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An Interaction Process Guided Framework for Small-Group Performance Prediction
Abstract
A small group is a fundamental interaction unit for achieving a shared goal. Group performance can be automatically predicted using computational methods to analyze members’ verbal behavior in task-oriented interactions, as has been proven in several recent works. Most of the prior works focus on lower-level verbal behaviors, such as acoustics and turn-taking patterns, using either hand-crafted features or even advanced end-to-end methods. However, higher-level group-based communicative functions used between group members during conversations have not yet been considered. In this work, we propose a two-stage training framework that effectively integrates the communication function, as defined using Bales’ interaction process analysis (IPA) coding system, with the embedding learned from the low-level features in order to improve the group performance prediction. Our result shows a significant improvement compared to the state-of-the-art methods (4.241 MSE and 0.341 Pearson’s correlation on NTUBA-task1 and 3.794 MSE and 0.291 Pearson’s correlation on NTUBA-task2) on the NTUBA (National Taiwan University Business Administration) small-group interaction database. Furthermore, based on the design of IPA, our computational framework can provide a time-grained analysis of the group communication process and interpret the beneficial communicative behaviors for achieving better group performance.
Collaborative problem-solving abilities are considered crucial skills in the 21st century. However, learners often struggle with collaborative problem-solving tasks. This empirical study aimed to propose and investigate the impacts of a novel group cognitive graph (GCG) approach on collaborative problem solving. In total, 90 college students voluntarily participated in the present study, and they were assigned to three conditions: the group cognitive graph (GCG) condition, the group knowledge graph (GKG) condition, and the control group (CG) condition. The findings revealed that the GCG approach significantly improved the collaborative problem-solving performance and collaborative knowledge building of the participants. Moreover, the group cognitive graph approach did not increase the imposed cognitive load. The findings and their implications for research and practice are discussed in depth.
The interaction of multidisciplinary parties during management and design team meetings should be a subject of real interest, yet research based on professional interact is limited. Using the Bales interaction process analysis technique, thirty management and design team meetings associated with ten different construction projects were observed. On completion of each project, data were collected on each project's performance, in respect of whether the project was completed within the scheduled time and budgeted cost, or not. The findings suggest that communication during management and design meetings exhibit regular patterns of interaction. On examination of the Interaction Profiles against project outcomes, initial results suggest that there are differences in the interaction associated with successful and unsuccessful projects.
Studying group dynamics requires fine-grained spatial and temporal understanding of human behavior. Social psychologists studying human interaction patterns in face-to-face group meetings often find themselves struggling with huge volumes of data that require many hours of tedious manual coding. There are only a few publicly available multi-modal datasets of face-to-face group meetings that enable the development of automated methods to study verbal and non-verbal human behavior. In this paper, we present a new, publicly available multi-modal dataset for group dynamics study that differs from previous datasets in its use of ceiling-mounted, unobtrusive depth sensors. These can be used for fine-grained analysis of head and body pose and gestures, without any concerns about participants' privacy or inhibited behavior. The dataset is complemented by synchronized and time-stamped meeting transcripts that allow analysis of spoken content. The dataset comprises 22 group meetings in which participants perform a standard collaborative group task designed to measure leadership and productivity. Participants' post-task questionnaires, including demographic information, are also provided as part of the dataset. We show the utility of the dataset in analyzing perceived leadership, contribution, and performance, by presenting results of multi-modal analysis using our sensor-fusion algorithms designed to automatically understand audio-visual interactions.
This paper summarizes the latest, final version of ISO standard 24617-2 "Semantic annotation framework, Part 2: Dialogue acts". Compared to the preliminary version ISO DIS 24617-2:2010, described in Bunt et al. (2010), the final version additionally includes concepts for annotating rhetorical relations between dialogue units, defines a full-blown compositional semantics for the Dialogue Act Markup Language DiAML (resulting, as a side-effect, in a different treatment of functional dependence relations among dialogue acts and feedback dependence relations); and specifies an optimally transparent XML-based reference format for the representation of DiAML annotations, based on the systematic application of the notion of 'ideal concrete syntax'. We describe these differences and briefly discuss the design and implementation of an incremental method for dialogue act recognition, which proves the usability of the ISO standard for automatic dialogue annotation.
