Collaboration and Author Order: Changing Patterns in IS Research

In this article we examine changes in the patterns of collaboration among information systems researchers since 1987, in terms of number of authors and order of authorship. The proportion of multiple authored papers, particularly among articles published in more prestigious journals, increased significantly. One possible explanation may be in increased research complexity, as evidenced by much longer papers. At the same time, among prestigious journals, the alphabetical model for ordering authorship all but disappeared. The article calls for consideration of a standard for authorship order in IS research

A Balanced Theory of Knowledge Management in Software Process Improvement

This research employs a social network analysis (SNA) approach to study the longitudinal changes in co-authorship and affiliations of authors, who published in the Australasian Conference on Information Systems (ACIS) from 2001 to 2011. The research explores the structural patterns of co-authorship at the institution and individual author levels, and found research collaboration tend to occur between authors in the same regions and institutions. Descriptive findings further revealed key authors with rich and diverse co-authorship ties, as well as the tendency of authors to collaborate in silos within institutions. A longitudinal SNA method was performed to statistically deduce the changing patterns of co-authorship and affiliations from a sample of the authors in this 11-year period, which complements the descriptive findings. The discussion of our findings results in recommendations to improve the ACIS community’s productivity and in directions for future studies concerning the applications of SNA in examining research collaboration

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Publishing patterns within the UK accounting and finance academic community

This study reports on publishing patterns in the UK and Irish accounting and finance academic community for the 2-year period 1998-1999 using the data contained in the BAR Research Register. It is found that the community has been growing modestly since 1991, with a doubling in the number of PhD-qualified staff (to 30%) and a reduction in the number with a professional qualification (from 81 to 58%). Nearly half of all outputs appear in other than academic journals. The mean number of publications is 1.76 per capita, with significantly more staff active in publishing than in 1991 (44% compared to 35%). However, only 17% publish in a subset of 60 'top' journals. Just over half of all articles are published in the core discipline journals, the rest appearing mainly in management, economics, sociology, education and IT journals. This may indicate a growing maturity in the disciplines, whereby applied research findings are flowing back into related foundation and business disciplines. Nearly two-thirds of academic articles are co-authored, with 25% of contributions coming from outside the community, indicating an openness to interdisciplinary collaboration, collaboration with overseas academics and collaboration with individuals in practice. The findings of this study will be of assistance to those making career decisions (either their own career or decisions involving other people's careers). They also raise awareness of the way in which the accounting and finance disciplines are developing

Probing the topological properties of complex networks modeling short written texts

In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well -- many informative discoveries have been made this way -- but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyzes performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks

Two-layer classification and distinguished representations of users and documents for grouping and authorship identification

Most studies on authorship identification reported a drop in the identification result when the number of authors exceeds 20-25. In this paper, we introduce a new user representation to address this problem and split classification across two layers. There are at least 3 novelties in this paper. First, the two-layer approach allows applying authorship identification over larger number of authors (tested over 100 authors), and it is extendable. The authors are divided into groups that contain smaller number of authors. Given an anonymous document, the primary layer detects the group to which the document belongs. Then, the secondary layer determines the particular author inside the selected group. In order to extract the groups linking similar authors, clustering is applied over users rather than documents. Hence, the second novelty of this paper is introducing a new user representation that is different from document representation. Without the proposed user representation, the clustering over documents will result in documents of author(s) distributed over several clusters, instead of a single cluster membership for each author. Third, the extracted clusters are descriptive and meaningful of their users as the dimensions have psychological backgrounds. For authorship identification, the documents are labelled with the extracted groups and fed into machine learning to build classification models that predicts the group and author of a given document. The results show that the documents are highly correlated with the extracted corresponding groups, and the proposed model can be accurately trained to determine the group and the author identity