Examining the needs to adopt big data analytics in B2B organizations: development of propositions and model of needs

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.

Exploring the role of relationship management and relationship quality in B2B: empirical insights and future research directions

“…For instance, digital technology (DT) has transformed B2B firms in that they can now use a wide range of digital systems to manage their interactions with customers and how they approach value creation (Richard and Devinney, 2005). The digitalization of firms has directly impacted relationships with customers from the use of social media pages and mobile marketing (Lamberton and Stephen, 2016), to the use of big data (Ram and Zhang, 2022; Wedel and Kannan, 2016), the Internet of Things (IoT) (Ng and Wakenshaw, 2017) and AI (Davenport et al , 2020). As Paschen et al (2019b) have noted, “more than mere mediators of B2B transactions, DTs shape industrial marketing processes and outcomes in significant ways and give rise to altogether new industrial marketing opportunities” (p. 1401).…”

The outcomes of B2B data-driven customer focused value creation

“…Empirical research on Big Data remains limited and tends to focus on large organisations in the business-to-consumer (B2C) domain (Fosso Wamba et al , 2015; Ram and Zhang, 2021). Despite increasing attention, B2B applications are under-represented (Corsaro and Anzivino, 2021; Ram and Zhang, 2021). There is a lack of understanding about how customer value is created from data (Hofacker et al , 2020) or that data provide a heterogenous resource in B2B networks that should be shared (Turunen et al , 2018).…”

“…Projects based on Big Data are seen as complex and challenging (Rantala et al , 2020). There is also a lack of direction as to how to link Big Data to firm strategy (Ram and Zhang, 2021; Khan et al , 2022) and a lack of suitable talent to exploit data (Qaffas et al , 2022). Of Big Data projects that are started, it is estimated that half are never completed because of lack of skills, technology or market driven requirements (Marr, 2015).…”

“…The potential importance of business analytics and technology in B2B contexts has been noted (Cortez and Johnston, 2017; Ram and Zhang, 2021). Yet, despite some attention (Holland et al , 2020; Sun et al , 2020) and the recognition of data’s role in the co-creation of value (Elia et al , 2020; Jayashankar et al , 2020), big data adoption remains under explored (Ram and Zhang, 2021) or limited to theoretical conceptualisation and application by academics, with a lack of exploration of what is done in practice by B2B organisations (Wright et al , 2019; Wang and Wang, 2020). Empirical research on Big Data remains limited and tends to focus on large organisations in the business-to-consumer (B2C) domain (Fosso Wamba et al , 2015; Ram and Zhang, 2021).…”

Customer churn prediction model: a case of the telecommunication market

“…Big data analytics is becoming a strategic tool for achieving business efficiency and implementing data-based marketing. Ram et al (2022) argue that the introduction of data analysis helps to increase customer lifetime value, respond to market fluctuations and changes, improve the process and quality of management. Nowadays, there is a significant need to implement advanced datadriven decisions based on Data Science technologies, as simple analytics will not cover the business demand (Fedirko et al, 2021;Mašić et al, 2018).…”