Interaction effects in models of consumer spatial behaviour

Cadwallader, Martin

doi:10.1016/0143-6228(94)00003-9

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…This finding is consistent with previous research about interaction effects in models of consumer spatial behavior (Cadwallader, 1995;Fotheringham, 1983). Namely, Cadwallader (1995) noted that ''the presence or absence of such interaction effects is of great importance in the context of predicting spatial choice'' (p. 143). In this study, when independently treated, both the estimates and inaccuracy of cognitive distance showed little impact on tourists' visiting intentions.…”

Section: Discussionsupporting

confidence: 91%

The Spatial Clustering Effect of Destination Distribution on Cognitive Distance Estimates and Its Impact on Tourists' Destination Choices

Lin

Morais

2008

Journal of Travel & Tourism Marketing

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Section: Discussionsupporting

confidence: 91%

The Spatial Clustering Effect of Destination Distribution on Cognitive Distance Estimates and Its Impact on Tourists' Destination Choices

Lin

Morais

2008

Journal of Travel & Tourism Marketing

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“…This disregards the significance of cross-sectoral agglomeration, as highlighted by Jacobs’ externalities [ 20 ]. From a geographical perspective, interactions between neighboring geographic features should be considered [ 21 ], in addition to spatial distribution. Therefore, there exists a research gap in the lack of analysis on the interactions between firms, especially after distinguishing the specific sectors to which they belong.…”

Section: Introductionmentioning

confidence: 99%

Mining co-location patterns of manufacturing firms using Q statistic and additive color mixing

Song,

Li,

Wang

et al. 2024

PLoS ONE

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The agglomeration effect significantly influences firms’ site selection. Manufacturing firms often exhibit intricate spatial co-location patterns that are indicative of agglomerations due to their reliance on material input and product output across various subdivisions of manufacture. In this study, we present an analytical approach employing the Q statistic and additive color mixing visualization to assess co-location patterns of manufacturing firms. We identified frequent pairs and triplets of manufacturing divisions, mapping them to reveal distinct categories: labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters. These agglomeration categories concentrate in different regions of the city. Policy implications are proposed to promote the upgrade of labor-intensive divisions, enhance the operational efficiency of upstream/downstream industrial chains, and reinforce the spillover effects of technology-intensive divisions.

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Consumer Spatial Behavior

Dion

Cliquet

2013

Geomarketing

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Interaction effects in models of consumer spatial behaviour

Cited by 9 publications

References 20 publications

The Spatial Clustering Effect of Destination Distribution on Cognitive Distance Estimates and Its Impact on Tourists' Destination Choices

The Spatial Clustering Effect of Destination Distribution on Cognitive Distance Estimates and Its Impact on Tourists' Destination Choices

Mining co-location patterns of manufacturing firms using Q statistic and additive color mixing

Consumer Spatial Behavior

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