Advance booking and hotel price variability online: Any opportunity for business customers?

Guizzardi, Andrea; Pons, Flavio; Ranieri, Ercolino

doi:10.1016/j.ijhm.2017.05.002

Cited by 51 publications

(40 citation statements)

References 37 publications

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“…In line with previous literature, besides the dependent variable and focal explanatory variables, we also embedded in our model two control variables: seasonality and the cumulated response volume. As far as the former variable is concerned, demand fluctuates significantly based on seasonality and an increasing number of hotels today deploy revenue management systems to accommodate their pricing strategies and tactics [17,59,141]. Accordingly, controlling for seasonality allows us also to control for revenue management strategies that can change very rapidly over time through dynamic pricing.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, we control for both seasonality and response volume that are relevant time-variant variables in the hospitality sector. As far as the first control is concerned, demand fluctuates significantly based on seasonality, and an increasing number of hotels today deploy revenue management systems to accommodate their pricing strategies and tactics [17,59,141]. Accordingly, controlling for seasonality allows us to control for revenue management strategies too.…”

Section: The Moderating Effect Of the Degree Of Orh On The Relationshmentioning

confidence: 99%

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Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry

Mariani

Borghi

2020

International Journal of Electronic Commerce

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This study aims to bridge a significant research gap in the electronic word-of-mouth (eWOM) literature: measuring the effect of the degree of online review helpfulness (ORH) on firms' financial performance. As studies of the impact of ORH on firm performance in the context of service industries in general and more specifically in the hospitality sector are virtually nonexistent, this work intends to offer insights to eWOM researchers by analyzing if and to what extent ORH affects the financial performance of hospitality firms. Based on a re-visitation of the antecedents of ORH stemming from information adoption models, social influence theory and dual process theory, we analyze the moderating effects of the degree of ORH on the relationships between online review valence/volume and firms' financial performance. Based on the examination of 395,964 online reviews related to 261 higherend hotels located in London, the third most visited destination worldwide, we find that the degree of ORH positively moderates the positive effect of the reviews' valence on financial performance, while it does not moderate significantly the positive effect of the reviews' volume on financial performance. Theoretical contributions to the nascent research stream taking an outcome-oriented approach to the study of eWOM helpfulness and managerial implications are discussed.

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Section: Methodsmentioning

confidence: 99%

Section: The Moderating Effect Of the Degree Of Orh On The Relationshmentioning

confidence: 99%

Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry

Mariani

Borghi

2020

International Journal of Electronic Commerce

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“…This assumption is equivalent to assuming that relative prices remain constant over time, what complements the assumption that the cost is lower than the budget constraint. This assumption implies that companies do not perform a strategic customer segmentation; therefore, they cannot use pricing strategies (Guizzardi, Pons, & Ranieri, ; Ladany, ). Melis and Piga () suggest that the use of fixed prices might be cost‐based (menu costs) or strategic‐based (fairness concerns, demand uncertainty, or uninformed customers).…”

Section: The Modelmentioning

confidence: 99%

An agent‐based model of tourism destinations choice

Álvarez

Brida

2018

Journal of Tourism Research

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This study introduces a dynamic agent-based model to represent the evolution of the choice of destinations by tourists. The model assumes that this choice depends on their personal tastes (characterized by their preferences for goods and services) and certain characteristics of other tourists (called crowding types), on the degree of inertia of the individuals and relevant information about the characteristics of visitors to other destinations. The model shows that the individual choices and the characterization of destinations are emergent properties of the system. The simulations show the formation of stable groups of individuals sharing destination, an endogenously increasing level of specialization of destinations, as well as an increasing market concentration, and that individuals of different characteristics can share destinations.

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“…The overall objective is to generate an intelligence that is able to set prices that increase value for the customers, boosting the number of bookings. Thus, dynamic pricing has become both a strategic management tool to address competition and customers' perceptions and a short‐term tactical tool focused on marketing (Abrate & Viglia, ; Guizzardi, Pons, & Ranieri, ).…”

Section: Introductionmentioning

confidence: 99%

“…In this respect, it is worth noting that price trajectories across the advance bookings display a significant autocorrelation (i.e. they are generally well‐described by a stationary AR[1] process, see Guizzardi et al, ).…”

Section: Introductionmentioning

confidence: 99%

Does advance booking matter in hedonic pricing? A new multivariate approach

Guizzardi

Angelini

Pons

2019

Journal of Tourism Research

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We consider prices across the advance bookings as a multivariate variable, modelling the time effect by means of a structural vector autoregression. Its unit-specific fixed effect is taken as the dependent variable in a set of hedonic regressions allowing us to estimate the marginal contribution to price of the (time-invariant) attributes along the price trajectory. Results from our two-step approach indicate that although past occupancy rates, events, bank holidays, and seasonality differentiate pricing strategies across advance bookings, time-invariant services/attributes tend to increase in hedonic value as the advance booking period decreases, reducing the contribution of dynamic pricing. K E Y W O R D Sbusiness tourism, dynamic pricing, fairs, Milan, seasonality, structural vector autoregression

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Advance booking and hotel price variability online: Any opportunity for business customers?

Cited by 51 publications

References 37 publications

Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry

Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry

An agent‐based model of tourism destinations choice

Does advance booking matter in hedonic pricing? A new multivariate approach

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