Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Efficiency and resistance to extinction of lottery-based incentives inhuman: Survey response behavior in 12-week real-world field experiment
Satoshi Nakano、Ryo Kato、Makito Takeuchi、Takahiro Hoshino

1 November, 2020
DP2020-020
https://ies.keio.ac.jp/en/publications/13455/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
1 November, 2020

Efficiency and resistance to extinction of lottery-based incentives in human: Survey
response behavior in 12-week real-world field experiment
Satoshi Nakano、Ryo Kato、Makito Takeuchi、Takahiro Hoshino
Keio-IES DP2020-020
1 November, 2020
JEL Classification: D10; D91; M31
Keywords: Resistance to extinction; Financial incentive; Underreporting bias;
Schedule of reinforcement; Survey response

Abstract
This study aims to improve survey response behavior by including schedules ofreinforcement with financial incentives in a 12-week real-world field experiment. We
found that lottery based-incentives produce more resistance to extinction and are also
more cost-efficient than fixed honorariums, despite having the same expectations. We
also found that immediate reinforcement and the high chance, low prize incentives
reduce underreporting bias. In addition, we confirmed the resistance to extinction whenswitching incentives and sustained responses due to lottery-based incentives. Moreover,
we evaluated the past reward winning experience and the habit of past responses, andfound that experience influenced subsequent survey responses. Thus, participants’
responses are dependent on incentive programs.

Satoshi Nakano
R&D Division, INTAGE Inc.
INTAGE Akihabara Building, 3 Kanda-Neribeicho, Chiyoda-ku, Tokyo
nakanosatoshi21@gmail.com
Ryo Kato
Research Institute for Economics and Business Administration, Kobe University
2-1, Rokkodai-cho, Nada-ku, Kobe, Hyogo
le.voiture.rouge@gmail.com
Makito Takeuchi
Faculty of Business Administration, Tohoku Gakuin University
1-3-1 Tsuchitoi, Aoba-ku, Sendai, Miyagi
makito.the.730@gmail.com

Takahiro Hoshino
Department of Economics, Keio University
2-15-45, Mita, Minato-ku, Tokyo
hoshino@econ.keio.ac.jp

Acknowledgement : This work was supported by KAKENHI 18H03209 2018-2022.

Efficiency and resistance to extinction of lottery-based incentives inhuman: Survey response behavior in 12-week real-world field
experiment
Satoshi Nakano1, Ryo Kato2, Makito Takeuchi3, Takahiro Hoshino4*
November 1, 2020
1

R&D Division, INTAGE Inc., Tokyo, Japan.

2

Research Institute for Economics and Business Administration, Kobe University,
Kobe, Japan.

3

Department of Business Administration, Tohoku Gakuin University, Sendai, Japan.

4

Department of Economics, Keio University, Tokyo, Japan / RIKEN Center for
Advanced Intelligence Project, Tokyo, Japan.

*Correspondence to: hoshino@econ.keio.ac.jp

Abstract: This study aims to improve survey response behavior by including
schedules of reinforcement with financial incentives in a 12-week real-world field
experiment. We found that lottery based-incentives produce more resistance toextinction and are also more cost-efficient than fixed honorariums, despite having thesame expectations. We also found that immediate reinforcement and the high chance,
low prize incentives reduce underreporting bias. In addition, we confirmed theresistance to extinction when switching incentives and sustained responses due tolottery-based incentives. Moreover, we evaluated the past reward winning experienceand the habit of past responses, and found that experience influenced subsequent survey
responses. Thus, participants’ responses are dependent on incentive programs.

Keywords: Resistance to extinction; Financial incentive; Schedule of reinforcement;
Survey response; Underreporting bias
JEL classification: D10, D91, M31

1

Introduction
Although many types of data, such as individual’s location details and web log data, cannow be collected automatically, data collected from surveys are still essential in certain
academic studies, including studies in medical research (1), political psychology (2),sociology (3), and economics (4). However, survey response data often suffer frombiases like overreporting and underreporting (5). Existing studies have attempted to
improve response rates by assessing insufficient effort responding (IER) (6) andinstructional manipulation checks (7). Contrarily, we focus on designing effective
financial incentives for survey responses to examine whether probabilistic financial
incentives drastically improve response rates.

Many experiments have shown that partial reinforcement—reinforcement that only
occurs at certain ratios of time or intervals—is more likely to strength the aimed
behavior and its resistance to extinction than continuous reinforcement—reinforcementthat occurs every single time (8, 9). In particular, many randomized controlled trials(RCT) using cash or gifts as reinforcements, called financial incentives, have been
conducted to promote changes in behavior. Consequently, some research revealed that
respondents offered financial incentives perform significantly better than those offeredno financial incentives (10), while other research indicated no significant effect (10, 11).
Financial incentives based on a variable ratio schedule, such as a lottery-based
incentive, is also considered to change behaviors more effectively than no incentives(12), though other research showed insignificant differences (11). Research on survey
response rates showed that financial incentives based on a fixed ratio of time tend to beeffective (13, 14), and financial incentives based on a variable ratio of time, namely
lottery-based incentives, are more effective than no incentive (15-17). Bosnjak and
Tuten (18) compared fixed-ratio and variable ratio incentives in the context of web
survey completion rates, and showed that, although the former has no advantages
compared with offering no incentive, the latter doubled the completion rates.

We conduct a randomized experiment for online panel respondents to investigate: (1)whether partial reinforcement, or a lottery-based incentive, increases the survey
response rate compared to the control group that has a weekly honorarium. Unlike thestudies that used web survey response rates, this study is designed so that the expected
incentive value is equal between the control and partial reinforcement groups; (2) which2

type of lottery increases the response rate: lotteries with small prizes but with higher
chances, or large prizes but lower chances; (3) whether the effect of partial
reinforcement lasts for a certain period; (4) whether immediate reinforcement is more
effective than delayed reinforcement; (5) whether reinforced responses continue after
lottery incentives cease (resistance to extinction); and (6) whether participants' response
efforts are enhanced by past rewards or habit.

Data and Design
We collected scanner panel data in which respondents report every purchase of abeverage. The data were collected using INTAGE Inc., one of the largest Japanesemarketing research companies. Every time participants purchase the product, they can
respond to the survey by scanning its barcode. These data include the amount ofpurchases and the number of units purchased in stock-keeping units, regardless of where
they were purchased. The product categories covered in this study are beverages
including tea, coffee, milk, juice, carbonated drinks, and nutritional drinks. The reason
for covering beverages is that many people, regardless of gender and age, frequently
purchase beverages. Since the reward attached to one beverage is smaller than the priceof the beverage, we assume that participants would not overreport. The field experiment
was conducted over a 12-week period from March 8, 2020 to May 30, 2020.

We designed five reward programs (grouped A–E) and two time periods (pre-week: firsteight weeks, post-week: following four weeks). The five groups consist of one control
group (group A) with a weekly honorarium and four partial reinforcement groups(groups B–E). In pre-week, the control group receives a uniform weekly honorarium,
while the other four groups receive lottery-based incentives. In post-week, we grant
weekly honorariums to all groups A–E. This manipulation, in which the same person isrewarded in different ways between pre-week and post-week, is intended to capture theresistance to extinction when partial reinforcement is withdrawn. The partial
reinforcement groups consist of a 2 × 2 experimental design. One of the factors relates
to the probability and amount of lottery-based incentive, defined as: (1) high chance andlow prize, and (2) low chance and large prize. Both expectation values are equal. This
allows us to test whether rewarding participants with lower prizes but higher chances(or vice versa) will increase their survey responses. Another factor is the timing of the3reinforcement, defined as: (1) immediate reinforcement: a lottery is drawn every time a
product is scanned, and (2) delayed reinforcement: a lottery is awarded only once a day.

