Bring me my Meal on your Wheel - An Empirical Analysis of the Impact of Food Delivery Platforms on Local Restaurant Employment

2023 | HICSS | Citations: 0

Authors: Müller, Michelle; Neumann, Jürgen

Abstract: Food delivery platforms have become an established part of the urban dining cult ... Expand

Abstract: Food delivery platforms have become an established part of the urban dining culture, but their success is reliant upon gig workers. While prior research has uncovered substantial socioeconomic consequences associated with the platform economy, little is known about how delivery platforms affect local employment. Using a quasi-experimental research design, this paper explores the impact of the spatiotemporal market entry of Grubhub, Postmates, DoorDash, and UberEats on local restaurant employment. Expanding upon prior theoretical work, our analysis suggests that the entry of delivery platforms does not affect restaurants’ demand, as the number of food preparation-related workers remain unchanged. However, as those platforms fundamentally reduce the number of dine-in service workers, we find an overall negative impact on local restaurant employment, which is only partially compensated for by an increase in gig workers (i.e., delivery drivers). Our findings inform policy makers and the restaurant industry on the macroeconomic impact of such platforms. Collapse

Topics: platform worker pandemic mobile application

Methods: quasi experiment survey difference in differences longitudinal research robustness check

The Role of Dependency Networks in Developer Participation Decisions in Open Source Software Ecosystems: An Application of Stochastic Actor-Oriented Models

2023 | HICSS | Citations: 0

Authors: Müller, M.; Rosenkranz, C.

Abstract: Open source software relies on the contributions of developers who participate ... Expand

Abstract: Open source software relies on the contributions of developers who participate voluntarily in projects. While prior research has investigated social characteristics, relations, and connections that influence a developer's participation, we argue that the technical relations and connections of projects, which emerge through dependencies between packages in software ecosystems, play a focal role in that decision as well. We empirically test these assertions by applying stochastic actor-oriented models to an affiliation network in the JavaScript software ecosystem. Our results show that while the number of dependencies of a project does not influence participation, developers are more likely to participate in projects to which their own projects have dependency relations. This study thereby contributes to the understanding of antecedents that influence developers' participation decisions by highlighting the importance of project interdependencies in software ecosystems. Collapse

Topics: digital ecosystem JavaScript network effect accounting application programming interface

Methods: simulation digital trace data computational algorithm cluster analysis statistical hypothesis test

Trust and Willingness to Share Health Related Information: A quantitative study of Danish students' use of mHealth applications

2023 | HICSS | Citations: 0

Authors: Müller, S.; Frydensbjerg, S.; Overgaard, E.

Abstract: mHealth technology has the potential to transform healthcare and realize the go ... Expand

Abstract: mHealth technology has the potential to transform healthcare and realize the goal of precision medicine through systematic data collection and use. Meanwhile, mHealth applications developed during COVID-19 have had limited effect, as people have been reluctant to adopt them due to a lack of trust and willingness to share data. The aim of this empirical study is to provide insights into young people's use, trust, and willingness to share data through mHealth apps as future users of healthcare services. A survey comprising 484 Danish students was conducted. It focuses on mHealth app use, willingness to share data, and trust. The findings show that the trustworthiness of the technology and data requesting organization is important for establishing trust in mHealth apps. These insights indicate how young people can be motivated to trust mHealth apps, which can be used to develop future apps and exploit the untapped potential of the collected data. Collapse

Topics: mobile application mobile health smartphone pandemic healthcare data

Methods: survey regression analysis method qualitative content analysis goodness of fit statistical test chi squared test

A Process Model for the Practical Adoption of Federated Machine Learning

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Müller, Tobias; Zahn, Milena; Matthes, Florian

Abstract: The wealth of digitized data forms the fundamental basis for the disruptive imp ... Expand

Abstract: The wealth of digitized data forms the fundamental basis for the disruptive impact of Machine Learning. Yet a significant amount of data is scattered and locked in data silos, leaving its full potential untouched. Federated Machine Learning is a novel Machine Learning paradigm with the ability to overcome data silos by enabling the training of Machine Learning models on decentralized, potentially siloed data. Despite its advantages, most Federated Machine Learning projects fail in the project initiation phase due to their decentralized structure and incomprehensive interrelations. The current literature lacks a comprehensible overview of the complex project structure. Through a Design Science Research approach, we provide a process model of a Federated Machine Learning life cycle including required activities, roles, resources, artifacts, and interrelations. Thereby, we aim to aid practitioners in the project initiation phase by providing transparency and facilitating comprehensibility over the entire project life cycle. Collapse

Topics: systems development privacy client-server model knowledge base

Methods: business process modeling qualitative interview group interview survey literature study

Unlocking the Potential of Collaborative AI - On the Socio-Technical Challenges of Federated Machine Learning

2023 | European Conference On Information Systems | Citations: 0

Authors: Müller, Tobias; Zahn, Milena; Matthes, Florian

Abstract: The disruptive potential of AI systems roots in the emergence of big data. Yet, ... Expand

