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This research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.

By analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.

Examining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.

Studies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.

This study examines the effect of contextual perceived value activated by contextual marketing offers and information privacy concerns on consumer loyalty in mobile commerce.

The survey was conducted through 340 mobile users in Morocco and the collected data were analyzed using structural equation modeling.

This study's results show that contextual marketing and information privacy concerns are key determinants in improving customer loyalty in the m-commerce context. Perceived ubiquity has a positive impact on perceived trust, which also impacts consumer loyalty. Information privacy concerns also have a positive impact on customer satisfaction, yet it does not impact perceived trust, which is contrary to the results of other researchers. It can also be concluded that customer satisfaction and trust are important antecedents of consumer loyalty.

This research gives rise to some important managerial and strategic implications in order to integrate contextual marketing strategies, as well as theoretical implications that concern this field of study.

This research makes a significant contribution to knowledge by examining the role of contextual marketing and information privacy concerns in the m-commerce context. These results will be considered useful for marketers and for businesses in general who wish to integrate a marketing strategy that is based on a customer-centric approach. It also contributes to the related literature, as there are few studies focused on m-commerce and contextual marketing within the context of Morocco.

The paper proposes an answer to one of the most important questions in corporate innovation management: what mechanisms of technological diversification exist within multinational companies? It is ascertained that research and development (R&D) intra-firm co-invention or co-patenting is one of those mechanisms. Co-invention implies knowledge-sharing, which should lead to unique combinations of knowledge and expertise and hence technological diversification of patent applications.

This paper offers a novel conceptual framework exploring the relationship between patents’ technological diversification and a detailed classification of different forms of international co-invention. Based on the case of Siemens’ Patent Cooperation Treaty (PCT) applications, the revealed technological advantage (RTA) index is utilized to measure the extent of the technological diversification of patent output.

The results show that patent applications generated by subsidiaries in advanced economies in cooperation with other subsidiaries feature unique technological areas that deviate from the company's overall technological specializations. These results provide a strong argument in favor of inter-subsidiary or horizontal co-patenting as a mechanism of new knowledge creation.

On the conceptual level, the results accentuate inter-subsidiary patenting being an important mechanism of knowledge meta-integration boosting technological diversification. The obvious limitation of this paper lies in exploring a single company case, which restricts the generalizability of our findings. Due to the dynamic nature of technological change, the author’s dataset also suffers from a lack of temporal external validity. Future research can expand the scope in both regards in applying our co-invention mode typology.

Based on the results, to diversify knowledge portfolio, companies should strengthen the co-patenting effort and reinforce horizontal (inter-subsidiary) R&D collaborations.

To the author’s knowledge, this is the first time when such a nuanced typology of co-invention modes is being utilized to understand the effect of different co-invention categories on knowledge diversification.

The intelligent connected vehicle (ICV) is an important trend in automobile development, but little research has been conducted on the technological differences in the ICV industry across countries. In this regard, the authors select China, the United States (US) and the European Union (EU) as countries with developed ICV industries to reveal these differences based on the perspective of subdivision technology.

The authors use logistic regression to fit lifecycles at technology level and country level based on ICV-related patents from China, the US and the EU, then use the Revealed Technological Advantage (RTA) index, Fast-Growing Specialization Index (FGSI) and International Patent Classification (IPC) numbers to conduct comparison of national technology advantages, finally use the social network analysis to investigate the evolution of characteristics and intermediate nodes of each technology innovation network.

Technology lifecycles vary according to the subdivision technology and country. The global development of the ICV industry has reached the mature stage, and 2030 may be a watershed moment, ushering in a wave of new technology iterations. In various subdivision technologies, China and the US have more leading RTAs, and China and the EU have more leading FGSIs. Innovation networks in different countries expand with technology lifecycles, with that in China being the fastest. China's Universities, the US's enterprises and the EU's research institutes are active in cooperative innovation as intermediaries.

This is the first study to compare the development of the ICV industry in major countries from the perspective of subdivision technology and reveal characteristics of innovation networks in each.

