Deepening Big Data Sustainable Value Creation: Insights using IPMA, NCA, and cIPMA

Randy Riggs, Carmen M. Felipe, José L. Roldán,  Juan C. Real

 

Palabras clave: Big data analytics capabilities; Circular economy practices; Supply chain management capabilities; Sustainable performance; IT-enabled capabilities perspective; Importance-performance map analysis (IPMA); Necessary condition analysis (NCA); Combined importance-performance map analysis (cIPMA)

The paper discusses the need for innovative methods to address the uncertainty and complexity of sustainability challenges. Leveraging big data through organizing for insight, increasing data storage and processing, and using improved methods can help achieve actual impact across disciplines. The expanded use of big data in infrastructure, analytics, and services has contributed to sustainable development by helping navigate this environment and measuring progress toward SDGs.

Specifically, the paper emphasizes integrating big data analytics capabilities (BDACs) with supply chain management capabilities (SCMCs) and circular economy practices (CEPs) to enhance economic, environmental, and social performance, thereby improving overall sustainable performance (SP). BDACs are highlighted as necessary for achieving SP and its antecedents, offering practical guidance to managers. The research shows that while BDACs positively impact SP, there is still room for improvement, and high levels of BDACs are required for excellence in SP.

There is an emphasis on the importance of sustainable development, which is now a key priority for governments and international institutions. Businesses play a crucial role in moving toward a sustainable future, as evidenced by initiatives like the United Nations Global Compact, which engages companies worldwide in committing to the Sustainable Development Goals (SDGs) as part of its 2030 Agenda. The SDGs challenge businesses to incorporate sustainability into their strategy to ensure viability.

The necessity for innovative approaches to manage the uncertainty and complexity associated with sustainability challenges is addressed. Significant impact across various disciplines can be achieved by leveraging big data through structured insight organization, enhancing data storage and processing capabilities, and employing advanced methods. Sustainable development has been facilitated by the increased utilization of big data in infrastructure, analytics, and services, enabling the measurement of progress towards Sustainable Development Goals (SDGs).

The impact of big data analytics (BDA) on business value, especially its sustainability aspects, is studied. While there's ample research on BDA's economic impact, its environmental and social considerations, and a holistic view of sustainable performance under a triple bottom line, need further investigation. The paper calls for empirical studies to show how organizations can effectively capture BDA's value.

The information technology-enabled capabilities perspective is introduced as a framework for understanding how IT can facilitate other organizational capabilities that improve performance. BDACs, defined as IT capabilities designed for efficient and effective management and exploitation of big data, are considered enabling capabilities. The study posits that integrating and coordinating BDACs within other organizational capabilities, such as supply chain management capabilities (SCMCs) and circular economy practices (CEPs), can positively impact economic, environmental, and social performance.

The decline in big data investment plans necessitates understanding the necessary conditions for big data to improve outcomes. The study proposes using IPMA to evaluate existing underperformance in SCMCs and CEPs, suggesting that there is potential for improving sustainable results through their influence. It seeks to answer whether there is room for improvement in the level of BDACs to positively impact SP and whether BDACs are a necessary condition for SP and its antecedents.

The research methodology involves a comprehensive sample of 210 Spanish companies. Data was collected through a detailed questionnaire, which was completed by top-level managers within these companies.

The study applies three advanced analytic techniques: Importance-Performance Map Analysis (IPMA), Necessary Condition Analysis (NCA), and Configurational Importance-Performance Map Analysis (cIPMA), which provide a robust framework for analyzing the data and drawing meaningful conclusions.

The joint use of Partial Least Squares Structural Equation Modeling (PLS-SEM) and NCA helps to identify the "should-have" and "must-have" factors for achieving the best possible outcomes, leading to a comprehensive understanding of the factors that are critical for success and those that are necessary but not sufficient on their own.

The study's results indicate that Big Data Analytics Capabilities (BDACs) are essential for explaining Sustainable Performance (SP). BDACs have a more significant impact on SP than Supply Chain Management Capabilities (SCMCs), although they lag Circular Economy Practices (CEPs) in terms of impact.

This paper provides significant theoretical and empirical insights into BDA mechanisms for creating sustainable value using novel research approaches. It emphasizes the importance of BDACs as a necessary condition for achieving SP and its antecedents, offering practical guidance to managers seeking to improve the effectiveness of their big data investments and increase their positive impact on sustainable performance.