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This article investigates how teleworking adoption influenced the labor market and workforce dynamic using bibliometric methods to overview 86 years of teleworking research [1936–2022]. By grouping the retrieved articles available on the Web of Science (WOS) core collection database, we revealed a holistic and topical view of teleworking literature using clustering and visualization techniques. Our results reflect the situation where the adoption of teleworking in the last three years was accelerated by the pandemic and facilitated by innovation in remote work technologies. We discussed the factors influencing one's decision to join the workforce or a specific company, besides the unintended consequences of the rapid adoption of teleworking. The study can aid organizations in developing adequate teleworking arrangements, enhancing employee outcomes, and improving retention rates. Furthermore, it can help policymakers design more effective policies to support employees, improve labor force participation rates, and improve societal well-being. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates competitive intelligence (CI)-related practices in companies, including process, scope and organizational structure. As these aspects have not been sufficiently discussed in the literature, the study objective is to determine (1) the type of CI process being employed, (2) whether the CI scope is limited to competitor analysis or spans a broader business environment and (3) whether the CI process and scope vary depending on organizational CI. Design/methodology/approach: An interview was conducted at two types of Japanese companies: one established a CI department to implement CI, whereas the other did not establish a CI department and conducted CI in an ad hoc manner. Multicase studies were performed to examine companies with different organizational structures. Findings: The CI scopes included a broad range of factors (e.g. technology, customers, markets, suppliers, economy, society, politics, legislation and regulation), and not only competitor analysis. An established CI department did not guarantee a well-organized CI process. Furthermore, the lack of such a department did not preclude systematic CI processes or activities. Originality/value: The authors classified the CI in the companies the authors inspected as either systematic (organized CI) or ad hoc (unorganized CI) methods. The advantages and disadvantages of both are discussed. The authors found the promotion mechanisms in company-wide CI process, which can cause intelligence transfers from CI to absorptive capacity processes. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis (CAD) has been a promising area of research over the last two decades. However, CAD is a very complicated subject because it involves a number of medicine and engineering-related fields. To develop a research overview of CAD, we conducted a literature survey with bibliometric analysis, which we report here. Our study determined that CAD research has been classified and categorized according to disease type and imaging modality. This classification began with the CAD of mammograms and eventually progressed to that of brain disease. Furthermore, based on our results, we discuss future directions and opportunities for CAD research. First, in contrast to the typical hypothetical approach, the data-driven approach has shown promise. Second, the normalization of the test datasets and an evaluation method is necessary when adopting an algorithm and a system. Third, we discuss opportunities for the co-evolution of CAD research and imaging instruments-for example, the CAD of bones and pancreatic cancer. Fourth, the potential of synergy with CAD and clinical decision support systems is also discussed. [ABSTRACT FROM AUTHOR]

This study proposes a multilevel conceptual framework for a deeper understanding of the relationship between employee well-being and innovativeness. We overview 49 years of well-being research [1972–2021] and 54 years of research on innovativeness [1967–2021] to uncover 24 dominant themes in well-being and ten primary topics in innovativeness research. Citation network analysis and text semantic similarity were used to develop a conceptual framework featuring 21 components and three levels: individual, organizational, and market. These components consist of constructs, domains, and factors that can influence or be influenced by employee well-being and innovativeness either directly or indirectly. This is the first study to use citation network analysis and data mining techniques to investigate the relationship between employee well-being and innovativeness. This novel framework can aid organizations in identifying more holistic and efficient strategies for fostering innovativeness and enhancing the well-being of their workforce. It can also assist in developing new theories and serve as a roadmap for future research. We discuss the research limitations and theoretical and practical implications and propose three research themes that future studies may address. [ABSTRACT FROM AUTHOR]

Highlights: [•] The analysis reveals that energy security research is highly multidisciplinary. [•] Diversification is important for ensuring security in the energy supply process. [•] A multilayered overview of the energy supply process is important for energy risk management. [•] Consumer lifestyle innovation will be a part of energy security in the future. [ABSTRACT FROM AUTHOR]

Highlights: [•] Chernobyl accident had limited influence on basic research in nuclear energy. [•] Budget allocation to R&D and number of published papers have recently decreased. [•] Citation network analysis revealed reactor safety and fusion as current research trend. [•] Nuclear energy research policy will change after Fukushima disaster. [Copyright &y& Elsevier]

