134 research outputs found

    In the eyes of Janus:the intellectual structure of HRM-performance debate and its future prospects

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    Purpose The purpose of this paper is to offer a perspective on the future of the human resource management (HRM)-performance debate and its prospects for interaction with practice by evaluating the debate's intellectual structure. Design/methodology/approach With co-citation analysis the paper examines the intellectual structure that informed the HRM-performance debate. The findings were presented to a group of academics, who have been influential in the development of the debate. In several rounds of a quasi-Delphi interaction they discussed the state of the art, future development of the debate, upcoming theoretical sources of inspiration and topics on which they (dis)agreed. Findings The dominant knowledge domain is built upon resource-based view, social exchange theory, human capital theory, institutional theory and critical perspective. It became well established in the mid 1990s, when the strategic HRM domain merged with the high performance work systems domain, thus forming the conceptual backbone of the debate. More recently the debate has been informed by review studies, meta-analyses and critical reflections on the current methodological paradigms, which is aligned with the debate's life cycle stage. Originality/value The paper highlights the theoretical foundations of the HRM-performance debate and gives valuable suggestions on how to take the field forward along with important implications for researchers and their relationship with the business community. Keywords: High performance work systems, HR strategy, Organization effectivenes

    Diffusion of Latent Semantic Analysis as a Research Tool: A Social Network Analysis Approach

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    Latent Semantic Analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from information retrieval to machine learning and so on. In this paper, we chart the development and diffusion of LSA as a research tool using Social Network Analysis (SNA) approach that reveals the social structure of a discipline in terms of collaboration among scientists. Using Thomson Reuters’ Web of Science (WoS), we identified 65 papers with “Latent Semantic Analysis” in their titles and 250 papers in their topics (but not in titles) between 1990 and 2008. We then analyzed those papers using bibliometric and SNA techniques such as co-authorship and cluster analysis. It appears that as the emphasis moves from the research tool (LSA) itself to its applications in different fields, citations to papers with LSA in their titles tend to decrease. The productivity of authors fits Lotka’s Law while the network of authors is quite loose. Networks of journals cited in papers with LSA in their titles and topics are well connected

    The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

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    A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks.Comment: 33 pages, 11 figures, 10 tables. To appear in the Journal of the American Society for Information Science and Technolog

    THE INTELLECTUAL STRUCTURE OF ELECTRONIC RECORDS MANAGEMENT

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    A number of countries have launched projects with a particular emphasis on using information technologies (IT) to provide electronic information and services to citizens and businesses. Through various IT, tremendous amount of electronic records in government agencies are created. These records and archives are the basis of knowledge management. Electronic records management (ERM) is a fast growing field throughout the last decades. Theoretical foundations for ERM have remained obscure from the research community. To map the intellectual structure of ERM research, this study identifies the high-impact articles as well as the correlations among these scholar publications. In this study, co-citation, co-word, association rule and cluster analysis techniques are used to investigate the intellectual pillars of the ERM literature. This study exposes researchers to a new way of profiling knowledge networks and their relationships the area of ERM, thereby helping academia and practitioners better understand contemporary studies. The results of the mapping can help identify the research direction of ERM research, provide a valuable tool for researchers to access ERM literature, and acts as an exemplary model for future researches

    Information Science in the web era: a term-based approach to domain mapping.

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    International audienceWe propose a methodology for mapping the research in Information Science (IS) field based on a combined use of symbolic (linguistic) and numeric information. Using the same list of 12 IS journals as in earlier studies on this same topic (White & McCain 1998 ; Zhao & Strotmann 2008a&b), we mapped the structure of research in IS for two consecutive periods: 1996-2005 and 2006-2008. We focused on mapping the content of scientific publications from the title and abstract fields of underlying publications. The labels of clusters were automatically derived from titles and abstracts of scientific publications based on linguistic criteria. The results showed that while Information Retrieval (IR) and Citation studies continued to be the two structuring poles of research in IS, other prominent poles have emerged: webometrics in the first period (1996-2005) evolved into general web studies in the second period, integrating more aspects of IR research. Hence web studies and IR are more interwoven. There is still persistence of user studies in IS but now dispersed among the web studies and the IR poles. The presence of some recent trends in IR research such as automatic summarization and the use of language models were also highlighted by our method. Theoretic research on "information science" continue to occupy a smaller but persistence place. Citation studies on the other hand remains a monolithic block, isolated from the two other poles (IR and web studies) save for a tenuous link through user studies. Citation studies have also recently evolved internally to accommodate newcomers like "h-index, Google scholar and the open access model". All these results were automatically generated by our method without resorting to manual labeling of specialties nor reading the publication titles. Our results show that mapping domain knowledge structures at the term level offers a more detailed and intuitive picture of the field as well as capturing emerging trends

    Data Science: A Study from the Scientometric, Curricular, and Altmetric Perspectives

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    This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1. What are the scientometric features of the data science field? 2. What are the contributing fields to the establishment of data science? 3. What are the major research areas of the data science discipline? 4. What are the salient topics taught in the data science curriculum? 5. What topics appear in the Twitter-sphere regarding data science? 6. What can be learned about data science from the scientometric, curricular, and altmetric analyses of the data collected? Using bibliometric data from the Scopus database for 1983 – 2021, the current study addresses the first three research questions. The fourth research question is answered with curricular data collected from U.S. educational institutions that offer data science programs. Altmetric data was gathered from Twitter for over 20 days to answer the fifth research question. All three sets of data are analyzed quantitatively and qualitatively. The scientometric portion of this study revealed a growing field, expanding beyond the borders of the United States and the United Kingdom into a more global undertaking. Computer Science and Statistics are foundational contributing fields with a host of additional fields contributing data sets for new data scientists to act, including, for example, the Biomedical and Information Science fields. When it comes to the question of salient topics across all three aspects of this research, it was revealed that a large degree of coherence between the three resulted in highlighting thirteen core topics of data science. However, it can be noted that Artificial Intelligence stood out among all the other groups with leading topics such as Machine Learning, Neural Networks, and Natural Language Processing. The findings of this study not only identify the major parameters of the data science field (e.g., leading researchers, the composition of the discipline) but also reveal its underlying intellectual structure and research fronts. They can help researchers to ascertain emerging topics and research fronts in the field. Educational programs in data science can learn from this study about how to update their curriculums and better prepare students for the rapidly growing field. Practitioners and other stakeholders of data science can also benefit from the present research to stay tuned and current in the field. Furthermore, the triple-pronged approach of this research provides a panoramic view of the data science field that no prior study has ever examined and will have a lasting impact on related investigations of an emerging discipline

    Methodological Advances in Bibliometric Mapping of Science

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    Bibliometric mapping of science is concerned with quantitative methods for visually representing scientific literature based on bibliographic data. Since the first pioneering efforts in the 1970s, a large number of methods and techniques for bibliometric mapping have been proposed and tested. Although this has not resulted in a single generally accepted methodological standard, it did result in a limited set of commonly used methods and techniques. In this thesis, a new methodology for bibliometric mapping is presented. It is argued that some well-known methods and techniques for bibliometric mapping have serious shortcomings. For instance, the mathematical justification of a number of commonly used normalization methods is criticized, and popular multidimensional-scaling-based approaches for constructing bibliometric maps are shown to suffer from artifacts, especially when working with larger data sets. The methodology introduced in this thesis aims to provide improved methods and techniques for bibliometric mapping. The thesis contains an extensive mathematical analysis of normalization methods, indicating that the so-called association strength measure has the most satisfactory mathematical properties. The thesis also introduces the VOS technique for constructing bibliometric maps, where VOS stands for visualization of similarities. Compared with well-known multidimensional-scaling-based approaches, the VOS technique is shown to produce more satisfactory maps. In addition to the VOS mapping technique, the thesis also presents the VOS clustering technique. Together, these two techniques provide a unified framework for mapping and clustering. Finally, the VOSviewer software for constructing, displaying, and exploring bibliometric maps is introduced
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