Content area
Full text
1. Introduction
Big data are paramount in current business scenarios to achieve superior organizational performance (Dubey et al., 2023). According to a Deloitte report, big data analytics is one of the top three investment priorities among all digital technologies (Gurumurthy et al., 2020), which can improve revenue and customer value by 15% and 23%, respectively, during the early adoption stage and 45% and 41%, respectively, during the maturity stage. Thus, big data adoption (BA) is crucial for organizations to improve performance. BA refers to integrating big data analytics into organizational decision-making (Arias-Pérez et al., 2022).
BA differs from other technological innovations in its ability to collect, analyze and interpret voluminous data to generate valuable insights (Cappa et al., 2021; Chatterjee et al., 2022). Unlike radio frequency identification (RFID), electronic data interchange and other traditional information technologies, BA is characterized by its ability to handle massive volumes of data derived from a wide variety of sources and processed at high speeds. These three characteristics − volume, variety and velocity − are unique to BA and fundamentally change how supply chains operate (Cappa et al., 2021). Nevertheless, few studies also extend the distinguishing features of BA to four or even five characteristics, including veracity and value (Joubert et al., 2023; Li et al., 2023). Here, veracity refers to the quality of generated data, and value represents the disclosure of underexploited insights from big data for effectively managing the supply chain operations (Talwar et al., 2021). These unique characteristics of BA allow supply chains to analyze vast amounts of structured and unstructured data in real-time, enabling more informed decision-making and proactive responses to market changes. While technologies such as RFID focus primarily on tracking and identification, BA goes beyond providing predictive and prescriptive analytics (Talwar et al., 2021; Li et al., 2023). BA can predict future trends and optimize supply chain operations based on complex data analysis, providing a unique capacity to the supply chains. For example, by analyzing real-time data from multiple sources, BA can identify potential disruptions in the supply chain and suggest alternative strategies to mitigate risks. This level of insight and agility is not achievable with technologies like RFID, which are more focused...