The Inspector

Gogolʹ, Nikolaĭ Vasilʹevich

doi:10.5040/9781636700403.00000007

Cited by 1 publication

(2 citation statements)

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“…To model B 's available delay information, we consider the notion of updating periods : Customers' knowledge about the expected delay at B is updated at the end of each period, assuming that periods are long enough for the system stationarity. For example, published delays for some ERs are based on annual averages (Groeger, 2019). We index the updating periods by t , referring to the status quo as Period 0 (

t=0

).…”

Section: Model Setupmentioning

confidence: 99%

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Real‐time delay announcement under competition

Singh¹,

Delasay

Scheller‐Wolf

2023

Production and Operations Management

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Internet-based technology enables firms to disseminate real-time delay information to delay-sensitive customers. We study how such delay announcements impact service providers in a competitive environment with two service providers who compete for market share. We model the service providers' strategies based on an endogenous timing game, investigating strategies that emerge in equilibrium. We determine the service providers' market shares under the various game outcomes by analyzing continuoustime Markov chains, which capture customers' joining decisions, and by developing a novel computational technique to analyze the intractable asymmetric Join-the-Shortest Queue system, providing bounds on the market shares. We find that only the lower capacity service provider announces its real-time delay under intermediate system loads and highly imbalanced capacities. However, for most parameter settings, the mere presence of a competitor induces both providers to announce delays in equilibrium, leaving customers better off on average. We relate our findings to the single-provider delay announcement literature by discussing the impact of competition on service providers, delay announcement technology firms, and customers. K E Y W O R D S delay announcement, endogenous timing game, Markov chain, service systems 1

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t=0

).…”

Section: Model Setupmentioning

confidence: 99%

“…(2004), we do not model customers as being able to infer B 's expected delay conditioned on A 's delay announcement. Instead, we let customers rely on the available historical average delay at B ; this type of information has become more readily available in the recent past (e.g., Perez, 2015; Groeger, 2019).…”

Section: Model Setupmentioning

confidence: 99%