Fieldwork with teams: practical problems and proposed solutions - - PowerPoint PPT Presentation

Helen Hughes & Mark Robinson

Fieldwork with teams: practical problems and proposed solutions

Advancing the Science of Multi-Team Systems EAWOP Small Group Meeting Varenna, Italy, October 27-29th 2012

Objectives − To identify some potential problems encountered when undertaking applied MTS research − To propose some potential solutions and future directions for MTS research − To encourage debate on these issues

Problem 1: Multi-team membership

Matrix ¡structure ¡ Vehicle ¡model ¡A ¡ Vehicle ¡model ¡B ¡ Vehicle ¡model ¡C ¡ Vehicle ¡model ¡D ¡ Vehicle ¡model ¡E ¡ Component ¡1 ¡  ¡  ¡  ¡  ¡  ¡ Component ¡2 ¡  ¡  ¡  ¡  ¡  ¡ Component ¡3 ¡  ¡  ¡  ¡  ¡  ¡ Component ¡4 ¡  ¡  ¡  ¡  ¡  ¡ Component ¡5 ¡  ¡  ¡  ¡  ¡  ¡

• Matrix organizational structures are increasingly common
• Results in a multi-team system (MTS) with multi-team memberships
• Conflicting demands and competing goals at inter- and intra-team levels
• Optimal MTS performance requires trade-offs in team performance

Problem 1: Multi-team membership (continued...)

People: Shared cognition Culture: Multiple social identities Processes/ procedures: Concurrent engineering Goals: Conflicting time demands Technology: Software integration Buildings/ infrastructure: Co-location

Academic definitions (e.g., Hackman, 2002; Salas et al., 2004)

• 2-8 people
• Integrated working
• Social interaction
• Shared identity
• Shared objectives
• Shared cognition
• Shared accountability
• Laboratory experiments
• Emergency services, military

Problem 2: Non-aligned team definitions (continued...)

Problem 2: Non-aligned team definitions (continued...)

Organizations’ own definitions

• 2-50 people
• Isolated working + meetings
• Time allocation (Robinson, 2012):
• 60% solo work
• 40% social work

(27% unplanned + 13% planned)

• Extra-team working
• Some competing goals
• Membership ambiguity
• Real teams do exist, but may not be labelled as such

Problem 3: Transitory teams and membership

Solution 1: Develop and modify variables

• For team performance, distinguish between completion time and

working time, by incorporating the variable availability (Crowder et al., 2012)

• Increased focus on work tasks and structures and implications for MTS

Member 1 Member 2 Member 3 Member 4 Member 5 Member 1 Member 2 Member 3 Member 4 Member 5

Solution 2: More sensitive methodologies − Social network analysis − Agent based modelling and simulation

Social network analysis

Knowledge sharing within and between different supply chain units Shepherd (2008)

SNA as a tool for researching teams

− Highly sensitive methodology (Murase et al, 2012) − Multi-level information − Dyadic characteristics − Team emergence and evolution − How is work actually being done? (e.g., Cross & Parker, 2004) − Might help researchers to better understand: − E.g. Leadership, cohesion – what does an ‘effective’ MTS look like? − Team fit within the wider MTS system.

SNA as a tool for organizations:

− To capture the ‘as is’; − Design better interventions; − Design more effective

• rganizational structures;

− As a diagnostic tool; − Can/should networks be managed?

− Micro-level decisions - macro-level

• utcomes

− Developed from the ‘bottom up’ − Run over ‘time’ − Simple rules → complex behaviour − Can later increase complexity of rules and behaviours

(e.g., see Gilbert & Troitzsch, 2005)

Agent based modelling and simulation (ABMS)

Source: SeSAm 2.0 Multi-agent simulation Reynolds (1987)

Crowder et al. (2012, p. 1433)

• The nature and

structure of work tasks are key within ABMS

• 1. Forces researchers to make assumptions explicit
• 2. Bring together knowledge and data from different sources
• 3. Helps researchers understand dynamic, real-world processes and
• utputs
• 7. Operate in an artificial environment

The benefits of ABMS for teams – see Hughes et al, 2012

Conclusions? − Applied research fundamental to theoretical advancements − ABMS and SNA ideally placed to study MTS complexity − Complementary, not competitive approaches − Need to consider MTSs within a socio-technical framework

Thank you for listening Questions and comments?

Contact details: h.hughes@leeds.ac.uk m.robinson@lubs.leeds.ac.uk

References

− Cross, R., & Parker, A. (2004). The hidden power of social networks. Understanding how work really gets done in organizations. Boston, USA :Harvard Business School Press. − Crowder, R. M., Robinson, M. A., Hughes, H. P. N., & Sim, Y. W. (2012). The development of an agent-based modeling framework for simulating engineering team work. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 42(6), 1425–1439. − Gilbert, N., & Troitzsch, K.G. (2005). Simulation for the social scientist. Milton Keynes: Open University Press. − Hughes, H. P. N., Clegg, C. W., Robinson, M. A., & Crowder, R. M. (2012). Agent- based modelling and simulation: The potential contribution to organizational

• psychology. Journal of Occupational and Organizational Psychology, 85(3), 487–

502.

− Murase, T., Doty, D., Wax, A., DeChurch, L.A. & Contractor, N.S. (2012). Teams are changing: Time to ‘think networks’. Industrial and Organizational Psychology, 5(1), 41-44. − Reynolds, C. W. (1987). Flocks, herds and schools: a distributed behavioral

• model. Computer Graphics, 21, 25–34.

− Robinson, M. A. (2012). How design engineers spend their time: Job content and task satisfaction. Design Studies, 33(4), 391–425. − Salas, E., Stagl, K.C., & Burke, S. (2004). 25 years of team effectiveness in

• rganisations: Research themes and emerging needs. In Cooper, CL. and

Robertson I.T. (eds.) International Review of Industrial and Organisational Psychology Volume 19 (pp. 47–91). John Wiley & Sons Ltd, London

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