Designing a conversational requirements elicitation system for end-users

[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 contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of end-users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] The presented dissertation project utilizes design science research to develop a requirements self-elicitation system, LadderBot. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human interviewer’s capability to rephrase questions and provide assistance in the process and allows users to converse in their natural language. Furthermore, the tool will assist requirements analysts with the subsequent aggregation and analysis of collected data. [Contribution] The dissertation project makes a practical contribution in the form of a ready-to-use system for wide audience end-user RE and subsequent analysis utilizing laddering as cognitive elicitation technique. A theoretical contribution is provided by developing a design theory for the application of conversational agents for RE, including the laboratory and field evaluation of design principles

LadderBot: A requirements self-elicitation system

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 to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] We present the prototypical self-elicitation system “LadderBot”. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human (expert) interviewer’s capability to rephrase questions and provide assistance in the process. An experimental study is proposed to evaluate LadderBot against an established questionnaire-based laddering approach. [Contribution] This work-in-progress introduces the chatbot LadderBot as a tool to guide novice users during requirements self-elicitation using the laddering technique. Furthermore, we present the design of an experimental study and outline the next steps and a vision for the future

MyEcoCost - forming the nucleus of a novel environmental accounting system: vision, prototype and way forward

The innovative software system "myEcoCost" enables to gather and communicate resource and environmental data for products and services in global value chains. The system has been developed in the consortium of the European research project myEcoCost and forms a basis of a new, highly automated environmental accounting system für companies and consumers. The prototype of the system, linked to financial accounting of companies, was developed and tested in close collaboration with large and small companies. This brochure gives a brief introduction to the vision linked to myEcoCost: a network formed by collaborative environmental accounting nodes collecting environmental data at each step in a product's value chains. It shows why better life cycle data are needed and how myEcoCost addresses and solves this problem. Furthermore, it presents options for a future upscaling of highly automated environmenal accounting for prodcuts and services

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups

Co-designing MOOCs with CoDe-Graph

As MOOCs have become a standard format of online learning, it is increasingly important to design courses that ft the needs and contexts of the targeted learners. One way to do so is by actively designing with the subject experts, instructors, and other stakeholders. Within the context of designing MOOCs for disadvantaged groups in Southeast Asia, we explore the three-phase process of co-design. We present a graphical modeling language, CoDe-Graph, which can be used to facilitate the co-design process. We examine how diverse groups of experts provide feedback on design elements and create a com mon understanding using shared artifacts. Four case studies illustrate how the tool can be used by co-design teams to create and visualize custom MOOC designs

CARI Project Evaluation Report

This report is primarily intended for internal reporting purposes; however, it is made available in the interest of sharing our lessons learned and to inform future police-academia collaborations. Individual workstream evaluation reports may be provided upon request

Understanding how people choose to pursue taught postgraduate study

HEFC

How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

Diverse perceptions of smart spaces

This is the era of smart technology and of ‘smart’ as a meme, so we have run three workshops to examine the ‘smart’ meme and the exploitation of smart environments. The literature relating to smart spaces focuses primarily on technologies and their capabilities. Our three workshops demonstrated that we require a stronger user focus if we are advantageously to exploit spaces ascribed as smart: we examined the concept of smartness from a variety of perspectives, in collaboration with a broad range of contributors. We have prepared this monograph mainly to report on the third workshop, held at Bournemouth University in April 2012, but do also consider the lessons learned from all three. We conclude with a roadmap for a fourth (and final) workshop, which is intended to emphasise the overarching importance of the humans using the spac

Quantum surveillance and 'shared secrets'. A biometric step too far? CEPS Liberty and Security in Europe, July 2010

It is no longer sensible to regard biometrics as having neutral socio-economic, legal and political impacts. Newer generation biometrics are fluid and include behavioural and emotional data that can be combined with other data. Therefore, a range of issues needs to be reviewed in light of the increasing privatisation of ‘security’ that escapes effective, democratic parliamentary and regulatory control and oversight at national, international and EU levels, argues Juliet Lodge, Professor and co-Director of the Jean Monnet European Centre of Excellence at the University of Leeds, U