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Special Issue "Big Data and Predictive Analytics for Sustainability"

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

Deadline for manuscript submissions: closed (30 November 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Benjamin T. Hazen

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
Website | E-Mail
Interests: closed-loop supply chains; reverse logistics; innovation; supply chain information systems

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 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 monthly 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 1400 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 (7 papers)

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Research

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Open AccessArticle Human-Scale Sustainability Assessment of Urban Intersections Based upon Multi-Source Big Data
Sustainability 2017, 9(7), 1148; doi:10.3390/su9071148
Received: 24 May 2017 / Revised: 23 June 2017 / Accepted: 26 June 2017 / Published: 2 July 2017
PDF Full-text (11287 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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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
Sustainability 2017, 9(7), 1100; doi:10.3390/su9071100
Received: 19 May 2017 / Revised: 16 June 2017 / Accepted: 19 June 2017 / Published: 23 June 2017
Cited by 1 | PDF Full-text (1532 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessArticle Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain
Sustainability 2017, 9(4), 608; doi:10.3390/su9040608
Received: 14 December 2016 / Revised: 14 March 2017 / Accepted: 10 April 2017 / Published: 14 April 2017
Cited by 3 | PDF Full-text (10179 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessArticle Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information
Sustainability 2017, 9(4), 497; doi:10.3390/su9040497
Received: 4 January 2017 / Revised: 20 March 2017 / Accepted: 23 March 2017 / Published: 25 March 2017
Cited by 2 | PDF Full-text (3181 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessArticle A New Perspective on Formation of Haze-Fog: The Fuzzy Cognitive Map and Its Approaches to Data Mining
Sustainability 2017, 9(3), 352; doi:10.3390/su9030352
Received: 9 January 2017 / Accepted: 23 February 2017 / Published: 27 February 2017
Cited by 1 | PDF Full-text (1031 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessArticle Determinants of Pro-Environmental Consumption: Multicountry Comparison Based upon Big Data Search
Sustainability 2017, 9(2), 183; doi:10.3390/su9020183
Received: 5 November 2016 / Accepted: 23 January 2017 / Published: 27 January 2017
Cited by 2 | PDF Full-text (1328 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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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Open AccessReview Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector
Sustainability 2017, 9(12), 2160; doi:10.3390/su9122160
Received: 1 September 2017 / Revised: 17 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
PDF Full-text (521 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
[...] Read more.
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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