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INFO 300: Research Methods Winter 2023
Abstract Monica
Your proposal abstract should be ~150 words and describe the work you propose to do. Briefly include your motivation for doing the study in your abstract. Focus on describing what you plan to do in your study and what you expect to learn.
The rapid advancement of autonomous vehicle (AV) technology has brought unprecedented impacts to the private transportation employment market. With the possible replacement of millions of drivers’ livelihoods, this research proposal will investigate how the private transportation workforce is being affected by AV implementation. It is urgent to (get a comprehensive understanding of how different segments of the private transportation stakeholders reacted to this huge technological transition so that the affected demographic can identify viable economic adaptation strategies and career transition pathways. Through a mixed-methods approach, we will proceed in two phases: first, conducting in-depth interviews with randomly selected drivers from major ride-sharing platforms and traditional taxi services to explore their experiences and concerns; second, deploying a comprehensive survey using 5-point Likert scales to measure perceived job security, adaptation readiness, and anticipated impact of
Introduction Monica
The rapid growth of autonomous vehicle (AV) technology is bringing major changes to the private transportation industry. While this innovation offers benefits like improved efficiency and fewer accidents, it also raises serious concerns about its impact on jobs. Millions of drivers who rely on ride-sharing platforms and traditional taxi services for their income are at risk of losing their livelihoods, making it essential to understand the broader effects of this technological shift.
Research so far highlights both the opportunities and challenges of AV adoption. Studies suggest that AVs may disrupt traditional transportation jobs but could also create new roles in related fields (Nikitas et al., 2021). However, there is still significant uncertainty about the long-term effects on job markets, especially for vulnerable groups like lower-income workers who may struggle to adapt quickly to these changes (Kim et al., 2020). Public perceptions of AVs reflect a mix of hope for productivity improvements and serious concerns about safety, trust, and job security (Liu et al., 2019). These findings show the need to close the gap between the rapid development of this technology and the ability of affected workers to adjust.
Our motivation for this study comes from the clear disconnect between how fast AV technology is advancing and how prepared workers are for its impact. By focusing on the experiences and perspectives of private transportation workers, we aim to highlight the human side of AV adoption, which is often overlooked in technology-focused research. Through interviews and surveys, we hope to better understand how these workers feel about their job security, their ability to adapt, and the challenges they face.
Literature Review/Prior Work Frecesca
Autonomous Vehicles (AV) technology has become a revolutionary force in the transportation sector in recent years. Its rapid development has had a wide-ranging impact on all aspects of society and the economy. In terms of the labor market, autonomous driving technology may lead to widespread job replacement, causing a major impact on traditional employment models (Acemoglu & Restrepo, 2017). The impact on the labor force is not only manifested in a direct reduction in jobs, but also in the decline in the salary level of traditional auto workers and the polarization of employment distribution. Students studying traditional mechanical majors will no longer find it easy to find suitable jobs after graduation, while students studying electronics and artificial intelligence related majors will have better treatment.
In this context, existing research focuses on two aspects: one is the discussion on whether the technological changes of autonomous vehicles can be in line with human development; the other is the impact of autonomous vehicles on the traditional automobile market and related industries, and the analysis of the long-term equilibrium effect on employment and wages in the labor industry (Acemoglu & Restrepo, 2017). In terms of the feasibility of technological change, Frey and Osborne (2013) used the innovative method of Gaussian Process Classifier to quantify the possibility of computerization of 702 detailed occupations. Based on the characteristics of occupational tasks, they matched the core tasks of each occupation in the data with knowledge, skills and ability variables to construct a computerization probability model for occupations. Studies have shown that about 47% of jobs in the United States may be at risk of being replaced by computers in the future (Frey & Osborne, 2017).
In terms of the analysis of long-term industrial equilibrium, Acemoglu and Restrepo (2017) proposed a model to quantify the specific impact of robotics on employment and wages by linking the specific functions of industrial robots with the activity of the local job market. The results show that between 1990 and 2007, the penetration of robotics technology could lead to a decrease in the employment-to-population ratio of approximately 0.18 to 0.34 percentage points and a reduction in wages of 0.25 to 0.5% for every additional robot added to one thousand workers (Acemoglu & Restrepo, 2017).
We are asking you to synthesize existing theories and research related to your research topic. By the end of the literature review, the reader should have a good sense of the current state of knowledge related to your topic, as well as a sense of specific area/s in need of future research.
Each time you discuss the findings and ideas from one of your references, you should cite the reference in the text. At the end of the proposal, include a reference list (not counted as part of the page limit) of all works cited in the text. The in-text citations and the reference list should be formatted using APA guidelines.
