Introduction to Social and Psychological Perspectices in Collaboration Research Minitrack

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Achieving Success in Community Crowdsourcing: Lessons from the Field

Community crowdsourcing is a relatively new phenomenon where local institutions, such as cities and neighborhoods, invite citizens to engage in a public discussion and solve problems that directly affect them. While community crowdsourcing has been around for over a decade, relatively little is known about what drives the success of these initiatives. In this exploratory study, we analyze field data from over 1,000 community crowdsourcing projects that were hosted on a professional community crowdsourcing platform. Our exploration reveals interesting insights into characteristics of community crowdsourcing projects that are associated with higher levels of user engagement. These insights allow us to speculate on guidelines to organize and execute community crowdsourcing initiatives

Accepting the Familiar: The Effect of Perceived Similarity with AI Agents on Intention to Use and the Mediating Effect of IT Identity

With the rise and integration of AI technologies within organizations, our understanding of the impact of this technology on individuals remains limited. Although the IS use literature provides important guidance for organization to increase employees’ willingness to work with new technology, the utilitarian view of prior IS use research limits its application considering the new evolving social interaction between humans and AI agents. We contribute to the IS use literature by implementing a social view to understand the impact of AI agents on an individual’s perception and behavior. By focusing on the main design dimensions of AI agents, we propose a framework that utilizes social psychology theories to explain the impact of those design dimensions on individuals. Specifically, we build on Similarity Attraction Theory to propose an AI similarity-continuance model that aims to explain how similarity with AI agents influence individuals’ IT identity and intention to continue working with it. Through an online brainstorming experiment, we found that similarity with AI agents indeed has a positive impact on IT identity and on the intention to continue working with the AI agent

Design Foundations for AI Assisted Decision Making: A Self Determination Theory Approach

Progress of technology and processing power has enabled the advent of sophisticated technology including Artificial Intelligence (AI) agents. AI agents have penetrated society in many forms including conversation agents or chatbots. As these chatbots have a social component to them, is it critical to evaluate the social aspects of their design and its impact on user outcomes. This study employs Social Determination Theory to examine the effect of the three motivational needs on user interaction outcome variables of a decision-making chatbot. Specifically, this study looks at the influence of relatedness, competency, and autonomy on user satisfaction, engagement, decision efficiency, and decision accuracy. A carefully designed experiment revealed that all three needs are important for user satisfaction and engagement while competency and autonomy is associated with decision accuracy. These findings highlight the importance of considering psychological constructs during AI design. Our findings also offer useful implications for AI designers and organizations that plan on using AI assisted chatbots to improve decision-making efforts

Disruption and Deception in Crowdsourcing: Towards a Crowdsourcing Risk Framework

While crowdsourcing has become increasingly popular among organizations, it also has become increasingly susceptible to unethical and malicious activities. This paper discusses recent examples of disruptive and deceptive efforts on crowdsourcing sites, which impacted the confidentiality, integrity, and availability of the crowdsourcing efforts’ service, stakeholders, and data. From these examples, we derive an organizing framework of risk types associated with disruption and deception in crowdsourcing based on commonalities among incidents. The framework includes prank activities, the intentional placement of false information, hacking attempts, DDoS attacks, botnet attacks, privacy violation attempts, and data breaches. Finally, we discuss example controls that can assist in identifying and mitigating disruption and deception risks in crowdsourcing