Individuals' psychosocial voice barriers in lean problem-solving teams

PurposeVoice role identification and the psychosocial voice barriers represented by implicit voice theories (IVTs) affect lean team members' prosocial voice behavior and thereby lean team performance. This paper investigates how role definition and IVTs influence individual lean team-members' prosocial voice behavior during lean implementation.Design/methodology/approachThis research was conducted in four case readymade garment (RMG) factories in Bangladesh following a mixed-method research approach dominated by a qualitative research methodology. Under the mixed-method design, this research followed multiple research strategies, including intervention-based action research and case studies.FindingsThe findings suggest that voice role perception affects the voice behavior of the individual lean team members. The findings also demonstrate that voice role definition significantly influences individually held implicit voice beliefs in lean teams.Research limitations/implicationsThis research was conducted in four sewing lines in four RMG factories in Bangladesh. There is a need for a cross-sector and cross-country large-scale study that follows the quantitative research methods in different contexts.Practical implicationsThis research contributes to the operations management literature, especially in lean manufacturing, by presenting the difficulties of mobilizing employee voice in lean problem-solving teams. This work provides new knowledge to managers to address challenges and opportunities to ensure decent work and to improve productivity.Originality/valueThis research raises a key issue of employee voice and its influence on lean performance which addresses two critical areas of employee voice behavior in lean teams: team-members' voice role perception and implicit voice beliefs that influence their voice behavior in the workplace, thereby influencing team performance.

Teaching Machines to Recognize Neurodynamic Correlates of Team and Team Member Uncertainty

We describe efforts to make humans more transparent to machines by focusing on uncertainty, a concept with roots in neuronal populations that scales through social interactions. To be effective team partners, machines will need to learn why uncertainty happens, how it happens, how long it will last, and possible mitigations the machine can supply. Electroencephalography-derived measures of team neurodynamic organization were used to identify times of uncertainty in military, health care, and high school problem-solving teams. A set of neurodynamic sequences was assembled that differed in the magnitudes and durations of uncertainty with the goal of training machines to detect the onset of prolonged periods of high level uncertainty, that is, when a team might require support. Variations in uncertainty onset were identified by classifying the first 70 s of the exemplars using self-organizing maps (SOM), a machine architecture that develops a topology during training that separates closely related from desperate data. Clusters developed during training that distinguished patterns of no uncertainty, low-level and quickly resolved uncertainty, and prolonged high-level uncertainty, creating opportunities for neurodynamic-based systems that can interpret the ebbs and flows in team uncertainty and provide recommendations to the trainer or team in near real time when needed.

Collaborating With Style: Using an Agent-Based Model to Simulate Cognitive Style Diversity in Problem Solving Teams

Collaborative problem solving can be successful or counterproductive. The performance of collaborative teams depends not only on team members’ abilities, but also on their cognitive styles. Cognitive style measures differences in problem-solving behavior: how people generate solutions, manage structure, and interact. While teamwork and problem solving have been studied separately, their interactions are less understood. This paper introduces the KAI Agent-Based Organizational Optimization Model (KABOOM), the first model to simulate cognitive style in collaborative problem solving. KABOOM simulates the performance of teams of agents with heterogeneous cognitive styles on two contextualized design problems. Results demonstrate that, depending on the problem, certain cognitive styles may be more effective than others. Also, intentionally aligning agents’ cognitive styles with their roles can improve team performance. These experiments demonstrate that KABOOM is a useful tool for studying the effects of cognitive style on collaborative problem solving.

Collaborating with Style: Using an Agent-Based Model to Simulate Cognitive Style Diversity in Problem Solving Teams

Collaborative problem solving can be successful or counterproductive. The performance of collaborative teams depends not only on team members' abilities, but also on their cognitive styles. Cognitive style measures differences in problem-solving behavior: how people generate solutions, manage structure, and interact. While teamwork and problem solving have been studied separately, their interactions are less understood. This paper introduces the KAI Agent-Based Organizational Optimization Model (KABOOM), the first model to simulate cognitive style in collaborative problem solving. KABOOM simulates the performance of teams of agents with heterogeneous cognitive styles on two contextualized design problems. Results demonstrate that, depending on the problem, certain cognitive styles may be more effective than others. Also, intentionally aligning agents' cognitive styles with their roles can improve team performance. These experiments demonstrate that KABOOM is a useful tool for studying the effects of cognitive style on collaborative problem solving.

Culturally Responsive Data-Based Decision Making in High School Settings

Engaging in culturally responsive practices is considered best practice in classroom instruction, particularly within diverse schools, however, when making data-based decisions, there is little guidance for culturally responsive practitioners on how to engage in this work. This article introduces a model of culturally responsive data-based decision making (CR-DBDM) by outlining culturally responsive practices and combining them with Deno’s IDEAL (Identify, Define, Explore, Apply, and Look) problem solving model. The article provides members of high school-based problem-solving teams (PSTs), working within a response to intervention (RTI) framework, suggestions for decision making in ways that do not contribute to the disproportionate representation of culturally and linguistically diverse students in special education and empowers students and families by valuing their voices throughout the RTI process.

Effects of consultation on professional learning communities

School consultation has been used to increase fidelity of implementation for team processes (Burns, Peters, and Noell, 2008) and resulting interventions (Noell, Witt, Gilbertson, Ranier, and Freeland, 1997). Professional learning communities are teacher teams with the overall purpose of changing educator behavior through collaborative engagement with colleagues and use of data to inform instructional practices (DuFour, Eaker, and DuFour, 2005; McLaughlin and Talbert, 2006). School-based teams, such as problem-solving teams, do not often follow implementation guidelines (Burns and Symington, 2002), thus hindering a crucial element of a successful response to intervention (RTI) model (Burns and Coolong-Chaffin, 2006). The discussion of student data and intervention strategies happening within PLCs is important for the continuation of an RTI model within schools (Burns and Gibbons, 2012). ... Each team was observed with the rubric using a multiple baseline design, including baseline, intervention, and maintenance phases. The intervention phase involved the researcher providing consultation on an identified area of weakness and guiding the team through an intervention, using an instructional consultation framework. The results from the study indicated a change in PLC implementation when consultation was added. Each team displayed an improvement in their implementation of PLC practices that was maintained after consultation ended. However, the improvements for each team during the intervention and maintenance phases were small, in comparison to the baseline phase. Future research is needed to determine the impact of consultation with PLCs on student outcomes. Implications for research and practice, limitations, and future directions are discussed.