Building the Infrastructure: The Effects of Role Identification Behaviors on Team Cognition Development and Performance

The primary purpose of this study was to extend theory and research regarding the emergence of mental models and transactive memory in teams. Utilizing Kozlowski et al.’s (1999) model of team compilation, we examine the effect of role identification behaviors and argue that such behaviors represent the initial building blocks of team cognition during the role compilation phase of team development. We then hypothesized that team mental models and transactive memory would convey the effects of these behaviors onto team performance in the team compilation phase of development. Results from 60 teams working on a command and control simulation supported our hypotheses

The relationship between the perceived level of contribution of virtual team members and their energization source as described by Jung\u27s typology

The increasing use of virtual teams as a result of advances in technology has altered the manner in which team members communicate and interact (Holton, 2001). The media-rich faceto- face team environment has frequently given way to asynchronous communication, using tools such as emails and discussion threads (Ohler, 2004). This study focused on the role of personality type in the emerging academic asynchronous environment; specifically, it explored the relationship between the Jungian dimension of energization (introversion vs. extroversion) of a team member and the perceived level of contribution of that team member to a team in an academic asynchronous virtual environment. The sample for this study included 144 university students who were participating in several courses that required virtual team activities. Respondents completed both an online personality survey similar to that of a Myer Briggs Type Inventory (MBTI), as well as an online teammate contribution questionnaire based on McGrath’s (1964) measures of team efficiency. The null hypothesis that no relationship exists between energization source and perceived virtual team contribution was tested. Nine of the 14 questions that addressed individual contribution to the team were correlated with energization at the ³ 95% confidence (£ 0.05 significance) level. When the individual rating items were grouped consistent with the McGrath (1964) team contribution model, a £ 0.05 significance level correlation was found with two of the three groupings. The null hypothesis was thus rejected, and it was concluded that at the university level, there was a significant relationship between Jung’s energization dimension of personality scale and perceived contribution to a virtual team. It was also concluded that at the university level, a relationship between an individual’s levels of introversion vs. extroversion likely impacts the vi manner in which a team member communicates and contributes in a virtual team environment. This conclusion suggested that future virtual team leaders and team members should be aware of, and give consideration to, the levels of introversion vs. extroversion of their teammates because this is an aspect of personality that may influence how team members communicate most effectively

The COVID-19 Pandemic: A Cross Sectional Analysis of Canadian University Students' and Student-Athletes' Mental Health

Student-athletes have shown to display poorer mental health than student non-athletes, typically due to the unique stressors of participating in collegiate sport. During the COVID-19 pandemic and with the implementation of public health response measures Şenışık et al. (2020) discovered that depression and anxiety symptoms were significantly lower in Turkish professional athletes than non- athletes, and similar among genders and sport types. Further research is required, and this study aims to identify differences among Canadian university student-athletes and non-athletes, males and females, and team and individual sport athletes on symptoms of depression, anxiety, stress, and distress during the 2019/2020 academic year. The Depression Anxiety Stress Scale – 21 and Impact of Events Scale – Revised were completed by 349 student-athletes (241 male and 108 female) and 142 non-athletes (77 male and 65 female). There were no main effects for gender or sport type, but student-athletes scored significantly higher than student non-athletes in depression (p < .001), anxiety (p = .014), stress (p < 0.001), and distress (p = .001). Interestingly, female team sport athletes reported greater levels of each measure than female individual sport athletes (p = .011). In conclusion, Canadian university student- athletes reported significantly higher levels of mental distress than student non-athletes during the 2019/2020 academic year, and there were no differences by gender or sport type. Although, female team sport athletes reported higher symptoms of depression, anxiety, stress, and distress than female individual sport athletes. This data was inconsistent with Şenışık et al. (2020), highlighting the need for more research to be done comparing post-secondary students and student-athletes to identify how the COVID-19 pandemic affected them, and how academic institutions can mitigate, and aid mental health disturbances caused by events of this nature

