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This article aims to examine considerations and strategies for improving team performance in decision‐making by teaching teams to use collaborative modeling based on team mental models.

The article describes the nature of shared mental models and collaborative modeling, the potential effects of collaborative modeling on team performance, and a perspective on communication imperatives that facilitate collaborative modeling. The articles builds upon this information to suggest five imperatives for teams to help them develop collaborative modeling skills

The article offers strategies in the form of five imperatives for teams to observe in order to build skills in collaborative modeling and improve team performance by improving team members’ ability to effect collaborative modeling to accomplish team tasks and goals.

Research over the years in mental modeling and communication has created a powerful argument that effective communication and shared mental models improves team performance. However there is little about application of this concept in the literature. The next step for researchers is to develop application models for collaborative modeling and test those models through empirical research. This paper offers an application model based on imperatives to be observed by decision‐making teams in order to facilitate the creation of shared mental models of team tasks and processes.

This paper aims to present a new approach for developing a framework based on a model driven architecture (MDA) for the modelling of technology‐independent collaborative processes.

The paper suggests a new collaborative process modelling approach based on an MDA and a metamodelling technique. The research method, based on the design science approach, was started by identifying the characteristics of the collaborative processes, which distinguish them from the classical intraorganizational ones. Then, the generic collaborative business process (CBP) modelling framework is developed based on MDA approach and definition of a set of transformation rules through three layers: business, process, and technical. After that, the core component of the framework was the proposition of a generic CBP metamodel at PIM/MDA level. The specific collaboration participant's business processes (expressed as BPMN model) are generated from the generic CBP model represented as an UML2 Profile activity diagram, which is compliant to CBP metamodel. Finally, as proof‐of‐concept, the architecture of an Eclipse‐based open development platform is developed implementing an e‐Procurement collaborative process.

The proposed framework for CBP modelling and the generic CBP metamodel contribute towards a more efficient methodology and have consequences for BPM‐related collaboration, facilitating the B2B processes modelling and implementation. In order to demonstrate and evaluate the practical applicability of the framework, the architecture of an Eclipse‐based open development platform is developed implementing a collaborative business application on the basis of an e‐Procurement use case.

There is a need to focus future research efforts on the improvement of the semi‐automatic transformation phase from public to private processes which needs human intervention by adding a suitable interfaces at both sides of the B2B interaction. In addition, the problem of semantic heterogeneities regarding the partner's business process elements (business documents, activity/task names) should be tackled by developing an approach that uses ontology.

Business processes developers find a B2B technology‐independent solution for implementing and using interorganizational information systems.

The paper provides a framework that enables the CBP modelling and integrates a generic CBP metamodel. Currently, to the best of the authors' knowledge, such a generic metamodel and his instantiation have not so far been developed.

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

The purpose of this paper is to reveal the underlying principles of knowledge processing in a new era of mass collaboration and provide an integrated guideline for organizational knowledge management (KM) based on identifying the gaps between the existing KM theories and emerging knowledge initiatives such as Web 2.0, Pro-Am, Crowdsourcing, as well as Open Innovation.

This research mainly employs three types of research methodologies: Literature study was conducted to connect this study with conventional theories in KM and propose the main principles of Mass Collaborative Knowledge Management (MCKM). Object-oriented modeling was used for designing its interaction model. The case study method was employed to discuss the two typical practices carried out by Goldcorp Inc. as well as the Defence Advanced Research Projects Agency.

This paper proposes the novel KM paradigm called MCKM and also provides its main principles and the interaction model. First, it identifies the gaps between emerging practices and existing KM theories. Second, it embraces the long tails into the scope of organizational KM and extends the scope of prevailing KM studies. Third, it falls back on Pro-Ams to save the costs of and to reduce the risk to organizational KM as well. Fourth, it highlights the advantages of opening organizational internal knowledge and transforms the core beliefs in conventional KM. Finally, it classifies organizational knowledge into two types, domain knowledge and non-domain knowledge, and provides some managing policies, respectively.

Introducing MCKM into organizational KM will not only enhance the organizational knowledge creation and sharing, but also help an organization build its open knowledge ecosystem.

This is a paper to introduce a new direction of KM studies, which guides an organization to build an open knowledge ecosystem by implementing mass collaborations and taking advantages of the complementary advantages of men and machines in knowledge processing.

This paper aims to present results of a study which examined students’ collaborative information behavior (CIB) in comparison with behavioral patterns illustrated in Wilson’s (1996) model of information behavior.

A total of six groups of undergraduate students; four from Sokoine University of Agriculture (SUA) and two from Ardhi University (ARU) were purposively selected. Data were collected using semi participant observation, critical incident interviews and focus group discussion methods.

Results indicate that students’ CIB is mainly shaped by collaborative learning environment, learning tasks objectives and requirements. Despite its wider applicability in different domains and contexts, Wilson’s (1996) model is partially appropriate in modeling students’ group-based learning information behavioral activities. Person(s) in context and active and passive information seeking are aspects of the model which are observed to be relevant in students’ CIB.

The study has implications on teaching and learning practices in higher learning institutions.

The study provides new insights on how students exhibit different information behavioral patterns during collaborative learning. The study fills a gap on how solitary models of information behavior can be used to model students’ information behavior in team-based learning.

Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation stage, team knowledge building during the execution stage, and team reflexivity and team knowledge sharing interventions during the reflection stage can improve collaborative problem solving.

The purpose of this paper is to explore the differences between collaborative and individual search techniques in a scenario-based task focussed on query behavior, cognitive load, search time, and task type about the search.

To help understand the influences on searching for relevant information in pairs or individual contexts, the authors conducted an exploratory user study with 30 participants, using two search tasks completed in a controlled laboratory setting.

On the basis of the analysis, the authors found that collaborative search teams resulted in more queries, more diverse query terms, and more varied query results compared to those working individually. The study results indicated that the cognitive load imposed on the participants did not differ between a collaborative search and an individual search except for the component of performance on the NASA Task Load Index. The results further showed that the total search time was a significant difference on average between the two conditions (i.e. individual information search and collaborative information search) for the second task. And there were significant differences of the mean of total search time between the two tasks for the both conditions. The authors also found that there was no significant relationship between query behavior and the total cognitive load.

The findings from this study have implications for a better understanding of collaborative search interface design, searchers’ cognitive load, query behavior, and general collaborative information search.

Sensemaking is an integral element of human cognition. It is the activity whereby we find answers to questions that arise from experience. It is at the core of collaborative management research as researchers and practitioners work together to build a shared understanding of organizational phenomena and take action based on that understanding, thereby generating actionable knowledge. However, mutual understanding is a complex process which requires a great deal of effort by both researchers and practitioners. While research has described in great detail the consequences of collaborative research endeavors, the challenge of creating a shared sensemaking conducive to these results has been left partially explored. The chapter examines the nature of sensemaking in collaborative management research. A comprehensive framework is proposed and then utilized to examine a collaborative management research effort carried out with an Italian social cooperative.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Health-care systems currently face great challenges, including an increasing elderly population. To respond to this problem, a hospital emergency department, three municipalities, and self-employed general practitioners in Denmark decided to collaborate with the aim of reorganizing treatment of elderly acute ill patients. By establishing a small-scale collaborative community and through an action research process, we show, how to jointly explore and develop a new organization design for in-home hospital treatment that enables the health professionals to collaborate in new ways, and at the same time to investigate and improve this cocreation process and codesign of knowledge among multiple different stakeholders.