Synchronized verbal behavior can reveal important information about social dynamics. This study introduces the linguistic style matching (LSM) algorithm for calculating verbal mimicry based on an automated textual analysis of function words. The LSM algorithm was applied to language generated during a small group discussion in which 70 groups comprised of 324 individuals engaged in an information search task either face-to-face or via text-based computer-mediated communication. As a metric, LSM predicted the cohesiveness of groups in both communication environments, and it predicted task performance in face-to-face groups. Other language features were also related to the groups’ cohesiveness and performance, including word count, pronoun patterns, and verb tense. The results reveal that this type of automated measure of verbal mimicry can be an objective, efficient, and unobtrusive tool for predicting underlying social dynamics. In total, the study demonstrates the effectiveness of using language to predict change in social psychological factors of interest.
The relationship between the quality of communication cycles and performance was tested for triad and dyads, and the relationship between thought cycles and performance was tested for individuals. High-quality cycles conform to an ideal structure. This means that they start with action preparatory functions (orientation or planning) and end with the evaluation of a behavior performed. It was hypothesized that quality of cycles predicts performance above and beyond other process variables. For triads, a significant amount of additional variance in performance was explained by cycle quality after accounting for the effect of number of cycles communicated and cycle length (Study 1). The main findings are replicated for dyads (Study 2). In Study 3, individual actors performing the same task were asked to think aloud, and the protocols were analyzed in the same manner as group communication. Again, quality of thought cycles was related to higher performance, indicating similar functions of thinking for individual action and of communication for groups.
A 2 × 2 factorial design was used to explore the process and outcome of small group problem-solving discussions for two modes of communication (face-to-face and computerized conferencing) and two types of tasks (a qualitative human relations task and a scientific ranking test with a criterion solution). Interaction process was coded using Bales Interaction Process Analysis. There were two to three times as many communication units in the face to-face groups consisting of five members each as in the computerized conferencing mode of communication during the same elapsed time. Group decisions were equally good in the two modes, but the groups were less likely to reach agreement in the computerized conferencing mode. There were proportionately more of the types of task-oriented communication associated with decision quality in the computerized conferences.
Investigated the relationship between the quality of task-related communication and group productivity with 36 college students (aged 19–24 yrs). The flow of communication was divided into cycles of related communication during which Ss talked about the same part of the task. 12 groups worked on a construction task involving strategic and manual task requirements. High- and low-performing groups were compared with regard to the amount of orientation, planning, and evaluation, as well as quality of communication cycles. High-performing groups showed a higher proportion of the ideal communication cycle, which starts with communication referring to task preparation, either orienting or planning, and ends with an evaluation. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Identifying emergent leaders in organizations is a key issue in organizational behavioral research, and a new problem in social computing. This paper presents an analysis on how an emergent leader is perceived in newly formed, small groups, and then tackles the task of automatically inferring emergent leaders, using a variety of communicative nonverbal cues extracted from audio and video channels. The inference task uses rule-based and collective classification approaches with the combination of acoustic and visual features extracted from a new small group corpus specifically collected to analyze the emergent leadership phenomenon. Our results show that the emergent leader is perceived by his/her peers as an active and dominant person; that visual information augments acoustic information; and that adding relational information to the nonverbal cues improves the inference of each participant's leadership rankings in the group.
This article presents Second Messenger, a system of dynamic awareness dis-plays that reveal speaker participation patterns in a face-to-face discussion. The system has been used by a variety of groups during face-to-face meetings, in-creasing individuals' awareness of their own and others' participation in discus-sions. Experimental results indicate that these displays influence the amount an individual participates in a discussion and the process of information sharing used during a decision-making task. These findings suggest that awareness ap-plications bring about systematic changes in group communication styles, high-lighting the potential for such applications to be designed to improve group interactions. Joan DiMicco is an HCI researcher interested in human–human communica-tion; she is a researcher at IBM Research in Cambridge, MA. Katherine Hollenbach is an undergraduate at MIT with an interest in computer science and design; she is an undergraduate researcher at the MIT Media Lab. Anna Pandolfo is an experimental psychology researcher with an interest in research methodology; she is a research affiliate at the MIT Media Lab. Walter Bender studies new information technologies that affect people directly; he is President, Software and Content, of the One Laptop per Child Foundation.
