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Theorizing Routines with Computational Sequence Analysis: A Critical Realism Framework
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2022 | Journal of the Association for Information Systems | Citations: 6
Authors: Zhang, Zhewei; Lee, Habin; Yoo, Youngjin; Choi, Youngseok

Abstract: We develop a methodological framework to develop process theories on routines b ... Expand

Abstract: We develop a methodological framework to develop process theories on routines by leveraging large volumes of digital trace data following critical realism principles. Our framework begins with collecting and preprocessing digital trace data, corresponding to the empirically observed experience of critical realism. In the second and third steps of the framework, we identify a finite set of similar repetitive patterns (routines) through computational analysis. We accomplish this by combining frequent subsequence mining and clustering analysis to transform empirical observation into a set of routines that correspond to actual happening in critical realism. Then, we employ a retroduction approach to identify generative mechanisms of the routines. In the final step, we validate the generative mechanisms by evaluating proposed processual explanations and/or eliminating alternatives. We provide an illustrative example of developing a process theory in relation to the collaboration pattern in Wikipedia.Suprateek Sarker was the accepting senior editor. This research article was submitted on April 17, 2017 and underwent three revisions.Zhewei Zhang is an assistant professor in the Information Systems Management group at Warwick Business School, University of Warwick. He received his PhD at Temple University (2016). His research primarily focuses on digital innovation, particularly the organizational and societal impacts brought by the adoption of emerging technologies and innovation through collaboration in nontraditional organizational settings. He is experienced in applying computational approaches, such as sequence analysis and machine learning, to support his research. He served as the co-investigator of several funded projects, such as Innovate UK and GCRF, leading the development of artificial intelligence solutions.Habin Lee is a chair in digital business analytics at Brunel Business School, Brunel University London. He obtained his PhD and MEng in management engineering and management science respectively at KAIST (Korea Advanced Institute of Science and Technology). His research interests include computational social science, digital governance, data analytics for policy modeling and simulation, and sustainable operations management. He published articles in Collapse

Semantic filters: partitional clustering

Topics: Wikipedia online community knowledge representation computer supported cooperative work information technology infrastructure

Methods: digital trace data cluster analysis hierarchical clustering computational algorithm theory development
Towards the Use of Clustering Algorithms in Recommender Systems

2020 | Americas Conference on Information Systems | Citations: 0
Authors: Miranda, Leandro; Viterbo, Jose; Bernardini, Flávia

Abstract: Recommender Systems have been intensively used in Information Systems in the la ... Expand

Abstract: Recommender Systems have been intensively used in Information Systems in the last decades, facilitating the choice of items individually for each user based on your historical. Clustering techniques have been frequently used in commercial and scientific domains in data mining tasks and visualization tools. However, there is a lack of secondary studies in the literature that analyze the use of clustering algorithms in Recommender Systems and their behavior in different aspects. In this work, we present a Systematic Literature Review (SLR), which discusses the different types of information systems with the use of the clustering algorithm in Recommender Systems, which typically involves three main recommendation approaches found in literature: collaborative filtering, content-based filtering, and hybrid recommendation. In the end, we did a quantitative analysis using K-means clustering for finding patterns between clustering algorithms, recommendation approaches, and some datasets used in their publications. Collapse

Semantic filters: partitional clustering

Topics: collaborative filtering recommender system database system content-based filtering IT project

Methods: cluster analysis experiment computational algorithm structured literature research design methodology
A network-based concept extraction for managing customer requests in a social media care context
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2020 | International Journal of Information Management | Citations: 0
Authors: Misuraca, Michelangelo; Scepi, Germana; Spano, Maria

Abstract: Web 2.0 changed everyday life in many aspects, including the whole system that o ... Expand

Abstract: Web 2.0 changed everyday life in many aspects, including the whole system that orbits around the purchase of products and services. This revolution necessarily involved also companies, because customers became increasingly demanding. The diffusion of social media platforms pushed customers to prefer this channel for quickly obtaining information and feedback about what they want to buy, as well as for asking help after the selling. In this framework, many organisations adopted a new way of providing assistance known as social customer care. A direct link to companies allows customers to obtain real-time solutions. In this paper, we introduce a new strategy for automatically managing the information listed in the requests that customers send to the social media accounts of companies. Our proposal relies on the use of network techniques for extracting high-level structures from texts, highlighting the different concepts expressed into the customers’ written requests. The texts can be then organised on the basis of this new emerging information. An application to the requests sent to the AppleSupport service on Twitter shows the effectiveness of the strategy. Collapse

Semantic filters: partitional clustering

Topics: Twitter social media information management knowledge base backup

Methods: computational algorithm hierarchical clustering topic model cluster analysis partitional clustering
A Visual Framework for Knowledge Discovery on the Web: An Empirical Study of Business Intelligence Exploration

2005 | Journal of Management Information Systems | Citations: 0
Authors: Wingyan Chung; Hsinchun Chen; Nunamaker Jr., Jay F.

Abstract: Information overload often hinders knowledge discovery on the Web. Existing tool ... Expand

Abstract: Information overload often hinders knowledge discovery on the Web. Existing tools lack analysis and visualization capabilities. Search engine displays often overwhelm users with irrelevant information. This research proposes a visual framework for knowledge discovery on the Web. The framework incorporates Web mining, clustering, and visualization techniques to support effective exploration of knowledge. Two new browsing methods were developed and applied to the business intelligence domain: Web community uses a genetic algorithm to organize Web sites into a tree format; knowledge map uses a multidimensional scaling algorithm to place Web sites as points on a screen. Experimental results show that knowledge map out-performed Kartoo, a commercial search engine with graphical display, in terms of effectiveness and efficiency. Web community was found to be more effective, efficient, and usable than result list. Our visual framework thus helps to alleviate information overload on the Web and offers practical implications for search engine developers. Collapse

Semantic filters: partitional clustering

Topics: online community web application website business intelligence web page

Methods: computational algorithm cluster analysis experiment literature study experimental task
Partitioning-based clustering for Web document categorization
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1999 | Decision Support Systems | Citations: 0
Authors: Boley, Daniel; Gini, Maria; Gross, Robert; Han, Eui-Hong (Sam); Hastings, Kyle; Karypis, George; Kumar, Vipin; Mobasher, Bamshad; Moore, Jerome

Abstract: Clustering techniques have been used by many intelligent software agents in orde ... Expand

Abstract: Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document structures to define a distance or similarity among these documents, or use probabilistic techniques such as Bayesian classification. Many of these traditional algorithms, however, falter when the dimensionality of the feature space becomes high relative to the size of the document space. In this paper, we introduce two new clustering algorithms that can effectively cluster documents, even in the presence of a very high dimensional feature space. These clustering techniques, which are based on generalizations of graph partitioning, do not require pre-specified ad hoc distance functions, and are capable of automatically discovering document similarities or associations. We conduct several experiments on real Web data using various feature selection heuristics, and compare our clustering schemes to standard distance-based techniques, such as hierarchical agglomeration clustering, and Bayesian classification methods, such as AutoClass. Collapse

Semantic filters: partitional clustering

Topics: information retrieval website web page

Methods: experiment cluster analysis computational algorithm term frequency–inverse document frequency association rule learning

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Found 5 articles

Theorizing Routines with Computational Sequence Analysis: A Critical Realism Framework

Towards the Use of Clustering Algorithms in Recommender Systems

A network-based concept extraction for managing customer requests in a social media care context

A Visual Framework for Knowledge Discovery on the Web: An Empirical Study of Business Intelligence Exploration

Partitioning-based clustering for Web document categorization