Information Privacy Concerns in the Age of Internet of Things

Internet of things (IoT) offer new opportunities for advancement in many domains including healthcare, home automation, manufacturing and transportation. In recent years, the number of IoT devices have exponentially risen and this meteoric rise is poised to continue according to the industry. Advances in the IoT integrated with ambient intelligence are intended to make our lives easier. Yet for all these advancements, IoT also has a dark side. Privacy and security were already priorities when personal computers, devices and work stations were the only point of vulnerability to personal information, however, with the ubiquitous nature of smart technologies has increased data collection points around us exponentially. Beyond that, the massive amount of data collected by IoT devices is relatively unknown and uncontrolled by users thereby exacerbating privacy issues and concerns. This study aims to create better understanding of privacy concerns stemming from most popular smart technologies, categorizing the data collected by them. We investigate how the data collection raises information privacy concerns among users of IoT

Building Trust in Healthcare IoT

Advances in Internet of Things (IoT) have given users the ability to monitor heart rate, calories burned, steps walked, blood pressure, time spent exercising, and electrocardiogram (ECG/EKG). Although major players in the wearable industry have marketed their wearables using the health and activity tracking features, they haven’t yet become the primary purpose of these devices. A prominent barrier to adoption of healthcare features in these devices is lack of user trust. This research studies the formation of user’s initial trust in wearables. We argue that the users project their perceptions about trustworthiness of the device and trustworthiness of device manufacturer on the wearable system. Understanding the formation of initial trust on wearable devices’ healthcare features can lead to improvement in user’s information acceptance from healthcare IoT, which in turn has the potential to cause a societal change in primary healthcare delivery

Building Trust in Wearables for Health Behavior

Advances in Internet of Things (IoT) have given users the ability to monitor heart rate, calories burned, steps walked, time spent exercising, and the electrical activity of the heartbeat. Although major players in the wearable industry have marketed their wearables using the health and activity tracking features, a noteworthy health behavior change has not been observed at individual or societal level. A prominent barrier to adoption of healthcare features in these devices is lack of user trust. This research conceptualizes the formation of user’s initial trust in wearables. Here, wearable systems are proposed as three-dimensional framework constituting the device, the organization (manufacturer or app-maker), and the Internet. Understanding the formation of initial trust on wearable systems’ healthcare features can lead to improvement in user’s health-related behaviors, which in turn has the potential to cause a societal change in primary healthcare delivery

Jack of All Trades vs Master of Some: Searching Ideal Knowledge Portfolio for Tech Start-Ups

17 pagesSenior leadership is indisputably central to firm performance. Numerous studies have delved into various attributes of firm leadership as predictors of performance, primarily focusing on educational background and prior tenure in other organizations. Surprisingly, the role of technical skills within firm leaders remains an under-researched area. Given that these leaders often serve as chief decision-makers in technology-centric firms, managing numerous engineers, their technical skills likely play a crucial role in ensuring seamless operations and fostering productive teams. This study addresses this gap by examining the influence of leaders' technical skills, specifically evaluating the diversity of these skills, and their depth and breadth within each technical domain on firm performance. Using data from Angel.co and LinkedIn, we constructed technical profiles for 100 firms based on the technical skills of their founders. Our analysis focused on the relationship between the Euclidean distance of technical profiles, their breadth and depth, and firm performance was measured in terms of the capital raised. Our findings suggest that the diversity and depth of technical profiles affect firm performance. We further discuss the broader implications of our results for both research and practical application

Examination of risks in AI/ML applications

Artificial Intelligence (AI) and Machine Learning (ML) systems powered by Natural Language Processing (NLP) and Computer Vision (CV) are permeating across industries and in our daily lives. Due to novelty of technology, AI/ML applications expose organizations to social, legal, and financial risks. Notable examples: Amazon’s AI hiring tool, Microsoft’s chatbot, Uber’s autonomous car accident. New regulations are being introduced across the world to govern AI applications. This dissertation explores the causes of these risks and mitigation strategies through three essays. Essay 1 uses grounded theory approach to propose a unifying theoretical framework for unintended consequences in AI projects. In this essay, 840 quotes from key informants about 30 unique AI cases using multiple news articles for each case were analyzed. The analysis of media discourse revealed signals of intended actions concerning the implementation of AI tools, which led to unintended consequences through various linking mechanisms. Essay 2 provides a conceptual framework using socio-technical systems theory to study effects of risk factors on AI project risk assumed by organization in developing and implementing AI systems. Essay 3 attempts to explore risk factors disclosed by AI oriented organizations in their annual disclosures using a dataset of 112 SEC annual 10-K filings. Together, this dissertation attempts to contribute to risk management literature in context of AI. Expected findings can inform organizations of critical sources of risk in AI projects and help mitigate them

Too good for malware: Investigating effects of entitlement on cybersecurity threat assessment and piracy behavior

When employees use work resources to commit digital piracy, they are putting their employer’s security and data at risk. This study investigates the effects of technology entitlement, the belief that one is more deserving of technology resources resulting in an expectation of special privileges in its use. Specifically, we explore the influence of technology entitlement on the relationship between perceived cyber security threat and attitude towards digital piracy. Using technology entitlement, we better understand the perception of risk that goes into a decision to pirate content and commit computer abuse using employer information technology

