Mergers & acquisitions research: A bibliometric study of top strategy and international business journals, 1980–2010

Mergers and acquisitions (M&As) are important modes through which firms carry out their domestic and international strategies and have been noted as the CEOs favorite strategy. As a significant field of study, M&Aresearch has accumulated substantial knowledge. This bibliometric study examines the extant strategy and international business literature on M&As. Methodologically, we examined a sample of 334 articles published in sixteen leading management/business journals, during a 31 year period — from 1980 to 2010. The results provide a global perspective of the field, identifying the works that have had the greater impact, the intellectual interconnections among authors and works, the main research traditions, or themes, delved upon on M&Arelated research. Structural and longitudinal analyses reveal the changes in the intellectual structure of the field over time. A discussion on the accumulated knowledge and future research avenues concludes this paper.info:eu-repo/semantics/publishedVersio

A Conceptual Framework for Integration Development of GSFLOW Model: Concerns and Issues Identified and Addressed for Model Development Efficiency

In Coupled Groundwater and Surface-Water Flow (GSFLOW) model, the three-dimensional finite-difference groundwater model (MODFLOW) plays a critical role of groundwater flow simulation, together with which the Precipitation-Runoff Modeling System (PRMS) simulates the surface hydrologic processes. While the model development of each individual PRMS and MODFLOW model requires tremendous time and efforts, further integration development of these two models exerts additional concerns and issues due to different simulation realm, data communication, and computation algorithms. To address these concerns and issues in GSFLOW, the present paper proposes a conceptual framework from perspectives of: Model Conceptualization, Data Linkages and Transference, Model Calibration, and Sensitivity Analysis. As a demonstration, a MODFLOW groundwater flow system was developed and coupled with the PRMS model in the Lehman Creek watershed, eastern Nevada, resulting in a smooth and efficient integration as the hydrogeologic features were well captured and represented. The proposed conceptual integration framework with techniques and concerns identified substantially improves GSFLOW model development efficiency and help better model result interpretations. This may also find applications in other integrated hydrologic modelings

Three decades of strategic management research on M&As: Citations, co-citations, and topics

Merger and acquisitions (M&As) strategies have been growingly deployed by firms for their domestic and international expansion, to redefine their business scope or take advantage of emerging opportunities. In this paper we conduct a bibliometric study of the extant strategy research on M&As, assessed by the articles published in the main journal for strategic management studies over the period 1984-2010. Results reveal the highest impact works (articles and books), the intellectual ties among authors and theories that form five main clusters of research, and the topics delved into. Performance effects, M&As as diversification strategies and RBV and capabilities-based topics have dominated the extant research. The study contributes to the extant knowledge on M&As by taking stock of the accumulated knowledge and research direction, complementing other literature reviews with a strategic management specific perspective. Thus, we provide a rear view of the field which facilitates detecting untapped gaps that may be munificent avenues for future research.info:eu-repo/semantics/publishedVersio

Text summarization towards scientific information extraction

Despite the exponential growth in scientific textual content, research publications are still the primary means for disseminating vital discoveries to experts within their respective fields. These texts are predominantly written for human consumption resulting in two primary challenges; experts cannot efficiently remain well-informed to leverage the latest discoveries, and applications that rely on valuable insights buried in these texts cannot effectively build upon published results. As a result, scientific progress stalls. Automatic Text Summarization (ATS) and Information Extraction (IE) are two essential fields that address this problem. While the two research topics are often studied independently, this work proposes to look at ATS in the context of IE, specifically in relation to Scientific IE. However, Scientific IE faces several challenges, chiefly, the scarcity of relevant entities and insufficient training data. In this paper, we focus on extractive ATS, which identifies the most valuable sentences from textual content for the purpose of ultimately extracting scientific relations. We account for the associated challenges by means of an ensemble method through the integration of three weakly supervised learning models, one for each entity of the target relation. It is important to note that while the relation is well defined, we do not require previously annotated data for the entities composing the relation. Our central objective is to generate balanced training data, which many advanced natural language processing models require. We apply our idea in the domain of materials science, extracting the polymer-glass transition temperature relation and achieve 94.7% recall (i.e., sentences that contain relations annotated by humans), while reducing the text by 99.3% of the original document

Human side of open innovation : review, analysis and recommendations

In recent years, open innovation (OI) has emerged as a prominent field of study within management and innovation literature, focusing on the collaborative and open nature of various organizational processes. However, much of the research in this area has been dominated by a firm and technological perspective, with limited attention paid to the human aspects of OI. As such, this thesis aims to address this research gap by conducting a comprehensive review of the OI with focus on human side and individual (micro) perspective. The study involves a systematic review of 54 articles published in top-tier management journals, which are analysed using thematic analysis and content analysis techniques. The review highlights the importance of factors such as trust, communication, collaboration, knowledge sharing, and leadership in facilitating successful OI practices. Moreover, it underscores the critical role of social and cultural factors in shaping the human dynamics of OI. Based on the findings, the thesis proposes research questions that aim to shed further light on the human dimensions around OI, and how they can be leveraged to enhance innovation outcomes. The study contributes to the existing literature by providing a more comprehensive understanding of the role of human factors in OI, and by providing insights that can inform the design of effective OI strategies.Nos últimos anos, a inovação aberta (IA) emergiu como um campo proeminente de estudo na literatura de gestão e inovação, concentrando-se na natureza colaborativa e aberta de vários processos organizacionais. No entanto, grande parte da investigação nesse campo tem sido dominada por uma firma perspetiva tecnológica, com pouca atenção dedicada aos aspetos humanos da IA. Como tal, a presente tese visa colmatar essa lacuna de investigação através da realização de uma revisão abrangente da OI com foco no lado humano e na perspetiva (micro) individual. O estudo envolve uma revisão sistemática de 54 artigos publicados em revistas emblemáticas de gestão, que são analisados usando técnicas de análise temática e de conteúdo. A revisão destaca a importância de fatores como confiança, comunicação, colaboração, compartilhamento de conhecimento e liderança na facilitação de práticas bem-sucedidas de IA. Além disso, enfatiza o papel crítico de fatores sociais e culturais na formação da dinâmica humana da IA. Com base nos resultados, a tese propõe questões de investigação que visam esclarecer as dimensões humanas da IA e como estas podem ser aproveitadas para melhorar os resultados da inovação. O estudo contribui para a literatura existente, fornecendo uma compreensão mais abrangente do papel dos fatores humanos na IA e oferecendo insights que podem informar o design de estratégias eficazes de IA

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed