Team Task Research Articles

Software Development Process Considerations in GNSS-Denied Navigation Project for Drones

This article will discuss the software development process utilised in the GNSS-denied navigation project targeted for drones. The process was implemented in an environment where software developers were oriented to developing a source code only and who showed great reluctance to follow any formal process. The mentioned process was a lightweight AGILE-based process that, by assumption, minimised developers’ engagement in activities other than those related to the source code development. The process described in this paper was designed to support and reflect product quality characteristics like functional completeness and correctness, time behaviour, resource utilisation, analysability, modifiability and testability in an “implicit” way. It allowed the developers to achieve those characteristics in an “invisible” manner. This allowed us to achieve acceptable product quality without needing to engage experienced architects. The described process needs improvements and extensions to satisfy certification needs. However, for the prototyping or research phases, it may be a perfect solution for facilitating future product modifications and saving costs. The process improves product quality by underlying the value of transparency, performing specific actions and incorporating specific attributes related to team tasks. This research also focused on finding a metric related to process acceptance in a given environment, resulting in a three-degree scale showing whether the process needed improvements or was accepted by the environment. Such a metric is a new finding and the metric implies that there are some factors that stand behind any process, which determine whether it will be friendly to the environment (people with their habits, work style, personal traits, etc.) or not. However, investigating factors that determine the process acceptance and that are connected to the environment traits were beyond the scope of this research and will be considered in the future.

Embracing Levin’s Legacy: Advancing Socio-Technical Learning and Development in Human-Robot Team Design Through STS Approaches

AbstractThis paper investigates the synergy between Levin’s theories on technology transfer as a socio-technical learning and developmental process (TLD process), and what we learnt about socio-technical systems (STS) theories in a case study developing human robot solutions for the construction sector. Levin’s extensive work highlights the significance of technology transfer as a means for organizational development. His TLD process emphasizes the intricate interplay between technology, organizational change, and learning and highlights the importance of incorporating cultural knowledge and skills into the technological transfer process. Contemporary STS views developed through our own work are introduced to complement and extend Levin’s theories by providing a systemic lens to understand the broader socio-technical context in which technology transfer occurs. To illustrate the synergies and potential challenges from Levin’s theories of technology transfer with contemporary STS concepts, we use a qualitative study of a unique case about the design and development of human-robot teams (HRTs) for construction tasks. Our findings reveal that while Levin’s theories provide a valuable foundation for understanding technology transfer and organizational change, contemporary socio-technical systems face unique challenges in the context of AI-driven human-robot teams, where intelligent robots also contribute to the socio-technical learning process. Moreover, the rapidly evolving nature of technology and innovations could exponentially impact on multidisciplinary design teams, stakeholder participation and inter-organizational dynamics. The discussions suggest an extension of co-generative learning to incorporate ‘collaborative intelligence’ between human-robot teams enabled by artificial intelligence (AI). Consequently, we suggest that Levin’s theories of technology transfer, developed before the rapid application of AI, may not have fully considered further social challenges caused by the introduction of autonomous systems such as AI-driven HRT systems. We extend Levin’s important work by suggesting that addressing such challenges requires ongoing dialogue and collaboration among researchers, practitioners, and policymakers with different disciplinary backgrounds to develop robust and reliable socio-technical systems frameworks to navigate the complexities of robotics and AI in today’s rapidly evolving technological landscape.

Enhancing team crafting through proactive motivation: An intervention study.

Although the literature on individual job crafting has proliferated over the past decade, research on the collaborative crafting efforts of organizational teams has lagged behind, which is surprising given the prominence of team-based arrangements in contemporary work and the importance of team proactivity in today's business environment. Drawing on proactive motivation theory and the literature on proactive performance in teams, this article presents a large-scale intervention study aimed at increasing team proactive motivation, including a pretest/posttest control group with 96 teams and 1,077 employees. We study the extent to which a team proactive motivation intervention is associated with changes in three dimensions of team crafting (task team crafting, relational team crafting, and cognitive team crafting) at both 6 months and 1 year after the intervention. We also examine the mediating role of change in the three team-level crafting dimensions in explaining the association between the intervention and change in team performance over time. Our results show that the intervention is positively associated with change in all three forms of team crafting. Furthermore, change in team crafting positively associates with change in team performance 6 months and 1 year after the intervention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

UI/UX Design of Mobile-Based Human Resource Management Applications Using the Design Thinking Method (Study Case: Habibi Garden)