Teaching Programming to the Post-Millennial Generation: Pedagogic Considerations for an IS Course

Teaching introductory programming to IS students is challenging. The educational, technological, demographic, and cultural landscape has changed dramatically in recent years. The post-millennial generation has different needs and expectations in an era of open resources. Learning to program is perceived as difficult, teaching approaches are diverse, and there is little research on what works best. In this paper, we share our experiences in developing, testing, and implementing a new design for teaching introductory IS programming at the undergraduate level. We describe pedagogic considerations and present teaching tips for a blended course that combines best practices with experimentation. Our approach recognizes the changing nature of the student body, the needs of an IS major in the current environment, and the worldwide shift in education from instructor-centered to student-centered learning

What Went Wrong? Identifying Risk Factors for Popular Negative Consequences in AI

The technologies that we have come to know as artificial intelligence (AI), such as machine learning, deep learning, computer vision, and natural language processing, are becoming general-purpose tools that significantly impact organizational and societal economic and social structures. However, that impact has not been entirely positive. We have already seen many projects where undesirable or negative consequences of AI systems have harmed their respective organizations in social, financial, and legal spheres. In this study, we examine common intended objectives and risk factors that lead to negative consequences in AI. Using a qualitative approach, we propose a unifying theoretical framework for negative consequences in AI projects. We analyzed 840 quotes from key informants about 30 unique AI projects using multiple news articles for each project. We identified intended objectives for implementing AI systems that lead to negative consequences through various linking risk factors

Chronic Electronic Cigarette Use and Atherosclerosis Risk in Young People: A Cross-Sectional Study—Brief Report

BackgroundLittle is known whether electronic cigarettes (ECIG) increase vulnerability to future atherosclerotic cardiovascular disease. We determined, using an ex vivo mechanistic atherogenesis assay, whether proatherogenic changes including monocyte transendothelial migration and monocyte-derived foam cell formation are increased in people who use ECIGs.MethodsIn a cross-sectional single-center study using plasma and peripheral blood mononuclear cells from healthy participants who are nonsmokers or with exclusive use of ECIGs or tobacco cigarettes (TCIGs), autologous peripheral blood mononuclear cells with patient plasma and pooled peripheral blood mononuclear cells from healthy nonsmokers with patient plasma were utilized to dissect patient-specific ex vivo proatherogenic circulating factors present in plasma and cellular factors present in monocytes. Our main outcomes were monocyte transendothelial migration (% of blood monocyte cells that undergo transendothelial migration through a collagen gel) and monocyte-derived foam cell formation as determined by flow cytometry and the median fluorescence intensity of the lipid-staining fluorochrome BODIPY in monocytes of participants in the setting of an ex vivo model of atherogenesis.ResultsStudy participants (N=60) had median age of 24.0 years (interquartile range [IQR], 22.0-25.0 years), and 31 were females. Monocyte transendothelial migration was increased in people who exclusively used TCIGs (n=18; median [IQR], 2.30 [ 1.29-2.82]; P<0.001) and in people who exclusively used ECIGs (n=21; median [IQR], 1.42 [ 0.96-1.91]; P<0.01) compared with nonsmoking controls (n=21; median [IQR], 1.05 [0.66-1.24]). Monocyte-derived foam cell formation was increased in people who exclusively used TCIGs (median [IQR], 2.01 [ 1.59-2.49]; P<0.001) and in people who exclusively used ECIGs (median [IQR], 1.54 [ 1.10-1.86]; P<0.001) compared with nonsmoker controls (median [IQR], 0.97 [0.86-1.22]). Both monocyte transendothelial migration and monocyte-derived foam cell formation were higher in TCIG smokers compared with ECIG users and in ECIG users who were former smokers versus ECIG users who were never smokers (P<0.05 for all comparisons).ConclusionsThe finding of alterations in proatherogenic properties of blood monocytes and plasma in TCIG smokers compared with nonsmokers validates this assay as a strong ex vivo mechanistic tool with which to measure proatherogenic changes in people who use ECIGs. Similar yet significantly less severe alterations in proatherogenic properties of monocytes and plasma were detected in the blood from ECIG users. Future studies are necessary to determine whether these findings are attributable to a residual effect of prior smoking or are a direct effect of current ECIG use

Machine learning driven prediction of lattice constants in transition metal dichalcogenides

Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential due to the significant need for innovative high-performance materials on a large scale. In this report, the gradient boosting regression tree model of machine learning was applied to predict the lattice constants of cubic and trigonal MX2 systems (M=transition metal and X=chalcogen atoms). The theoretical/experimental values of the materials were compared to the predicted values to calculate the standard errors such as RMSE (root mean square error) and MAE (mean absolute error). The features used to predict lattice constants were ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density and oxidation states. The features versus contribution barplot has been drawn to reveal the contribution level of each parameter in the degree of [0,1] to obtain the predictions. This report provides a precise account of the prediction methodology for lattice parameters of the transition metal dichalcogenides family, a process that was previously not reported