Journal articles: 'Sustainability Analytics'

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Lv, Zhihan, Rahat Iqbal, and Victor Chang. "Big data analytics for sustainability." Future Generation Computer Systems 86 (September 2018): 1238–41. http://dx.doi.org/10.1016/j.future.2018.05.020.

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Lein, James. "Projecting Regional Sustainability Trends Using Geospatial Analytics." Papers in Applied Geography 1, no. 2 (April 3, 2015): 119–27. http://dx.doi.org/10.1080/23754931.2015.1012428.

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Kobusińska, Anna, Kamil Pawluczuk, and Jerzy Brzeziński. "Big Data fingerprinting information analytics for sustainability." Future Generation Computer Systems 86 (September 2018): 1321–37. http://dx.doi.org/10.1016/j.future.2017.12.061.

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Marjaba, G. E., S. E. Chidiac, and A. A. Kubursi. "Sustainability framework for buildings via data analytics." Building and Environment 172 (April 2020): 106730. http://dx.doi.org/10.1016/j.buildenv.2020.106730.

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Aliabadi, Majid Moradi, and Yinlun Huang. "Vector-Based Sustainability Analytics: A Methodological Study on System Transition toward Sustainability." Industrial & Engineering Chemistry Research 55, no. 12 (December 28, 2015): 3239–52. http://dx.doi.org/10.1021/acs.iecr.5b03391.

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Kim, Kyoung-jae, Kichun Lee, and Hyunchul Ahn. "Predicting Corporate Financial Sustainability Using Novel Business Analytics." Sustainability 11, no. 1 (December 22, 2018): 64. http://dx.doi.org/10.3390/su11010064.

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Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

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Ojokoh, Bolanle A., Oluwarotimi W. Samuel, Olatunji M. Omisore, Oluwafemi A. Sarumi, Peter A. Idowu, Emile R. Chimusa, Ashraf Darwish, Adebayo F. Adekoya, and Ferdinand A. Katsriku. "Big data, analytics and artificial intelligence for sustainability." Scientific African 9 (September 2020): e00551. http://dx.doi.org/10.1016/j.sciaf.2020.e00551.

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Lee, Picheng, Gary Kleinman, and Chu‐hua Kuei. "Using text analytics to apprehend urban sustainability development." Sustainable Development 28, no. 4 (February 19, 2020): 897–921. http://dx.doi.org/10.1002/sd.2045.

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Paz, Cristhian, Josune Sáenz, and Ana Ortiz-de-Guinea. "Data analytics in organic farming: Impact on environmental sustainability." European Conference on Knowledge Management 23, no. 2 (August 25, 2022): 895–903. http://dx.doi.org/10.34190/eckm.23.2.292.

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The production of healthy food while preserving the environment constitutes one of the main challenges of the 21st century. Along these lines, organic farming has emerged as a farm management and food production system that encourages environmental sustainability. To enhance such sustainability, data analytics both as an asset and as a capability could play a substantial role. Indeed, data analytics could be used to interpret the past and predict the future and to make more timely or accurate decisions regarding the use and protection of natural resources. Using survey data from 119 Spanish organic farms whose digitization degree as reported by the farmer is above 0, and structural equation modeling based on partial least squares to test research hypotheses, we found that even though data analytics in organic farming is clearly underdeveloped, it still contributes to enhancing farms’ environmental sustainability. Thus, investments in environmental data analytics appear to pay off.

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Raghupathi, Viju, Jie Ren, and Wullianallur Raghupathi. "Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach." Sustainability 12, no. 11 (June 10, 2020): 4753. http://dx.doi.org/10.3390/su12114753.

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Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders’ perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience.

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Wehnert, Peter, Christoph Kollwitz, Christofer Daiberl, Barbara Dinter, and Markus Beckmann. "Capturing the Bigger Picture? Applying Text Analytics to Foster Open Innovation Processes for Sustainability-Oriented Innovation." Sustainability 10, no. 10 (October 16, 2018): 3710. http://dx.doi.org/10.3390/su10103710.

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In open innovation initiatives for sustainability-oriented innovations, it is indispensable to have a wide array of engaging stakeholders. Yet, as not all relevant actors are able or willing to participate, important opinions can go unnoticed. Due to such stakeholder selection effects, aspects of high relevance may remain uncaptured. To address this issue, we first define the concept of silent stakeholders and relate it to sustainability-oriented innovations. We then discuss the new approach of employing analytical methods to examine existing sources outside the innovation process for silent stakeholder opinions. For this purpose, we conduct an action research study demonstrating how to examine broad discourse data with text analytics for an open innovation project aiming to create a sustainability-oriented innovation. To this end, we develop an approach for the efficient integration of external sources in open innovation processes. We find that text analytics of broad discourse data can particularly support the orientation and idea generation phase for sustainability-oriented innovation. Furthermore, we identify possibilities for the application of further data mining methods to complement open innovation approaches along the innovation process. Building on that, we propose an integrated framework. Hence, we add to the literature on stakeholder participation, analytical methods and innovation management, as well as sustainability-oriented innovation.

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Zhu, Xiangyu, and Yang Yang. "Big Data Analytics for Improving Financial Performance and Sustainability." Journal of Systems Science and Information 9, no. 2 (April 1, 2021): 175–91. http://dx.doi.org/10.21078/jssi-2021-175-17.

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Abstract In this study, the key drivers of sustainability commitment, green supply chain management, big data integration and green human resource practice are explored, and the impact of these sustainable capabilities on the environmental and financial performance of banks is also elaborated. In addition, the influence of green management practices on integrating big data technology into operations is presented. As for the concept of dynamic ability, it has been used to recommend and empirically test conceptual models. Data were collected through a self-administrated survey questionnaire on 317 people working in 37 banks in six Asian countries. Research suggests that big data analytics strategies have an impact on internal processes and on the stability and financial performance of banks. Besides, it is indicated that banks are committed to proper data monitoring of their customers to complete operational efficiency and sustainability goals. Furthermore, our result proved that banks practicing Green Innovation strategies experience better environmental and economic performance because their employees are already trained in Green HR. Finally, from our study, it was found that internal and external green supply chain management practices have a positive effect on the environmental and financial performance of banks, thus ensuring that the bank of Association of Southeast Asian Nations (ASEAN) mitigates the environmental impact through its operations and ultimately experiences an increase in financial performance.

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Edgeman, Rick, and Joseph A. Williams. "Enterprise self-assessment analytics for sustainability, resilience and robustness." TQM Journal 26, no. 4 (June 3, 2014): 368–81. http://dx.doi.org/10.1108/tqm-01-2014-0012.

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Purpose – The purpose of this paper is to integrate resilience, robustness, and resplendence (R 3) with sustainable enterprise excellence (SEE) and social-ecological innovation (SEI) that assist firms to progress toward continuously relevant performance proceeding from continuously responsible strategy, behavior, and other actions. Design/methodology/approach – Sustainable enterprise excellence, resilience, robustness, and resplendence (SEER3) model and the associated means of SEER 3 maturity assessment are introduced to explain the organizational concept. Findings – SEER3 balances the complementary and competing interests of key stakeholder segments, including society and the natural environment and increases the likelihood of superior and sustainable competitive positioning and hence long-term enterprise success that is defined by continuously relevant and responsible governance, strategy, actions, and performance consistent with high-level organizational R3. Originality/value – This paper adapts the established principles from physics to characterize enterprise R3 to come up with SEE model.

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Relich, Marcin. "Predictive and Prescriptive Analytics in Identifying Opportunities for Improving Sustainable Manufacturing." Sustainability 15, no. 9 (May 7, 2023): 7667. http://dx.doi.org/10.3390/su15097667.

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Environmental issues and sustainability performance are more and more significant in today’s business world. A growing number of manufacturing companies are searching for changes to improve their sustainability in the areas of products and manufacturing processes. These changes should be introduced in the design process and affect the whole product life cycle. This paper is concerned with developing a method based on predictive and prescriptive analytics to identify opportunities for increasing sustainable manufacturing through changes incorporated at the product design stage. Predictive analytics uses parametric models obtained from regression analysis and artificial neural networks in order to predict sustainability performance. In turn, prescriptive analytics refers to the identification of opportunities for improving sustainability performance in manufacturing, and it is based on a constraint programming implemented within a constraint satisfaction problem (CSP). The specification of sustainability performance in terms of a CSP provides a pertinent framework for identifying all admissible solutions (if there are any) of the considered problem. The identified opportunities for improving sustainability performance are dedicated to specialists in product development, and aim to reduce both resources used in manufacturing and negative effects on the environment. The applicability of the proposed method is illustrated through reducing the number of defective products in manufacturing.

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Shokouhyar, Sajjad, Mohammad Reza Seddigh, and Farhad Panahifar. "Impact of big data analytics capabilities on supply chain sustainability." World Journal of Science, Technology and Sustainable Development 17, no. 1 (January 6, 2020): 33–57. http://dx.doi.org/10.1108/wjstsd-06-2019-0031.

