Big data analytics business value and firm performance: linking with environmental context

被引:42
作者
Vitari, Claudio [1 ]
Raguseo, Elisabetta [2 ]
机构
[1] Univ Toulon & Var, Aix Marseille Univ, CERGAM, Aix En Provence, France
[2] Politecn Torino, Dept Management & Prod Engn, Observ Online Platform Econ, European Commiss, Turin, Italy
关键词
resource based view; contingency theory; big data analytics; customer satisfaction; financial performance; market performance; munificence; dynamism; SUPPLY CHAIN MANAGEMENT; INFORMATION-TECHNOLOGY; ENTREPRENEURIAL ORIENTATION; PREDICTIVE ANALYTICS; CAPABILITY; INDUSTRY; LOGISTICS; SYSTEMS; FUTURE; TRANSFORMATION;
D O I
10.1080/00207543.2019.1660822
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Previous studies, grounded on the resource based view, have already explored the relationship between the business value that Big Data Analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.
引用
收藏
页码:5456 / 5476
页数:21
相关论文
共 122 条
[1]  
Abbasi A, 2016, J ASSOC INF SYST, V17, pI
[2]   Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research [J].
Agarwal, Ritu ;
Dhar, Vasant .
INFORMATION SYSTEMS RESEARCH, 2014, 25 (03) :443-448
[3]   How to improve firm performance using big data analytics capability and business strategy alignment? [J].
Akter, Shahriar ;
Wamba, Samuel Fosso ;
Gunasekaran, Angappa ;
Dubey, Rameshwar ;
Childe, Stephen J. .
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2016, 182 :113-131
[4]   Global business and emerging economies: Towards a new perspective on the effects of e-waste [J].
Amankwah-Amoah, Joseph .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2016, 105 :20-26
[5]  
[Anonymous], WORLDW BIG DAT BUS A
[6]  
[Anonymous], 1994, MANAGEMENT INNOVATIO
[7]  
[Anonymous], 1995, Contingency theory
[8]  
Aral S., 2006, 27 INT C INF SYST MI
[9]   A bibliometric analysis of research on Big Data analytics for business and management [J].
Ardito, Lorenzo ;
Scuotto, Veronica ;
Del Giudice, Manlio ;
Petruzzelli, Antonio Messeni .
MANAGEMENT DECISION, 2019, 57 (08) :1993-2009
[10]  
Avital Michel, 2016, P 37 INT C INF SYST