Habibi Garden is a company operating in agricultural technology. The current issue at Habibi Garden is the absence of a human resource management application. COOs and Agronomists face operational challenges. COOs struggle to track project progress in the field and sometimes forget to log their attendance. Agronomists have difficulty organizing team tasks as some employees forget to submit assignments, and various assignments, reports, and projects are jumbled in Google Drive. Additionally, employees have to search for reimbursement links mixed with other assignment links, leading to potential errors. This research aims to develop a UI/UX design for a mobile-based human resource management application for Habibi Garden to aid employees in operational activities. The method used is Design Thinking which consists of 5 stages, namely Empathize, Define, Ideate, Prototype, and Test. The results show that the interface design performs well. The effectiveness parameter scored 93.75% for COOs and 91.25% for Agronomists. The efficiency parameter achieved 0.04 goals/sec for COOs and 0.06 goals/sec for Agronomists. User satisfaction, measured using the SUS questionnaire, averaged 88 for COOs, categorized as excellent and graded B, and 77 for Agronomists, categorized as good and also graded B. Design Thinking enables designers to create application solutions that truly fit user expectations and needs by understanding user problems, needs, and behavior through the empathize stage. Based on the prototype design that has been designed in this study, the UI/UX generated can serve as a foundation for the development of mobile applications based on Human Resource Management.

Athlete Identity and Satisfaction of Student-Athletes in Selected Universities in Kenya

Strong athlete identity and satisfaction with being an athlete are linked to better performance. This is because student-athletes are more motivated and feel a sense of belonging. However, research on how these factors connect with details like gender or competition level in student-athletes, especially in Kenya, is lacking. Using an analytical cross-sectional design and the Athletic Identity Measurement Scale-Plus (AIMS-Plus) and Athlete Satisfaction Questionnaire (ASQ), this study examined the relationship between athlete identity and satisfaction among university student-athletes in relation to gender, year of study, and level of competition. A total of 309 (52.4% males and 47.6% females) Kenyan university student-athletes participated in the study. Results revealed females had a slightly stronger athlete identity (4.00 ± 0.478) than male student-athletes (3.93 ± 0.503), but there was no significant correlation between gender and athlete identity. Similarly, females had higher athlete satisfaction (4.24 ± 0.443) than male student-athletes (4.09 ± 0.576), and the relationship between gender and athlete satisfaction was statistically significant (p =.017). Investigated demographic characteristics only explained (3.5%) variance in student-athlete identity (R2 adjusted =.035, F (4, 263) = 3.391, p =.010) and (3.2%) in athlete satisfaction (R2 adjusted =.032, F(4,263) = 3.210, p =.013). The study concluded that universities should provide enhanced training facilities, regular competition opportunities, and a supportive environment for athletes in low-level competitions. Future research could address athlete identity beyond demographics such as coach leadership, education goals, team task contribution, and variables that provide a more comprehensive understanding of athletic identity and satisfaction.

A snapshot of Bulgarian school teachers’ familiarity with, use of, and opinions on artificial intelligence at the threshold of its incorporation into the educational process

The current study was designed within a survey-based quantitative research methodology in order to investigate Bulgarian teachers’ familiarity with, use of, and attitudes toward artificial intelligence (AI) at the nascent stage of its integration into the Bulgarian education system. The survey was completed by 2252 teachers, of whom 16.30% were men and 83.70% were women. The majority of the teachers (72%) reported some level of familiarity with AI technology. Higher levels of familiarity were significantly associated with younger teachers (aged 20 to 29), mathematics, sciences, and technology subject areas, as well as the male gender. More than 50% of the teachers reported using AI technology in their instruction. Familiarity with AI technology was the only significant predictor of AI use, overriding the effect of other variables. The teachers used AI technology to prepare teaching, assessment, and homework materials; design individual and team tasks; and grade students' work. The teachers who regularly used AI technology were more favorable towards AI in contrast to those who seldom or never employed AI. The majority of the respondents recognized the need for teacher training in AI technology and expressed willingness to participate in teacher training initiatives. Therefore, the current situation requires teacher education programs to harness teachers’ needs by equipping them with relevant AI literacy and skills that they could apply to the critical adoption of suitable AI tools. Educational institutions and stakeholders should provide guidelines and consultancy regarding technical, methodological, and ethical issues concerning the instructional use of AI technology.

Multi-task oriented team formation in online collaborative learning

Rational and personalized team formation can greatly enhance the efficiency of online collaborative learning. However, in the process of forming learning teams and completing learning tasks, online synchronous communication among team members is often overlooked, and the abilities of team members may change during task execution, which may have an effect on the completion of team tasks. To address these problems, this paper comprehensively considers the impact of learners’ online time and skill levels on team formation. We quantify the potential development space of learners’ skills and design optimization objectives accordingly based on the Zone of Proximal Development theory. Additionally, we propose a Near-Informer deep learning model to predict learners’ future online states, aiming to measure communication efficiency. Then, we propose a multi-task oriented multi-objective optimization team formation problem, and transform it into a single-objective one by using weighted aggregation of all objectives. After that, we present a Three-dimensional Monte Carlo Tree Search algorithm (TDMCTS). The experiments are conducted based on two real datasets. Compared with the baselines, our Near-informer model achieved the highest number of optimal predictions, with 22 out of 32. Besides, TDMCTS algorithm achieved more comprehensive team formation results, with average skill satisfaction degrees of 99.8% and 98.9% on two task sets, respectively, while satisfying the task requirements.

A dynamic surface water extent service for Africa developed through continental-scale collaboration

Spatially explicit, near real time information on surface water dynamics is critical for understanding changes in water resources, and for long-term water security planning. The distribution of surface water across the African continent since 1984 and updated as every new Landsat scene becomes available is presented here, and validated for the continent for the first time. We applied the Water Observations from Space (WOfS) algorithm, developed and well-tested in Australia, to every Landsat scene acquired over Africa since the mid 1980s to provide spatial information on surface water dynamics over the past 30+ years. We assessed the accuracy of WOfS using aerial and satellite imagery. Four regional geospatial organisations, coordinated through the Digital Earth Africa Product Development Task Team, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. We assessed whether the point was wet, dry, or cloud covered, for each of the 12 months in 2018, resulting in 34,800 labelled observations. As waterbodies larger than 100 km2 are easy to identify with Landsat resolution data and can thus boost accuracy, these were masked out. The resulting overall accuracy of the water classification was 82%. WOfS in Africa is expected to be used by ministries and departments of agriculture and water across the continent, by international organisations, academia, and the private sector. A large-scale collaborative effort, which included regional and technical skills spanning two continents was required to create a service that is regionally accurate and is both hosted on, and implemented operationally from, the African continent.

What you say vs what you do: Utilizing positive emotional expressions to relay AI teammate intent within human–AI teams

With the expansive growth of AI’s capabilities in recent years, researchers have been tasked with developing and improving human-centered AI collaborations, necessitating the creation of human–AI teams (HATs). However, the differences in communication styles between humans and AI often prevent human teammates from fully understanding the intent and needs of AI teammates. One core difference is that humans naturally leverage a positive emotional tone during communication to convey their confidence or lack thereof to convey doubt in their ability to complete a task. Yet, this communication strategy must be explicitly designed in order for an AI teammate to be human-centered. In this mixed-methods study, 45 participants completed a study examining how human teammates interpret the behaviors of their AI teammates when they express different positive emotions via specific words/phrases. Quantitative results show that, based on corresponding behaviors, AI teammates were able to use displays of emotion to increase trust in AI teammates and the positive mood of the human teammate. Additionally, our qualitative findings indicate that participants preferred their AI teammates to increase the intensity of their displayed emotions to help reduce the perceived risk of their AI teammate’s behavior. When taken in sum, these findings describe the relevance of AI teammates expressing intensities of emotion while performing various behavioral decisions as a continued means to provide social support to the wider team and better task performance.

The Efficacy of Tournaments for Nonroutine Team Tasks

Previous articleNext article FreeThe Efficacy of Tournaments for Non-Routine Team TasksFlorian Englmaier, Stefan Grimm, Dominik Grothe, David Schindler, and Simeon SchudyFlorian Englmaier Search for more articles by this author , Stefan Grimm Search for more articles by this author , Dominik Grothe Search for more articles by this author , David Schindler Search for more articles by this author , and Simeon Schudy Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Journal of Labor Economics Just Accepted Published for the Society of Labor Economists, Economics Research Center/ NORC Article DOIhttps://doi.org/10.1086/725553 HistoryAccepted April 19, 2023 © 2023 The University of Chicago. All Rights reserved.PDF download Crossref reports no articles citing this article.