Abstract: The disruptive potential of AI systems roots in the emergence of big data. Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects. Collapse

Topics: business model artificial intelligence participatory design innovation management business process management

Methods: qualitative interview focus group structured literature research literature study personal interview

Theories: theory of task technology fit

What Disrupts Flow in Office Work? The Impact of Frequency and Relevance of IT-Mediated Interruptions

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Nadj, Mario; Rissler, Raphael; Adam, Marc; Knierim, Michael; Li, Maximilian; Maedche, Alexander; Riedl, René

Abstract: Flow, the holistic sensation people experience when they act with total involve ... Expand

Abstract: Flow, the holistic sensation people experience when they act with total involvement, is a known driver for desired work outcomes like task performance. However, the increasing ubiquity of IT at work can disrupt employees' flow. Thus, the impact of IT-mediated interruptions on flow warrants more attention in research and practice. We conducted a NeuroIS laboratory experiment focusing on a typical office work task-an invoice matching task (i.e., matching customer payments to invoices). We manipulated interruption frequency (low, high) and content relevance (irrelevant, relevant) to study the impact of interruptions on self-reported flow, its dimensions, and high-frequency heart rate variability (HF-HRV; calculated from electrocardiography recordings) as a proxy for parasympathetic nervous system (PNS) activation. We found that content relevance moderated the relationship between interruption frequency and self-reported flow and that these results vary along flow dimensions. Content relevance also moderated the relationship between interruption frequency and PNS activation. Furthermore, selfreported flow was positively associated with both perceived and objective task performance, while PNS Collapse

Topics: task productivity cognitive absorption Python technostress information system use

Methods: electrocardiography NeuroIS multivariate analysis of variance experiment descriptive statistic

Theories: flow theory

IDENTIFICATION, ABSTRACTION AND CLASSIFICATION OF INCONSISTENCY STRUCTURES IN DECLARATIVE PROCESS MODELS

2023 | European Conference On Information Systems | Citations: 0

Authors: Nagel, Sabine; Delfmann, Patrick

Abstract: Handling inconsistencies in declarative process models (DPMs) has been of incr ... Expand

Abstract: Handling inconsistencies in declarative process models (DPMs) has been of increased interest in previous years as even a single contradiction within a constraint set makes the entire DPM unsatisfiable. To support inconsistency detection and resolution, we provide a collection of generic inconsistency structures in this work. To this aim, we (1) iteratively identify inconsistency structures, (2) generalize them by analyzing their extendibility, (3) classify them based on their characteristics and (4) provide a visual representation of each generic structure. The resulting collection of structures provides the basis for future inconsistency detection and visualization approaches. single activity as opposed to activity combinations. Therefore, it is currently not possible to automatically identify all (potential) MIS within a DPM. In addition to being able to detect inconsistencies, it is also important for humans to understand the problem at hand to be able to resolve inconsistencies in the context of interactive inconsistency resolution approaches (Nagel and Delfmann, 2022). This requires being able to distinguish between different inconsistency structures, which can be defined as a unique and generic combination of constraints that represents a minimal inconsistent subset and can be characterized and visualized based on several dimensions. Thus, our work is guided by the following research question: How can recurring patterns of inconsistencies in declarative process models be abstracted and classified? To answer this question, we (1) iteratively identify inconsistency structures, (2) generalize them by analyzing their extendibility, (3) classify them based on their characteristics, and (4) provide a visual representation of each generic structure. In Section 2 we will introduce preliminaries and related work. Section 3 will provide an overview of the applied methodology and Section 4 will introduce basic concepts for the definition and abstraction of inconsistency structures. In Section 5, we describe the iterative inconsistency structure identification process, followed by a classification and visualization of the resulting inconsistency structures in Section 6. We conclude this work with a summary, as well as a discussion of limitations and opportunities for further research. Response(a,b) ChainResponse (a,b) Precedence (a,b) ChainPrecedence (a,b) Succession (a,b) ChainSuccession (a,b) AlternateResponse (a,b) AlternatePrecedence (a,b) AlternateSuccession (a,b) b a c k w a r d c o u p l i n g f o r w a r dRespondedExistence(a,b) CoExistence(a,b) Absence (a) Collapse

Topics: digital dashboard decision support system

Methods: computational algorithm literature study business process modeling design science

Revealing the Dark and Bright Sides of ChatGPT: An Exploratory Study on User Perceptions

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Nah, Fiona; Cai, Jingyuan; Zheng, Ruilin; Chen, Langtao

Abstract: A state-of-the-art AI conversational agent, ChatGPT, has attracted overwhelming ... Expand