Table 1 shows the details of the incentive design for each group in pre-week. The
expected values per week are 300 yen (about US$2.8) for all groups. The amount of 300
yen per week is reasonable compared to the reward amounts in the Syndicated
Consumer Index (SCI), which is Japan's largest consumer individual-level scanner panel
data managed by INTAGE Inc and has been in constant operation since 2012 with
50,000 panelists. In the case of lottery-based incentives, each group is awarded a lottery
according to the odds of winning, the winning amount, and the number of lots per week,
as shown in Table 1. On the days when the participants do not purchase anything, they
can draw the lottery by reporting the fact that they did not purchase anything. Thus, we
designated the number of lots per week at seven in groups B and C, and 10.1 in groups
D and E1). For groups D and E, the expected value per lottery is about 30 yen, which ismuch lower than the price of a beverage. Therefore, participants do not purchaseadditional goods to increase the likelihood of gaining a reward, as this is considered aloss.

In post-week, groups A–E receive a weekly honorarium, the same as the control group
A in pre-week, of 300 yen per week, regardless of their scan record status. Participantsin groups B–E were informed of this change to the reward program through an email on
the morning of the first day of week 9 (May 3, 2020). We did not disclose toparticipants until week 8 that the rewards would change after week 9. Therefore, thedata from participants up to week 8 are unaffected by the change in the incentive design.

Table 1. The incentive design for the first eight weeks
Groups Reward programs
A
B
C
D
E

The control group with weekly honorarium
Variable ratio
DelayedHigh chance, low prize
Variable ratio
DelayedLow chance, high prize
High chance, low prize
Variable ratio Immediate
Variable ratio Immediate
Low chance, high prize

Opportunity to
Odds ofdraw lots
winning－
33.0%
Up to once a day
Up to once a day
16.5%
At each scan record 33.0%
At each scan record 16.5%

Winning Number of
Expectation
amount lots per week value
300 yen/week
130 yen7
300 yen/week
260 yen7
300 yen/week
90 yen10.1
300 yen/week
180 yen10.1
300 yen/week

We recruited a total of 700 individuals, who were randomly divided into each group(140 individuals in each group). Following this, a total of 595 individuals (A: 118
4

[84.3%], B: 117 [83.6%], C: 119 [85.0%], D: 120 [85.7%], E: 121 [86.4%]) partook in
the survey. A chi-square test was performed on a 5 × 2 contingency table (i.e., groups ×
permitted/denied), with 𝜒𝜒 2 (4) = 0.560 and p = 0.967. The results did not show asignificant difference. Therefore, the rate of participation in the survey was not related
to the incentive design.

In the analysis, we used three regression models (see Supplementary Materials). In
Model I, we regress the number of weekly scan records 𝑦𝑦𝑖𝑖𝑖𝑖 in participant 𝑖𝑖, week 𝑡𝑡 on
the group's dummy variables, weekly variables, and their interaction terms. This model
is aimed to capture the variation in scan records with the passage of weeks. We thenevaluate the past reward winning experience and the habit of responding on the numberof scan records thereafter. Model 2 is being evaluated in pre-week. The dependent
variable 𝑧𝑧𝑖𝑖 is defined as the number of scan records from week 3 to week 8 in preweek, and explanatory variables are the group's dummy variables, experience, and habit.
Experience is defined as the actual amount of received rewards and the habit is definedas the number of past scan records. Both experience and habit are calculated in the firsttwo weeks in pre-week. Model III has the same structure as Model II, but it evaluates
the relationship between pre-week and post-week. We evaluate the impact of experienceand habit in pre-week on the number of scan records in post-week. In addition, we also
calculated the parameters of the two reinforcement factors (high chance, low prize andimmediate reinforcement) by combining the parameters of the groups estimated in eachmodel using delta method (19). This is aimed to assess not only between groups, butalso between reinforcement schedules.

Results
A holistic view of the experimental results
Figure 1 shows the expected number of scan records per week by group calculated using
Model I. In pre-week, when the reinforcement schedule differed among groups, thenumber of scan records increase over time, especially in groups D and E, which have an
immediate reinforcement. However, a decrease in scan records over time is observed in
the control group A for all weeks. In addition, in post-week, during which weekly
honorariums are paid uniformly to all groups, there is a decrease in the number of scan
records in all groups. However, the resistance to extinction, which is measured as a5persistent high number of scan records, is seen in groups D and E, whose number ofscan records in the post-week is consistently higher than that of the other groups.

Fig. 1. The expected number of scan records per week by group. The lottery-based
incentives were given to groups B–E until week 8, and weekly honorariums were given
to all groups, A–E, from week 9.

Efficiency of monetary rewards and type of schedules
We find some interesting features regarding the number of scan records and efficiency
of monetary rewards. In terms of rewards per record, group A earned 86 yen, group B
earned 56 yen, group C earned 70 yen, group D earned 38 yen, and group E earned 43
yen (see Table S3). Groups D and E has more scan records, but the amounts of rewards
per record are lower than for other groups. In contrast, group A, which was given a
fixed weekly honorarium, has the highest amount of rewards per record. The reason forthis lies in the cost of paying for uncooperative participants. The participants in group A
were paid a fixed fee even if they did not complete a survey. As a result, uncooperative
participants increased the total survey costs. Therefore, paying a lottery-based incentive
to survey participants is more cost-efficient than paying a fixed honorarium.

6

Reporting behavior and the type of schedules
Figure 2(A) shows the differences in the number of scan records in pre-week for eachgroup. As a test for the difference of means, we adopted a linear regression analysis
with individual random effect considering multiple measurements from the same
individual (see Supplementary Materials). The result indicates that groups D and E havesignificantly more scan records than the control group (group A) or the delayed
reinforcement groups (groups B and C) have. On the contrary, no significant difference
is observed in groups B and C compared to group A. Similarly, Figure 3(A) shows theimpact of the groups on the number of scan records, excluding the effect of the passage
of weeks estimated by Model I. This result also indicates that the participants in groups
D and E have the highest survey responses. Figure 3(B) shows the effect of the passage
of weeks in pre-week by group estimated by Model I. The coefficients for groups D and
E are positively significant (p < .001), confirming an increase in the number of scan
records over time.

Fig. 2. Difference in scan records per week by groups. The bars indicate the mean of
the number of scan records. The error bars reflect standard errors. In pre-week (panelA), the multiple comparisons using Shaffer's method shows that significant differences
are found in groups C–D (p < .001), A–D, C–E, and B–D (p < .01), and A–E and B–E(p < .10). In post-week (panel B), significant differences are found in groups C–D (p< .01) and C–E (p < .10).

7

Fig. 3. Regression of the number of scan records per week on group dummies, preweek, and post-week (Model I). The impact of the groups on the number of records
excluding the effect of the passage of weeks (panel A). The effect of the passage ofweeks in pre-week by groups (panel B). The effect of the passage of weeks in post-weekby groups (panel C). The error bars reflect standard errors. In panel A, as a result ofmultiple comparisons using Shaffer's method, significant differences are found forgroups C–D, B–D, A–D, C–E, B–E, and A–E (p < .001), and D–E (p < .01). In panel B,
significant differences are found for groups A–E, A–D, C–E, B–E, and C–D (p < .001),
A–B (p < .01), B–D (p < .05), and D–E (p < .10). In panel C, significant differenceswere found for groups A–C (p < .001), A–D (p < .01), and A–E (p < .05).

We summarized these results by group into two reinforcement schedules factors in
Table 2 (Model I column). As shown in the coefficients of the dummy variables, thecoefficients of both factors are positively significant, thus it shows that both
reinforcement schedules are effective to improve survey responses. Compared to thecoefficients between the two factors, immediate reinforcement is more effective thanhigh chance, low prize reinforcement. In addition, looking at the coefficients of preweek, both factors are positively significant, suggesting that both reinforcement
schedules, especially immediate reinforcement, increase the number of scan records as
the week progressed.

8

Table 2. Estimation results among factors of reinforcement schedules
Dependent variable# of scan records
per week in pre-week(M odel I)
Coef
SE
ControlDummy
1.038
0.040 ***
-0.0260.004 ***
Week(A)
2.506
0.067 ***High chance, low prize Dummy
Pre-week
0.029
0.008 ***
Post-week -0.153
0.023 ***
0.064 ***
Immediate reinforcement Dummy
2.837
0.007 ***
0.071
Pre-week
0.022 ***
Post-week -0.158# of scan records
after the third week
(M odel II)
SE
Coef

Dummy
Experience
Habit
Dummy
Experience
Habit

4.627
0.955
0.608
4.756
0.828
0.168

0.090 ***
0.072 ***
0.054 ***
0.089 ***
0.063 ***
0.056 **

# of scan recordsin post-week(M odel III)
SE
Coef

3.695
1.466
0.837
3.767
1.623
-0.8080.128 ***
0.158 ***
0.120 ***
0.126 ***
0.149 ***
0.138 ***

Figure 4(A) shows the parameter estimates of the Model II, which is aimed to assess theimpact of past winning experience and the habit of past responding behavior. The
coefficients of experience are positively significant in all groups, suggesting that theexperience of receiving the reward at the beginning improves subsequent survey
responses. In contrast, the coefficients of habit are positively significant in groups B, C,
and E. Particularly the large coefficients are obtained for groups C and B. Moreover, as
shown in Table 2 (Model II column), which is summarized the results by two
reinforcement schedule factors, for the high chance, low prize reinforcement, bothexperience and habit are related to subsequent survey responses, but habit is more
effective. In contrast, for the immediate reinforcement, participants enhance theirsubsequent response efforts based on experience rather than habit.

9Fig. 4. Regression of the number of scan records on past winning experience andhabit. The regression of number of scan records after the third week in pre-week(Model II) (panel A). The regression of number of scan records in post-week (Model
III) (panel B). The error bars reflect standard errors. In Model II, for the coefficients ofthe experience, as a result of multiple comparisons using Shaffer's method, significant
differences are found for groups B–C (p < .05), and C–D and C–E (p < .10). For thecoefficients of habit, significant differences are found among all groups, C–D, C–E, B–
D, B–E, and B–C (p < .001), and D–E (p < .05). In Model III, for the coefficients of theexperience, significant differences are found for groups D–E (p < .001), C–E (p < .01),
and B–D (p < .10). For the coefficients of habit, significant differences (p < .001) arefound among all groups.

Resistance to extinction due to changes in the reinforcement schedule
Figure 2(B) shows the difference of the mean of the number of scan records in postweek by group. It is calculated similarly to the regression model with random effects in
Figure 2(A). We find that the number of scan records in groups D and E are higher inboth post-week and pre-week than in other groups. In addition, as shown for week 9 inFigure 1, the number of scan records for groups D and E of immediate reinforcement
are high at the beginning of the post-week. Therefore, we confirm the resistance toextinction when switching incentives and sustained responses enhance by lottery-based
incentives.

10Furthermore, Figure 3(B) shows the effect of the passage of weeks. Among all groups,
the coefficients are negative, indicating a decreasing trend. In addition, the speeds ofdecrease are greater in groups C, D, and E than in the control group A. Moreover, thecoefficients on post-week in Table 2 (Model I column) are negatively significant (p< .001) for both high chance, low prize and immediate reinforcement, and themagnitude of the coefficients are comparable. This suggests that, regardless of how thereinforcement schedule was set up in pre-week, the decreasing rates of the number ofscan records are comparable between the two factors.

Figure 4(B) shows the effect of winning experience and habit estimated by Model III.
The coefficients of experience are significantly positive (p < .001) in all groups.
Therefore, we confirm that the past winning experience of the lottery-based incentive
affects subsequent response behavior, even if the incentive is switched to honorariums.
However, the coefficients for habit are positively significant (p < .001) for groups B andC, and negatively significant (p < .001) for group E. In order to interpret these results
more clearly, Table 2 (Model III column) shows the summaries from the perspective ofthe two factors. It shows that, in the case of immediate reinforcement, the experience,but not habit, positively affects the number of scan records, whereas, in the case of highchance, low prize, both experience and habit positively affects the number of scan
records.

Discussion
We investigated the effects of financial incentives on survey response behavior.
Specifically, we conducted a 12-week real-world field experiment in an RCT toexamine the difference of reinforcement schedules. This field experiment was notintended to be an ad hoc survey, but rather a survey that requires long-term and ongoing
responses, such as a panel survey, which has rarely been addressed in previous studies.
As a result, this study suggests that the survey response effort varies substantiallydepending on the reinforcement schedule, even for less labor-intensive surveys, such as
scan-type surveys. This means that participants’ responses are dependent on the
incentive program. Many experiments on animals and humans have suggested thatpartial reinforcement is stronger than continuous reinforcement, despite the incentives
having the same expected value (8, 9); however, this is the first study to show thatsimilar results can be obtained in terms of survey response behavior. The results of this11

study are important to many researchers and practitioners in terms of the effect of
incentives on the subject matter of surveys, a methodology that is widely used in thesocial sciences, medicine, and public health.

Our results showed that partial reinforcement, such as lottery-based incentive, enhanced
the survey response behavior and reduced underreporting bias. In particular, immediate
reinforcement and high chance, low prize incentives were most effective. In addition,
switching from lottery-based incentives to weekly honorariums resulted in theresistance to extinction and sustained responses that were enhanced by lottery-based
incentives. Although the extinction resistance is revealed to be enhanced more by thepartial reinforcement than by the continuous reinforcement for animals (8), we firstly
showed that the similar phenomenon is observed for survey response behavior.
Moreover, we evaluated the past reward winning experience and the habit of past
responding behavior, and found that experience is related to subsequent survey
responses. However, in the frequent reinforcement scenario, such as high chance, lowprize incentives, both experience and habit were related to subsequent survey responses.

The implications of this study call for caution for many researchers and practitioners
who design surveys. Particularly in a panel survey that requires long-term cooperation,a weekly honorarium may lead to a greater decrease in the willingness to respond to asurvey over time than lottery-based incentives. To deal with the underreporting
problem, researchers and practitioners may need to consider practical applications, suchas giving lottery-based incentives, paying incentives according to survey response
actions, or shortening the intervals between incentives. This study also suggests that adesign that makes good use of the resistance to extinction is effective even if the
incentives were based on weekly honorariums.

Furthermore, this study provides practical implications in terms of the management ofmonetary resources in the survey design. We found that when a fixed amount of money
is given regardless of the status of cooperation in the survey, total expenditure washigher than when the incentives were given on a lottery-basis. This result was related to
the fact that participants with lower levels of cooperation were also rewarded.
Therefore, this study suggests that survey designers can more efficiently obtain thecooperation of participants if they pay a lottery-based incentive.
12

Finally, we discuss the limitations. The first is the issue of habituation in the case of alonger survey. In this study, the period of the lottery-based incentive was set at eightweeks, limiting the capture of the changes in the participants' behavior when partial
reinforcement is applied over a longer period of time. The second limitation is checking
the participants' survey responses to obtain more accurate data. In this study, thecorrectness of the participants' responses was assessed ex-post (see Robustness Checks
in Supplementary Materials). However, it is not clear whether the participants answered
correctly or not. Confirming the correctness of the responses to gain more accurate databy having the participants report visiting a store after obtaining data on their location,for example, would improve reliability and validity.

Endnote1) The expected number of lotteries is calculated from the average number of weekly
scan records for the beverage category in the SCI data. The SCI data are measured
in a system very similar to the data collection in this study and are panel data thatare actually operated as a business service. Therefore, we believe that the data canbe used as a reference for designing incentives.

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Supplementary Materials
A. Descriptive statistics by group
Table S1 shows the descriptive statistics by group. The average number of records per
week in pre-week is higher in groups D, E, B, A, and C, in that order. However, in postweek, the records are higher in groups D, E, A, B, and C, in that order. Furthermore,corresponding to Figure 1, Table S2 shows the mean values of the number of scan
records in the week by group. Group A, which received the weekly honorarium, has thehighest average amount of incentives in pre-week. It should be noted that the lotterybased incentive groups B–E have a higher number of scan records, although theamounts of incentives are lower than for group A. Table S3 shows the efficiency ofmonetary rewards for calculating the rewards per record.

