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Towards a taxonomy of crowdsourced delivery business models

Towards a taxonomy of crowdsourced delivery business models The paper aims to develop (1) a comprehensive framework for classifying crowdshipping business models and (2) a taxonomy of currently implemented crowdshipping business models.Design/methodology/approachThe business models of 105 companies offering crowdsourced delivery services are analysed. Cluster analysis and principal component analysis are applied to develop a business model taxonomy.FindingsA detailed crowdsourced delivery business model framework with 74 features is developed. Based on it, six distinct clusters of crowdshipping business models are identified. One cluster stands out as the most appealing to customers based on social media metrics, indicating which type of crowdshipping business models is the most successful.Research limitations/implicationsDetailed investigations of each of the six clusters and of recent crowdshipping business model developments are needed in further research in order to enhance the derived taxonomy.Practical implicationsThis paper serves as a best-practices guide for both start-ups and global logistics operators for establishing or further developing their crowdsourced delivery business models.Originality/valueThis paper provides a holistic understanding of the business models applied in the crowdshipping industry and is a valuable contribution to the yet small amount of studies in the crowd logistics field. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Physical Distribution & Logistics Management Emerald Publishing

Towards a taxonomy of crowdsourced delivery business models

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0960-0035
DOI
10.1108/ijpdlm-10-2019-0326
Publisher site
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Abstract

The paper aims to develop (1) a comprehensive framework for classifying crowdshipping business models and (2) a taxonomy of currently implemented crowdshipping business models.Design/methodology/approachThe business models of 105 companies offering crowdsourced delivery services are analysed. Cluster analysis and principal component analysis are applied to develop a business model taxonomy.FindingsA detailed crowdsourced delivery business model framework with 74 features is developed. Based on it, six distinct clusters of crowdshipping business models are identified. One cluster stands out as the most appealing to customers based on social media metrics, indicating which type of crowdshipping business models is the most successful.Research limitations/implicationsDetailed investigations of each of the six clusters and of recent crowdshipping business model developments are needed in further research in order to enhance the derived taxonomy.Practical implicationsThis paper serves as a best-practices guide for both start-ups and global logistics operators for establishing or further developing their crowdsourced delivery business models.Originality/valueThis paper provides a holistic understanding of the business models applied in the crowdshipping industry and is a valuable contribution to the yet small amount of studies in the crowd logistics field.

Journal

International Journal of Physical Distribution & Logistics Management – Emerald Publishing

Published: May 27, 2021

Keywords: Crowd logistics; Crowdshipping; Crowdsourced delivery; Sharing economy; Cluster analysis; Business model

References

  • Value creation in e-business
    Amit, R.; Zott, C.
  • A taxonomy of business start-up reasons and their impact on firm growth and size
    Birley, S.; Westhead, P.
  • Crowdshipping: using the crowd to transform delivery [WWW Document]
    Botsman, R.
  • Crowd logistics: an opportunity for more sustainable urban freight transport?
    Buldeo Rai, H.; Verlinde, S.; Merckx, J.; Macharis, C.
  • Shipping outside the box. Environmental impact and stakeholder analysis of a crowd logistics platform in Belgium
    Buldeo Rai, H.; Verlinde, S.; Macharis, C.
  • Can the crowd deliver? Analysis of crowd logistics' types and stakeholder support
    Buldeo Rai, H.; Verlinde, S.; Merckx, J.; Macharis, C.
  • ‘Carried away by the crowd’: what types of logistics characterise collaborative consumption?
    Carbone, V.; Rouquet, A.; Roussat, C.
  • The rise of crowd logistics: a new way to co-create logistics value
    Carbone, V.; Rouquet, A.; Roussat, C.
  • A typology of logistics at work in collaborative consumption
    Carbone, V.; Rouquet, A.; Roussat, C.
  • The Scree test for the number of factors
    Cattell, R.B.
  • Crowdsourced delivery for last-mile distribution: an agent-based modelling and simulation approach
    Chen, P.; Chankov, S.M.
  • Using taxis to collect citywide E-commerce reverse flows: a crowdsourcing solution
    Chen, C.; Pan, S.; Wang, Z.; Zhong, R.Y.
  • Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers
    Devari, A.; Nikolaev, A.G.; He, Q.
  • K-means clustering via principal component analysis
    Ding, C.; He, X.
  • The 4I-framework of business model innovation: a structured view on process phases and challenges
    Frankenberger, K.; Weiblen, T.; Csik, M.; Gassmann, O.
  • Understanding and assessing crowd logistics business models – using everyday people for last mile delivery
    Frehe, V.; Mehmann, J.; Teuteberg, F.
  • Characteristics and typology of last-mile logistics from an innovation perspective in an urban context, city distribution and urban freight transport: multiple perspectives
    Gevaers, R.; Vanelslander, T.; Van de Voorde, E.
  • Taxonomic approaches to studying strategy: some conceptual and methodological issues
    Hambrick, D.C.
  • To be or not to be in social media: how brand loyalty is affected by social media?
    Laroche, M.; Habibi, M.R.; Richard, M.-O.
  • Crowd-shipping services for last mile delivery: analysis from American survey data
    Le, T.V.; Ukkusuri, S.V.
  • Supply, demand, operations, and management of crowd-shipping services: a review and empirical evidence
    Le, T.V.; Stathopoulos, A.; Van Woensel, T.; Ukkusuri, S.V.
  • The next big thing in the sharing economy? [WWW Document]
    McKinnon, A.
  • Crowd logistics − a literature review and maturity model
    Mehmann, J.; Frehe, V.; Teuteberg, F.
  • Crowdsourced urban package delivery
    Miller, J.; Nie, Y.; Stathopoulos, A.
  • Crowdsourcing in logistics: concepts and applications using the social crowd
    Mladenow, A.; Bauer, C.; Strauss, C.
  • Cluster Analysis
    Mooi, E.; Sarstedt, M.
  • The impact of e-commerce on final deliveries: alternative Parcel delivery services in France and Germany
    Morganti, E.; Seidel, S.; Blanquart, C.; Dablanc, L.; Lenz, B.
  • The use of social media in the supply chain: survey and extensions
    O'Leary, D.E.
  • Modeling the acceptability of crowdsourced goods deliveries: role of context and experience effects
    Punel, A.; Stathopoulos, A.
  • Studying determinants of crowd-shipping use
    Punel, A.; Ermagun, A.; Stathopoulos, A.
  • Crowdsourcing delivery: new interconnected business models to reinvent delivery
    Rougès, J.; Montreuil, B.
  • Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
    Rousseeuw, P.J.
  • Crowd-based city logistics
    Sampaio, A.; Savelsbergh, M.; Veelenturf, L.; van Woensel, T.
  • A tutorial on principal components analysis introduction
    Smith, L.I.
  • Crowdsourcing: a snapshot of published research
    Tarrell, A.; Tahmasbi, N.; Kocsis, D.; Pedersen, J.; Tripathi, A.; Xiong, J.; Oh, O.; De Vreede, G.-J.
  • Business model design: an activity system perspective
    Zott, C.; Amit, R.
  • The business model: recent developments and future research
    Zott, C.; Amit, R.; Massa, L.; Zott, C.; Massa, L.