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Description
Social media influencers are a marketing tactic that has become very relevant in present-day marketing within the past decade. The way that social media influencers succeed is by utilizing strategies that capitalize on both marketing and social media perspectives. Based on research findings, it was found that advertising and social media separately negatively affect mental well-being and perceptions of body image. Since social media influencers have a role within both spheres, the question on if they have the same effects on mental health has become a topic of discussion.
This interview-style podcast highlights the history of marketing and advertising, social media and its effects on users, and social media influencers and their roles in consumers’ lives. Furthermore, expert opinions from faculty at Arizona State University will help answer the question: do influencers have an adverse effect on mental health?
Professor Naomi Mandel, a consumer behavior professor at the W. P. Carey School of Business, and Dr. Mary Ingram-Waters, an Honors Faculty Fellow at Barrett, The Honors College, provide insight on the topic of social media influencers. The full interviews are found in the podcast. Professor Naomi Mandel’s interview is found at 29:45, and Dr. Mary Ingram-Waters’ interview is found at 46:00.
This interview-style podcast highlights the history of marketing and advertising, social media and its effects on users, and social media influencers and their roles in consumers’ lives. Furthermore, expert opinions from faculty at Arizona State University will help answer the question: do influencers have an adverse effect on mental health?
Professor Naomi Mandel, a consumer behavior professor at the W. P. Carey School of Business, and Dr. Mary Ingram-Waters, an Honors Faculty Fellow at Barrett, The Honors College, provide insight on the topic of social media influencers. The full interviews are found in the podcast. Professor Naomi Mandel’s interview is found at 29:45, and Dr. Mary Ingram-Waters’ interview is found at 46:00.
ContributorsJenkins, Mallory Erin (Author) / Schmidt, Peter (Thesis director) / Giles, Charles (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The way that people consume media is changing. While every platform seems to shift to video, there is a not-so-quiet revolution going on in the podcast industry. Each week, 10,000 new podcasts are created and uploaded and this number continues to grow (Moore, 2018). As the prevalence of smartphones, faster download speeds, and streaming platforms have proliferated across the globe, more and more people are turning to podcasts to get their content fix. Young professionals are especially drawn to the format because it fits perfectly into their busy lifestyles. This thesis explored how to create, produce, and market a podcast to college students and entry level workers that are interested in pursuing a career in advertising. We collected data through conducting depth interviews and an online survey to podcast listeners as well marketing and design students. The insights drawn from this research were combined with a thorough trend analysis of the podcast market to find the factors that matter most to the target consumer. From there we produced eight episodes of the podcast and released them over a timespan of two and a half months. Finally, paid social media advertisements were used to target students at major advertising colleges around the country. The results of this thesis found that there are a number of important takeaways from the process that can help anyone build a podcast brand, audience and media strategy. Our research found that prospective podcasters should: maintain a consistent upload schedule, invest in audio quality, experiment with content strategy, know their target audience, own your show’s brand, and not rely on just one audio streaming platform.
ContributorsLarkin, Brianna Nicole (Co-author) / Larkin, Brianna (Co-author) / Teixeira, Trent (Thesis director) / Eaton, Kathryn Karnos (Committee member) / Giles, Charles (Committee member) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Intelligence is a loosely defined term, but it is a quality that we try to measure in humans, animals, and recently machines. Progress in artificial intelligence is slow, but we have recently made breakthroughs by paying attention to biology and neuroscience. We have not fully explored what biology has to offer us in AI research, and this paper explores aspects of intelligent behavior in nature that machines still struggle with.
ContributorsLahtinen, David (Author) / Gaffar, Ashraf (Thesis director) / Sanchez, Javier Gonzalez (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This Creative Thesis is a popularization on the subject of Dark Patterns. Dark Patterns are deceptive functionality implemented online by developers seeking to manipulate users and benefit from their misfortune. They work by using psychological techniques to influence a user’s behavior, like by toying with a user’s emotion. I hope to spread the knowledge of Dark Patterns to as many people as possible. Once people know how Dark Patterns work, Dark Patterns will not be effective on them anymore.
ContributorsEaton, Conor (Author) / Meloy, Elizabeth (Thesis director) / Carradini, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Aviation Programs (Contributor)
Created2022-05
Description
Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances, the list of distractive activities grows along with crash risk. Additionally, the distractive activities become more common and complicated, especially with regard to In-Car Interactive System. This work's main focus is on driver distraction caused by the in-car interactive System. There have been many User Interaction Designs (Buttons, Speech, Visual) for Human-Car communication, in the past and currently present. And, all related studies suggest that driver distraction level is still high and there is a need for a better design. Multimodal Interaction is a design approach, which relies on using multiple modes for humans to interact with the car & hence reducing driver distraction by allowing the driver to choose the most suitable mode with minimum distraction. Additionally, combining multiple modes simultaneously provides more natural interaction, which could lead to less distraction. The main goal of MMI is to enable the driver to be more attentive to driving tasks and spend less time fiddling with distractive tasks. Engineering based method is used to measure driver distraction. This method uses metrics like Reaction time, Acceleration, Lane Departure obtained from test cases.