B. Models
B.1. Model I – the holistic view of the experimental results
We modeled the number of scan records 𝑦𝑦𝑖𝑖𝑖𝑖 in participant 𝑖𝑖, week 𝑡𝑡, using thefollowing zero-inflated Poisson (ZIP) regression (Model I).
𝑝𝑝 + (1 − 𝑝𝑝) exp(−𝜆𝜆𝑖𝑖𝑖𝑖 )𝜆𝜆𝑖𝑖𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 exp(−𝜆𝜆𝑖𝑖𝑖𝑖 )𝑃𝑃𝑃𝑃(𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑘𝑘) = �(1 − 𝑝𝑝)𝑦𝑦𝑖𝑖𝑖𝑖 !

where, 𝑖𝑖𝑖𝑖 𝑘𝑘 = 0, 𝑖𝑖𝑖𝑖 𝑘𝑘 > 0

(B.1)

4

𝜆𝜆𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + � 𝛽𝛽1𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) + 𝛽𝛽2 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝐴𝐴) +𝑗𝑗=1

𝛽𝛽3 𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝐴𝐴̅) +

(B.2)

4

� 𝛽𝛽4𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) × 𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝐴𝐴̅) +𝑗𝑗=2

𝛽𝛽5 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝐴𝐴̅) +16

4

� 𝛽𝛽6𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) × 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝐴𝐴̅) + 𝛽𝛽7 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖 + 𝛽𝛽8 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖
𝑗𝑗=2

and
𝑙𝑙𝑙𝑙𝑙𝑙 �𝑝𝑝� = 𝛼𝛼01 − 𝑝𝑝(B.3)

The ZIP model assumes that 𝑦𝑦𝑖𝑖𝑖𝑖 is 0 with probability 𝑝𝑝 and Pois(𝜆𝜆𝑖𝑖𝑖𝑖 ) with probability
1 − 𝑝𝑝. The Poisson regression part of the ZIP includes dummy variables for the groups,
weekly variables, and their interaction terms, gender, and age as explanatory variables.
Specifically, 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) is a dummy variable of participant 𝑖𝑖’s group (𝑗𝑗 = 1, …, 4,
from group B to group E). The control group, group A, is the reference group that has thecoefficient of the parameter is normalized to zero. 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝐴𝐴) is a dummy variablewhich is equal to 1 if the participant belongs to group A, and 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑌𝑌𝑖𝑖 (𝐴𝐴̅) is a dummy

variable which is equal to 1 if the participant does not belong to group A. 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 is aweekly variable that contains 1 to 12. In group A, which has a weekly honorarium in bothpre- and post-week, we use the interaction term of 𝑊𝑊𝑊𝑊𝑊𝑊𝐾𝐾𝑡𝑡 and the group A dummy.
𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 is a weekly variable that can be set to 1–8 in the pre-week and 0 in the
post-week. Conversely, 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 is a weekly variable with 0 in the pre-week and
1–4 in the post-week. The interactions between 𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 or 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 andgroup dummy variables are set for the dummy variables of groups C-E (j=2, …, 4) withgroup B as the reference group. 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖 is a dummy variable that is equal to 1 if
participant 𝑖𝑖 is male, and 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖 is a continuous variable. 𝛽𝛽0 is a constant term, and
𝛽𝛽1 -𝛽𝛽8 are coefficients of the explanatory variables. In addition, as in Equation (B.3), we
do not set explanatory variables in the logit model part of the ZIP, but only a constantterm.
B.2. Models II and III – to evaluate the experience and the habit on the number ofsubsequent scan records.
We evaluate the past reward winning experience and the habit of past survey responses.
We modeled the number of scan records 𝑧𝑧𝑖𝑖 using the following Poisson regression.
Assuming 𝑧𝑧𝑖𝑖 ~Pois(𝜇𝜇𝑖𝑖 ), 𝜇𝜇𝑖𝑖 is the mean and variance of the distribution. The Poisson17regression model is a generalized linear model that can be expressed as 𝑙𝑙𝑙𝑙𝑙𝑙(𝜇𝜇𝑖𝑖 ) = 𝜂𝜂𝑖𝑖
using the link function of the natural logarithm. The linear predictor 𝜂𝜂𝑖𝑖 is set up withdummy variables for the groups, experience, group dummies×experience, habit, group
dummies×habit, gender, and age, as follows:3𝜂𝜂𝑖𝑖 = 𝛾𝛾0 + � 𝛾𝛾1𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) + 𝛾𝛾2 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 +3𝑗𝑗=1

� Υ3𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) × 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 +
𝑗𝑗=1

(B.4)

3

𝛾𝛾4 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑖𝑖 + � Υ5𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) × 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑖𝑖 + 𝛾𝛾6 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖 + 𝛾𝛾7 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖
𝑗𝑗=1

where 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 is the past reward winning experience, which is defined as theactual amount of past incentives, and 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑖𝑖 is the habit of past survey responses,
which is defined as the number of past scan records. Note that the variables of experienceand habit are standardized so that the parameters can be compared when interpreting theresults. 𝛾𝛾0 is a constant term, and 𝛾𝛾1 - 𝛾𝛾7 are coefficients of the variables. The
participants of group A are excluded from the analysis because they are given a weekly
honorarium. Therefore, the analysis was conducted for those in groups B–D (n=477). The
reference group was set as group C.
We set up two models with different periods of analysis. In Model II, the total number ofscan records after the third week in the pre-week is the dependent variable, and theexperience and the habit, up to second week, are explanatory variables. In Model III, thedependent variable is the total records in post-week, and explanatory variables are up to
eighth week (pre-week).

18

C. The results of Models I, II and III
C1. Estimation results of Model I
Table S4 shows the ZIP regression results of Model I. As shown in Supplementary
Materials D.1, the results of ZIP with combinations of parameters by the delta method areused in the table. First, the main effects of the dummy variables for each group are
positively significant (p < .001) for all of groups A–E. Moreover, the coefficients forgroups D and E are larger than those of the others. The weekly variable for group A is
negatively significant (p < .001), indicating a gradual decrease in the number of scan
records as the weekly honorarium is continued. However, with regard to the weekly
variables for the pre-week of groups B–E, groups D and E are positively significant (p< .001) and groups B and C are not. This result suggests that immediate reinforcement
promotes an increase in the number of scan records. In addition, the weekly variables inpost-week of groups B–E are negatively significant (p<.001) in all groups. Hence, it issuggested that switching to a weekly honorarium may reduce participants' willingness to
respond to surveys, compared to a lottery-based incentive.
C2. Estimation results of Model II
Table S5 shows the results of the parameter estimation of the Poisson regression model(Model II). Note that the values in this table are after the combinations of parameters
shown in Supplementary Materials D.2.
The experience is positively significant (p < .001) for all groups. It is suggested thatparticipants who received more money at the beginning of the survey are more willing to
respond in the subsequent period. The coefficients are especially large in the high chance,
low prize reinforcement schedule groups (groups B and D). Multiple comparisons using
Shaffer's method for parameter equality reveal significant differences between groups B–
C (p < .05), and C–D and C–E (p < .10). The effects of past scan records are positively
significant (p < .001) in groups B, C, and E. Multiple comparisons using Shaffer's method
show that there are significant differences among all groups, C–D, C–E, B–D, B–E, and
B–C (p < .001), and D–E (p < .05).
In additions, the parameters of habit are larger than those of experience for groups B andC, suggesting that both experience and habit have the effects. However, the parameters19

of habit are smaller in groups D and E than those of experience, suggesting that only pastwinning experience have an effect.
Note that the correlation coefficients between experience and habit are B = 0.494, C =0.542, D = 0.888, and E = 0.906, which are not linearly dependent, but the coefficientsare high. In this regard, we compared the information criterions for the model including
only experience (LL = -5353.7, the Akaike Information Criterion (AIC) = 10727.3, BIC= 10769.0), only habit (LL = -5080.4, AIC = 10180.9, BIC = 10222.5), and bothexperience and habit (LL = -4897.3, AIC = 9822.6, BIC = 9880.9). Based on theseinformation criterions, we adopted the model including both experience and habit in thisstudy.
C3. Estimation results of Model III
Table S6 shows the results of the parameter estimation of the Poisson regression model(Model III). Because the coefficients of experience are positively significant (p < .001)
for all groups, it is suggested that participants who received more money are more willing
to respond when lottery-based incentives are switched to weekly honorariums. Multiple
comparisons using Shaffer's method for parameter equality confirm significant
differences in D–E (p < .001), C–E (p < .01) and B–D (p < .10).
The parameters of the habit are positively significant (p < .001) in groups B and C, andnegatively significant (p < .001) in group E. Multiple comparisons using Shaffer's method
confirm that there are significant differences (p < .001) in the magnitude of the parametervalues between all groups. For groups E and D with immediate reinforcement, the effects
of experience in pre-week are stronger than those of habit. However, for groups B and C
with delayed reinforcement, both experience and habit are related to the number of scan
records in post-week.
Note that the correlation coefficients between experience and habit are B = 0.558, C =0.659, D = 0.987, E = 0.973. In this regard, we compared the information criterion for themodel including only experience (LL = -3165.7, AIC = 6351.4, BIC = 6393.1), only habit(LL = -2974.2, AIC = 5968.4, BIC = 6010.0) and both experience and habit (LL = -2807.6,
AIC =5643.2, BIC = 5615.2). Since the model including both is the best model, we
adopted it in this study.

20

D. Combinations of parameters
D.1. Combinations of parameters for Model I
In this section, we show the combinations of parameters in Model I using the delta method(19). Table S7 shows the results of the ZIP model (Model I) before the combinations ofparameters shown in Supplementary Materials B.1. For this result, the design matrix 𝑨𝑨
is used to integrate the parameters, as shown in Equation D.1. Here, we show the exampleof the combinations of the intercept and Dummy(j).
𝛽𝛽̂
⎡ 0⎤
𝛿𝛿𝑏𝑏
1
⎢𝛽𝛽̂1𝑏𝑏 ⎥𝛿𝛿𝑐𝑐 � ⎢ ̂ ⎥𝜹𝜹 = � � , 𝜷𝜷 = 𝛽𝛽1𝑐𝑐 , 𝑨𝑨 = �1
𝛿𝛿𝑑𝑑
1
⎢̂ ⎥
1
𝛿𝛿𝑒𝑒
⎢𝛽𝛽1𝑑𝑑 ⎥̂⎣ 𝛽𝛽1𝑒𝑒 ⎦

1
0
0
0

0
1
0
0

0
0
1
0

0
0� , 𝜹𝜹
� = 𝑨𝑨𝜷𝜷� ~𝑵𝑵(𝑨𝑨𝜷𝜷� , 𝑨𝑨𝑨𝑨�𝜷𝜷� �𝑨𝑨𝒕𝒕 )
0
1

(D.1)

where 𝛿𝛿𝑏𝑏 is a combination of the intercept (the coefficient of group A) and groupB. 𝛿𝛿𝑐𝑐 , 𝛿𝛿𝑑𝑑 , 𝛿𝛿𝑒𝑒 are similarly combined with the intercept and the coefficients, respectively.
� is the parameter vector of the group's dummy variables. 𝑽𝑽�𝜷𝜷� � is the variance𝜷𝜷
covariance matrix of the parameters. For the pre-week and post-week variables, thecoefficients of groups C, D, and E are combined with the reference group B.
For the results of each factor presented in Table 2 (Model I column), the parameters are
combined using the design matrices 𝑨𝑨 and 𝑩𝑩 as shown in Equation D.2. We show theexample of the combinations of the intercept and Dummy(j).
𝛽𝛽̂
⎡ 0⎤
1
⎡1
𝛿𝛿0
⎢𝛽𝛽̂1𝑏𝑏 ⎥� = ⎢ 𝛽𝛽̂1𝑐𝑐 ⎥ , 𝑨𝑨 = ⎢1𝜹𝜹 = �𝛿𝛿1 � , 𝜷𝜷
⎢
⎢̂ ⎥
⎢1
𝛿𝛿2
𝛽𝛽
⎢ 1𝑑𝑑 ⎥
⎣1
⎣ 𝛽𝛽̂1𝑒𝑒 ⎦

0
1
0
0
0

0
0
1
0
0

0
0
0
1
0

0
1
0⎤
⎥
0⎥ , 𝑩𝑩 = �00⎥01⎦

� = 𝑩𝑩𝑩𝑩𝜷𝜷� ~𝑵𝑵(𝑩𝑩𝑩𝑩𝜷𝜷
� , 𝑩𝑩𝑩𝑩𝑩𝑩�𝜷𝜷� �𝑨𝑨𝒕𝒕 𝑩𝑩𝒕𝒕 )
𝜹𝜹

0
1
0

0 0
0 1
0 1

0
0�,
1

(D.2)

where 𝛿𝛿0 is a parameter of weekly honorarium, 𝛿𝛿1 is a parameter of high chance low
prize, and 𝛿𝛿2 is a parameter of immediate reinforcement. The parameters of theinteraction terms between pre-week and group dummies, estimated with group B as thereference group, are combined as in Equation D.3. The interaction terms between
21

post_week and group dummies are also operated in the same way.
𝛽𝛽̂1 0 0 0⎡ 2⎤
𝛿𝛿1 � ⎢ 𝛽𝛽̂3𝑐𝑐 ⎥1 1 0 01𝜹𝜹 = � � , 𝜷𝜷 = ⎢ ⎥ , 𝑨𝑨 = �� , 𝑩𝑩 = �
̂
1
0
1
0
𝛿𝛿2
0
⎢𝛽𝛽3𝑑𝑑 ⎥1 0 0 1⎣ 𝛽𝛽̂3𝑒𝑒 ⎦� = 𝑩𝑩𝑩𝑩𝜷𝜷� ~𝑵𝑵(𝑩𝑩𝑩𝑩𝜷𝜷
� , 𝑩𝑩𝑩𝑩𝑩𝑩�𝜷𝜷� �𝑨𝑨𝒕𝒕 𝑩𝑩𝒕𝒕 )
𝜹𝜹

0
0

1 0
�,
1 1

(D.3)

D.2. Combinations of parameters for Models II and III

Next, we show the combinations of parameters used in Tables S5 (Model II) and S6(Model III). Table S8 shows the Poisson regression results of Model II before the
combinations of parameters. This is the results of the model in Equation B.4 of
Supplementary Materials B.2, where the reference group is group C. For this result, we
combine the parameters using the design matrix. We show the example of the
combinations of the intercept and Dummy(j) in Equation D.4. Similarly, Table S9 shows
the Poisson regression results of Model III before the combinations of parameters.
𝛾𝛾�1𝑏𝑏𝛿𝛿𝑏𝑏1𝛾𝛾�� = � 0 � , 𝑨𝑨 = �0𝜹𝜹 = �𝛿𝛿𝑑𝑑 � , 𝜸𝜸𝛾𝛾�1𝑑𝑑0𝛿𝛿𝑒𝑒
𝛾𝛾�1𝑒𝑒

1
1
1

0
1
0

0
� = 𝑨𝑨𝜸𝜸
0� , 𝜹𝜹
�~𝑵𝑵(𝑨𝑨𝜸𝜸
�, 𝑨𝑨𝑨𝑨(𝜸𝜸�)𝑨𝑨𝒕𝒕 )1(D.4)

For the results of each factor presented in Table 2 (Model II and Model III columns), theparameters are combined using the design matrices 𝑨𝑨 and 𝑩𝑩 as shown in Equation D.5.
We show the example of the combinations of the intercept and Dummy(j), where thereference group is group C. The parameters of experience and habit are also operated in
the same way as in Equation D.5.
𝛾𝛾�1𝑏𝑏1𝛾𝛾�0𝛿𝛿10� = � � , 𝑨𝑨 = �𝜹𝜹 = � � , 𝜸𝜸0𝛿𝛿2𝛾𝛾�1𝑑𝑑0𝛾𝛾�1𝑒𝑒

1
1
1
1

0
0
1
0

0
0
1 0
� , 𝑩𝑩 = �00 01

� = 𝑩𝑩𝑩𝑩𝜸𝜸
�~𝑵𝑵(𝑩𝑩𝑩𝑩𝜸𝜸
�, 𝑩𝑩𝑩𝑩𝑩𝑩(𝜸𝜸�)𝑨𝑨𝒕𝒕 𝑩𝑩𝒕𝒕 )
𝜹𝜹

22

1
1

0
�,
1

(D.5)

D.2. Difference test in the number of scan records between groups using a random
effects model
We describe the random effects model and its combinations of parameters used to test of
the differences in the number of scan records between groups. Here, we employ a linear
regression model with the number of scan records 𝑦𝑦𝑖𝑖𝑖𝑖 as the dependent variable and thegroup dummies as the explanatory variable, with individual’s random effect. Since 𝑦𝑦𝑖𝑖𝑖𝑖
has been measured multiple times from the same person, the measurements are correlated,
and therefore we have adopted this method rather than simply used a test of the difference
in means.
4𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + � 𝛽𝛽1𝑗𝑗 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 (𝑗𝑗) + 𝜇𝜇𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖

(D.6)

𝑗𝑗=1

where 𝜇𝜇𝑖𝑖 is a random effect and assumed to be normally distributed with a mean of zero
independent of the explanatory variables, and 𝜀𝜀𝑖𝑖𝑖𝑖 is an error term. 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑌𝑌𝑖𝑖 (𝑗𝑗) is adummy variable for groups B to E, and, here, we analyze group A as the reference group.
For the estimated parameters 𝛽𝛽0 and 𝛽𝛽1𝑗𝑗 , we performed the combinations of parameters
shown in Equation D.7 and the results are shown in Figure 2.
𝛽𝛽̂0
𝛿𝛿𝑎𝑎
⎡
⎤
1
⎡ ⎤
̂
⎡1
𝛽𝛽
𝛿𝛿𝑏𝑏
⎢
1𝑏𝑏 ⎥
⎢ ⎥ �
𝜹𝜹 = ⎢ 𝛿𝛿𝑐𝑐 ⎥ , 𝜷𝜷
= ⎢ 𝛽𝛽̂1𝑐𝑐 ⎥ , 𝑨𝑨 = ⎢⎢1⎢̂ ⎥⎢1⎢𝛿𝛿𝑑𝑑 ⎥
⎢𝛽𝛽1𝑑𝑑 ⎥⎣1⎣ 𝛿𝛿𝑒𝑒 ⎦⎣ 𝛽𝛽̂1𝑒𝑒 ⎦

0
1
0
0
0

0
0
1
0
0

0
0
0
1
0

0
0⎤⎥
� = 𝑨𝑨𝜷𝜷� ~𝑵𝑵(𝑨𝑨𝜷𝜷� , 𝑨𝑨𝑨𝑨�𝜷𝜷� �𝑨𝑨𝒕𝒕 )0⎥ , 𝜹𝜹
0⎥
1⎦

(D.7)

E. An extension of Model I to include quadratic terms
Figure 1 shows that the number of scan records continued to increase in groups D and E
in pre-week, while the number of scan records continue to decrease for all groups in postweek. This trend may be related to the weekly variables of Model I, estimated as a firstorder term. Therefore, we extended the analysis by including quadratic terms for 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 ，
𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 and 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡 in the Equation (B.2). Figure S1 shows the expected23

value of the number of scan records by group including quadratic terms. We find convex
downward shapes in pre-week and convex upward shapes in post-week, suggesting thatthe number of scan records does not continue to fall in the post-weeks. However, after
comparing the information criterions among the models with only first-order terms andthe model including quadratic terms, the model with only first-order terms is adopted inseveral information criterions of Bayesian Information Criterion (BIC) and consistent
AIC (CAIC). For the model with only first-order terms, AIC = 47027.8, BIC = 47144.6
and CAIC = 47161.6. For the model including quadratic terms, AIC = 47002.4, BIC =47181.2, CAIC = 47207.2. Therefore, the model with only first-order terms is adopted inthis study.

F. Robustness checks
F.1. Methods
In this section, we discuss the robustness checks of our results. Participants may respondfalsely in a self-reported survey. In particular, when offered a form of lottery-based
incentives, for which the participant is paid according to the response effort, dishonest
scanning may lead to falsely implying that an effort was made. Hence, this study examines
the robustness of the model’s estimation results when participants are excluded for thefollowing two cases.
First, the study excluded those who were suspected of falsely response with incorrect scan
records. For example, we excluded those who scanned the same product multiple times
in order to win the lottery, specifically, those who scanned the same stock-keeping units
three or more times on the same day.
Second, this study excluded those who were prone to IER (insufficient effort responding).
We used the following procedure to identify IER respondents. First, we conducted aquestionnaire survey with participants before the experiment began and asked them about
the IER scale. IER is defined as “a response set in which the respondent answers a survey
measure with low or little motivation to comply with survey instructions, correctlyinterpret item content, and provide accurate responses” (6, p. 100). In the case of thisstudy, participants who are prone to IER may not cooperate earnestly in the survey andmay be underreported. Although various indices have been developed to identify theparticipants who are prone to IER (6, 20-22), we use the infrequency IER scale (22),24which is associated with short response time and inconsistent response to items. We use
four items including “I can run 3 kilometers in 2 minutes,” “I am interested in pursuing a
degree in managemental genetics,” “I have never used a computer,” “I work twenty-eight
hours in a typical work day” 1). The response alternatives used a seven-point Likert scales(1 = strongly disagree, 7 = strongly agree). Although the infrequency IER scale originallyconsisted of eight items, in this study, four of the above-mentioned items were includedin the questionnaire for ethical reasons presented by the marketing research firm that
conducted the survey. These items are interspersed with the other items in thequestionnaire to hide the fact that we were asking about IER from the participants.
The infrequency IER scale identifies IER based on the participants' agreement on items
that do not exist or are not feasible. Therefore, we set IER respondents (IER1 = 1) if theygave an agreeable rating (from 5 = slightly agree to 7 = strongly agree) on any of theitems and non-IER respondents (IER1 = 0) if they did not. In addition, in the item for the
degree, Curran and Hauser (23) indicated that respondents could respond to the degree
presented in the survey as interesting even if they did not know it, or they could not
disagree because it might be a degree that existed. Therefore, we set only three items
without using the item of the degree as IER2.
F.2. Results
We checked the robustness of the parameters by excluding: (1) those who were suspectedof falsely responding with incorrect scan records, and (2) those who were prone to IER(IER1 and IER2). As the result of extracting those who were suspected of falsely
responding, 11 participants remained (B: 2, D: 3, E: 6). In addition, for the IER, IER1
included 111 participants (A: 21, B: 21, C: 23, D: 28, E: 18), and IER2 included 84
participants (A: 14, B: 17, C: 17, D: 23, E: 13).
Tables S10 and S11 show the results for Model I. The results show no difference insignificance or sign compared to the variables that become significant in Table S4, except
for Pre_week(C) in the case of IER. In addition, in Pre_week(C), under the IER2 criterion,
which is less likely to inadvertently remove non-IER respondents, the direction of thesign is the same and the result in Table S11 is marginally significant instead of nonsignificant in the original model, with a slight difference. Therefore, the implications of
this study remain the same, suggesting that there is robustness in the results of this study.

25

Similarly, Tables S12 and S13 show the results for Model II. There is no change in thesignificance or sign of the parameters in the case excluding those who were suspected offalsely responding. However, in the case excluding those who were prone to IER, there
is a tendency for the coefficient of experience to be positive and for the coefficient of
habit to be negative in group D. However, the main findings of this study remain thesame: in the original model, experience is more effective at improving the participant's
survey response efforts than habit is. Therefore, we support the results of Model II. In
addition, Tables S14 and S15 show the results for Model III. There is no change in thesignificance and sign of the results, suggesting that the results for Model III are robust.

