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In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.

This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.

The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.

There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.

This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.

The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

This paper aims to investigate the law and logic mechanism of management control matching pattern and company strategy aggressiveness under different strategies by textual analysis, based on the empirical data of Chinese A-share listed companies during the period from 2010 to 2018. Additional analyses further investigate the moderating effect of environmental uncertainty and R&D intensity on the relationship between management control matching type and strategy aggressiveness. The conclusion can help relevant departments to develop management control theory and method system with Chinese characteristics and provide theoretical reference for the matching mode of dual control.

This paper uses the text analysis method. The main explanatory variables are analyzed using the computer SQL Server database software through the relevant text of the board of directors report in the company annual report. Other financial data came from the CSMAR database, excluding ST and PT and companies with missing data, and 16,902 samples were finally obtained. This paper conducted statistical analysis through Stata12.

This paper shows that the matching pattern between formal and informal control is divided into three types. They have different impacts on strategy aggressiveness. Specifically, consistent matching type II significantly positively influences the aggressiveness of offensive strategy. Consistent matching type I significantly positively influences the aggressiveness of defensive strategy. Complementary matching type I significantly positively influences the aggressiveness of analytical strategy. Additional analyses find that compared with non-high-tech companies, high-tech companies have more significant influence on the relationship between management control matching pattern and company strategic aggressiveness. And compared with other two “strategy-control” matching patterns, both environmental uncertainty and product innovation have more significant influence on the relationship between consistent matching II and offensive strategy aggressiveness.

To the best of the authors’ knowledge, this paper divides the formal and informal control matching patterns of management control into three categories for the first time. It examines the relationship between the formal and informal control matching of management accounting and the degree of strategy aggressiveness. The conclusion provides new empirical evidence to promote the effective implementation of development strategies for companies. It can help relevant departments to develop management control theory and method systems with Chinese characteristics and provide theoretical references for the matching mode of dual control.

This paper deconstructs the boundary-spanning technology innovation (BSTI) of manufacturing enterprises based on dual-meta and dual-degree perspective. We aim to explore the impact of differentiated internal reconfigurations on networking capabilities and, thus, different BSTIs and then reveal the optimal transformation paths of different BSTIs.

We use fuzzy-set qualitative comparative analysis (fsQCA) to conduct an empirical study on 128 manufacturing enterprises in China to reveal the heterogeneous combinatorial path of internal reconfigurations on BSTI through the networking capability, and the case analysis in specific technology fields of Haier, Gree, Midea and TBEA is used to verify our results. The transfer entropy (TE) method is used to reveal the best transformation paths of different BSTIs.

The results show that the manufacturing enterprises follow the “I,” “T,” “⊥” and “|” reconfiguration logic to effectively realize multiple boundaries breakthrough (MBB), tick boundaries breakthrough (TBB), multiple boundaries reproducing (MBR) and tick boundaries reproducing (TBR) BSTI, respectively. The BSTI has two adjacent transformations named “dual-meta transformations” and “dual-degree transformations.” Nonadjacent transformations follow the “clockwise” transformation law. In “quality transformation,” “degree” transforms first and then “meta” follows, while in “feature transformation,” “meta” transforms first and then degree follows.

Firstly, the scientific classification of BSTI is carried out to guide enterprises to carry out accurate BSTI. Secondly, the “internal reconfiguration-networking capability-BSTI” paths of manufacturing enterprises are explored. Finally, the different laws of different BSTIs’ transformations are revealed.

This paper aims to develop a dynamic two-sided stable matching method based on preference information of the matching objects in uncertain environments, so as to solve the matching problem of cloud manufacturing tasks and services with load balancing.

For dynamic two-sided matching, due to the complexity of social environment and the limitation of human cognition, hesitation and fuzziness always exist in the process of multi-criteria assessment. First, in order to obtain the accurate preference information of each matching object, uncertain linguistic variables, uncertain preference ordinal and incomplete complementary matrices are used to evaluate multi-criteria preference information. This process is undertaken by considering the probability of each possible matching pair. Second, the preference information at different times is integrated by using the time-series weight to obtain the comprehensive satisfaction degree matrices of the matching objects. Further, the load adjustment parameter is used to increase the satisfaction degree of the matching objects. Afterward, a dynamic two-sided stable matching optimization model is constructed by considering stable matching conditions. The model aims to maximize the satisfaction degree and minimizes the difference in the satisfaction degree of matching objects. The optimal stable matching results can be obtained by solving the optimization model. Finally, a numerical example and comparative analysis are presented to demonstrate the characteristics of the proposed method.

Uncertain linguistic variables, uncertain preference orders and incomplete complementary matrices are used to describe multi-criteria preference information of the matching objects in uncertain environments. A dynamic two-sided stable matching method is proposed, based on which a DTSMDM (dynamic two-sided matching decision-making) model of cloud manufacturing with load balancing can be constructed. The study proved that the authors can use the proposed method to obtain stable matching pairs and higher matching objective value through comparative analysis and the sensitivity analysis.

A new method for the two-sided matching decision-making problem of cloud manufacturing with load balancing is proposed in this paper, which allows the matching objects to elicit language evaluation under uncertain environment more flexibly to implement dynamic two-sided matching based on preference information at different times. This method is suitable for dealing with a variety of TSMDM (two-sided matching decision-making) problems.

The online health community's success depends on doctors' active participation, so it is essential to understand the factors that affect doctors' knowledge contribution behavior in the online health communities. From the perspective of peer effect, this paper discusses the influence of focal doctors' peers on focal doctors' knowledge contribution behavior and the mechanism behind it. This paper aims to solve these problems.

Empirical data of 1,938 doctors were collected from a Chinese online health community, and propensity score matching and ordinary least squares were employed to verify the proposed theoretical model.

The results show that the presence of focal doctors' peers in online health communities has a positive effect on the knowledge contribution behavior of focal doctors, and the economic returns and social returns of focal doctors' peers have a significant mediating effect.

This paper discusses focal doctors' knowledge contribution behavior from the perspective of peer effect. It enhances the understanding of focal doctors' behavior in the online health communities by exploring the mediating role of their peers' economic and social returns. The results of this paper extend the research in the field of peer effect and online health and provide management implications and suggestions for online health platforms and doctors.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

The Million Farmers School (MFS) program—also known as Kisan Pathshala was launched to impart training to the farmers by the government of the state of Uttar Pradesh (India) in December 2017. This study estimates the impact of training on agricultural knowledge of the farmers.

The study is based on household survey conducted in Uttar Pradesh (UP), India, during March–May 2019. The authors employed matching methods, the two-stage least square (2SLS)-residual and endogenous switching regression approaches to control for selection bias and endogeneity.

The results suggest that knowledge outcomes are significantly better among participants vis-à-vis non-participants. The results are robust to different model specifications. Further, the benefits are observed across different regions and social groups.

The MFS program can go a long way in enhancing agricultural know-how and the farmers' economic well-being, bringing a transformative change in the agricultural landscape of UP.

This study is based on a field survey data and analyzes various aspects of the program's impact, design and implementation, and offers implementation advice for greater efficacy in future.