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Three-dimensional computer-aided design (CAD) assembly model has become important resource for design reuse in enterprises, which implicates plenty of design intent, assembly intent, design experience knowledge and functional structures. To acquire quickly CAD assembly models associated with specific functions by using product function requirement information in the product conceptual design phase for model reuse, this paper aims to find an approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model before functional semantic-based assembly retrieval.

An approach for structure-function correlations analysis and functional semantic annotation of CAD assembly model is proposed. First, the product knowledge model is constructed based on ontology including design knowledge and function knowledge. Then, CAD assembly model is represented by part attributed adjacency graph and partitioned into multiple functional regions. Assembly region and flow-activity region are defined for structure-function correlations analysis of CAD assembly model. Meanwhile, the extraction process of assembly region and flow-activity region is given in detail. Furthermore, structure-function correlations analysis and functional semantic annotation are achieved by considering comprehensively assembly structure and assembled part shape structure in CAD assembly model. After that, a structure-function relation model is established based on polychromatic sets for expressing explicitly and formally relationships between functional structures, assembled parts and functional semantics.

The correlation between structure and function is analyzed effectively, and functional semantics corresponding to structures in CAD assembly model are labeled. Additionally, the relationships between functional structures, assembled parts and functional semantics can be described explicitly and formally.

The approach can be used to help designers accomplish functional semantic annotation of CAD assembly models in model repository, which provides support for functional semantic-based CAD assembly retrieval in the product conceptual design phase. These assembly models can be reused for product structure design and assembly process design.

The paper proposes a novel approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model. Functional structures in assembly model are extracted and analyzed from the point of view of assembly structure and function part structure. Furthermore, the correlation relation between structures, assembled parts and functional semantics is expressed explicitly and formally based on polychromatic sets.

The proposed method is to generate the 3 D model of frame assemblies based on their topological model automatedly. It was a very demanding task and there was no appropriate automated method to facilitate this work.

The proposed method includes two stages. The first stage is decisive. In this stage, a deep learning network and the Chu–Liu–Edmonds algorithm are used to recognize contact relations among parts. Based on this recognition, the authors perform a geometrical computation in the second stage to finalize the 3 D model.

The authors verify the feasibility of the proposed method using a case study and find that the classification rate of the deep learning network for part contact relations is higher than 75 per cent. Furthermore, more accurate results could be achieved with modification by the Chu–Liu–Edmonds algorithm. The proposed method has lower computational complexity compared with traditional heuristic methods, and its results are more consistent with existing designs.

The paper introduces machine learning method into assembly modelling issue. The proposed method divides the assembly modelling into two steps and solves the assemble relation creatively.

Frame assemblies are fundamental to many areas. The proposed method could automate frame assembly modelling in a viable way. It could benefit design and manufacture process significantly.

The proposed method expands the application of machine learning into a new field. It would be more useful than simple machine learning in industry. The proposed method is better than general heuristic algorithms. It outputs identical results when the inputs are the same. Meanwhile, the algorithmic complexity in worst situation is better than general heuristic algorithms.

The purpose of this paper is to find a method for key assembly structure identification in complex mechanical assembly. Three-dimensional model reuse plays an increasingly important role in complex product design and innovative design. Assembly model has become important resource of models reuse in enterprises, which contains certain function assembly structures. These assembly structures implicating plenty of design intent and design experience knowledge can be used to support function-structure design, modular design reuse and semantics analysis for complex product.

A method for identifying key assembly structures in assembly model is presented from the viewpoint of assembly topology and multi-source attributes. First, assembly model is represented based on complex network. Then, a two-level evaluation model is put forward to evaluate importance of parts assembled, and the key function parts in assembly can be obtained. After that, on the basis of the function parts, a heuristic algorithm upon breadth first searching is given to identify key assembly structures.

The method could be used to evaluate key function parts and identify key assembly structures in complex mechanical assembly according to the specific circumstances.

The method can not only help designers find the key assembly structure in complex mechanical assembly model, facilitate the function-structure designing and semantics analyzing, and thereby improve the efficiency of product knowledge reuse, but also assist in analyzing influence scope of key function part changing and optimization of the assembly process for complex mechanical assembly.

The paper is the first to propose a method for key assembly structure identification in complex mechanical assembly, where the key function parts can be evaluated through a two-level evaluation model, and the key assembly structures are identified automatically based on complex network.

The purpose of this paper is to establish an adaptive assembly, to realize the adaptive changing of the models and to improve the flexibility and reliability of assembly change. For a three-dimensional (3D) computer-aided design (CAD) assembly in a changing process, there are two practical problems. One is delivering parameters’ information not smoothly. The other one is to easily destroy an assembly structure.

The paper establishes associated parameters design structure matrix of related parts, and predicts possible propagation paths of the parameters. Based on the predicted path, structured storage is made for the affected parameters, tolerance range and the calculation relations. The study combines structured path information and all constrained assemblies to build the adaptive assembly, proposes an adaptive change algorithm for assembly changing and discusses the extendibility of the adaptive assembly.

The approach would improve the flexibility and reliability of assembly change and be applied to different CAD platform.

The examples illustrate the construction and adaptive behavior of the assembly and verify the feasibility and reasonability of the adaptive assembly in practical application.

The adaptive assembly model proposed in the paper is an original method to assembly change. And compared with other methods, good results have been obtained.

