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High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

This study aims to analyse how multinational corporations (MNCs) organise value chain activities to penetrate new market segments. It contributes by expanding traditional decisions regarding the vertical fine-slicing of value chain activities (whether performed internally or externally) and the consideration of resource-sharing decisions (integration or separation) for each value chain function.

The authors draw on primary data collected from two case study firms operating in the large emerging Chinese market: Volvo Construction Equipment AB and Epiroc AB. In-depth cases illustrate how foreign MNCs expand into new market segments and simultaneously target both the lower-priced mid-market and the premium segments in the Chinese mining and construction industry.

The results reveal that product diversification creates challenges for managers who must oversee new (vertical) value chains, often simultaneously. Beyond geography and modes of governance, managers must decide whether to integrate or separate value chain activities for the new product lines. The study identifies four main strategic choices for firms to address this complexity, focusing on the decision to internalise or externalise (i.e. within or across organisational boundaries) and integrate or separate value chain activities between different product lines.

This study builds upon the internalisation theory and recent international business contributions that focus on value chain configurations to explain MNCs’ product diversification as a growth strategy in a host emerging market. It also sheds light on the choice of conducting new activities in-house or externally and elucidates firms’ managerial decisions to operationally integrate or separate individual value chain activities. The study provides insights into the drivers explaining managerial decisions to configure value chain activities across product lines and contributes to the growing body of literature on MNC activities in emerging economies by highlighting that product diversification impacts entry mode diversity and resource sharing across units.

This study aims to explore the patterns and transformational dynamics of the executive–interpreter network in the innovation processes of small- and medium-sized enterprises in hospitality.

Grounded in social network theory and adopting a case-based approach, this study features a multi-case design focusing on three Chinese boutique hotels. Data were collected through Web page and document reviews, participant observations and semistructured interviews.

Results capture how executive–interpreter network dynamics contribute to innovation in small- and medium-sized hotels. Key factors in social networks (e.g. size, scope and strength) shift throughout innovation. This study presents a design-driven approach as a means of innovation. Findings also delineate the network development conditions under which innovation dynamically occurs in boutique hotels.

Practical implications center on how network dynamics help small- and medium-sized hotels innovate more effectively. These insights can assist hotel operators and prospective market entrants in improving their hotels’ performance and competitiveness.

This study blends social network theory with a design-driven approach to explore innovation mechanisms in small- and medium-sized hotels. It offers empirical evidence for practitioners regarding design-driven innovation. Findings enrich the body of knowledge on both design-driven innovation theory and hospitality innovation.

This paper explains the mechanisms underlying the generation of two-way knowledge spillovers through the interaction of subsidiaries with differentiated local responsibilities and domestic firms.

The study is based on firm-level panel data from a census of Colombian manufacturing firms for the period 2003–2012. The estimation procedure involves two stages. In the first one, total factor productivity (TFP) of foreign and domestic firms is estimated. In a second step, we estimate conventional spillovers (from foreign-owned to local firms) and reverse spillovers (from local to foreign-owned firms) separately, using a random effect approach.

This study’s findings reveal that only locally creative subsidiaries enjoy positive and significant two-way knowledge spillover effects. The connectivity of subsidiaries to local and international networks is reinforced by reciprocal relationships among actors that enhance bidirectional knowledge flows, these being favored by the dynamics of clustering effects.

The paper contributes with new empirical evidence about the mechanism explaining how the technological heterogeneity of subsidiaries plays a determinant role in the generation of both knowledge flows from foreign to domestic firms and to the reverse, all integrated into the same framework.

In recent years, environmental issues and resource depletion have posed significant challenges to firms and society. To address these environmental challenges, firms seek to build strategic alliances of green supply chain management (GSCM) with their supply chain partner. As the largest developing country in the Asia–Pacific region, China needs to take more responsibility for environmental protection, which requires more Chinese firms to participate in GSCM. Therefore, focusing on the issue of GSCM and innovation persistence in the context of an increasingly harsh ecological environment is essential.

To test the hypothesis, the authors perform an empirical analysis on a sample of 124 listed firms in China from 2014 to 2019. The results are robust to a battery of robustness analyses the authors performed to take care of endogeneity.

Empirical results indicate that GSCM can promote innovation persistence and both market environment turbulence and technology environment turbulence have a positive moderating effect on the relationship between the two. Mechanism tests show that GSCM can improve innovation efficiency, ensure innovation quality and alleviate financing constraints, thus promoting the innovation persistence of firms.

This study can provide a theoretical basis for the country to promote GSCM orientation, raise firms' awareness of the value of GSCM, convey the significance of GSCM to investors, influence firms' investment decisions and give experience to other developing countries.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

To provide insights into the role of formal and informal accounts in preventing the liquidation of a professional football club and in post-crisis rebuilding.

This case study, framed as a conflict arena, covers an eight-year period of a high-profile struggle over the future of a professional football club. It uses a mixed methods design, including direct engagement with key actors involved in administration proceedings and transformation to a hybrid supporter-owned organisation.

Our findings suggest that within the arena:• formal accounting and governance were of limited use in managing the complex network of relationships and preventing the abuse of power or existential crises. • informal accounting helped mobilise critical resources and maintain supporters’ emotional investment during periods of conflict. • informal accounts enabled both resistance and coalition-building in response to perceived abuse of power. • informal accounts were used by the Club as part of its legitimation activities.

This study provides theoretical and empirical insights into an unfolding crisis with evidence gathered directly from actors involved in the process. The conceptual framework developed in this paper creates new visibilities and possibilities for developing more effective accounting practices in settings that enable continuing emotional investment from supporters.

Business research has highlighted the importance of knowledge transfer and innovation in multinational firms for better performance outcomes. However, the existing body of literature is characterized by differentiated theories, antecedents and outcomes. This study aims to address this gap by adopting a systematic approach to analyze knowledge transfer and innovation literature from the perspective of multinational organizations.

This study follows “preferred reporting items for systematic reviews and meta-analyses” (PRISMA) guidelines for conducting a systematic literature review. The study adopts a systematic approach for analyzing the literature using School of thought (S), Contexts (C), Methodologies (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) framework (SCM-TBFO framework) devised for holistic literature review. The study analyzes 75 articles from reputed journals from 2000 to 2022.

In general, knowledge transfer and innovation in multinationals is a relatively new area and is evolving rapidly. There are many opportunities to study the various perspectives that are included in the SCM-TBFO framework. The key schools of thought included the evolutionary theory of innovation, institutional theory and internationalization theory. The studies had differing settings or contexts, including China, Europe, the USA and Taiwan. Further, key methodologies that were used included regression, case studies, structural equation modeling (SEM) and theoretical studies. Knowledge transfer and innovation triggers included competitive advantage, competitive pressure, constant requirements for better products and services, foreign direct investment (FDI) and globalization. Knowledge transfer and innovation facilitators were categorized into strategy-related facilitators, organization culture and orientation-related facilitators, and resource-related facilitators. Knowledge transfer and innovation barriers included autonomy, international knowledge dispersion, risk of knowledge leakage, search breadth, ambiguity and institutional voids. Key outcomes of knowledge transfer and innovation in multinationals included financial performance, innovation performance, knowledge flow, transfer effectiveness, patents and new product development.

By synthesizing the literature, the study aims to provide an overview of the current state of research on knowledge transfer and innovation in multinationals. The study develops a holistic model for fostering knowledge transfer and innovation in multinationals. The proposed novel framework can also be applied to perform a holistic assessment of the current literature in various research domains. Further, the study suggests future theory development and research agendas. The study also provides implications for practitioners using the framework to achieve more desirable outcomes.