We describe a social visualization system that monitors the vocal arousal levels of the participants in a simulated two-party employment negotiation. In a 3x2 factorial experiment (N = 84), we manipulate two variables of interest for social visualization systems: the feedback configuration of the system's display (participants receive self feedback vs. partner feedback vs. no feedback) and the status of the interactants (high vs. low). Receiving feedback about one's own arousal level has negative consequences for performance in and feelings about the negotiation. Receiving feedback about one's partner's arousal level interacts with status: high-status individuals benefit from the visualization, while low-status individuals do not.
This study focused on leadership style (participative leadership/directive leadership) as a key factor, which has an intervening impact on a functionally heterogeneous team’s process and outcomes. In a study of 136 primary care teams, the author found that in high functionally heterogeneous teams, participative leadership style was positively associated with team reflection, which in turn fostered team innovation; however, this leadership style decreased team in-role performance. The impact of directive leadership was in promoting team reflection under the condition of low functional heterogeneity, whereas no such impact was found under the condition of high functional heterogeneity.
Meeting of Minds
The performance of humans across a range of different kinds of cognitive tasks has been encapsulated as a common statistical factor called g or general intelligence factor. What intelligence actually is, is unclear and hotly debated, yet there is a reproducible association of g with performance outcomes, such as income and academic achievement. Woolley et al. (p. 686 , published online 30 September) report a psychometric methodology for quantifying a factor termed “collective intelligence” ( c ), which reflects how well groups perform on a similarly diverse set of group problem-solving tasks. The primary contributors to c appear to be the g factors of the group members, along with a propensity toward social sensitivity—in essence, how well individuals work with others.
The task of flying a multipilot transport aircraft is a classic small-group performance situation where a number of social, organizational, and personality factors are relevant to important outcome variables such as safety. The aviation community is becoming increasingly aware of the importance of these factors but is hampered in its efforts to improve the system because of research psychology's problems in defining the nature of the group process. This article identifies some of the problem areas as well as methods used to address these issues. It is argued that high fidelity flight simulators provide an environment that offers unique opportunities for work meeting both basic and applied research criteria.
Approaches to mitigating the likelihood of psychosocial problems during space missions emphasize preflight measures such as team training and team composition. Additionally, it may be necessary to monitor team interactions during missions for signs of interpersonal stress. The present research was conducted to identify features in team members' communications indicative of team functioning.
Team interactions were studied in the context of six computer-simulated search and rescue missions. There were 12 teams of 4 U.S. men who participated; however, the present analyses contrast the top two teams with the two least successful teams. Communications between team members were analyzed using linguistic analysis software and a coding scheme developed to characterize task-related and social dimensions of team interactions. Coding reliability was established by having two raters independently code three transcripts. Between-rater agreement ranged from 78.1 to 97.9%.
Team performance was significantly associated with team members' task-related communications, specifically with the extent to which task-critical information was shared. Successful and unsuccessful teams also showed different interactive patterns, in particular concerning the frequencies of elaborations and no-responses. Moreover, task success was negatively correlated with variability in team members' word count, and positively correlated with the number of positive emotion words and the frequency of assenting relative to dissenting responses.
Analyses isolated certain task-related and social features of team communication related to team functioning. Team success was associated with the extent to which team members shared task-critical information, equally participated and built on each other's contributions, showed agreement, and positive affect.
In qualifying and analyzing the performance of group interaction, interaction processing analysis (IPA) defined by Bale is considered a useful approach. IPA is a system for labeling a total of 12 interaction categories for the interaction process. Automatic IPA can manually encompass the gap in spending manpower and can efficiently qualify group performance. In this paper, we present computational interaction processing analysis by developing a model to recognize categories of IPA. We extract both verbal features and nonverbal features for IPA category recognition modeling with SVM, RF, DNN and LSTM machine learning algorithms and analyze the contribution of multimodal features and unimodal features for the total data and each label. We also investigate the effect of context information by training sequences with different lengths with an LSTM and evaluating them. The results show that multimodal features achieve the best performance with an F1 score of 0.601 for the recognition of 12 IPA categories using the total data. Multimodal features are better than the unimodal features for the total data and most labels. The results of investigating context information show that a suitable length of sequence enables a longer sequence to achieve the best F1 score of 0.602 and a better performance for recognition.
We explored the effectiveness of external observable behaviors in multi-party discussions to estimate an individual’s empathy skill level. In our previous research, we estimated personal empathy skills from the external observable behavior in multi-person dialogues. We demonstrated that the gaze behavior towards the end of utterances and dialogue act (DA), i.e., verbal-behavior information indicating the intension of an utterance during turn-keeping/changing, are important for estimating empathy level. We focused on Davis’ Interpersonal Reactivity Index (IRI), which measures empathy skill level and consists of four dimensions of empathy, i.e., empathic concern (EC), perspective taking (PT), personal distress (PD), and fantasy (FS), as the estimation target. We particularly focused on estimating an individual’s EC score. In this research, we explored whether gaze behavior and DA during turn-keeping/changing are useful regarding the other three dimensions, i.e., PT, PD, and FS by constructing and evaluating estimation models based on these dimensions. We found that gaze behavior and DA are useful for estimating the scores of these three dimensions. Therefore, gaze behavior and DA during turn-changing/keeping are useful for estimating the scores of all four Davis’ IRI dimensions.
In this paper, we present the Group Affect and Performance (GAP) corpus, a publicly available dataset of thirteen small group meetings. The GAP corpus contains meeting audio, transcriptions, annotations, decision-making performance, as well as group member influence, post-meeting ratings of satisfaction, and demographics. In this paper, we discuss all aspects of data collection and preparation. We also present preliminary analyses and findings concerning decision-making performance, group member influence, group member satisfaction, and additional meeting characteristics. We conclude with future directions. In creating and releasing this corpus, it is our goal to stimulate research on the computational analysis of small group meetings, and to supplement the relatively small amount of currently available group interaction data.
We explored the gaze behavior towards the end of utterances and dialogue act (DA), i.e., verbal-behavior information indicating the intension of an utterance, during turn-keeping/changing to estimate empathy skill levels in multiparty discussions. This is the first attempt to explore the relationship between such a combination. First, we collected data on Davis' Interpersonal Reactivity Index (which measures empathy skill level), utterances that include the DA categories of Provision, Self-disclosure, Empathy, Turn-yielding, and Others, and gaze behavior from participants in four-person discussions. The results of analysis indicate that the gaze behavior accompanying utterances that include these DA categories during turn-keeping/changing differs in accordance with people's empathy skill levels. The most noteworthy result was that speakers with low empathy skill levels tend to avoid making eye contact with the listener when the DA category is Self-disclosure during turn-keeping. However, they tend to maintain eye contact when the DA category is Empathy. A listener who has a high empathy skill level often looks away from the speaker during turn-changing when the DA category of a speaker's utterance is Provision or Empathy. There was also no difference in gaze behavior between empathy skill levels when the DA category of the speaker's utterance was turn-yielding. From these findings, we constructed and evaluated models for estimating empathy skill level using gaze behavior and DA information. The evaluation results indicate that using both gaze behavior and DA during turn-keeping/changing is effective for estimating an individual's empathy skill level in multi-party discussions.
Small group interaction occurs often in workplace and education settings. Its dynamic progression is an essential factor in dictating the final group performance outcomes. The personality of each individual within the group is reflected in his/her interpersonal behaviors with other members of the group as they engage in these task-oriented interactions. In this work, we propose an interlocutor-modulated attention BSLTM (IM-aBLSTM) architecture that models an individual's vocal behaviors during small group interactions in order to automatically infer his/her personality traits. The interlocutor-modulated attention mechanism jointly optimize the relevant interpersonal vocal behaviors of other members of group during interactions. In specifics, we evaluate our proposed IM-aBLSTM in one of the largest small group interaction database, the ELEA corpus. Our framework achieves a promising unweighted recall accuracy of 87.9% in ten different binary personality trait prediction tasks, which outperforms the best results previously reported on the same database by 10.4% absolute. Finally, by analyzing the interpersonal vocal behaviors in the region of high attention weights, we observe several distinct intra- and inter-personal vocal behavior patterns that vary as a function of personality traits.
