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The rapid growth of emerging market firms (EMFs) has been a topic of interest for the past two decades, especially in China. However, few studies have discussed how and why EMFs can impel the upgrading of their capabilities to quickly win competitive advantages in the global market. In this context, the purpose of this paper is to unravel the implausible upgrading phenomenon from the perspective of technological proximity.

This paper adopts a single case study, specifically that of a leading Chinese e-bike firm, with a special focus on the dynamic nature of the capability upgrading process and underlying mechanisms.

The results show that taking advantage of technological proximity is an important way for EMFs to climb the ladder of capability upgrading. The stage-based process reveals how capability upgrading is achieved through elaborate actions related to technological proximity. Furthermore, this study finds three learning mechanisms behind the technological proximity, which enable firms to successfully upgrade to higher levels of capabilities. In particular, the trigger role played by contextual conditions in guiding firms' capability upgrading is highlighted and characterized.

This study enriches traditional capability upgrading literature from a technological proximity perspective, especially the traditional static upgrading research related to EMFs. The authors also contribute to the conceptualization of technological proximity. However, the research setting is China's e-bike industry; therefore, the study's generalizability to other emerging markets and industries may be limited.

The results show that it is important to recognize the value of the transfer and sharing of technology between proximal industries for local governments. Also, appropriate policies should be developed to break down the technology barriers between these industries. Moreover, rather than catching up with the superior technologies of multinational corporations in advanced countries, focusing on products with high technological proximity in local or regional areas may be more helpful for EMFs' upgrading.

This paper investigates the capability upgrading process and mechanisms in EMFs, particularly with respect to the role played by technological proximity.

The purpose of the study is to explore how technological capability and exogenous pressure interactively influence business model (BM) dynamics over time in new technology-based ventures.

The study adopts a longitudinal case study of the BM innovations of a Chinese financial technology venture. The structural approach and temporal bracket are used to analyze and theorize the data.

The findings indicate that distinct contextual changes impel a firm to refine or abandon existing BMs over time. In different stages, the antecedents interactively influence BM dynamics with three successive patterns, namely pressure dominance, parallel influence and hybrid influence. While both antecedents trigger changes during the initiation and implementation of new BMs, they also serve as the filter and the enabler, respectively, during the ideation and integration of BMs.

The study inductively develops three propositions regarding the relationship between BM dynamics and its antecedents, which is based on the data collected from one single firm. Future research should test the propositions in other domains and take more cross-level antecedents into consideration.

The study contributes to the nascent research stream of BM dynamics by offering in-depth insights into the interaction of internal and external antecedents and by linking the differentiated roles of antecedents to the BM innovation process. The research offers some practical implications for new technology-based ventures seeking to develop BMs in a fast-changing environment.

In the face of fierce international competition for those participating in global value chains (GVCs), upgrading has been a central concern of emerging market firms (EMFs) that are trying to occupy higher value-added positions. However, although the innovation capabilities (ICs) have been generally considered critical to upgrading in GVCs, few studies have examined how IC is built up and then applied to the EMF upgrading process over time. To this end, the purpose of this paper is to investigate why and how EMFs can upgrade in GVCs through the development of their IC.

This paper adopts a multiple-case study of three supplier firms in China and their IC development processes, with a special focus on the nature of the firm-level upgrading in GVCs.

The results generate a process model of EMFs upgrading with respect to the development of IC. The model reveals how IC is built up through the firms' underlying systematic innovation activities, which enable firms to successfully upgrade within GVCs. In particular, the role played by contextual vulnerability in guiding firms to develop the appropriate IC, and the corresponding upgrading, is highlighted.

This study contributes to the micro-foundation in GVCs literature, especially the traditional static upgrading research of EMFs. The authors also contribute to existing IC development research. Meanwhile, the study focuses on the upgrading of three Chinese firms in the phone and LED industries. The generalizability to other emerging markets and industries may therefore be limited.

The study results show that EMFs could initially develop endogenous IC that focuses on process innovation as a means to establish a foundation for further upgrading. In addition, firms need to improve their ability to accurately sense contextual changes. As such, it would be valuable to understand their positions and characteristics within GVCs.

This paper investigates a process model of upgrading in GVCs through IC development in EMFs. This study also adds a dynamic micro-foundation to existing, rather macro and static GVCs studies.

Building on the resource- and knowledge-based views, this paper aims to explore how coopetition affects radical innovation and the roles of knowledge structure and external knowledge integration in the relationship between coopetition and radical innovation.

This study proposes a research model to examine the mediating role of external knowledge integration on the coopetition-radical innovation link, where the mediation is moderated by the firm’s knowledge structure (including component knowledge and architectural knowledge). The authors use regression and bootstrapping to test the proposed model with survey data from 241 Chinese technology firms.

This study finds that coopetition positively affects radical innovation and the effect is fully mediated by external knowledge integration. Additionally, component knowledge negatively moderates the coopetition-external knowledge integration link and architectural knowledge positively moderates this relationship. Further, the mediating effect of external knowledge integration is also moderated by component knowledge and architectural knowledge.

Firms should engage in coopetition to promote radical innovation. Further, it is necessary for firms to appropriately manage coopetition according to their internal knowledge structure.