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Purpose The purpose of this paper is to develop a theoretical model to explain the impact of big data analytics capabilities (BDAC) on company’s supply chain sustainability (CSCS). The secondary objective of the study is to assess the relationship between different dimensions of supply chain sustainability and companies’ BDAC. Design/methodology/approach This research was carried out by conducting a survey among 234 pharmaceutical companies in Iran (a case study of Iran), using a standard questionnaire of BDAC and United Nations (UN) online self-assessment on supply chain sustainability. However, the respond of managers of 188 companies were usable in this research. Smart PLS3 was used to employ partial least squares method to examine the validity and reliability of the measurement and structural model. Findings The results of this study demonstrate that BDAC have a strong impact on both pharmaceutical supply chain sustainability, and the dimensions including vision, engage and internal. It is found that the relationships between BDAC and the other dimensions of supply chain sustainability including expect, scope and goals are not significant but positive. Research limitations/implications Research on the relationship between BDAC and CSCS, especially in the pharmaceutical supply chain, is scanty, and this gap is highlighted in developing countries and the pharmaceutical supply chain that plays a prominent role in public health. This paper discusses several important barriers to forming a sustainable supply chain and strong BDA capabilities. Practical implications This paper could be a guide to managers and consultants who are involved in big data analytics and sustainable development. Since UN urges companies do the online self-assessment, the results of this paper would be attractive and useful for UN global compact specialists. Originality/value No study has directly measured the relation between BDAC and CSCS and different dimensions of CSCS, using a comprehensive survey throughout all pharmaceutical companies in Iran. Moreover, this research assesses the different dimensions of BDA capabilities and supply chain sustainability. This paper represents the facts about situation of sustainability of pharmaceutical supply chain and BDAC in these companies, and discloses several related issues that are serious barriers to forming a sustainable supply chain and strong BDAC. In addition, this paper provided academic support for UN questionnaire about CSCS and used it in the survey.

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Whiteman, Heather, Solange Charas, Hieu Bui, Lee S. Webster, and James Gaskin. "The Critical Need for Human Capital Measurement Standards and Transparency in Healthcare." Journal of Healthcare Management Standards 3, no. 1 (August 18, 2023): 1–8. http://dx.doi.org/10.4018/jhms.328520.

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Despite long histories, the disciplines of healthcare performance analytics and human capital analytics followed parallel but separate tracks during the 19th and 20th centuries. Little has been done to integrate these two analytic disciplines to improve the delivery of medical care and the sustainability of healthcare organizations. Today, there is an increased demand for healthcare to meet the aging world population, spiraling healthcare costs, and a shortage of human resources to meet patient needs. It is imperative that healthcare professionals apply innovations to explore and optimize value from a combined discipline of healthcare human capital measurement and reporting.

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Lemsa, Santa. "READINESS OF LATVIA’S ORGANIZATIONS FOR ADVANCED ANALYTICS." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 13, 2023): 61–66. http://dx.doi.org/10.17770/etr2023vol2.7256.

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The advanced analytics is one of the core tools to provide competitive advantage, sustainable development and foster productivity of the organization. Digital transformation and advanced analytics are two key trends in the emerging age of data, analytics, and automation. Digital transformation is the process of transforming how businesses operate when faced with digital disruption. Advanced analytics is the application of predictive and prescriptive models to analyse large, complex datasets in order to make critical business decisions. The focus of the paper is to assess the maturity level of advanced analytics in the organizations of Latvia by region, size and industry. Assessment was done by several domains like Organization, People, Data, Analytics, Technologies. The quantitative online survey was performed to assess the readiness of Latvia’s organizations for advanced analytics. The questionnaire was developed based on an academic literature review, reports and publications by researchers, analytical sector, industry experts and Author’s professionals experience in advanced analytics industry. The overall readiness level of Latvia’s organizations is 2.4 in 5 points scale. It differs by region, size of the organization and industry. Most of organizations do not have Analytics strategy, majority use spreadsheets based analytical tools, half of organizations use mostly only internal data, more than third part of organizations do not have any analytical resources. It leads to conclusion that majority of Latvia’s organizations are far from ability to improve productivity, be able to maximize the potential of the digital environment, to exploit data to make data-driven and automated decisions and are far from 21st century digital opportunities. Thus, puts under danger the sustainability of the organizations itself.

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Bani-Hani, Imad, and Eva Shepherd. "Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective." Informatics 8, no. 3 (July 15, 2021): 45. http://dx.doi.org/10.3390/informatics8030045.

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The capabilities of the people, processes, and technology are important factors to consider when exploring continuous use to create value. Multiple perceptions and attitudes towards self-service systems lead to various usage levels and outcomes. With complex analytical structures, organizations need a better understanding of IS value and users’ satisfaction. Incompatibility reduces the purpose of self-service analytics, decreasing its value and making it obsolete. In a qualitative, single case study, 20 interviews in a major digital Scandinavian marketplace were explored using the expectation–confirmation theory of continuous use to explore the mechanisms influencing the sustainability of self-service value. Two main mechanisms were identified: the personal capability reinforcement mechanism and the environment value reinforcement mechanism. This study contributes to the post-implementation and continuous use literature and self-service analytics literature and provides some practice implications to the related industry.

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Elliott, Matthew, Lisa Elliott, and Evert Sluis. "A Predictive Analytics Understanding of Cooperative Membership Heterogeneity and Sustainability." Sustainability 10, no. 6 (June 16, 2018): 2048. http://dx.doi.org/10.3390/su10062048.

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Benkert, Julia. "Reconciling Analytics with Holistic Thinking in Business Sustainability Decision-Making." Academy of Management Proceedings 2019, no. 1 (August 1, 2019): 17339. http://dx.doi.org/10.5465/ambpp.2019.17339abstract.

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Land, Anna, Andrew Buus, and Alana Platt. "Data Analytics in Rail Transportation: Applications and Effects for Sustainability." IEEE Engineering Management Review 48, no. 1 (March 1, 2020): 85–91. http://dx.doi.org/10.1109/emr.2019.2951559.

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Reisi, Marzieh, Soheil Sabri, Muyiwa Agunbiade, Abbas Rajabifard, Yiqun Chen, Mohsen Kalantari, Azadeh Keshtiarast, and Yan Li. "Transport sustainability indicators for an enhanced urban analytics data infrastructure." Sustainable Cities and Society 59 (August 2020): 102095. http://dx.doi.org/10.1016/j.scs.2020.102095.

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Amirmokhtar Radi, Sanaz, and Sajjad Shokouhyar. "Toward consumer perception of cellphones sustainability: A social media analytics." Sustainable Production and Consumption 25 (January 2021): 217–33. http://dx.doi.org/10.1016/j.spc.2020.08.012.

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Putra, Rino Dwi, Sri Mulyani, Sugiono Poulus, and Citra Sukmadilaga. "Data quality analytics, business ethics, and cyber risk management on operational performance and fintech sustainability." International Journal of Data and Network Science 6, no. 4 (2022): 1659–68. http://dx.doi.org/10.5267/j.ijdns.2022.4.008.

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This study conducted a test to see the influence of data quality analytics, business ethics, and cyber risk management on operational performance and its implication on corporate sustainability of Fintech P2P Lending companies registered and licensed in Indonesian Financial Services Authority (OJK). This study used descriptive analysis and statistical method Structural Equation Modeling (SEM)-Lisrel. The data was collected by using questionnaires given to 104 managers from 91 Fintech P2P Lending companies registered and licensed at OJK until the end of December 2021. The results show that data quality analytics and cyber risk management had a positive and significant influence on operational performance. The results also show that analytical data quality, business ethics and cyber risk management had a positive and significant influence on operational performance. The findings of this study added to the limitations of the research literature on the elaboration of variables that determine performance and business sustainability in Fintech P2P lending.

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Alzboun, Nidal Mohammed. "Big data analytics capabilities and supply chain sustainability: Evidence from the hospitality industry." Uncertain Supply Chain Management 11, no. 4 (2023): 1427–32. http://dx.doi.org/10.5267/j.uscm.2023.8.004.

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Big data analytics capabilities have piqued the curiosity of academics and practitioners in recent years. However, there has been little research conducted on the effects of these capabilities on supply chain sustainability, particularly in emerging economies. To address this gap, this article attempted to investigate the impact of big data analytics capabilities on the supply chain sustainability of Jordan's hospitality industry using quantitative data derived from 512 managers in senior and middle levels of hotels listed in Jordan Hotels Association (JHA). Structural Equation Modelling (SEM) was conducted for hypothesis evaluation. The research findings proved that the dimensions of big data analytics capabilities, which were infrastructure flexibility, management capabilities, and personnel capabilities, had a significant positive role in enhancing supply chain sustainability. Therefore, the research provided a series of recommendations for managers in these hotels, the most important of which was allocating significant investments in modern data-collecting technologies to record key changes across the supply chain, including manufacturing, transportation, and distribution.

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Comm, Clare L., and Dennis F. X. Mathaisel. "The use of analytics to market the sustainability of “Unique” products." Journal of Marketing Analytics 6, no. 4 (June 26, 2018): 150–56. http://dx.doi.org/10.1057/s41270-018-0038-6.

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Arikat, Tarek, and Marvine Hamner. "Utilizing Analytics to Assess the Sustainability of Telecommunications in Developing Nations." Procedia Computer Science 118 (2017): 67–94. http://dx.doi.org/10.1016/j.procs.2017.11.148.

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Dubey, Rameshwar, Angappa Gunasekaran, Stephen J. Childe, Thanos Papadopoulos, Zongwei Luo, Samuel Fosso Wamba, and David Roubaud. "Can big data and predictive analytics improve social and environmental sustainability?" Technological Forecasting and Social Change 144 (July 2019): 534–45. http://dx.doi.org/10.1016/j.techfore.2017.06.020.

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Zhang, Haili, Michael Song, and Huanhuan He. "Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability." Sustainability 12, no. 3 (January 28, 2020): 949. http://dx.doi.org/10.3390/su12030949.

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There has been increased interest in studying how big data analytics capability (BDAC) and artificial intelligence capability (AIC) lead to sustainable innovation and performance. Yet, few studies have investigated how these two emerging capabilities affect the success of sustainability development projects through the mediating effects of the sustainability design and commercialization processes. Based on Day and Wensley’s theoretical framework for diagnosing competitive superiority, we propose a research model to investigate how sustainability design and commercialization mediate the relationships between two emerging capabilities and sustainable growth and performance. To test the proposed research model, we collected empirical data from 905 sustainability development projects from China and the United States. This study makes theoretical and managerial contributions to sustainable development theory. The study findings reveal several interesting results. First, BDAC and AIC not only increase the proficiency of sustainability design and commercialization but also directly enhance sustainable growth and performance. Second, sustainability design and commercialization mediate the positive effects of BDAC and AIC on sustainable growth and performance. Finally, the empirical analyses uncovered several cross-national differences. For sustainability design, BDAC is more important than AIC in the United States, while AIC is more important than BDAC in China.

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Kozlova, Mariia, and Julian Scott Yeomans. "Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics." Sustainability 14, no. 3 (January 31, 2022): 1655. http://dx.doi.org/10.3390/su14031655.

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In practice, environmental analytics involves an integration of science, methods, and techniques involving a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to address “real-world” environmental and sustainability problems [...]

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Teplovs, Chris. "Commentary On “Theory-led design of instruments and representations in learning analytics: Developing a novel tool for orchestration of online collaborative learning”." Journal of Learning Analytics 2, no. 2 (December 7, 2015): 44–46. http://dx.doi.org/10.18608/jla.2015.22.4.

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This commentary reflects on the contributions to learning analytics and theory by a paper that describes how multiple theoretical frameworks were woven together to inform the creation of a new automated discourse analysis tool. The commentary highlights the contributions of the original paper, provides some alternative approaches, and touches on issues of sustainability and scalability of learning analytics innovations.

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Cho, Jungwon, Sangmi Shin, Youngmi Jeong, Eunsook Lee, Soyeon Ahn, Seunghyun Won, and Euni Lee. "Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry." Healthcare 9, no. 9 (September 9, 2021): 1187. http://dx.doi.org/10.3390/healthcare9091187.

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Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients’ dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan–Do–Study–Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations.

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Jeble, Shirish, Rameshwar Dubey, Stephen J. Childe, Thanos Papadopoulos, David Roubaud, and Anand Prakash. "Impact of big data and predictive analytics capability on supply chain sustainability." International Journal of Logistics Management 29, no. 2 (May 14, 2018): 513–38. http://dx.doi.org/10.1108/ijlm-05-2017-0134.

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PurposeThe purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.Design/methodology/approachThe authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.FindingsThe statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support forH4. Although the authors did not find support forH4(moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.Originality/valueThis study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.

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Raut, Rakesh D., Sachin Kumar Mangla, Vaibhav S. Narwane, Bhaskar B. Gardas, Pragati Priyadarshinee, and Balkrishna E. Narkhede. "Linking big data analytics and operational sustainability practices for sustainable business management." Journal of Cleaner Production 224 (July 2019): 10–24. http://dx.doi.org/10.1016/j.jclepro.2019.03.181.

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Ponnusamy, Vinoth Kumar, Padmanathan Kasinathan, Rajvikram Madurai Elavarasan, Vinoth Ramanathan, Ranjith Kumar Anandan, Umashankar Subramaniam, Aritra Ghosh, and Eklas Hossain. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid." Sustainability 13, no. 23 (December 1, 2021): 13322. http://dx.doi.org/10.3390/su132313322.

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The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

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Lee, In, and George Mangalaraj. "Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions." Big Data and Cognitive Computing 6, no. 1 (February 1, 2022): 17. http://dx.doi.org/10.3390/bdcc6010017.

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Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.

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Agbozo, Reuben Seyram Komla, Tao Peng, Huajun Cao, and Renzhong Tang. "Enhancing big data for greentelligence across the production value chain." Green Manufacturing Open 1 (2022): 4. http://dx.doi.org/10.20517/gmo.2022.02.

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The big data concept has been explosive, revealing, and transformative across manufacturing industries as it provides deeper insights into manufacturing operations for decision-making. However, big green data (BGA), a dedicated subset of big data, is not adequately structured for comprehensive sustainability analysis, particularly in smart factories. With our proposed green data balance (GDB), there will be accountability for each input and output composition in a production unit within the production value chain (PVC). Data will be exhaustively and accurately collected in each workshop to help uncover unknown issues in a production value chain while facilitating the development of sustainability metrics or index systems. Additionally, a structured big green data system will fuel “greentelligence”, using intelligent systems and technologies to speed up digitalization toward sustainable manufacturing by measuring, tracking, and minimizing adverse environmental impacts. Lastly, with the support of the cognitive intelligence data analytic system (CIDAS), real-time and near real-time comprehensive sustainability analytics can be performed, leading to Self-X metacognitive adjustments and corrective actions.

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Zerbino, Pierluigi, Davide Aloini, Riccardo Dulmin, and Valeria Mininno. "Towards Analytics-Enabled Efficiency Improvements in Maritime Transportation: A Case Study in a Mediterranean Port." Sustainability 11, no. 16 (August 18, 2019): 4473. http://dx.doi.org/10.3390/su11164473.

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The current digitalization trend, the increased attention towards sustainability, and the spread of the business analytics call for higher efficiency in port operations and for investigating the quantitative approaches for maritime logistics and freight transport systems. Thus, this manuscript aims at enabling analytics-driven improvements in the port transportation processes efficiency by streamlining the related information flow, i.e., by attaining shorter time frames of the information and document sharing among the export stakeholders. We developed a case study in a mid-sized European port, in which we applied Process Mining (PM)—an emerging type of business analytics—to a seven-month dataset from the freight export process. Four process inefficiencies and an issue that can jeopardize the reliability of the time performance measurements were detected, and we proposed a draft of solutions to cope with them. PM enabled enhancements in the overall export time length, which might improve the vessels’ turnover and reduce the corresponding operational costs, and supported the potential re-design of performance indicators in process control and monitoring. The results answer the above-mentioned calls and they offer a valuable, analytics-based alternative to the extant approaches for improving port performance, because it focuses on the port information flow, which is often related to sustainability issues, rather than the physical one.

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Corrin, Linda, Maren Scheffel, and Dragan Gašević. "Learning Analytics: Pathways to Impact." Australasian Journal of Educational Technology 36, no. 6 (December 31, 2020): 1–6. http://dx.doi.org/10.14742/ajet.6853.

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The field of learning analytics has evolved over the past decade to provide new ways to view, understand and enhance learning activities and environments in higher education. It brings together research and practice traditions from multiple disciplines to provide an evidence base to inform student support and effective design for learning. This has resulted in a plethora of ideas and research exploring how data can be analysed and utilised to not only inform educators, but also to drive online learning systems that offer personalised learning experiences and/or feedback for students. However, a core challenge that the learning analytics community continues to face is how the impact of these innovations can be demonstrated. Where impact is positive, there is a case for continuing or increasing the use of learning analytics, however, there is also the potential for negative impact which is something that needs to be identified quickly and managed. As more institutions implement strategies to take advantage of learning analytics as part of core business, it is important that impact can be evaluated and addressed to ensure effectiveness and sustainability. In this editorial of the AJET special issue dedicated to the impact of learning analytics in higher education, we consider what impact can mean in the context of learning analytics and what the field needs to do to ensure that there are clear pathways to impact that result in the development of systems, analyses, and interventions that improve the educational environment.

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Alcaide-Ruiz, María Dolores, Francisco Bravo-Urquiza, and Elena Moreno-Ureba. "Sustainability Committee Research: A Bibliometric Study." Sustainability 14, no. 23 (December 2, 2022): 16136. http://dx.doi.org/10.3390/su142316136.

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This study conducts a bibliometric analysis of research on sustainability committees. Specifically, our paper analyses the development of this field of research by identifying the most influential articles, authors, and relevant research themes, and highlighting potential future lines of research. Our sample is composed of the publications from the main collection of the Clarivate Analytics Web of Science database (WOS) for the period 1900–2021. Our findings stress the interdisciplinary nature of research about sustainability committees. In addition, our evidence emphasizes the need for more research to understand how firms respond to regulatory and societal pressures on sustainability matters. In addition, the network analysis highlights the main research themes and provides a basis for recognizing future research opportunities. Our paper is the first to perform a comprehensive bibliometric analysis for sustainability committees. Our evidence presents relevant implications for academics in the definition of their research projects.

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Adel Abdo Mukred, Maged, and Zheng Jianguo. "Use of Big Data to Improve Environmental Sustainability in Developing Countries." International Journal of Business and Management 12, no. 11 (October 18, 2017): 249. http://dx.doi.org/10.5539/ijbm.v12n11p249.

