Competitive response to unbundled services: An empirical look at Spirit Airlines

Author(s):  
Lei He ◽  
Myongjin Kim ◽  
Qihong Liu
Keyword(s):  
Author(s):  
Corey Tazzara

Chapter 6 offers a quantitative examination of the commercial development of Livorno, showing how it plugged local and regional exchange networks into the currents of global commerce. Livorno was at the epicenter of the reorganization of maritime trade in the Tyrrhenian and throughout the Mediterranean. Despite dense connections between north-central Italy and the free port, however, international commerce did not substantially affect productive relations in the hinterland. North-central Italy remained an autonomous region; rather than a colonial outpost subservient to northern capitalism, Livorno was a large marketplace connecting otherwise distinct economies. The Tuscan city’s success in organizing trade eventually provoked a competitive response by neighboring ports.


2020 ◽  
Author(s):  
Olivia L. Cope ◽  
Richard L. Lindroth ◽  
Andrew Helm ◽  
Ken Keefover‐Ring ◽  
Eric L. Kruger

2010 ◽  
pp. 1020-1029
Author(s):  
Maria Manuela Cunha

Most definitions of virtual enterprise (VE) incorporate the idea of extended and collaborative outsourcing to suppliers and subcontractors in order to achieve a competitive response to market demands (Webster, Sugden, & Tayles, 2004). As suggested by several authors (Browne & Zhang, 1999; Byrne, 1993; Camarinha- Matos & Afsarmanesh, 1999; Cunha, Putnik, & Ávila, 2000; Davidow & Malone, 1992; Preiss, Goldman, & Nagel, 1996), a VE consists of a network of independent enterprises (resources providers) with reconfiguration capability in useful time, permanently aligned with the market requirements, created to take profit from a specific market opportunity, and where each participant contributes with her best practices and core competencies to the success and competitiveness of the structure as a whole. Even during the operation phase of the VE, the configuration can change to assure business alignment with the market demands, traduced by the identification of reconfiguration opportunities and constant readjustment or reconfiguration of the VE network to meet unexpected situations or to keep permanent competitiveness and maximum performance (Cunha & Putnik, 2002, 2005a, 2005b)


2012 ◽  
pp. 126-149 ◽  
Author(s):  
Hubert Gatignon ◽  
David Soberman

2020 ◽  
Vol 84 (6) ◽  
pp. 3-21 ◽  
Author(s):  
Joon Ho Lim ◽  
Rishika Rishika ◽  
Ramkumar Janakiraman ◽  
P.K. Kannan

“Facts Up Front” nutrition labels are a front-of-package (FOP) nutrition labeling system that presents key nutrient information on the front of packaged food and beverage products in an easy-to-read format. The authors conduct a large-scale empirical study to examine the effect of adoption of FOP labeling on products’ nutritional quality. The authors assemble a unique data set on packaged food products in the United States across 44 categories over 16 years. By using a difference-in-differences estimator, the authors find that FOP adoption in a product category leads to an improvement in the nutritional quality of other products in that category. This competitive response is stronger for premium brands and brands with narrower product line breadth as well as for categories involving unhealthy products and those that are more competitive in nature. The authors offer evidence regarding the role of nutrition information salience as the underlying mechanism; they also perform supplementary analyses to rule out potential self-selection issues and conduct a battery of robustness checks and falsification tests. The authors discuss the implications of the findings for public policy makers, consumers, manufacturers, and food retailers.


2020 ◽  
Vol 12 (18) ◽  
pp. 2977
Author(s):  
Bishwa Sapkota ◽  
Vijay Singh ◽  
Clark Neely ◽  
Nithya Rajan ◽  
Muthukumar Bagavathiannan

Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions.


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