This study explains why scholars have different ideas about the relationship between coopetition and radical innovation by exploring the mediating role of external knowledge integration and the moderating effect of knowledge structure. Firms possess increased possibilities for knowledge leakage and partner opportunism with high levels of component knowledge, which will reduce the positive effect coopetition on external knowledge integration; thus, they are less likely to realize radical innovation. Instead, firms possess increased opportunities for resource sharing with high levels of architectural knowledge, thus improving the positive effect coopetition on external knowledge integration and they are more likely to achieve radical innovation.

The purpose of this study is to present a system dynamic (SD)-based remanufacturing economic analysis model of used automobile engine under two recycling modes. The authors will compare the remanufacturing cost, sales profit and sales revenue from time and space dimensions incurred in different recycling modes in the long run.

The remanufacturing economic analysis model is based on SD methodology. The authors can simulate the relations of impact factors on automobile engine recycling and remanufacturing and further analyze and compare the cost, sales profit and sales revenue incurred in different recycling modes in the long term.

Sinotruk Steyr engine remanufacturing in Shandong province is taken as the research case subject. The revenue, cost and profit under the two recycling modes from 2015 to 2035 are analyzed and compared. The results show that different recycling modes have significant varying influence on the economy of engine remanufacturing.

This economic analysis model can provide a method reference to decide the recycling mode for auto components and other product remanufacturing. Moreover, this model can guide and support the sustainable development of remanufacturing industry.

The purpose of this paper is to apply Maslow's hierarchy of needs to extend the expectation‐confirmation model of information systems continuance (ECM‐IS) to analyze users' continuance intention of social networking services (SNSs).

A survey is conducted on 202 users of social networking services in China.

Fulfillment of self‐actualization needs has a significant impact on continuance intention; however, the direct impact of fulfillment of social needs on continuance intention is not significant but fully mediated by satisfaction.

The first limitation is that the participants in the sample are undergraduates. Second, this study has used cross‐sectional survey data to empirically test the model. Third, the survey is conducted in a single country.

The results of this paper provide several marketing implications to better manage SNSs. First, SNS managers should enhance instant communication functions, develop a platform that is convenient for users to express themselves and provide more entertainment functions. Second, SNS managers should focus on users' expectations and experiences about website functions and adjust or update website functions accordingly.

This paper contributes to the research on continuance intention of social networking services from the perspective of Maslow's hierarchy of needs to capture motivations of continuance intention. The authors believe their conceptualizations of fulfillment of self‐actualization needs and fulfillment of social needs, as well as their substantial findings, would be useful to researchers and practitioners alike to better study and manage continuance intention of socially‐oriented online services.

In recent years, rough set theory has evolved as one of the most promising classification techniques. One of the cardinal uses of rough set theory is its application for rule generation. The purpose of this paper is to propose a real-time fabric inspection technique. This work deals with the multi-class classification of fabric defects using rough set theory.

This technique focuses on the classification of fabric defects using the effective decision rules envisaged by rough set theory. In the proposed work, the six features of 50 images have been used for multiclass classification of fabric defects.

In this work, 40 images were used for generation of decision rules and 10 unseen images were used for validation out of which nine images are accurately predicted by the proposed technique.

The proposed method accurately identified 9 out of 10 testing defects. The obtained decision rules provide an insight about the classification method which ensures that the prediction accuracy can be improved further by framing more robust decision rules with the help of a large training data set. Thus, with the support of modern computational systems this method is potent in getting recognition from the textile industry as a real-time classification technique.

The purpose of this paper is to present a conceptual framework for a hybrid simulation model that can be used to study the decision making and behaviors of humanitarian logistics actors to determine how/whether certain coordination mechanisms enable better relief chain efficiency and effectiveness over time.

The agent-based portion of the model is used to represent human decision making and interactions in a more realistic way than has been done previously, and the discrete-event simulation (DES) portion of the model allows the movement of vehicles, materials, and information throughout a supply network to be represented in a way that allows for dynamic and stochastic behavior.

Coordinated efforts by actors in humanitarian logistics operations involve complex interactions and adaptations over time, which can be capture and explored via hybrid agent-based model (ABM)-DES modeling.

This paper describes a framework for a hybrid ABM-DES model. The actual development and implementation of the model, including input data collection and analysis, model development, experimentation, and output data collection and analysis, will be the subject of future work.

The hybrid model framework provides other researchers with a starting point for model development.

This paper provides a basis for future modeling and assessment of coordination in humanitarian logistics, an area that is in need of research.

The hybrid simulation modeling framework presented in this paper is a novel application of a new modeling methodology to the problem of coordination in humanitarian logistics.

The purpose of this paper is to review Olympic mascots in the electronic and traditional communications environments.

Olympic mascots from 2006 to 2012 are analyzed using a descriptive semiotic analysis technique.

Results found that none of the 2006-2012 mascots clearly represented the two most recognizable icons of the Olympic movement, the Olympic Rings and the Olympic Flame. The association of the London 2012 mascots with the Olympic Games are found to be limited.

This research sets the stage for a number of future studies to further assess the management issues, social benefits, and potential missteps regarding mascots at the Olympic Games and other mega-events.

The practitioner of today working for a mega-event like the Olympic Games needs to be aware of the potential benefits and inherent risks of developing and implementing a mascot.

This research is the first to look specifically at Olympic mascots in the electronic age and contrast their use to traditional communications.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.