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Corporate Sustainable Resource Management in Artificial Intelligent Era

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 36649

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Special Issue Editors

Dr. Kuo-Jui Wu

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Guest Editor

School of Business, Dalian University of Technology, Panjin, China
Interests: industrial management; sustainable supply chain management; multi-criteria decision-making
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ming-Lang Tseng

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Guest Editor

Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan
Interests: corporate sustainability; sustainable supply chain management; supply chain management; operations management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fuyume Sai

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Guest Editor

Faculty of Business Administration, Daito Bunka University, Tokyo, Japan
Interests: business information systems

Special Issue Information

Dear Colleagues,

As natural resources are becoming exhausted, researchers and practitioners are striving to identify an optimal way to manage corporate resources to achieve sustainability. Corporate sustainable resource management seeks to address such sustainability issues. Each corporate economic system rests upon utilizing natural resources in a balanced way to maximize human well-being without obstructing the support of living society (Bringezu & Bleischwitz, 2017). However, there are few prior studies on sustainable resource management. Corporate sustainable resource management must take into account the fields of marketing, human resource management, research and development, products and finance to discover the optimal way to achieve sustainability. It also relies on establishing qualitative and quantitative data that deals with corporate sustainability issues (Lee et al., 2018; Lei et al, 2017; Cui et al., 2017).

Recently, artificial intelligence (AI) research has aimed to include reasoning, knowledge representation, marketing planning, self-learning, natural language processing, human perception and the ability to move and manipulate objects. There are multiple approaches to AI, including statistical methods, computational intelligence, and traditional symbolic approaches. Many tools are used in AI, including mathematical optimization, artificial neural networks, statistics, probability and economics. Corporate sustainable resource Management combines these qualitative and quantitative features, and recent studies attempted to adopt AI to increase the accuracy and speed of decision-making, including as artificial neural networks based on the prediction model and fuzzy logic (Aibinu et al., 2017; Hwang et al., 2010). AI is a device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals (Legg & Hutter, 2007; Poole et al., 1998). It refers to applications where a machine imitates human cognitive function, such as learning and problem solving (Russell & Norving, 2016). Thus, this Special Issue seeks to present research that enhances or guides corporations in managing sustainable resources in this era of artificial intelligence.

Interested topics include, but are not limited to:

Systematic frameworks for corporate sustainable resource management and its influential attributes;
AI approaches to promoting corporate sustainable resource management;
Innovative approaches for the sustainable assessment and improvement of AI tools;
Novel theories and methods for corporate sustainable resource management in the AI era.

Contributors are encouraged to communicate with the editors by e-mail: [email protected]. Accepted papers will be published online immediately. Please follow the Sustainability “Instructions for Authors” when preparing your manuscripts. Please submit your manuscripts via https://www.mdpi.com/journal/sustainability/instructions.

References:

Aibinu, A.M.; Onumanyi, A.J.; Adedigba, A.P.; Ipinyomi, M.; Folorunso, T.A.; Salami, M.J.E. Development of hybrid artificial intelligent based handover decision algorithm. Sci. Tech. Inter. J. 2017, 20, 381–390.
Bringezu, S.; Bleischwitz, R. (Eds.) Sustainable Resource Management: Global Trends, Visions and Policies; Routledge: London, UK, 2017.
Cui, L. Fuzzy approach to eco-innovation for enhancing business functions: A case study in China. Manage. Data Syst. 2017, 117, 967–987.
Hwang, R.C.; Chen, Y.J.; Huang, H.C. Artificial intelligent analyzer for mechanical properties of rolled steel bar by using neural networks. Expert Syst. Appl. 2010, 37, 3136–3139.
Lee, C.H.; Wu, K.J.; Tseng, M.L. Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data. Clean. Prod. 2018, 202, 120–129.
Legg, S.; Hutter, M. A collection of definitions of intelligence. Frontiers Artificial Intelligence Appl. 2007, 157, 17.
Poole, D.L.; Mackworth, A.K.; Goebel, R. Computational Intelligence: A Logical Approach (Vol. 1); Oxford University Press: New York, NY, USA, 1998.
Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach; Pearson Education Limited: Petaling Jaya, Malaysia, 2016.
Shi, L.; Wu, K.J.; Tseng, M.L. Improving corporate sustainable development by using an interdependent closed-loop hierarchical structure. Conserv. Recy. 2017, 119, 24–35.

Prof. Dr. Kuo-Jui Wu
Prof. Dr. Ming-Lang Tseng
Prof. Dr. Fuyume Sai
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.

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Published Papers (7 papers)

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Research

26 pages, 1605 KiB

Open AccessArticle

Exploring Carry Trade and Exchange Rate toward Sustainable Financial Resources: An application of the Artificial Intelligence UKF Method

by Qian Zhang, Kuo-Jui Wu and Ming-Lang Tseng

Sustainability 2019, 11(12), 3240; https://doi.org/10.3390/su11123240 - 12 Jun 2019

Cited by 2 | Viewed by 2745

Abstract

This paper constructs a heterogeneous agent model for the foreign exchange market that is based on the law of supply and demand and includes carry trade, central bank intervention, and macroeconomic fundamentals. With the artificial intelligence method of the unscented Kalman filter, this paper investigates carry traders’ expectation formation and risk aversion and the impact of their activities on the movement of the Chinese yuan exchange rate and on the efficiency of central bank intervention. The findings demonstrate that carry traders’ activities are partially responsible for fluctuations in the Chinese yuan exchange rate; carry traders behave with obvious risk aversion; their activities tend to weaken the ability of the central bank to intervene in China’s foreign exchange market; and the volatility of the Chinese yuan exchange rate and the weight of carry traders are negatively related. Based on these empirical results, specific suggestions for exploring sustainable financial resources are provided. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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15 pages, 2185 KiB

Open AccessArticle

Environmental Sustainability on Tourist Hotels’ Image Development

by Tsai-Chiao Wang, Jen-Son Cheng, Hsin-Yu Shih, Chia-Liang Tsai, Ta-Wei Tang, Ming-Lang Tseng and Ying-Sheng Yao

Sustainability 2019, 11(8), 2378; https://doi.org/10.3390/su11082378 - 22 Apr 2019

Cited by 12 | Viewed by 4450

Abstract

Previous studies are lacking that explore the added values of sustainable practices perceived by consumers. To achieve a balanced development of economy and environmental protection, tourist hotels should develop a service differentiation strategy based on the sustainable practices. By examining the environmental characteristics and performing art in marketing images of a tourist hotel that are attractive to customers, this study built on the attention restoration theory and triple-bottom-line perspective, and employed the eye-tracking analysis technique to investigate the effect of image characteristics on customers’ visual attention. Sixty-three individuals participated in the experiment and observed the performing arts images. This study confirmed that, first, a natural image could attract more of customers’ visual attention than a built image. In particular, the coupling of nature and performing arts can get the most visual attention from customers. Second, older adults prefer natural images, but younger adults do not. However, there is no significant difference in the impact of gender on the customer’s visual attention. Those findings imply that tourist hotels should use the marketing image design to highlight the value-added services derived from environmental protection. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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15 pages, 712 KiB

Open AccessArticle

Sustainable Investment: Interrelated among Corporate Governance, Economic Performance and Market Risks Using Investor Preference Approach

by Ming-Lang Tseng, Phan Anh Tan, Shiou-Yun Jeng, Chun-Wei Remen Lin, Yeneneh Tamirat Negash and Susilo Nur Aji Cokro Darsono

Sustainability 2019, 11(7), 2108; https://doi.org/10.3390/su11072108 - 09 Apr 2019

Cited by 39 | Viewed by 7488

Abstract

Prior studies are lacking on the drivers of sustainable investment. Hence, this study examines the relationship between the social aspects, environmental aspects, economic benefits, market conditions, and corporate governance issues on sustainable investment. Sustainable investment has been rising since the last decade. However, sustainable investment is preceded by ethical investment, green investment, and socially responsible investment. In order to understand the sustainability of an investment before decision-making, it proposed a set of attributes to measure its sustainability using investor’s linguistics preferences. The proposed attributes are interrelated and based on investor’s linguistic preferences. The study employs the fuzzy set theory to handle the uncertainty resulting from the vagueness of linguistic terms and applies decision making trial and evaluation laboratory (DEMATEL) to determine the nature of interrelationships among sustainable investment attributes. The result indicates that corporate governance, economic performance, and market risks are the causal aspects of sustainable investment. In addition, this study found that transparency, anti-corruption, and board diversity were the two most important criteria of corporate governance. Furthermore, the three most important criteria of economic performance presented the model were excess return, market value, and shareholder loyalty. The theoretical and practical implications of sustainable investment are discussed. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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15 pages, 1490 KiB

Open AccessEditor’s ChoiceArticle

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

by Chao Fu, Guo-Quan Li, Kuo-Ping Lin and Hui-Juan Zhang

Sustainability 2019, 11(2), 512; https://doi.org/10.3390/su11020512 - 18 Jan 2019

Cited by 34 | Viewed by 3777

Abstract

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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20 pages, 3858 KiB

Open AccessArticle

Social Network Analysis of Sustainable Human Resource Management from the Employee Training’s Perspective

by Lu Zhang, Xiaochao Guo, Zhimei Lei and Ming K. Lim

Sustainability 2019, 11(2), 380; https://doi.org/10.3390/su11020380 - 13 Jan 2019

