The Effect of Digital Nudging Techniques on Customers’ Product Choice and Attitudes towards E-Commerce Sites

Digital nudging is receiving increasing attention by academics and practitioners in recent years. In this research, our main goal is to determine the relative impact of different nudging techniques on the customers’ product choice processes and their attitudes towards e-commerce sites employing these techniques. Specifically, we are interested in the interaction effects of defaulting, customer reviews (star ratings of products) and purchase pressure cues with the centrality choice bias. Prior research has predominantly investigated nudging techniques or positioning effects in separation. We try to fill this gap and explore possible interaction effects in an eye-tracking experiment. In our study, we plan to research not only the effects of digital nudging techniques on product choice, but also in how far they shape users’ attitudes towards an e-commerce site

Identification of Decision Rules from Legislative Documents Using Machine Learning and Natural Language Processing

Decision logic extraction from natural language texts can be a tedious, labor-intensive task. This is especially true for legislative texts, since they do not always follow usual speech and writing patterns. This paper explores the possibility of using machine learning and natural language processing approaches to identify decision rules within legislative documents, and ultimately provides the possibility of building an extraction algorithm on top of the solution to extract and visualize decision logic automatically. Such a novel method for decision rules identification bears the potential to reduce human labor, minimize mistakes, and lessen context dependency. To accomplish this, we use pre-trained word vectorization in conjunction with a complex multi-layer convolutional neural network (CNN). The relevant data used in this project was generated from the Austrian income tax code and labeled by hand. A quantitative evaluation shows that our approach can be trained on as little as a single code of law and still obtain significant accuracy

Process Mining Supported Process Redesign: Matching Problems with Solutions

Process mining is a widely used technique to understand and analyze business process executions through event data. It offers insights into process problems but leaves analysts barehanded to translate these problems into concrete solutions. Research on business process management discusses both process mining and improvement patterns in isolation. In this paper, we address this research gap. More specifically, we identify six categories of process problems that can be identified with process mining and map them to applicable best practices of business processes. We analyze the relevance of our approach using a thematic analysis of reports that were handed in to the Business Process Intelligence Challenges over recent years, and observe the dire need for better guidance to translate process problems identified by process mining into suitable process designs. Conceptually, we position process mining into the problem and solution space of process redesign and thereby offer a language to describe potentials and limitations of the technique

Impact of Influencer Type and Advertisement Disclosure on Perceived Trust, Credibility and Purchase Intention

Influencer marketing is a new type of marketing, in which companies pay individuals with extensive social network for endorsing their products. So far, there is a lack of studies that take into account the joint effects of multiple factors of influencer marketing on consumer behavior, and none of the related studies considers an experimental setting. In this research-in-progress paper, we aim to close this gap. More specifically, we focus on investigating the impact of influencer type and advertisement disclosure on perceived credibility and trustworthiness of the influencer, and consumers’ purchase intention. We develop hypotheses on their individual and collective impact on the task performance, based on previous research, dual-process theory, and affect heuristic. We plan to carry out a laboratory experiment to test our hypotheses

Effective Presentation of Ontological Overlap and Conceptual Model Comprehension

Conceptual models serve as an important means to help IS professionals grasp a holistic picture of an information system. However, in most cases, the use of only one model is not sufficient to provide this information. For that reason, pieces of information are represented using multiple models and grammars, which partially overlap. The literature is still silent on how this overlap can be effectively presented to the users. Our research aims to close this gap. We plan to conduct an eye-tracking experiment to investigate our hypotheses. The results will contribute to the theory of combined ontological completeness and overlap and provide practical advice on how the connection between multiple models should be presented

The Art of Inspiring Creativity: Exploring the Unique Impact of AI-generated Images

This paper examines whether AI-generated art can serve as a source of inspiration to enhance individual creative performance. Specifically, the study investigates whether AI-generated art has associative potential that can stimulate idea generation and enhance individual creativity in terms of originality and the number of ideas generated by humans. To address this research problem, we focus on DALL-E-2, a generative AI system that can create images from textual descriptions. We first provide an overview of situational creativity support systems and then present the design of an online experiment in which 298 participants used (artificially generated vs. traditional) art or none to ideate. The data shows that art in general, but AI-generated especially, has the potential to enhance creative performance by stimulating idea generation. We discuss the implications of using AI-generated art as a creative support tool