Discovering the most important data quality dimensions in health big data using latent semantic analysis

Big Data quality is a field which is emerging. Many authors nowadays agree that data quality is still very relevant, even for Big Data uses. However, there is a lack of frameworks or guidelines focusing on how to carry out big data quality initiatives. The starting point of any data quality work is to determine the properties of data quality, termed ‘data quality dimensions’ (DQDs). Even these dimensions lack precise rigour in terms of definition in existing literature. This current research aims to contribute towards identifying the most important DQDs for big data in the health industry. It is a continuation of previous work, which, using relevant literature, identified five DQDs (accuracy, completeness, consistency, reliability and timeliness) as being the most important DQDs in health datasets. The previous work used a human judgement based research method known as an inner hermeneutic cycle (IHC). To remove the potential bias coming from the human judgement aspect, this research study used the same set of literature but applied a statistical research method (used to extract knowledge from a set of documents) known as latent semantic analysis (LSA). Use of LSA concluded that accuracy and completeness were the only similar DQDs classed as the most important in health Big Data for both IHC and LSA

Discovering the most important data quality dimensions in health big data using latent semantic analysis

Big Data quality is a field which is emerging. Many authors nowadays agree that data quality is still very relevant, even for Big Data uses. However, there is a lack of frameworks or guidelines focusing on how to carry out big data quality initiatives. The starting point of any data quality work is to determine the properties of data quality, termed ‘data quality dimensions’ (DQDs). Even these dimensions lack precise rigour in terms of definition in existing literature. This current research aims to contribute towards identifying the most important DQDs for big data in the health industry. It is a continuation of previous work, which, using relevant literature, identified five DQDs (accuracy, completeness, consistency, reliability and timeliness) as being the most important DQDs in health datasets. The previous work used a human judgement based research method known as an inner hermeneutic cycle (IHC). To remove the potential bias coming from the human judgement aspect, this research study used the same set of literature but applied a statistical research method (used to extract knowledge from a set of documents) known as latent semantic analysis (LSA). Use of LSA concluded that accuracy and completeness were the only similar DQDs classed as the most important in health Big Data for both IHC and LSA

Assessing the Exchange of Knowledge Between Operations Management and other Fields: Some Challenges and Opportunities

Addressing a suspicion that the field of Operations Management (OM) draws substantially more knowledge from non-OM journals than those journals draw from OM journals in return, we studied the citations of the top 30 research journals of interest to our field. We conducted citation analyses of the three oldest OM journals over three decades in comparison to the 27 other journals representing the fields of Management, Operations Research/Management Science (OR/MS), Marketing, Practice, and Engineering. We examined both the entire 30-year period and then each decade separately. Our suspicions were confirmed—although citations from these 27 journals to these three OM journals have increased by a factor of 7 over the three decades, we in OM still cite these journals about twenty-five times more often than they cite our journals, giving an indication of the knowledge development and flows among these fields. We then describe some challenges for the field of OM in providing more research knowledge to other fields but also some opportunities that OM should be able to capitalize on, such as our historic ties to practice and our escalating research in strategic and organizational issues

Agenda setting and active audiences in online coverage of human trafficking

Online news platforms and social media increasingly influence the public agenda on social issues such as human trafficking. Yet despite the popularity of online news and the availability of sophisticated tools for analyzing digital texts, little is known about the relations between news coverage of human trafficking and audiences’ reactions to and interpretations of such coverage. In this paper, we examine journalists’ and commenters’ topic choices in coverage and discussion of human trafficking in the British newspaper The Guardian from 2009 to 2014. We use latent semantic analysis to identify 11 topics discussed by both journalists and readers, and analyze each topic in terms of the degree to which journalists and readers agree or disagree in their topic preferences. We find that four topics were preferred equally by journalists and commenters, four were preferred by journalists, and three were preferred by commenters. Our findings suggest that theories of ‘agenda setting’ and of the ‘active audience’ are not mutually exclusive, and the scope of explanation of each depends partly on the specific topic or subtopic that is analyzed

The Diffusion Network of Research Knowledge in Operations Management

Purpose: To examine how the research knowledge in OM has been obtained and distributed since the first journals in OM began publication in 1980, changes in the interests of OM over the decades and where they are heading in the future, and to explore the changing roles of individual journals in the development of OM. Design/methodology/approach: A two-stage bibliometric study was employed, first using citation analysis to examine the changing research interests in OM through an analysis of the OM journals. Then the top journals of most importance to OM were analysed to determine the role that each one played in the knowledge distribution network and how that changed over the decades. Findings: OM’s journal base consists of 7 research knowledge sources, 12 transmitters linking different journal groups, and 11 sinks with limited input. Research attention changed from practice, engineering, and OR to general management, strategy, and production management in the 2000s, with strategy, organizational issues, and logistics surfacing in the 2010s. OM features increasingly academic research with less interest in practice. OM journals’ network importance has increased substantially, with JOM now a bridge between the quantitative and management journals. Practical implications Both researchers and managers gain in understanding the history and identifying the future direction of OM, as well as which journals will have the most relevant papers to their interests. Originality/value: This research identifies the history of the OM field in terms of its constituents and where it is going in the future. This history is related to the role OM plays among the knowledge network of top journals and presents a novel way of classifying and labeling journals based on their contribution