Mental Health and Student Athletes

The purpose of this study is to gain a better understanding of the relationship between mental health and collegiate student athletes. The participants of the study were Merrimack College student athletes and those strongly associated with Merrimack. The purpose of this study was to educate and bring awareness to mental health issues in collegiate student athletes, as well as topics associated with mental health; such as social stigma, personal stigma, social support, referral skills and resources. Participants participated in an online workshop that included three activities focused on stigma, social support and help seeking. Student athlete participants were asked to complete a post workshop evaluation. The findings from this research provided insight on the quality of the workshop as well as Merrimack College’s student athlete’s attitudes toward the importance of mental health issues, help seeking, social support and stigma

Ergonomics and human factors as a requirement to implement safer collaborative robotic workstations: a literature review

There is a worldwide interest in implementing collaborative robots (Cobots) to reduce work-related Musculoskeletal Disorders (WMSD) risk. While prior work in this field has recognized the importance of considering Ergonomics & Human Factors (E&HF) in the design phase, most works tend to highlight workstations’ improvements due to Human-Robot Collaboration (HRC). Based on a literature review, the current study summarises studies where E&HF was considered a requirement rather than an output. In this article, the authors are interested in understanding the existing studies focused on Cobots’ implementation with ergonomic requirements, and the methods applied to design safer collaborative workstations. This review was performed in four prominent publications databases: Scopus, Web of Science, Pubmed, and Google Scholar, searching for the keywords ‘Collaborative robots’ or ‘Cobots’ or ‘HRC’ and ‘Ergonomics’ or ‘Human factors’. Based on the inclusion criterion, 20 articles were reviewed, and the main conclusions of each are provided. Additionally, the focus was given to the segmentation between studies considering E&HF during the design phase of HRC systems and studies applying E&HF in real-time on HRC systems. The results demonstrate the novelty of this topic, especially of the real-time applications of ergonomics as a requirement. Globally, the results of the reviewed studies showed the potential of E&HF requirements integrated into HRC systems as a relevant input for reducing WMSD risk.This work has been supported by FCT–Fundação para a Ciência e Tecnologia and MIT Portugal Program under the doctoral Grant SFRH/BD/151365/2021. This work has been also supported by NORTE-06-3559-FSE-000018, integrated in the invitation NORTE-59-2018-41, aiming the Hiring of Highly Qualified Human Resources, co-financed by the Regional Operational Programme of the North 2020, thematic area of Competitiveness and Employment, through the European Social Fund. Additionally, has been also supported by FCT within the Project “I-CATER–Intelligent robotic Coworker Assistant for industrial Tasks with an Ergonomics Rationale”, Ref. PTDC/EEIROB/3488/2021, and within R&D Units Project Scope: UIDB/00319/2020

Structuring AI Teammate Communication: An Exploration of AI\u27s Communication Strategies in Human-AI Teams

