An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Ahangama, Supunmali

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Topics: Twitter social influence social media social identity robotic

Methods: chatGPT sentiment analysis multiple linear regression term frequency–inverse document frequency NLTK

DrugExBERT for Pharmacovigilance – A Novel Approach for Detecting Drug Experiences from User-Generated Content

2023 | International Conference on Information Systems | Citations: 0

Authors: Bohnen, Eva; Erlebach, Stefanie; Zimmermann, Steffen

Abstract: Pharmaceutical companies have to maintain drug safety through pharmacovigilance ... Expand

Abstract: Pharmaceutical companies have to maintain drug safety through pharmacovigilance systems by monitoring various sources of information about adverse drug experiences. Recently, user-generated content (UGC) has emerged as a valuable source of real-world drug experiences, posing new challenges due to its high volume and variety. We present DrugExBERT, a novel approach to extract adverse drug experiences (adverse reaction, lack of effect) and supportive drug experiences (effectiveness, intervention, indication, and off-label use) from UGC. To be able to verify the extracted drug experiences, DrugExBERT additionally provides explications in the form of UGC phrases that were critical for the extraction. In our evaluation, we demonstrate that DrugExBERT outperforms state-of-the-art pharmacovigilance approaches as well as ChatGPT on several performance measures and that DrugExBERT is data-and drug-agnostic. Thus, our novel approach can help pharmaceutical companies meet their legal obligations and ethical responsibility while ensuring patient safety and monitoring drug effectiveness. Collapse

Topics: health information system application programming interface generative AI database system

Methods: chatGPT natural language processing RoBERTa BERT machine learning

Too Good to Be True? An Empirical Study of ChatGPT Capabilities for Academic Writing and Implications for Academic Misconduct

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Busch, Peter André; Hausvik, Geir Inge

Abstract: This study evaluated the capabilities of ChatGPT, a conversational artificial i ... Expand

Abstract: This study evaluated the capabilities of ChatGPT, a conversational artificial intelligence (AI) agent, to generate quality answers to exam questions that are indistinguishable from human-generated text, normally requiring high-level cognitive abilities. This potential capacity is a major threat to academic integrity since ChatGPT may be used for academic misconduct. To determine its capabilities, we asked 15 faculty members to assess a total of ten answers (two each) to an actual IS exam question, of which students wrote five, and ChatGPT generated five. The study found that ChatGPT can generate answers of generally good quality that may pass as human-written text by examiners. The contributions of the study lay in providing empirical results in a novel research area, providing recommendations for teachers, and suggesting ways forward for research. Collapse

Topics: artificial intelligence smartphone evaluation criteria smartphone addiction mobile system

Methods: chatGPT machine learning descriptive statistic correlation analysis personal interview

Understanding Emojis for Financial Sentiment Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Siyi; Xing, Frank

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Topics: application programming interface social media website cryptocurrency Python

Methods: sentiment analysis machine learning BERT deep learning experiment

Pickled eggs: Generative AI as research assistant or co-author?

2023 | Information Systems Journal | Citations: 0

Authors: Davison, Robert M.; Laumer, Sven; Tarafdar, Monideepa; Wong, Louie H. M.

Abstract: ChatGPT' indicates approximately 416 million hits, while Google Scholar suggest ... Expand

Abstract: ChatGPT' indicates approximately 416 million hits, while Google Scholar suggests 4710 articles, a number that is likely to rise fast. Generative AI is clearly here to stay, though there are already calls to curtail its use and some countries appear to be banning it. Interestingly, we already see the roll-out of generative AI detection software with tools like GPTZero. The plagiarism detection tool, iThenticate, is likely to follow up and embed generative AI detection as well. In order to reflect on this rapidly developing phenomenon, we conducted a sentiment and topic analysis of Twitter tweets about ChatGPT in academia. We analysed 12 400 tweets over a 3-month period from late 2022 to early 2023. 3680 tweets display a positive sentiment, 2612 display a negative one and 6109 display a neutral view. This pattern already exhibits a shift from the earlier reactions which were predominantly negative. Nevertheless, the topics discussed in these tweets are similar and can be subsumed within two major streams: research and publication. | WHY DOES IT MATTER TO ISJ READERS?Our focus in this editorial is on the practicalities of applying generative AI in the academic research community. The stakeholders of relevance here are authors, reviewers, editors and readers (scholars and practitioners) of ISJ, and also its publisher. All of these stakeholders have a commitment to and interest in the undertaking and publishing of academic research. Indeed, research and publication are the twin pillars on which a scholarly journal stands. Emerging articles comment on the strengths, weaknesses, opportunities and threats of generative AI in the context of research (see for instance Dwivedi et al., 2023, for an extensive account). In academia, there is an ongoing debate on the various implications that the technology may have. | WHY IS GENERATIVE AI RELEVANT TO RESEARCH?The title of this editorial, viz.: "pickled eggs", should have given some pause for thought. As it happened the collaborators of one of the co-authors of this editorial used a generative AI assistant (Microsoft Office 365's transcription function) to convert an audio file (of an interview with a research subject) to text. In that interview, reference was made to "pico-x", the name of a software. The generative AI assistant duly rendered this as "pickled eggs", presumably the closest term in its vocabulary. Reading through the transcript, numerous errors appeared: personal, product or place names were routinely misspelled, but so too were more mundane expressions. Similar errors occurred in Chinese where an interviewee used the English letters EDI (Electronic Data Interchange) in the middle of what was otherwise a Chinese sentence. The generative AI assistant, which was set up to transcribe Chinese audio to Chinese text, correctly transcribed all the Chinese words into characters, but made an error with EDI, which it also converted Collapse

Topics: Twitter knowledge sharing electronic health digital ecosystem participatory design

Methods: experiment survey design methodology action research chatGPT

Theories: affect theory

What’s the Harm in Waiting? Examining disfluency from system response time in decision tasks with conversational agents

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Duron, Edgar; Ray, Soumya; Hsia, Po Jen

Abstract: Using conversational agents as decision-support tools brings together an intere ... Expand

Abstract: Using conversational agents as decision-support tools brings together an interesting mix of theoretical lenses. On the one hand, user experiences with agents must still conform to characteristics of fluent use as any interactive system. But at the same time, the more human-like nature of back-and-forth discourse, instead of static graphical interfaces, provides users with social cues that they must then contend with. This study demonstrates how users facing disfluency, such as goal-oriented decision-making with large choice sets, can aid themselves with filters as a tool to reach the end of their task. It also demonstrates how a slowed system response (a social cue) will have a negative effect in disfluent situations where users are trying to regain confidence. A slowed system response time discourages users from using filters leaving them making a suboptimal decision. This study shows how decision fluency and social cues have interesting interactions in their effect on users performing tasks with conversational agents. Collapse

Topics: conversational agent decision making application programming interface Facebook Messenger social media

Methods: chatbot experiment regression analysis method factorial experiment descriptive statistic

Theories: social response theory

Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

2023 | International Journal of Information Management | Citations: 0

Authors: Dwivedi, Yogesh K.; Kshetri, Nir; Hughes, Laurie; Slade, Emma Louise; Jeyaraj, Anand; Kar, Arpan Kumar; Baabdullah, Abdullah M.; Koohang, Alex; Raghavan, Vishnupriya; Ahuja, Manju; Albanna, Hanaa; Albashrawi, Mousa Ahmad; Al-Busaidi, Adil S.; Balakrishnan, Janarthanan; Barlette, Yves; Basu, Sriparna; Bose, Indranil; Brooks, Laurence; Buhalis, Dimitrios; Carter, Lemuria; Chowdhury, Soumyadeb; Crick, Tom; Cunningham, Scott W.; Davies, Gareth H.; Davison, Robert M.; Dé, Rahul; Dennehy, Denis; Duan, Yanqing; Dubey, Rameshwar; Dwivedi, Rohita; Edwards, John S.; Flavián, Carlos; Gauld, Robin; Grover, Varun; Hu, Mei-Chih; Janssen, Marijn; Jones, Paul; Junglas, Iris; Khorana, Sangeeta; Kraus, Sascha; Larsen, Kai R.; Latreille, Paul; Laumer, Sven; Malik, F. Tegwen; Mardani, Abbas; Mariani, Marcello; Mithas, Sunil; Mogaji, Emmanuel; Nord, Jeretta Horn; O’Connor, Siobhan; Okumus, Fevzi; Pagani, Margherita; Pandey, Neeraj; Papagiannidis, Savvas; Pappas, Ilias O.; Pathak, Nishith; Pries-Heje, Jan; Raman, Ramakrishnan; Rana, Nripendra P.; Rehm, Sven-Volker; Ribeiro-Navarrete, Samuel; Richter, Alexander; Rowe, Frantz; Sarker, Suprateek; Stahl, Bernd Carsten; Tiwari, Manoj Kumar; Van Der Aalst, Wil; Venkatesh, Viswanath; Viglia, Giampaolo; Wade, Michael; Walton, Paul; Wirtz, Jochen; Wright, Ryan

Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential ... Expand

Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential impact of generative AI technologies such as ChatGPT in the domains of education, business, and society. Its objective is to offer initial guidance on the opportunities, challenges, and implications associated with these technologies. It is worth noting that, given its nature as an editorial opinion piece, this submission has not undergone a formal double-blind review process but has been reviewed informally by appropriate experts.Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer 1 Equal contributions Y.K. Dwivedi et al.International Journal of Information Management 71 (2023) 102642 3 significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts. Collapse

