Big Data Analytics, Decision-Making Models, and Their Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 808

Special Issue Editor


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Guest Editor
Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Interests: big data management and intelligent analytics; marketing; customer behavior; decision making; design management

Special Issue Information

Dear Colleagues,

Technological innovations such as digitalization, automation, artificial intelligence, and big data are rapidly progressing according to the fourth industrial revolution. In addition, these technological innovations are causing a paradigm shift in various fields such as product development, design, manufacturing, and operation. Artificial intelligence technology in particular is an important factor in the fourth industrial revolution, showing high performance in areas such as computer vision, voice recognition, and natural language processing due to the development of machine learning and deep learning algorithms; these technologies are already being applied to various commercial products and services. Innovative products, services, and business models are created in these applications. Therefore, technology trend analysis offers a flexible instrument to understand both opportunities and competition for emerging technologies. Specifically, a foundation study that can identify important influencing factors and make clear decisions for a more effective approach to technology utilization and application is needed. 

Prof. Dr. Boyoung Kim
Guest Editor

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Keywords

  • multi-criteria decision-making
  • optimization techniques and applications
  • big data and data mining
  • analytic hierarchy process and decision making
  • technology adoption and decision model
  • applications in digital engineering decision making
  • semantic-based technology trend analysis
  • forecast analysis of technology trend

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Published Papers (1 paper)

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Research

11 pages, 1043 KiB  
Article
Mining Product Reviews for Important Product Features of Refurbished iPhones
by Atefeh Anisi, Gül E. Okudan Kremer and Sigurdur Olafsson
Information 2025, 16(4), 276; https://doi.org/10.3390/info16040276 - 29 Mar 2025
Viewed by 220
Abstract
Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most [...] Read more.
Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand. Full article
(This article belongs to the Special Issue Big Data Analytics, Decision-Making Models, and Their Applications)
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