Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments

2023 | Information Systems Research | Citations: 0

Authors: Khern-am-nuai, Warut; Hashim, Matthew J.; Pinsonneault, Alain; Yang, Weining; Li, Ninghui

Abstract: Password-based authentication is the most commonly used method for gaining acces ... Expand

Abstract: Password-based authentication is the most commonly used method for gaining access to secured systems. Unfortunately, empirical evidence highlights the fact that most passwords are significantly weak, and encouraging users to create stronger passwords is a significant challenge. In this research, we propose a theoretically augmented password strength meter design that is guided by the elaboration likelihood model of persuasion (ELM). We evaluate our design by leveraging three independent and complementary methods: a survey-based experiment using students to evaluate the saliency of our conceptual design (proof of concept), a controlled laboratory experiment conducted on Amazon Mechanical Turk to test the effectiveness of the proposed design (proof of value), and a randomized field experiment conducted in collaboration with an online forum in Asia to establish proof of use. In each study, we observe the changes in users’ behavior in response to our proposed password strength meter. We find that the ELM-augmented password strength meter is significantly effective at addressing the challenges of password-based authentication. Users exposed to this strength meter are more likely to change their passwords, leading to a new password that is significantly stronger. Our findings suggest that the proposed design of augmented password strength meters is an effective method for promoting secure password behavior among end users.History: Alessandro Acquisti, Senior Editor; Pallab Sanyal, Associate Editor.Funding: The authors gratefully acknowledge financial support from the Social Science and Humanities Research Council of Canada (SSHRC) [Grant 435-2018-0605].Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1125. Collapse

Topics: password website data security electronic authentication application programming interface

Methods: experiment survey laboratory experiment experimental group field experiment

Theories: elaboration likelihood model

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

The Innovation Waltz: Unpacking Developers’ Response to Market Feedback and Its Effects on App Performance

2023 | International Conference on Information Systems | Citations: 0

Authors: Zhou, Yusheng; Xiong, Bingqing; Zhu, Jiantao; Lim, Eric; Tan, Chee-Wee

Abstract: AbstractsTo remain competitive in the intensely competitive mobile app market, ... Expand

Abstract: AbstractsTo remain competitive in the intensely competitive mobile app market, developers often rely on user feedback to fuel the innovation process. Past studies, however, have rarely examined the impact of developers' incremental innovation strategies by treating app innovation as a continuous process. This knowledge gap prompted us to advance a framework of developers' incremental innovation strategies comprising four coping strategies: sailing, optimizing, supplementing, and patching. Employing a multi-state Markov model to capture the probability of a developer employing an incremental innovation strategy in response to distinct types of market feedback during the app innovation process, we analyze data sourced from the Android app store that consists of 4,583 apps, 29,307 updates, and 231,817 reviews. We discovered that market feedback affects the adoption of the four incremental innovation strategies differently. Additionally, we found that sailing, supplementing, and optimizing strategies boost app downloads, while supplementing, optimizing, and patching strategies improve app ratings. Collapse

Topics: innovation management mobile application mobile application market digital distribution Android

Methods: Markov model theoretical contribution structural equation modeling SEM tool descriptive statistic

A multistate modeling approach for organizational cybersecurity exploration and exploitation

2022 | Decision Support Systems | Citations: 0

Authors: Zadeh, Amir; Jeyaraj, Anand

Abstract: This study examines the dynamic stages of exploration and exploitation efforts b ... Expand

Abstract: This study examines the dynamic stages of exploration and exploitation efforts by organizations in their cybersecurity responses using multistate modeling. Using textual data from the annual 10-K reports of S&P 100 organizations, this study uses a combination of text analytics and Markov chain approach to quantify exploration and exploitation in organizational cybersecurity responses. The study models two and four states of exploration and exploitation based on their cybersecurity responses over time and uses a continuous-time Markov chain approach to analyze transitions between states as organizations adapt their responses over time to achieve ambidexterity. The two-state Markov model focuses on the firm-level Exploration and Exploitation states whereas the four-state model captures deeper levels of exploration and exploitation by considering Surviving, Investigating, Reinforcing, and Balancing as possible states for exploration and exploitation of cybersecurity responses. We analyze the dynamics of organizational exploration-exploitation behaviors by modeling longitu­ dinal transition probabilities across different states. Implications for research and practice are discussed. Collapse