In the past decades, artificial intelligence (AI) has been implemented in various domains to facilitate humans in their work, such as healthcare and the automotive industry. Such application of AI has led to increasing attention on human-AI teaming, where AI closely collaborates with humans as a teammate. AI as a teammate is expected to have the ability to coordinate with humans by sharing task-related information, predicting other teammates’ behaviors, and progressing team tasks accordingly. To complete these team activities effectively, AI teammates must communicate with humans, such as sharing updates and checking team progress. Even though communication is a core element of teamwork that helps to achieve effective coordination, how to design and structure human-AI communication in teaming environments still remains unclear. Given the context-dependent characteristics of communication, research on human-AI teaming communication needs to narrow down and focus on specific communication elements/components, such as the proactivity of communication and communication content. In doing so, this dissertation explores how AI teammates’ communication should be structured by modifying communication components through three studies, each of which details a critical component of effective AI communication: (1) communication proactivity, (2) communication content (explanation), and (3) communication approach (verbal vs. non-verbal). These studies provide insights into how AI teammates’ communication ii can be integrated into teamwork and how to design AI teammate communication in human-AI teaming. Study 1 explores an important communication element, communication proactivity, and its impact on team processes and team performance. Specifically, communication proactivity in this dissertation refers to whether an AI teammate proactively communicates with human teammates, i.e., proactively pushing information to human teammates. Experimental analysis shows that AI teammates’ proactive communication plays a crucial role in impacting human perceptions, such as perceived teammate performance and satisfaction with the teammate. Importantly, teams with a non-proactive communication AI teammate increase team performance more than teams with a proactive communication AI as the human and the AI collaborate more. This study identifies the positive impact of AI being proactive in communication at the initial stage of task coordination, as well as the potential need for AI’s flexibility in their communication proactivity (i.e., once human and AI teammates’ coordination pattern forms, AI can be non-proactive in communication). Study 2 examines communication content by focusing on AI’s explanation and its impact on human perceptions in teaming environments. Results indicate that AI’s explanation, as part of communication content, does not always positively impact human trust in human-AI teaming. Instead, the impact of AI’s explanations on human perceptions depends on specific collaboration scenarios. Specifically, AI’s explanations facilitate trust in the AI teammate when explaining why AI disobeys humans’ orders, but hinder trust when explaining why AI lies to humans. In addition, AI giving an explanation of why they ignored the human teammate’s injury was perceived to be more effective than AI not providing such an explanation. The findings emphasize the context-dependent characteristic of AI’s communication content with a focus on AI’s explanation of their actions. iii Study 3 investigates AI’s communication approach, which was manipulated as verbal vs. non-verbal communication. Results indicate that AI teammates’ verbal/nonverbal communication does not impact human trust in the AI teammate, but facilitates the maintenance of humans’ situation awareness in task coordination. In addition, AI with non-verbal communication is perceived as having lower communication quality and lower performance. Importantly, AI with non-verbal communication has better team performance in human-human-AI teams than human-AI-AI teams, whereas AI with verbal communication has better team performance in human-AI-AI teams than human-human-AI teams. These three studies together address multiple research gaps in human-AI team communication and provide a holistic view of the design and structure of AI’s communication by examining three specific aspects of communication in human-AI teaming. In addition, each study in this dissertation proposes practical design implications on AI’s communication in human-AI teams, which will assist AI designers and developers to create better AI teammates that facilitate humans in teaming environments

Encouraging Trust and Cooperation in Digital Negotiations

One of the most important issues in modern society is how society modifies the way in which its members develop relationships and foster cooperation in the face of new communication technologies. I explore theoretical and empirical parameters of this process and their implications for encouraging trust and cooperation in negotiations. I begin with an argument for the role of trust and cooperation as part of the foundation of digital commerce by expanding the reach of the social contract theory (ISCT) of Donaldson and Dunfee (1994; 1999). I argue that a digital community is a community in the ISCT sense, and that the basic framework of ISCT can apply to the digital business world. I then analyze the roles of trust and cooperation within this framework, explaining their moral relevance for e-commerce. I follow this discussion with two empirical papers to begin to uncover the nature of digital norms. In Negotiating with the Millennial Generation I use a series of behavioral studies and online chat analyses to show that people build trusting relationships online, often resulting in more cooperation than when they talk face to face. I then look at what type of texting creates stronger relationships, showing that longer texting conversations that go beyond small talk generated greater trust and rapport. I also use a behavioral study involving a smartphone application to discuss how over time people learn to use new forms of communication to build trusting relationships through digital media. In the third paper Why the F*** Don\u27t They TRUST I develop the notion that particular behaviors can affect online trust development. Using analyses of online texts and additional behavioral studies I show how norm-defying online incivility decreases trust while norm-abiding use of capital letters does not. I show that encouraging people to abide by civility norms develops more trusting and cooperative online environments

Developing and Facilitating Temporary Team Mental Models Through an Information-Sharing Recommender System

