Mahei Li
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Dr.
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Li
First name
Mahei
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mahei.li@unisg.ch

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Publication

The Rise of Generative AI in Low Code Development Platforms – An Analysis and Future Directions

2024-01-06 , Olivia Bruhin , Ernestine Dickhaut , Edona Elshan , Mahei Li

This study investigates the relationship between Generative AI (GenAI) and Low Code Development Platforms (LCDPs), providing preliminary insights into Gen's transformative potential in this context. It is based on expert interviews and provides insight into the changing landscape of LCDPs influenced by GenAI. The findings highlight the promising benefits of GenAI in LCDPs, such as increased efficiency and decreased errors, while also emphasizing the importance of human oversight and collaboration. The findings also highlight the importance of interpersonal skills in IT, even in an increasingly automated environment. While the economic efficiency and broader implications of GenAI are still being investigated, the study lays the groundwork for future research in this rapidly evolving domain.

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Publication

Take the Wheel - Technology-driven Change in the Energy Sector

2019 , Li, Mahei , Müller, Jennifer , Ernst, Sissy-Josefina , Peters, Christoph

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Publication

Generative AI in Customer Support Services: A Framework for Augmenting the Routines of Frontline Service Employees

2024-01-06 , Philipp Reinhard , Mahei Li , Christoph Peters , Jan Marco Leimeister

Customer support service employees are facing an increased workload, while artificial intelligence (AI) appears to possess the potential to change the way we work. With the advent of modern types of generative AI, new opportunities to augment frontline service employees have emerged. However, little is known about how to integrate generative AI in customer support service organizations and purposefully change service employee work routines. Following multi-method qualitative research, we performed a literature review, conducted workshops, and interviewed IT support agents, managers, and AI experts. Thereby, we examine AI augmentation for frontline service employees in the context of IT support to carve out where and how GenAI can be leveraged to develop more efficient and higher-quality customer support. Our resulting framework reveals that especially adapting solutions and retaining knowledge is subject to a high degree of AI augmentation.

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From Service Systems Engineering to Service Innovation - A Modeling Approach

2019-06-10 , Li, Mahei , Peters, Christoph

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.

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Publication

Sense of Control for Empowerment in Hybrid Intelligence Service Systems

2021-12-15 , Eilers, Karen , Li, Mahei

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Publication

A service system modelling approach

2018 , Li, Mahei

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