Download Lewin in China: Replicating and extending Lewin`s group decision

Lewin in China:
Replicating and extending Lewin’s group decision-making intervention
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Sherry Jueyu Wu
Elizabeth Levy Paluck
Princeton University
July 5, 2016
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Introduction
Kurt Lewin, the founding father of experimental social psychology, conceptualized the
idea of group decision making (GDM) – a structured effort to discuss and reach a goal in a group
setting – as a way to change group behavior in both the short and the long term. To test his
particular style of group decision making, Lewin conducted a series of field experiments with
workers in the Harwood factory in 1940s. Workers were invited to contribute to regular 30minute informal discussions on barriers to increasing their productivity, followed by a group vote
on their aspirational levels of daily output, compared to groups that only discussed or listened to
a lecture. The experiments demonstrated that group discussion on workers’ approach to their
tasks followed by decision making increased and sustained a high level of worker productivity.
Lewin’s groundbreaking experiments demonstrated that group decision making, and in
his words, discussion of a democratic nature, can significantly increase productive behavior. Not
only were Lewin’s Harwood experiments important for understanding motivation and
productivity, but also they formed part of the foundation for how the next several generations of
social psychologists would understand the group-based social motivations for individual
behavior and attitudes.
Notwithstanding their foundational status in the history of social psychology, Lewin’s
Harwood experiments have never been replicated and extended. Some of the principles of the
experiments were distilled and investigated by his students in follow up laboratory studies.
However, much about the original studies remains unspecified and unevaluated, including
Lewin’s model of change. There is still much to be learned from a field investigation that revisits
a group decision making paradigm in a workplace, and particularly in a context that differs from
the Harwood World War II-era American setting.
We plan to conduct a field experiment in a modern textile factory in Eastern China. We
hope to address two questions with this project. First, can we reproduce Lewin’s group-decision
effects, which have guided much of social psychological thought on the power of groups?
Specifically, does participation in group decision making about production increase workers’
output in a much different context like a Chinese factory, which is a hierarchical institution
situated in a non-democratic state? Second, how might experience with a more democratic style
of group decision making at work shift not only worker productivity, but also worker attitudes
toward participation in decisions about their livelihoods, and more generally, attitudes at work
and in politics?
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Hypotheses
We predict that participation in GDM will enhance individual and team productivity. We
also test whether GDM affects a cluster of related attitudes and preferences that we term
generally “workplace empowerment.” For example, positive attitudes toward participatory work
and group life, job satisfaction, a sense of control over one’s work situation, and a feeling of
“individuation” – that one’s identity is known and recognized at work. We also test whether
GDM changes less obviously related attitudes, as a direct result of participation in GDM or
conditional on an increase in productivity and workplace empowerment following GDM. These
potential changes include attitudes toward authority and political participation, perception in
justice and fairness, and confidence and well-being. (Our experimental design cannot distinguish
between a direct and a conditional effect, and so we will analyze the direct causal effect and will
explore correlational relationships between productivity, workplace empowerment, and
downstream attitudes.) Based on our confidence in these theoretical predictions, we elaborate the
following three hypotheses:
Primary Hypothesis: Participation in group decision making  Higher productivity. We treat
worker productivity as the primary outcome because this is a direct replication of Lewin’s
original effect, which is substantiated by his theories of change. Productivity will be objectively
measured from workers’ salary and productivity data provided by the factory. We can also assess
whether productivity is higher as measured by lower worker turnover.
Secondary Hypothesis: Participation in group decision making  Higher workplace
empowerment. Apart from productivity, there are different ways in which workers may feel
more recognition, satisfaction, and motivation from the GDM intervention. We will test the
secondary outcomes using post-intervention surveys. These empowerment outcomes may
consequently help to promote more productivity, and we will explore correlations between
empowerment and productivity.
Tertiary Hypothesis: Participation in group decision making  Attitudinal change related to
social, familial, and political issues. Because these attitudes are less directly related to the
intervention, we predict that these changes that might take place only after a longer period of
participation in the intervention, or perhaps only conditional on changes at work including more
productivity as a result of the initial intervention. We will investigate these attitudes as direct
effects of the intervention, and will explore correlational relationships between these attitudes,
the participants’ productivity, and their workplace empowerment. In addition, we will explore
how they behave in response to the intervention, including their regular and overtime working
hours and days of leaves.
