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Retail Research in the Age of Big Data: Guiding the Search for Answers.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Retail Research in the Age of Big Data: Guiding the Search for Answers./
作者:	Comber, Sam.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	350 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28827639
ISBN:	9798494461001

Retail Research in the Age of Big Data: Guiding the Search for Answers.
Comber, Sam.

Retail Research in the Age of Big Data: Guiding the Search for Answers. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 350 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2021.

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

Consumption binds the worlds of production and marketing to possession and ownership, satisfying the consumer's functional wants and needs, but also fulfilling hedonic desires related to self-gratification and recreational aspiration. Across the last fifty years, growing consumer individualisation through increased personal incomes, education and political empowerment has transformed the importance of consumption spaces, which have further systematized changes in urban growth dynamics. The preservation of attractive, thriving and cohesive retail systems embedded within the urban fabric are now increasingly coupled to urban sustainability. For this reason, building understandings of retail environments has never been more pertinent, with many amenity-led theories of urban development premised on the distribution of place-specific assets like consumption spaces.A rich history of theories that, for example, describe how urban spatial structure relates configurations of people, households and firms to clusters of attractive consumption environments have guided our understandings of these systems. Yet, despite this rich history, establishing theories that describe retail environments have often relied on coarse approximations of the phenomena under study. While theories of urban consumption behaviour are predicated at a fine spatial granularity (often describing consumeror store-level activities), their representativeness are often constrained by the misgiving of coarse underlying data. This emanated from the traditional difficulties of acquiring highly-detailed data required to support the design of research hypotheses that describe consumption spaces.Most recently, the ability to glance into the inner workings of urban systems like consumption spaces has grown exponentially through the increased digitisation of retail transactions, many of which were traditionally fulfilled offline. Vast quantities of data that reflect many aspects of human behaviour have recently emerged from three main sources: open datasets exposed by public organisations; data emergent as a by-product of companies moving their business offerings online; and data streams produced by sensor technologies like smartphones and tablets. This transformation of the data landscape accessible to urban researchers has increased the volume and diversity of available data sources, enabling the design of research questions at a degree of scale previously unthinkable. Unlike traditional sources of data like censuses or economic surveys, however, these new forms of data were not produced in view of research or policy analysis. Their usefulness emanates from an accidental nature, but a major flaw of this new data pertains to the quality, degree of completeness and unsuitability to traditional statistical techniques. Emphasising the latter point, new forms of semi-structured and unstructured data (such as street-level imagery and digitised text documents) require techniques borrowed from outside the field of retail geography. Methods that have emerged from significant innovation within the fields of computer science and data mining have been identified as ripe for potential cross-pollination to research problems in retail geography, but require non-trivial programming skills to access. Therefore, researchers equipped with coding ability to analyse non-traditional datasets with untraditional methods are uniquely placed to explore urban phenomena through a much more granular lens than what has been used previously to drive the construction of theoretical premises.In this thesis, we embrace a data sharing partnership with the Local Data Company that provides unprecedented access to characteristics describing consumption spaces.

ISBN: 9798494461001Subjects--Topical Terms:

524010
Geography.

Retail Research in the Age of Big Data: Guiding the Search for Answers.
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520 $a Consumption binds the worlds of production and marketing to possession and ownership, satisfying the consumer's functional wants and needs, but also fulfilling hedonic desires related to self-gratification and recreational aspiration. Across the last fifty years, growing consumer individualisation through increased personal incomes, education and political empowerment has transformed the importance of consumption spaces, which have further systematized changes in urban growth dynamics. The preservation of attractive, thriving and cohesive retail systems embedded within the urban fabric are now increasingly coupled to urban sustainability. For this reason, building understandings of retail environments has never been more pertinent, with many amenity-led theories of urban development premised on the distribution of place-specific assets like consumption spaces.A rich history of theories that, for example, describe how urban spatial structure relates configurations of people, households and firms to clusters of attractive consumption environments have guided our understandings of these systems. Yet, despite this rich history, establishing theories that describe retail environments have often relied on coarse approximations of the phenomena under study. While theories of urban consumption behaviour are predicated at a fine spatial granularity (often describing consumeror store-level activities), their representativeness are often constrained by the misgiving of coarse underlying data. This emanated from the traditional difficulties of acquiring highly-detailed data required to support the design of research hypotheses that describe consumption spaces.Most recently, the ability to glance into the inner workings of urban systems like consumption spaces has grown exponentially through the increased digitisation of retail transactions, many of which were traditionally fulfilled offline. Vast quantities of data that reflect many aspects of human behaviour have recently emerged from three main sources: open datasets exposed by public organisations; data emergent as a by-product of companies moving their business offerings online; and data streams produced by sensor technologies like smartphones and tablets. This transformation of the data landscape accessible to urban researchers has increased the volume and diversity of available data sources, enabling the design of research questions at a degree of scale previously unthinkable. Unlike traditional sources of data like censuses or economic surveys, however, these new forms of data were not produced in view of research or policy analysis. Their usefulness emanates from an accidental nature, but a major flaw of this new data pertains to the quality, degree of completeness and unsuitability to traditional statistical techniques. Emphasising the latter point, new forms of semi-structured and unstructured data (such as street-level imagery and digitised text documents) require techniques borrowed from outside the field of retail geography. Methods that have emerged from significant innovation within the fields of computer science and data mining have been identified as ripe for potential cross-pollination to research problems in retail geography, but require non-trivial programming skills to access. Therefore, researchers equipped with coding ability to analyse non-traditional datasets with untraditional methods are uniquely placed to explore urban phenomena through a much more granular lens than what has been used previously to drive the construction of theoretical premises.In this thesis, we embrace a data sharing partnership with the Local Data Company that provides unprecedented access to characteristics describing consumption spaces.
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