This study aims to investigate the thermal fracture mechanism of moisture-preconditioned SAC305 ball grid array (BGA) solder joints subjected to multiple reflow and thermal cycling.

The BGA package samples are subjected to JEDEC Level 1 accelerated moisture treatment (85 °C/85%RH/168 h) with five times reflow at 270 °C. This is followed by multiple thermal cycling from 0 °C to 100 °C for 40 min per cycle, per IPC-7351B standards. For fracture investigation, the cross-sections of the samples are examined and analysed using the dye-and-pry technique and backscattered scanning electron microscopy. The packages' microstructures are characterized using an energy-dispersive X-ray spectroscopy approach. Also, the package assembly is investigated using the Darveaux numerical simulation method.

The study found that critical strain density is exhibited at the component pad/solder interface of the solder joint located at the most distant point from the axes of symmetry of the package assembly. The fracture mechanism is a crack fracture formed at the solder's exterior edges and grows across the joint's transverse section. It was established that Au content in the formed intermetallic compound greatly impacts fracture growth in the solder joint interface, with a composition above 5 Wt.% Au regarded as an unsafe level for reliability. The elongation of the crack is aided by the brittle nature of the Au-Sn interface through which the crack propagates. It is inferred that refining the solder matrix elemental compound can strengthen and improve the reliability of solder joints.

Inspection lead time and additional manufacturing expenses spent on investigating reliability issues in BGA solder joints can be reduced using the study's findings on understanding the solder joint fracture mechanism.

Limited studies exist on the thermal fracture mechanism of moisture-preconditioned BGA solder joints exposed to both multiple reflow and thermal cycling. This study applied both numerical and experimental techniques to examine the reliability issue.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Digital transformation (DT) is among the vital factors contributing to innovation ambidexterity, especially for advanced manufacturing firms (AMFs). However, the empirical studies on the relationship between DT and innovation ambidexterity in AMFs from the perspective of knowledge management are inadequate. Therefore, this study aims to systematically analyze the impact of DT on innovation ambidexterity and its mechanism of action.

This study selects 254 listed firms within the ten key areas of “Made in China 2025,” as they occupy a key position in China’s advanced manufacturing system. Based on the knowledge-based view (KBV) and contingency theory, it constructs a model of the influence mechanism of DT on innovation ambidexterity.

The results show that the DT of AMFs positively influence innovation ambidexterity. External pressure from environmental turbulence enhances the positive relationship between DT and innovation ambidexterity, demonstrating the “resilience effect,” external knowledge search (EKS) and broadening the knowledge base mediating roles between them, highlighting the “accumulation effect.”

By identifying this mediation mechanism of DT and innovation ambidexterity, this study provides new ideas for path research on the KBV. Moreover, this study explores the triggering effect of market environmental turbulence on the DT of firms. It reveals the boundary conditions of DT acting on innovation ambidexterity, expands the research perspective on organizational resilience and enriches the theory of power change.

The economic crisis of 2008 resulted in a unique context for the empirical experimentation of organizational learning and entrepreneurship theories from Schumpeterian inspiration. The purpose of this paper is to investigate how the economic downturns change the limits and possibilities to pursue both exploration and exploitation and thus achieve innovative performance.

This study examines an original database with the patent portfolio of 2,309 firms based in Brazil, covering a period of eight years preceding the 2008 economic crisis, that is 2000–2007, and five years thereafter, 2008–2013. To test the hypotheses empirically, the authors use the negative binomial model.

The findings add to the literature in two ways. First, this study found a positive association between exploitation and exploration behaviour and innovative performance. However, this relation is curvilinear because there are diminishing negative effects on innovation. Second, regarding the distinction between creative destruction and creative accumulation, the results support the prevalence of creative accumulation, although consistent with the notion of co-occurrence of both models.

This study expands the discussion on how entrepreneurs respond to the economic crisis in specific contexts of an emerging economy. We provide new empirical evidence through the exploration of a unique data set on the patent portfolio of 2,309 firms based in Brazil. Most of the previous literature has applied this topic to developed countries, and there are few studies that apply this topic outside the focus of US and European countries.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.