Selective Sharing is Caring: Toward the Design of a Collaborative Tool to Facilitate Team Sharing

Temporary teams are commonly limited by the amount of experience with their new teammates, leading to poor understanding and coordination. Collaborative tools can promote teammate team mental models (e.g., teammate attitudes, tendencies, and preferences) by sharing personal information between teammates during team formation. The current study utilizes 89 participants engaging in real-world temporary teams to better understand user perceptions of sharing personal information. Qualitative and quantitative results revealed unique findings including: 1) Users perceived personality and conflict management style assessments to be accurate and sharing these assessments to be helpful, but had mixed perceptions regarding the appropriateness of sharing; 2) Users of the collaborative tool had higher perceptions of sharing in terms of helpfulness and appropriateness; and 3) User feedback highlighted the need for tools to selectively share less data with more context to improve appropriateness and helpfulness while reducing the amount of time to read

How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions?

When deciding where to visit next while traveling in a group, people have to make a trade-off in an interactive group recommender system between (a) disclosing their personal information to explain and support their arguments about what places to visit or to avoid (e.g., this place is too expensive for my budget) and (b) protecting their privacy by not disclosing too much. Arguably, this trade-off crucially depends on who the other group members are and how cooperative one aims to be in making the decision. This paper studies how an individual's personality, trust in group, and general privacy concern as well as their preference scenario and the task design serve as antecedents to their trade-off between disclosure benefit and privacy risk when disclosing their personal information (e.g., their current location, financial information, etc.) in a group recommendation explanation. We aim to design a model which helps us understand the relationship between risk and benefit and their moderating factors on final information disclosure in the group. To create realistic scenarios of group decision making where users can control the amount of information disclosed, we developed TouryBot. This chat-bot agent generates natural language explanations to help group members explain their arguments for suggestions to the group in the tourism domain [more specifically, the initial POI options were selected from the category of "Food" in Amsterdam (see Sect. 3.2 for the details)]. To understand the dynamics between the factors mentioned above and information disclosure, we conducted an online, between-subjects user experiment that involved 278 participants who were exposed to either a competitive task (i.e., instructed to convince the group to visit or skip a recommended place) or a cooperative task (i.e., instructed to reach a decision in the group). Results show that participants' personality and whether their preferences align with the majority affect their general privacy concern perception. This, in turn, affects their trust in the group, which affects their perception of privacy risk and disclosure benefit when disclosing personal information in the group, which ultimately influences the amount of personal information they disclose. A surprising finding was that the effect of privacy risk on information disclosure is different for different types of tasks: privacy risk significantly impacts information disclosure when the task of finding a suitable destination is framed competitively but not when it is framed cooperatively. These findings contribute to a better understanding of the moderating factors of information disclosure in group decision making and shed new light on the role of task design on information disclosure. We conclude with design recommendations for developing explanations in group decision-making systems. Further, we propose a theory of user modeling that shows what factors need to be considered when generating such group explanations automatically

To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other’s working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality-derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.Web Information System

\u3ci\u3eDrosophila\u3c/i\u3e Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution

The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu