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The purpose of this paper is to document the relationship between intra-group coopetition and subsidiaries' innovation performance and the moderating impact of the intensity of external competition.

Data were collected from 75 subsidiaries in China through a questionnaire survey of their R&D and general managers. The total number of individual respondents was 205. We tested our hypothesis by using ordinary least squares regression.

Intra-group cooperation was found to promote a subsidiary's performance in product and process innovation. Intra-group competition was found to have a U-shaped relationship with product and process innovation. Intra-group cooperation strengthens the U-shaped relationship between intra-group competition and process innovation.

This study involved firms from more than one industry. Studies of specific industries might reach more specific conclusions. And all of the data were self-reported by the managers of the firms concerned. Future studies would be well-advised to consider more objective data describing pairs of parent firms and subsidiaries.

Subsidiaries ought to build their internal networks to cooperate with each other. That can bring significant advantages in terms of information and synergy in innovation. Subsidiaries are also suggested to take full advantage of the opportunities that intra-group competition brings.

This study is the first one to explore coopetition phenomenon in the context of business group. By taking Chinese business group subsidiaries as the research samples, this research not only extends the coopetition research but also reveals that cooperation and competition are co-existed and exert influence in subsidiaries.

The purpose of this paper is to explore the moderating effects of non‐market factors on partnership management in transitional economies. Design/methodology/approach – Based on a literature review, it is proposed that the performance implications of control and trust in international partnerships are subject to four main non‐market factors, which include the ownership system, government regulations, cultural differences and the level of regional economic development. Findings – The arguments presented suggest that the effectiveness of a multinational enterprise's (MNE) control is greater in uncertain situations but relatively weaker in highly regulated environments. The impact of trust is weaker when MNEs are faced with greater differences in institutions and national cultures. Research limitations/implications – This paper identifies key contingency factors and provides directions for future empirical research on partnership management. Practical implications – To successfully manage international partnerships, MNEs must adjust to the institutional and cultural environments. Originality/value – This paper identifies key contingency factors and provides directions for future empirical research on partnership management, especially in China.

This paper aims to examine the internal logical relationship between two intergenerational inheritance ways of passing property rights and residual control rights (RCR) and to construct a conceptual model comprising transfer elements, paths and timing of succession in this process.

Driven by the cases of Haixin, Tianyijiao and Changhe Group, this paper applies research methods of copying and expanding analysis logic, progressive deduction, content analysis and comparative research based on the perspective of HeXie theory to explore the deep interrelation of transfer elements, paths and timing during family business succession.

The findings present that the content of intergenerational inheritance of a family firm is the inheritance of property rights and RCR. First, the inheritance of property rights is a static inheritance of time-point delivery, whereas the inheritance of RCR is a dynamic inheritance process for a period of time. Second, the inheritance of property rights and RCR are not independent; only a “HeXie” succession of both rights can realize a successful inheritance of family firms.

This paper constructs the paths and timing model of intergenerational inheritance of property rights and RCR in family firms. This paper integrates the current literature studies on the family inheritance of property rights and RCR and explains their internal mechanisms. This paper also provides a theoretical foundation and empirical evidence for family business transitions in the business world.

In this paper, the organism model for knowledge‐based enterprise is proposed. A dynamic capacity grey set is defined and analyzed based on the definition of the growth and development for knowledge‐based enterprise organism. The structure of the capacity whiten core, a subset of the capacity grey set, is optimized for different periods of the organism's life cycle. The organism grey topological structure model of knowledge‐based enterprise is described to possess the essential capacity grey set.

This study aims to propose a comprehensive optimization and scheduling method for the combined heat and power (CHP) systems that takes into account the uncertainties of wind power and demand response.

The uncertainty of wind power and the “thermal-electric coupling” characteristics of CHP units have led to an increasing issue of wind power curtailment in CHP systems. With the objective of minimizing the overall scheduling cost of the CHP system, this paper considers the characteristics of interactive loads and wind power uncertainty, and establishes a coordinated optimization scheduling model for the generation-load-storage of the system, based on the inclusion of thermal energy storage devices.

During the optimization scheduling process, the proposed method in this paper reduces the scheduling cost by ¥99,900 (approximately 36.3%) compared to traditional methods, and significantly decreases the wind power curtailment rate by 53.7%. These results clearly demonstrate the significant advantages of the proposed method in enhancing the economic efficiency of the system and improving wind power integration.

However, the planning process did not take into account the impact of unit combinations and grid structures.

This study proposes a comprehensive optimization and scheduling method for the CHP systems that takes into account the uncertainties of wind power and demand response. The objective function is to minimize the wind curtailment rate’s total scheduling cost, considering the impact of wind power uncertainties and demand response. A coordinated optimization and scheduling model for the generation-load-storage of CHP system is established.

CHP units achieve the coupling of electric and thermal energy, significantly improving energy efficiency. In this study, the planning of the CHP system considers the coupling relationships among multiple energy sources, various devices and the pricing optimization spaces of electric and thermal forms of generation, storage and load-side. This approach has achieved favorable results in terms of economic operation scheduling and wind power accommodation improvement.

The case method is used to handle the uncertainty of wind power output on the generation side. Demand response is integrated on the load side to adjust user load curves. On the storage side, the thermal-electric coupling constraints of the CHP units are decoupled using thermal energy storage devices, while considering the economic benefits of all three parties involved: the power source, the load and the energy storage.

The purpose of this paper is to determine barriers jeopardizing the adoption and usage intention of mobile banking (M-banking) in Pakistan and provide deeper insights to fix such deteriorating factors.

Data was collected in countrywide regional headquarters to mark the utmost generalizability of the results, which included seven largest cities of Pakistan. SEM path analysis was used to analyze data collected from Pakistan’s top 5 bank customers incorporating both users and non-users.

Results revealed that lack of awareness, initial trust and compatibility and perceived risk were the core barriers that stood out as obstacles to the adoption and usage of M-banking in Pakistan. It was also approved that having fixed these core barriers would outcome in existing users’ continuity intent besides raising new users’ inclination toward M-banking.

The study has unveiled the core barriers that have so far impeded the adoption and usage of M-banking. There is not a unified position concerning adoption and usage blockades. Factors differ with contexts, markets, time and kinds of innovations. However, this study is unlike past studies that merely studied students within a specified institute in a restricted jurisdiction. This is the first study to have nationally explored adoption and usage issues; thus, it is anticipated to potentially contribute to the prevailing literature especially in Pakistani context where a few studies prevail, addressing M-banking adoption and usage barriers.

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues.

In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel.

The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.

The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

Utilizing the extended transportation-imagery model, this study categorizes three storytelling elements into six distinct factors – character types, influencer-character congruence, imaginable titles, concrete details, replication difficulty and artistic processing – to explore how these factors enhance influencer engagement.

This study utilized a quantitative research design, analyzing 1,660 influencer-created videos over a six-month period. Narrative elements were examined through manual coding, and their impact on live comments was assessed using negative binomial regression to identify key factors driving audience engagement.

Research results show that non-fictional characters, imaginable titles and concrete details significantly increased live comments. Conversely, high replication difficulty negatively influenced engagement. Notably, influencer-character congruence and artistic processing showed no significant effect.

This study advances the extended transportation-imagery model by integrating narrative elements with live comments, offering new perspectives on real-time audience engagement. The findings deepen our understanding of how storytelling techniques enhance the effectiveness of influencer marketing. From a managerial standpoint, this research provides strategic insights for influencers and brands to refine their content strategies, ultimately boosting audience engagement.

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.