Towards Human Digital Twins for Improving Customer Experience

Applications of digital twin (DT) technology have gained momentum in IS research and cognate disciplines. Several studies have documented how DTs create value in contexts such as manufacturing or smart cities through virtual monitoring and decision-making. While these contexts benefit from DTs of products or production steps, this research is the first to investigate the potentials of human DTs to improve customer experience (CX) (i.e., customer twins). Drawing on a structured literature review, we derive new conceptualizations of DTs as (i) virtual mirrors that depict a physical entity and its interactions in virtual space, and (ii) virtual orchestrators which extend the virtual mirror by also simulating potential virtual interactions. These new conceptualizations, by applying them to human DTs, enable us to discuss DT’s implications to approach current CX potentials. The results of the discussion indicate that human DTs can support CX management to improve CX throughout the whole customer journey

Making Data Valuable for Smart City Service Systems - A Citizen Journey Map for Data-driven Service Design

Due to the digital transformation of smart cities (SCs), improved access to digital technologies can enable gathering and utilization of data which can serve as key resources for services to improve citizens’ quality of life. SCs face challenges making data valuable for the design of such data-driven services. Service literature lacks in providing methods to facilitate the design of these services while addressing the requirements of SCs as smart service systems. This paper presents the Data-driven Citizen Journey Map (DCJM), a method which supports designing data-driven services in collaborative Design Thinking (DT) workshops. Following design science research (DSR), we developed and evaluated our method through five iterations of workshops, interviews, and questionnaires with SC experts and students. Our evaluations indicate that the DCJM, including all promoted constructs, is useful to support data-driven service design in SCs and that it can be combined with existing methods in comprehensive service development processes