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.

In our digital world, all companies need IT support. The IT support staff is under high pressure solving user-based issues and facing an increase of heterogeneous systems. Hybrid intelligence could solve many issues due to the combination of machine power and the individual strengths of humans. As a part of a bigger design science research project, this paper derives requirements for an IT support system based on hybrid intelligence (ISSHI). 17 requirements from the literature and 21 requirements from 24 indepth interviews with IT support managers and support-agents from three different companies have been derived. These were evaluated and refined with a second interview series of five IT support stakeholders that led to a total of 24 consolidated requirements. Finally, these requirements were used to inform a system architecture for an ISSHI. This architecture shall serve as a foundation for future research directions regarding hybrid intelligence in IT support.

Higher legal standards regarding the data protection of individuals, such as the European General Data Protection Regulation, increase the pressure on developing lawful systems. In the development of technologies, not only developers are involved. It also requires knowledge from other stakeholders, such as legal experts, that lack technical knowledge but are required to understand IT artifacts. We see two strings that can benefit from the use of design patterns: first, the well-known use of design patterns to support developers in case of recurring problems. Second, we see potential that legal experts, who have to interact with and understand complicated, novel technologies, benefit from the same patterns. We conduct a revelatory case study using design patterns to develop and assess a smart learning assistant. We scaffolded the case interpretation through the human-centered view of socio-materiality and provide contributions concerning the use of design patterns in the development and assessment of lawful technologies.

As more and more business processes are based on IT services the high availability of these processes is dependent on the IT-Support. Thus, making the IT-Support a critical success factor of companies. This paper presents how this department can be supported by providing the staff with domain-specific and high-quality solution material to help employees faster when errors occur. The solution material is based on previously solved tickets because these contain precise domain-specific solutions narrowed down to e.g., specific versions and configurations of hard-/software used in the company. To retrieve the solution material ontologies are used that contain the domain-specific vocabulary needed. Because not all previously solved tickets contain high-quality solution material that helps the staff to fix issues the designed IT-Support system separates lowfrom high-quality solution material. This paper presents (a) theory- and practicalmotivated design requirements that describe the need for automatically retrieved solution material, (b) develops two major design principles to retrieve domainspecific and high-quality solution material, and (c) evaluates the instantiations of them as a prototype with organic real-world data. The results show that previously solved tickets of a company can be pre-processed and retrieved to ITSupport staff based on their current queries.

In today’s VUCA world, that is characterized by high volatility, uncertainty, complexity and ambiguity, service provisioning is required to realize flexible and adaptable reconfiguration of service delivery systems and its stakeholders’ resources. However, services are often embedded in greater service systems and the context information of both customer and service provider form both its boundary conditions the suitable solution service. To capture the complexity and leverage the dynamic of service systems, we propose the formal service system model (SSM) method. Following general systems theory, we define boundaries for service delivery and show SSM’s applicability for ad-hoc service operations. We show its usefulness for structuring a service system for service operations, specifically scheduling, planning, and pricing of service provisioning. We contribute to service systems engineering by applying one generalizable mathematical model for both structuring and operationalizing service systems and provide insights in-to capturing the complex relationships of its components