Endnote1) The scales of Huang et al. (2015) includes “I can run two miles in two minutes,” and“I am interested in obtaining a degree in Parabanjology.” However, since the survey
was conducted among people in Japan, we use the familiar kilometer notation forthe survey. In addition, since it is not possible to translate “Parabanjology” into
Japanese, we have replaced the term with a non-existent degree.

26

Fig. S1. The expected value of the number of scan records by group using a model
including quadratic terms

27

Table S1. Descriptive statistics by groupn (participants)

A
118

B
117

C
119

D
120

E
121

Total
595

n (participants-weeks)
Average number of records per week

mean

1416
3.48

1404
3.76

1428
2.94

1440
6.53

1452
5.76

7140
4.50

in the pre-week
Average number of records per week

sd
mean

3.47
3.02

4.01
2.97

3.27
2.26

11.10
4.25

10.30
3.76

7.49
3.26

in the post-week
Acutual amount of rewards

sd
mean

3.20
2.40

3.57
1.68

2.79
1.66

6.34
1.96

6.64
2.00

4.86
1.83

in the pre-week (thousand yen)

sd

0.00

0.89

1.08

2.36

2.32

1.81

Acutual amount of rewards
in first two weeks of the pre-week (thousand yen)

mean
sd

0.60
0.00

0.41
0.24

0.39
0.29

0.45
0.52

0.46
0.51

0.43
0.41

Age

mean
sd

41.2
11.1

41.4
10.7

42.1
10.9

42.0
10.6

41.0
11.2

41.5
10.9

51.7

51.3

51.3

49.2

51.2

50.9

Male (%)

28

Table S2. Mean values of the number of scan records in the week by group

weeks

groupsA
1 3.952 3.85
3 3.764 3.81
5 3.24
6 3.05
7 3.08
8 3.06
9 2.87
10 3.07
11 3.20
12 2.92

B
3.64
3.97
3.88
3.81
3.95
3.47
3.68
3.67
3.33
2.84
2.96
2.75

C
3.34
3.17
3.05
3.06
2.73
2.82
2.77
2.61
2.45
2.18
2.35
2.07

29

D
5.94
6.47
6.84
6.32
6.53
6.23
6.93
6.98
4.51
4.22
4.08
4.18

E
5.27
6.02
5.79
6.07
5.37
5.64
5.39
6.50
3.90
3.85
3.54
3.74

Table S3. Efficiency of monetary rewards
Records per week
Rewards per week (yen)Rewards per record (yen)

A
B
C
D
E
3.48 (3.47) 3.76 (4.01) 2.94 (3.27) 6.53 (11.10) 5.76 (10.30)
300 (0)211 (111)
207 (135)
246 (295)
250 (290)
86
56
70
38
43

Note. For records per week and rewards per week, we show the mean (sd). For rewards
per record, we divided the mean of rewards per week by the mean of records per week.

30

Table S4. The ZIP regression results of Model I
Poisson regression partSE
z-value
β
1.038
0.040
25.746 ***Dummy(A)
1.002
0.042
23.674 ***Dummy(B)
0.934
0.045
20.662 ***Dummy(C)
1.504
0.036
41.233 ***Dummy(D)
1.333
0.037
35.753 ***Dummy(E)-0.0260.004-5.932 ***Week(A)
0.003
0.007
0.421
Pre_week(B)-0.0110.008-1.390Pre_week(C)
0.027
0.005
5.316 ***
Pre_week(D)
0.044
0.005
8.512 ***
Pre_week(E)-0.0590.015-3.842 ***Post_week(B)-0.1050.012-8.799 ***Post_week(C)-0.0930.018-5.288 ***Post_week(D)-0.0640.013-5.104 ***Post_week(E)
0.345
0.012
28.528 ***Gender
Age
0.010
0.001
16.090 ***Logistic regression part predicting records to be 0SEz-valueβ-0.8580.026 -32.410 ***Intercept
Log-likelihood-23496.9

Note. *** p < .001, ** p < .01, * p < .05.

31

Table S5. The results of Poisson regression of Model II
Dummy(B)
Dummy(C)Dummy(D)Dummy(E)
Experience(B)Experience(C)Experience(D)Experience(E)Habit(B)
Habit(C)
Habit(D)
Habit(E)
GenderAge
Log-likelihood

γ
2.149
2.010
2.478
2.278
0.485
0.320
0.471
0.357
0.588
0.946
0.020
0.149
0.069
0.019
-4897.3

SE
0.048
0.048
0.047
0.046
0.047
0.039
0.054
0.032
0.025
0.045
0.048
0.028
0.018
0.001

z-value
45.18641.74853.02349.54410.237
8.312
8.637
11.11523.81421.174
0.409
5.262
3.715
20.507Note. *** p < .001, ** p < .01, * p < .05.

32

***
***
***
***
***
***
***
***
***
***
***
***
***

Table S6. The results of Poisson regression of Model III
Dummy(B)
Dummy(C)Dummy(D)Dummy(E)
Experience(B)Experience(C)Experience(D)Experience(E)Habit(B)
Habit(C)
Habit(D)
Habit(E)
GenderAge
Log-likelihood

γ
1.704
1.615
1.991
1.776
0.935
0.696
0.531
1.092
0.977
1.379
-0.141-0.667-0.1460.016-2807.6

SE
0.068
0.071
0.067
0.066
0.103
0.091
0.120
0.087
0.044
0.089
0.111
0.081
0.027
0.001

z-value
24.98422.65529.65127.085
9.054
7.679
4.416
12.48422.27115.496-1.263-8.219-5.497
12.200Note. *** p < .001, ** p < .01, * p < .05.

33

***
***
***
***
***
***
***
***
***
***
***
***
***

Table S7. The ZIP regression results of Model I before the combinations ofparameters
Poisson regression partβSEz-value
Intercept
1.038
0.040
25.746
Dummy(B)-0.0360.044-0.817
Dummy(C)-0.1040.047-2.204
Dummy(D)
0.466
0.039
11.854
Dummy(E)
0.295
0.040
7.361
Week(A)-0.0260.004-5.932
Pre_week
0.003
0.007
0.421
Pre_week(C)-0.0130.010-1.325Pre_week(D)
0.024
0.008
2.855
Pre_week(E)
0.042
0.008
4.916
Post_week-0.0590.015-3.842
Post_week(C)-0.0460.020-2.358
Post_week(D)-0.0340.023-1.453
Post_week(E)-0.0050.020-0.258Gender0.345
0.012
28.528
Age
0.010
0.001
16.090Logistic regression part predicting records to be 0βSEz-value
Intercept-0.8580.026-32.410Log-likelihood-23496.9

Note. *** p < .001, ** p < .01, * p < .05.

34

***
*
***
***
***

**
***
***
*

***
***

***

Table S8. The Poisson regression results of Model II before the combinations ofparameters
Intercept
Dummy(B)
Dummy(D)Dummy(E)
Experience
Experience×Dummy(B)Experience×Dummy(D)Experience×Dummy(E)Habit
Habit×Dummy(B)Habit×Dummy(D)Habit×Dummy(E)GenderAge
Log-likelihood

γ
2.010
0.139
0.468
0.268
0.320
0.165
0.150
0.037
0.946
-0.357-0.926-0.7970.069
0.019-4897.3

SE
0.048
0.032
0.030
0.031
0.039
0.061
0.067
0.050
0.045
0.051
0.066
0.053
0.018
0.001

Note. *** p < .001, ** p < .01, * p < .05.

35

z-value
41.7484.304
15.445
8.716
8.312
2.703
2.256
0.748
21.174-6.981-14.078-15.1443.715
20.507

***
***
***
***
***
**
*
***
***
***
***
***
***

Table S9. The Poisson regression results of Model III before the combinations ofparameters
Intercept
Dummy(B)
Dummy(D)Dummy(E)
Experience
Experience×Dummy(B)Experience×Dummy(D)Experience×Dummy(E)Habit
Habit×Dummy(B)Habit×Dummy(D)Habit×Dummy(E)GenderAge
Log-likelihood

γ
1.615
0.089
0.376
0.161
0.696
0.239
-0.1660.396
1.379-0.401-1.519-2.046-0.1460.016-2807.6

SE
0.071
0.049
0.047
0.047
0.091
0.137
0.150
0.125
0.089
0.099
0.142
0.121
0.027
0.001

z-value
22.655
1.812
7.922
3.421
7.679
1.738
-1.101
3.155
15.496-4.062-10.668-16.962-5.497
12.200Note. *** p < .001, ** p < .01, * p < .05, † p < .10.

36

***
†
***
***
***
†
**
***
***
***
***
***
***

Table S10. The results of Model I excluding those who were suspected of falsely
responding
Poisson regression partSE
z-value
β
1.143
0.041
28.047 ***Dummy(A)
1.099
0.043
25.487 ***Dummy(B)
Dummy(C)
1.038
0.046
22.794 ***
1.527
0.038
40.141 ***Dummy(D)Dummy(E)
1.413
0.039
36.328 ***Week(A)-0.0250.004-5.898 ***Pre_week(B)
0.003
0.007
0.503
Pre_week(C) -0.010
0.008-1.315Pre_week(D)
0.025
0.005
4.705 ***
0.025
0.006
4.311 ***
Pre_week(E)Post_week(B) -0.059
0.016-3.785 ***0.013-8.140 ***Post_week(C) -0.103
0.018-5.155 ***Post_week(D) -0.091
Post_week(E) -0.052
0.013-3.932 ***Gender0.277
0.012
22.176 ***
Age
0.008
0.001
13.148 ***Logistic regression part predicting records to be 0βSEz-value
Intercept-0.8390.027 -31.570 ***Log-likelihood -21686.8

Note. *** p < .001, ** p < .01, * p < .05.

37

Table S11. The results of Model I excluding those who were prone to IER
IER1Poisson regression partSE
z-value
β
0.946
0.046
20.530 ***Dummy(A)
0.938
0.049
19.310 ***Dummy(B)
Dummy(C)
0.840
0.051
16.326 ***Dummy(D)
1.305
0.044
29.865 ***Dummy(E)
1.206
0.043
28.297 ***-0.0250.005-5.223 ***Week(A)-0.0120.008-1.543
Pre_week(B)-0.0220.009-2.468 *Pre_week(C)Pre_week(D)
0.019
0.006
2.925 **
Pre_week(E)
0.046
0.006
7.776 ***
0.018
-4.979 ***Post_week(B) -0.088
0.015-6.070 ***Post_week(C) -0.089
0.020-4.403 ***Post_week(D) -0.086
0.014-5.557 ***Post_week(E) -0.080
0.352
0.014
25.176 ***Gender
Age
0.011
0.001
15.999 ***Logistic regression part predicting records to be 0SEz-valueβIntercept-0.851
0.029-28.910 ***Log-likelihood -18007.6

IER2
β
0.972
0.914
0.854
1.287
1.190
-0.026-0.004-0.0160.017
0.050-0.085-0.093-0.089-0.072
0.355
0.011

SE
0.044
0.047
0.050
0.043
0.042
0.005
0.007
0.008
0.006
0.006
0.017
0.014
0.019
0.014
0.014
0.001

z-value
21.91319.24917.16630.09728.610-5.697-0.540-1.881
2.777
8.652-4.857-6.404-4.682-5.11525.97016.312

†
**
***
***
***
***
***
***
***

β
-0.842-18895.1

SE
0.029

z-value-29.460***

Note. *** p < .001, ** p < .01, * p < .05, † p < .10.

38

***
***
***
***
***
***

Table S12. The results of Model II excluding those who were suspected of falsely
responding

Dummy(B)
Dummy(C)Dummy(D)Dummy(E)
Experience(B)Experience(C)Experience(D)Experience(E)Habit(B)
Habit(C)
Habit(D)
Habit(E)
GenderAge
Log-likelihood

γ
2.250
2.122
2.474
2.289
0.514
0.331
0.496
0.345
0.592
0.939
0.071
0.166
0.049
0.017
-4011.4

SE
0.049
0.049
0.048
0.048
0.048
0.038
0.054
0.036
0.025
0.044
0.048
0.031
0.019
0.001

z-value
46.06743.22751.23248.07910.639
8.607
9.192
9.593
23.72321.152
1.483
5.395
2.571
17.440

***
***
***
***
***
***
***
***
***
***
***
*
***

Note. *** p < .001, ** p < .01, * p < .05.

39

Table S13. The results of Model II excluding those who were prone to IER
IER1γ
Dummy(B)1.989
Dummy(C)1.790
Dummy(D)2.268
Dummy(E)
2.126
Experience(B) 0.374
Experience(C) 0.538
Experience(D) 0.918
Experience(E)0.308
Habit(B)
0.627
Habit(C)0.967
Habit(D)-0.386
Habit(E)
0.188
Gender
0.131
Age
0.022
Log-likelihood -3892.1

SE
0.055
0.057
0.053
0.053
0.051
0.047
0.068
0.036
0.032
0.050
0.060
0.032
0.022
0.001

z-value
36.45531.61542.67140.3747.363
11.40913.546
8.455
19.79619.215-6.4075.930
5.861
19.903

IER2
***
***
***
***
***
***
***
***
***
***
***
***
***
***

Note. *** p < .001, ** p < .01, * p < .05.

40

γ
1.957
1.784
2.236
2.109
0.395
0.503
0.857
0.304
0.630
1.071
-0.334
0.186
0.153
0.023
-4147.68

SE
0.053
0.055
0.052
0.051
0.050
0.045
0.066
0.036
0.031
0.048
0.059
0.031
0.022
0.001

z-value
36.70632.72043.16541.0907.886
11.22112.9088.494
20.32422.369-5.6505.950
7.069
21.149

***
***
***
***
***
***
***
***
***
***
***
***
***
***

Table S14. The results of Model III excluding those who were suspected of falsely
responding
Dummy(B)
Dummy(C)Dummy(D)Dummy(E)
Experience(B)Experience(C)Experience(D)Experience(E)Habit(B)
Habit(C)
Habit(D)
Habit(E)
GenderAge
Log-likelihood

γ
1.645
1.578
1.941
1.740
0.998
0.697
0.535
1.016
0.957
1.370
-0.144-0.523-0.1230.017-2572.7

SEz-value
0.070 23.339 ***0.073 21.616 ***0.069 28.158 ***0.068 25.698 ***0.107
9.316 ***
0.091
7.697 ***
0.122
4.379 ***
0.087 11.705 ***0.044 21.576 ***0.089 15.419 ***0.113 -1.280
0.081 -6.494 ***0.027 -4.503 ***0.001 12.275 ***Note. *** p < .001, ** p < .01, * p < .05.

41

Table S15. The results of Model II excluding those who were prone to IER
IER1Dummy(B)
Dummy(C)Dummy(D)Dummy(E)
Experience(B)Experience(C)Experience(D)Experience(E)Habit(B)
Habit(C)
Habit(D)
Habit(E)
GenderAge
Log-likelihood

γ
1.885
1.845
1.998
1.877
0.846
0.672
0.820
0.785
1.268
1.896
-0.285-0.380-0.167
0.013-1954.2

SEz-value
0.077
24.345 ***0.084
22.087 ***0.075
26.666 ***0.074
25.306 ***0.111
7.655 ***
0.109
6.143 ***
0.151
5.426 ***
0.108
7.260 ***
0.068
18.772 ***0.126
15.063 ***0.141-2.019 *0.100-3.796 ***0.032-5.224 ***0.002
8.532 ***

Note. *** p < .001, ** p < .01, * p < .05.

42

γ
1.922
1.819
2.048
1.938
0.841
0.834
0.660
0.863
1.205
1.532
-0.134-0.450-0.2140.013-2077.2

SEz-value
0.076
25.270 ***0.081
22.524 ***0.074
27.772 ***0.073
26.604 ***0.110
7.662 ***
0.104
8.050 ***
0.146
4.513 ***
0.106
8.180 ***
0.066
18.284 ***0.101
15.138 ***0.137-0.9780.098-4.600 ***0.031-6.805 ***0.002
8.211 ***