Topics: IT security organization learning IT security threat organizational productivity analytical information system

Methods: Markov chain Monte Carlo Markov model natural language processing post-hoc analysis computational algorithm

Theories: punctuated equilibrium theory

Career Path Clustering via Sequential Job Embedding and Mixture Markov Models

2022 | International Conference on Information Systems | Citations: 0

Authors: Zhong, Hao; Liu, Chuanren

Abstract: Extracting typical career paths from largescale and unstructured talent profile ... Expand

Abstract: Extracting typical career paths from largescale and unstructured talent profiles has re cently attracted increasing research attention. However, various challenges arise in effec tively analyzing selfreported career records. Inspired by recent advances in neural net works and embedding models, we develop a novel career path clustering approach with two major components. First, we formulate an embedded Markov framework to learn job embeddings from longitudinal career records and further use them to compute dynamic embeddings of career paths. Second, to cope with heterogeneous career path clusters, we estimate a mixture of Markov models to optimize clusterwise job embeddings with a prior embedded space shared by multiple clusters. We conduct extensive experiments with our framework to investigate its algorithmic performance and extract meaningful patterns of career paths in the information technology (IT) industry. The results show that our ap proach can naturally discover distinct career path clusters and reveal valuable insights. Collapse

Topics: IT career cloud computing IT workforce computer hardware implicit knowledge

Methods: cluster analysis Markov model experiment computational algorithm data transformation

Effectiveness of Integrated Offline-and-Online Promotions in Omnichannel Targeting: A Randomized Field Experiment

2021 | Journal of Management Information Systems | Citations: 0

Authors: Lee, Seung Yoon; Son, Yoonseock; Oh, Wonseok

Abstract: Leveraging omnichannel has become a new norm of strategic marketing in the retai ... Expand

Abstract: Leveraging omnichannel has become a new norm of strategic marketing in the retail industry, with many vendors foregrounding the value of customers who wish to maximize their shopping experiences across all channels. Notwithstanding such heightened attention, little is known about the effectiveness of omnichannel targeting and promotional strategies. Whereas previous studies assessed the economic worth of channel promotions independently of each other, our study delved into the effects of integrated omnichannel campaigns. Using a randomized field experiment design, we provide empirical evidence of an offline direct experience effect and revealed short-term channel substitution (spillover) effects among customers who only use the online-channel (offline-channel). We further examined omnichannel conversion behaviors after exposure to online promotion and developed different coupon schemes based on responses to the previously offered offline initiative. Finally, we detected significant patterns of post-treatment omnichannel migration and confirmed the effectiveness of integrated omnichannel promotions in fostering a shift to omnichannel shopping. Collapse

Topics: information system use electronic commerce mobile application marketing management accounting

Methods: experimental group experiment propensity score method field experiment descriptive statistic

Theories: construal level theory

A Bayesian Optimal Stopping Framework for Traumatic Brain Injuries Patients

2021 | International Conference on Information Systems | Citations: 0

Authors: Zavadskiy, Gleb; Zantedeschi, Daniel; Jank, Wolfgang

Abstract: One of the central veins of the Information System (IS) discipline is to help p ... Expand

Abstract: One of the central veins of the Information System (IS) discipline is to help practitioners in other fields to solve their real-world problems. This article focuses on building the decision support system for patients with Traumatic Brain Injuries (TBI) using Hidden Markov Models (HMM). Most of the existing literature focuses on the long-term outcomes arising from TBIs. Our development's key distinguishing factor is that we predict the likelihood of stability within the next 24 hours to undergo a non-cranial surgery of polytrauma patients with TBI in acute settings. Our goal in this work is to build an HMMbased Bayesian optimization framework to help doctors to determine the optimal release schedule for TBI patients. Collapse

Topics: decision making decision support system health information system

Methods: hidden Markov model Markov chain Monte Carlo logistic regression generalized additive model descriptive statistic

