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  <doc>
    <id>33095</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>49</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>articlenr</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-03-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Empirical Dynamic Modelling for Exploring Complex Time Series in Management and Marketing Research</title>
    <abstract language="eng">Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). The authors introduce empirical dynamic models (EDM) to management and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying, predicting and attributing relationships between variables. We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, the authors confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels.</abstract>
    <parentTitle language="eng">SSRN eLibrary</parentTitle>
    <identifier type="doi">10.2139/ssrn.4036834</identifier>
    <identifier type="issn">1556-5068</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Florian</firstName>
      <lastName>Dost</lastName>
    </author>
    <submitter>
      <firstName>Florian</firstName>
      <lastName>Dost</lastName>
    </submitter>
    <author>
      <firstName>Erik</firstName>
      <lastName>Maier</lastName>
    </author>
    <author>
      <firstName>Tammo</firstName>
      <lastName>Bijmolt</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Empirical dynamic models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>nonlinear time series models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>omnichannel system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>customer journey</value>
    </subject>
    <collection role="institutes" number="5304">FG ABWL, insbesondere Marketing</collection>
  </doc>
  <doc>
    <id>33097</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>70</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>articlenr</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-03-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Retail Location Assessment with Urban Scaling</title>
    <abstract language="eng">Location is an impactful but irrevocable driver of retail store performance. Unless retailers rely on their gut feelings for finding high potential locations, they have to invest in extensive location research, calibrating performance models on expensive rich data (e.g., income or education of households in each prospective trading area). To prevent “the death of the high street”, also public administrators care for location potentials. This research proposes a parsimonious new model for location potentials, drawing from emerging urban scaling literature outside of marketing. We show that a measure of the local urban scale explains stores’ sales, local competitive intensity, and defining aspects of store lifecycles (managers’ location choice, sales ramp-up to a steady state after opening, store closure). We demonstrate these capabilities of the scaling approach using six datasets, including data from two retail chains (grocery and variety stores), public data, map data, and an experiment with retail managers. Our parsimonious model compares well to more complex multivariate benchmarks and remains more robust across modeling choices. We put forth a scale measure that can be cheaply obtained from map data, offering accessible applications for retail and public policy managers (e.g., “heat maps” across all potential locations in a city) and to marketing research in general (e.g., as input or control variable for geo or mobile marketing).</abstract>
    <parentTitle language="eng">SSRN eLibrary</parentTitle>
    <identifier type="doi">10.2139/ssrn.3738003</identifier>
    <identifier type="issn">1556-5068</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Lennard</firstName>
      <lastName>Schmidt</lastName>
    </author>
    <submitter>
      <firstName>Florian</firstName>
      <lastName>Dost</lastName>
    </submitter>
    <author>
      <firstName>Erik</firstName>
      <lastName>Maier</lastName>
    </author>
    <author>
      <firstName>Florian</firstName>
      <lastName>Dost</lastName>
    </author>
    <collection role="institutes" number="5304">FG ABWL, insbesondere Marketing</collection>
  </doc>
</export-example>