It is well understood that teams are essential and common in many aspects of life, both work and leisure. Due to the importance of teams, much research attention has focused on how to improve team processes and outcomes. Of particular interest are the cognitive aspects of teamwork including team mental models (TMMs). Among many other benefits, TMMs involve team members forming a compatible understanding of the task and team in order to more efficiently make decisions. This understanding is sometimes classified using four TMM domains: equipment (e.g., operating procedures), task (e.g., strategies), team interactions (e.g., interdependencies) and teammates (e.g., tendencies). Of particular interest to this dissertation is accelerating the development of teammate TMMs which include members understanding the knowledge, skills, attitudes, preferences, and tendencies of their teammates. An accurate teammate TMM allows teams to predict and account for the needs and behaviors of their teammates. Although much research has highlighted how the development of the four TMM domains can be supported, promoting the development of teammate TMMs is particularly challenging for a specific type of team: temporary teams. Temporary teams, in contrast to ongoing teams, involve unknown teammates, novel tasks, short task times (alternatively limited interactions), and members disbanding after completing their task. These teams are increasingly used by organizations as they can be agilely formed with individual members selected to accomplish a specific task. Such teams are commonly used in contexts such as film production, the military, emergency response, and software development, just to name a few. Importantly, although these teams benefit greatly from teammate TMMs due to the efficiencies gained in decision making while working under limited deadlines, the literature is severely limited in understanding how to support temporary teams in this way. As prior research has suggested, an opportunity to accelerate teammate TMM development on temporary teams is through the use of technology to selectively share teammate information to support these TMMs. However, this solution poses numerous privacy concerns. This dissertation uses four studies to create a foundational and thorough understanding of how recommender system technology can be used to promote teammate TMMs through information sharing while limiting privacy concerns. Study 1 takes a highly exploratory approach to set a foundation for future dissertation studies. This study investigates what information is perceived to be helpful for promoting teammate TMMs on actual temporary teams. Qualitative data suggests that sharing teammate information related to skills/preferences, conflict management styles, and work ethic/reliability is perceived as beneficial to supporting teammate TMMs. Also, this data provides a foundational understanding for what should be involved in information-sharing recommendations for promoting teammate TMMs. Quantitative results indicate that conflict management data is perceived as more helpful and appropriate to share than personality data. Study 2 investigates the presentation of these recommendations through the factors of anonymity and explanations. Although explanations did not improve trust or satisfaction in the system, providing recommendations associated with a specific teammate name significantly improved several team measures associated with TMMs for actual temporary teams compared to teams who received anonymous recommendations. This study also sheds light on what temporary team members perceive as the benefits to sharing this information and what they perceive as concerns to their privacy. Study 3 investigates how the group/team context and individual differences can influence disclosure behavior when using an information-sharing recommender system. Findings suggest that members of teams who are fully assessed as a team are more willing to unconditionally disclose personal information than members who are assessed as an individual or members who are mixed assessed as an individual and a team. The results also show how different individual differences and different information types are associated with disclosure behavior. Finally, Study 4 investigates how the occurrence and content of explanations can influence disclosure behavior and system perceptions of an information-sharing recommender system. Data from this study highlights how benefit explanations provided during disclosure can increase disclosure and explanations provided during recommendations can influence perceptions of trust competence. Meanwhile, benefit-related explanations can decrease privacy concerns. The aforementioned studies fill numerous research gaps relating to teamwork literature (i.e., TMMs and temporary teams) and recommender system research. In addition to contributions to these fields, this dissertation results in design recommendations that inform both the design of group recommender systems and the novel technology conceptualized through this dissertation, information-sharing recommender systems

Prototyping a Conversational Agent for AI-Supported Ideation in Organizational Creativity Processes

In this study, we present design guidelines (DGs) for the development and improvement of a virtual collaborator (VC) for Design Thinking (DT). Based on interviews in an ex ante study, we designed a first prototype of a VC. From an ex post evaluation using focus group discussions, we derive strengths, weaknesses, opportunities and threats of the VC. Strengths of the VC are good structure, giving inspiration as well as pace and accuracy. Opportunities are to set level of detail, give a more humane representation, and linking search with other DT phases. Weaknesses are not always suitable content and the VC being rather suitable for research phases as well as one-sided communication and no empathy. Threats are questionable search parameters and too narrow focus of search. We then derived DGs for further improvement of the VC, addressing the weaknesses, threats and ideas from participants