteristics and organization mode of logistics economy of 21 cities in Guangdong Province under the background of the integrated development of Guangdong, Hong Kong and Macao Bay area, and explore the spatial development characteristics and influencing factors of logistics economy in Guangdong Province.

This paper constructs the development level model of urban logistics economy in Guangdong Province from three aspects: demand level, supply level and support level, and uses the entropy weight method to measure the development level index of urban logistics economy in Guangdong Province. Then, the traffic accessibility index model is used to measure the traffic accessibility index between cities in Guangdong Province. Finally, using the social network analysis method, combined with the development level index of urban logistics economy in Guangdong Province and the urban traffic access index in Guangdong Province, this paper analyzes the spatial connection characteristics and influencing factors of logistics economy network in Guangdong Province.

There are regional differences in the development level of logistics economy in Guangdong Province; The overall network density of its logistics economic connection is large, but there is an imbalance in the network structure, and the core edge phenomenon is obvious; Logistics economic space presents the characteristics of double core development.

Because the research object is the spatial connection characteristics of logistics economy in Guangdong Province, the research results may lack universality. Therefore, researchers are encouraged to put forward further tests.

By studying the spatial connection mode of logistics economy in 21 cities in Guangdong Province, China, this paper promotes the original methods and empirical contributions, and constructs the research framework of spatial relationship of logistics economy. This research framework is universal to a certain extent.

This paper is conducive to promoting the integrated development of logistics economy in Guangdong Province and improving the balance of regional development of logistics economy.

Firstly, this study provides a new perspective to understand the spatial relationship and spatial spillover of logistics economy from relational data rather than attribute data. Secondly, This study enriched and broadened the research topic of spatial correlation of logistics economy. Thirdly, this research aims to promote the original methods and empirical contributions. Specifically, this study establishes a comprehensive research framework on the spatial network structure of logistics economy.

This paper aims to identify all variables and parameters related to business and emission within the petrochemical industry. The variables and parameters specified will be modeled into a system dynamic model that will be a baseline for the proposed best scenario(s) to address the business issue related to emission reduction in the petrochemical industry.

Literature review and stakeholder interviews were conducted to define the key factors contributing to the emission reduction of the petrochemical industry. The key factors are then developed into a system dynamic model to measure the quantitative impact of changes in those variables on emission and industry profitability.

This paper provides an analysis of system dynamic model. It suggests that process optimization can lead to a slight amount reduction in emissions. In contrast, a significant reduction shows in the simulation result of bio-based feedstock utilization and implementation of advanced technology. To sustain the emission reduction, strong commitment from stakeholders and support from the government will play an important role.

This research is limited to problem analysis of the primary product (high-value chemical) of the petrochemical industry by only considering the changes in the key factors of emission reduction.

This paper includes implications for interventions that can be imposed to reduce emission while retaining the business profitability.

The contribution of this study is to find the best scenario that can boost emission reduction within Indonesia’s petrochemical industry.

The phenomenon of defamilization of family firms is gradually increasing for the growth of family firms, that is, nonfamily executives are increasingly present in the executive teams of family firms. Although previous scholars have identified various determinants of family firms' defamilization, whether and when innovation underperformance affects the decision to defamilize family firms has not been explore. This study aims to fill the aforementioned research gaps.

This study empirically tests the theoretical view based on the data of Chinese A-share family listed companies from 2009 to 2017.

The authors found that innovation underperformance drives family companies to increase the percentage of nonfamily executives in their executive teams. Further, the authors found that family firms are less willing to hire nonfamily executives with an increase in socioemotional wealth, particularly when founders of such businesses serve as directors or are major shareholders, even when they are not directors.

This study shows that innovation underperformance and socioemotional wealth are important predictors of family firms’ defamilization decisions.