Special Issue "Business Analytics and Data Mining for Business Sustainability"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Prof. Dr. Se-hak Chun
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Guest Editor
Department of Business Administration, Seoul National University of Science and Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Korea
Interests: artificial intelligence; big data research; business analytics; data mining; economics of information system; electronic commerce; financial forecasting
Prof. Dr. Young-Woong Ko
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Guest Editor
Department of Software, Hallym University, 1 Hallymdaehak-gil, Chucheon, Gangwon 24252, Korea
Interests: big data research; cloud computing; computer software; system software
Prof. Dr. Moon Young Kang
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Guest Editor
Department of Entrepreneurship and Small Business, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 06978, Korea
Interests: business analytics; big data research; marketing analytics; small and medium enterprise (SME); startup; sustainability
Prof. Dr. Jinho Choi
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Guest Editor
School of Business, Sejong University, Seoul 05006, Korea
Interests: agent-based model; business analytics; big data research; network analysis; sustainability

Special Issue Information

Dear Colleagues,

With the advent of new technologies, such as the Internet of Things, mobile technologies, and a wide variety of social media applications, not only do businesses generate a huge volume of data in different formats and from various sources, but also, sustainable business under such an environment has moved beyond being just a trend to becoming an important objective.

Analyzing such data enables businesses to explore the new possibility of uncovering hidden knowledge, improve decision making, and support strategic planning with precise diagnosis, prediction, innovation, and prescription.

Thus, businesses are interested in developing new insights and understanding of business performance based on data and statistical methods, so-called business analytics for sustainable business and performance. Business analytics makes extensive use of analytical modeling and numerical analysis, including explanatory and predictive modeling, and fact-based scientific management to drive optimal decision making and sustainable strategies.

Applying techniques from data mining to extract relevant knowledge from available data sources has become vital for various stakeholders to ensure competitiveness for sustainability and, thus, already attracted a large amount of research attention.

This Special Issue will address innovative approaches in the general area of business analytics and data mining, as well as specific approaches dealing with the topic big data and web data mining in businesses and other domains.

Topics of interest for this Special Issue include (but are not limited to):

  • Artificial intelligence and Its applications
  • Analysis of customer behavior and CRM
  • Big data business analytics
  • Big algorithms and software development
  • Business analytics and decision support
  • Data mining and financial forecasting
  • Data mining and knowledge discovery
  • Data science for sustainable applications
  • Firm sustainability
  • Improving forecasting models using big data analytics
  • Innovative methods for big data analytics
  • Machine learning and big data
  • Network analysis in social communities
  • Network-mediated human interactivity
  • Online community and big data
  • Parallel, accelerated, and distributed big data analytics
  • Real-world applications of big data analytics, such as default detection, cybercrime, e-commerce, e-health, e-sports, online gamble, etc.
  • Search and optimization for big data
  • Security and privacy in big data era
  • Sustainable business performance
  • Sustainable innovation
  • Sustainable management
  • Sustainable SMEs and startups
  • Sustainable marketing and sustainable political marketing
  • Techniques for mining unstructured, spatial–temporal, streaming and/or multimedia data
  • Value and performance of big data analytics
  • Web data mining (web content mining, web usage mining)

Prof. Dr. Se-hak Chun
Prof. Dr. Young-Woong Ko
Prof. Dr. Moon Young Kang
Prof. Dr. Jinho Choi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • business analytics
  • big data
  • data mining
  • data science
  • firm sustainability
  • marketing
  • information system
  • information technology
  • political marketing
  • sustainable management
  • social media
  • sustainable innovation
  • sustainable business performance
  • sustainable SMEs and startups

Published Papers (8 papers)