TOPIC MODELLING METHODOLOGY: ITS USE IN INFORMATION SYSTEMS AND OTHER MANAGERIAL DISCIPLINES

Over the last decade, quantitative text mining approaches to content analysis have gained increasing traction within information systems research, and related fields, such as business administration. Recently, topic models, which are supposed to provide their user with an overview of themes being dis-cussed in documents, have gained popularity. However, while convenient tools for the creation of this model class exist, the evaluation of topic models poses significant challenges to their users. In this research, we investigate how questions of model validity and trustworthiness of presented analyses are addressed across disciplines. We accomplish this by providing a structured review of methodological approaches across the Financial Times 50 journal ranking. We identify 59 methodological research papers, 24 implementations of topic models, as well as 33 research papers using topic models in In-formation Systems (IS) research, and 29 papers using such models in other managerial disciplines. Results indicate a need for model implementations usable by a wider audience, as well as the need for more implementations of model validation techniques, and the need for a discussion about the theoretical foundations of topic modelling based research

Artificial Intelligence in Business: A Literature Review and Research Agenda

The rise of artificial intelligence (AI) technologies has created promising research opportunities for the information systems (IS) discipline. Through applying latent semantic analysis, we examine the correspondence between key themes in the academic and practitioner discourses on AI. Our findings suggest that business academic research has predominantly focused on designing and applying early AI technologies, while practitioner interest has been more diverse. We examine these differences in the socio-technical continuum context and relate existing literature on AI to core IS research areas. In doing so, we identify existing research gaps and propose future research directions for IS scholars related to AI and organizations, AI and markets, AI and groups, AI and individuals, and AI development

ARGUMENTOS DA DECISÃO DE VOTO DE DEPUTADOS DURANTE A VOTAÇÃO DO IMPEACHMENT

The advances in techniques for analyzing unstructured data can help to better understand the positioning and votes of politicians who represent a population. This article analyses the underlying semantic relationship between the themes present in the arguments for the voting decision of parliamentarians of different political parties. For this, it uses discourse data from all the deputies during the impeachment voting, which took place in 2015. Weiss's (1983) perspective on the decision-making of politicians, and Festinger's (1957) theory of cognitive dissonance were used as the theoretical basis for the analysis. Additionally, using the technique of LSA (Latent semantic analysis) — a text mining technique based on matrix decomposition¬¬¬¬ — it aims to contribute to the analyses by bringing results related to the main associated terms, and the use of certain words in the political context. It was found that for the case presented, the deputies' discourse is not an element that enables the different voting groups to be distinguished, indicating that in order to understand the position of a politician, and better choose their representative, citizens need to go beyond the politicians’ discourse.El avance de técnicas para análisis de datos no estructurados puede ayudar a comprender mejor el posicionamiento y los votos de los políticos que representan una población. El objetivo del presente artículo es analizar la relación semántica latente de las temáticas presentes en los argumentos de la decisión de voto de los parlamentarios de diferentes partidos políticos. Para esto, se utilizaron datos de discurso de todos los diputados durante la votación del impeachment, ocurrida en 2015. En ese sentido, se utilizó como base teórica para la realización de los análisis la perspectiva de Weiss (1983) sobre la toma de decisión de políticos y la teoría de la disonancia cognitiva de Festinger (1957). Además, a partir del uso de la técnica LSA (Latent semantic analysis), técnica de minería de texto basada en descomposición matricial, se buscó contribuir con los análisis al traer resultados relacionados a los principales términos asociados y uso de determinadas palabras en el contexto político. Como resultados, se constató que, para el caso presentado, el discurso de los diputados no es elemento que permite separar a los diferentes grupos votantes, lo que indica que para comprender la posición de un político y elegir mejor su representante, los ciudadanos deben ir más allá de su discurso.O avanço de técnicas para análise de dados não estruturados pode auxiliar a compreender melhor o posicionamento e os votos dos políticos que representam uma população. O objetivo do presente artigo é analisar a relação semântica latente das temáticas presentes nos argumentos da decisão de voto dos parlamentares de diferentes partidos políticos. Para tal, foram utilizados dados de discurso de todos os deputados durante a votação do impeachment, ocorrida em 2015. Nesse sentido, utilizaram-se como base teórica para a realização das análises a perspectiva de Weiss (1983) sobre a tomada de decisão de políticos e a teoria da dissonância cognitiva de Festinger (1957). Adicionalmente, a partir do uso da técnica LSA (Latent semantic analysis), técnica de mineração de texto baseada em decomposição matricial, buscou-se contribuir com as análises ao trazer resultados relacionados aos principais termos associados e uso de determinadas palavras no contexto político. Como resultados, verificou-se que, para o caso apresentado, o discurso dos deputados não é um elemento que permite separar os diferentes grupos votantes, o que indica que, para compreender a posição de um político e escolher melhor seu representante, os cidadãos precisam ir além do seu discurso