Tim Rietz

Tim Rietz
Karlsruhe Institute of Technology | KIT · Institute of Information Systems and Marketing

M.Sc. Industrial Engineering and Management

About

16
Publications
9,921
Reads
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118
Citations
Citations since 2017
16 Research Items
118 Citations
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Introduction
Dear reader, Thank you for your interest in my work. In general, my research evolves around Designing Conversational Requirements Engineering Systems for end-users. That's a very high level description. Specifically, that means that I am looking at how to learn something about what users of software want - or actually, making artificial intelligence tell me something about what users want and analyze it accordingly. Sounds interesting? Do you want to discuss research, theories, or are a student interested in the topic and looking for a thesis supervisor? Feel free to contact me anytime or learn more at my institute webpage.

Publications

Publications (16)
Conference Paper
Full-text available
Information technology is rapidly changing the way how people collaborate in enterprises. Chatbots integrated into enterprise collaboration systems can strengthen collaboration culture and help reduce work overload. In light of a growing usage of chatbots in enterprise collaboration systems, we examine the influence of anthropomorphic and functiona...
Conference Paper
Full-text available
[Context] Digital transformation impacts an ever-increasing amount of everyone's business and private life. It is imperative to incorporate user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by users that are novices at contributing requirements...
Conference Paper
User feedback on mobile app stores, product forums, and on social media can contain product development insights. There has been a lot of recent research studying this feedback and developing methods to automatically extract requirement-related information. This feedback is generally considered to be the "voice of the users"; however, only a subset...
Conference Paper
Full-text available
Coding is an important process in qualitative research. However, qualitative coding is highly time-consuming even for small datasets. To accelerate this process, qualitative coding systems increasingly utilize machine learning (ML) to automatically recommend codes. Existing literature on ML-assisted coding reveals two major issues: (1) ML model tra...
Conference Paper
Qualitative research can produce a rich understanding of a phenomenon but requires an essential and strenuous data annotation process known as coding. Coding can be repetitive and time-consuming, particularly for large datasets. Existing AI-based approaches for partially automating coding, like supervised machine learning (ML) or explicit knowledge...
Article
In user research, laddering interviews are particularly helpful in eliciting goals and underlying values. However, laddering interviews do not scale due to being time and training intensive. In this study, we propose and evaluate Ladderbot, a text-based conversational agent (CA) capable of facilitating human-like online laddering interviews. Ladder...
Article
Full-text available
Many software users give feedback online about the applications they use. This feedback often contains valuable requirements information that can be used to guide the effective maintenance and evolution of a software product. Yet, not all software users give online feedback. If the demographics of a user-base aren’t fairly represented, there is a d...
Conference Paper
Full-text available
While qualitative research can produce a rich understanding of peoples’ mind, it requires an essential and strenuous data annotation process known as coding. Coding can be repetitive and timeconsuming, particularly for large datasets. Crowdsourcing provides flexible access toworkers all around theworld, however, researchers remain doubtful about it...
Conference Paper
Full-text available
Qualitative coding, the process of assigning labels to text as part of qualitative analysis, is time-consuming and repetitive, especially for large datasets. While available QDAS sometimes allows the semi-automated extension of annotations to unseen data, recent user studies revealed critical issues. In particular , the integration of automated cod...
Conference Paper
Full-text available
Information systems development is driven by a variety of stakeholders – each with specific requirements. Modern agile development methods, like Scrum, allocate the vital step of prioritizing requirements to dedicated roles like the product owner. However, this can create a bottleneck and may lead to misunderstandings and conflicts between stakehol...
Chapter
Full-text available
Non-profit sport organizations fulfill an important role in society but face various problems regarding the attraction, retention and management of members and volunteers. Digitalization in the form of platform-based software ecosystems is a promising alternative to costly tailor-made or inflexible standard-software. Such ecosystems for sport organ...
Conference Paper
[Context] Digital transformation impacts an ever-increasing degree of everyone’s business and private life. It is imperative to incorporate a wide audience of user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by end-users that are novices at con...

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Projects

Projects (3)
Project
Qualitative research can produce a rich understanding of a phenomenon but requires an essential and strenuous data annotation process known as coding. Coding can be repetitive and time-consuming, particularly for large datasets. Existing AI-based approaches for partially automating coding, like supervised machine learning (ML) or explicit knowledge represented in code rules, require high technical literacy and lack transparency. Further, little is known about the interaction of researchers with AI-based coding assistance. We introduce Cody, an AI-based system that semi-automates coding through code rules and supervised ML. Cody supports researchers with interactively (re)defining code rules and uses ML to extend coding to unseen data.
Project
We use conversational technology to develop a requirements elicitation system to be used with end-users. We want to enable the engagement with a wide audience of individual end-users, independent of previous experience with contributing requirements to development projects. Therefore, we look into requirements self-elicitation as well as (semi-)automated interview analysis.