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Explanation of Secondary Hypothesis
There are different ways that a worker can be empowered in her workplace. We propose
three groups of secondary outcomes under the general umbrella of workplace empowerment that
will result from the group decision making intervention: informational gain, individual
empowerment, and group empowerment. These outcomes are predicted to be interdependent and
may correlate with each other. All three types will be measured in the immediate postintervention survey.
Survey items are listed as bullet points below the description of the outcome grouping
and the logic for our specific prediction. Survey items are grouped together according to the
theoretical and face validity of their similarity, and they should not be interpreted as forming
coherent scales. Most of the questions are adapted from established scales, and have been piloted
with local factory workers so that we are confident that subjects understand the language and
content in the same way as researchers (refer to Wu & Paluck, 2015). The items have been
translated and back-translated into Mandarin Chinese by two English-Chinese bilingual speakers.
Informational gain. Participation in group decision making facilitates informational and
strategic exchanges, which help remove workers’ behavioral inefficiencies. By discussing work
strategies and engaging in direct problem solving, workers learn from each other on how to most
efficiently prepare before work, arrange materials, pass on finished pieces, develop good
gestures with machines, and communicate work related issues to coworkers. Thus the
intervention helps remove existing logistical and procedural obstacles that tend to keep
production down.
[Informational and strategic exchanges]
 How many different gestures or strategies are you aware of that you can use to do your
task? (have them give the number) how many variations to the gestures (give the number)
 Do you know who to ask for if your machine needs fixing during the order switch?
Individual empowerment. Group decision making facilitates the transition of individual
motivation into action – individuals are expected to be more likely to commit to the goal when it
is announced in a group setting. This active participatory meeting style encourages workers to be
more involved in voicing and solving their production problems, and more interested to learn a
local solution, feeding into informational gain. Participation in a workplace decision making
group is expected to help a worker to see herself as a fundamental building block in the factory,
rather than as the lowest or most helpless layer of a strict workplace hierarchy within a largely
de-individuating context.
[Individuation (sense of being a unique individual)]
 Most of the people on my team know my name (y/n)
[Job satisfaction]
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 All in all I am satisfied with my job.
 In general, I don’t like my job
 I often think about quitting.
 I am looking for a new job.
 Being frustrated comes with this job.
[General happiness/wellbeing]
 Did you experience happiness during a lot of the day yesterday?
 All things considered, how satisfied are you with your life as a whole these days? Use a 0
to 10 scale, where 0 is dissatisfied and 10 is satisfied.
[Voicing out opinion on your own]
 I feel comfortable speaking up in meetings with coworkers and leaders.
 I feel comfortable talking to my coworkers and leaders when a work problem occurs.
 I feel that my team leader listens to me.
 The treatment that I have generally received here at [corporate name] has been fair.
[Sense of control]
 To what extent do you have control over what happens on your job?
 I sometimes feel I am being pushed around in my life.
 There is really no way I can solve all the problems I have at work.
Group empowerment. The third group of workplace empowerment items are related to
workers’ team identification and to the collaboration developed by the GDM intervention.
Workers will gradually observe that “I’m part of and contribute to the performance of my
working team” “I can help others, not only myself, work better”, etc. Moreover, workers may
infer more care and respect from the factory authority since they are now given a chance to speak
and make a decision. A complimentary outcome from increased job affiliation and satisfaction is
the lower turnover rate, as opposed to the existing high turnover rate in the factory1.
[Attitudes toward group life]
 How do you like your coworkers?
 How often do you socialize with your coworkers during work?
 I feel I am really part of my team.
 I have confidence and trust in my coworkers.
 I like my team.
 I feel that in the factory, everyone’s part of a big family.
 I get involved to benefit my work group.
 I help others in my work group learn about the work.
 I assist others in my group with their work for the benefit of the group.
[Friendship]
 Think about your good friend(s) in the factory, and list the number of people you can go
to at the factory when you have problems because they will help you. (list #)
1
As estimated by the Human Resource Department of our experimental sites, around half of the
new workers leave within a year.
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 I feel lonely in this factory -- agreement
[Higher opinion of their role]
 The work we do here is important to the factory
 The factory (“the higher” as in Chinese) cares about and respects us.
*Note: All of the survey questions will be read aloud by trained enumerators. Participants will be
given answer sheets where they only need to circle yes or no, or the numbers as instructed.
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Explanation of Tertiary Hypothesis
The social, familial, and political attitudes listed below are less directly related to the
GDM intervention, and may not be detectable immediately following the intervention. We will
test the tertiary hypothesis in the second wave survey. For details on specific predictions why
each of the following constructs would result from the treatment, refer to Wu & Paluck (2015).
[Participation in politics]
 How interested would you say you are in politics?
 How much impact do you feel government policies have on your daily life?
[Participation in family and social life]
 How’s your relationship with your family?
 How often have you participated in your family’s decision making lately? (never;
sometimes; often; always)
 How much influence do you intend to have on your kids or your future kids?
 How often do you follow news about social issues?
 How often do you socialize with your coworkers off-work?
 I know many people’s name who work on my floor (y/n)
[Attitudes toward authority and Justice and fairness]
 Obedience and respect for authority are the most important virtues children should learn.
 If people would talk less and work more, everybody would be better off.
 Every person should have complete faith in some supernatural power whose decisions he
obeys without question.
 Although evil men may hold political power for a while, in the general course of history
good wins out.
 It is often impossible for a person to receive a fair trial in China. (R)
 By and large, people deserve what they get.
 Do you think most people would try to take advantage of you if they got a chance, or
would they try to be fair? Please show your response on this card, where 1 means that
“people would try to take advantage of you,” and 10 means that “people would try to be
fair” (code one number).
[Social mobility: marginalization and class]
 There is a lot of social mobility – it is not too difficult for people to change their position
in life.
 In China, it is possible to go “from rags to riches.”
 If you are born poor, it is very unlikely you will ever be rich.
 In our society, some are positioned on the top, while others are positioned on the bottom.
Think of the following scale as a ladder representing where people stand in China, where
10 = top of the ladder: people who are the best off (those who have the most money, the
most education, and the most respected jobs) and 1 = bottom of the ladder: people who
are the worst off (those who have the least money, the least education, and the least
respected jobs or no job). Where would you place yourself on this ladder at the current
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
life? (a pictorial ladder will be displayed)
Same instruction as above. Where would you place yourself on this ladder 10 years ago?
Same instruction as above. Where would you expect yourself to stand on this ladder 10
years from now on?
In your mind, to what extent do the following social groups have conflict with each other?
The rich and the poor
The working class and the middle class
Managers and workers
Those at the top and those at the bottom of the society
[Well-being]
 All in all, how would you describe your state of health these days? Would you say it is
(read out): very good, good, fair, poor [physical health]
 How much do you think life will get better? (not at all, slightly, moderately, strongly
better) [optimism]
 All things considered, how satisfied are you with your life as a whole these days? Use a 0
to 10 scale, where 0 is dissatisfied and 10 is satisfied. [life satisfaction]
 I felt depressed. [depression measure]
*Note: In the second wave survey on downstream social attitudes, we will repeat several
questions from survey wave 1 on workplace empowerment. The selection criteria for the
repeating questions will be based on the significance and magnitude of the attitudinal changes,
comparing treatment and control. We are interested to see how long-lasting those effects/changes
are by repeating the questions in survey wave 2.
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Study Setting and Sample
This field experiment will take place in Suzhou, China, and in one of the world’s largest
original equipment manufacturer for garments2. The corporation is a multi-national firm and a
world-class manufacturer for textiles and garments. It has multiple manufacturing facilities
across different countries in Asia, and the China (Suzhou) branch is the largest in scale among all
its branches and the backbone for its garment production.
The basic production units in the factory are departments. Departments are further
divided into teams which vary in size, ranging from 10 to 30 persons per team. Team assignment
within each department is random. Once a worker is assigned into a team, she rarely rotates to
another team. Each team has its own team leader who oversees the work of their assigned
frontline workers. Each team is required to have a daily morning meeting led by its team leader
to summarize problems occurred during production.
The sample population will be sewing workers (individual factory employees) who
voluntarily participate in the study. The estimated total number of workers (excluding team
leaders, supervisors, and staff) from the factory’s sewing department is 1,963, structured into 77
sewing teams. They are spread on seven different production floors in six factory buildings.
These seven floors do sewing jobs only, and workers remain in their own working area and
rarely walk around different floors. From extensive field observation during the past summer, we
predict there will be little spillover across production floors. The study has gained support and
been promoted by the factory’s top management as an effort to improve workplace culture using
insights from behavioral sciences.
The study has been approved by Princeton Institutional Review Board (#7262).
2
Corporation name is kept for confidentiality.
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Treatment and Randomization
We will randomize work teams within each department into treatment (participation in
GDM) and control. All the sample workers will be aware that there is a research project about
work experience in the factory, as told by our research assistants (or discussion facilitators in the
treatment condition).
GDM Treatment
There will be six weekly meetings for each treatment team on a fixed day of the week in
the experimental period, conducted by trained discussion facilitators. In other words, each
treatment team will have one GDM meeting taking over their regular morning meeting per week,
and will have six meetings in total over the experimental period. The meeting will be directed
into three parts: warming-up, problem solving, and goal setting.
1. Warming-up. Before the start of the discussion, each worker gives a round of selfintroduction, articulating her name. The discussion facilitator starts the discussion with opening
questions, such as what they are working on together as a team and what each of them is working
on that day.
2. Problem solving. The discussion progresses to questions encouraging information
exchange to close behavioral inefficiencies, and eliciting workers’ concerns and thoughts on
production. For example, discussion questions include but are not limited to: “What are some of
the strategies you’ve used for this task? Do you mind sharing with others?” “How can you work
well on this as a team?” “What can you do to make work faster and better as a team?” “What do
you need from the person next to you, to do your job faster or better, such as being more
carefully or making fewer mistakes?”
3. Goal setting. After having a discussion on productivity related issues and how to solve
them, each worker votes on specific production amounts they aim to reach in the present week,
along with voicing aspirations on what they will do to achieve the goal themselves, and to help
their group achieve the goal.
The meetings on group decision making for the treatment groups will be of similar length
with regular meetings led by team leaders in the control groups.
Control
The control work teams will have a required daily meeting led by their team leader, a
normal practice as before the intervention. At the same time, one of our research assistants will
observe one meeting on a weekly basis.
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Implementation Plan
As requested by the factory, we will start the experiment by randomizing one entire
department prior to the large-scale intervention to ensure there is no negative consequence due to
the experiment3. Specifically, our data collection for this first department will start before the rest
of the departments, while the content and length of the intervention in this first remain exactly
the same as the other departments. Below is a timeline for the research:
4/4 – 5/14: First “test” department4
Week 1: 4/4 – 4/9
Week 2: 4/11 – 4/16
Week 3: 4/18 – 4/23
Week 4: 4/25 – 4/30
Week 5: 5/2 – 5/7
Week 6: 5/ 9 – 5/14
5/26 – 7/6: Rest of sample
Week 1: 5/26 – 6/1
Week 2: 6/2 – 6/8
Week 3: 6/9 – 6/15
Week 4: 6/16 – 6/22
Week 5: 6/23 – 6/29
Week 6: 6/30 – 7/6
7/11– 7/19: Survey wave 1
First round data analysis
8/1 – 8/9: Survey wave 2
3
The factory retains the right to terminate the experiment when immediate noticeable negative
consequences of the intervention occur, such as worker strikes, rising absenteeism, and apparent
low worker morale.
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Empirical Estimation
We predict that productivity (P) and two classes of social outcomes will result from
GDM treatment. The first class of social outcomes is work related that results directly from the
intervention, which we call “workplace empowerment (E)”. The second class of social outcomes
is attitudinal change (A). Because A is less directly related to the intervention and might result
from a generalization of changes at work to broader changes in attitudes toward familial, social,
and political life, we see them as changes that might take place only after a longer period of
participation in GDM. We will investigate A as direct effects of GDM, with the expectation that
they may not be detectable immediately following the intervention, and we will explore
correlations among P, E, and A.