Understanding Cardiovascular Disease Progression Behavior from Patient Cohort Data using Markov Chain Model

2020 | International Conference on Information Systems | Citations: 0

Authors: Brahma, Arin; Chatterjee, Samir; Fitzpatrick, Ben G; Seal, Kala Chand

Abstract: Cardiovascular Diseases (CVDs) are the number one cause of deaths worldwide and ... Expand

Abstract: Cardiovascular Diseases (CVDs) are the number one cause of deaths worldwide and management of these highly chronic diseases is a major concern to healthcare providers. Progression of CVDs often involves several comorbidities, multi-morbidities, and multiple episodic occurrences, involving recurring hospitalization over a period of time. Using longitudinal data of 4839 CVD episodes of 1274 real patients and continuous-time Markov model as the kernel theory, this research finds the CVD progression paths and transition probabilities. The resultant probability data and the transition paths open the door for building simulation models and tools which can help the hospital administrators to improve resource and capacity planning. Practitioners can compare a patient’s disease progression trend against the pattern revealed by the model. Results are actionable and can influence treatment and intervention strategies in overall CVD progression management by clinicians and providers. The framework developed is repeatable, reusable, and extensible to other diseases and populations. Collapse

Topics: healthcare data analytical information system IT skill electronic health record

Methods: longitudinal research Markov chain Monte Carlo Markov model time series analysis kernel theory

How Much Method-in-Use Matters? A Case Study of Agile and Waterfall Software Projects

2020 | Journal of the Association for Information Systems | Citations: 0

Authors: Thummadi, Babu Veeresh; Lyytinen, Kalle

Abstract: Development methods are rarely followed to the letter, and, consequently, their ... Expand

Abstract: Development methods are rarely followed to the letter, and, consequently, their effects are often in doubt. At the same time, information systems scholars know little about the extent to which a given method truly influences software design and its outcomes. In this paper, we approach this gap by adopting a routine lens and using a novel methodological approach. Theoretically, we treat methods as (organizational) ostensive routine specifications and deploy routine construct as a feasible unit of analysis to analyze the effects of a method on actual, “performed” design routines. We formulated a research framework that identifies method, situation fitness, agency, and random noise as main sources of software design routine variation. Empirically, we applied the framework to examine the extent to which waterfall and agile methods induce variation in software design routines. We traceenacted design activities in three software projects in a large IT organization that followed an objectoriented waterfall method and three software projects that followed an agile method and then analyzed these traces using a mixed-methods approach involving gene sequencing methods, Markov models, and qualitative content analysis. Our analysis shows that, in both cases, method-induced variation using agile and waterfall methods accounts for about 40% of all activities, while the remaining 60% can be explained by a designer’s personal habits, the project’s fitness conditions, and environmental noise. Generally, the effect of method on software design activities is smaller than assumed and the impact of designer and project conditions on software processes and outcomes should thus not be understated. Collapse

Topics: waterfall model systems development agile software development IT project requirements elicitation

Methods: design methodology design process qualitative interview Markov chain cluster analysis

Theories: measurement theory

Opioid Use Disorder: Studying Quality of Life with IT-based Interventions

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Varshney, Upkar; Singh, Neetu

Abstract: Opioid Use Disorder (OUD) has become a major public health challenge. There hav ... Expand

Abstract: Opioid Use Disorder (OUD) has become a major public health challenge. There have been several interventions, including those based on health-IT, proposed recently. There is a major need to study these interventions. We are interested in exploring how different IT-based interventions impact opioid related Quality of Life. We developed a model using Markov chain for three different states in OUD. The model and results can lead to better decision making by healthcare professionals, patients and insurance companies. More specifically, the proposed model and results can help in (a) whether to prescribe opioids to different types of patients, (b) what IT-based interventions are suitable with an opioid prescription, and (c) how patients and healthcare professionals can select an intervention out of multiple available interventions.Keywords IT-based interventions, opioid use disorder, Markov model, evaluation. Collapse

Topics: decision making mobile application IS effectiveness

Methods: Markov chain Monte Carlo Markov model field study experiment computational algorithm

Theories: health promotion model