Exploring Consumers’ Attitudes of Smart TV Related Privacy Risks

A number of privacy risks are inherent in the Smart TV ecosystem. It is likely that many consumers are unaware of these privacy risks. Alternatively, they might be aware but consider the privacy risks acceptable. In order to explore this, we carried out an online survey with 200 participants to determine whether consumers were aware of Smart TV related privacy risks. The responses revealed a meagre level of awareness. We also explored consumers’ attitudes towards specific Smart TV related privacy risks. We isolated a number of factors that influenced rankings and used these to develop awareness-raising messages. We tested these messages in an online survey with 155 participants. The main finding was that participants were generally unwilling to disconnect their Smart TVs from the Internet because they valued the Smart TV’s Internet functionality more than their privacy. We subsequently evaluated the awareness-raising messages in a second survey with 169 participants, framing the question differently. We asked participants to choose between five different Smart TV Internet connection options, two of which retained functionality but entailed expending time and/or effort to preserve privacy

An Archaeological Approach to Understanding the Meaning of Beads Using the Example of Korean National Treasure 634, A Bead from a 5th/6th-Century Royal Silla Tomb

An ancient bead is a document from the past—a message in a bottle—written in some lost symbolic language. Archaeologists try to understand that language by integrating scientific and technological approaches with the social, economic, political, and symbolic/ religious context in which the bead was found. As an example, we use Korean National Treasure 634 (NT634), a dark blue glass bead adorned with mosaic decorations of a bird, a flowering tree, and a human face, found in a 5th-6th century Korean tomb. This bead suggests its meaning by how and where it was made, and what its images may represent

Predicting Facebook Continuance Intention: The Roles of Interpersonal and Technology Trust

This paper examines trust’s role in predicting Facebook continuance intention. We examine the relative influence of two types of trusting beliefs including interpersonal-related trust beliefs and technology-related trust beliefs on technology trusting intentions. Interpersonal trusting beliefs include integrity, competence, and benevolence. Technology-related trusting beliefs include three conceptually similar, yet distinct beliefs including reliability, functionality, and helpfulness. We find that college-aged Facebook users’ interpersonal and technology-related beliefs have similar effects on trusting intentions. Thus the two types of beliefs are conceptually and functionally equivalent. Our results also show that trusting intention mediates the effects of trusting beliefs on continuance intentions. This initial study presents future research opportunities to explore the importance of these two types of trusting beliefs in other technology contexts

Managing the Unseen: Pandemic Prophylactics, Placebos & Panaciea

Introduction: The World Health Organization declared COVID-19 as a pandemic on March 11, 2020, when the disease had spread to 114 countries, infecting more than 118,000 people and killing almost 4,300.1 As of mid-February 2021, over 109 million people worldwide had been infected with the virus and over 2.4 million people had died. COVID-19 is a pandemic of epic proportions that has changed the way we live, work, and do business. This call for papers solicited manuscripts describing and analyzing the intersection of management, COVID-19, and the workplace. The goal was to create a special issue that would inform academics and practitioners and help them emerge from the pandemic with new and effective approaches. While most of us are familiar with the term, ‘the new normal’ that describes new ways of living and working because of the pandemic, McKinsey has coined a new term called the ‘next normal.’ The next normal relates to the next phase of the pandemic in which vaccines are distributed, businesses reopen, and companies gear up for a very different competitive environment.[1] According to McKinsey, resiliency is the key to keeping organizations on top of their game and in a position to compete effectively. Each of the papers in this special issue taps into aspects of resiliency or the ability or quality of something (e.g., people and organizations) to be knocked down by adversities and come back as strong as before, if not stronger.[2] Resiliency can advance management into and beyond the ‘next normal.’ [1] https://www.mckinsey.com/business-functions/operations/our-insights/the-need-for-resiliency [2] https://www.psychologytoday.com/us/basics/resilienc

Paying Attention to News Briefs about Innovative Technologies

News sources about innovative technologies like Google’s driverless car and Apple’s Siri feature can help potential users evaluate the benefits and risks involved. However, individuals must pay attention to this information before they can make sense of it, and decide to change their technology trusting intention. While other fields investigate attention, no research to date has investigated why people pay attention to news briefs about innovative technologies. We propose four factors based on information processing theory. An exploratory study in which respondents are given a series of news briefs and asked how much they paid attention to them and why, provides support for four of our eight propositions. We find the strongest reasons for paying attention/(disregarding) the news briefs are the positive/(negative) nature of the news brief content characteristics. However, the biggest changes in trust are from positive and negative technology involvement factors

Making and Evaluating Participant Choice in Experimental Research on Information Technology: A Framework and Assessment

Evaluations of participant samples for experiments in information systems research often appear to be informal and intuitive. Appropriate participant choice becomes a more salient issue as the population of information technology professionals and users grows increasingly diverse, and the distribution of relevant characteristics in participant samples such as age, gender, nationality, and experience can often be unrepresentative of the characteristics’ distribution in target populations. In this paper, we present a framework based on widely accepted standards for evaluating participant choice and providing rationale that the choice is appropriate. Using a step-by-step approach, we compare current practice in experimental studies from top information systems journals to this framework. Based on this comparison, we recommend how to improve the treatment of participant choice when evaluating the validity of study inferences and how to discuss the tradeoffs involved in choosing participant samples

Effects of Prior Use, Intention, and Habit on IT Continuance Across Sporadic Use and Frequent Use Conditions

This article is motivated by the desire to integrate and expand two literature streams, one that models effects of prior information technology (IT) use and habit strength on continued IT use and another that studies how to apply such models to IT that are used in a characteristically sporadic manner. We find that joint predictions of continuance intention, prior IT use, and habit strength within our research model are superior to subsets of the model across the extended range of usage frequency we studied. However, subsets of the model can also provide reasonable predictions where all measures are not available

Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.

By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods