Abstract
Purpose
Big data analytics (BDA) is becoming a strategic tool to harness data to achieve business efficiencies. While business-to-customer organizations have adopted BDA, its adoption in business-to-business (B2B) has been slow, raising concerns about the lack of understanding of the need to adopt BDA. Little knowledge exists on the subject and the purpose of this study is to examine BDA adoption needs among B2B organizations.
Design/methodology/approach
A systematic literature review (SLR) following the six-step SLR guidelines of Templier and Paré (2015) involved 1,051 articles, which were content analyzed.
Findings
The authors offer two-pronged findings. First, on the basis of the SLR, the authors develop a new four-category classification scheme of needs to adopt BDA and present a consolidated review of the current knowledge base along with these categories (i.e. innovation, operational efficiency, customer satisfaction and digital transformation). Second, underpinned by the theory of organizational motivation and literature evidence, the authors develop propositions and a corresponding model of BDA adoption needs. The authors show that BDA adoption among B2B organizations is driven by the need to augment customer lifetime value, champion the change, improve managerial decision cycle-time, tap into social media benefits and align with market transformation.
Research limitations/implications
The results facilitate theory development as the study creates a new classification scheme of needs and a model of needs to adopt BDA in large B2B organizations.
Practical implications
The findings will serve as a guideline framework for managers to examine their BDA adoption needs and strategize its adoption.
Originality/value
The study develops a new four-category classification scheme for understanding B2B organizations’ needs to adopt big data analytics. The study also develops a new model of needs which will serve as a stepping stone for the development of a theory of needs of technology adoption.
Subject
Marketing,Business and International Management
Reference89 articles.
1. The effect of a SECoS in crude palm oil forecasting to improve business intelligence;Bulletin of Electrical Engineering and Informatics,2020
2. Relational selling: past, present and future;Industrial Marketing Management,2018
3. Atkins, C., Valdivieso De Uster, M., Mahdavian, M. and Yee, L. (2016), “Unlocking the power of data in sales”, available at: www.mckinsey.com/business-functions/marketing-and-sales/our-insights/unlocking-the-power-of-data-in-sales
4. Barfar, A. (2015), “Predictive analytics of organizational decisions and the role of rationality”, Doctor of Philosophy thesis, University of South Florida.
5. Applying behavioral economics in predictive analytics for B2B churn: findings from service quality data;Decision Support Systems,2017
Cited by
25 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献