Technology Affordances and IT Identity

The study attempts to understand the impact of technology affordances on identifying the self with technology (IT identity). Furthermore, it seeks to understand the role of experiences in mediating the relationship between technology affordances and IT identity. To answer our research questions, we will conduct a cross-sectional survey

Capturing the Complexity of Cognitive Computing Systems: Co Adaptation Theory for Individuals

Cognitive computing systems (CCS) are the new generation of automated IT systems that mimic human cognitive capabilities. CCS reshape the interaction between humans and machines and challenge our traditional assumptions of technology use and adoption. This work introduces co adaptation and defines it as the series of activities that a user and a system engage in simultaneously to make the system fit the user. Co adaptation involves two types of adaptation: human adaptation and machine adaptation. Human adaptation refers to the user either changing their behavior to adjust to the technology or changing the technology to adjust to their use. Machine adaptation refers to the system adapting itself to fit users’ needs. We use polynomial regression and response surface analysis to examine the impact of co adaptation on individual performance. We add to previous work by offering a solid theoretical argument with supporting evidence that congruence between human adaptation and machine adaptation plays a critical rol e in determining the impact of technology use on individuals and their performance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167182/1/Alahmad and Robert 2021.pdfDescription of Alahmad and Robert 2021.pdf : PreprintSEL

Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153812/4/AI Fairness Final to Online Feb 24 2020.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153812/1/AI Fairness Final to Online Feb 21 2020.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153812/6/Robert et al. 2020 AI Fairness New Proof.pdfDescription of AI Fairness Final to Online Feb 24 2020.pdf : Update Preprint Feb 24 2020Description of AI Fairness Final to Online Feb 21 2020.pdf : PreprintDescription of Robert et al. 2020 AI Fairness New Proof.pdf : Corrected Proof Mar 1 202

The Impacts of Platform Quality on Gig Workers’ Autonomy and Job Satisfaction

Gig economy jobs rely heavily on the use of platforms including mobile applications. Even though such platforms are necessary to participate in the gig economy, we know very little about how the quality of these platforms affects gig workers. Drawing from a survey of Uber drivers, in this paper we examine the impacts of platform quality on gig workers’ job autonomy and job satisfaction. Preliminary results suggest that gig workers working in the high quality of platforms are more likely to have greater job autonomy and satisfaction. This study contributes to the literature by identifying platform quality as an important factor of gig workers’ job autonomy and satisfaction and suggesting possible applications of the preliminary findings in future research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145612/1/cscwp044-kimA.pd

Impacts of Perceived Behavior Control and Emotional Labor on Gig Workers

Gig economy workers enjoy flexibility in choosing certain aspects of their work. Nonetheless, platform companies still need to control workers’ behaviors to scale their business and ensure customers quality service. Mechanisms of control have been widely studied in traditional organizations; however, work in the gig economy differs from traditional organizations in that the role of a human supervisor is replaced with digital systems. Thus, there is reason to suspect that our traditional theories of control may not hold for new forms of work in the gig economy. To address these concerns, this study examines how gig economy workers, specifically Uber drivers, perceive behavior control and its effect on their job satisfaction. Our results suggest that emotional labor mediates the relationship between perceived behavior control and job satisfaction.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145617/1/Marquis_cscwp072_Abstract.pd

A Review of Personality in Human Robot Interactions

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.Comment: 70 pages, 2 figure

Accumulation, Source Identification, and Cancer Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in Different Jordanian Vegetables

The accumulation of polyaromatic hydrocarbons in plants is considered one of the most serious threats faced by mankind because of their persistence in the environment and their carcinogenic and teratogenic effect on human health. The concentrations of sixteen priority polycyclic aromatic hydrocarbons (16 PAHs) were determined in four types of edible vegetables (tomatoes, zucchini, eggplants, and cucumbers), irrigation water, and agriculture soil, where samples were collected from the Jordan Valley, Jordan. The mean total concentration of 16 PAHs (∑16PAHs) ranged from 10.649 to 21.774 µg kg−1 in vegetables, 28.72 µg kg−1 in soil, and 0.218 µg L−1 in the water samples. The tomato samples posed the highest ∑16PAH concentration level in the vegetables, whereas the zucchini samples had the lowest. Generally, the PAHs with a high molecular weight and four or more benzene rings prevailed among the studied samples. The diagnostic ratios and the principal component analysis (PCA) revealed that the PAH contamination sources in soil and vegetables mainly originated from a pyrogenic origin, traffic emission sources, and biomass combustion. The bioconcentration factors (BCF) for ∑16PAHs have been observed in the order of tomatoes > cucumbers and eggplants > zucchini. A potential cancer risk related to lifetime consumption was revealed based on calculating the incremental lifetime cancer risk of PAHs (ILCR). Therefore, sustainable agricultural practices and avoiding biomass combusting would greatly help in minimizing the potential health risk from dietary exposure to PAHs

Artificial Intelligence and IT Identity: Towards a Comprehensive Understanding of Human-Machine Integration in the Workplace

Cognitive computing systems (CCS) are a new generation of automated IT systems that simulate human cognitive capabilities. Cognitive computing reshapes the interaction between humans and machines and challenges the way we study technology use and adaptation in the Information Systems field. The present work introduces co-adaptation theory, which occurs when both the user and the CCS adapt simultaneously to make the system fit the user. Co-adaptation involves two types of adaptation: human adaptation and machine adaptation. Human adaptation refers to the degree to which the user adapts to CCS by either changing system features or changing the way they interact with the system. Machine adaptation refers to the degree to which the user perceives that the CCS adapts itself to fit the user’s needs. Using polynomial modeling, moderated polynomial regression, mediated polynomial regression, and response surface analysis, we examine longitudinal survey data of 248 Intelligent Assistant users. The findings show that when individuals and CCS both adapt at the same rate, it has the greatest effect on individual relationships with the CCS (i.e., strong IT identity). Furthermore, IT identity fully mediates the association between co-adaptation and individual innovative performance. Lastly, anthropomorphism moderates the association between co-adaptation and IT identity. The data shows that in low anthropomorphism individuals expect CCS to adapt more to them.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174563/1/rashama_1.pd

Artificial Intelligence (AI) and IT identity: Antecedents Identifying with AI Applications

In the age of Artificial Intelligence and automation, machines have taken over many key managerial tasks. Replacing managers with AI systems may have a negative impact on workers’ outcomes. It is unclear if workers receive the same benefits from their relationships with AI systems, raising the question: What degree does the relationship between AI systems and workers impact worker outcomes? We draw on IT identity to understand the influence of identification with AI systems on job performance. From this theoretical perspective, we propose a research model and conduct a survey of 97 MTurk workers to test the model. The findings reveal that work role identity and organizational identity are key determinants of identification with AI systems. Furthermore, the findings show that identification with AI systems does increase job performance