Pink Arrow exPat Team

1. Pink Arrow exPat Team Julia Puebla Fortier (Health & Public Policy, exPat), Milagritos Gonzalez (Social Psychology, Puerto Rico), Bill Hefley (Service Science, American),Farzana Nayani (Cross-cultural Communication, exPat/Canada), Yvette Reisinger (Tourism/Marketing, exPat, EU/Poland/Australia)Martin Renzo Rosales (Anthropology, exPat/Panama), Kostas Triantis (Systems Engineering, exPat, Greece)

2. Culture in service research: challenges in multidisciplinary research • Scoping of the research problem • Understand the context • Articulation of the impacts (desired outcomes) • What are the deliverables? • Models, papers, implementation and evaluation • Costs of multi-cultural studies • Availability of researchers • Availability of participants • Costs (multi-lingual materials, games/sims/modeling tools) • Data collection • Research team profile

3. Framework/Model • At two levels • Profiles of intercultural variables • Guidance for working as a multidisciplinary team

4. Profiles of intercultural variables • Concepts/Definitions: What is the service problem being addressed? What variables do you use to assess culture? Which matter in services? • Need to develop a Vocabulary for service systems – a common language for describing and explaining these complex systems • Catalog of xxx thousand attributes, which ones matter for the study at hand? • What is the unit of analysis? What are the variables?What is communication in intercultural…what are the differences? Are we addressing inter-cultural, inter-racial, inter-organizational • Measurement issues – some are too abstract, lack definition, lack valid and reliable scales, lack equivalence, and have translation/interpretation/communication difficulties • Consider multilingual, culturally appropriate instruments

5. Guidance for multidisciplinary teams • Organic teams – let them coalesce and form, don’t force fit teams together • People are invited because they have done previous work in this area in their own disciplines. • Consider multiple co-PIs from different disciplines. • Want complementarities and to embrace heterogeneity between team members in order to be successful. • Need to look at incorporating other relevant disciplines (CS/software skills (if using games, micro worlds)) • Need a Boundary spanner – who is willing to work outside comfort zone – and also important for an individual within the group to coordinate, span, be a good facilitator • Optimum team size for the core/main team can be around 5, plus perhaps others who contribute in specific tasks

6. Teams - 2 • Team needs a Coalescing period • As teams form, they should have a planning session/retreat to: • develop emotional ties and connections among the team • describe/discuss the problem and approaches • appreciate strengths and validate contribution of other disciplines • assess personal and academic biases • - Team members should share their methodological skills. • Each person brings background about their discipline’s main literature and methods they would like considered. • Collective discussion about scope and parameters of the project, define common purpose, different goals • All potential team members come together at the beginning, but may later contribute at appropriate phases…. • The initial planning is to develop emotional commitment/attachment (we all own what we are building) + (what we are building changes over time)

7. Teams - 3 • Teams need to develop a common purpose • should focus on applications to public or private sector that are real-world problems (or their analogues) • Key foundations for the success of the team • Need MDT for description and definition of variables, norms, attitudes. What matters in each discipline, and why? Need to capture these intercultural attributes in quantitative and/or analytic approaches. • Example from Rik Warren – developing a team diversity scale • Need multidisciplinary teams (MDT) to work on the design of measurement tools (data collection, measurement) for behavior and cognitive variables • Apply relevant methodologies from differing disciplines as the problem dictates • May have team leader rotate by phase (based on skills and tasks)

8. Problem design • Team needs to identify what requirements they are satisfying? What questions are we trying to answer? • Levels of analysis – is it all individual – need psychologists for measurement at individual level, sociologists or anthropologists could inform about population, or if at higher level (towards societies), definitely need sociologists or anthropologists. • How to reconcile discrete disciplinary points of view? Or how to integrate? Need recognition of each others biases and paradigms. • ISSUE: Can we make predictions about behavior? Can we really model? • What to model? Which variables to include? Which matter? How to specify the variables to include? How to interpret, and use at the individual, agency, policy levels? This needs the collective wisdom of the interdiscisplinary team.

9. Methods • Methods – all methods are legitimate, unless proven otherwise. We could use methods from any of the disciplines, but need to select which methods to apply when. • Equivalence concepts • Conceptual equivalence • Construct equivalence • Sampling equivalence • Measurement equivalence • Data collection equivalence • Translation equivalence • Interpretation equivalence • Reporting equivalence • Equivalence = is defined as = same or similar • Ex. Conceptual equivalence….Socialscapes – social context… • Ex. Timing of data collection in summer in both north and south hemispheres as example of data collection equivalence

10. Research paradigms • Challenge – multicultural studies – not just bilateral. Need to gather data and information across cultures…. • What is the context of the service setting? Other stakeholders? What is their involvement? What are collateral damages / impacts of the services? More meaningful to have real datasets for study – representing real service settings. • Data helps us test models, models allow communication of issues in a structured way. • Want to be able to do Modeling scenarios – do what if studies? • Models need to be adaptable for cultural nuances, differences. • Need to get representative users (not token representatives) involved in studying and in service design – ethnographic research, stakeholder analysis, participatory design • Modeling challenges: • Uncertainty in information that we use in modeling, • what biases are in the data we use? • capture interactions • assess current state of the system • assess the impact of the intervention /. Interactions • outcomes • Data  Simulations  Experiments  feedback and calibration with experimental data • Grounding to the real world • What are the multiple levels – interaction (socialscape), service encounter, service systems, outcomes (of the service systems), societal levels? How do we model these levels? Aggregation and multi-level analysis, how do you adapt models for differing cultures.

11. Problem spaces • Took perspective of service systems • Transportation systems • Environmental / green in tourism

12. Example Problem Space 1 – transportation systems • Design of transportation systems that would be used in different cultures • in PR: • individual transport (cars), cars are a symbol of status • traffic congestion, lack of public transport • needing to live near where they work • Understand the demography of community, transportation needs • Identify where to locate stations, near where people live • Options – public transport, congestion pricing, • Who should be on MDT: sociologists, urban planners, psychologists, civil engineers (infrastructure), economists (willingness to pay – both taxes, fares), marketing (to sell the new solution and educate people about it), financial, systems engineers • What inputs could we get from a researcher located in a geography where they have done this? Congestion pricing; for example, Stockholm. Put congestion pricing in as a trial, removed it, then asked for a referendum on whether to implement.

13. Example Problem Space 2 – Environmental / green in tourism • Need to understand intentions, cultural nuances of environmental decision making • Service settings • Service retailing – consumer decision making • Hotelling – how they do it? • Catering – waste management • Transport – shared rides (In PR public cars, carros publicos). vs taxis? • Political? Policy level?

14. For funding opportunities, • Contact Bill & Associates • DBA Pink Arrow Multicultural Research