The digital transformation changes the way how organizations exchange data in supply chains. Data traditionally shared, is enriched by detailed data sets captured by sensors in the production itself. In addition to the promised benefits, also new risks arise. Advanced data analytic approaches make it possible to extract knowledge from such data sets and thus increase the risk that competitive knowledge unintentionally spills over. From a knowledge management perspective, little attention is paid to such knowledge risks arising from data-centric collaborations. Hence, this paper has the goal to investigate knowledge risks in data-centric collaborations by conducting a structured literature review. Thereby, we focus on digital supply chains, as data-centric collaborations play a central role within them. Based on our review, we identify four characteristics of digital supply chains relevant for managing knowledge risks. Based on these characteristics, we present causes, risks and countermeasures from an organizational, technical and legal perspective.
Digital supply chains (SCs) and data-centric collaborations have boosted data exchange between companies and, combined with recent advancements in data science, have brought a new type of knowledge risks . This paper presents an exploratory interview study investigating knowledge risks in data-centric collaborations. The aim is to gain insights into the current perception and awareness of knowledge risks and approaches to data-centric collaborations to encounter them. The authors conducted 27 interviews with 15 experts in a two-stage semistructured interview study. The first stage identified three kinds of approaches for managing knowledge risks in data-centric collaborations: (1) informal, (2) preventive and (3) proactive, which were validated in follow-up interviews. All three approaches lead to different perspectives of sharing and protecting knowledge within the digital SC and relate to the business model and the level of innovation within the organisation.
Although research on risk management (RM) in small- and medium-sized enterprises (SMEs) in general and regarding supply chains (SCs) has increased recently, our understanding is still rather fragmented and underdeveloped. This refers particularly to new types of risks such as dynamic crises or emerging risks associated with digital transformation (DT). Therefore, the purpose of this exploratory paper is to investigate RM in SMEs in SCs. More precisely, the aim is to identify patterns that can be used to group SMEs according to their risk behavior (i.e., risk attitude and perception). Drawing from a data set of 181 European SMEs, this paper empirically conceptualizes a typology of SMEs. The typology consists of four distinct types of SMEs that emerged from a cluster analysis: collective risk eliminators, collective playing it safe seekers, collective risk-ignoring knights of fortune, and collective neglecting imperturbable ones. The findings indicate that different risk behavior leads to different degrees of collaboration within the SC. Furthermore, the close interconnection between RM as found in the different clusters and the respective firm’s innovation performance can be shown. By acknowledging the heterogeneity found in SMEs, this paper breaks away from mainstream research that tends to consider SMEs as a homogeneous entity.
Unintentional disclosure of sensitive data is a critical challenge for many organizations and a serious barrier for open data platforms. Within this research in progress paper, we propose a data anonymization tool to tackle this challenge. The goal of this paper is to elicit design requirements to increase the willingness to share data and collaborate with others on an open data platform. For this purpose, a demonstrator for a data anonymization tool was evaluated within a workshop setting with representatives from companies, science, and public authorities. We found that the willingness to share data can be increased by implementing an anonymization tool and identified further requirements to improve design and to reach the participants' involvement.
The digital transformation changes the way how organizations exchange data in supply chains (SC). Data traditionally shared, is enriched by detailed data sets captured by sensors in the production itself. Advanced data analytic approaches make it possible to extract knowledge from such data sets and thus increase the risk that competitive knowledge unintentionally spills over. From a knowledge management perspective, little attention is paid to such knowledge risks arising from data-centric collaborations. Hence, this proposed PhD project aims at investigating this, by using the overall method of Design Science Research. The project focuses on digital SC, as data-centric collaborations play a central role within them. To contribute to knowledge research, a framework is being sought. The elaborated framework should allow an assessment of knowledge risks and support the selection of suitable measures and it should contribute on how to support the management of knowledge risks in digital SC.
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