Team Familiarity, Task Familiarity, and Product Innovation: Evidence from Japanese Sake Brewing

Team Familiarity, Task Familiarity, and Product Innovation: Evidence from Japanese Sake Brewing

A “Rearview Mirror” Look at COVID-19: Successes and Further Lessons at Botswana Open University

In retrospect, as if looking into a rearview mirror, the COVID-19 pandemic came with a lot of challenges to business processes, where workplaces had to reinvent themselves to remain afloat. This has been particularly the case with universities, where embracing online teaching and learning was precipitated, usually without enough know-how in ODL. The Botswana Open University took steps to reposition itself to ensure a smooth running of all its processes, thus managing the COVID-19 risks and challenges adequately. This has come as an opportunity for BOU, which operates through the ODL methodology. However, the institution had to take conscious steps to circumvent the challenges posed by the pandemic. The management proactively put in place a versatile task team that led the response and adaptation of the university to COVID-19. Indeed, BOU’s level of success during these uncertain times has been admirable, given that the advent of the pandemic coincided with BOU’s strength as an ODL tertiary institution. Numerous other lessons have also been learned, calling for, among other necessities, moving together with supervisees, looking into the future, and being prepared for emergencies. Thus, looking back at the time concerning the pandemic, the experience emanating from it points to many successes and many lessons to be learned and these have been expanded in this work.

On Shared Leadership Modeling: Contrasting Network and Dyadic Approaches

Shared leadership is a dynamic phenomenon that has gained attention in behavioral science and management research over the last two decades. Network modeling is frequently employed to study this phenomenon, with the recent literature favoring a node-based approach over the traditional dyad-based approach. In this study, we investigate the differential impact of these approaches on shared leadership dynamics in student teams, specifically examining their effects on team task cohesion, team social cohesion, and team performance. We utilized multilevel structural equation modeling to compare node-based and dyad-based approaches in modeling shared leadership networks. Our findings indicate that increased leadership interactions positively influenced team performance and cohesion across both approaches. The dyad-based approach demonstrated a greater effect of leadership interactions on team performance, while leadership centrality significantly impacted performance exclusively in the node-based approach. This research contributes to the field by elucidating the differential impacts of node-based and dyad-based approaches, highlighting their strengths in capturing shared leadership dynamics and centrality effects. Our results underscore the critical importance of aligning theoretical foundations and research objectives with methodological choices in shared leadership studies. These insights enhance our understanding of shared leadership measurement and its implications for team outcomes, offering valuable guidance for future empirical investigations in this domain.

The effect of information integration on team communication in a simulated submarine control room task

Submarine control rooms are characterised by dedicated individual roles for information types (e.g. Sonar operator processes sound energy), with individuals verbally reporting the information that they receive to other team members to help resolve uncertainty in the operational environment (low information integration). We compared this work design with one that ensured critical information was more readily available to all team members (high information integration). We used the Event Analysis of Systemic Teamwork (EAST) method to analyse task, information, and social networks for novice teams operating within a simulated submarine control room under low versus high information integration. Integration impacted team member centrality (importance relative to other operators) and the nature of information shared. Team members with greater centrality reported higher workload. Higher integration across consoles altered how team members interacted and their relative status, the information shared, and how workload was distributed. However, overall network structures remained intact.

Chasing two hares at once: The effects of goal orientation (in)congruence in teams

AbstractOrganizations must excel at what they do well while also learning new ways of operating to achieve long‐term success. Work teams may thus find themselves pursuing contradictory objectives to support the organization's strategy. We investigated teams' goal orientation (in)congruence and its impact on task meaningfulness and, ultimately, performance, hypothesizing the potential pitfalls of teams simultaneously pursuing both learning‐ and performance‐goal orientations. Three‐wave, multisource data were collected from 109 teams at a large North American mortgage company. In a polynomial regression and response surface analytical framework, team task meaningfulness—and subsequent team performance—was enhanced when teams had greater divergence between their learning‐ and performance‐goal orientations but suffered when both goal orientations were more aligned. Our investigation thus revealed the potential pitfalls of teams simultaneously pursuing both learning‐ and performance‐goal orientations. We discuss the theoretical contributions of the team goal orientation incongruence effect substantiated in this study, as well as implications for practice and future research.

The Use of Weather, Water, Ice, and Climate (WWIC) Information in the Polar Regions: What Is Known after the Decadelong Polar Prediction Project?

