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AI-based industrial full-service offerings: A model for payment structure selection considering predictive power
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2022 | Decision Support Systems | Citations: 0
Authors: Häckel, Björn; Karnebogen, Philip; Ritter, Christian

Abstract: Artificial Intelligence and servitization reshape the way that manufacturing com ... Expand

Abstract: Artificial Intelligence and servitization reshape the way that manufacturing companies derive value. Aiming to sustain competitive advantage and intensify customer loyalty, full-service providers offer the use of their products as a service to achieve continuous revenues. For this purpose, companies implement AI classification algorithms to enable high levels of service at controllable costs. However, traditional asset sellers who become service providers require previously atypical payment structures, as classic payment methods involving a onetime fee for production costs and profit margins are unsuitable. In addition, a low predictive power of the implemented classification algorithm can lead to misclassifications, which diminish the achievable level of service and the intended net present value of the resultant service. While previous works focus solely on the costs of such misclassifications, our decision model highlights implications for payment structures, service levels, and –ultimately – the net present value of such data-driven service offerings. Our research suggests that predictive power can be a major factor in selecting a suitable payment structure and the overall design of service level agreements. Therefore, we compare common payment structures for data-driven services and investigate their relationship to predictive power. We develop our model using a design science methodology and iteratively evaluate our results using a four-step approach that includes interviews with industry experts and the application of our model to a real-world use case. In summary, our research extends the existing knowledge of servitization and data-driven services in the manufacturing industry through a quantitative decision model. Collapse

Semantic filters: pseudonymity mechanical engineering

Topics: artificial intelligence decision support decision making business model value creation

Methods: machine learning classification method qualitative interview mathematical model design process
Crafting an Information Security Policy: Insights from an Ethnographic Study

2016 | International Conference on Information Systems | Citations: 1
Authors: Niemimaa, Elina

Abstract: Industry surveys report that information security management is a top concern fo ... Expand

Abstract: Industry surveys report that information security management is a top concern for managers. While information security policy is central for organizational information security, research and practitioner-oriented guidelines provide little advice on crafting such a policy. Therefore, this ethnographic study seeks to understand the crafting of an organization-wide policy in practice and the practices that lead to successful policy crafting. Drawing on ethnographic evidence from 15-month participant observation at a global corporation, the study provides insight into and suggests five implications for successful policy crafting. The implications advice managers in (1) building the foundation of their information security policy; (2) contextualizing policy's practices; (3) inviting participation of organizational members; (4) legitimizing the policy; and (5) estimating the ramifications of the policy implementation. Contributions are discussed and conclusion drawn. Collapse

Semantic filters: pseudonymity mechanical engineering

Topics: data security organizational context information security management privacy pseudonymity

Methods: ethnography company material field study participant observation

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AI-based industrial full-service offerings: A model for payment structure selection considering predictive power

Crafting an Information Security Policy: Insights from an Ethnographic Study