Cited by 32 | Viewed by 9052

Abstract

Employee training is not only important for the continuous growth of human resources but also guarantees sustainable human resource management in enterprises. It is very important to understand corporate behaviour related to employee training not only from the perspective of a single enterprise but also from that of multiple enterprises. The purpose of this study is to explore multiple enterprises’ employee training behaviours by conducting a content analysis of corporate social responsibility (sustainability) reports and a social network analysis. This study also seeks to find a way to achieve sustainable employee training by analysing the similarities in the different types of corporate training behaviours. Our analysis shows that, in 2017, 108 types of training activities were implemented by 53 enterprises; the key employee trainings (e.g., security training and skills training) and enterprises (e.g., bank of communication) are identified. The training behaviours of some of the enterprises are similar to some extent, and eight groups of firms that are very similar are identified. The results of this study show that social network analysis performs well for studying corporate employee training behaviours. Some suggestions to minimize the investment costs of training and to improve the sustainability of human resource management from the employee training perspective are provided. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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18 pages, 2693 KiB

Open AccessArticle

Estimation and Analysis of Energy Conservation and Emissions Reduction Effects of Warm-Mix Crumb Rubber-Modified Asphalts during Construction Period

by Qing-Zhou Wang, Zhan-Di Chen, Kuo-Ping Lin and Ching-Hsin Wang

Sustainability 2018, 10(12), 4521; https://doi.org/10.3390/su10124521 - 30 Nov 2018

Cited by 12 | Viewed by 5954

Abstract

In order to solve the serious environmental problems caused by the rapid increase in the number of waste tires and unproper storage of waste tires, modifying the asphalt mix for roadway pavement by adding rubber crumb from recycled waste tires is one of the highly effective approach to solve the problem and can achieve the sustainable use of rubber resources. The application of warm-mix crumb rubber-modified asphalt (CRMA) overcomes some issues of the hot-mix CRMA, such as high temperature and high energy consumption. However, there is a lack of estimation methodology for the energy conservation and emission reduction during the production process of warm-mix CRMA. This study develops the estimation models for the evaluation of energy conservation and emissions reduction during different production stages of waste rubber powder, asphalt, CRMA, hot-mix CRMA, and warm-mix CRMA. A list for gas emissions during the mixing and paving process of CRMA mixtures was established through the simulated mixing measurement and paving site measurement. The results show that for each metric ton of CRMA mixture produced, warm mixing can reduce energy consumption by 18~36% and decrease gas emissions during different stages by 15~87% compared to hot mixing. The Evotherm warm-mix CRMA mixture with DAT as warm mix agent (Ev-DAT warm-mix CRMA mixture) is more energy-efficient by saving approximately 108.56 MJ of energy and reducing gas emissions during mixing and paving by at least 32% and 73%, respectively. This model can improves the technical standard of warm-mix CRMA and the energy conservation assessment. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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19 pages, 5110 KiB

Open AccessArticle

New Energy Empowerment Using Kernel Principal Component Analysis in Insulated Gate Bipolar Transistors Module Monitoring

by Bo-Ying Liu, Gao-Sheng Wang, Ming-Lang Tseng, Zhi-Gang Li and Kuo-Jui Wu

Sustainability 2018, 10(10), 3644; https://doi.org/10.3390/su10103644 - 11 Oct 2018

Cited by 1 | Viewed by 2399

Abstract

At present, energy exhausted and environmental pollution are important issues, vigorously promoting new energy and improving the utilization efficiency and management level of new energy is an important way to achieve sustainable social development. Insulated gate bipolar transistors are important components in power converters and are widely used in new energy generation, new energy vehicles, high-speed rail and industrial production. However, the power module’s age is related to all aspects of its performance change, precluding the use of a single parameter to fully and accurately express the aging state. To monitor this state and evaluate the aging state, this study presents a method to analyze and process the state data of Insulated gate bipolar transistors power module aging tests using kernel principal component analysis and establishes a multi-dimensional grey model to evaluate the power module aging state. Using the temperature cycle aging test platform, the 7000 temperature cycling tests are implemented to accelerate the age of the power module to failure, the dynamic parameters of the power modules are measured after every 1000 cycles. During the accelerated aging process, the case temperature change rate, collector-emitter voltage drop V_ce(SAT) and Miller platform of the gate signal of V_ge are found to exhibit different variation trends at different aging stages. The result showed that multiple parameters are combined into integrated attributes to enable more accurate implementation of the state monitoring of power modules using the proposed method, which improves the status monitoring level of Insulated gate bipolar transistors modules. The proposed method is beneficial to improve the utilization efficiency and new energy source management level. Full article

(This article belongs to the Special Issue Corporate Sustainable Resource Management in Artificial Intelligent Era)

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