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Big Data and Predictive Analytics for Sustainability

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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 (30 November 2017) | Viewed by 93795

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Special Issue Editor

Prof. Dr. Benjamin T. Hazen

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Guest Editor

1. Department of Marketing and Supply Chain Management, University of Tennessee, Knoxville, TN 37996, USA
2. Department of Operational Sciences, Air Force Institute of Technology, USA
Interests: closed-loop supply chains; reverse logistics; innovation; supply chain information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Because of the volume, velocity and variety of data generated and consumed across the globe, big data and predictive analytics (BDPA) are driving the means through which firms compete in today’s marketplace. Business research over the past several years has been instrumental in developing an understanding of what BDPA are (and are not), and mechanisms for their employment. Research has more recently centered upon how firms can achieve a sustainable financial advantage via employment of BDPA. However, there remains a dearth of understanding regarding the role that BDPA can play toward achiving social and environmental sustainability.

This Special Issue on “Big Data and Predictive Analytics for Sustainability” will address this important and potentially wide-ranging topic. The broad scope of this Special Issue is hoped to encourage submissions from across several disciplines. Theory-based research that links BDPA with social and/or environmental sustainabiliy is especially encouaged, as is research that is cross-disciplinary in nature. However, any research that falls under the scope of this call for papers is certainly most welcome.

Prof. Dr. Benjamin T. Hazen
Guest Editor

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

Big Data
Data Science
Data Mining
Data Management
Predictive Analytics
Environmental Sustainability
Social Sustainability
Circular Economy
Information Systems
Information Technology
Triple Bottom Line
Decision-Making
Operations Research
Applied Statistics
Operations Management
Supply Chain Management

Published Papers (10 papers)

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Research

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19 pages, 2127 KiB

Open AccessArticle

The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model

by Yongli Zhang, Sanggyun Na, Jianguang Niu and Beichen Jiang

Sustainability 2018, 10(1), 187; https://doi.org/10.3390/su10010187 - 15 Jan 2018

Cited by 22 | Viewed by 4188

Abstract

Technology innovation is a motivating force for sustainable development. The recognition and measurement of influencing factors are a basic prerequisite of technology innovation research. In response to the gaps and shortages of existing theories and methods, this paper builds the impact indicators of technology innovation, the proposed FOA-GRNN model, and analyzes the influencing factors, regional differences and temporal variations of technology innovation based on industrial above-scale enterprises of 31 provinces in China from 2008 to 2015. The empirical results show that innovation investment is a determinant of technology innovation in China, and is more and more significant; meanwhile a wide gap of innovation resource between Eastern China and Western China exists. In general, the enterprise scale has a negative effect: with enlargement of enterprise in China, the innovation efficiency of enterprise will decline, while the effect has regional disparity, with positive influence in Central and Western China, and negative influence in Eastern China. Government support has negative effects on technology innovation: indirect equity investment contributes more to technology innovation than direct fund support. Innovation environment has positive and weak effects on technology innovation, but it is the biggest obstacle in Western China, and the innovation environment in China has improved continuously. This paper provides new evidence that can shine some light on determining the factors affecting technology innovation, and also presents a novel approach, which comprises characteristics of nonlinear function approximation, high accuracy and a small sample. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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18 pages, 6239 KiB

Open AccessArticle

A Bibliometric Analysis and Visualization of Medical Big Data Research

by Huchang Liao, Ming Tang, Li Luo, Chunyang Li, Francisco Chiclana and Xiao-Jun Zeng

Sustainability 2018, 10(1), 166; https://doi.org/10.3390/su10010166 - 11 Jan 2018

Cited by 406 | Viewed by 22060

Abstract

With the rapid development of “Internet plus”, medical care has entered the era of big data. However, there is little research on medical big data (MBD) from the perspectives of bibliometrics and visualization. The substantive research on the basic aspects of MBD itself is also rare. This study aims to explore the current status of medical big data through visualization analysis on the journal papers related to MBD. We analyze a total of 988 references which were downloaded from the Science Citation Index Expanded and the Social Science Citation Index databases from Web of Science and the time span was defined as “all years”. The GraphPad Prism 5, VOSviewer and CiteSpace softwares are used for analysis. Many results concerning the annual trends, the top players in terms of journal and institute levels, the citations and H-index in terms of country level, the keywords distribution, the highly cited papers, the co-authorship status and the most influential journals and authors are presented in this paper. This study points out the development status and trends on MBD. It can help people in the medical profession to get comprehensive understanding on the state of the art of MBD. It also has reference values for the research and application of the MBD visualization methods. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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13 pages, 1098 KiB

Open AccessArticle

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

by Yan Guo, Chengxin Yin, Mingfu Li, Xiaoting Ren and Ping Liu

Sustainability 2018, 10(1), 147; https://doi.org/10.3390/su10010147 - 09 Jan 2018

Cited by 63 | Viewed by 10545

Abstract

A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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11287 KiB

Open AccessArticle

Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data

by Yuhuan Zhang, Huapu Lu, Shengxi Luo, Zhiyuan Sun and Wencong Qu

Sustainability 2017, 9(7), 1148; https://doi.org/10.3390/su9071148 - 02 Jul 2017

Cited by 10 | Viewed by 5067

Abstract

To evaluate the sustainability of an enormous number of urban intersections, a novel assessment model is proposed, along with an indicator system and corresponding methods to determine the indicators. Considering mainly the demands and feelings of the urban residents, the three aspects of safety, functionality, and image perception are taken into account in the indicator system. Based on technologies such as street view picture crawling, image segmentation, and edge detection, GIS spatial data analysis, a rapid automated assessment method, and a corresponding multi-source database are built up to determine the indicators. The improved information entropy method is applied to obtain the entropy weights of each indicator. A case study shows the efficiency and applicability of the proposed assessment model, indicator system and algorithm. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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1532 KiB

Open AccessArticle

Exploring Suitable Technology for Small and Medium-Sized Enterprises (SMEs) Based on a Hidden Markov Model Using Patent Information and Value Chain Analysis

by Keeeun Lee, Deaun Go, Inchae Park and Byungun Yoon

Sustainability 2017, 9(7), 1100; https://doi.org/10.3390/su9071100 - 23 Jun 2017

Cited by 20 | Viewed by 6116

Abstract

R&D cooperative efforts between large firms and small and medium-sized enterprises (SMEs) have been accelerated to develop innovative projects and deploy profitable businesses. In general, win-win alliances between large firms and SMEs for sustainable growth require the pre-evaluation of their capabilities to explore high potential partners for successful collaborations. Thus, this research proposes a systematic method that identifies SME-suitable technology where SMEs have a competitive edge in R&D collaborations. First, such technology fields are identified by various factors that influence successful R&D activities by applying the Hidden Markov Model (HMM) and using information on value chains of an industry. To identify these fields, innovation factors such as the current impact index and technology cycle time are composed using the bibliographic information of patents. Second, patent information is analyzed to obtain observation probability in terms of technical competitiveness, and value chain data is used to calculate transition probability in HMMs. Finally, the Viterbi algorithm is employed to formulate the aforementioned two types of probability as a tool for selecting appropriate fields for SMEs. This paper applies the proposed approach to the solar photovoltaic industry to explore SME-suitable technologies. This research can contribute to help develop successful R&D partnership between large firms and SMEs. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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10179 KiB

Open AccessArticle

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

by Venkatesh Mani, Catarina Delgado, Benjamin T. Hazen and Purvishkumar Patel

Sustainability 2017, 9(4), 608; https://doi.org/10.3390/su9040608 - 14 Apr 2017

Cited by 102 | Viewed by 15181

Abstract

The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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3181 KiB

