Implementing data-based services: a socio-technical model

dc.accessRightsAnonymous*
dc.contributor.authorCampos, Adrian
dc.contributor.authorWäfler, Toni
dc.contributor.authorHavelka, Anina
dc.contributor.authorDeflorin, Patricia
dc.contributor.editorLeitner, Christine
dc.contributor.editorGanz, Walter
dc.contributor.editorBassano, Clara
dc.contributor.editorSatterfield, Debra
dc.date.accessioned2023-04-27T14:05:12Z
dc.date.available2022-11-17T15:34:24Z
dc.date.available2023-04-27T14:05:12Z
dc.date.issued2022-07-21
dc.description.abstractThe trend towards digitalization requires equipment manufacturers to complement their products with data-based services. Successfully implementing appropriate processes into existing organizational structures poses several challenges, which we address in this research. Based on two in-depth case studies together with Swiss industrial companies we developed a sociotechnical model that supports the successful implementation of data-based services. Following, the challenges as well as the model and its application are briefly described. Challenges: Adding data-based services to existing products requires not only new skills, such as the ability to collect valid data or to apply the latest algorithms of data analytics. If needed, such methodological abilities can be obtained on the labor market. Rather, it is a matter of identifying the right indicators on which to base data-driven services, or interpreting the results of analysis in a way that adds value for customers. Consequently, novel methodological abilities need to be combined with in-depth domain expertise. However, the required expertise is usually distributed throughout an organization. This is because, industrial companies over the years optimize their organizational processes and structures regarding the production, commissioning and maintenance of their traditional products. Therefore, experiences and knowledge are gained by different organizational units. Combining this distributed expertise to create synergies in the analysis and interpretation of data can be a major challenge. It becomes even more difficult, when part of the expertise is tacit or when its relevance is not obvious but requires the linking of seemingly unimportant information. Hence, organizational processes and structures must be found that enable co-production and hence the knowledge synergies required for the data-based services. The sociotechnical model: Based on an extended literature review and the needs of our industrial partners we developed a sociotechnical model. It provides operationalized criteria for designing and evaluating organizational processes and structures, which enable knowledge-oriented collaboration. The model is onion-like structured with different spheres. The data-based service to be implemented builds the core of the model. It is surrounded by spheres of technology, knowledge, human workforce, and organizational structures. For each sphere as well as for interfaces between spheres operationalized criteria are provided, which represent preconditions of successful knowledge-oriented collaboration. Examples for the criteria are: process transparency, perceived self-efficacy or mutual trust. Furthermore, the upper hemisphere of the model represents the service provider, whereas its lower hemisphere represents the customer. It is very important to have both included as the targeted synergies require boundary-spanning collaboration between the two. Model application: The model and its criteria are applied when designing the collaboration of everyone required for service delivery. As long as a company still mainly manufactures its traditional mechanical industrial products, it cannot radically reorganize. In this case, the model supports the design of processes and structures of collaboration across organizational boundaries based on defined roles and networking. In periodical meetings the collaboration within these networks is evaluated and optimized by means of the model's criteria. The model as well as its application will be presented in the paper.
dc.event13th International Conference on Applied Human Factors and Ergonomicsen_US
dc.event.end2022-07-28
dc.event.start2022-07-24
dc.identifier.doihttps://doi.org/10.54941/ahfe1002566
dc.identifier.isbn978-1-958651-38-4
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/34032
dc.identifier.urihttps://doi.org/10.26041/fhnw-4814
dc.language.isoenen_US
dc.publisherAHFE Open Access
dc.relation.ispartofThe Human Side of Service Engineeringen_US
dc.relation.ispartofseriesAHFE International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.spatialNew York Cityen_US
dc.subject.ddc003 - Systemeen_US
dc.subject.ddc330 - Wirtschaft
dc.titleImplementing data-based services: a socio-technical modelen_US
dc.type04B - Beitrag Konferenzschrift*
dspace.entity.typePublication
fhnw.InventedHereYesen_US
fhnw.IsStudentsWorknoen_US
fhnw.ReviewTypeLectoring (ex ante)en_US
fhnw.affiliation.hochschuleHochschule für Angewandte Psychologie FHNWde_CH
fhnw.affiliation.hochschuleHochschule für Wirtschaft FHNWde_CH
fhnw.affiliation.institutInstitut Mensch in komplexen Systemende_CH
fhnw.affiliation.institutInstitut für Personalmanagement und Organisationde_CH
fhnw.openAccessCategoryGolden_US
fhnw.pagination262-270
fhnw.publicationStatePublisheden_US
fhnw.seriesNumber62
relation.isAuthorOfPublication5ab198a7-45df-411c-a548-7622a4b4211f
relation.isAuthorOfPublication82d34b36-8c33-40cc-863b-bfbca0b7ff35
relation.isAuthorOfPublication.latestForDiscovery82d34b36-8c33-40cc-863b-bfbca0b7ff35
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