Abstract The polar regions are facing a wide range of compounding challenges, from climate change to increased human activity. Infrastructure, rescue services, and disaster response capabilities are limited in these remote environments. Relevant and usable weather, water, ice, and climate (WWIC) information is vital for safety, activity success, adaptation, and environmental protection. This has been a key focus for the World Meteorological Organization’s (WMO) Polar Prediction Project (PPP), and in particular its “Societal and Economic Research and Applications” (PPP-SERA) Task Team, which together over a decade have sought to understand polar WWIC information use in relation to operational needs, constraints, and decision contexts to inform the development of relevant services. To understand research progress and gaps on WWIC information use during the PPP (2013–23), we undertook a systematic bibliometric review of aligned scholarly peer-reviewed journal articles (n = 43), examining collaborations, topics, methods, and regional differences. Themes to emerge included activity and context, human factors, information needs, situational awareness, experience, local and Indigenous knowledge, and sharing of information. We observed an uneven representation of disciplinary backgrounds, geographic locations, research topics, and sectoral foci. Our review signifies an overall lack of Antarctic WWIC services research and a dominant focus on Arctic sea ice operations and risks. We noted with concern a mismatch between user needs and services provided. Our findings can help to improve WWIC services’ dissemination, communication effectiveness, and actionable knowledge provision for users and guide future research as the critical need for salient weather services across the polar regions remains beyond the PPP. Significance Statement Every day, people in the Arctic and Antarctic use weather, water, ice, and climate information to plan and carry out outdoor activities and operations in a safe way. Despite advances in numerical weather prediction, technology, and product development, barriers to accessing and effectively communicating high-quality usable observations, forecasts, and actionable knowledge remain. Poorer services, prediction accuracy, and interpretation are exacerbated by a lack of integrated social science research on relevant topics and a mismatch between the services provided and user needs. As a result, continued user engagement, research focusing on information use, risk communication, decision-making processes, and the application of science for services remain highly relevant to reducing risks and improving safety for people living, visiting, and working in the polar regions.

From interaction to integration: leveraging AI in enhancing team communication and task efficiency

From interaction to integration: leveraging AI in enhancing team communication and task efficiency

Theoretical and methodological aspects of the development of science, technology and innovation for SDGs roadmaps

The article examines theoretical and methodological aspects of the development and implementation of science, technology and innovation (STI) for SDGs Roadmaps. The measures and instruments of science, technology and innovation policy for the Sustainable Development Goals according to the classifications of the United Nations Conference on Trade and Development, World Bank, European Commission and United Nations Interagency Task Team on Science, Technology and Innovation for the SDGs are revealed.

Video analysis of real-life shoulder dystocia to assess technical and non-technical performance.

Managing obstetric shoulder dystocia requires swift action using correct maneuvers. However, knowledge of obstetric teams' performance during management of real-life shoulder dystocia is limited, and the impact of non-technical skills has not been adequately evaluated. We aimed to analyze videos of teams managing real-life shoulder dystocia to identify clinical challenges associated with correct management and particular non-technical skills correlated with high technical performance. We included 17 videos depicting teams managing shoulder dystocia in two Danish delivery wards, where deliveries were initially handled by midwives, and consultants were available for complications. Delivery rooms contained two or three cameras activated by Bluetooth upon obstetrician entry. Videos were captured 5 min before and after activation. Two obstetricians assessed the videos; technical performances were scored as low (0-59), average (60-84), or high (85-100). Two other assessors evaluated non-technical skills using the Global Assessment of Team Performance checklist, scoring 6 (poor) to 30 (excellent). We used a spline regression model to explore associations between these two score sets. Inter-rater agreement was assessed using interclass correlation coefficients. Interclass correlation coefficients were 0.71 (95% confidence interval 0.23-0.89) and 0.82 (95% confidence interval 0.52-0.94) for clinical and non-technical performances, respectively. Two teams had low technical performance scores; four teams achieved high scores. Teams adhered well to guidelines, demonstrating limited head traction, McRoberts maneuver, and internal rotation maneuvers. Several clinical skills posed challenges, notably recognizing shoulder impaction, applying suprapubic pressure, and discouraging women from pushing. Two non-technical skills were associated with high technical performance: effective patient communication, with teams calming the mother and guiding her collaboration during internal rotational maneuvers, and situation awareness, where teams promptly mobilized all essential personnel (senior midwife, consultant, pediatric team). Team communication, stress management, and task management skills were not associated with high technical performance. Videos capturing teams managing real-life shoulder dystocia are an effective tool to reveal challenges with certain technical and non-technical skills. Teams with high technical performance are associated with effective patient communication and situational awareness. Future training should include technical skills and non-technical skills, patient communication, and situation awareness.

The Effect of Incentives in Nonroutine Analytical Team Tasks

The Effect of Incentives in Nonroutine Analytical Team Tasks