Abstract: A state-of-the-art AI conversational agent, ChatGPT, has attracted overwhelming attention and interest. ChatGPT draws on existing knowledge repositories and demonstrates excellent comprehension and content-generation capabilities. The capabilities of conversational agents have advanced significantly due to advancements in natural language interaction and deep learning technologies such as the transformer architecture (Vaswani et al., 2017) and generative adversarial networks (Xu et al., 2017). To better understand the potential and limitations of human-conversational agent interaction, we conducted an exploratory qualitative content analysis of user posts available on the social media platform, Reddit. We downloaded the posts in the first 10 weeks of the release of ChatGPT (i.e., from December 1, 2022 to February 8, 2023) under the subreddit r/ChatGPT, which resulted in 7871 posts. The software package LIWC (Linguistic Inquiry and Word Count) was used to identify the top 300 posts with the highest scores in positive emotions and negative emotions, respectively. Hence, we coded 600 posts in total. Manifestations of the dark side of ChatGPT include algorithm and product limitations, the possibility of singularity, access and content restrictions, violations of content policy, addictive tendencies, inappropriate use for cheating and plagiarism as well as ethical concerns (Siau & Wang, 2020) such as risks of job displacement and threats to information privacy and security. The bright side of ChatGPT includes assistance with tasks, exploratory use, and value creation. Although ChatGPT can offer unique values, there are concerns relating to its dark side. Given the current state of advancement in AI, we need to study how to embrace generative AI in society and specific industry segments. This research highlights the importance of understanding user perceptions of AI conversational agents that can be used to guide the design, implementation, and regulation of generative AI products. Collapse

Topics: artificial intelligence generative AI Reddit social media conversational agent

Methods: chatGPT chatbot qualitative content analysis

Bayesian Social Subgraph Generative Models: Social Network Twins using Belief Networks and Ego Behavior Models

2023 | HICSS | Citations: 0

Authors: Naidu, N.; El-Gayar, O.

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is t ... Expand

Abstract: A key assumption of a Subgraph Generative Model (SUGM) for sparse networks is that a subgraph is independent of lower order subgraphs in a sparse network. This is not entirely true especially for nonsparse networks. Additionally, the generated networks lack the typical properties of a social network because of an assumption of random growth for nodes and edges. Finally, there is no concept for explicit ego choice or bias when connecting to dyadic or triadic relationships. We develop a novel graph generative model referred to as the Bayesian Social Subgraph Generative Model (BASSUGM). We ground the BASSUGM in a proposed sociological model and leverage Bayesian tools like belief networks. We introduce novel concepts like the networks' macro theme when combines with an ego's individuality realizes the ego's intent. We also demonstrate how the social network twin generated with BASSUGM outperforms SUGM for non-sparse, small, social, networks. Collapse

Topics: social network logistics management telephone

Methods: cluster analysis Bayesian network experiment hierarchical linear modeling computational algorithm

Theories: probability theory graph theory

Information System Articulation Development - Managing Veracity Attributes and Quantifying Relationship with Readability of Textual Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Naik, Neelam; Nimmagadda, Shastri; Purohit, Seema; Reiners, Torsten; Mani, Dr Neel

Abstract: Often the textual data are either disorganized or misinterpreted because of uns ... Expand

Abstract: Often the textual data are either disorganized or misinterpreted because of unstructured Big Data in multiple dimensions. Managing readable textual alphanumeric data and its analytics is challenging. In spatial dimensions, the facts can be ambiguous and inconsistent, posing interpretation and new knowledge discovery challenges. The information can be wordy, erratic, and noisy. The research aims to assimilate the data characteristics through Information System (IS) artefacts that are appropriate to data analytics, especially in application domains that involve big data sources. Data heterogeneity and multidimensionality can make and preclude IS-guided veracity models in the data integration process, including customer analytics services. The veracity of big data thus can impact visualization and value, including knowledge enhancement in the vast amount of textual data qualitatively. The manner the veracity features construed in each schematic, semantic and syntactic attribute dimension in several IS artefacts and relevant documents can enhance the readability of textual data robustly. 2Modelling Veracity Attributes of Textual Data concepts and contexts (Lacasta et al. 2010). We have proposed a holistic methodology to build relationships between veracity attributes and instances and ease the readability of textual data. We can ontologically structure textual facts to explore connections between wordy readable, erratic, and noisy data. For example, ontologically structured alphanumeric data can logically and physically be integrated into unified textual metadata in a repository system. The metadata can be cost-effective for explicitly exploring new knowledge in a textual form (Vasarhelyi et al. 2015).The rest of the research paper is structured in various sections. The research objectives are highlighted, along with the motivation and significance of the research. The literature review identifies the research gaps in textual data mining. Various issues and challenges of Big textual Data mining are discussed in the next section. IS guided data mapping and modelling methods are described, including how the veracity attribute instances are mappable qualitatively with textual data mining artefacts that are construed with readability attributes and their occurrences, which are the highlights of the framework development. The value of information systems in various business contexts is highlighted in Rainer and Prince (2022). Several IS artefacts are articulated in the framework development. The integrated methodological framework adapts the ontology-based alphanumeric metadata with various mining artefacts, all accommodated in a repository. Results and discussions are analyzed, along with contributions and the intended research audience. The research is concluded with future scopes and limitations. Collapse

Topics: Python big data website Twitter decision making

Methods: experiment machine learning data modeling design artifact product review