How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management

By Daniel Q. Chen, David S. Preston, Morgan Swink. Journal of Management Information Systems, 2015

756 citations recorded by the source corpus.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/5c4878878d4d8da5d53188423237863c9b32ea49

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Citations in the corpus

How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service (2018, background): “Technological advancements in the tools and methods of business analytics provide unprecedented access to vast amounts of data beyond the firms’ business transactions— big data [4, 28].”
Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation (2021, neutral): “The DCV is an appropriate lens to examine business analytics utilisation (Chen et al., 2015).”
Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2017, methodology): “Apart from that, frameworks such as Technology Organisation Environment (TOE) are also used to investigate organisations’ BDA adoption behaviour (Chen et al., 2015).”
Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations (2020, background): “ED is a key factor in DC theory (see Schilke, 2014), which suggests that the differential effects of dynamic capabilities on organisational performance (see Helfat and Winter, 2011; Chen et al., 2015) are contingent on the level of dynamism of the”
The adoption of digital technologies in supply chains: Drivers, process and impact (2021, methodology): “Several papers indicated that customer needs is an important driver of manufacturing firms adopting digital technologies (Soliman and Meade, 2005; Chen et al., 2015; Seethamraju, 2014).”
Big data analytics adoption: Determinants and performances among small to medium-sized enterprises (2020, background): “The insignificant effect of compatibility on BDA adoption contrasts with the findings of Verma and Bhattacharyya (2017) and Chen et al. (2015). The extent of adjustability of SMEs’ procedures and practices is the potential reason for the insignificant relationship between compatibility and BDA adopt”
Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience (2019, background): “2019) and technological innovation (Singh, 2011; Chen et al. 2015; Aydiner et al. 2019) are two important building blocks of competitive advantage.”
Debating big data: A literature review on realizing value from big data (2017, methodology): “Alongside big data, algorithms allow for increased automation of operational and strategic decision making (Loebbecke and Picot, 2015; Markus, 2015), which have traditionally been considered complex and therefore requiring human judgment (Chen et al., 2015; Gillon et al., 2014).”
Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022, background): “In addition, other benefits include coor - dination and knowledge sharing across the entire supply chain (Chen et al., 2015) as shown from several of the identified studies (e.g., Belhadi, Kamble, Jabbour, et al., 2021; Mandal, 2018; Rajesh, 2016; Singh & Singh, 2019).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, positive): “Only few studies utilize a theory-focused approach to explain how BDPA can help to achieve enhanced performance (see Chen et al., 2015; Gunasekaran et al., 2017; Srinivasan and Swink, 2018; Aydiner et al., 2019).”
Role of big data analytics in developing sustainable capabilities (2019, positive): “Big data analytics use unique information processing capabilities in the organizational value creation processes to enhance a ﬁ rm's competitive advantage and the support and commitment of top management play a critical role in that direction (e.g., El-Kassar and Singh, 2018; Chen et al., 2015).”
Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, result): “Whereas some studies argue for the existence of a positive relationship between big data and firm performance (Chen, Preston, & Swink, 2015; Wamba et al., 2017), other studies suggest the use of big data may not lead to improved firm performance (Ghasemaghaei, Hassanein, et al., 2017; Ghasemaghaei,…”