Consequential factors of Big Data's Analytics Capability: how firms use data in the competitive scenario

Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter

Purpose

Despite the increase in studies focused on analyzing the potential of big data analytics capability (BDAC) as a driver of product and process innovation, it is still necessary to understand how the use of insights generated by BDAC in innovation may be maximized through articulation with individuals' intellect and other processes involving the assimilation and transformation of knowledge. This study thus aims to analyze the impact of BDAC's deployment on innovation capability (IC – process and product innovation capabilities), taking absorptive capacity (AC) as mediating variable in this relationship.

Design/methodology/approach

Structural equations were used to test the research model with survey data from 112 firms located in an emerging country that is one of the digital transformation leaders in the region.

Findings

The results show that 37% of process IC variance is explained by the indirect relationship via the variable mediator (AC), while in the case of product IC this percentage is 34%.

Originality/value

These results allow us to ascertain the extent to which individuals continue to be relevant to generating product and process innovation in the digital age at a time when the literature anticipates a total loss of prominence due to the arrival of new digital technologies. However, in the case of the relationship between BDAC and ICs, the existence of a partial mediation of AC indicates that individuals continue to play a role that, albeit not being the most prominent, remains relevant in ensuring that a company maximizes the assimilation and transformation of the insights generated by BDAC in new products and processes.

The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

Purpose

In this age of digitalization, organizations are generating large data from the organizations' manufacturing processes that are valuable for capturing a competitive edge. Chinese small and medium enterprises (SMEs) can bring organizations radical innovation by investing in innovation projects (i.e. big data use; BDU) using the SMEs' scarce resources. Thus, the authors' research aims to predict Chinese SMEs' radical innovation (RI) through BDU using the theoretical lenses of the resource-based view. Moreover, the authors' study also pursues to realize the undermining mediating process of business strategy alignment (BSA) and the buffering role of information sharing in BDU–RI linkage.

Design/methodology/approach

In total, 297 Chinese SMEs entrepreneurs and managers were recruited from the Yangtze River Economic Belt, China using a survey approach. In Mplus 7.4, the authors tested the proposed hypotheses.

Findings

The sample of 297 shows that BDU is directly and indirectly (via BSA) related to RI. Further, information sharing moderates the linkage between BDU and BSA and BSA and RI. The association between BSA and RI is only significant and stronger when information sharing is high.

Practical implications

This research is beneficial for SME entrepreneurs/managers to enhance the understanding of BDU, eliminate challenges of BSA via BDU and align business strategies to bring RI to Chinese SMEs.

Originality/value

SMEs always search for new ways to enhance SMEs' productivity using scarce resources. This is the first research that advances big data and innovation literature by predicting firm RI through BDU using a resource-based view. Moreover, this study is novel because the study investigates the mediation role of BSA and the moderating role of information sharing in the linkage between BDU and firm RI in Chinese SMEs.

Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece

Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing.