Mahei Li

Recent years have seen a growing adoption of Low Code Development Platforms (LCDPs) in organizations. The increasing affinity for technology development across all user groups, consumerization of development, and advancing digitalization are opening up a new target group for the low code movement. This change in software development allows bottom-up user innovators within a company to leverage their domain knowledge and quickly deploy much-needed digital services. However, a clear understanding of this paradigm of software development in organizations and the influence on end-user acceptance is still missing. In this paper, we present the results of an interview study conducted with 18 LCDP experts and discuss the implications of our findings, highlighting the role of LCDPs and context in bottom-up innovation as well as user-centricity. Our research contributes to the literature on LCDPs and offers valuable insights for organizations looking to leverage their workforce's innovative potential.

Due to the high drop-out rates in IT support desks, efficient onboarding of novices becomes a relevant and recurring challenge. Especially in the case of IT support, solving technical issues and service requests while the conversation with the customer is still ongoing imposes high demands on novice support agents. As artificial intelligence (AI) can already classify service requests and help find solutions, AIbased augmentation holds great potential for improving the onboarding phase and reducing time-to-performance. For this reason, we propose an AI-based conversational (co-)agent during the onboarding phase of customer support novices to reduce the time spent on service tasks and enable on-the-job training. Following action design research, we aim to develop an instantiation of an AI-based co-agent to reduce the job demand for the service center agent novices and augment problem-solving capabilities by considering cognitive load. The co-agent will be implemented with one development partner and evaluated with two different case partner organizations. In this research-in-progress project, we developed a low-fidelity prototype and derived a tentative architecture that allows for a generalized development of such conversational agents in customer service organizations.

Due to the advent of digitization, service innovation has become even more important for both business and service research alike. Current service systems engineering approaches have employed a recombinant perspective that follows innovation mechanisms to leverage existing company resources for new service innovations. Employing these innovation mechanisms is still challenging, since there is little support on how to structure and identify these mechanisms. We propose a model-based service system engineering approach to structure existing resources into one formal model, enabling the formalization of service innovation mechanisms. The formalized service innovation mechanisms allow for a graphical illustration and enable future research to apply functions to analyze how innovation impacts entire or specific parts of service systems. Furthermore, the mathematical model enables an object-oriented value-driven perspective on service systems and is basis for graphical software tools. We contribute to literature by formalizing service innovations and its mechanisms in the context of service systems and by combining concepts of service innovation and service systems engineering. We do so by a) formalizing service innovation mechanisms and b) demonstrating the application of formal service innovations along one specific software implementation case. For practice, the service system model can with simulating the effects of service innovations.