Response to Maltoni, Puccetti, Poli, et al.

Oesterreich, Steffi; Lee, Adrian V.; Carleton, Neil

doi:10.1093/jnci/djad022

Cited by 1 publication

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Increased Process Efficiency [13,15,18,25,26,27,32,34] 8 Increased Productivity [5,9,26] 3 Improved Resource Utilization [13,17,18,31,33] 5 Reduced Waste [29] 1 Increased Process Performance [2,15,17,18,25,27,28,31,34,37] 10 Firm & Organization Performance Improved Organizational Performance [6,13,22,26,32] 5…”

Section: Operational Performancementioning

confidence: 99%

“…Increased Competitive Advantage [5,7,11,14,21] 5 Increased Market Value [5] 1 Improved Market Perception [17] 1 Improved Inter-Organizational Collaboration [13,27,34] 3 Improved Supply Chain Performance [10] 1…”

Section: Strategic and Competitive Performancementioning

confidence: 99%

“…For instance, BDA supports "effective and personalized treatment decisions" (Grover et al, 2018, p. 406), leading to "better and individualized therapies" (Oesterreich et al, 2020, p. 12). This causes a higher satisfaction of the customers, i.e., increasing patient satisfaction in individualized therapies through data-driven insights (Adjerid et al, 2018;Grover et al, 2018;Oesterreich et al, 2020). This holds also true for improved customer satisfaction in a business context (Du et al, 2020;Grover et al, 2018).…”

Section: Service and Innovation Capabilitymentioning

confidence: 99%

See 2 more Smart Citations

Data Anonymization as Instrument to manage Knowledge Risks in Supply Chains

Zeiringer,

Fleiß,

Thalmann

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

A key objective of data-driven transformations is to utilize big data analytics (BDA) to create data-driven business value (DDBV). While prior research shows the potential of BDA to achieve DDBV, the concept remains blurry and an overview of realizable DDBVs is still lacking. To better understand the multidimensionality of the DDBV concept and to obtain insights into the bandwidth of achievable DDBVs, we conducted a systematic review of the information systems literature. Based on our results, we present a comprehensive overview of 34 DDBVs, which are classified according to their tangibility and locus of value realization. Furthermore, we describe three research deficiencies:(1) the missing operationalization of the DDBV concept, (2) the lack of explanatory mechanisms for DDBV realization, and (3) missing qualitative, in-depth insights into DDBV realization processes. Future research may build upon our systematization and help closing these research gaps, thereby increasing the success likelihood of data-driven initiatives.

show abstract