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Big data inhibits the ability to significantly impact a wide range of fields in an economy, from the government sector to commercial sectors like retail and healthcare. Not only has it altered the way companies assess their product’s demand and supply patterns but has also phenomenally helped in making the environment healthier in recent years. It carries the ability to identify valuable data from a huge dataset with exceptional parallel processing. This study presents the general introduction of big data bringing forth its various features and advantages along with the challenges which organizations face while using with respect to environmental sustainability. Observations have also been made on the findings of various researches, and studies and surveys performed by some international organizations in the recent years on the urgent need of taking necessary measures and initiatives to prevent further depletion of natural resources thus making the environment sustainable. Making the issue the study aim, future studies must intend to explore how multinational corporations can enhance environmental sustainability through big data analytics. Lastly, recommendations have been made to organisations– private and public in hiring adequate expertise and set-up, thereby making big data analytics more efficient and reliable.

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Munir, Muhammad Adeel, Amjad Hussain, Muhammad Farooq, Muhammad Salman Habib, and Muhammad Faisal Shahzad. "Data-Driven Transformation: The Role of Ambidexterity and Analytics Capability in Building Dynamic and Sustainable Supply Chains." Sustainability 15, no. 14 (July 11, 2023): 10896. http://dx.doi.org/10.3390/su151410896.

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Data-driven supply chain analytics skills are seen as the next frontier of the supply chain transformation. The potential of data analytics-enabled dynamic capability for improving organizational performance and agility has been investigated in past research. However, there has not been sufficient research on the potential benefits of the data analytics capability and supply chain ambidexterity paradox to develop a sustainable and agile supply chain that can integrate and reorganize all of its resources in order to respond to rapidly changing business circumstances. This study aimed to empirically validate how an organization’s SC ambidexterity affects its sustainability and dynamic capability, and the mediating role of supply chain analytics capability (SCAC) in their relationship. The research’s theoretical framework is founded on dynamic capability theory. A pretested questionnaire was used to collect responses from 427 supply chain specialists who worked in diverse product-based industries across Pakistan, Bangladesh, and India. Using partial least squares structural equation modeling (PLS-SEM), a total of six hypotheses were evaluated, and the results show that supply chain ambidexterity has a positive effect on dynamic capability and sustainability, and SCAC plays a complementary, partially mediating role in their interaction. The findings of the research reveal the expected results of investing in the analytics capability of the supply chain and provide firms with some recommendations for improving their dynamic capabilities. This study will facilitate in creating an agile and sustainable supply chain, enabling it to adapt to both short- and long-term changes in the market while simultaneously considering the social, economic, and environmental vitality.

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Mageto, Joash. "Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains." Sustainability 13, no. 13 (June 24, 2021): 7101. http://dx.doi.org/10.3390/su13137101.

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Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.

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Santoso, I., M. Purnomo, A. A. Sulianto, and A. Choirun. "Machine learning application for sustainable agri-food supply chain performance: a review." IOP Conference Series: Earth and Environmental Science 924, no. 1 (November 1, 2021): 012059. http://dx.doi.org/10.1088/1755-1315/924/1/012059.

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Abstract The agri-food supply chain consists of activities in “farm-to-fork” order, including agriculture (i.e., land cultivation and crop production), production processes, packaging, warehousing systems, distribution, transportation, and marketing. Data analytics hold the key to ensuring future food security, food safety, and ecological sustainability. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing can change production management practices. The current study presents a systematic review of machine learning (ML) applications in the agri-food supply chain. This framework identifies the role of ML algorithms in providing real-time analytical insights to assist proactive data-driven decision-making processes in the agri-food supply chain. It also guides researchers, practitioners, and policymakers on successful management to increase the productivity and sustainability of agri-food.

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Hilali, Wail El, Abdellah El Manouar, and Mohammed Abdou Janati Idrissi. "The mediating role of big data analytics in enhancing firms’ commitment to sustainability." International Journal of Advanced Technology and Engineering Exploration 8, no. 80 (July 31, 2021): 932–44. http://dx.doi.org/10.19101/ijatee.2021.874114.

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Queiroz, Maciel M. "A framework based on Twitter and big data analytics to enhance sustainability performance." Environmental Quality Management 28, no. 1 (September 2018): 95–100. http://dx.doi.org/10.1002/tqem.21576.

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Said, Fareyha, Azlina Abdul Jalil, and Dalilawati Zainal. "Big Data Analytics Capabilities, Sustainability Reporting on Social Media, and Competitive Advantage: An Exploratory Study." Asian Journal of Business and Accounting 16, no. 1 (June 30, 2023): 129–60. http://dx.doi.org/10.22452/ajba.vol16no1.5.