Abstract: Business development is vital for any firms. However, globalization and the rapid development of technologies have made it difficult to find appropriate business partners such as suppliers and customers, and build reciprocal relationships among them, while it simultaneously offers many opportunities. In this contribution, we propose AI-based approach to find plausible candidates of business partners using firm profiles and transactional relationships among them. We employ machine learning techniques to build a prediction model of customer–supplier relationships. We applied our approach to the large amount of actual business data. The results showed that our approach successfully found potential business partners with F-values of about 84% and reciprocity among them with F-values of about 77%. Using our method, we also developed the Web-based system that helps people in actual businesses to find their new business partners. These contribute to developing one’s own business in the complicated, specialized and rapidly changing business environments of recent years. [Copyright &y& Elsevier]

In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas-research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks. [ABSTRACT FROM AUTHOR]

Purpose – This paper seeks to propose a method of discovering uncommercialized research fronts by comparing scientific papers and patents. A comparative study was performed to measure the semantic similarity between academic papers and patents in order to discover research fronts that do not correspond to any patents.Design/methodology/approach – The authors compared structures of citation networks of scientific publications with those of patents by citation analysis and measured the similarity between sets of academic papers and sets of patents by natural language processing. After the documents (papers/patents) in each layer were categorized by a citation-based method, the authors compared three semantic similarity measurements between a set of academic papers and a set of patents: Jaccard coefficient, cosine similarity of term frequency-inverse document frequency (tfidf) vector, and cosine similarity of log-tfidf vector. A case study was performed in solar cells.Findings – As a result, the cosine similarity of tfidf was found to be the best way of discovering corresponding relationships.Social implications – This proposed approach makes it possible to obtain candidates of unexplored research fronts, where academic researches exist but patents do not. This methodology can be immediately applied to support the decision making of R&D investment by both R&D managers in companies and policy makers in government.Originality/value – This paper enables comparison of scientific outcomes and patents in more detail by citation analysis and natural language processing than previous studies which just count the direct linkage from patents to papers. [ABSTRACT FROM AUTHOR]

As academic disciplines are segmented and specialized, it becomes more difficult to capture relevant research areas precisely by common retrieval strategies using either keywords or journal categories. This paper proposes a method of measuring the relatedness among sets of academic papers in order to detect unrelated communities which are not related to target topic. A citation network, extracted by given keywords, is divided into communities based on the density of links. We measured and compared four measures of relatedness between two communities in a citation network for three large-scale citation datasets. We used both link and semantic similarities. The topological distance from the center in a citation network is a more efficient measure for removing the unrelated communities than the other three measures: the ratio of the number of intercluster links over the all links, the ratio of the number of common terms over all terms, cosine similarity of tf-idf vectors. [ABSTRACT FROM AUTHOR]

In this article, we performed a comparative study to investigate the performance of methods for detecting emerging research fronts. Three types of citation network, co-citation, bibliographic coupling, and direct citation, were tested in three research domains, gallium nitride (GaN), complex network (CNW), and carbon nanotube (CNT). Three types of citation network were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. We evaluated the performance of each type of citation network in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, measured by average publication year, and topological relevance, measured by density. Direct citation, which could detect large and young emerging clusters earlier, shows the best performance in detecting a research front, and co-citation shows the worst. Additionally, in direct citation networks, the clustering coefficient was the largest, which suggests that the content similarity of papers connected by direct citations is the greatest and that direct citation networks have the least risk of missing emerging research domains because core papers are included in the largest component. [ABSTRACT FROM AUTHOR]

Abstract: Surface roughness with a broad range of scale that is not suitable for application in most cases is often produced during chemical vapor deposition (CVD). In this work, we survey the mechanisms causing roughness evolution during CVD from a phenomenological viewpoint. We classify roughness evolution processes into two stages, i.e., the initial stage of deposition on the substrate and the growth stage of continuous films. Thermodynamic and kinetic factors are discussed in the initial stage. In the growth stage, roughness evolution can be classified into two types: positive-feedback growth and non-positive-feedback growth. The former includes shadowing, concentration gradient, and preferential deposition of particles. The latter includes the effect of crystallographic planes, crystallinity, composition, and catalytic activities. [Copyright &y& Elsevier]

In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a “similar” position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also analyzed the effect of aging as well as of self-correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain. [ABSTRACT FROM AUTHOR]

Scientific publications written in natural language still play a central role as our knowledge source. However, due to the flood of publications, obtaining a comprehensive view even on a topic of limited scope, from a stack of publications is becoming an arduous task. Examples are presented from our recent experiences in the materials science field, where information is not shared among researchers studying different materials and different methods. To overcome the limitation, we propose a structured keywords method to reinforce the functionality of a future e-library. [ABSTRACT FROM AUTHOR]

