Evaluating the impact of big data analytics usage on the decision-making quality of organizations

Li, Lei; Lin, Jiabao; Ouyang, Yibo; Luo, Xin

doi:10.1016/j.techfore.2021.121355

Cited by 87 publications

(40 citation statements)

References 59 publications

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“…It is a complete process that generally includes three stages: data preparation, data mining processing, and result interpretation and evaluation. This process is often repeated and approaches the essence of things until a solution to the target problem is found [ 17 ]. The complete process of data mining is shown in Figure 5 .…”

Section: Methodsmentioning

confidence: 99%

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Lin

2022

Computational Intelligence and Neuroscience

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In the era of Internet +, modern industry has developed rapidly, the network economy has promoted the great development of the industrial economy, and the traditional industrial economic statistics method has not been suitable for the development needs of modern enterprises. In today’s society, it can be described as the era of big data, the use of big data technology for industrial economic statistics is needed for the development of industrial modernization, and it is also a new requirement for industrial economic statistics put forward by social development. With the wide application of Internet of Things, cloud computing, mobile Internet, remote sensing, and geographic information technology in the economic field, precise economic policies have gradually developed and matured. Especially for different industries in the regional economy, according to the big data in the region, the big data mining technology and analysis technology can be used to obtain the development situation and future trend of the industrial economy in a timely and effective manner. Applying big data technology to macrodecision of regional economic information is an effective way to make macrodecision of current economy. Based on this background, this paper proposes a macroeconomic decision-making method for regional industries based on big data technology. Using data mining technology, time series data analysis methods combined with artificial intelligence analysis, the development trend of regional industries is obtained, and then the development trend of the industry is obtained. Development makes macroeconomic decisions. Taking agriculture as an example, the most popular analysis of the price trend of a certain agricultural product provides an effective reference for the development strategy of this agricultural product. The results show that the method proposed in this paper can effectively apply big data technology to the macrodecision-making of regional industrial economy. And it has better promotion significance.

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Section: Methodsmentioning

confidence: 99%

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Lin

2022

Computational Intelligence and Neuroscience

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“…Additionally, CA can be achieved through lower-cost leadership because BDAC provides rapid data access, response, and real-time data processing (Wang et al, 2018) and enhances operational excellence (Bag et al, 2020;Gunasekaran et al, 2017), which reduces the waste of resources and the operational costs of the firm (Belhadi et al, 2020). Studies in the literature have confirmed the positive relationship between BDAC and the quality of products and services provided by firms that adopt BDA in their activities (Li et al, 2021;Ma et al, 2021). Fosso Wamba et al (2019) and Wamba et al (2018) confirmed that BDAC provides more efficient ways to improve production processes and service delivery and to study the efficiency of these products more accurately than traditional methods to enhance organizational performance (Ransbotham et al, 2016).…”

Section: Theoretical Framework and Hypothesis Development 21 Big Data...mentioning

confidence: 99%

Can big data analytics capabilities promote a competitive advantage? Green radical innovation, green incremental innovation and data-driven culture in a moderated mediation model

Al-Khatib¹

2022

BPMJ

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PurposeThis study explores the connection between big data analytics capabilities and the competitive advantage of the manufacturing sector in Jordan through the mediating role of green radical innovation and green incremental innovation as well as the moderating role of a data-driven culture.Design/methodology/approachFor the purpose of this study, 356 questionnaires were analysed. Convergent validity and discriminant validity tests were performed through structural equation modelling in the Smart-PLS programme, and the data reliability was confirmed. A bootstrapping technique was used to analyse the data. The mediating effect for green radical and green incremental innovation and the moderating effect for data-driven culture were performed.FindingsThe empirical results showed that the proposed moderated-mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between big data analytics capabilities and the competitive advantage as well as a mediating effect of green radical innovation and green incremental innovation. It was confirmed that there is a moderating relationship for data-driven culture between green radical innovation, green incremental innovation and competitive advantage.Research limitations/implicationsThis cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalization of its results, and the results are limited to one country.Originality/valueThis research developed a theoretical model to incorporate big data analytics capabilities, green radical innovation, green incremental innovation, data-driven culture, and competitive advantage. This study provides new findings that bridge the existing research gap in the literature by testing the moderated mediation model with a focus on the organizational benefits of big data analytics capabilities to improve levels of green innovation and competitive advantage in the Jordanian manufacturing sector.

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“…Secondly, compared with longitudinal data, cross-sectional data still has some limitations on testing the hypotheses. Although several works that tried to test the robustness of the data collected by questionnaire have been done to rule out potential alternative explanations, future researches still should try their best to adopt case study or longitudinal tracking data to enhance the robustness of the findings (Ciampi et al, 2021;Li et al, 2021). Thirdly, this study only explained the special mechanism of the relationship between BDAC and competitive performance by considering resource integration.…”

Section: Limitations and Future Researchmentioning

confidence: 94%

“…More importantly, PLS-SEM can effectively deal with both non-normal and normal sample data, which contributes to exploratory forecasting research (Fornell and Bookstein, 1982). In recent years, PLS-SEM has been widely used in organization behavior, strategy, marketing and entrepreneurship (Ali, 2021;Ciampi et al, 2021;Li et al, 2021). Therefore, it is suitable to use PLS-SEM to test the relationships between complicated constructs because the theoretical framework developed in this study includes multiple variables and paths.…”

Section: Statistical Techniquesmentioning

confidence: 99%

“…Second, compared with BDA technology capability, enterprises should pay more attention to the construction and cultivation of BDA management capability in order to better expand the potential positive effect of undervalued resources on performance (Senyard et al, 2014). Therefore, enterprises should build data-driven culture (Li et al, 2021), promote digital communications between departments (Xie et al, 2020) and strengthen data sharing in interdepartmental cooperation (Sun and Liu, 2020). These measurements will be highly beneficial for achieving digital management of high-quality resources.…”

Section: Managerial Implicationsmentioning

confidence: 99%

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The Effect of Big Data Analytics Capability on Competitive Performance: The Mediating Role of Resource Optimization and Resource Bricolage

et al. 2022

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Although big data analytics capability (BDAC) leads to competitive performance, the mechanism of the relationship is still unclear. To narrow the research gap, this paper investigates the mediating roles of two forms of resource integration (resource optimization and resource bricolage) in the relationship between two forms of BDAC [big data analytics (BDA) management capability and BDA technology capability] and competitive performance. Supported by Partial Least Squares-Structural Equation Modeling (PLS-SEM) and the cross-sectional survey data from 219 Chinese enterprises, the results show that the resource bricolage plays a significantly mediating role in the relationships between BDA management capability and competitive performance as well as in the relationship between BDA technology capability and competitive performance. Furthermore, the mediating effect in the former relationship is stronger than that in the latter relationship. Additionally, BDA technology capability only has a direct effect on resource bricolage, while BDA management capability has a stronger effect on resource optimization than that on resource bricolage. Finally, resource bricolage has a stronger impact on competitive performance than resource optimization. These findings contribute to understanding how enterprises could apply different forms of BDAC to other kinds of resource integration to achieve outstanding competitive performance.

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Evaluating the impact of big data analytics usage on the decision-making quality of organizations

Cited by 87 publications

References 59 publications

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Big Data Technology in the Macrodecision-Making Model of Regional Industrial Economic Information Applied Research

Can big data analytics capabilities promote a competitive advantage? Green radical innovation, green incremental innovation and data-driven culture in a moderated mediation model

The Effect of Big Data Analytics Capability on Competitive Performance: The Mediating Role of Resource Optimization and Resource Bricolage

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