Download pdf
Unlocking Enterprise Innovation: The Impact of Big Data Analytics and External Network Relationships
- 1 The University of Queensland
- 2 University of Sheffield
- 3 The University of Hong Kong
* Author to whom correspondence should be addressed.
Abstract
Innovation drives modern industry and enhances enterprise competitiveness. Despite China's progress, evident by its 14th position in the Global Innovation Index 2020, a gap remains compared to Western developed nations. Enterprises are key to national innovation, especially in the rapidly evolving tech landscape. Big data presents new opportunities and challenges for innovation. This study explores how enterprises can leverage big data to improve innovation outcomes, identifying factors that influence this process. Grounded in resource-based and social network theories, the research employs questionnaires to assess big data analytic capabilities, external network relationships, and innovation performance. Using hierarchical regression analysis and reliability tests, findings reveal that foundational and management capabilities of big data significantly impact innovation, while technical capabilities do not. External network relationships partially mediate this effect. The results offer insights for managers on utilizing big data and strengthening external ties to drive innovation and competitive advantage.
Keywords
Big Data Analytics Capability, External Network Relationships, Enterprise Innovation Performance.
[1]. Raut, Rakesh, et al. "Unlocking causal relations of barriers to big data analytics in manufacturing firms." Industrial Management & Data Systems 121.9 (2021): 1939-1968.
[2]. Lozada, Nelson, Jose Arias-Pérez, and Geovanny Perdomo-Charry. "Big data analytics capability and co-innovation: An empirical study." Heliyon 5.10 (2019).
[3]. Bhatti, Sabeen Hussain, et al. "Big data analytics capabilities and MSME innovation and performance: A double mediation model of digital platform and network capabilities." Annals of Operations Research (2022): 1-24.
[4]. Côrte-Real, Nadine, Pedro Ruivo, and Tiago Oliveira. "Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?." Information & Management 57.1 (2020): 103141.
[5]. Sestino, Andrea, et al. "Internet of Things and Big Data as enablers for business digitalization strategies." Technovation 98 (2020): 102173.
[6]. Benzidia, Smail, Naouel Makaoui, and Omar Bentahar. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance." Technological forecasting and social change 165 (2021): 120557.
[7]. Hung, Jui-Long, Wu He, and Jiancheng Shen. "Big data analytics for supply chain relationship in banking." Industrial Marketing Management 86 (2020): 144-153.
[8]. Aljumah, Ahmad Ibrahim, Mohammed T. Nuseir, and Md Mahmudul Alam. "Organizational performance and capabilities to analyze big data: do the ambidexterity and business value of big data analytics matter?." Business Process Management Journal 27.4 (2021): 1088-1107.
[9]. Tsang, Y. P., et al. "Unlocking the power of big data analytics in new product development: An intelligent product design framework in the furniture industry." Journal of Manufacturing Systems 62 (2022): 777-791.
[10]. Sestino, Andrea, et al. "Internet of Things and Big Data as enablers for business digitalization strategies." Technovation 98 (2020): 102173.
[11]. ur Rehman, Muhammad Habib, et al. "The role of big data analytics in industrial Internet of Things." Future Generation Computer Systems 99 (2019): 247-259.
Cite this article
Wu,Z.;Yao,K.;Liu,T. (2024). Unlocking Enterprise Innovation: The Impact of Big Data Analytics and External Network Relationships. Theoretical and Natural Science,55,24-29.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).