Abstract: Preferred orientation and related film morphology of ZnO films have been extensively studied over the last decades. The aim of this paper is conducting a comprehensive survey to organize the information of previous works and to summarize the current status of research. We first collected reported experimental results concerning the relation between operating conditions and preferred orientation. Ion flux, gas composition, and substrate temperature were main factors to control preferred orientation. Then, we collected proposed models. Preferential nucleation, sticking, surface diffusion, grain growth were considered as major phenomena determining preferred orientation. Finally, we discussed the origin of preferred orientation of polycrystalline ZnO films. Unfortunately, the lack of kinetic data such as sticking probability of precursors onto each crystallographic plane and uncertainty of data such as surface energy hampers us to draw a determinate conclusion. But this survey is expected to play a starting point for further elucidation of the mechanism underlying preferred orientation. [Copyright &y& Elsevier]

Abstract: The initial stage during vapor deposition has been extensively studied in physical vapor deposition (PVD) processes, and nucleation theories have been successfully used to model island nucleation processes during PVD. Compared with the extensive research in PVD, there has been less work on understanding the initial stage in chemical vapor deposition (CVD) processes, despite the technological and commercial importance of CVD-based manufacturing systems. In this work we briefly review the nucleation theories developed for PVD processes and consider the validity of them for modeling the initial stage of CVD processes. One characteristic of CVD processes is the existence of an incubation time. Recent research indicates that the incubation time can be caused by the different reactivity of precursors nucleating on substrates and islands. We proposed process indices to evaluate the relative importance of sticking probabilities and desorption of adsorbates on the incubation time. The differing precursor reactivity between islands and substrates may also affect the island growth mode. This situation in CVD processes differs from that in PVD processes, for which current nucleation theories were developed, and therefore prevents the direct application of PVD nucleation theories to CVD processes. Therefore, to model CVD processes, a nucleation model is needed that is sensitive to the different reactivity of precursors to islands and substrates. [Copyright &y& Elsevier]

Vapor deposition is a complex process, including gas-phase, surface, and solid-phase phenomena. Because of the complexity of chemical and physical processes occurring in vapor deposition processes, it is difficult to form a comprehensive, fundamental understanding of vapor deposition and to control such systems for obtaining desirable structures and performance. To overcome this difficulty, we present a method for simplifying the complex description of such systems. One simplification method is to separate complex systems into multiple elements, and determine which of these are important elements. We call this method abridgement. The abridgement method retains only the dominant processes in a description of the system, and discards the others. Abridgement can be achieved by using process indices to evaluate the relative importance of the elementary processes. We describe the formulation and use of these process indices through examples of the growth of continuous films, initial deposition processes, and the formation of the preferred orientation of polycrystalline films. In this paper, we propose a method for representing complex vapor deposition processes as a set of simpler processes. [Copyright &y& Elsevier]

Deposition flux is an important factor that determines the structures of vapor-deposited materials. However, controlling this flux over a wide range is difficult using only a single apparatus. In this work, we developed a simple method, called combinatorial masked deposition (CMD), that enables a series of deposition fluxes and their respective distribution to be realized on a single sample by just setting a mask with holes of different sizes above a substrate. The degree of reduction in deposition flux can be controlled by the hole size and distance between the given point and the hole. The characteristics and applicability of CMD were evaluated by two experiments. In the first experiment, Cu nanoparticles were formed by sputter–deposition on a-SiO2 at different Cu deposition fluxes. The nanoparticles had a higher number density and smaller size when deposited at 0.80 nm/s for 2.5 s than when deposited at 0.014 nm/s for 140 s. In the second experiment, metal-induced crystallization of amorphous Si (a-Si) was done with spatially distributed Ni additives. The CMD method can realize a series of Ni flux distributions and was successfully used to form 100 different profiles of Ni concentration on a single sample, thus enabling efficient screening of concentration profiles to enhance grain size. [Copyright &y& Elsevier]

• Major trends in energy storage are uncovered through an exhaustive analysis of papers and patents. • Exclusive and overlapping topics between academia and industry are discussed. • The leading role of industry research is revealed and discussed. • The findings can be used as support in energy policy decisions. Energy storage technologies are a critical component of the rapidly growing global demand for reliable electric power supply. Consequently, researchers in both academia and industry have intensified their focus on enhancing electric power generation and distribution systems, publishing thousands of related documents each year. This abundance of information makes keeping up with new developments in the field difficult for researchers and practitioners. To help in this matter, this present study leverages computational methods that allow the exhaustive navigation of the scientific and technological landscape. We identified 52 emerging topics in academic publications and 34 topics in industry after mining more than 100,000 academic articles and patents on energy storage. The key emerging topics in academia were found to be primarily related to the optimization of lithium-sulfur batteries, led by studies focused on decreasing the formation of dendrites in the anode or avoiding the insulation of sulfur in the cathode after repeated charging cycles. Meanwhile, industry research has been directed toward electric digital data processing for multi-power systems. This includes the development of devices to orchestrate the integration of multiple systems in the grid, including renewables and virtual power plants. We also found that a much larger proportion of advances in new energy storage technologies is being led by industry, even before being scrutinized in academia. The identification of exclusive and overlapping topics presented in this study aims to offer a basis for discovering potential opportunities either for industrial commercialization or for university-industry collaboration. [ABSTRACT FROM AUTHOR]

Digitally dependent firms, which are linked to a large number of other firms with varying capabilities, are challenged on evaluating networks with which to engage. Empirically supported heuristic guidance can aid firms in identifying alliances to strengthen capability. We report a case study of firms linked to the Internet of Things (IoT) with a broad view of firms that leverage digital technologies to offer a diverse range of next generation consumer and industrial products and services. This article includes a network analysis of their publicly declared deals to reveal clusters of high volume deals-making activity and their respective hub firms. Based on the rationale of deals of the hub firms, the firms are categorized in a perceptual framework of market-oriented versus technology-seeking inclination. The inclinations of these hub firms were found to vary between technology-seeking, market-oriented, and ambidextrous. The results of this article can inform hub firms in assessing investment portfolios, nonhub firms in evaluating networks governed by hub firms, and policy makers to design incentives for digital adoption. [ABSTRACT FROM AUTHOR]

We analyze a topological structure of networks formed according to the entries and trackbacks in the blogosphere, which is a collection of weblog articles. The analysis is performed based on community extraction, network visualization and keyword analysis. It is shown that the large-scale structure of the blogosphere has a globally sparse, but locally dense structure. The entries in a community yield a dense structure while the trackbacks that interconnect communities are sparse. The visualized results show sparkling-firework-like patterns. We then attempt to characterize the communities using a tf-idf technique. It is found that specific topics are discussed in each community. These results will help us to identify the communities in which certain specific topics discussed and to detect trends in the blogosphere. [ABSTRACT FROM AUTHOR]

There is increasing recognition of the need to incorporate the complex and dynamic interactions between society and water in studies of water resource systems. The study of human-water dynamics requires the involvement of researchers from different disciplines, including hydrology and social science. This paper tracks recent trends in socio-hydrological research using quantitative and qualitative methods. Bibliographic metrics and network analysis are used to identify general trends and illustrate the internal connections in this small but growing interdisciplinary field. Results show that hydrologists dominate research in socio-hydrology as presently defined, with far less participation from social scientists. Research questions for socio-hydrology tend to focus on system dynamics and are often not framed in a way that engages the interests and expertise of social scientists. A qualitative analysis addresses key barriers to the interdisciplinary development of socio-hydrology and identifies research needs and directions for better integration of the human dimensions of water science. Attracting social scientists to this field requires a broader conceptualization of socio-hydrology to focus on sustainable development, risk assessment, hazard management, resilience, adaptation, and knowledge mobilization in addition to hydrology’s traditional focus on systems modeling and decision support. We propose three themes for thematic focus: systemic risk and natural hazards; sustainability science; and adaptive governance. This broader framing of socio-hydrology is inherently more interdisciplinary, brings new methods and viewpoints to the field, and ensures that place-based local values are included in studies of water system dynamics. [ABSTRACT FROM AUTHOR]

Online communication is an indispensable tool for communication and management. The network structure of communication is considered to affect team and individual performances, but it has not been not empirically tested. In this article, we collected a set of 1-month e-mail logs of a company and conducted an e-mail network analysis. We calculated the network centralities of 72 managerial candidates, and investigated the relationship between positions in the network and leadership performance with partial least squares structural equation modeling. Betweenness and in-degree network centralities of those middle managers are correlated with their leadership performance; on the other hand, for this management group, out-degree has no correlation, and PageRank is a negative indicator of leadership. Leaders with high performance are trusted in their communities as a hub of the information channel of the communication network. [ABSTRACT FROM AUTHOR]