Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics
| dc.contributor.author | Herden, Tino T. | |
| dc.date.accessioned | 2020-02-13T17:14:29Z | |
| dc.date.available | 2020-02-13T17:14:29Z | |
| dc.date.issued | 2020-02-11 | |
| dc.description.abstract | Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives. | en |
| dc.description.sponsorship | DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlin | en |
| dc.identifier.eissn | 2013-0953 | |
| dc.identifier.issn | 2013-8423 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/10776 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-9671 | |
| dc.language.iso | en | en |
| dc.relation.ispartof | 10.14279/depositonce-10028 | en |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | en |
| dc.subject.ddc | 670 Industrielle Fertigung | de |
| dc.subject.other | supply chain analytics | en |
| dc.subject.other | logistics | en |
| dc.subject.other | supply chain management | en |
| dc.subject.other | data science | en |
| dc.subject.other | business analytics | en |
| dc.subject.other | grounded theory | en |
| dc.title | Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.3926/jiem.3004 | en |
| dcterms.bibliographicCitation.issue | 1 | en |
| dcterms.bibliographicCitation.journaltitle | Journal of Industrial Engineering and Management | en |
| dcterms.bibliographicCitation.originalpublishername | OmniaScience | en |
| dcterms.bibliographicCitation.originalpublisherplace | [S.l.] | en |
| dcterms.bibliographicCitation.pageend | 78 | en |
| dcterms.bibliographicCitation.pagestart | 56 | en |
| dcterms.bibliographicCitation.volume | 13 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 7 Wirtschaft und Management::Inst. Technologie und Management (ITM)::FG Logistik | de |
| tub.affiliation.faculty | Fak. 7 Wirtschaft und Management | de |
| tub.affiliation.group | FG Logistik | de |
| tub.affiliation.institute | Inst. Technologie und Management (ITM) | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |