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Big Data Usage in Transient Hotel Ro...

Haynes, Natalie.

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Big Data Usage in Transient Hotel Room Pricing: Deconstructing a Black Box.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big Data Usage in Transient Hotel Room Pricing: Deconstructing a Black Box./
Author:	Haynes, Natalie.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	243 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-03, Section: A.
Contained By:	Dissertations Abstracts International81-03A.
Subject:	Recreation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27528181
ISBN:	9781085644501

Big Data Usage in Transient Hotel Room Pricing: Deconstructing a Black Box.
Haynes, Natalie.

Big Data Usage in Transient Hotel Room Pricing: Deconstructing a Black Box. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 243 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: A.

Thesis (Ph.D.)--Sheffield Hallam University (United Kingdom), 2018.

This item must not be sold to any third party vendors.

This research explains the use of big data in transient hotel room price decision-making, where transient prices are those charged to individuals rather than groups or those with specially negotiated corporate rates (Hayes & Miller, 2011; Ideas, 2018). From a practice-based viewpoint, this issue had not been fully explored in the literature and the links between big data and pricing in the hospitality literature appeared particularly blurred. It was also directly suggested that more empirical research was needed into big data "issues" (Raguseo, 2018, p.187). Crucially, it was felt that the complexities and realities of the use of big data in transient hotel room price decision-making, in particular at the individual property level, were situated within a black box that required deconstruction. To achieve this, Straussian grounded theory was utilised. The speed of development of the literature on big data and the many gaps in the literature in this area of hotel pricing made it a challenge to develop hypotheses to test. Instead, this approach allowed for the successful deconstruction of the black box by generating a substantive theoretical framework that could explain the use of big data on the transient hotel room price decision-making process. This resulted in three main contributions to knowledge. The first was that big data was not the only input into the price decision-making process. In fact, through various discussion processes the general manager and revenue specialists, where present, interacted to reinterpret the big data with small data, which was characterised by customer insights locally generated in the hotel property. This formed a new type of hybridised data. The discovery of this hybridised data also meant it was possible to reconstruct the Vs framework, commonly used to define big data. This resulted in the contribution of a new typology of pricing data within the hotel context. The second contribution was uncovered whilst observing the use of hybridised data within the price decision-making process. Here the countervailing forces of local market dynamics, characterised by the stability and predictability of demand factors, resulted in a simplified interpretation of the hybridised data. General Managers felt a pressure to make a decision that often, given the unpredictability of the market, became a decision made using trial and error, short-term, tactical approaches that did not incorporate the full range of hybridised data available to them. Observing these processes also allowed for a more general contribution by allowing fresh insights into the role of the general manager to bring up-to-date the existing literature on the role. Ultimately it discovered that the impacts of big data on price decision-making were not as significant as the hype around big data would suggest. Market forces proved more powerful than the data. This suggests not only that economics should become a greater part of revenue education but also that although the technology is capable of making constant, instantaneous price changes the process of decision-making should be slowed down. This would, in turn, make decision-making less reactive as there would be time to factor in all the hybridised data that has been generated as overall fewer decisions would be made.

ISBN: 9781085644501Subjects--Topical Terms:

535376
Recreation.
Subjects--Index Terms:

Hotels

Big Data Usage in Transient Hotel Room Pricing: Deconstructing a Black Box.
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500 $a Advisor: Egan, David.
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506 $a This item must not be sold to any third party vendors.
520 $a This research explains the use of big data in transient hotel room price decision-making, where transient prices are those charged to individuals rather than groups or those with specially negotiated corporate rates (Hayes & Miller, 2011; Ideas, 2018). From a practice-based viewpoint, this issue had not been fully explored in the literature and the links between big data and pricing in the hospitality literature appeared particularly blurred. It was also directly suggested that more empirical research was needed into big data "issues" (Raguseo, 2018, p.187). Crucially, it was felt that the complexities and realities of the use of big data in transient hotel room price decision-making, in particular at the individual property level, were situated within a black box that required deconstruction. To achieve this, Straussian grounded theory was utilised. The speed of development of the literature on big data and the many gaps in the literature in this area of hotel pricing made it a challenge to develop hypotheses to test. Instead, this approach allowed for the successful deconstruction of the black box by generating a substantive theoretical framework that could explain the use of big data on the transient hotel room price decision-making process. This resulted in three main contributions to knowledge. The first was that big data was not the only input into the price decision-making process. In fact, through various discussion processes the general manager and revenue specialists, where present, interacted to reinterpret the big data with small data, which was characterised by customer insights locally generated in the hotel property. This formed a new type of hybridised data. The discovery of this hybridised data also meant it was possible to reconstruct the Vs framework, commonly used to define big data. This resulted in the contribution of a new typology of pricing data within the hotel context. The second contribution was uncovered whilst observing the use of hybridised data within the price decision-making process. Here the countervailing forces of local market dynamics, characterised by the stability and predictability of demand factors, resulted in a simplified interpretation of the hybridised data. General Managers felt a pressure to make a decision that often, given the unpredictability of the market, became a decision made using trial and error, short-term, tactical approaches that did not incorporate the full range of hybridised data available to them. Observing these processes also allowed for a more general contribution by allowing fresh insights into the role of the general manager to bring up-to-date the existing literature on the role. Ultimately it discovered that the impacts of big data on price decision-making were not as significant as the hype around big data would suggest. Market forces proved more powerful than the data. This suggests not only that economics should become a greater part of revenue education but also that although the technology is capable of making constant, instantaneous price changes the process of decision-making should be slowed down. This would, in turn, make decision-making less reactive as there would be time to factor in all the hybridised data that has been generated as overall fewer decisions would be made.
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