The purpose of this paper is to present an automated method for complicated truss structure subassembly identification.

A community-detecting algorithm is introduced and adapted to reach the target. The ratio between oriented bounding boxes of parts is used as the weight to reflect the compact degree of assembly relationships. The authors also propose a method to merge nodes together at cut-vertex in model, by which the solving process could be accelerated.

This method could identify the subassemblies of complex truss structures according to the specific requirements.

This research area is limited to truss structures. This research offers a new method in assembly sequences planning area. It could identify subassemblies in complex truss structures, with which the existing method is not adequate to deal.

This method could facilitate the complex truss structures assembly planning, lower the human errors and reduce the planning time.

The method could inspire general assembly analysis planning.

All authors of this paper confirm that this manuscript is original and has not been submitted or published elsewhere.

The purpose of this paper is to solve the asynchrony problem between the logistics stream and the information stream in the complex product assembly executive process.

States of assembly and logistics are described by transitions, and implemented via logistics agents and assembly agents. Events in the assembly executive process are described by places, and mapped to radio frequency identification (RFID) tags' states. An agent‐based complex product assembly framework is proposed. Mobile agents are used to encapsulate task and data, and RFID tags' states are able to trigger dispatching of assembly agents and executing of assembly tasks. Assisted by mobile agents' retraction mechanism, on‐site data are carried back when assembly tasks are finished.

An assembly executive process Petri net and a mobile agent‐based complex product assembly framework are proposed.

Dynamic matching mechanism between assembly tasks and materials is achieved, and controlling and monitoring methods of complex product assembly executive process are enhanced.

This paper aims to explore how external creditors assess firms’ financial aggressiveness in China.

Using bank loan-specific data, the authors investigate whether firms exhibit greater costs of bank loans when they engage in earnings manipulation and whether this association changes when restrictions on lenders’ compensation are promulgated.

The authors find compelling evidence that bank executives charge higher premiums on firms with accrual earnings management to compensate for additional financial risk but do not charge extra loan prices for firms conducting real earnings management (REM). The authors also find that the enactment of Robust Bank Executive Compensation (REBC) enhances the vigilance of bank executives on the overall client firms’ earnings manipulation, with the exception of REM conducted by state-owned firms.

The authors extend the current literature on the cost of external loans by focusing on bank loans and the influence of REBC. This study offers implications for policymakers in China and other emerging economics to control loan default and financial risk.

The purpose of this paper is to further understand the joint effect of project‐level exploratory and exploitative learning in new product development. It aims to examine the complicated relationships among exploratory learning, exploitative learning and new product performance at a single project level. In addition, it seeks to shed light on the contextual effects of a firm's market orientation on the relationship between joint occurrence of both learning activities and new product development performance.

The paper is based on a questionnaire survey/analysis of a sample of 298 projects from high‐tech firms in Taiwan.

The findings suggest that the joint occurrence of both learning activities has a positive effect on new product performance and depends upon a high level of one learning activity coupled with a small dose of the other. Drawing on cultural and behavioral perspectives of market orientation, the results also indicate that market orientation may enhance the joint effect of both learning activities on new product performance.

This paper offers insight to project managers with regard to the importance of rationally mixing with exploratory and exploitative learning during new product development. Furthermore, the study argues that market orientation is an alternative of organizational design that fosters the positive joint effect of both learning behaviors.

The results empirically support the theoretical argument that a high‐low matching of exploratory and exploitative learning can enhance performance at the level of a single project. The study provides a multiple‐level framework to understand how the firm‐level MO strengthens the positive effects of joint occurrence of project‐level exploratory and exploitative learning activities during new product development.

Drawing upon the resource-based view and the contingency theory, the purpose of this paper is to build and test a framework of: the interaction between market orientation (MO) and accountants’ participation in strategic decision making; and its subsequent effect on the use of management accounting systems (MASs), which, in turn, enhances firm performance.

The hypotheses were empirically tested using partial least square-structural equation modeling with survey data from 171 large business firms in Vietnam. The standardized root mean squared residual value of the composite model was also examined using SmartPLS3 to test the model fit. The marker-variable technique was employed to test common method bias.

This study has two key findings: first, the use of MAS (in terms of broad scope, timeliness, aggregation, and integration) mediates the effect of MO on firm performance. Second, the degree of accountants’ participation in strategic decision making elevates the positive relationship between MO and the use of MAS.

This study is one of the first empirical attempts to test the contingent roles of accountants’ participation in strategic decision making and the use of MAS information in driving performance of market-oriented firms in the context of a transition market.

The purpose of this study is to investigate factors that affect smallholders' adoption of organic rice production in the Hai Lang district of Vietnam.

This study employed a cross-sectional survey research design. A sample size of 215 was drawn from a total of 455 smallholders. Descriptive and inferential statistics, including a binary logistic regression, were used to analyse the data.

The study concludes that younger female smallholders participating in credit/training programs and community-based organisations, who have a high level of education, a large household size and more experience in rice farming, and who grow mixed crops, have a greater tendency to adopt organic rice production.

A combination of demographic, socio-economic and institutional characteristics of rice smallholders should be considered when promoting organic rice farming practices by smallholders and when choosing organic agricultural development strategies for smallholders in developing countries.

This research provides significant understanding of the determinants of organic rice adoption by smallholder farmers and highlights factors that need to be considered when designing policies to enhance the uptake of organic farming in developing countries.