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Manuscript type: Research paper Research aims: Drawing from the lens of dynamic capability view (DCV), this study investigates whether companies with big data analytics (BDA) capabilities, specifically BDA management, infrastructure, and personnel capabilities, disclose more sustainability posts on social media and whether such disclosure affects their competitive advantage. Design/Methodology/Approach: Data from 100 public listed firms in Malaysia were obtained from questionnaires and content analysis of Facebook pages. Smart PLS was employed to analyse the data. Research findings: The results suggest that in the context of Malaysia, BDA management capability significantly impacts sustainability reporting on social media (SRSM). The evidence also points to SSRM positively impacting a company’s competitive advantage. Theoretical contribution/Originality: Theoretically, this study contributes to the literature on DCV. The findings provide insights into how BDA capabilities can help organisations focus on social media platforms and communicate with their stakeholders on sustainability performance. It also suggests that sustainability reporting on social media is associated with competitive advantage, as it allows for two-way interaction between organisations and its stakeholders. Practitioner/Policy implications: Practically, this study provides insights into the roles of accounting, social media, and big data within the current digital revolution. Specifically, it offers guidance to executives and managers on identifying the conditions that need to be present for BDA capability to add value to SRSM. Additionally, the findings here have implications for policymakers and businesses looking to use BDA in the context of SRSM to gain competitive advantage. Research limitation/Implications: Future studies could consider increasing the sample size. This study sheds light on the relevance of BDA capabilities in promoting sustainability issues using social media.

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Relich, Marcin. "A Data-Driven Approach for Improving Sustainable Product Development." Sustainability 15, no. 8 (April 17, 2023): 6736. http://dx.doi.org/10.3390/su15086736.

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A product’s impact on environmental issues in its complete life cycle is significantly determined by decisions taken during product development. Thus, it is of vital importance to integrate a sustainability perspective in methods and tools for product development. The paper aims at the development of a method based on a data-driven approach, which is dedicated to identifying opportunities for improving product sustainability at the design stage. The proposed method consists of two main parts: predictive analytics and simulations. Predictive analytics use parametric models to identify relationships within product sustainability. In turn, simulations are performed using a constraint programming technique, which enables the identification of all possible solutions (if there are any) to a constraint satisfaction problem. These solutions support R&D specialists in finding improvement opportunities for eco-design related to reducing harmful impacts on the environment in the manufacturing, product use, and post-use stages. The results indicate that constraint-satisfaction modeling is a pertinent framework for searching for admissible changes at the design stage to improve sustainable product development within the full scope of socio-ecological sustainability. The applicability of the proposed approach is verified through an illustrative example which refers to reducing the number of defective products and quantity of energy consumption.

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Zhang, Ming, and Bolin Lan. "Detect Megaregional Communities Using Network Science Analytics." Urban Science 6, no. 1 (February 16, 2022): 12. http://dx.doi.org/10.3390/urbansci6010012.

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Urban science research and the research on megaregions share a common interest in the system of cities and its implications for world urbanization and sustainability. The two lines of inquiry currently remain largely separate efforts. This study aims to bridge urban science and megaregion research by applying network science’s community detection algorithm to explore the spatial pattern of megaregions in the contiguous United States. A network file was constructed consisting of county centroids as nodes, the direct links between each pair of counties as edges, and inter-county commuting flows as the weight to capture spatial interactions. Analyses were carried out at two levels, one at the national level using Gephi and the other for the State of Texas involving NetworkX, an open-source Python programming package to implement a weighted community detection algorithm. Results show the detected communities largely conforming to the qualitative knowledge on megaregions. Despite a number of limitations, the study indicates the great potential of applying network science analytics to improve understanding of the spatial process of megaregions.

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Hack-Polay, Dieu, Mahfuzur Rahman, Md Morsaline Billah, and Hesham Z. Al-Sabbahy. "Big data analytics and sustainable textile manufacturing." Management Decision 58, no. 8 (March 22, 2020): 1699–714. http://dx.doi.org/10.1108/md-09-2019-1323.

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PurposeThe purpose of this article is to discuss issues associated with the application big data analytics for decision-making about the introduction of new technologies in the textile industry in the developing world.Design/methodology/approachThe leader–member exchange theoretical framework to consider the nature of the relationships between owners and followers to identify the potential issues that affect decision-making was used. However, decisions to adopt such environmentally friendly biotechnologies are hampered by the lack of awareness amongst owners, intergenerational conflict and cultural impediments.FindingsThe article found that the limited use of this valuable technological resource is linked to several factors, mainly cultural, generational and educational factors. The article exposes two key new technologies that could help the industry reduce its carbon footprint.Originality/valueThe study suggests more awareness raising amongst plant owners and greater empowerment of new generations in decision-making in the industry. This study, therefore, bears significant implications for environmental sustainability in the developing world where the textile industry is one of the major polluting industries affecting water quality and human health.

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Journal articles on the topic 'Sustainability Analytics'

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