First, we will do a manipulation check with the following survey questions, using an
independent sample t-test:
 How often do you talk with your team members about how to do your job well? (never,
once or twice, once per week, a few times per week)
 Do you know some information about the order you are working on (e.g. order amount,
deadline)? (y/n)
To test our predicted outcomes we will employ fixed-effects regressions with a dummy
variable indicating treatment, a vector of pre-treatment individual covariates to improve
efficiency, and with clustered standard errors.
Specifically, to test our productivity outcome, for which we have pre-treatment and posttreatment time series data, we will estimate a regression that tests the average worker earnings
and productivity for the six weeks during the intervention, controlling for average productivity in
six week time periods prior to the intervention. Suppose the average productivity (e.g. workers’
salary earnings) from the first week of the study to the sixth week of an individual worker i of
team j,
Pij = 0 + Dij1 + Zij1 + Hij1 + gj +ij.
(1)
The regression coefficient 1 represents the average causal effect of the intervention on worker
productivity, as measured by Pij (averaged over the first 6 weeks following the start of the
intervention). Dij refers a binary variable of experimental manipulation randomly assigned to the
individual, in which Dij = 1 refers to participation in group decision making and Dij = 0 refers to
the control condition. Zij is a vector of individual-level worker characteristics that are unaffected
by the treatment (working experience, education, age, gender, marital status, living arrangement,
and regional origin). Hij denotes a vector of controls for pre-treatment productivity, broken up
into 6-week averages (depending on the amount of data provided by the factory, this could be
approximately 8 covariates). gi denotes a departmental fixed-effect.  is a zero-mean error term,
assumed to be mutually independent across (but not within) groups. We will use robust standard
error clustered at the group treatment level.
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We will measure productivity gains following the intervention in the longer term and test
whether the productivity gains in the GDM condition will sustain after the intervention ends.
With the same specification as in Equation 1, the outcome variables will be post-treatment
productivity in the first 6-week following the end of the intervention, post-treatment productivity
in the second 6-week following the end of the intervention, and more (assuming we continue to
get the data from the factory).
For our analysis of workplace empowerment (Eij) and downstream attitude change (Aij),
we will use the same specification but without the pre-treatment controls. We will conduct
principal component analysis for the empowerment and attitude items, and create indices based
on the main components. We do not have strong predictions as to which survey items belong to
the same constructs5, apart from face validity. Thus we would prefer this bottom-up data driven
approach in analyzing the social outcomes.
Because we will test several regressions for our secondary and tertiary hypotheses, we
will use a joint significance test of the null that all of the regressions are nonsignificant.
5
Using Western established instruments in whole prove inefficient for these blue-collar workers
from pilot, a considerable part of whom are illiterate. In the meantime, due to the amount of time
when workers are available, we have a length constraint for the survey measurement.
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Dealing with Attrition, Missing Data, and Limited Variation Outcomes
The experimental design minimizes attrition, given that the treatment meetings represent
minimal interruption (once per week in workers’ pre-existing morning meeting time), and given
that the factory does not shift workers to new teams. We will adopt an intent-to-treat analysis
method.
Missing data could result from attrition (an old worker resigning from the job or exiting
the experiment), a new working joining an existing team, or a technical failure on the part of our
enumeration team to properly conduct both survey waves with any one worker.
If a treatment or control worker resigns from her job, her resignation will be counted
towards the worker turnover rate, which is one of our dependent variables. However, we will
have to deal with missing productivity data and survey data following her resignation date. If a
new worker joins an existing treatment or control team, we will have to deal with missing
productivity data prior to her joining the labor force. In both cases, we will use a few
specifications that account for missing values in different ways. For example, we will use
listwise deletion (complete case analysis) and pairwise deletion, missing value imputation, and
we will use inverse probability weighting to rebalance our estimates by overweighting
individuals whose covariate profiles are most similar to those who attrited from our sample. We
will use all the covariates that are unaffected by the treatment (work experience, education, age,
gender, marital status, living arrangement6, and howntown) in inverse probability weiting to
rebalance our estimates for productivity. For those who do not complete the survey, we will use
their productivity data in inverse probability weighting to rebalance our estimates for survey
responses.
6
Living arrangement refers to whether a worker lives in factory dormitories or rented apartments,
and whether she is living with her child, if any.
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