Edona Elshan
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Dr.
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Elshan
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Edona
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edona.elshan@unisg.ch
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+41 71 224 3304

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Publication

The Future of Work Unleashed: Generative AI's Role in Shaping Knowledge Workers' Autonomous Motivation

2024-06 , Andreas Oesinghaus , Edona Elshan , Sandvik Hakon Osland

Generative artificial intelligence (AI) such as ChatGPT is increasingly used by knowledge workers. The benefits of its use for productivity are obvious. However, prior literature has overlooked a potentially crucial benefit of using generative AI: that its use can motivate users. Building on self-determination theory, we argue that knowledge workers using generative AI experience greater perceived competence, autonomy, and relatedness as a result of its use. We thereby advance self-determination theory, uncover a potential additional benefit of using generative AI, and show fruitful avenues for future research.

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Publication

An Investigation of Why Low Code Platforms Provide Answers and New Challenges

2023-01-06 , Elshan, Edona , Dickhaut, Ernestine , Ebel, Philipp

Although the idea of low code development is not new, the market for these oftentimes platform-based development approaches is exponentially growing. Especially factors such as increasing affinity for technology development across all user groups, consumerization of development, and advancing digitalization are opening a new target group for the low code movement. The broad application possibilities of low code, as well as the benefits, are therefore getting more important for businesses. Especially for small and medium-sized enterprises (SMEs), low code constitutes a promising avenue to survive and succeed in the rapidly changing world. However, a clear understanding regarding the application of this paradigm of software development in SMEs is still missing. To provide a coherent understanding of the phenomenon low code in SMEs, we review extant literature and conduct interviews, identifying potential application domains and conceptualizing the benefits and challenges of low code from a holistic perspective.

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Assessing the Reusability of Design Principles in the Realm of Conversational Agents

2022-06 , Elshan, Edona , Engel, Christian , Ebel, Philipp Alexander , Siemon, Dominik

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ICT-enabled job crafting: How Business Unit Developers use Low-code Development Platforms to craft jobs

2022-12-14 , Li, Mahei , Peters, Christoph , Poser, Mathis , Eilers, Karen , Elshan, Edona

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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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What to Learn Next? Designing Personalized Learning Paths for Re-&Upskilling in Organizations

2023-01-06 , Eva Ritz , Leonie Freise , Edona Elshan , Roman Rietsche , Ulrich Bretschneider

The fast-paced acceleration of digitalization requires extensive re-&upskilling, impacting a significant proportion of jobs worldwide. Technology-mediated learning platforms have become instrumental in addressing these efforts, as they can analyze platform data to provide personalized learning journeys. Such personalization is expected to increase employees’ empowerment, job satisfaction, and learning outcomes. However, the challenge lies in efficiently deploying these opportunities using novel technologies, prompting questions about the design and analysis of generating personalized learning paths in organizational learning. We, therefore, analyze and classify recent research on personalized learning paths into four major concepts (learning context, data, interface, and adaptation) with ten dimensions and 34 characteristics. Six expert interviews validate the taxonomy’s use and outline three exemplary use cases, undermining its feasibility. Information Systems researchers can use our taxonomy to develop theoretical models to study the effectiveness of personalized learning paths in intra-organizational re-&upskilling.

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Requirements for AI-based Teammates: A Qualitative Inquiry in the Context of Creative Workshops

2022-01-04 , Elshan, Edona , Siemon, Dominik , de Vreede, Triparna , de Vreede, Gert-Jan , Oeste-Reiß, Sarah , Ebel, Philipp

Innovation requires organizations to tap into the knowledge and creativity of teams. However, teams are confronted with massive amounts of data and information, necessitating a broad set of knowledge, methodologies, and approaches to solve problems and innovate. Consequently, team composition has become a critical challenge. Recent advances in artificial intelligence (AI) may assist in addressing this challenge. As AI is permeating both business and private sectors, organizational teams may be augmented with AI team members. However, given the nascent nature of this phenomenon, little is known about the specific roles and requirements for such AI teammates. Within an interview study we discover common challenges in teams and identify recurring capability gaps of participants and behaviors that negatively impact the team's collective performance. Based on our findings, we propose requirements for AI-based teammates to address these gaps and support beneficial collaboration between humans and AI in teams.

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Faster, Cheaper, Better? Analyzing how Lowcode Developoment Platforms drive Bottom-Up Innovation

2023 , Edona Elshan , Dominic Germann , Ernestine Dickhaut , 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.

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Artificial Socialization? How Artificial Intelligence Applications Can Shape A New Era of Employee Onboarding Practices

2023-01-06 , Ritz, Eva , Fabio, Donisi , Elshan, Edona , Rietsche, Roman

Onboarding has always emphasized personal contact with new employees. Excellent onboarding can extend employee retention and improve loyalty. Even in a physical setting, the onboarding process is demanding for both the newcomer and the onboarding organization. Remote work, in contrast, has made this process even more challenging by forcing a rapid shift from offline to online onboarding practices. Organizations are adopting new technologies like artificial intelligence (AI) to support work processes, such as hiring processes or innovation facilitation, which could shape a new era of work practices. However, it has not been studied how AI applications can or should support onboarding. Therefore, our research conducts a literature review on current onboarding practices and uses expert interviews to evaluate AI's potential and pitfalls for each action. We contribute to the literature by presenting a holistic picture of onboarding practices and assessing potential application areas of AI in the onboarding process.

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Publication

How Conversational Agents Relieve Teams from Innovation Blockages

2022-08-09 , Elshan, Edona , Zierau, Naim , Janson, Andreas , Leimeister, Jan Marco

Innovation is one of the most important antecedents of a company's competitive advantage and long-term survival. Prior research has alluded to teamwork being a primary driver of a firm's innovation capacity. Still, many firms struggle with providing an environment that supports innovation teams in working efficiently together. Thereby, a team's failure can be attributed to several factors, such as inefficient working methods or a lack of internal communication that leads to so-called innovation blockages. There are a number of approaches that are targeted at supporting teams to overcome innovation blockages, but they mainly focus on the collaboration process and rarely consider the needs and potentials of individual team members. In this paper, we argue that Conversational Agents (CAs) can efficiently support teams in overcoming innovation blockages by enhancing collaborative work practices and, specifically, by facilitating the contribution of each individual team member. To that end, we design a CA as a team facilitator that provides nudges to reduce innovation blocking actions according to requirements we systematically derived from scientific literature and practice. Based on a rigorous evaluation, we demonstrate the potential of CAs to reduce the frequency of innovation blockages. The research implications for the development and deployment of CAs as team facilitators are explored.

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