2019
DOI: 10.1016/j.ejor.2019.03.029
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On the interaction between asymmetric demand signal and forecast accuracy information

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Cited by 14 publications
(3 citation statements)
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References 48 publications
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“…Big data technology enables sensing data across social and business contexts, increases analysis productivity, 45 helps companies reshape their competitive strategies, and improves innovativeness and market competence 46 . Besides, demand signal management (DSiM) solutions enable fast response to volatile market forces and improve forecast information accuracy 47 . Moreover, they allow merging signals sensed from different sources such as point of sales, operations, and public data to adjust demand data nearly in real‐time.…”
Section: Review Results and Theoretical Backgroundmentioning
confidence: 99%
See 1 more Smart Citation
“…Big data technology enables sensing data across social and business contexts, increases analysis productivity, 45 helps companies reshape their competitive strategies, and improves innovativeness and market competence 46 . Besides, demand signal management (DSiM) solutions enable fast response to volatile market forces and improve forecast information accuracy 47 . Moreover, they allow merging signals sensed from different sources such as point of sales, operations, and public data to adjust demand data nearly in real‐time.…”
Section: Review Results and Theoretical Backgroundmentioning
confidence: 99%
“…46 Besides, demand signal management (DSiM) solutions enable fast response to volatile market forces and improve forecast information accuracy. 47 Moreover, they allow merging signals sensed from different sources such as point of sales, operations, and public data to adjust demand data nearly in real-time. These solutions seem promising for identifying weak signals with high potential for opportunities and improving strategic agility.…”
Section: Getting a Better Sense Of Innovation Opportunitiesmentioning
confidence: 99%
“…They also stated that vertical information sharing benefits upstream suppliers and hurts downstream retailers. Subsequently, Jain et al [14], Qian et al [15], and Wang et al [16] built a Stackelberg game to investigate the information sharing equilibrium of downstream enterprises when upstream enterprises exhibit information leakage behavior. Anand and Goyal [17], Kong et al [3], Liu et al [8], and Chen and Ozer [9] explored upstream enterprises' equilibrium information leakage decisions by considering the two retailers from the Stackelberg Cournot competition, who have asymmetric information.…”
Section: Information Leakage In An Scmentioning
confidence: 99%