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Article
Announcement Effects of Convertible and Warrant Bond Issues with Embedded Refixing Option: Evidence from Korea
Sustainability 2020, 12(21), 8933; https://doi.org/10.3390/su12218933 - 27 Oct 2020
Viewed by 679
Abstract
This study examines the announcement effects of convertible and warrant bond issues with embedded refixing option in Korea from January 2001 to December 2018. Refixing option denotes an adjustment right of the conversion price embedded in equity-linked debt when the underlying stock price [...] Read more.
This study examines the announcement effects of convertible and warrant bond issues with embedded refixing option in Korea from January 2001 to December 2018. Refixing option denotes an adjustment right of the conversion price embedded in equity-linked debt when the underlying stock price falls under conversion price. I find statistically significant declines of 2.6 to 2.7 percentage points in cumulative abnormal returns for the inclusion of a refixing clause and especially further declines of 6.2 to 6.3 percentage points during the period from 2016 to 2018. This result implies that the market’s concerns about the dilution of existing shareholder value due to the exercise of the refixing rights are reflected in the market response. I further find that the degree of negative market response varies according to the changes in macroeconomic conditions and the stock exchange on which the issuing firms are listed. The findings are robust after controlling for the effect of firm-, issue-, and market-specific characteristics. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends
Sustainability 2020, 12(18), 7696; https://doi.org/10.3390/su12187696 - 17 Sep 2020
Cited by 1 | Viewed by 629
Abstract
In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have [...] Read more.
In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
Geometric Case Based Reasoning for Stock Market Prediction
Sustainability 2020, 12(17), 7124; https://doi.org/10.3390/su12177124 - 01 Sep 2020
Viewed by 629
Abstract
Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision [...] Read more.
Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p < 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
Sustainable Political Social Media Marketing: Effects of Structural Features in Plain Text Messages
Sustainability 2020, 12(15), 5997; https://doi.org/10.3390/su12155997 - 25 Jul 2020
Cited by 1 | Viewed by 896
Abstract
The success of Barack Obama’s 2008 U.S. presidential campaign led politicians and voters all over the world to pay attention to social media. Including Donald Trump for his upcoming 2020 re-election, many politicians around the world have used social media for their political [...] Read more.
The success of Barack Obama’s 2008 U.S. presidential campaign led politicians and voters all over the world to pay attention to social media. Including Donald Trump for his upcoming 2020 re-election, many politicians around the world have used social media for their political campaigns. While some social media can deliver information in various forms (i.e., video, audio, and interactive content), some popular ones, such as Twitter, are still focused mostly on plain text messaging. With political marketing using simple text messages via social media, there is a need to examine ways of creating messages that ultimately help shape voters’ perception of politicians and eventually win the election. Based on communication science, this study attempts to test the limited capacity model of motivated mediated message processing by examining whether this model can be applied to the simplest form of mediated message, which is plain text. In order to do so, structural features of text messages exchanged on social media engaged in political campaigns, namely linguistic formality and network-mediated human interactivity, are manipulated in an experiment. Findings suggest that linguistic formality and human interaction in plain text messages influence perceived friendliness, truthfulness, and dependability of the message source (politicians), as well as the receivers’ (constituents’) behavioral intent to vote for the message source in an upcoming election. This implies that politicians should pay more attention on sustainable political marketing through appropriate manipulation of structural features in social media messages. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
Too Costly to Disregard: The Cost Competitiveness of Environmental Operating Practices
Sustainability 2020, 12(15), 5971; https://doi.org/10.3390/su12155971 - 24 Jul 2020
Viewed by 784
Abstract
Achieving the dual goal of improved environmental and financial performance has become a universal business concern. Our study distinguishes between firms’ environmental behaviors and their environmental performance, a distinction that has been largely disregarded in previous empirical studies that analyze the association between [...] Read more.
Achieving the dual goal of improved environmental and financial performance has become a universal business concern. Our study distinguishes between firms’ environmental behaviors and their environmental performance, a distinction that has been largely disregarded in previous empirical studies that analyze the association between environmental performance and financial performance. As an improvement in environmental performance itself does not necessarily guarantee positive financial returns, our study pays particular attention to the value-added nature of preemptive environmental activities, investigating the effects of plant-level pollution prevention activities (PPAs) on environmental performance and financial performance in terms of cost competitiveness and market valuation. Drawing on detailed environmental information about 18,743 chemical plants in the U.S. and analyzing a multi-level panel dataset constructed bottom-up from plant-level data to their parent firms’ performance data, we find that more intensive PPAs are associated with both superior environmental performance and improved cost competitiveness but do not necessarily lead to higher market valuation. Our study illuminates the specific environmental activities and conditions linked to environmental and financial performance, thereby offering managers practical guidance in pursuing both sustainable and profitable businesses under increasingly stringent environmental standards. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
A Business Intelligence Framework for Analyzing Educational Data
Sustainability 2020, 12(14), 5745; https://doi.org/10.3390/su12145745 - 17 Jul 2020
Cited by 5 | Viewed by 1593
Abstract
Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a [...] Read more.
Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
Exploring the Dynamics between M&A Activities and Industry-Level Performance
Sustainability 2020, 12(11), 4399; https://doi.org/10.3390/su12114399 - 27 May 2020
Cited by 2 | Viewed by 1044
Abstract
This study investigates the correlation between mergers and acquisitions (M&As) activities and industry-level performance. While extensive research on M&As has focused on financial performance at the firm-level around the merger announcement, not much focus has been given to the relationship between M&A activities [...] Read more.
This study investigates the correlation between mergers and acquisitions (M&As) activities and industry-level performance. While extensive research on M&As has focused on financial performance at the firm-level around the merger announcement, not much focus has been given to the relationship between M&A activities and financial performance at the industry level. Using global data from the S&P (Standard & Poor’s) Capital IQ platform database, this study examines the significance of relationships of 12 industry-level financial values with M&A frequency and transaction value across 11 industry sectors throughout 2009–2018. The results show that M&A activities play a key role in identifying industries with lots of potential and that strategic investment planning can be drawn from both industry and time lag perspectives. This study bridges the gap by exploring the complexity of M&A performance across various firms and industries, and supports forward-looking investment processes by delineating emerging industries with expected positive returns. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Erratum
Erratum: Villegas-Ch, W., et al. A Business Intelligence Framework for Analyzing Educational Data. Sustainability 2020, 12, 5745
Sustainability 2021, 13(6), 3105; https://doi.org/10.3390/su13063105 - 12 Mar 2021
Viewed by 455
Abstract
The authors would like to make the following corrections to the published paper [...] Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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