Your reference list should include at least 6 scholarly references (i.e. peer-reviewed publications, conference proceedings, and books published by a university press). Aim for 8-12 scholarly references (20 is probably too many).
Your literature review should contain the following:
Research question(s) Monica
Therefore, the proposed study seeks to understand this problem by addressing the following research question: How can autonomous driving technology impact the private transportation employment market on the societal level?
Research design (including the epistemological / paradigm, depending on the qualitative or quantitative or mixed research methods) David
(Will be further enriched after group discussion in week 10)
***[This section describes your proposed research approach and provides a justification for its use. Why have you decided on this particular approach and rejected others? How does your proposed research approach relate to epistemological standpoints and theoretical perspectives? Can you foresee any problems with this approach, and, if so, how do you intend to overcome them? Methods are the tools that will be used to generate your data, conduct your analysis, and provide insight into your research question/s. You need to illustrate how these methods relate to your methodology and discuss why they are the most appropriate to answer your research question/s.]***
Based on our research topic of autonomous vehicles and their impact on the private transportation market, several research epistemologies are possible for us to choose. However, considering that our research topic and intended findings could be subjective to numerous factors, including geographic location, technology advancements, and personal opinions, we have decided to start with a hypothesis that there are negative impacts on the labor market for private transportation services once autonomous driving technologies are fully incorporated. Given that, we are going with a primarily positivist research methodology that combines both qualitative and quantitative, which we hope to achieve through conducting semi-structured interviews and surveys of randomly selected private transportation service drivers (Uber, Lyft, Taxi) in the city of Seattle, with interviews being qualitative focusing on detailed questions regarding their experience and opinion on the topic as well as quantitative Likert scale questions that focus on how much they think autonomous driving technology and vehicles will impact their job, respectively. Additionally, we will be using a simple random sampling method to pick our sample group of Uber/Lyft/Taxi drivers out of the Seattle fleet at 11 a.m. every Monday until we recruit forty-five participants (under the assumption that the active feed of all drivers on each fleet is accessible and order by sequential numbers from 1 to n, representing the total number of active drivers. We will use random.org to generate the driver number, five from each provider each Monday.
In terms of our anticipated challenges and possible mitigation strategies, we are aware that our current research design heavily relies on accessing the active driver rosters from each provider in order for us to recruit participants, which may pose logistical or privacy-related constraints that we are hoping to address through cooperating with local transportation authorities or service providers for research purposes while complying with all regulations. More importantly, we understand that our data collection method of survey and interview on the topic of how Uber/Lyft/Taxi drivers might be impacted by the coming of autonomous technology might not get us the most desired response rate given how challenging and sensitive this topic might be to some as well as the time commitment of drivers, which is considered as part of the non-response bias that comes with our methodology. To mitigate this, we will be fully obeying the wills of our participants (even potential participants if they hesitate to be a part) and following all due process to ensure informed consent in all circumstances, even if doing so would delay our data collection period. We will also be offering flexible scheduling options for drivers to best incorporate their time preferences. At this stage, we are not considering adding any forms of incentives for drivers to participate in our data collection process since we do not want to complicate the data collection bias any further.
In addition to our strategies mentioned above on what data collection methods we are using as well as how we might mitigate some of the logistical and ethical challenges, our strategy for generating data will have two phases to grasp both qualitative and quantitative perspectives as we mentioned in our research design. The initial phase will involve conducting semi-structured dialogues with a cohort of 45 private transportation drivers, equitably divided among Uber, Lyft, and traditional taxi operators in Seattle (15 from each category). These conversations will be guided by an open-ended framework, carefully crafted to unearth their lived experiences, perspectives, and apprehensions about the potential infiltration of autonomous vehicles into their sector. Key discussion points will encompass themes such as employment stability, strategies for adaptation, and personal forecasts regarding their trajectory within the industry. Each interaction will occur either face-to-face or via virtual conferencing, contingent on participant preference, and will be meticulously recorded for subsequent transcription and thematic dissection.
As for the quantitative measurement, we will deploy a structured questionnaire targeting the same participant pool to quantify metrics such as perceived occupational security, adaptability, and anticipated levels of disruption. Utilizing 5-point Likert scale indicators, the survey will capture these variables, supplemented by demographic inquiries to contextualize the data and ensure a multiplicity of viewpoints is represented. Statistical analyses will then be employed to discern patterns and correlations among the surveyed groups, enriching the overarching findings.
Analyzing data (such as how we do data transcripts, deductive? or inductive?) Frecesca
You are not expected to go into great detail in data analysis; a few sentences will suffice.