GDPR Privacy Type Clustering: Motivational Factors for Consumer Data Sharing

2023 | European Conference On Information Systems | Citations: 7

Authors: Hanneke, Björn; Baum, Lorenz; Hinz, Oliver

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the dail ... Expand

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the daily life of (online) consumers. Moreover, literature shows that privacy preferences are constantly evolving. This is the first study introducing a GDPR exercising-oriented approach to identify consumer privacy types. Based on a representative sample of the German online population, we cluster consumers according to their privacy importance ("intention to act") and GDPR knowledge ("ability to act") and derive four consumer privacy type clusters: fundamentalists, amateurs, pragmatists, and unconcerned. We investigate motivational factors for changing privacy settings and find significant differences between consumers' intentions and actions for selected factors. This provides evidence for the privacy paradox. Contrarily, intentions and actions align for other factors, which supports the hypothesis that actionbased consent might lower the privacy paradox. Finally, we suggest the development of standardized scales and corresponding clustering methodologies for consumer privacy type clustering to increase comparability over time and across populations. Collapse

Topics: privacy legal context General Data Protection Regulation privacy behavior advertising management

Methods: cluster analysis survey archival research k-means clustering survey design

Password and Passphrase Guessing with Recurrent Neural Networks

2023 | Information Systems Frontiers | Citations: 0

Authors: Nosenko, Alex; Cheng, Yuan; Chen, Haiquan

Abstract: Most online services continue their reliance on text-based passwords as the prim ... Expand

Abstract: Most online services continue their reliance on text-based passwords as the primary authentication mechanism. With a growing number of these services and the limited creativity to devise new memorable passwords, users tend to reuse their passwords across multiple platforms. These factors, combined with the increasing number of leaked passwords, make passwords vulnerable to cross-site guessing attacks. Over the years, researchers have proposed several prevalent methods to predict subsequently used passwords, such as dictionary attacks, rule-based approaches, neural networks, and combinations of the above. We exploit the correlation between the similarity and predictability of these subsequent passwords in a dataset of 28.8 million users and their 61.5 million passwords. We use a rule-based approach but delegate rule derivation, classification, and prediction to a Recurrent Neural Network (RNN). We limit the number of guessing attempts to ten yet get an astonishingly high prediction accuracy of up to 83% in under five attempts, twice as much as any other known model. The result makes our model effective for targeted online password guessing without getting spotted or locked out. To the best of our knowledge, this study is the first attempt of its kind using RNN. We also explore the use of RNN models in passphrase guessing. Passphrases are perceived to be more secure and easier to remember than passwords of the same length. We use a dataset that contains around 100,000 distinct phrases. We demonstrate that RNN models can predict complete passphrases given the initial word with rate up to 40%, which is twice better than other known approaches. Furthermore, our predictions can succeed in under 5,000 attempts, a 100% improvement compared to existing algorithms. In addition, this approach provides ease of deployment and low resource consumption. To our knowledge, it is the first attempt to exploit RNN for passphrase guessing. Collapse

Topics: password Google+ computer hardware electronic authentication Python

Methods: artificial neural network experiment computational algorithm naïve bayes Markov chain Monte Carlo

Artificial Intelligence for Sign Language Translation – A Design Science Research Study

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

Authors: Strobel, Gero; Schoormann, Thorsten; Banh, Leonardo; Möller, Frederik

Abstract: Although our digitalized society is able to foster social inclusion and integra ... Expand

Abstract: Although our digitalized society is able to foster social inclusion and integration, there are still numerous communities suffering from inequality. This is also the case with deaf people. About 750,000 deaf people in the European Union and over 4 million deaf people in the United States face daily challenges in terms of communication and participation. This occurs not only in leisure activities but also, and more importantly, in emergency situations. To provide equal environments and allow people with hearing handicaps to communicate in their native language, this paper presents an AI-based sign language translator. We adopted a transformer neural network capable of analyzing over 500 data points from a person's gestures and face to translate sign language into text. We have designed a machine learning pipeline that enables the translator to evolve, build new datasets, and train sign language recognition models. As proof of concept, we instantiated a sign language interpreter for an emergency call with over 200 phrases. The overall goal is to support people with hearing inabilities by enabling them to participate in economic, social, political, and cultural life. Collapse

Topics: artificial intelligence communication service infrastructure smartphone information system use innovation management

Methods: machine learning design artifact machine translation artificial neural network qualitative interview

Theories: socio technical theory

“Just Google Translate It!” ICT Use of Migrant IT professionals for Mitigating Workplace Integration Challenges

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Berbyuk Lindström, Nataliya; Cordeiro, Cheryl

Abstract: IT professionals represent a growing group of highly educated migrants in diffe ... Expand

Abstract: IT professionals represent a growing group of highly educated migrants in different countries, yet research on their workplace integration is scarce. Applying a combined theoretical framework of Hofstede's culture dimensions and functional theory of language approach, this paper addresses the research need in investigating how migrant IT professionals to Sweden integrate into the workplace and the role of ICTs in mitigating integration challenges. Fifteen (15) interviews with IT professionals from India and Pakistan were analyzed using Thematic Content Analysis. Results uncover migrants experiencing language barriers and cultural differences, which impede developing relationships with colleagues and career opportunities. Our findings indicate that although ICTs, primarily machine translation applications, are indispensable for supporting communication between migrants and locals, collegial support is still essential for managing intercultural interactions, contributing to migrants' feelings of connectedness at work and a sense of belonging. Workplace inclusion and suggestions for practitioners are discussed. Collapse

Topics: IT workforce electronic mail IT career

Methods: qualitative interview qualitative content analysis personal interview case study machine translation

Theories: cultural dimensions theory theory of language

Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Ebrahimi, Mohammadreza; Chai, Yidong; Samtani, Sagar; Chen, Hsinchun

Abstract: International dark web platforms operating within multiple geopolitical regions ... Expand

Abstract: International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels. Collapse

Topics: darknet IT security knowledge sharing shapley additive explanation spoofing

Methods: machine learning design science design artifact case study deep learning

Strategic Directions for AI: The Role of CIOs and Boards of Directors

2021 | Management Information Systems Quarterly | Citations: 57

Authors: Li, Jingyu; Li, Mengxiang; Wang, Xincheng; Thatcher, Jason

Abstract: This paper applies upper echelons theory to investigate whether chief informati ... Expand

Abstract: This paper applies upper echelons theory to investigate whether chief information officers (CIOs) and boards of directors affect the development of AI orientation, which represents firms' overall strategic direction and goals regarding the introduction and application of artificial intelligence (AI) technology. We tested our model using a dataset drawn from 1,454 publicly listed firms in China. Our findings show that the presence of a CIO positively influences AI orientation and that board educational diversity, R&D experience, and AI experience positively moderate the CIO's effect on AI orientation. Our post hoc analysis further demonstrates that these board characteristics represent contingencies that impact AI orientation but not conventional IT orientation. This paper contributes to the upper echelons literature and IT management research by offering contextualized arguments that explain new business and IT strategies such as AI orientation. Further, our findings suggest important implications about how to build top management teams and boards capable of effectively developing AI orientations. Collapse

Topics: artificial intelligence Chief Information Officer research and development enterprise resource planning robotic

Methods: statistical hypothesis test natural language processing post-hoc analysis cluster analysis instrumental variables estimation

Theories: upper echelons theory resource based view of the firm

From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research

2021 | European Journal of Information Systems | Citations: 0

Authors: Li, Yan; Thomas, Manoj a; Liu, Dapeng

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile a ... Expand

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between humans and computers. Although there is a large body of NLP studies published in Information Systems (IS), a comprehensive review of how NLP research is conceptualised and realised in the context of IS has not been conducted. To assess the current state of NLP research in IS, we use a variety of techniques to analyse a literature corpus comprising 356 NLP research articles published in IS journals between 2004 and 2018. Our analysis indicates the need to move from semantics to pragmatics. More importantly, our findings unpack the challenges and assumptions underlying current research trends in NLP. We argue that overcoming these challenges will require a renewed disciplinary IS focus. By proposing a roadmap of NLP research in IS, we draw attention to three NLP research perspectives and present future directions that IS researchers are uniquely positioned to address. Collapse

Topics: database system knowledge representation social media analytics social media information retrieval

Methods: natural language processing qualitative content analysis literature study computational algorithm literature sample

Towards End-to-End Multilingual Question Answering

2021 | Information Systems Frontiers | Citations: 0

Authors: Loginova, Ekaterina; Varanasi, Stalin; Neumann, Günter

Abstract: Multilingual question answering (MLQA) is a critical part of an accessible natur ... Expand

Abstract: Multilingual question answering (MLQA) is a critical part of an accessible natural language interface. However, current solutions demonstrate performance far below that of monolingual systems. We believe that deep learning approaches are likely to improve performance in MLQA drastically. This work aims to discuss the current state-of-the-art and remaining challenges. We outline requirements and suggestions for practical parallel data collection and describe existing methods, benchmarks and datasets. We also demonstrate that a simple translation of texts can be inadequate in case of Arabic, English and German languages (on InsuranceQA and SemEval datasets), and thus more sophisticated models are required. We hope that our overview will re-ignite interest in multilingual question answering, especially with regard to neural approaches. Collapse

Topics: Google+ Wikipedia search engine knowledge sharing

Methods: deep learning machine translation experiment question answering word embedding

Introduction to WITS 2018 Special Issue in TMIS

2020 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Dutta, Kaushik; Fang, Xiao; Jiang, Zhengrui (Jeffrey)

Abstract: ... Expand

Abstract: Collapse

Topics: electronic commerce online review online learning system knowledge representation

Methods: classification method machine translation WordNet experiment literature sample

An OOV-Aware Curation Process for Psycholinguistic Analysis of Social Media Text - A Hybrid Approach

2020 | DESRIST | Citations: 0

Authors: Liu, Kun; Li, Yan

Abstract: Massive user generated social media text posits new opportunities as well as cha ... Expand

Abstract: Massive user generated social media text posits new opportunities as well as challenges for psycholinguistic analysis to understand individual differences such as personality. Traditional off-the-shelf NLP (Natural Language Processing) tools perform poorly when analyzing text from social media because of the frequent out-of-vocabulary (OOV) words usage. Existing dictionary-based, closed-vocabulary approach and data-driven, open-vocabulary approach are both limited in handling OOV words. This research designs an OOV-aware curation process with a specific focus on OOV words. Following a design science research process model, the curation process is designed through four design cycles. The curation process includes a hybrid approach that integrates closed-vocabulary dictionary with expanded OOV categories and open-vocabulary approach with normalized OOV words. The curation process would enable psycholinguistic researchers and practitioners to exploit more psycholinguistic cues for tasks similar to personality predictions using social media text. Collapse

Topics: personality social media Python individual characteristic logistics management

Methods: design science experiment regression analysis method conceptual modelling ensemble learning