Browsing by Subject "Gig Economy"

Now showing 1 - 2 of 2
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    A Descriptive Mixed-Methods Examination of Corporate Social Responsibility, Accountability, and HRD in the Access Economy
    (2021-04) Hart-Mrema, Tasha
    App-based companies have emerged as legitimate forms of business, giving way to gigs, odd jobs, and convenient access to goods, products, or services. As with all business, growth brings the need for socially responsible change. This study presents an original exploration of factors that underlie stakeholder perceptions of corporate social responsibility (CSR) in the access economy (i.e. gig/sharing economy). The access economy is framed as a complex adaptive system and benefits of platform companies are discussed along with prevalent criticisms regarding worker classifications and unethical business practices. By utilizing a mixed methods survey, empirical evidence is provided that not only identifies a negative association between observed versus expected CSR in the access economy, but also provides insight on the need for four types of platform stakeholder responsibility. Although findings revealed that perceptions on accountability were spread across stakeholder groups, the overarching theme is that more accountability should be allocated to platform companies. The data support the idea that consumers expect platform companies to go beyond mere onboarding and to establish some form of worker protections in line with what would generally be expected from a more conventional business model. Opinions of platform accountability appear to be influenced by expectations associated with specific industries (e.g. transportation vs. tourism) and whether or not workers would have direct contact with consumers. Individual factors such as ethical ideology, risk aversion, desire for certain types of service attributes, and propensity toward social proof techniques are shown to be associated with perceptions of CSR. This study serves as a starting point for the field of HRD to enter the access economy.
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    A Human-Centered Cyber-Physical System Framework and its Applications in Gig Delivery
    (2022-10) Ding, Yi
    With wider and deeper interaction between humans and systems in modern society, the study of human-centered cyber-physical systems (human-centered CPS) has become increasingly important. Thanks to the massive data collected by ubiquitous devices (e.g., smartphones) and advanced machine learning and data mining techniques, numerous human-centered CPS applications and studies are emerging. However, two essential problems still exist: (1) unlike purely internet-based systems, in human-centered CPS, different people engage the system at different places using different devices, which brings technical challenges like scalability and heterogeneity; (2) unlike CPS without wide and deep human participation, in human-centered CPS, human behavior (e.g., locations, mobility, activity) plays a key role, but human behavior is difficult to predict given its inherent uncertainty. To address the challenges, we have done a variety of works that can be organized under the three-layer framework of sensing, prediction, and decision-making. In the sensing layer, we design and build wireless sensing systems to capture human behavior like the arrival and departure at certain locations. We address the scalability challenge by studying human mobility and adopting their smartphones as virtual sensors, and we address the heterogeneity challenge by studying the impacts of environment and hardware on sensing and modeling the similarity with graph learning. In the prediction layer, we study the indoor localization problem by transforming it into a travel time prediction problem and solving it with graph learning based on the human behavior data collected from wireless sensing. In the decision-making layer, we utilize the data from the sensing and the knowledge from prediction to make decisions that lead to higher efficiency compared to the state-of-the-art. We also show how to utilize the feedback from humans to benefit the system design and achieve human-system synergy. In addition to the in-lab design and experiments, we implement our works in one of the latest and largest human-centered CPS applications, gig delivery. By studying couriers’ and merchants’ behavior and building corresponding sensing, prediction, and decision-making systems, we not only improve the system performance but also achieve the synergy between the couriers and systems, saving millions of dollars for the platform and benefiting millions and merchants, couriers, and customers.