A Qualitative Study on Project Failure in Agile Teams Using Socio- technical Systems Theory

2023 | HICSS | Citations: 0

Authors: Kolukuluri, Meenakshi; Singh, Jang Bahadur

Abstract: Companies are adopting agile methodology in response to fulfill a growing demand ... Expand

Abstract: Companies are adopting agile methodology in response to fulfill a growing demand for agility. Specifically, the scrum method gained popularity, despite having no guarantee of success. This study employs socio-technical systems interaction as a framework to study the reasons for agile project failure. It reflects on misalignments as an outcome of socio- technical systems interaction. In this study, we identify the events that are most commonly responsible for imbalance by using socio-technical systems theory as a diagnostic tool. We conducted a qualitative interpretive study interviewing twenty-seven individuals in agile information systems delivery (ISD) team settings. Our findings reveal how an individual's lack of belief about being agile, doing agile, and both result in events of failure in IT projects. This study provides a nuanced understanding of the challenges and reasons for failure in agile teams. Collapse

Topics: organizational value meeting support system knowledge base IT project

Methods: qualitative interview cluster analysis personal interview theoretical contribution theory development

Theories: sociotechnical systems theory socio technical theory

MANTRA: A Topic Modeling-Based Tool to Support Automated Trend Analysis on Unstructured Social Media Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Konadl, Daniel; Wörner, Janik; Schmid, Isabel; Leist, Susanne

Abstract: ... Expand

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Topics: marketing management social media new product development social media analytics spreadsheet

Methods: topic model machine learning design theory survey kernel theory

Theories: innovation theory

Would You Accept Doctor ChatGPT: An Empirical Study Based on the UTAUT Model

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Kong, Deli; Qi, Jiayin; Fu, Xiangling

Abstract: Since its introduction, ChatGPT has generated significant interest in many area ... Expand

Abstract: Since its introduction, ChatGPT has generated significant interest in many areas in just a few months, including healthcare. Recently, researchers have claimed that it has passed the U.S. medical licensure examination (Kung et. al, 2022). However, few empirical studies have investigated whether ChatGPT would be valued and accepted by public in the healthcare context. To understand the public's willingness to accept Artificial Intelligence-Generated Content (AIGC) applications like ChatGPT in the healthcare sector, this study proposes a model of factors affecting the user acceptance of ChatGPT for healthcare purposes integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Trust Theory, the Perceived Risk Theory, and the Perceived Illness Theory. We will analyze the data collected from questionnaires using Structural Equation Modeling (SEM). The findings of this study will provide insights into the factors affecting the user acceptance of ChatGPT for healthcare services. KeywordsChatGPT, UTAUT model, perceived risk, trust theory, perceived illness.ChatGPT is an AI language model developed by OpenAI, a leading AI research organization. It is a generative language model that uses deep learning techniques to produce human-like responses to natural language queries. ChatGPT is based on the GPT (Generative Pre-trained Transformer) architecture, which uses unsupervised learning techniques to pre-train the model on large volumes of text data. The pre-training enables ChatGPT to learn the underlying patterns and structures of natural language, making it highly effective in generating contextually relevant responses (Kirmani, 2022). In healthcare, ChatGPT has the potential to serve as a health counselor, providing personalized advice and recommendations to patients. Researches have shown that AI-enable technologies have the ability to provide accurate and reliable health-related information and can be an effective tool for health communication (Palanica et al., 2019;Jiang et al., 2017).The literature on technology adoption mainly applies traditional behavioral models, including the Technology Acceptance Model (TAM: Davis, 1989), as well as its extension, the Unified Theory of Acceptance and Use of Technology (UTAUT: Venkatesh et al., 2003).Artificial intelligence has the potential to transform the healthcare industry, making diagnosis and treatment more accurate and cost-effective (Liu et al., 2019). However, the acceptance and adoption of Acceptance of Doctor ChatGPT Collapse

Topics: behavioral intention artificial intelligence user acceptance healthcare data social influence

Methods: chatGPT survey structural equation modeling SEM tool

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

From the “rush to ethics” to the “race for governance” in Artificial Intelligence

2023 | Information Systems Frontiers | Citations: 0

Authors: Koniakou, Vasiliki

Abstract: This paper engages with the emerging field of Artificial Intelligence (AI) gover ... Expand

Abstract: This paper engages with the emerging field of Artificial Intelligence (AI) governance wishing to contribute to the relevant literature from three angles grounded in international human rights law, Law and Technology, Science and Technology Studies (STS) and theories of technology. Focusing on the shift from ethics to governance, it offers a bird-eye overview of the developments in AI governance, focusing on the comparison between ethical principles and binding rules for the governance of AI, and critically reviewing the latest regulatory developments. Secondly, focusing on the role of human rights, it takes the argument that human rights offer a more robust and effective framework a step further, arguing for the necessity to extend human rights obligations to also directly apply to private actors in the context of AI governance. Finally, it offers insights for AI governance borrowing from the Internet Governance history and the broader technology governance field. Collapse

Topics: artificial intelligence IT governance disruptive innovation decision making responsible artificial intelligence

Methods: computational algorithm

Cyber threat detection: Unsupervised hunting of anomalous commands (UHAC)

2023 | Decision Support Systems | Citations: 0

Authors: Kayhan, Varol O.; Agrawal, Manish; Shivendu, Shivendu

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive to ... Expand

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive tool for early and faster detection of suspected malicious actors. In this paper, we propose a machine learning-based method, Unsupervised Hunting of Anomalous Commands (UHAC), to detect text-based anomalous commands in security information and event management (SIEM) logs that are good candidates for threat hunting. A unique feature of the proposed method is that it first creates a feature set based on the augmentation of document-term and document-character matrices. Then, an autoencoder-based detector is trained on this feature set using a custom loss function. UHAC consis­ tently outperforms other feature sets and algorithms such as one-class support vector machine, density-based spatial clustering of applications with noise, and word-embedding based models such as word2vec. The UHAC detector identifies 84–89% of anomalies in the top 10% of the data. Findings have implications for cybersecurity analysts who perform threat hunting in SIEM logs for process auditing on endpoint devices. Collapse

Topics: IT security artificial intelligence Python decision support system email phishing

Methods: autoencoder data transformation DBSCAN anomaly detection machine learning

Unsupervised Threat Hunting using Continuous Bag of Terms and Time (CBoTT)

2023 | International Conference on Information Systems | Citations: 0

Authors: Kayhan, Varol; Shivendu, Shivendu; Behnia, Rouzbeh; Daniel, Clinton; Agrawal, Manish

Abstract: Threat hunting is sifting through system logs to detect malicious activities th ... Expand

Abstract: Threat hunting is sifting through system logs to detect malicious activities that might have bypassed existing security measures. It can be performed in several ways, one of which is based on detecting anomalies. We propose an unsupervised framework, called continuous bag-of-terms-and-time (CBoTT), and publish its application programming interface (API) to help researchers and cybersecurity analysts perform anomaly-based threat hunting among SIEM logs geared toward process auditing on endpoint devices. Analyses show that our framework consistently outperforms benchmark approaches. When logs are sorted by likelihood of being an anomaly (from most likely to least), our approach identifies anomalies at higher percentiles (between 1.82-6.46) while benchmark approaches identify the same anomalies at lower percentiles (between 3.25-80.92). This framework can be used by other researchers to conduct benchmark analyses and cybersecurity analysts to find anomalies in SIEM logs. Collapse

Topics: IT security application programming interface Hadoop Distributed File System website web analytics

Methods: natural language processing artificial neural network support vector machine term frequency–inverse document frequency DBSCAN

Seeking Information Using Search Engines: The Impact of Negation on Judgments

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Kayhan, Varol; Warkentin, Merrill

Abstract: We examine how online searches lead to judgment formation in two contexts where ... Expand

Abstract: We examine how online searches lead to judgment formation in two contexts where statements are negated differently. In one context, a statement (of the form "if P then Q") is negated using "not" (as in "if P then not Q"), while in the other context, it is negated using a different term or phrase (as in "if P then R"). We show that online searches to find information about the validity of this statement generate different results in these contexts. Our findings contribute to extant work on online searches by suggesting that when individuals test the validity of a statement using search engines, their searches lead to categorically different results contingent on the search context. From a practical standpoint, we make recommendations to search engine developers and content providers so online searches generate more inclusive results. Collapse

Topics: search engine Google+ web page decision making accounting

Methods: experiment blood pressure experimental task statistical hypothesis test analysis of covariance

How can Extended Reality Help Individuals with Depth Misperception?

2023 | HICSS | Citations: 0

Authors: Kazemi, Gholamhossein; Stendal, Karen

Abstract: Despite the recent actual uses of Extended Reality (XR) in treatment of patients ... Expand

Abstract: Despite the recent actual uses of Extended Reality (XR) in treatment of patients, some areas are less explored. One gap in research is how XR can improve depth perception for patients. Accordingly, the depth perception process in XR settings and in human vision are explored and trackers, visual sensors, and displays as assistive tools of XR settings are scrutinized to extract their potentials in influencing users’ depth perception experience. Depth perception enhancement is relying not only on depth perception algorithms, but also on visualization algorithms, display new technologies, computation power enhancements, and vision apparatus neural mechanism knowledge advancements. Finally, it is discussed that XR holds assistive features not only for the improvement of vision impairments but also for the diagnosis part. Although, each specific patient requires a specific set of XR setting due to different neural or cognition reactions in different individuals with same the disease. Collapse

Topics: virtual reality privacy augmented reality smart device extended reality

Methods: literature study literature sample structured literature research experiment

She? The Role of Perceived Agent Gender in Social Media Customer Service

2023 | International Conference on Information Systems | Citations: 0

Authors: Ke, Junyuan; Gao, Yang; Sun, Shujing; Rui, Huaxia

Abstract: This paper investigated the role of perceived agent gender in customer behavior ... Expand

Abstract: This paper investigated the role of perceived agent gender in customer behavior using a unique dataset from Southwest Airlines' Twitter account. We inferred agent gender based on the first names provided by agents when responding to customers. We measured customer behavior using three outcomes: whether a customer decided to continue the service conversation upon receiving an agent's initial response as well as the valence and arousal levels in their second tweet if the customer chose to continue the interaction. Our identification strategy relied on the Backdoor Criterion and hinged on the assumption that customer service requests are assigned to the next available agent, independent of agent gender. The findings revealed that customers were more likely to continue interactions with female agents than male agents and they were more negative in valence but less intense in arousal with the former group than with the latter. Collapse

Topics: Twitter customer service application programming interface Google+ service quality

Methods: word embedding computational algorithm machine learning support vector machine experiment

Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic

2023 | Decision Support Systems | Citations: 0

Authors: Kellner, Domenic; Lowin, Maximilian; Hinz, Oliver

Abstract: Managing an extreme event like a healthcare disaster requires accurate informati ... Expand

Abstract: Managing an extreme event like a healthcare disaster requires accurate information about the event's circum­ stances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit. Collapse

Topics: Twitter Reddit social media pandemic website

Methods: BERT information extraction computational algorithm autoregressive integrated moving average time series analysis