Team mental model characteristics and performance in a simulation experiment

The aim of the present study was to investigate whether enhancing team mental models (TMM), more specifically task models and team interaction models, improved teamwork in dynamic situations. Measures of performance, coordination, and communication were collected during a forest firefighting simulation task (C3Fire) and compared across three learning conditions. The purpose of these learning conditions was to either enhance task TMM by providing additional information on environmental dynamics or team TMM by providing additional information on the roles of each team member and possible interaction strategies. In the control condition, no additional information was provided. Also, task complexity was varied through transparency of courses of action (COA). The results showed better team performance and coordination in conditions with a more obvious COA. However, there was no significant effect of learning condition on team effectiveness. A trend in the data suggests that teams given additional information on team interaction or task factors spent more time communicating. These findings are discussed in the context of previous research and potential avenues for future investigations.

Development and initial validation of the Team Mental Models Instrument (TMMI): A psychometric tool to measure shared and complementary mental models in sports

This thesis hypothesizes that providing task specific information to individual team members will improve coordination and decision-making, and therefore team performance, at time critical tasks. Major themes addressed in this research include teams and team processes, mental models, team mental models, work domain analysis, and hierarchical task analysis. Furthermore, the theory behind the development of complementary models is introduced. A unique method to identify the information sources and requirements in a complex team environment is first discussed in general and then specifically applied in two domains. The findings are presented of two experiments examining the effects of imposing different information distribution strategies that range from no complementariness to full complementariness of information. Team communication, team and individual task performance, workload, and timeliness and effectiveness of team decision making were assessed in nominal and off-nominal conditions. The first experiment used an automobile simulator and examined team navigation while driving. A second experiment was designed to incorporate additional measures to more specifically investigate individual performance, team workload, and clarity of information requirements using a UH-60 Black Hawk helicopter simulator. The procedures used for both experiments provided for dynamic yet controlled environments through which critical factors that influence team process and performance could be evaluated accurately. Results of these experiments provide empirical evidence that providing task relevant information to individual team members in a time critical environment, while limiting their access to non-relevant information, improves individual and team performance. Furthermore, there is evidence of increased individual performance that indicates this method of distributing information among team members may provide individual crewmembers with a more accurate “task relevant” mental model of their own environment. This research provides new insight into how the distribution of information among team members effects the development of mental models, information requirements, team and individual performance, and communications, and highlights several directions for future research. The information distribution design principles presented in this thesis address the heterogeneity of teams; teams cannot be thought of as groups of identical individuals. The results concerning the communication, workload, performance and team of mental models were consistent across the domains in this research.

Because research on team mental models is still in its formative stages, there is a need for continued conceptual development of the construct and direct empirical support linking team mental models to team outcomes. Researchers in other fields have developed concepts that are distinct from, but clearly related to team mental models, including information sharing, transactive memory, group learning, and cognitive consensus. Although these research streams currently exist in parallel with little cross‐fertilization, there is much to be gained from integration across disciplinary boundaries. Therefore, the purpose of this paper is to enrich the theoretical understanding of team mental models and to broaden the empirical research base by adopting a cross‐disciplinary focus and incorporating related team knowledge domains from other literatures. Based on a synthesis of various literatures, we develop a framework that delineates the relationships among team knowledge constructs. Copyright © 2001 John Wiley & Sons, Ltd.

Interprofessional collaboration between doctors and nurses is based on team mental models, in particular for each professional’s roles. Our objective was to identify factors influencing concordance on the expectations of doctors’ and nurses’ roles and responsibilities in an Internal Medicine ward. Using a dataset of 196 doctor-nurse pairs (14x14 = 196), we analyzed choices and prioritized management actions of 14 doctors and 14 nurses in six clinical nurse role scenarios, and in five doctor role scenarios (6 options per scenario). In logistic regression models with a non-nested correlation structure, we evaluated concordance among doctors and nurses, and adjusted for potential confounders (including prior experience in Internal Medicine, acuteness of case and gender). Concordance was associated with number of female professionals (adjusted OR 1.32, 95% CI 1.02 to 1.73), for acute situations (adjusted OR 2.02, 95% CI 1.13 to 3.62), and in doctor role scenarios (adjusted OR 2.19, 95% CI 1.32 to 3.65). Prior experience and country of training were not significant predictors of concordance. In conclusion, our concordance-based approach helped us identify areas of lower concordance in expected doctor-nurse roles and responsibilities, particularly in non-acute situations, which can be targeted by future interprofessional, educational interventions.

Purpose – This study aims to evaluate the extent to which similar team mental models (TMMs) at the beginning of a team's lifecycle influence the level of relationship conflict within the team, TMM-similarity at the middle of the team lifecycle, and in turn team effectiveness. Thus far, no research has analysed the mediating role of a dysfunctional team process between TMM-similarity and effectiveness. Design/methodology/approach – The study was conducted in a strategy and management competition involving 414 individuals who comprised 92 teams (3-5 members). Data were collected at four moments in time. The questionnaires were developed based on validated scales and adapted for the specific context. Findings – The results provide support for the mediating role of conflict between the similarity of team-TMMs at the beginning of team lifecycle and effectiveness. The results also provide support for the mediating role of task-TMMs in the middle of team lifecycle between task-TMMs at the beginning of team lifecycle and effectiveness. Findings suggest that teams with more similar TMMs, experience less relationship conflict which in turn improves effectiveness. Research limitations/implications – In this study TMM-accuracy was not analysed. Future research should analyse the role of TMM similarity and accuracy. Further, future research should explore the optimal level of TMM-similarity and when the similarity of TMM is disruptive to teams. Originality/value – This paper sheds light on the role of conflict as a dysfunctional team process between TMM-similarity and effectiveness. Moreover, this paper shows that more research on TMM evolution is needed.

With so many natural and man-made disasters bringing catastrophic losses worldwide, the initial response is the one during which real-time decision-making is particularly important for the overall performance. During the response phase, first responders with diverse backgrounds (e.g., fire, medical, law enforcement, or public work) need to work together as an ad hoc team to save lives and infrastructures at risk. For successful response operations, cognition (i.e., the ability to support timely and effective decision-making) needs to be understood not only at the individual level but also at the team level. Yet, team cognition still remains to be an inadequately addressed topic in emergency response literature (Bigley & Roberts, 2001; Comfort, 2007; Majchrzak, Jarvenpaa, & Hollingshead, 2007). While the need to study real-world teams in the context of “broader sociotechnical systems” has been documented in team cognition and behavior literature (Kozlowski & Ilgen, 2006; Salas, Cooke & Rosen, 2008; DeChurch and Zaccaro, 2010), emergency response literature lacks attention to emergency responders’ collective efforts. Unlike many other cognitive psychology research topics, the theoretical growth of team cognition has been primarily driven by application needs – i.e., “there was no time to wait for a psychology of team cognition” because applications were “needed yesterday” (Cooke et al. 2007). That is, the scarcity of literature can be traced back to lack of efforts in conceptualizing and operationalizing team cognition in the unique context of emergency response. Most of all, the emergency response field must first come to consensus on what it means by team cognition. As it stands, there are at least five research domains of team cognition (Wildman, Salas, & Scott, 2014), i.e., team mental models, transactive memory systems, team situation awareness, strategic consensus, and interactive team cognition (ITC). While each of these domains highlights some aspects of the multifaceted construct of team cognition, the direction of future research hinges on having a clear conceptualization of team cognition – particularly in the context of emergency response. A scoping review of literature was conducted as an initial effort to outline and synthesize how team cognition has been defined in the field of emergency response. Using targeted keyword searches in MEDLINE, COMPENDEX and CINAHL, the total of 1,799 articles published in English after 1994, which either empirically or otherwise investigated team cognition in emergency response were retrieved. After the initial search, paper titles, abstracts, and full texts were subsequently reviewed to exclude irrelevant ones and to define the subset for detailed review. Our qualitative analysis identified inconsistencies in definitions that may generate redundant research efforts and hinder the generalization of findings. Three main research gaps with respect to defining team cognition in emergency response are identified. (1) Team cognition needs to be defined at the team level, viewing a team either as an aggregated group of individuals or a cognitive system. (2) Product-based and process-based definitions of team cognition need to be clearly differentiated and aligned with two distinct views on a team. While team cognition has a nuanced literature that describes subtle distinctions between two perspectives (i.e., an emergent state vs. a cognitive process) (Durso, Rawson, & Girotto, 2007; Cooke et al. 2007; Saner et al. 2009), emergency response literature has yet to produce research that shows satisfactory exploration on those distinctions. (3) Team cognition needs to be redefined in the unique context of emergency response. As posited by the ITC theory (“team cognition is inextricably tied to context”, Cooke et al. 2013), team cognition in emergency response could differ depending on the nature of the surrounding dynamic context including team, task, and environment. However, different constructs are taken directly from other disciplines and only very few of them are redefined in the context of emergency response (Sætrevik, 2015). This research documents the first component of the multistage on-going project that investigates how interactions among human, team, and technology affect team cognition and performance in emergency response. Based on the synthesis, a working definition for future work is presented as: “a collective cognitive process of team members 1) perceiving changes in the status of critical elements, 2) adapting in response to the perceived changes, and 3) learning from past performance, which supports timely and effective coordinated decision-making and manifests itself as behavioral patterns of dynamic interactions among individuals and technologies”. Future work in progress includes developing an appropriate measurement technique for team cognition based on working definition, designing and evaluating potential technologies to support team cognition in the simulated emergency response environment (i.e., Emergency Operations Training Center, TEEX, College Station, TX).

Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method.

In open‐end mutual funds (unit trusts), there is a strong positive cross‐sectional relationship between net inflows to individual funds and past performance, as if investors attributed performance to managerial skill. Performance shows only very weak persistence, however, so at first sight investors do not appear to gain anything by responding to past performance information. This behaviour can be explained by the fact that past performance is effectively unpriced in the unit trust market, since management fees are unresponsive to demand. If investors believe that there is a non‐zero probability that future performance will turn out to be positively correlated with past performance (i.e. that there is an element of managerial skill in performance), but a zero probability that this correlation will be negative, it is rational to prefer funds with better past performance when performance is not priced. In other words, it costs nothing to insure against the possibility of some managerial skill effect.If this explanation of the flow–performance relationship in unit trusts is correct, one would expect the relationship between investor demand and past fund performance to be much weaker if past performance were to be priced. We test this hypothesis in the market for closed‐end funds (investment trusts). Because closed‐end funds do not trade at net asset value, but at a price determined in the market, strong demand will raise the ratio of price to net asset value (known as the premium). Since it is well established that premiums are mean‐reverting, future shareholder returns on funds currently on high premiums tend to be depressed by the reversion of the premium to the mean. In the closed‐end fund market, as for open‐end funds, there is little evidence of performance persistence, and therefore, to the extent that funds with good past performance are pushed to higher premiums, the expected return on them is less than on the average fund. This implicit pricing mechanism should mean that demand is a declining function of the premium, so that, even if demand is an increasing function of past performance for a given premium, any effect on the premium itself will be muted.We test this hypothesis for closed‐end funds traded in the US and the UK. We find that there is a statistically significant effect of past performance on the premium in both countries. However, consistent with the hypothesis, it has limited economic significance, since it represents only a small component of premium variability. Copyright © 2008 John Wiley & Sons, Ltd.

In the current organizations, successful management of diversity at work has become the most important and challenging issue for managers. While scholars have deepened the understanding on the nature of diversity by proposing new concept such as faultline, previous studies on diversity and faultline have focused more on structural aspect of diversity, while neglecting the role of psychological aspect of diversity among team members. In addition, although team task design plays an important role in interactions among team members, the extent literature has not examined how team task design changes team processes under the different structural and psychological team compositions. To overcome the above limitations, this study constitutes an integrative model of team faultline, value diversity, and team task design on team process and on team effectiveness. Integrating the categorization-elaboration model and the false-consensus bias theory, we suggest that team faultline has positive effect on team effectiveness through team mental model only when both team value diversity and team task design are high. This paper intends to contribute to the literature of team diversity, team design, and team mental model.

Students often make incorrect predictions about their exam performance, with the lowest-performing students showing the greatest inaccuracies in their predictions. The reasons why low-performing students make inaccurate predictions are not fully understood. In two studies, we tested the hypothesis that low-performing students erroneously predict their exam performance in part because their past performance varies considerably, yielding unreliable data from which to make their predictions. In contrast, high-performing students tend to have consistently high past performance that they can rely on to make relatively accurate predictions of future test performance. Results showed that across different exams (Study 1) and different courses (Study 2), low-performing students had more variable past performance than high-performing students. Further, results from Study 2 showed that variability in past course performance (but not past exam performance) was associated with poor calibration. Results suggest that variability in past performance may be one factor that contributes to low-performing students’ erroneous performance predictions.

Purpose This study investigates how CEOs’ temporal focus shapes firms’ R&D responses to inconsistent performance signals, where past performance feedback and future performance prospects convey conflicting signals. Design/methodology/approach Using an unbalanced panel of 1,969 Chinese A-share listed firms (8,801 firm-year observations), we employ fixed-effects models to assess how CEO temporal focus influences firms’ R&D search in response to inconsistent feedback. Findings When past performance is strong but future prospects are weak, past-focused CEOs tend to reduce R&D search, whereas future-focused CEOs are more likely to expand it. Robustness tests reveal a reversed pattern when past performance is weak but future prospects are strong. Originality/value This study demonstrates that CEOs’ temporal orientation determines how conflicting past and future performance signals are attended to and prioritized, providing new insights into attention-based mechanisms underlying heterogeneity in firms’ R&D search and strategic responses.

Purpose. Digital transformation of education determines the need to rethink the goals and meanings of the educational process and, at present, is associated with the problems of our time, the expansion of the role of artificial intelligence. The paper is devoted to the presentation of the taxonomy of educational goals from the standpoint of a mental model of thinking, which allows designing new educational results in the digital transformation of education. Methodology and methods. Unlike Bloom’s taxonomy, the main goal of modern education is the formation and development of computational thinking. The basis for constructing the taxonomy of educational goals is a mental approach, which is a set of principles and strategies for organizing the educational process aimed at forming and developing the cognitive abilities of the student. To identify the essence of computational thinking, a mental model of thinking is used, in which cognitive functions are determined by the mechanisms of perception, memorization, structuring and retrieval of information. In this model, knowledge is a structural set of mental images; mental schemes and mental models based on the first, second, and third signal systems. Results. Consideration of the mental characteristics of a modern person in a digital society, definition of the 3rd signal system in the “person + ICT + artificial intelligence” link, in which mental schemes and mental models play the main role, made it possible to formulate the stages of the educational process target settings in the form of the taxonomy of educational goals. The taxonomy of educational goals begins with the assignment of mental entropy - as a subject field of the future professional activity of a specialist, containing questions, tasks and problems. They can be formulated by traditional educational goals in the form of: know, be able to, master. The specification of these goals leads to target guidelines for the composition, volume and content of subject mental images, mental schemes and mental models. The set of mental images, schemes and models represent a knowledge block. In parallel, an ascending target block of universal cognitive operations is set, that a modern specialist must master. The pinnacle of the taxonomy is computational thinking. Based on the mental model of thinking, the essence of computational thinking is revealed and a set of universal cognitive operations is compiled. The basic composition of mental models that determine human cognitive behavior in a digital society is formed. Computational thinking is a type of thinking with the activation of conceptual-abstract and conceptual-machine superstructures of mental images, mental schemes and models in the circuit of the third signal system. Conclusion. The proposed mental taxonomy of educational goals determines the vector of the direction of digital transformation of education to achieve the main result - computational thinking. The stages of formation and the quality of computational thinking are determined by the formation of a set of universal cognitive operations, the volume and content of mental images, mental schemes and mental models of the subject area.

Preschool children are generally inaccurate at evaluating past and predicting future performance. The present study examines the effect of performance feedback on the accuracy of preschoolers’ predictive judgments and tests whether performance feedback acts as an anchor for postdictive judgments. In Experiment 1, preschool children (n = 40) solved number patterns, and in Experiment 2 they solved object patterns (n = 59). The results in both experiments revealed, firstly, that children receiving performance feedback made more accurate predictive judgments and lowered their certainty after their incorrect answer. Secondly, the children relied on performance feedback more than on actual task experience when making postdictive judgments, indicating that performance feedback was used as an anchor for subsequent postdictive judgments.

The effects of two types of Intelligent Computer Aided Instruction (ICAI) and a non-intelligent tutoring system on the development of students' mental models and on performance was explored. One of the two ICAI systems provided both principle-based and task specific advice while the other provided task specific advice only. The non-intelligent CAI provided no advice. The problems presented to students increased in difficulty as the student progressed. It was hypothesized that the mental models of students who interacted with the ICAI that provided both principle-based and task specific advice would be most similar in structure to a “gold standard” model, and that these students would also exhibit the best performance. It was also hypothesized that students who were exposed to the ICAI that provided only task specific advice would perform better, and exhibit more accurate and detailed mental models than would students who interacted with the CAI that provided no advice. Study results indicated that students in both ICAI conditions achieved a higher similarity index, fewer errors, and faster task time, than did students in the no advice group. However, there was no significant difference between the two ICAIs for any of these measures. There was a significant interaction between version and problem difficulty for task time. As problem difficulty increased, task time increased for both of the two ICAI conditions, but not for the no-advice condition. These results are discussed in terms of their implications for the predictive validity of the Pathfinder measure, and in terms of the appropriate use and design of ICAI.