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

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 12405

Special Issue Editors

Prof. Dr. Se-hak Chun
E-Mail Website
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 systems; electronic commerce; financial forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Young-Woong Ko
E-Mail Website
Guest Editor
Department of Software, Hallym University, 1 Hallymdaehak-gil, Chucheon, Gangwon 24252, Korea
Interests: big data research; cloud computing; computer software; system software
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moon Young Kang
E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinho Choi
E-Mail Website
Guest Editor
School of Business, Sejong University, Seoul 05006, Korea
Interests: agent-based model; business analytics; big data research; network analysis; sustainability
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 2000 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 (12 papers)

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Article
The Effect of Cash Incentive Projects on the Social Value Performances of Social Enterprises: An Empirical Analysis of SK’s Social Progress Credit in Korea
Sustainability 2022, 14(10), 6310; https://doi.org/10.3390/su14106310 - 22 May 2022
Viewed by 231
Abstract
Social enterprises seek to maximize benefits to society and the environment while obtaining profits. Social enterprises are increasing in number; however, their size and growth rates are very small. In addition, many social enterprises face difficulties in obtaining profits through social activities that [...] Read more.
Social enterprises seek to maximize benefits to society and the environment while obtaining profits. Social enterprises are increasing in number; however, their size and growth rates are very small. In addition, many social enterprises face difficulties in obtaining profits through social activities that generate social value, even though they are supported by government policy. Previous research has focused on the relationship between social performance and financial performance, compensation, and policy making, as well as the effect of incentives on social performance within organizations. To our knowledge, there is lack of empirical research on cash incentives for activities that generate social value. This paper analyzes the behavior of companies with regard to fostering a social enterprise ecosystem and a cash incentive system for social enterprises. In particular, we investigate the relationship between SK’s cash incentive system, which is called social progress credit (SPC), and the activities of social enterprises, and we examine which social value activities are affected by a cash incentive system. Furthermore, through empirical analysis, this paper analyzes how the amount of cash for incentives is determined by specific social activities, such as social service performance, employment performance, environmental performance, and social ecosystem performance, as well as by the size of the social enterprise and its financial performance (i.e., revenue and net profit). The results show that employment performance is the most important factor for incentive payments, reflecting the social atmosphere and government policy in Korea, and that it can be a simpler measurement of performance than other social performance measures. Moreover, the results show that there is a significant positive (+) relationship between incentive payments and financial performance, such as sales and net profit of social enterprises. In addition, it was found that more incentives were paid to small social enterprises with higher sales growth. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
Article
Predicting Advertisement Revenue of Social-Media-Driven Content Websites: Toward More Efficient and Sustainable Social Media Posting
Sustainability 2022, 14(7), 4225; https://doi.org/10.3390/su14074225 - 02 Apr 2022
Viewed by 406
Abstract
Social media platforms such as Facebook have been a crucial web traffic source for content providers. Content providers build websites and apps to publish their content and attract as many readers as possible. More readers mean more influence and revenue through advertisement. As [...] Read more.
Social media platforms such as Facebook have been a crucial web traffic source for content providers. Content providers build websites and apps to publish their content and attract as many readers as possible. More readers mean more influence and revenue through advertisement. As Internet users spend more and more time on social media platforms, content websites also create social media presence, such as Facebook pages, to generate more traffic and thus revenue from advertisements. With so much content competing for limited real estate on social media users’ timelines, social media platforms begin to rank the contents by user engagements of previous posts. Posting content to social media that receives little user interaction will hurt the content providers’ future presence on social media. Content websites need to consider business sustainability when utilizing social media, to ensure that they can respond to short-term financial needs without compromising their ability to meet their future needs. The present study aims to achieve this goal by building a model to predict the advertisement revenue, which is highly correlated with user engagements, of an intended social media post. The study examined combinations of classification methods and data resampling techniques. A content provider can choose the combination that suits their needs by comparing the confusion matrices. For example, the XGBoost model with undersampled data can reduce the total post number by 87%, while still making sure that 49% of the high-performance posts will be posted. If the content provider wants to make sure more high-performance posts are posted, then they can choose the DNN(Deep Neural Network) model with undersampled data to post 66% of high-performance posts, while reducing the number of total posts by 69%. The study shows that predictive models could be helpful for content providers to balance their needs between short-term revenue income and long-term social media presence. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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Article
Stick or Twist—The Rise of Blockchain Applications in Marketing Management
Sustainability 2022, 14(7), 4172; https://doi.org/10.3390/su14074172 - 31 Mar 2022
Viewed by 589
Abstract
The adoption of blockchain technology by companies can change the way they interact with stakeholders, redefining communication strategies and other marketing processes. In this study, we investigated the relevance of blockchain applications for marketing management from the perspective of marketing-related professionals. Answers about [...] Read more.
The adoption of blockchain technology by companies can change the way they interact with stakeholders, redefining communication strategies and other marketing processes. In this study, we investigated the relevance of blockchain applications for marketing management from the perspective of marketing-related professionals. Answers about blockchain technology application in the marketing arena were collected from the social platform Quora. The data were analyzed through text mining and Spearman’s correlation coefficient to assess the degree of association, inherent intensity, and the association significance between the variables payments, supply chain, loyalty programs, digital marketing, credential management, and marketing management, using Quora-specific metrics, namely, upvotes, shares, and views. The results posit blockchain technology as being an asset for marketing, with greater relevance in supply chain and internal management among marketing operations. Professionals will be able to potentially improve internal management systems and marketing campaigns, which will enhance companies’ competitive advantage. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
Article
A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions
Sustainability 2022, 14(3), 1366; https://doi.org/10.3390/su14031366 - 25 Jan 2022
Viewed by 646
Abstract
In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based [...] Read more.
In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based reasoning, which does not consider how to retrieve similar neighbors from previous patterns in terms of a graphical pattern. In this paper, we show how the proposed method can be used when traders find similar time series patterns among nearest cases. For this, we suggest an interactive prediction system where traders can select similar patterns with individual knowledge among automatically recommended neighbors by case-based reasoning. In this paper, we demonstrate how traders can use their knowledge to select similar patterns using a graphical interface, serving as an exemplar for the target. These concepts are investigated against the backdrop of a practical application involving the prediction of three individual stock prices, i.e., Zoom, Airbnb, and Twitter, as well as the prediction of the Dow Jones Industrial Average (DJIA). The verification of the prediction results is compared with a random walk model based on the RMSE and Hit ratio. The results show that the proposed technique is more effective than the random walk model but it does not statistically surpass the random walk model. Full article
(This article belongs to the Special Issue Business Analytics and Data Mining for Business Sustainability)
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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 887
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 3 | Viewed by 899
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
Cited by 3 | Viewed by 933
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 2 | Viewed by 1197
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
Cited by 1 | Viewed by 1081
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 7 | Viewed by 2300
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 3 | Viewed by 1376
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 610
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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