We address the problem of automatically predicting group performance on a task, using multimodal features derived from the group conversation. These include acoustic features extracted from the speech signal, and linguistic features derived from the conversation transcripts. Because much work on social signal processing has focused on nonverbal features such as voice prosody and gestures, we explicitly investigate whether features of linguistic content are useful for predicting group performance. The conclusion is that the best-performing models utilize both linguistic and acoustic features, and that linguistic features alone can also yield good performance on this task. Because there is a relatively small amount of task data available, we present experimental approaches using domain adaptation and a simple data augmentation method, both of which yield drastic improvements in predictive performance, compared with a target-only model.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
This paper focuses on the computational analysis of the individual communication skills of participants in a group. The computational analysis was conducted using three novel aspects to tackle the problem. First, we extracted features from dialogue (dialog) act labels capturing how each participant communicates with the others. Second, the communication skills of each participant were assessed by 21 external raters with experience in human resource management to obtain reliable skill scores for each of the participants. Third, we used the MATRICS corpus, which includes three types of group discussion datasets to analyze the influence of situational variability regarding to the discussion types. We developed a regression model to infer the score for communication skill using multimodal features including linguistic and nonverbal features: prosodic, speaking turn, and head activity. The experimental results show that the multimodal fusing model with feature selection achieved the best accuracy, 0.74 in R² of the communication skill. A feature analysis of the models revealed the task-dependent and task-independent features to contribute to the prediction performance.
This paper addresses the problem of predicting the performance of decision-making groups. Towards this goal, we evaluate the predictive power of group attributes and discussion dynamics by using automatically extracted features, such as group members' aural and visual cues, interaction between team members, and influence of each team member, as well as selfreported features such as personality-and perception-related cues, hierarchical structure of the group, and individual-and grouplevel task performances. We tackle the inference problem from two angles depending on the way that features are extracted: 1) a holistic approach based on the entire meeting, and 2) a sequential approach based on the thin slices of the meeting. In the former, key factors affecting the group performance are identified and the prediction is achieved by support vector machines. As for the latter, we compare and contrast the classification performance of an influence model-based novel classifier with that of hidden Markov model (HMM). Experimental results indicate that the group looking cues and the influence cues are major predictors of group performance and the influence model outperforms the HMM in almost all experimental conditions. We also show that combining classifiers covering unique aspects of data results in improvement in the classification performance.
Purpose: The purpose of this paper is to contribute to further studies of theoretical and conceptual understanding of teachers' team learning processes, with a main focus on team work, team atmosphere, and collective reflections. Design/methodology/approach: The empirical study was designed as a multi-case study in a research and development project. The case studies include three teacher teams from different schools. Data were collected though observations and in-depth interviews and analysed qualitatively. Findings: The main findings show that the teams differ with regard to collaboration and team atmosphere, and willingness to learn collectively. The analyses of talk at team meetings show the importance of collective reflection loops through which the teachers transform the contents of their conversations. A facilitating team atmosphere seems vitally important for the emergence of the identified collective reflection loops. Collective reflections potentially increase team learning. Research limitations/implications: Case study and conversation analyses which were mainly focused on verbal communication have certain limitations. A multi-case design and different methods for data collection were used to offset these presumed weaknesses. Practical implications: One of the purposes with the research and development approach was to support teachers' team learning processes. The findings provide insights and model of team learning with further practical implications for teacher teams. Originality/value: The findings show that a facilitating team atmosphere supports collective reflection loops, with potential to increase the team's collective competence. These findings provide valuable contributions to further conceptual understanding of team learning.
