Search results

This paper aims to provide a comprehensive review of the state-of–the-art design methods for additive manufacturing (AM) technologies to improve functional performance.

In this survey, design methods for AM to improve functional performance are divided into two main groups. They are design methods for a specific objective and general design methods. Design methods in the first group primarily focus on the improvement of functional performance, while the second group also takes other important factors such as manufacturability and cost into consideration with a more general framework. Design methods in each groups are carefully reviewed with discussion and comparison.

The advantages and disadvantages of different design methods for AM are discussed in this paper. Some general issues of existing methods are summarized below: most existing design methods only focus on a single design scale with a single function; few product-level design methods are available for both products’ functionality and assembly; and some existing design methods are hard to implement for the lack of suitable computer-aided design software.

This study is a useful source for designers to select an appropriate design method to take full advantage of AM.

In this survey, a novel classification method is used to categorize existing design methods for AM. Based on this classification method, a comprehensive review is provided in this paper as an informative source for designers and researchers working in this field.

Digital technologies have transformed business management practices and adapted them to shorter product lifecycles. As a result, firms are shifting their approach to building new competitive advantage from cost-oriented to entrepreneurial orientation (EO). This study aims to analyze the innovation performance (IP) in the context of EO from a knowledge management perspective. It constructs the functional path of the relationships among EO, knowledge coupling (KC) and IP of Chinese manufacturing firms to achieve business success.

Using data from 157 listed Chinese manufacturing firms from 2012 to 2021, the authors construct a panel data model to test the effect of EO on IP. This study classifies KC into existing knowledge coupling (EKC) and new and existing knowledge coupling (NKC) and analyzes their mediating effects in the above relationships.

This study finds that EO has an insignificant, inverted U-shaped relationship with IP. Both EKC and NKC have a significant, inverted U-shaped relationship with IP; in other words, if EKC and NKC increase, the IP of Chinese manufacturing firms first increases and then decreases, and EKC and NKC have a complete mediating effect on the relationship between EO and IP.

This study provides an in-depth analysis of IP from an EO perspective. The study’s findings enrich and extend the theoretical relationship between EO and IP. The authors also propose a knowledge management perspective for entrepreneurship research. These findings improve the current understanding of the role and function of KC in EO.

Firms are driven to ride on the digital wave in today’s open innovation ecosystem. This study aims to explore the effect of digital transformation (DT) on knowledge-intensive business services (KIBS) firms’ innovation ambidexterity, namely, radical versus incremental innovation, respectively. Meanwhile, the authors evaluated the moderating role of the complexity of R&D collaboration portfolio (i.e. organizational diversity and geographic diversity) in the above relationships.

Using a panel data set of 171 Chinese listed firms in the information and communications technology services industry from 2010 to 2018, the proposed hypotheses were empirically attested.

It is found that DT has a positive relationship with radical innovation and an inverted U-shaped relationship with incremental innovation. In terms of the R&D collaboration portfolio, organizational diversity positively moderates the relationships between DT and innovation ambidexterity, respectively. The geographic diversity weakens the inverted U-shaped effect of DT on incremental innovation; however, its moderating role in the link between DT and radical innovation is not empirically verified.

Extant scholars mainly addressed the interplay between KIBS firms and their manufacturing clients, while this study reveals the different consequences of DT on KIBS firms’ innovation ambidexterity to highlight the role of KIBS firms is an independent and essential innovator in a knowledge-driven economy. Notably, the findings contribute to knowledge management (KM) and R&D literature by confirming the diversity of the R&D collaboration portfolio is a critical KM strategy for KIBS firms to develop and promote external knowledge resources.

The purpose of this paper is to evaluate the forecasting performance of grey prediction models on educational attainment vis-à-vis that of exponential smoothing combined with multiple linear regression employed by the National Center for Education Statistics (NCES).

An out-of-sample forecasting experiment was carried out to compare the forecasting performances on educational attainments among GM(1,1), GM(1,1) rolling, FGM(1,1) derived from the grey system theory and exponential smoothing prediction combined with multivariate regression. The predictive power of each model was measured based on MAD, MAPE, RMSE and simple F-test of equal variance.

The forecasting efficiency evaluated by MAD, MAPE, RMSE and simple F-test of equal variance revealed that the GM(1,1) rolling model displays promise for use in forecasting educational attainment.

Since the possible inadequacy of MAD, MAPE, RMSE and F-type test of equal variance was documented in the literature, further large-scale forecasting comparison studies may be done to test the prediction powers of grey prediction and its competing out-of-sample forecasts by other alternative measures of accuracy.

The findings of this study would be useful for NCES and professional forecasters who are expected to provide government authorities and education policy makers with accurate information for planning future policy directions and optimizing decision-making.

As a continuing effort to evaluate the forecasting efficiency of grey prediction models, the present study provided accumulated evidence for the predictive power of grey prediction on short-term forecasts of educational statistics.

Given that environmental issues have become increasingly critical in business operations, from the lens of guanxi, this study explores the impact of relational capital on green innovation in a knowledge-driven context of new energy enterprises. Additionally, the moderating effect of corporate environmental responsibility (CER) on the above relationship is analyzed.

This study takes 162 Chinese new energy enterprises from 2010 to 2020 as the research sample. For empirical analysis, factor analysis is adopted to comprehensively measure relational capital, while green innovation is embodied in two dimensions, namely radical green innovation (RGI) and incremental green innovation (IGI).

Relational capital significantly promotes RGI and IGI. Moreover, it is found that implementing CER strengthens the positive relationship between relational capital and RGI but weakens the positive relationship between relational capital and IGI.