Topics: artificial intelligence generative AI Twitter privacy innovation management

Methods: chatGPT chatbot generative pre-trained transformer literature study language model

Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial Intelligence

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Emdad, Forhan Bin; Ho, Shuyuan; Ravuri, Benhur; Hussain, Shezin

Abstract: Artificial Intelligence (AI) aims to elevate healthcare to a pinnacle by aiding ... Expand

Abstract: Artificial Intelligence (AI) aims to elevate healthcare to a pinnacle by aiding clinical decision support. Overcoming the challenges related to the design of ethical AI will enable clinicians, physicians, healthcare professionals, and other stakeholders to use and trust AI in healthcare settings. This study attempts to identify the major ethical principles influencing the utility performance of AI at different technological levels such as data access, algorithms, and systems through a thematic analysis. We observed that justice, privacy, bias, lack of regulations, risks, and interpretability are the most important principles to consider for ethical AI. This data-driven study has analyzed secondary survey data from the Pew Research Center (2020) of 36 AI experts to categorize the top ethical principles of AI design. To resolve the ethical issues identified by the meta-analysis and domain experts, we propose a new utilitarian ethics-based theoretical framework for designing ethical AI for the healthcare domain. Collapse

Topics: artificial intelligence privacy cloud computing

Methods: survey qualitative content analysis computational algorithm chatGPT meta analysis

Is Academic Integrity at Risk? Perceived Ethics and Technology Acceptance of ChatGPT

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Farrell, Wendy; Bogodistov, Yevgen; Mössenlechner, Claudia

Abstract: The academic community is experiencing a significant disruption thanks to a new ... Expand

Abstract: The academic community is experiencing a significant disruption thanks to a new technology called ChatGPT. There is news almost daily about how Large Language Models technologies have the potential to allow students to cheat undetected. This leads many universities to discuss ways to regulate and even ban its use. But is this necessary? The previous research focuses mainly on universities' and lecturers' views but fails to consider the students' views. Do students even want to use the system, and if they do, will they use it for nefarious purposes? Extending the Technology Acceptance Model with Perceived Ethics, this quantitative study researched how students perceive ChatGPT from an ethical perspective and how that impacted their intention to use the system for school work. 277 Students from 40 countries took part in the survey. Results indicated that Perceived Ethics has a direct positive influence on the attitude toward usage but only an indirect effect on the behavioral intention to use the system for school work. This study is one of the first to consider how students perceive ChatGPT from an ethical perspective and how that impacts their perception of and intention to use the Large Language Models technology system. Collapse

Topics: usage intention perceived usefulness behavioral intention perceived ease of use information system use

Methods: chatGPT data transformation survey PLS tool partial least squares regression

Theories: technology acceptance model unified theory of acceptance and use of technology

Inquiry Frameworks for Research Question Scoping in DSR: A Realization for ChatGPT

2023 | DESRIST | Citations: 0

Authors: Díaz, Oscar; Garmendia, Xabier; Contell, Jeremías P.; Pereira, Juanan

Abstract: Research Question (RQ) Scoping refers to defining and refining a research questi ... Expand

Abstract: Research Question (RQ) Scoping refers to defining and refining a research question before conducting research. This step is crucial for ensuring the relevance and focus of the study, particularly in Design Science Research (DSR), where problems and solutions develop gradually. Literature reviews are a traditional method for comprehending the problem and determining key questions; however, they can be time-consuming and not worth it at the onset when lashing out in the dark. NLP chatbots such as ChatGPT can serve as a cost-effective alternative to clearing the way due to their capability to interact with users in a natural language-based manner and provide intuitive responses. The requirements for RQ Scoping extend beyond conversational support to include a framework for a sustained and iterative scoping process. An inquiry framework is necessary to guide and assist students and supervisors in fully harnessing the potential of NLP chatbots. This work incorporates ChatGPT into an inquiry framework for RQ Scoping, with mind maps as the visualization and the 5 Why technique as the inquiry strategy. Contributions include Design Principles, an IT artifact, and a Technology Acceptance Model evaluation (n = 9). Regarding perceived usefulness, the results indicate agreement on the intervention’s effectiveness in maintaining focus. However, there is less enthusiasm for mind maps as a communication tool. Perceived ease of use was also positive but revealed concerns about the query templates used by the framework. Collapse

Topics: artificial intelligence ease of use perceived usefulness perceived ease of use communication service infrastructure

Methods: chatGPT chatbot design principle survey natural language processing

Theories: technology acceptance model theory of affordance