DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEW AND DEVELOPMENT OF A RESEARCH AGENDA

The potential created by ongoing developments in data and analytics permeates a multitude of research areas, such as the field of Service Innovation. In this paper, we conduct a Systematic Literature Review (SLR) to investigate the integration of data and analytics as an analytical unit into the field of Service Innovation – referred to as Data-Driven Service Innovation (DDSI). Overall, the SLR reveals three main research perspectives that span the research field of Data-Driven Service Innovation: Explorative DDSI, validative DDSI, and generative DDSI. This integrated theoretical framework describes the distinct operant roles of data analytics for Service Innovation, and thus contributes to the body of knowledge in the field of DDSI by providing three unified lenses, which researchers can use to describe and locate their existing and future research endeavors in this ample field. Building up on the insights from the SLR, a research agenda is proposed in order to trigger and guide further discussions and future research surrounding DDSI. Ultimately, this paper aims at contributing to the body of knowledge of Service Innovation in general and Data-Driven Service Innovation in particular by presenting a three-dimensional research space model structuring DDSI towards its advancement

The Imprint of Design Science in Information Systems Research: An Empirical Analysis of the AIS Senior Scholars’ Basket

Design Science (DS) has become an established research paradigm in Information Systems (IS) research. However, existing research still considers it as a challenge to publish DS contributions in top IS journals, due to the rather strict guidelines that DS publications are expected to follow. Against this backdrop, we intend to emphasize the myriad of configurations and empirically describe the status-quo of DS publications in IS. Based on a Systematic Literature Review (SLR) and a conceptually derived analysis frame, we empirically analyze DS papers published in the AIS Senior Scholars’ Basket. Thereby, we intend to contribute conceptually and descriptively to the knowledge base of DS, by providing insights based on empirical evidence to aid and guide the discussion towards the advancement of the field. Overall, this shall lay the descriptive foundation for creating prescriptive knowledge on DS in IS by proposing and opening future research avenues

Deploying a Model for Assessing Cognitive Automation Use Cases: Insights from Action Research with a Leading European Manufacturing Company

Cognitive automation moves beyond rule-based automation and thus imposes novel challenges on organizations when assessing the automation potential of use cases. Thus, we present an empirically grounded and conceptually operationalized model for assessing cognitive automation use cases, which consists of four assessment dimensions: data, cognition, relationship, and transparency requirements. We apply the model in a real-world organizational context in the course of an action research project at the customer service department of ManuFact AG, and present unique empirical insights as well as the impact the application of the model had on the organization. The model shall help practitioners to make more informed decisions on selecting use cases for cognitive automation and to plan respective endeavors. For research, the identified factors affecting the suitability of a use case for cognitive automation shall deepen our understanding of cognitive automation in particular, and AI as the driving force behind cognitive automation in general

Opening the Black Box of Music Royalties with the Help of Hybrid Intelligence

The ever-increasing complexity of the music industry and the intensified resentment of artists towards collecting societies call for a transformation and a change of behavior within the music ecosystem. This article introduces a hybrid intelligence system, that ameliorates the current situation by combining the intelligence of humans and machines. This study proposes design requirements for hybrid intelligence systems in the music industry. Using a design science research approach, we identify design requirements both inductively from expert interviews and deductively from theory and present a first prototypical instantiation of a respective hybrid intelligence system. Overall, this shall enrich the body of knowledge of hybrid intelligence research by transferring its concepts into a new context. Furthermore, the identified design requirements shall serve as a foundation for researchers and practitioners to further explore and design hybrid intelligence in the music industry, and beyond

Regulating ChatGPT and other Large Generative AI Models

Large generative AI models (LGAIMs), such as ChatGPT or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest four strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. In particular, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers. In all areas, regulators and lawmakers need to act fast to keep track with the dynamics of ChatGPT et al.Comment: under revie

Folding and unfolding of a triple-branch DNA molecule with four conformational states

Single-molecule experiments provide new insights into biological processes hitherto not accessible by measurements performed on bulk systems. We report on a study of the kinetics of a triple-branch DNA molecule with four conformational states by pulling experiments with optical tweezers and theoretical modelling. Three distinct force rips associated with different transitions between the conformational states are observed in the folding and unfolding trajectories. By applying transition rate theory to a free energy model of the molecule, probability distributions for the first rupture forces of the different transitions are calculated. Good agreement of the theoretical predictions with the experimental findings is achieved. Furthermore, due to our specific design of the molecule, we found a useful method to identify permanently frayed molecules by estimating the number of opened basepairs from the measured force jump values.Comment: 17 pages, 12 figure

Structuring the Quest for Strategic Alignment of Artificial Intelligence (AI): A Taxonomy of the Organizational Business Value of AI Use Cases

The deployment of Artificial Intelligence (AI) in businesses is said to provide significant benefits to organizations. However, many businesses struggle to align single AI use cases with the overall strategic business value contribution. Thus, we investigate the strategic characteristics that determine the business value contribution of AI use cases at an organizational level. We draw on academic literature and 106 AI use cases to develop a conceptually sound and empirically grounded taxonomy of the organizational business value of AI use cases. With the developed taxonomy, decision-makers are presented with a tool to systematically align AI use cases with strategic objectives. Moreover, our findings reveal how an AI use case can generate different business value contributions in different contexts, which provides researchers with a conceptual frame for informing their empirical research endeavors at the organizational level