It is evident that existing literature on green innovation mainly focused on a single perspective rather than from different dimensions. In addition, few scholars have drawn from stakeholder theory to elucidate the interaction of relational capital with corporate responsibility practices. In this regard, this study examines the link between relational capital and green innovation while examining the moderating effect of CER, which provides valuable insights for future research on relational governance and innovation management. Furthermore, this study innovatively centers on new energy enterprises in China, which are pioneers and facilitators of green development, as the research subject. Considering relevant studies are still nascent in this domain, our empirical results are of extensive practical guidance for managers and practitioners to promote environmental sustainability.

Existing hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.

This study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.

The capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.

The initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

To adapt to the rapidly changing market environment, firms must constantly adjust and change their knowledge base to develop new technologies. The purpose of this paper is to analyze the improvement path of firms’ breakthrough innovation from the perspective of knowledge recombination in the context of dynamic change in the knowledge base. By analyzing the influencing mechanism of environmental dynamism on the relationship between the two, this paper provides a theoretical foundation for managers to make knowledge recombination decisions under a dynamic external environment while further enriching the firm’s innovation achievements.

Using data from 220 manufacturing firms listed on the Shanghai and Shenzhen A-share stock from 2010 to 2018, an extensive panel data set was constructed to investigate the effect of knowledge recombination, which was divided into recombination creation and recombination reuse, on firms’ breakthrough innovation. In addition, the authors differentiated environmental dynamism as market dynamism and technological dynamism and then examined its moderating role in the above relationships.

The research results show that various recombination behaviors of knowledge elements have a differentiated effect on firms’ breakthrough innovation presented as follows: Knowledge recombination creation is significantly positively correlated with firms’ breakthrough innovation, while knowledge recombination reuse is significantly negatively correlated with firms’ breakthrough innovation. In addition, environmental dynamism has a considerable moderating effect between knowledge recombination and firms’ breakthrough innovation further, emphasizing that the moderating effect on different types of knowledge recombination behaviors is significantly distinct.

First, given that this study refers to several Chinese noted databases to collect second-hand data for empirical analysis, future research could use first-hand data by collecting questionnaire survey and interview to provide a more practical and detailed research conclusion. Second, the authors focused on the contextual variable to explore the moderating role of environmental dynamism on the relationship between knowledge recombination and breakthrough innovation. Nevertheless, the indirect effects of other internal factors were not discussed. The authors advocate future studies to involve other moderators from employee social and phycological perspectives, such as trust in colleagues in the proposed theoretical models in this study.

This study is conducive for managers to attach great attention to knowledge management practices in the firm and to understand the critical role of knowledge recombination in affecting innovation performance under dynamic environmental changes. Moreover, this study provides practical guidance and serves as a reference for firms to strengthen their knowledge recombination ability as full utilization of existing knowledge elements and exploration of new knowledge values.

Primarily, from the perspective of dynamic changes in the knowledge base, this paper explores how the knowledge recombination behaviors affect firms’ breakthrough innovation, thereby enriching and extending the relationship theory between knowledge recombination capabilities and breakthrough innovation, while new and valuable ideas are provided in the study of issues related to the firms’ breakthrough innovation; Moreover, this study analyzes the moderating effects of diverse types of environmental dynamism on the relationship between knowledge recombination and firms’ breakthrough innovation from a multi-dimensional perspective proposing that the moderating effects of environmental dynamism on different knowledge recombination behaviors are distinct.

This study aims to analyze the moderating effect of managerial discretion on the relationship between cross-border knowledge search and the high-tech firms’ innovation quality in a global health emergency and addresses the following issues: the influence mechanism of different types of cross-border knowledge search on the high-tech firms’ innovation quality in a global health emergency; and the moderating role of different dimensions of manager discretion on the above relationship.

Based on the firms’ strategy selection methods, the authors divided cross-border knowledge search into three aspects, namely, breadth, depth and balance, and analyzed the impact of cross-border knowledge search on the innovation quality of high-tech firms in a global health emergency, taking managerial discretion as the moderating variable, and divided it into position rights, pay rights and operation rights according to the key rights of firms’ strategic management. Furthermore, the authors constructed a theoretical model, and for an analysis sample, the authors collected data from Chinese high-tech firms from 2013 to 2021.

The empirical results show that there is an inverted U-shaped relationship between cross-border knowledge search breadth, depth and balance and the innovation quality of high-tech firms. The position rights, pay rights and operation rights of managerial discretion have partially significant moderating effects on the relationship between cross-border knowledge search breadth, depth and balance and the high-tech firms’ innovation quality.

Considerable literature has grown around the theme of the impact of knowledge search on the firms’ innovation quality. Nevertheless, only a few studies draw on the combination of global health emergency, cross-border knowledge search and the innovation quality; in particular, no literature has analyzed the relationship between the three from the managerial discretion perspective. Exploring the above relationships has great theoretical value for enriching and improving knowledge management and innovation management theories and provides a theoretical basis and practical support for high-tech firms to face challenges of a global health emergency and to break through the innovation dilemma.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

This paper aims to anticipate the possible development direction of WAAM. For large-scale and complex components, the material loss and cycle time of wire arc additive manufacturing (WAAM) are lower than those of conventional manufacturing. However, the high-precision WAAM currently requires longer cycle times for correcting dimensional errors. Therefore, new technologies need to be developed to achieve high-precision and high-efficiency WAAM.

This paper analyses the innovations in high-precision WAAM in the past five years from a mechanistic point of view.

Controlling heat to improve precision is an effective method. Methods of heat control include reducing the amount of heat entering the deposited interlayer or transferring the accumulated heat out of the interlayer in time. Based on this, an effective and highly precise WAAM is achievable in combination with multi-scale sensors and a complete expert system.

Therefore, a development direction for intelligent WAAM is proposed. Using the optimised process parameters based on machine learning, adjusting the parameters according to the sensors’ in-process feedback, achieving heat control and high precision manufacturing.