Big data for social benefits: Innovation as a mediator of the relationship between big data and corporate social performance

Calic, Goran; Ghasemaghaei, Maryam

doi:10.1016/j.jbusres.2020.11.003

Cited by 49 publications

(40 citation statements)

References 72 publications

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“…For the authors, these different understandings about Big Data represent a major challenge for the definition of organizational and technical standards, making it difficult to reach an agreement on a future roadmap for the use of Big Data in companies, delaying the implementation of Big Data. In this context of using Big Data in organizations, the study by Calic & Ghasemaghaei (2020) demonstrated its importance for organizational innovation in business practices and external relations, as they significantly and substantially improve economic, ethical-legal, and philanthropic performance.…”

Section: Resultsmentioning

confidence: 99%

Permeability evaluation of Industry 4.0 technologies in cloud-based energy management systems environments - Energy Cloud

Schaefer

Carvalho

Ruhoff

et al. 2021

Prod.

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Section: Resultsmentioning

confidence: 99%

Permeability evaluation of Industry 4.0 technologies in cloud-based energy management systems environments - Energy Cloud

Schaefer

Carvalho

Ruhoff

et al. 2021

Prod.

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“…Moreover, according to Dubey et al, [50] world-class manufacturing companies benefit from BDA with superior economic, social, and environmental performance. Information from BD can improve corporate social performance and these improvements occur through organizational innovation in business practices [51]. Compared to this, the term of wisdom manufacturing used by Yao et al [52] incorporates social computing, crowdsourcing, customization, sustainability, and innovation promotion in manufacturing.…”

Section: B Bda Impact On Performance and Value Creation Within Iot Manufacturingmentioning

confidence: 99%

Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Rajnoha

Hadac

2024

IEEE Trans. Eng. Manage.

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Big Data (BD)-driven business intelligence (BI) is considered to be a new development stage within industrial engineering management to achieve higher business value creation. The article provides extensive research of BD-driven approaches to identify strategic knowledge and value-creating components within Internet of Things (IoT) manufacturing. Due to previous research studies focused mostly on partial solutions and lack a holistic view, this article topic is still far from being sufficiently explored and resolved. The article presents the results of an extensive research study of contemporary professional literature with a specific aim to provide a more holistic and systematical view on the current state and future trends in this issue. To solve this high complex research problem, both quantitative and qualitative research methodology approaches are used. The article is primarily based on a systematic literature review covering 187 essential and 36 additional current research papers within the Web of Science and Scopus database from 2012 to the present. Research findings identify the seven key strategic elements determining that Big Data analytics (BDA) can become a real and tremendous business value creation driver within IoT intelligent manufacturing. These research results provide important conclusions and implications for challenging future investigations into BDA and BI systems in the context of advancing the Industry 4.0 revolution. In conclusion, we propose a final suggestion in the form of a strategic future research chain from IoT through BDA to value creation based on BD intelligence within IoT manufacturing, whereas seven strategic elements might play a crucial.

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“…With the gradual improvement in the level of economic and social development in developing countries, the diversification of governance subjects and participation methods provides conditions for various stakeholders to jointly improve resource misallocation (Lu and Zhang, 2019;Chen and Lin, 2021;Silal and Saha, 2021). Previous studies have often regarded government power as the leading factor in the SMCL in developing countries, but practical cases and theoretical studies have begun to pay increasing attention to the role of market players and social forces in recent years (Calic and Ghasemaghaei, 2021;Yang and Lee, 2021). According to the classic theory of national and regional governance (Kollintzas et al, 2018;Lin et al, 2022), this study proposes a system of factors that influence the SMCL, composed of government, market, and social forces.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Assessment and Contributing Factors of the Spatial Misallocation of Construction Land: A Case Study of Shandong, China

Cao

Wang

et al. 2022

Front. Environ. Sci.

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Spatial misallocation is an essential reason for the low utilization efficiency of construction land. Optimizing the spatial pattern of construction land allocation can improve the efficiency of economic operations and resilience to food crisis and climate change challenges. This study constructs a quantitative measurement method for the spatial misallocation of construction land (SMCL), proposes a new government market society support (GMSS) analytical framework for the contributing factors with interlocked relationships, and conducts empirical research in Shandong, China, which is a typical area with a rapid development of construction land and significant regional disparity. It is concluded that the SMCL ensues through the interaction and coevolution of the GMSS system, which plays a key role in adjusting the construction land use sequence, structure, and efficiency under the regulation of the upper government. Effectively using the estimation method based on the equal marginal output principle, the SMCL in Shandong is established as a downward trend, with evident temporal and spatial differentiation characteristics and spatial morphological mode changes, although most sub-regions are significantly approaching the adaptation interval with fluctuation. Furthermore, the empirical results of the regression model indicate that there are different effects and intensities on the SMCL among the contributing factors under the GMSS framework, wherein the local government force has an aggravating and the greatest effect, the market forces have a dual and second-ranking effect, the social forces play a positive but still weak role, and the support system has a differentiated improvement effect. However, the impacts of various dimensional factors on the SMCL also have heterogeneity in the development stages and different regions. Generally, in the low-level development stage and underdeveloped areas, the effect of local government intervention is stronger, the market forces’ importance is lower, and the social forces and support systems remain insufficiently robust.

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Big data for social benefits: Innovation as a mediator of the relationship between big data and corporate social performance

Cited by 49 publications

References 72 publications

Permeability evaluation of Industry 4.0 technologies in cloud-based energy management systems environments - Energy Cloud

Permeability evaluation of Industry 4.0 technologies in cloud-based energy management systems environments - Energy Cloud

Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Assessment and Contributing Factors of the Spatial Misallocation of Construction Land: A Case Study of Shandong, China

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