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A knowledge management and sharing business model for dealing with disruption: The case of Aramex

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  • v. Alberti-Alhtaybat, Larissa
  • Al-Htaybat, Khaldoon
  • Hutaibat, Khalid

Abstract

The current study investigates the global logistics player Aramex and how it deals with disruptive technologies. In particular, it focuses on the unique business model that the case organisation has adopted and that allows for disruption to be managed through collaborative knowledge management. The study is qualitative and uses video, document/text and interview material for the case organisation. Data was analysed in two coding stages to derive at the categories/themes that have the most explanatory power. Aramex, a global logistics providers originating from the Middle East, is utilised to illustrate their business concept that determines and permeates their organisational culture. Disruptive technological innovations, such as Big Data Analytics, new hardware, smart apps that can connect individuals to the corporation in different contexts, feature strongly, to manage their collective knowledge of innovation and value creation. Disruption is embedded in their business model and an important part of their business operations.

Suggested Citation

  • v. Alberti-Alhtaybat, Larissa & Al-Htaybat, Khaldoon & Hutaibat, Khalid, 2019. "A knowledge management and sharing business model for dealing with disruption: The case of Aramex," Journal of Business Research, Elsevier, vol. 94(C), pages 400-407.
    • Handle: RePEc:eee:jbrese:v:94:y:2019:i:c:p:400-407
    DOI: 10.1016/j.jbusres.2017.11.037
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    References listed on IDEAS

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    1. Bouncken, Ricarda B. & Fredrich, Viktor, 2016. "Business model innovation in alliances: Successful configurations," Journal of Business Research, Elsevier, vol. 69(9), pages 3584-3590.
    2. Xu, Zhenning & Frankwick, Gary L. & Ramirez, Edward, 2016. "Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective," Journal of Business Research, Elsevier, vol. 69(5), pages 1562-1566.
    3. Battistella, Cinzia & De Toni, Alberto F. & De Zan, Giovanni & Pessot, Elena, 2017. "Cultivating business model agility through focused capabilities: A multiple case study," Journal of Business Research, Elsevier, vol. 73(C), pages 65-82.
    4. Juin-Ming Tsai & Shiu-Wan Hung, 2016. "Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach," International Journal of Production Research, Taylor & Francis Journals, vol. 54(9), pages 2757-2770, May.
    5. Ricciardi, Francesca & Zardini, Alessandro & Rossignoli, Cecilia, 2016. "Organizational dynamism and adaptive business model innovation: The triple paradox configuration," Journal of Business Research, Elsevier, vol. 69(11), pages 5487-5493.
    6. Derwik, Pernilla & Hellström, Daniel & Karlsson, Stefan, 2016. "Manager competences in logistics and supply chain practice," Journal of Business Research, Elsevier, vol. 69(11), pages 4820-4825.
    7. Mihalis Giannakis & Michalis Louis, 2016. "A Multi-Agent Based System with Big Data Processing for Enhanced Supply Chain Agility," Post-Print hal-01353916, HAL.
    8. Germain, Richard, 1996. "The role of context and structure in radical and incremental logistics innovation adoption," Journal of Business Research, Elsevier, vol. 35(2), pages 117-127, February.
    9. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    Full references (including those not matched with items on IDEAS)

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