ContributorsJahagirdar, Tanvi (Author) / Gaffar, Ashraf (Thesis advisor) / Ghazarian, Arbi (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2015
Description
For the past decade, mobile health applications are seeing greater acceptance due to their potential to remotely monitor and increase patient engagement, particularly for chronic disease. Sickle Cell Disease is an inherited chronic disorder of red blood cells requiring careful pain management. A significant number of mHealth applications have been developed in the market to help clinicians collect and monitor information of SCD patients. Surveys are the most common way to self-report patient conditions. These are non-engaging and suffer from poor compliance. The quality of data gathered from survey instruments while using technology can be questioned as patients may be motivated to complete a task but not motivated to do it well. A compromise in quality and quantity of the collected patient data hinders the clinicians' effort to be able to monitor patient's health on a regular basis and derive effective treatment measures. This research study has two goals. The first is to monitor user compliance and data quality in mHealth apps with long and repetitive surveys delivered. The second is to identify possible motivational interventions to help improve compliance and data quality. As a form of intervention, will introduce intrinsic and extrinsic motivational factors within the application and test it on a small target population. I will validate the impact of these motivational factors by performing a comparative analysis on the test results to determine improvements in user performance. This study is relevant, as it will help analyze user behavior in long and repetitive self-reporting tasks and derive measures to improve user performance. The results will assist software engineers working with doctors in designing and developing improved self-reporting mHealth applications for collecting better quality data and enhance user compliance.
ContributorsRallabhandi, Pooja (Author) / Gary, Kevin A (Thesis advisor) / Gaffar, Ashraf (Committee member) / Bansal, Srividya (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2017
Description
Driving is the coordinated operation of mind and body for movement of a vehicle, such as a car, or a bus. Driving, being considered an everyday activity for many people, still has an issue of safety. Driver distraction is becoming a critical safety problem. Speed, drunk driving as well as distracted driving are the three leading factors in the fatal car crashes. Distraction, which is defined as an excessive workload and limited attention, is the main paradigm that guides this research area. Driver behavior analysis can be used to address the distraction problem and provide an intelligent adaptive agent to work closely with the driver, fay beyond traditional algorithmic computational models. A variety of machine learning approaches has been proposed to estimate or predict drivers’ fatigue level using car data, driver status or a combination of them.
Three important features of intelligence and cognition are perception, attention and sensory memory. In this thesis, I focused on memory and attention as essential parts of highly intelligent systems. Without memory, systems will only show limited intelligence since their response would be exclusively based on spontaneous decision without considering the effect of previous events. I proposed a memory-based sequence to predict the driver behavior and distraction level using neural network. The work started with a large-scale experiment to collect data and make an artificial intelligence-friendly dataset. After that, the data was used to train a deep neural network to estimate the driver behavior. With a focus on memory by using Long Short Term Memory (LSTM) network to increase the level of intelligence in two dimensions: Forgiveness of minor glitches, and accumulation of anomalous behavior., I reduced the model error and computational expense by adding attention mechanism on the top of LSTM models. This system can be generalized to build and train highly intelligent agents in other domains.
Three important features of intelligence and cognition are perception, attention and sensory memory. In this thesis, I focused on memory and attention as essential parts of highly intelligent systems. Without memory, systems will only show limited intelligence since their response would be exclusively based on spontaneous decision without considering the effect of previous events. I proposed a memory-based sequence to predict the driver behavior and distraction level using neural network. The work started with a large-scale experiment to collect data and make an artificial intelligence-friendly dataset. After that, the data was used to train a deep neural network to estimate the driver behavior. With a focus on memory by using Long Short Term Memory (LSTM) network to increase the level of intelligence in two dimensions: Forgiveness of minor glitches, and accumulation of anomalous behavior., I reduced the model error and computational expense by adding attention mechanism on the top of LSTM models. This system can be generalized to build and train highly intelligent agents in other domains.
ContributorsMonjezi Kouchak, Shokoufeh (Author) / Gaffar, Ashraf (Thesis advisor) / Doupe, Adam (Committee member) / Ben Amor, Hani (Committee member) / Cheeks, Loretta (Committee member) / Arizona State University (Publisher)
Created2020
Description
Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption.
To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.
To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process.
ContributorsJonas, Michael (Author) / Gaffar, Ashraf (Thesis advisor) / Fainekos, Georgios (Committee member) / Doupe, Adam (Committee member) / Herley, Cormac (Committee member) / Arizona State University (Publisher)
Created2016
Description
The purpose of this project was to evaluate the State Bar of New Mexico's (SBNM) new podcast series, SBNM is Hear. The podcast was initially developed as a member outreach tool and a new platform for professional development and survey questions were developed to gauge the podcast’s effectiveness in these two areas. An electronic survey was deployed to active members of the SBNM through email. Respondents were asked questions regarding their demographics, whether they had listened to the series, and what content they would like to hear in the future. The survey resulted in 103 responses, of which 60% indicated that they had not listened to the podcast. The results showed that listenership was evenly divided between generations and that more females listened to at least one episode. The open-ended responses indicated that the two cohorts of respondents (listeners and non- listeners) viewed the podcast a potential connection to the New Mexico judiciary. Future recommendations include conducting an annual survey to continue to understand the effectiveness of the podcast and solicit feedback for continued growth and improvement
ContributorsPettit, Morgan (Author) / Lauer, Claire (Degree committee member) / Mara, Andrew (Degree committee member) / Carradini, Stephen (Degree committee member)
Created2020-12-10