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Task-solving in dialogue depends on the convergence of the situation models held by the dialogue partners. The Interactive Alignment Model (Pickering & Garrod, 2004) suggests that this convergence is the result of an interactive alignment process, which is based on mechanistic repetition at a number of linguistic levels. In this paper, we develop two predictions arising from the theory, along with two methods to quantify the known structural priming effects in the full inventory of syntactic choices found in text and speech corpora. (a) Under a rational perspective, we expect increased repetition in task-oriented dialogue compared to spontaneous conversation. We find within- and between-speaker priming in a corpus of spontaneous conversations, but stronger priming in task-oriented dialogue. (b) The Interactive Alignment Model predicts linguistic adaptation to be correlated with task success. We show this effect in a corpus of task-oriented dialogue, where we find a positive correlation of long-term adaptation and a quantifiable task success measure. We argue that the repetition tendency relevant for the high-level alignment of situation models is based on slow adaptation rather than short-term priming. We demonstrate that lexical and syntactic repetition are reliable and computationally exploitable predictors of task success.
We develop and evaluate a real-time language feedback system that monitors the communication patterns among students in a discussion group and provides real-time instructions to shape the way the group works together. As an initial step, we determine which group processes are related to better outcomes. We then experimentally test the efficacy of providing real-time instructions which target two of these group processes. The feedback system was successfully able to shape the way groups worked together. However, only appropriate feedback given to groups that were not working well together from the start was able to improve group performance.
This study explores the effects of computer-mediated communication (CMC) and face-to-face (FTF) media on group performance under four experimental conditions. There were CMC-only, FTF-only, FTF/CMC, and CMC/FTF groups. The study examined three variables: the number of unique ideas generated, the time to reach consensus, and the decision quality. Vigilant interaction theory was helpful in exploring the media effects on group performance. Results indicate that, in general, CMC groups generate a greater number of unique ideas than do FTF groups. However, the CMC effect was greater with CMC/FTF groups. This effect was attributed to CMC's showing a greater potential for preventing productivity loss when there is more likelihood for performance evaluation as group members anticipate future FTF interaction with their coparticipants in CMC. Results also indicate the CMC groups take longer to reach consensus than FTF groups. Decision quality was greater in both FTF/CMC and CMC/FTF groups than in either CMC- and FTF-only groups. This study affirms the need to explore group decision making as a nonunitary process and the need to combine FTF/CMC media for better group performance. The discussion includes implications and recommendations for media combination choice.
Work group literature has recently focused on team process improvement, which refers to a learning process within the team, including the review of recent work methods and objectives and the development of alternative working strategies. Until now, however, no systematic empirical effort has been undertaken to empirically explore the dimensionality of team process improvement, although a dual focus, namely, team reflection and team adaptation, is theoretically accepted. The authors thus examined a two-dimensional structure of team process improvement by distinguishing team reflection and team adaptation in two studies using an experimental and a field design. Confirmatory factor analyses results of both studies provide evidence for the hypothesized two-dimensional structure of team process improvement. Additionally, the field study of organizational teams show that team reflection and team adaptation predict team performance to some extent.
Purpose
This paper seeks to investigate the potential role of emotional intelligence (EI) abilities within learning in teams. The research focuses on examining how EI abilities are enacted within team contexts and how these are associated with critical reflection and team processes associated with learning.
Design/methodology/approach
A phenomenological approach to the investigation of EI abilities was adopted using a diary methodology to capture how EI abilities were enacted over a 14‐week team project by 80 MBA students from a range of international backgrounds. Such an approach is advocated to offer insights into the internal processes by which social action is perceived “ in situ ”.
Findings
The two EI abilities, emotional awareness and emotional management, were found to influence the three critical reflection processes: problem analysis, theorising cause and effect relationships, and action planning, as well as processes associated with team learning including team identification, social engagement, communication and conflict management.
Practical implications
EI may offer insights into how differences in the nature, direction and depth of critical reflection can occur in team learning contexts. Developmental initiatives that aim to improve the emotional abilities of team members may help individuals to better manage the emotional context of learning in teams.
Originality/value
Despite the increasing recognition of the role emotions play in learning, very little is known to date about how differences in the way in which emotional information is processed within social learning contexts can influence critical reflection or other learning processes. The paper fills some of the gaps.
This chapter reviews existing research and thought on the role of group interaction in task-oriented groups, and provides suggestion that part of the difficulty in understanding the relationship between group interaction and group effectiveness has to do with the nature of existing methodological and conceptual tools. It proposes an alternative framework for research on group effectiveness. The major functions group interaction serves in enhancing and depressing group effectiveness have been explored in the chapter and a set of strategies for influencing group interaction and group performance by alteration of “input” factors has been proposed within the new framework. The chapter presents an argument for a return to action-oriented research as a way to improve simultaneously the understanding of the determinants of group effectiveness and the capability to change and improve it. Implications for research and for action have been drawn and explored.
This review examines the role of groups in the organization. Recent literature is discussed through a heuristic model of group behavior. Group structure, strategies, leadership, and reward allocation to members are viewed as inputs to the model. Outcomes are defined as group performance, quality of work lifefor group members, and ability to work independently in the future. A number of group process variables are seen as significant in this model. Implications of the current literature are offeredfor practitioners and researchers.
This article develops a model of group problem solving in which performance is a function of group resources and strategies for their use. Resources are defined as the joint task knowledge of a group's two most expert members. Decision scheme is essentially defined as the degree of influence of these two experts and is shown, for certain composition conditions, to be related to group process, specifically, effective conflict management. Similarities between the model and other social influence theories are examined, and implications for organizational problem solving are explored. The task is the “Moon Survival” problem, and the subjects are 102 managers and graduate management students, working in 21 groups.
In this paper we present an annotated audio–video corpus of multi-party meetings. The multimodal corpus provides for each
subject involved in the experimental sessions six annotation dimensions referring to group dynamics; speech activity and body
activity. The corpus is based on 11 audio and video recorded sessions which took place in a lab setting appropriately equipped
with cameras and microphones. Our main concern in collecting this multimodal corpus was to explore the possibility of providing
feedback services to facilitate group processes and to enhance self awareness among small groups engaged in meetings. We therefore
introduce a coding scheme for annotating relevant functional roles that appear in a small group interaction. We also discuss
the reliability of the coding scheme and we present the first results for automatic classification.
The small amount of published research into construction project meetings demonstrates some of the principal difficulties of investigating such sensitive business environments. Using the Bales Interaction Process Analysis (IPA) research method, data on group interaction were collected. A project outcome, namely whether the project was within contract budget, was used as a basis of enquiry between interaction patterns. Analysis was concerned with the socio-emotional (relationship building) and the task-based components of communication and the positive and negative socio-emotional interaction characteristics. Socio-emotional interaction was found to be significantly greater in the projects completed within budget. Socio-emotional interaction is used to express feelings in relation to tasks and it serves as the flux that creates and sustains the group's social framework, which is crucial in a project environment. The data provide an indication of the importance of informal communication in the maintenance of relationships within project meetings.
Although a few studies have investigated the communication behaviour of construction professionals this research represents the first attempt to model the construction team's interaction in live project meetings. Using the established Bales interaction process analysis (IPA) method, both task and relational interaction were recorded and a model of group communication was produced. A total of 36 meetings were observed from 10 construction projects and the data aggregated to provide a single profile of the groups' interaction. The construction meetings' interaction is compared to previous research undertaken in other contexts. Differences were found between the interaction patterns of work, social and academic groups. Typical of the interaction previously observed in work groups, the participants in construction meetings use high levels of task-based interaction and low levels of socio-emotional interaction. The adversarial environment often associated with construction was not found, indeed the level of negative emotion and critical discussion was so low that it could be suggested that problems may pass unchallenged.
Robert F Bales. 1950. Interaction process analysis; a method for the study of small groups
- F Robert
- Bales
- Bales F
Blazeface: Sub-millisecond neural face detection on mobile gpus
- Valentin Bazarevsky
- Yury Kartynnik
- Andrey Vakunov
- Karthik Raveendran
- Matthias Grundmann
- Bazarevsky Valentin