1,721,094 research outputs found
Investigating end-user acceptance of autonomous electric buses to accelerate diffusion
No full textTo achieve the widespread diffusion of autonomous electric buses (AEBs) and thus harness their environmental potential, a broad acceptance of new technology-based mobility concepts must be fostered. Still, there remains little known about the factors determining their acceptance, especially in the combination of vehicles with alternative fuels and autonomous driving modes, as is the case with AEBs. In this study, we first conducted qualitative research to identify relevant factors influencing individual acceptance of autonomously driven electric buses. We then developed a comprehensive research model that was validated through a survey of 268 passengers of an AEB, operated in regular road traffic in Germany. The results indicate that a mix of individual factors, social impacts, and system characteristics determine an individual’s acceptance of AEBs. Notably, it is important that users perceive AEBs, not only as advantageous, but also trustworthy, enjoyable, and in a positive social light. Our research supplements the existing corpora by demonstrating the importance of individual acceptance and incorporating it to derive policy implications
Promoting Business Trip Ridesharing with Green Information Systems: A Blended Environment Perspective
No full textOn the Design of and Interaction with Conversational Agents: An Organizing and Assessing Review of Human-Computer Interaction Research
Get PDFConversational agents (CAs), described as software with which humans interact through natural language, have increasingly attracted interest in both academia and practice because of improved capabilities driven by advances in artificial intelligence and, specifically, natural language processing. CAs are used in contexts such as peoples private lives, education, and healthcare, as well as in organizations to innovate or automate tasks for example, in marketing, sales, or customer service. In addition to these application contexts, CAs take on different forms in terms of their embodiment, the communication mode, and their (often human-like) design. Despite their popularity, many CAs are unable to fulfill expectations, and fostering a positive user experience is challenging. To better understand how CAs can be designed to fulfill their intended purpose and how humans interact with them, a number of studies focusing on human-computer interaction have been carried out in recent years, which have contributed to our understanding of this technology. However, currently, a structured overview of this research is lacking, thus impeding the systematic identification of research gaps and knowledge on which future studies can build. To address this issue, we conducted an organizing and assessing review of 262 studies, applying a sociotechnical lens to analyze CA research regarding user interaction, context, agent design, as well as CA perceptions and outcomes. This study contributes an overview of the status quo of CA research, identifies four research streams through cluster analysis, and proposes a research agenda comprising six avenues and sixteen directions to move the field forwar
Two-sided sustainability: Simulating battery degradation in vehicle to grid applications within autonomous electric port transportation
No full texthttp://dx.doi.org/10.13039/501100006360 Bundesministerium für Wirtschaft und Energi
Adapting Carsharing Vehicle Relocation Strategies for Shared Autonomous Electric Vehicle Services
No full textGenerating Rental Data for Car Sharing Relocation Simulations on the Example of Station-Based One-Way Car Sharing
No full textDeveloping sophisticated car sharing simulations is a major task to improve car sharing as a sustainable means of transportation, because new algorithms for enhancing car sharing efficiency are formulated using them. Simulations rely on input data, which is often gathered in car sharing systems or artificially generated. Real-world data is often incomplete and biased while artificial data is mostly generated based on initial assumptions. Therefore, developing new ways for generating testing data is an important task for future research. In this paper, we propose a new approach for generating car sharing data for relocation simulations by utilizing machine learning. Based on real-world data, we could show that a combined methods approach consisting of a Gaussian Mixture Model and two classification trees can generate appropriate artificial testing data
Decoding the Motivational Black Box - The Case of Ranking, Self-Efficacy, and Subliminal Priming
No full textGame-based IS features are popular means to change behavior. While existing studies indicate a successful impact of gamified IS features, others show opposite effects. However, there are no studies that have investigated the underlying motivational processes of single gamified IS features and the additional possible support of subliminally primed IS features for the desired goal attainment. To address this gap, we examine the interaction between users and the gamified feature ‘Ranking’ on concentration enhancement, while studying the moderation effects of self-efficacy and a subliminally primed IS feature in a laboratory experiment (N=407). Therefore, our paper sheds light on the theoretically and practically relevant question: how can gamification features lead to proper interaction with the user to effectively support desired goal attainment. The results show varying reactions of either positive or negative feedback, to the ranking, depending on individual’s self-efficacy. While test persons with low self-efficacy show better performance results receiving negative feedback, participants with high selfefficacy perceptions reveal better performance rates receiving positive feedback. Furthermore, we could not observe a significant impact of the subliminally primed feature regarding mechanisms of the consciously perceived game feature ‘Ranking’ on concentration enhancement
Towards an Integrative View on Design Science Research Genres, Strategies, and Pivotal Concepts in Information Systems Research
No full textDesign science research (DSR) has been established as an essential part of information systems research. DSR can provide artificial solutions and prescriptive knowledge about how to solve problems relevant to our modern times. However, DSR has been reported to be in a state of "conceptual confusion." Thus, an ongoing and open discourse regarding how to overcome the causes of this confusion has arisen. Several causes and solutions have been proposed, ranging from conceptualizations of contributions, publication schemas, to the formulation of research strategies and genres. Prominently, the persisting confusion frequently leads editors and reviewers to assess the same study's merit substantially differently, depending on the individual editor's and reviewer's understanding of and preferences for DSR. Consequently, publishing DSR studies is challenging. Against this background, we propose DSR focus as a two-dimensional characteristic of a DSR study, comprising the two dimensions "contribution" and "research approach." Furthermore, we present a DSR focus matrix (DSRFM) as a framework and tool to describe the DSR focus of a study and identify relevant seminal work. Following this framework enables a grounded discussion with editors and reviewers, thus preventing diverting understandings and preferences that may skew the assessment of a study. We demonstrate this ability by positioning research strategies, genres, and seminal works within the matrix's quadrants
Decoding the Motivational Black Box - The Case of Ranking, Self-Efficacy, and Subliminal Priming
No full textGame-based IS features are popular means to change behavior. While existing studies indicate a successful impact of gamified IS features, others show opposite effects. However, there are no studies that have investigated the underlying motivational processes of single gamified IS features and the additional possible support of subliminally primed IS features for the desired goal attainment. To address this gap, we examine the interaction between users and the gamified feature ‘Ranking’ on concentration enhancement, while studying the moderation effects of self-efficacy and a subliminally primed IS feature in a laboratory experiment (N=407). Therefore, our paper sheds light on the theoretically and practically relevant question: how can gamification features lead to proper interaction with the user to effectively support desired goal attainment. The results show varying reactions of either positive or negative feedback, to the ranking, depending on individual’s self-efficacy. While test persons with low self-efficacy show better performance results receiving negative feedback, participants with high selfefficacy perceptions reveal better performance rates receiving positive feedback. Furthermore, we could not observe a significant impact of the subliminally primed feature regarding mechanisms of the consciously perceived game feature ‘Ranking’ on concentration enhancement
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