Open AccessArticle

Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information

by Byeongki Jeong and Janghyeok Yoon

Sustainability 2017, 9(4), 497; https://doi.org/10.3390/su9040497 - 25 Mar 2017

Cited by 38 | Viewed by 7673

Abstract

Augmented reality has recently achieved a rapid growth through its applications in various industries, including education and entertainment. Despite the growing attraction of augmented reality, trend analyses in this emerging technology have relied on qualitative literature review, failing to provide comprehensive competitive intelligence analysis using objective data. Therefore, tracing industrial competition trends in augmented reality will provide technology experts with a better understanding of evolving competition trends and insights for further technology and sustainable business planning. In this paper, we apply a topic modeling approach to 3595 patents related to augmented reality technology to identify technology subjects and their knowledge stocks, thereby analyzing industrial competitive intelligence in light of technology subject and firm levels. As a result, we were able to obtain some findings from an inventional viewpoint: technological development of augmented reality will soon enter a mature stage, technologies of infrastructural requirements have been a focal subject since 2001, and several software firms and camera manufacturing firms have dominated the recent development of augmented reality. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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1031 KiB

Open AccessArticle

A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining

by Zhen Peng and Lifeng Wu

Sustainability 2017, 9(3), 352; https://doi.org/10.3390/su9030352 - 27 Feb 2017

Cited by 9 | Viewed by 5594

Abstract

Haze-fog has seriously hindered the sustainable development of the ecological environment and caused great harm to the physical and mental health of residents in China. Therefore, it is important to probe the formation of haze-fog for its early warning and prevention. The formation of haze-fog is, in fact, a fuzzy nonlinear process. The formation of haze-fog is such a complex process that it is difficult to simulate its dynamic evolution using traditional methods, mainly because of the lack of their consideration of the nonlinear relationships. It is, therefore, essential to explore new perspectives on the formation of haze-fog. In this work, previous research on haze-fog formation is summarized first. Second, a new perspective is proposed on the application of fuzzy cognitive map to the formation of haze-fog. Third, a data mining method based on the genetic algorithm is used to discover the causality values of a fuzzy cognitive map (FCM) for hazefog formation. Finally, simulation results are obtained through an experiment using the fuzzy cognitive map and its data mining method for the formation of haze-fog. The validity of this approach is determined by definition of a simple rule and the Kappa values. Thus, this research not only provides a new idea using FCM modeling the formation of haze-fog, but also uses an effective method of FCM for solving the nonlinear dynamics of the haze-fog formation. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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1328 KiB

Open AccessArticle

Determinants of Pro-Environmental Consumption: Multicountry Comparison Based upon Big Data Search

by Donghyun Lee, Suna Kang and Jungwoo Shin

Sustainability 2017, 9(2), 183; https://doi.org/10.3390/su9020183 - 27 Jan 2017

Cited by 8 | Viewed by 5545

Abstract

The Korean government has promoted a variety of environmental policies to revitalize pro-environmental consumption, and the government’s budget for this purpose has increased. However, there is a lack of quantitative data and analysis regarding the effects upon the pro-environmental consumption of education and changing public awareness of the environment. In addition, to improve pro-environmental consumption, the determinant and hindrance factors of pro-environmental consumption should be analyzed in advance. Accordingly, herein we suggest a pro-environmental consumption index that represents the condition of pro-environmental consumption based on big data queries and use the index to analyze determinants of and hindrances to pro-environmental consumption. To verify the reliability of the proposed indicator, we examine the correlation between the proposed indicator and Greendex, an existing survey-based indicator. In addition, we conduct an analysis of the determinants of pro-environmental consumption across 13 countries based upon the proposed indicator. The index is highest for Argentina and average for Korea. An analysis of the determinants shows that the levels of health expenditure, the ratio of the population aged over 65 years, and past orientation are signiﬁcantly negatively related to the pro-environmental consumption index, but the level of preprimary education is signiﬁcantly positively related with it. We also ﬁnd that high-GDP countries have a signiﬁcantly positive relationship between economy growth and pro-environmental consumption, but low-GDP countries do not have this relationship. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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Review

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521 KiB

Open AccessReview

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

by Juan Manuel Ponce Romero, Stephen H. Hallett and Simon Jude

Sustainability 2017, 9(12), 2160; https://doi.org/10.3390/su9122160 - 23 Nov 2017

Cited by 44 | Viewed by 9297

Abstract

The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry. Full article

(This article belongs to the Special Issue Big Data and Predictive Analytics for Sustainability)

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