Unleashing Crowd Wisdom: Leveraging Cognitive Memory Structures to Increase Quality of User-Generated Content

In recent years, online information sharing platforms have opened new opportunities for people to share information and experiences with each other and with organizations that sponsor these platforms. Increasingly, data consumers, both at the organizational and at the individual level, hope to use these User-Generated Content (UGC) in their decision making. However, recent studies uncovered significant challenges associated with the interfaces used to collect high-quality UGC. While many aspects of the information quality (IQ) of UGC have been studied, the role of data structures in gathering UGC and the nature of shared content have yet to receive attention. UGC is created on online platforms with varying degrees of data structure, ranging from unstructured (e.g., open box fields) to highly structured formats (e.g., rigid and specific forms). Despite much research on UGC, we have little understanding of the appropriate degree of data structures in data collection and its impact on the quality of information. Moreover, we know that most of the produced UGC originates in the declarative memory of the contributors. Psychology literature shows that different types of memory are stored and managed differently, and that they are retrieved accordingly. Thus, we argue that the information collection interface for retrieving and collecting each type of memory should be aligned with the way that it was stored. Therefore, we posit that designing interfaces with sensitivity to human memory structures should result in improvements of the IQ of UGC. We conducted several experiments to examine differently-designed information collection interfaces for different types of information. We evaluated both data creators’ and data consumers’ perceived quality of information collection, at the individual level. The findings support our claims of the importance of these factors for information quality. This research demonstrates a connection between information system interface design and human memory, which eventually could result in changes to best practices in interface design. This could, in turn, lead to improved interaction between participants and organizations, including enhanced data creators’ self-expression, improved users’ attitudes toward UGC systems, and increased value-add from organizations’ use of UGC

Nonprofits and decisions to accept cryptocurrency donations: a qualitative study to examine potential opportunities and risks

Several studies believe that cryptocurrency is the future of money, and crypto fundraising is a new way to help nonprofit organizations (NPOs) raise funds. Recently, foundations have started to take cryptocurrency donations. Several third parties have assisted nonprofits in setting up for this type of donation to smooth the new fundraising process. In this qualitative study, in-depth interviews were conducted with five managers of nonprofits that accepted crypto donations in the last seven years. The data were analyzed and coded using NVIVO to classify conceptually similar themes mentioned by the interviewees. This qualitative paper is among the first steps to discussing the pros and cons of crypto donation and explaining essential grounds that a nonprofit should consider when taking crypto donations. The results offer practical implications as understanding these factors can help NPO managers identify potential benefits and risks in the planning, developing, and using crypto donation platforms

Data Collection Interfaces in Online Communities: The Impact of Data Structuredness and Nature of Shared Content on Perceived Information Quality

The growth of online communities has resulted in an increased availability of user-generated content (UGC). Given the varied sources of UGC, the quality of information it provides is a growing challenge. While many aspects of UGC have been studied, the role of data structures in gathering UGC and nature of to-be-shared content has yet to receive attention. UGC is created in online platforms with varying degrees of data structure, ranging from unstructured to highly-structured formats. These platforms are often designed without regard to how the structure of the input format impacts the quality of outcome. In this study, we investigate the impact of the degree of data structure on the perceived quality of information from the novel perspective of data creators. We also propose and evaluate a novel moderating effect due to the nature of content online users wish to share. The preliminary findings support our claims of the importance of these factors for information quality. We conclude the paper with directions for future research and expected contributions for theory and practice

Participatory Design for User-generated Content: Understanding the challenges and moving forward

Research on participatory design (PD) dates back to the 1970s, and has focused historically on internal organization settings. Recently, the proliferation of content-producing technologies such as social media and crowdsourcing has led to the explosion of user-generated content (UGC) that originates outside of organizations. Participative challenges in UGC differ from those in traditional organizational, as well as other distributed multi-user, settings; e.g.; open source software, multi-party systems. UGC is an interesting emerging domain and exploring PD in this context may contribute to knowledge and practices in PD itself. In this paper, we analyze the challenges and opportunities associated with PD in organization-directed UGC development, illustrate these with two UGC projects, and propose fruitful directions for future research

Unleashing Crowd Wisdom: Leveraging Cognitive Memory Structures to Increase Quality of User-Generated Content

In recent years, online information sharing platforms have opened new opportunities for people to share information and experiences with each other and with organizations that sponsor these platforms. Increasingly, data consumers, both at the organizational and at the individual level, hope to use these User-Generated Content (UGC) in their decision making. However, recent studies uncovered significant challenges associated with the interfaces used to collect high-quality UGC. While many aspects of the information quality (IQ) of UGC have been studied, the role of data structures in gathering UGC and the nature of shared content have yet to receive attention. UGC is created on online platforms with varying degrees of data structure, ranging from unstructured (e.g., open box fields) to highly structured formats (e.g., rigid and specific forms). Despite much research on UGC, we have little understanding of the appropriate degree of data structures in data collection and its impact on the quality of information. Moreover, we know that most of the produced UGC originates in the declarative memory of the contributors. Psychology literature shows that different types of memory are stored and managed differently, and that they are retrieved accordingly. Thus, we argue that the information collection interface for retrieving and collecting each type of memory should be aligned with the way that it was stored. Therefore, we posit that designing interfaces with sensitivity to human memory structures should result in improvements of the IQ of UGC. We conducted several experiments to examine differently-designed information collection interfaces for different types of information. We evaluated both data creators’ and data consumers’ perceived quality of information collection, at the individual level. The findings support our claims of the importance of these factors for information quality. This research demonstrates a connection between information system interface design and human memory, which eventually could result in changes to best practices in interface design. This could, in turn, lead to improved interaction between participants and organizations, including enhanced data creators’ self-expression, improved users’ attitudes toward UGC systems, and increased value-add from organizations’ use of UGC

A Qualitative Study on Aging Adults\u27 Opinions and Perceptions about Using Companion Robots in Daily Activities

In the 21st century, Agetech (age technology) and tech devices can be integral to healthcare systems in increasing patient engagement, maintaining independence, and helping seniors live happier and healthier lives. Studying how seniors use and interact with technology can help researchers, technology designers, and vendors understand the possible benefits and risks of using technology products designed to meet older adults’ needs. This study attempts to use a qualitative approach to examine the elderly opinions about using intuition robotics (i.e., companion robots). The findings indicate a positive attitude toward using this Agetech due to health and wellness, companionship and support, and technology design benefits. However, participants also raise concerns about digital dependency and social disconnection concerns, information integrity and online resilience risks, and implementation costs. Through a model, we suggest a need for more education and awareness about robotics\u27s potential gains and concerns

Exploring Patients’ Cognitive Trust in Diverse AI Representations: Implications for Patient-Centered Healthcare

This study aims to investigate the dynamics of cognitive trust among patients in healthcare settings, with a focus on three distinct Artificial Intelligence (AI) representations: robotic AI (physical robots), virtual AI (virtual agents or bots), and embedded AI (technology integrated within devices or software, invisible to the user). The rapid integration of AI into healthcare highlights the urgent need to understand how patients perceive and trust these technologies. Addressing this need, our research evaluates the effects of key variables—tangibility, transparency, reliability, and task nature—on cognitive trust in each AI form. By analyzing responses from healthcare recipients, the study aims to uncover how these variables influence patients\u27 cognitive trust level in AI-assisted healthcare services. The findings are expected to contribute valuable insights for the ethical design, implementation, and policy-making regarding AI in healthcare, ensuring these technologies are deployed in effective and trust-enhancing ways