scholarly journals Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

2020 ◽  
Vol 57 (2) ◽  
pp. 103169 ◽  
Author(s):  
Patrick Mikalef ◽  
John Krogstie ◽  
Ilias O. Pappas ◽  
Paul Pavlou
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Competitive Performance ◽  
Operational Capabilities ◽  
The Relationship ◽  
Analytics Capability

Big data analytics capability as a mediator in the impact of open innovation on firm performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
José Arias-Pérez ◽  
Alejandro Coronado-Medina ◽  
Geovanny Perdomo-Charry
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Financial Performance ◽  
Open Innovation ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Customer Involvement ◽  
The Relationship ◽  
Analytics Capability

PurposeBig data analytics capability (BDAC) is the ability of a firm to capture and analyze big data toward the generation of insights. The literature has mainly focused on analyzing the direct effects of BDAC on different aspects related to firm performance such as finances and innovation. However, the lack of works analyzing the intermediation role BDAC could play is noticeable, particularly in organizational situations that pose great challenges in terms of data processing. Thus, the aim of this paper is to analyze BDAC mediation in the relationship between open innovation (OI), particularly customer involvement, and firm performance (financial and non-financial).Design/methodology/approachStructural equation modeling was used to test the proposed model with survey data from a sample of 112 firms.FindingsThe results show that BDAC has a partial mediating effect on the relationship between OI and financial performance, and between OI and non-financial performance. Nevertheless, this mediation is greater in the first relationship.Originality/valueThe main contribution of the study is to offer a broader research perspective regarding the role of BDAC in the relationship between OI and firm performance. This study ultimately questions that research tradition in which this role has been reduced to that of a simple application of data analytics techniques. Instead, the results show BDAC is primarily an organizational skill that should be articulated with key processes, such as customer involvement, to maximize the financial and non-financial use of the large flow of data coming from the main OI activity of low and medium-technology companies.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

scholarly journals Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory

2020 ◽  
Vol 12 (5) ◽  
pp. 1984
Author(s):  
Michael Song ◽  
Haili Zhang ◽  
Jinjin Heng
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Information Processing Theory ◽  
Processing Theory ◽  
Study Results ◽  
Analytics Capability ◽  
Cross National

Service innovativeness is a key sustainable competitive advantage that increases sustainability of enterprise development. Literature suggests that big data and big data analytics capability (BDAC) enhance sustainable performance. Yet, no studies have examined how big data and BDAC affect service innovativeness. To fill this research gap, based on the information processing theory (IPT), we examine how fits and misfits between big data and BDAC affect service innovativeness. To increase cross-national generalizability of the study results, we collected data from 1403 new service development (NSD) projects in the United States, China and Singapore. Dummy regression method was used to test the model. The results indicate that for all three countries, high big data and high BDAC has the greatest effect on sustainable innovativeness. In China, fits are always better than misfits for creating sustainable innovativeness. In the U.S., high big data is always better for increasing sustainable innovativeness than low big data is. In contrast, in Singapore, high BDAC is always better for enhancing sustainable innovativeness than low BDAC is. This study extends the IPT and enriches cross-national research of big data and BDAC. We conclude the article with suggestions of research limitations and future research directions.

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