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Business Analytics and Big Data for Business Sustainability II
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Business Analytics and Big Data for Business Sustainability II

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 (29 February 2024) | Viewed by 9681

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, Republic of 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, Republic of 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, Republic of 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, Republic of 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 this 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; improving decision making; and supporting strategic planning with precise diagnosis, prediction, innovation, and prescription.

Thus, businesses are interested in developing new insight 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, as well as fact-based scientific management to drive optimal decision making and sustainable strategies.

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

This Special Issue will address innovative approaches in the general area of business analytics and big data, as well as specific approaches dealing with the topic of big data 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;
  • Big data and financial forecasting;
  • Big data 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;
  • Marketing analytics;
  • 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 gambling, 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 unstructured, spatial–temporal, streaming, and/or multimedia data;
  • Value and performance of big data analytics.

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 2400 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
  • firm sustainability
  • marketing analytics
  • information system
  • information technology
  • sustainable management
  • social media
  • sustainable innovation
  • sustainable business performance
  • sustainable SMEs and startups

Published Papers (4 papers)

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Research

28 pages, 1665 KiB  
Article
The Integration of Sustainable Technology and Big Data Analytics in Saudi Arabian SMEs: A Path to Improved Business Performance
by Arwa Mohammed Asiri, Sabah Abdullah Al-Somali and Rozan Omar Maghrabi
Sustainability 2024, 16(8), 3209; https://doi.org/10.3390/su16083209 - 11 Apr 2024
Viewed by 446
Abstract
Big data analytics technology offers significant opportunities for innovation and performance improvement for small- and medium-sized enterprises (SMEs) operating in competitive environments. However, reaping these benefits requires the adoption of such technologies by SMEs. This study investigates the factors influencing the adoption of [...] Read more.
Big data analytics technology offers significant opportunities for innovation and performance improvement for small- and medium-sized enterprises (SMEs) operating in competitive environments. However, reaping these benefits requires the adoption of such technologies by SMEs. This study investigates the factors influencing the adoption of big data and analytics in Saudi Arabian SMEs in the service and manufacturing sectors, with a particular focus on the role of facilitating sustainable technology in enabling sustainable business performance. Data were collected from managers of SMEs in Saudi Arabia using a quantitative method. The proposed hypotheses were tested using structural equation modeling with SmartPLS 4.0. The findings reveal that big data security and management support significantly influence the perceived ease of use and usefulness of big data analytics in SMEs. Perceived ease of use significantly influences the adoption of big data analytics. Furthermore, facilitating sustainable technology was a significant predictor of sustainable business performance. Additionally, the study revealed that the adoption of big data analytics significantly influenced business performance. The insights obtained from this study can be useful for the service and manufacturing industries operating in Saudi Arabia, particularly regarding the key influencing factor of perceived ease of use that determines the adoption of big data analytics in the Saudi Arabian SME market. Full article
(This article belongs to the Special Issue Business Analytics and Big Data for Business Sustainability II)
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25 pages, 1182 KiB  
Article
Evolution of the Coupling Coordination between the Marine Economy and Digital Economy
by Yang Liu, Yiying Jiang, Zhaobin Pei, Na Xia and Aijun Wang
Sustainability 2023, 15(6), 5600; https://doi.org/10.3390/su15065600 - 22 Mar 2023
Cited by 5 | Viewed by 1994
Abstract
Accelerating the high-quality integrated development of digital economy and marine economy is vital for the development of the marine economy in coastal countries and regions. However, few scholars examined such coordination. Here, based on panel data from 2012 to 2019 and the spatial [...] Read more.
Accelerating the high-quality integrated development of digital economy and marine economy is vital for the development of the marine economy in coastal countries and regions. However, few scholars examined such coordination. Here, based on panel data from 2012 to 2019 and the spatial scale of China’s coastal provinces and cities, the entropy method, coupling harmonious degree model (CCDM), Theil index, and Tobit model were adopted to measure and calculate the interval index differences in the marine economic quality and digital economy level. Exploring the coordination between the marine economy and digital economy, the interval difference index, and the coordination impact factors were also important. First, we found that the quality level of the marine economy and digital economy moved forward in waves and spiraled up, but that the quality of development was relatively low. Second, the coordination between the marine economy and digital economy gradually increased. Third, the coordination gap between the regional marine economy and digital economy was obvious. Fourth, the main factors that affected the coordination between the marine economy and digital economy were the level of digital infrastructure construction, the scale of the marine economy, the level of the marine industry, and industrial digitalization. The results have value for the sustainable development of the marine economy of coastal countries and regions. Full article
(This article belongs to the Special Issue Business Analytics and Big Data for Business Sustainability II)
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19 pages, 3099 KiB  
Article
Trust Evaluation Method of E-Commerce Enterprises with High-Involvement Experience Products
by Kun Liang, Jun He and Peng Wu
Sustainability 2022, 14(23), 15562; https://doi.org/10.3390/su142315562 - 23 Nov 2022
Cited by 1 | Viewed by 1170
Abstract
Purpose: High-involvement experience products (HIEP) are generally characterized by a high value and difficult purchasing decision for customers, and a wrong decision will bring large losses to consumers, severely affecting their trust in enterprises. The purpose of this paper is to solve the [...] Read more.
Purpose: High-involvement experience products (HIEP) are generally characterized by a high value and difficult purchasing decision for customers, and a wrong decision will bring large losses to consumers, severely affecting their trust in enterprises. The purpose of this paper is to solve the problem of trust evaluation of HIEP e-commerce enterprises. Tasks and research methods: First, given the heterogeneity of trust information in the big data context, this paper collects the reputation data of HIEP enterprises and the trust big data of enterprises in industry, commerce and justice by a crawler program. Next, we use the dictionary and pattern matching methods to extract the trust features in text big data and construct the trust evaluation feature set integrating judicial information. Then, based on machine learning methods, we propose a LAS-RS model, which aims to solve the problem of trust evaluation in an imbalanced and high-dimensional data context. Finally, by introducing signal theory, this paper reveals the differential influence mechanism of big data feature variables on the trust of HIEP e-commerce enterprises. Originality: This study further enriches the relevant theories and methods of e-commerce trust evaluation research and is conducive to a better understanding and control of potential trust risks. Full article
(This article belongs to the Special Issue Business Analytics and Big Data for Business Sustainability II)
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14 pages, 674 KiB  
Article
Technological Revolution and Circular Economy Practices: A Mechanism of Green Economy
by Syed Abdul Rehman Khan, Muhammad Umar, Alam Asadov, Muhammad Tanveer and Zhang Yu
Sustainability 2022, 14(8), 4524; https://doi.org/10.3390/su14084524 - 11 Apr 2022
Cited by 40 | Viewed by 4854
Abstract
Rising environmental concerns, Industry 4.0 technologies, and circular economy (CE) practices are the prevailing business considerations of the current time, and they are transforming business models. Keeping in view the importance of these considerations, this work looks into the role of Industry 4.0 [...] Read more.
Rising environmental concerns, Industry 4.0 technologies, and circular economy (CE) practices are the prevailing business considerations of the current time, and they are transforming business models. Keeping in view the importance of these considerations, this work looks into the role of Industry 4.0 technologies in adoption of CE practices and the impact of CE practices on firms’ performance. The current study collected data from 213 automotive firms located in Eastern European countries including Poland, Romania, and Ukraine. Using Covariance-Based Structural Equation Modelling (CB-SEM), the current study provides some important findings. Firstly, Industry 4.0 technologies significantly enhance circular economy practices. Secondly, circular economy practices are found to be positively related with environmental and operational performance. Lastly, higher economic and operational performance boost organizational performance. Hence, the current study provides deeper understanding regarding performance implications of Industry 4.0 technologies and offers insights about ways of promoting sustainable performance in the current age of digitization. Full article
(This article belongs to the Special Issue Business Analytics and Big Data for Business Sustainability II)
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