An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective

Hu, Feng; Liu, Wei; Tsai, Sang‐Bing; Gao, Junbin; Ning, Bin; Chen, Quan

doi:10.3390/su10030667

Cited by 20 publications

(16 citation statements)

References 36 publications

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“…Many firms have invested heavily in big data with the aim of deriving important insights to gain sustainable competitive advantages. Recent literature has suggested that the innovation process need to create sustainable new capabilities such as big data analytics capability (BDAC) [1][2][3][4]. BDAC can help companies process the enormous amounts of available data to identify market opportunities, predict customer needs, and analyze customer purchase decisions [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Sustainable organizational growth and performance is becoming a hot topic in the literature. The literature has suggested that sustainable organizations need to create sustainable new capabilities such as BDAC [1][2][3][4]. Prior studies have suggested that big data is an important sustainable resource for enterprises because they need new technologies to manage and analyze massive data and information [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, past studies suggest that if organizations do not pay attention to the sustainability of BDAC, the social impact will be temporary [2]. BDAC can promote sustainable development in many fields including the Internet of Things, social networks, sustainable development [1], and sustainable investment in supply chain [3].…”

Section: Introductionmentioning

confidence: 99%

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Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance

2019

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Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance

2019

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“…In addition, we applied a t-test for the differences in means of CARs between firms in heavy-polluting industries and non-heavy-polluting industries. The results show the differences between two groups were significantly different from zero based on two-tail p-values for most of the event windows [51]. Specifically, based on a one-tail p-value, the means of CARs for firms in heavy-polluting industries were significantly lower than the means of CARs for firms in non-heavy-polluting industries.…”

Section: The Overall Market Reactionmentioning

confidence: 77%

Valuation Impacts of Environmental Protection Taxes and Regulatory Costs in Heavy-Polluting Industries

Yue

Liu

et al. 2020

IJERPH

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In 2016, the issue of the Environmental Protection Tax Law indicated the enhancement of environmental protection in China. This study examines the market reaction to firms in heavy-polluting industries, and the effects of external legal institutional quality and internal environmental disclosure on firm value around the passage of Environmental Protection Tax Law. Using an event study approach coupled with ordinary least square regressions, the researchers find a significantly negative market reaction to firms in heavy-polluting industries, but this negative reaction varies depending on the expected increase in future regulatory costs. Specifically, the above negative reaction is stronger when the firm reveals that itself or its subsidiary belongs to heavy-polluting industry, however it would be mitigated when a firm is in a region with better quality of legal institutions or discloses environmental improvement activities. Overall, the results are consistent with the market perceiving that the environmental protection tax law enacted would increase regulatory costs for firms in heavy-polluting industries, and also show the higher-quality regional legal institutions and more efforts on environmental protection could relieve the market’s pessimism caused by uncertainty.

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“…The term "big data" is used to describe this phenomenon in the digital world [1]. Big data plays an increasingly significant role in the development of the economy and society [2], garnering attention in both academia and in business [3]. Recent literature describes big data as the next "management revolution" [4], "the fourth paradigm of science" [5], and the "the key to sustainable innovation within an Industry 4.0 factory" [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique

et al. 2018

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Globally, many enterprises are currently focusing on big data technology to improve their performance and operations. Recent literature points out several factors that influence the adoption of big data. However, enterprises often resist using the business value of big data due to a lack of knowledge. The purpose of this study is to investigate the factors influencing big data adoption by Chinese enterprises and to develop an indicator system based on the Motivation–Opportunity–Ability (MOA) model. Moreover, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to construct a network relationship map and to analyze its effects. Using the DEMATEL-based Analytic Network Process (ANP) (DANP) method to identify the weight distribution of index, this study quantitatively evaluates the influencing factors. The results show that leadership support, perceived usefulness, financial support, data resources, industrial development, data talents, and technical capability are key elements affecting the application of big data. Accordingly, some targeted suggestions are proposed.

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An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective

Abstract: Big data has been extensively applied to many fields and wanted for sustainable development. However, increasingly growing publications and the dynamic nature of research fronts pose challenges to understand the current research situation and sustainable development

Cited by 20 publications

References 36 publications

Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance

Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance

Valuation Impacts of Environmental Protection Taxes and Regulatory Costs in Heavy-Polluting Industries

Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique

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