Präventives Retourenmanagement im eCommerce

Möhring, Michael; Walsh, Gianfranco; Koot, C.W.; Härting, Ralf-Christian

doi:10.1007/bf03340854

Cited by 5 publications

(1 citation statement)

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“…This field is even more important because it meets both customer behaviour and the sustainability concept, as well as helping to easily understand the facets appearing in big data analysis. For instance, if an organisation wants to use online customer reviews (unstructured textual data) to predict the product returns probability (Schmidt and Möhring, 2013; Möhring et al , 2013), past customer order data from the CRM and ERP system (structured data) as well as images (unstructured image data) from offered goods should also be integrated into the analysis to enhance the quality of the prediction. Therefore, they must apply different methods like text mining for textual data, image pattern recognition for images and traditional data mining techniques like regression or correlation analysis.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Advancing beyond technicism when managing big data in companies’ decision-making

Caputo

Keller

Möhring

et al. 2023

JKM

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Purpose In recognising the key role of business intelligence and big data analytics in influencing companies’ decision-making processes, this paper aims to codify the main phases through which companies can approach, develop and manage big data analytics. Design/methodology/approach By adopting a research strategy based on case studies, this paper depicts the main phases and challenges that companies “live” through in approaching big data analytics as a way to support their decision-making processes. The analysis of case studies has been chosen as the main research method because it offers the possibility for different data sources to describe a phenomenon and subsequently to develop and test theories. Findings This paper provides a possible depiction of the main phases and challenges through which the approach(es) to big data analytics can emerge and evolve over time with reference to companies’ decision-making processes. Research limitations/implications This paper recalls the attention of researchers in defining clear patterns through which technology-based approaches should be developed. In its depiction of the main phases of the development of big data analytics in companies’ decision-making processes, this paper highlights the possible domains in which to define and renovate approaches to value. The proposed conceptual model derives from the adoption of an inductive approach. Despite its validity, it is discussed and questioned through multiple case studies. In addition, its generalisability requires further discussion and analysis in the light of alternative interpretative perspectives. Practical implications The reflections herein offer practitioners interested in company management the possibility to develop performance measurement tools that can evaluate how each phase can contribute to companies’ value creation processes. Originality/value This paper contributes to the ongoing debate about the role of digital technologies in influencing managerial and social models. This paper provides a conceptual model that is able to support both researchers and practitioners in understanding through which phases big data analytics can be approached and managed to enhance value processes.

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Section: Theoretical Backgroundmentioning

confidence: 99%