Business models (BM) have become increasingly important and versatile abstract tools to describe businesses on both, a strategic, as well as operational level (Wirtz et al. 2015). These BMs are used to understand what a business does to remain competitive, strategically (Johnson et al. 2008). Existing research (Johnson et al. 2008; Wirtz et al. 2015) identified the importance of BMs for the success of businesses. Also, practice revealed that financially successful companies ascertain twice the level of importance than less successful companies (IBM, 2007). A BM is a conceptual and structural implementation of a business strategy and the foundation of business processes (Osterwalder and Pigneur, 2002). BMs include a strategy, input factors, processes, with an underlying financial model to ensure the profitability. In recent years BMs have become synonymous with entire companies. Literature shows a heterogeneous abstraction of BMs, sometimes referring to BMs as business unit or products (Wirtz et al. 2015). Service systems literature shows similar structures, with service systems referring to exchanges on an individual level, among business units or entire ecosystems (Chandler and Lusch 2015). We propose that a service systems perspective can systematically model businesses and capture its inherent complexities (Peters et al. 2015). The service business model (SBM) includes (1) the value proposition, which is the firm’s offering to the customer; (2) the value creation and delivery mechanisms, reflecting the value chain; (3) value capture and analysis of the BM and its constituent elements to understand how the firm generates profit. Our model, thus, captures the holistic characteristic of BM, while retaining the detailed information on how the business is constructed. Businesses are confronted with a complex, digitalized world, in which important service innovations are continuously emerging, which need to be designed and linked to the BM of a company, thus addressing the strategy to execution gap (Kaplan and Norton 2009). Typical BM tools, such as the business model canvas (Osterwalder et al. 2005), only provide a descriptive framework for structuring businesses, missing the interrelations of its BM elements. This is important for operational decision makers, who implement new service innovations. In sum, without a SBM that relates the high level strategic information to the detailed operations perspective, BMs only reflect half the picture. Hence, we develop a hypergraph theory-based underlying model for businesses to understand how the business works both from a strategic perspective, as well as from a detailed operational perspective. Our SBM makes value creation visible, relying on a systems perspective linking multi-dimensional input factors with set of activities and actors and thus capturing value proposition, value creation and delivery and value capture mechanisms (Li & Peters 2018). Attached table shows an overview of our SBM, which relies on a formally model (Li et al. 2018). It represents a base structure of businesses and can be used for operational purposes too (e.g.: scheduling, planning and cost analyses) and thus enables a high level strategic perspective to be integrated into the operational perspective, bridging the gap between strategy and execution for successful businesses.

To make a fundamental shift towards value orientation, manufacturing companies strategically move to integrate services into their portfolio. While manufacturing firms rely on production information systems as the backbone of their operations, these systems are based on product structure models (e.g., bill of materials). This poses a problem because services do not adhere to the goods-dominant perspective of product structures. To solve this divide, this paper proposes an integrative mathematical model for both production systems and service systems. This model draws upon concepts of service-dominant logic and is based on hypergraph theory. To illustrate that the production structure model includes both product structures and process structures, we further demonstrate that the production structure model can be transformed into either. Therefore, our theoretical contribution lies in introducing a structural model for production systems that is compatible with structures of a service system model. For practice, this model enables the development of production information systems that can plan and control products, services and hybrids.

The creative potential from innovative contributions of the crowd constitutes some critical challenges. The quantity of contributions and the resource demands to identify valuable ideas is high and remains challenging for firms that apply open innovation initiatives. To solve these problems, research on algorithmic approaches proved to be a valuable way by identifying metrics to distinguish between high and low-quality ideas. However, such filtering approaches always risk missing promising ideas by classifying good ideas as bad ones. In response, organizations have turned to the crowd to not just for generating ideas but also to evaluate them to filter high quality contributions. However, such crowd-based filtering approaches tend to perform poorly in practice as they make unrealistic demands on the crowd. We, therefore, conduct a design science research project to provide prescriptive knowledge on how to combine machine learning techniques with crowd evaluation to adaptively assign humans to ideas.

Currently, research on service science has emerged as its own discipline, where service systems are its basic unit of analysis. However, without a clearly defined modeling approach for service systems, analyzing a service system is challenging. We therefore propose a conceptual hypergraph-based modeling approach, which can be used to model services for both traditional goods-dominant businesses, as well as service-businesses. We define key elements of a service system while drawing upon hypergraph theory and present three modeling properties which are required to model a service systems graph (SSG). The focus of SSGs is to describe the relationships between the various resources, actors and activities, thus configuring a service system. It provides the foundation for computer graphic simulations and database applications of service business structure for future research.